From 92520acd0ac5af56ea6297f8d0ac86b847988ed5 Mon Sep 17 00:00:00 2001 From: kljk345 Date: Tue, 2 Jul 2024 15:10:26 +0100 Subject: [PATCH] Update tests and docs --- .../doctrees/environment.pickle | Bin 1073388 -> 1073250 bytes docs/sphinx-builddir/doctrees/index.doctree | Bin 7747 -> 6624 bytes .../notebooks/QSARtuna_Tutorial.ipynb | 2276 +++-- .../notebooks/QSARtuna_Tutorial.doctree | Bin 10371273 -> 10349183 bytes .../html/_sources/index.rst.txt | 2 - docs/sphinx-builddir/html/index.html | 2 - .../html/notebooks/QSARtuna_Tutorial.html | 2191 ++-- .../html/notebooks/QSARtuna_Tutorial.ipynb | 2276 +++-- docs/sphinx-builddir/html/searchindex.js | 2 +- docs/sphinx-source/index.rst | 2 - .../building/calibration_chemprop_build.json | 43 + .../ChemProp_drd2_50_retrain.json | 29 + .../ChemProp_drd2_50_retrain_cls_error.json | 29 + .../ChemProp_drd2_50_retrain_missingfile.json | 36 + .../optimization/peptide_classification.json | 89 + examples/optimization/peptide_regression.json | 102 + notebooks/QSARtuna_Tutorial.ipynb | 2276 +++-- tests/data/PIM1_AZ.csv | 6194 ------------ tests/data/failed.csv | 187 - tests/data/peptide/permeability/train.csv | 53 + tests/data/peptide/toxinpred3/test.csv | 2209 +++++ tests/data/peptide/toxinpred3/train.csv | 8829 +++++++++++++++++ tests/data/pim_60.pkl | Bin 17774206 -> 0 bytes tests/data/pxc50/P24863.csv | 4 +- tests/data/reg_model.pkl | Bin 181485 -> 0 bytes tests/test_chemprop.py | 70 +- tests/test_composite_descriptor.py | 4 +- tests/test_data_transformers.py | 17 + tests/test_datareader.py | 59 + tests/test_descriptors.py | 61 +- tests/test_integration.py | 197 +- tests/test_model_builder.py | 10 + tests/test_optbuild.py | 30 + tests/test_physchem_descriptor.py | 12 +- tests/test_retrain.py | 167 - tests/test_retrain_full.py | 93 - tests/test_retrain_full2.py | 137 - tests/test_tracking_callback.py | 60 +- tests/test_uncertainty.py | 57 +- 39 files changed, 16434 insertions(+), 11371 deletions(-) create mode 100644 examples/building/calibration_chemprop_build.json create mode 100644 examples/optimization/ChemProp_drd2_50_retrain.json create mode 100644 examples/optimization/ChemProp_drd2_50_retrain_cls_error.json create mode 100644 examples/optimization/ChemProp_drd2_50_retrain_missingfile.json create mode 100644 examples/optimization/peptide_classification.json create mode 100644 examples/optimization/peptide_regression.json delete mode 100644 tests/data/PIM1_AZ.csv delete mode 100644 tests/data/failed.csv create mode 100644 tests/data/peptide/permeability/train.csv create mode 100644 tests/data/peptide/toxinpred3/test.csv create mode 100644 tests/data/peptide/toxinpred3/train.csv delete mode 100644 tests/data/pim_60.pkl delete mode 100644 tests/data/reg_model.pkl delete mode 100644 tests/test_retrain.py delete mode 100644 tests/test_retrain_full.py delete mode 100644 tests/test_retrain_full2.py diff --git a/docs/sphinx-builddir/doctrees/environment.pickle 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" ] @@ -1662,7 +1692,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1708,18 +1738,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:59:56,345] A new study created in memory with name: my_study\n", - "[I 2024-07-01 11:59:56,386] A new study created in memory with name: study_name_0\n", - "[W 2024-07-01 11:59:56,387] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", + "[I 2024-07-02 13:19:17,760] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:19:17,800] A new study created in memory with name: study_name_0\n", + "[W 2024-07-02 13:19:17,801] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", "Traceback (most recent call last):\n", - " File \"/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", + " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", " value_or_values = func(trial)\n", - " File \"/Users/kljk345/PycharmProjects/optuna_az/optunaz/objective.py\", line 128, in __call__\n", + " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/objective.py\", line 128, in __call__\n", " self._validate_algos()\n", - " File \"/Users/kljk345/PycharmProjects/optuna_az/optunaz/objective.py\", line 264, in _validate_algos\n", + " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/objective.py\", line 264, in _validate_algos\n", " raise ValueError(\n", "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n", - "[W 2024-07-01 11:59:56,395] Trial 0 failed with value None.\n" + "[W 2024-07-02 13:19:17,807] Trial 0 failed with value None.\n" ] }, { @@ -1838,11 +1868,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:59:56,446] A new study created in memory with name: my_study\n", - "[I 2024-07-01 11:59:56,448] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:19:17,867] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:19:17,868] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n", - "[I 2024-07-01 12:00:42,714] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", - "[I 2024-07-01 12:01:30,800] Trial 1 finished with value: -6594.8449774319415 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 152.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 110.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 6.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.32, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 2100.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': 0, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 900.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -2, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 1 with value: -6594.8449774319415.\n" + "[I 2024-07-02 13:20:11,301] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", + "[I 2024-07-02 13:21:02,026] Trial 1 finished with value: -6445.608102397302 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 78.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 105.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 5.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.16, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 1700.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -2, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 2300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -2, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 1 with value: -6445.608102397302.\n" ] } ], @@ -2138,26 +2168,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:32,104] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:01:32,106] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 12:01:34,080] Trial 0 finished with value: -5817.943727498592 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.943727498592.\n", - "[I 2024-07-01 12:01:34,134] Trial 1 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:21:03,347] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:21:03,350] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:21:05,443] Trial 0 finished with value: -5817.944008002311 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.944008002311.\n", + "[I 2024-07-02 13:21:05,495] Trial 1 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.943727498592]\n" + "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.944008002311]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:35,962] Trial 2 finished with value: -5796.34392897437 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.34392897437.\n", - "[I 2024-07-01 12:01:37,796] Trial 3 finished with value: -5795.086720713623 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-01 12:01:37,827] Trial 4 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:21:07,433] Trial 2 finished with value: -5796.34392897437 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.34392897437.\n", + "[I 2024-07-02 13:21:09,439] Trial 3 finished with value: -5795.086720713623 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086720713623.\n", + "[I 2024-07-02 13:21:09,470] Trial 4 pruned. Duplicate parameter set\n" ] }, { @@ -2171,8 +2201,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:39,445] Trial 5 finished with value: -5820.22730026582 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-01 12:01:39,477] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:21:11,241] Trial 5 finished with value: -5820.227555999914 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086720713623.\n", + "[I 2024-07-02 13:21:11,283] Trial 6 pruned. Duplicate parameter set\n" ] }, { @@ -2186,7 +2216,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:41,217] Trial 7 finished with value: -5852.160071204277 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086720713623.\n" + "[I 2024-07-02 13:21:13,322] Trial 7 finished with value: -5852.160071204277 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086720713623.\n" ] } ], @@ -2343,19 +2373,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:41,473] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:01:41,524] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", - "[I 2024-07-01 12:01:41,681] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", - "[I 2024-07-01 12:01:41,719] Trial 2 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:41,737] Trial 3 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:41,885] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-01 12:01:41,913] Trial 5 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:42,058] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-01 12:01:42,085] Trial 7 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:42,113] Trial 8 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:42,167] Trial 9 pruned. Incompatible subspace\n", - "[I 2024-07-01 12:01:42,421] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", - "[I 2024-07-01 12:01:42,456] Trial 11 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:21:13,577] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:21:13,629] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", + "[I 2024-07-02 13:21:13,777] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", + "[I 2024-07-02 13:21:13,819] Trial 2 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:13,849] Trial 3 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:13,997] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-02 13:21:14,021] Trial 5 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:14,167] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-02 13:21:14,196] Trial 7 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:14,228] Trial 8 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:14,269] Trial 9 pruned. Incompatible subspace\n", + "[I 2024-07-02 13:21:14,523] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", + "[I 2024-07-02 13:21:14,559] Trial 11 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:21:14,753] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" ] }, { @@ -2369,8 +2400,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:42,659] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n", - "[I 2024-07-01 12:01:42,692] Trial 13 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:21:14,790] Trial 13 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:21:14,960] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" ] }, { @@ -2379,13 +2410,6 @@ "text": [ "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4030.4577379164707]\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2024-07-01 12:01:42,899] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" - ] } ], "source": [ @@ -2493,10 +2517,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:01:43,184] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:01:43,185] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:21:15,255] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:21:15,256] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-01 12:02:30,197] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + "[I 2024-07-02 13:21:58,856] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", " \r" ] } @@ -2544,9 +2568,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:03:38,195] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:03:38,239] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 12:04:23,189] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" + "[I 2024-07-02 13:23:02,954] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:23:02,997] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:23:47,043] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" ] } ], @@ -2592,14 +2616,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:04:23,289] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:04:23,291] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:23:47,172] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:23:47,174] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-01 12:04:44,908] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-01 12:05:06,570] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 98.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 40.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-01 12:05:28,088] Trial 2 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5890.94653501547.\n", - "[I 2024-07-01 12:05:28,127] Trial 3 pruned. Duplicate parameter set\n", - "[I 2024-07-01 12:05:28,156] Trial 4 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:24:09,495] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-02 13:24:31,625] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 98.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 40.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-02 13:24:53,140] Trial 2 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5890.94653501547.\n", + "[I 2024-07-02 13:24:53,181] Trial 3 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:24:53,211] Trial 4 pruned. Duplicate parameter set\n" ] }, { @@ -2819,25 +2843,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:05:49,404] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-01 12:05:49,406] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-01 12:05:50,365] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" + "[I 2024-07-02 13:25:15,173] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-02 13:25:15,175] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:25:16,110] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" ] } ], @@ -2912,9 +2920,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:05:52,665] A new study created in memory with name: uncalibrated_rf\n", - "[I 2024-07-01 12:05:52,709] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 12:05:53,024] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" + "[I 2024-07-02 13:25:18,566] A new study created in memory with name: uncalibrated_rf\n", + "[I 2024-07-02 13:25:18,608] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:25:18,915] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" ] } ], @@ -3239,9 +3247,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:05:54,779] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-01 12:05:54,821] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 12:05:55,696] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" + "[I 2024-07-02 13:25:20,500] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-02 13:25:20,548] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:25:21,537] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" ] } ], @@ -3347,7 +3355,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -3396,10 +3404,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:05:59,867] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:05:59,911] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:25:25,916] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:25:25,959] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n", - "[I 2024-07-01 12:12:52,533] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", + "[I 2024-07-02 13:32:26,200] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", " \r" ] } @@ -3458,7 +3466,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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NePnll/HEE0/M+Gcmk+NCoLc/hef2DaG3PwUuxGwfEiGEEDLraHwkhJC5TZvtA9izZw+y2Sw2b94cfC2RSGD9+vXYsWMHrrrqqpLHP/744+jo6MApp5wSfO3cc88FYww7d+7EG97wBuRyOezYsQPf+973cOTIEXz6058ueY3NmzfjlltuQTgcDr6mKHKuIZVKzcTHJFOwu3cY923rQ/9wDq4roKoMXW1RXHl+N9b1tM324RFCCCGzgsZHQgiZ+2Y9wO7v7wcALFmypOTrixcvDr5X7NixYxMeaxgGWlpacPToUQAyQL/77rsBIPiz2PLly7F8+fKSr/3nf/4nwuEwNm3aVPdn0bRZTwg44VRVKflzunbtH8YPtu5FwXIRi2jQNAWOw3F4MIsfbN2L97xhHdavmj83EY0+PycbOj+To3NUHZ2f6ubC+aGxsTHm2/g4F373ZhudAzoHAJ0DYOGdg1kPsP3iZIZhlHw9FAohmUyWffz4x/qPr3f/9A9/+EP86Ec/wmc/+1m0tdU3OCkKQ2trrK7nngwSici0X4Nzgft3PA3T5ljUEgZjDABgaCoiIQ1DKRP37ziIzRuXQ1HYtN/vRGrE+TmZ0fmZHJ2j6uj8VDdb54fGxsac9/k8PtLfTToHAJ0DgM4BsHDOwawH2H6atmVZJSnbpmkiEpn4QwiHw2WLn5mmiWg0OqX3FkLgG9/4Br797W/jAx/4AN71rndN8ejHcC6QSuXqfv58paoKEokIUqk8XJdP/oQq9h9N4WB/CtGwCpcLAKX7yqIhFQf7U3hqTz9WLUlM671OlEaen5MRnZ/J0Tmqjs5PdfWcn0Qi0rBVBhobG/N7OR/HR/q7SecAoHMA0DkATo5zMJWxcdYDbD/de2BgACtXrgy+PjAwgDVr1kx4fFdXF373u9+VfM2yLIyOjmLx4sU1v69t2/j0pz+Ne++9F5/+9Kfx7ne/u74PUMRx5ucvTCO4Lp/250+mTTiuQFRVUK5mi6oqcFxHPq5jfp3rRpyfkxmdn8nROaqOzk91s3l+FvLPpVHnfT6Pj/R3k84BQOcAoHMALJxzMOuJ8GvXrkU8Hsdjjz0WfC2VSmHXrl1l90Nv2rQJ/f396OvrC762fft2AMA555xT8/t+4hOfwP3334+vfvWrDQmuyfTFozpUlVX8i+c4HKrKEI/qJ/jICCGEkNlD4yMhhMwfsx5gG4aBd77znfjKV76C3//+99izZw8++tGPoqurC6973evgui4GBwdRKBQAABs2bMDZZ5+Nj370o3jmmWewbds23HDDDXjzm9+Mzs7Omt7z7rvvxpYtW/DRj34U5557LgYHB4N//PchJ97KziZ0tUWRLTgQ46bohRDIFhx0tUWxsrNplo6QEEIIOfFofCSEkPlj1gNsALjuuutw9dVX47Of/Sze8Y53QFVVfP/734eu6zh69Che/epXY8uWLQAAxhi+9a1vYfny5bjmmmvwkY98BBdffDFuvPHGmt/v3nvvBQB8+ctfxqtf/eqSf/z3ISeewhiuPL8bYUPFaMaCZbvgQsCyXYxmLIQNFVee3w2FzUwBF+otSgghZC6aifGRxjxCCJkZTIyfCiV1cV2O4eHsbB/GCadpClpbYxgZyTZsT8Vs9PmcqfecifMzH3AhcOBYGpmcjXhUx8rOprI3fgv1/EwFnaPq6PxUV8/5aWuLNazIGY2Njf29bNRYdSLG2dn4u1nr2HOi0PWJzgFA5wA4Oc7BVMbGWS9yRsh463rasKa79YQNkrt7h3HH1r0oWA5iYR1aRPYWPTSYxR1b9+Kay9dM6YajeIBvbgqhublydfvJbgbm2s1CLWZjgoQQQmZLuev0TGnE+Ph87zBuu283CpaDaFhHLK5POubNh7GIxh5CyFxBATaZkxTG0NM1861GuBC4b1sfCpaDlnhorLeorkLXFIxmLNy3rQ9rultrupkYP8BrKsOKrv24YtMKnL6ipepjx98MzMebhUZPVhBCyFxW6Tr9pgtX4cIZ6v89nfFx1/4hfOee55ErOGAMMG2OTN5Gc8xAS9woO+bNh7GIxh5CyFwyJ/ZgEwLMzn6wA8fS6B/OIRbWg+DaxxhDLKyhfziHA8fSk76WP8AfGswgpKtIxA2EDBW9R1O4bctu7O4drv5YXQ1uBu5/rK/q94tfa64YP1lh6CoUxmDoKlriBgqWi/u29dE+P0LISaHadfy2Lbvx9IuDDXuvRoyPu3uH8f0te5AzHagKg6oqUBiD5XAMpQowLXfCmDfZWDUXxiIaewghcw2tYJM5YSoz5I1MVcvkbLnSHCk/16RpCnIFB5mcXfV1Kq2Eq7qKSEjD8dFCsCoAoOqq+UjaxH1/7oOqsoasqp8oU5msOBHZCYQQ0giVUsCrXceTGQt3/uFFfORvXjHt92/ECrI/RpmWA8YYGAMYADBAYwwOF0hmLXS0RoIxr9EZXjOFxh5CyFxDATaZdVNJ7Wp0qlpxb1FDVyd8v9beopMO8JHSVYFqjw3pKoZTBbQlwvPqZqFRkxWEEDJXVBpzzjm9Y9Jr/uGBDPr601jREZ/W+zci9dkfo6JhHaZtQsALsD0qY7AdjnzBCca8+RK40thDCJlrKEWczKqppHbNRKpao3qLBgO8VnmAd12BTM6e9LFggACgVFgQKH6tuaR4sqKcWicrCCFkLqg25vz60V6Yllv1mu+4HOlpXKcbmfrsjzvhkFx95kKUjnlMjnm5ojFvKuPabKKxhxAy11CATWZVrTPkvf3pGdlj1ajeolMZ4Ms9VggB03aRNx35dQHwCh9lrt4sNGqyghBCZttkwa3jcJi2W/War6kKmqZwnR6/z7q3v3E1Qvxxx3UFEjEDCmNwhQgCbcEFhEDJmDdfAlcaewghcw2liJNZVWtq174jyRlLVVvX04ZrLl8TpAHmvBS55R2xmlPP/QH+0GAWuqaUHKMQAtm8g2UdsWCAL35swXKRylqwHQ7hPZ4pQM60EQ1rE1+r4GB50WvNFf5kxR1b92I0YyEW1uQqjsORLTg1T1YQQshsm2zytymqw0zKa3d7c7jsNX/VsmZ0dzWBu5NP/JZLRY9HdFl4LFI+gJ1K6nPxGNUSN9CWCAfjDveOORrW8J4r1wVj3qTj2hwZi2jsIYTMNRRgk1lV6x5oJjCje6ym21u00gDvOhw5050wwPuPPZ6UlVuFEFAYAxcCCgM0VYVpcRxPFtAcM+bNzUIjJisIIWS2TTb5q+sqQkXFvsYHdRFDxdV/cZq8rqN6gF1pn/VQsoC86SKbt9EUNSY8byoryOXGqEUtYRRMF7mCg5Ch4r1vWIv1Rdfo+RS40thDCJlLKMAms6rWGfJwSIUrBPIFZ8KqLtCYVLXp9t4uN8BrKkPPksSEPtjretrwd687Hd++53lwLsC8fdchXUVzTLb3GhotAAAKlgNewLy5WZjuZAUhhMy2WiZ/DV3B5jO68Nz+YYxmTEDIyd7lHTG86cJV2HBaB0ZGslXfp1ql7rZECEeO55DMWIhH9GmvII8fo9yCXCnvWdJUcVyZT4ErjT2EkLmCAmwyqyabIVcYkM3b+OXD+5E3HWTzNtJ5GYRGQvLXdy6lqo0f4JubQti4tgvJZG7CPrZoREfIa+OlKgyKwkpu5BJxA6bl4i2XrEYiasyrm4XpTlYQQshsmmzyN5m1wABse/4YHC5rcrc0Gbhk41JcsnFZ2aC8nGqp6IqioDluYDRtYihZQKIB2Uz1BKHzKXClsYcQMhdQgE1mXaUZ8tamEJIZEyMZE7GwjnY1hKGUTKkecgpoaw5BU5Q5l6pWPMBrmgKlQjnwTM6GywViUb3scftp74mogTNXt8/oMRNCCBlTbfI3mbVgWi5CuoqQoSLmfX00Y2Hr9oPoao3irFMX1fQ+k6Wix7x92O3NYWTydkNWkOsJQilwJYSQ2lGATeaE8TPk0YiOu/74EkbSYixtTlexiDGMZkxYNsdw0kQips/JVLVaNKoHNyGEkMYrN/mrKLJ/dEhXsaglXJLS7e/Hvm9bH844pbZJ0VrGgZCh4por1oAxNudXkAkhhFCATeaQ4hny3v4Ujo3kJ6TNhUMaukIasnkbpu3irZecgvPP6JqXNxrzpUIrIYQsVOMnf1M5C3c/tA8hQ63a0aKvP432tvikr1/rONDdlZiX4xwhhCxE1AebzElB2pxW/lc0EtagMIZE1Ji3Nx2N6sFNCCFk5viTv2eubkciasDllccmTVPgugLpGjta0DhACCEnHwqwyZxUnDZXzlxKn+ZCoLc/hef2DaG3PwUuJu956vNTEJd3xGDaLlIZC6btYnlHDNdcvmbepb0TQsjJrNaxqWkKY9NcHwemM8YRQshCRCniZE6aL+nTu3uHx1qeuLLlSVdbdEp7wudThVZCCFnIak/pntrYNFfHgUaMcYQQstDQCjaZk+ZD2tzu3mHcsXUvDg1mENJVJOIGQrqKQ4NZ3LF1L3b3Dtf8WsUpiD20144QQuakmRyb5to40MgxjhBCFhIKsMmcNZfT5rgQuG9bHwqWg5Z4CIauQmGyj3VL3EDBcnHftj5KpSOEkJPMXB6bGoXGOEIIqR+liJM5ba6mzR04lkb/cG5ClXOgvkqyhBBC5o+5OjY1Sq1j3IFjaeqPTQgh41CATea84vZdc0VQ5TxSuZJsruDUXEmWEELI/DIXx6ZGqXWMy9AYRwghE1CATUgdiivJGro64fv1VJKdLi7ESbuaQgghjUbXzMpqHePmQicPQgiZayjAJqQO06kkOxM3dVTplRBCakfXzMq4EBBCIB7RMZQsoL05PGc7eRBCyFxEATYhdfAryd6xdS9GMxZiYQ2apsBxOLIFp2Il2Zm4qfMrvRYsB7GwDi0ij8Ov9HqyFN0hhJBGoGtmZcVjlGm5yJsODg9m0RwzEIvqk45xhBBCqIo4IXWbaiXZmWh5UlzptTlmQAAwLRcCQHNMp0qvhBBShKpjV1Y8RhmagnhURzSiQwiBkbSJ4WThpKuWTgghM4FWsAmZhloryY6/qfPT7Qxdha4pGM1YuG9bH9Z0t05pRcCv9KqpCgZHC7AdDgGAAdA1BVGq9EoIIQGqjl1e8Rjl9/gOxhPGACYQi2h4/5vORE8X7VUnhJBqKMAmZJpqqSTb1z8zN3WZnA3TcmHaLrgQUBiDAkAAsBwXTpYjpKtU6ZUQQkDVsSspnqwdTpkTxhPBgeOjBfT1p7B6ycKZeCCEkHpQijghJ0Dav6nTKt/Uua6Y8k1dLKLJ4JoLqIxBYQzM+1NlDJwLmLaLWITm0gghpLg6djkLtTp2JmcH+6u5mDieKAqDEMBDTx1ZkOnzhBAyFdO6637ooYfw6KOPYmBgANdffz12796NM844A8uWLWvU8ZEqqMXI1M3WOWuaoZYnAsz7E2VXxgVEyeMIIWQhq7UDxFyujl1uHAMwrbEtHtUBBtgODwLrYgwMjAmMZqwFlz5PCCFTVVeAnc/n8aEPfQiPPvoo4vE4stksrr32Wvz0pz/Frl278KMf/QinnXZao4+VFKEWI1M3m+esu2tmbupyeRshXUXBcuB4q9hgAATgCgGFASFdRS6/sNIdCSGknHo7QMwV5caxpogOMIZ0zqp7bFvZ2YSWeAjpnD0huAbkeKJ7GVgLLX2eEEKmqq4U8a997Wt4/vnncfvtt2Pbtm0QXrrQl770JXR2duIb3/hGQw+SlJqJatQnuxNxzrgQ6O1P4bl9Q+jtT5Wk0fk3dX7xGMvbM23ZLkYzVt03dfGojpChojkegqEp4EKAcwEuBAxNQXMshJChLrh0R0IIqWSqHSDminLjGAAcGMjgwLE0ANQ9timM4ZINS8HA4HABIby91wJwuJysjYV1aAswff5kU+1ehRDSGHWtYP/2t7/F9ddfj/PPPx+u6wZfX7x4MT7wgQ/gpptuatgBklIzVY36ZDb5OTNx18Mv469cjqaYUVfauL+qcHQoC9vmgMLQ0RzG2y47Fa8+JwZg7KbOX33IFRyoKsPyjljdq+jF6Y6LWyOwHQ7OBRSFBb8Pcz3dkRBCTrRaO0DMFeXGMc45Mn52kgByBQdNUWPK9wN+ynl7cxiL2yIYGM7B5XxsrNQUJKI6Cjan8WSeo+xHQk6MugLsVCpVcZ91c3MzcrnctA6KVEYtRqau2jkzLRcF00Xv0TS++5tdMAx1yoONv6qQyVtwXAHXFRBCoDdv4+s/fxoDaQuXbVgCoPE3deXSHQ1DhePwaa2ME0LIya6WDhBzxfhxLG86GE2bsIqKtRUsF+mchaaoUfP9wPiAiwsBVVWgMiAa1mHockvTfEifJ9X59yoFy5HZCBG5NcLPdpjL2RuEzDd1pYifdtpp+M1vflP2e3/4wx9o//UMysxQNeqTWaVzVjAdDKUKsF15gxIOazA0BX39afznvbvw4BOHJk2d8lcVMnkLls3huByMAarCoCoMtsPx4/t347n9Q8Fz/Ju6M1e3o6crMe2blfma7kgIIaS2lN3icSxvOhguGruKJTMWCqYDYPL7gXIp5/GIDkNV4ArAdjnypkvjyUlgfAaEoatQGIOhq2iJGyhYLu7b1kfp4oQ0SF0r2B/4wAfw4Q9/GKOjo7jsssvAGMOOHTtw991342c/+xm++tWvNvo4iSc+Q9WoT2aVzlkya4ELGQwLAbhc3ojYDgc3Hfz09y9i5wuDVVezDxxL4+hQFo4/819UfZUBgCqrst754Mv4zLvOmbGZ//mW7kgIIaT2lF1/HLNtF6ms5Y03gOPFQ15tSwghkMxaCIe0qvcD1bZOtbeEMZoxsag5jL+6cFXdW6fI3EHZj4ScWHWtYL/2ta/FzTffjL179+LGG2+EEAJf/OIXcf/99+PGG2/EFVdc0ejjJB5/z2224ATF5Xx+NequtijtkSpS7pxZtivbkUDeaCgKQzprwXJcuQLt9ZDu609XLRSTydmwbQ7XFWVbmyiMgQEYHM0HRWhmSqNXxgkhhMycqRTf9MextDcJ7I83/lVetmoEVEWB7XCYllP1fmDygEvHaMZCU8yg8eQkQNmPhJxYdffBfuMb34g3vvGN2LdvH0ZHR5FIJLB69WooSl0xO6nRfG8xMhvKnTPX5RBCBDclAEpWoAUDGAdiYS1InSpXKCYe1QGFQQhR9pz7ry8EtTYhhBAiTbVgqT+O/ee9u8BNByoYoDAoisy+AgCFAQKyBkgqKzOZKt0PBAFXpHLAlSs4NG6dJCj7kZATa9rR8OrVq3H22Wfj1FNPpeD6BKE9t1M3/pzlivaoJWIhWXm7eAXaSw5QVaUkdWq8lZ1N6GgOy9S8cd/jQsB1ZSVWxoBUzqL9TYQQsgCN32fd2197yq5vXU8b3nRBD1SFBS0ZAUDXFBiaAoAFX+tqj1S9HygOuMqZ6YCLWkWdWJT9SMiJVfMK9tq1aycMApUwxrBr1666D4pMjvbcTl3xOUtlLdzzp/04nixAUxkESmebXK+PtKGr4EJUnMlXGMNbL16Nb9z1LGyHw7vHgev1EQUA7gqkczZ+9vsX8ehz/dQOgxBCFgDOBfYfTeHZl47jiRcHMZq24HK5zzoe0WFaLmKR8gFspRXkS165DDtfGERffxqxsAZVleOUEAKW7SKVtdHZFsGn3nkOtCqLHsUtHnVNKbm/8wOumWrJRa2iTjzKfiTkxKo5wP7Qhz5Uc4BNToz51GJkrlEYw6tfsQRbtx9E1usjKrx/uUJAYUBzzABQOpPv9wstntRYv6odf33RKvzyf/bDrrAaAMgepb3enm7KNCCEkJPXrv3DuH/H09h3aBSZvA0h5EpzS9yAqioYShaQN11k8zaaosaE51daQS4OlAqWi5iqgAsBx+HImS7iUR1XX3JK1eB6/OucyIDr+d5h3HbfbhQsB9GwjlhcnxOtosqN7SdbsOln8vmTG7mCA1VlWN4Ro8kNQhqs5gD7//yf/zOTx0HIjCs3a94U0RE2VBw5noXrCjAmYOgqmmMGwiGtZCY/l7fxtf96quys+xXndWP54ji+ddezJX1Ji3EhK4rnTafinm5CCCHz2+7eYfzAC4ALtgtAdqtwuMBw2kR7Ioy2RAhHjueQzFiIR/QprSA3KlA60QHXrv1D+M49zyNXcMAYYNocmbyN5piBlrgxYd/5ibKQVtQp+5GQE6PuImf9/f34wQ9+gMcffxzJZBLt7e04//zz8a53vQutra2NPEZCps2v1lqwHMTCOrSInKkfyVgI6Qou3bgMj+8dgO1wJGIGNE2BZbvBTP6Zq9rwgwdemPD84ln3aFiHyycG18zrnyIgA+zmmEHtMAgh5CQ0VrzMRVNURyZvQ1FkkTIFgMNlG63Otiia4wZG0yaGkoVg3Kl1BblRgdKJCrh29w7j+1v2IGc6UBUGJiuywXI4hlIFtCfCs9IqqtK9wWyvqM8kyn4kZObVVZVs9+7duOqqq/CTn/wE0WgUZ555JjRNw3e/+128+c1vxsGDBxt9nITUbXy1VkNXoTAGQ1fREjdg2hxHhrJ471Xr0d3VNKFw3LsuX4Pn9g9XfL5fZfzlw0lwISu5ArIvaXCPUnSv4rqc2mEQQshJKGh/FdHgeoU4ikNVlTHYDodly/3XkZCG9uZwXQVLG9WacaZbPPpjsGk5QdFPf3zUFAYugGTWOuGtoia7N/DHdirARgiZqrpWsL/0pS9hxYoV+O53v4tFixYFXz969CiuvfZafOELX8Att9zSsIMkZDom7/eped/XcP3bN06Yya/1+SvTBe9rCFasy92muFz2oqylOmsj94UthD1mhBAym4r7DfsJTSVjgTc+cC73TYcMFddcsQaMsZP22tznVUyPhDQULBMu5OqODLJZMOmQ91LU66lcXs/4VuvYTtlmhJCpqivAfvLJJ/G1r32tJLgGgCVLluC6667Dpz71qYYcHCGNMJV+n+VSp2p9fls8JNun8KLZbu/OqngC3HE5li+OT1qdtZH7whbSHjNCCJktxe2vIiENuqbActwgmPT7OTKGYJ919wysGs8l6ZwN03LhuBzc62nJIcAAqArAFAbBZbeOniVNU65cXu/4Rr3ACSEzpa4U8ba2NmSz2bLfU1UVsVhsWgdFSCNNt99nrc8/ZXkzli2KyZi6qJ12cXCtMCBsaFjv7XmrlHrm7ws7NJhBSFeRiBsI6WqwL2x373CtH7+hr0UIIaSyoN9w3gEANMcNKIzBFUJW++YcqsKQMxdOa6TB0TzypgPb4VCL7joF5J501+UQAnWdj+mMb7PdC5wQcvKqK8D+wAc+gK9+9at4/vnnS75+8OBBfOMb38D73//+hhwcIbXgQqC3P4Xn9g2htz81IWgNbngKDoT3PSEETNtFrmAjlbXQ2RqpOGte7vk+v9prV1sU3V0JvO2yU9EckzdUSpl7BENXYbsc//34IXzr7mfxtf96asINQCP3hdEeM0IIOXH89ldhQ8VQyoSqMLQmQtBUBS6Xq7YhQ8XyjvisFNCabLxs+Ptxgcf3DIAxBiFkN42JxwREQirec+W6KZ2PWsc3h/Oyn7nWsX0meoETQk5uNaeI/8Vf/EXJHpXjx4/j6quvxooVK7Bo0SIkk0ns378fhmFg69at+Lu/+7sZOWBCitWSGja+36emMmQLcjZdCAEGhpzpYm/fSNnBfSr9Qtf1tOF9V63HvX/uxeHjWTgOh6IoiIZU5EzZmiQe0YPnl6tUOtV9YdX2ntEeM0IIObHW9bThPW9Yh/t3HMTB/hQcVyAW1tASD+Gc0zuwflXbrOyzno2tQvsOJ9E/lEUsrCGVs4MU+fHOXNWGdd1T60BTy/h2cCCDL/xwJ5JZq+xnno1e4ISQk1/NAfa555474QI23ite8YppHxAhtZpKew2/3+fPH3wJBwcyEEKmcRuaglhEx0jarNqSYyr9QovbnuRMF0sWN+HWe57FwcEMWuKh4O+RoavQNWVC78+p7Aub7IaJ9pgRQsiJt35VGzZvXI6n9vQjmTZnvXjZbLWjSmUtOK7MGPM6c6HcovnOF47ja//11JSC/cnGN9flyOZtuC5HczxU8TOfyF7ghJCFoeYA+4tf/OKMHQTnHN/61rfwi1/8Aul0Gps2bcINN9yAFStWlH38yMgIPv/5z+Phhx8GYwxXXnklPvGJTyASiUx47M6dO/HOd74Tu3fvrvs1yNwzPjVssqAVANZ0twZtUWJhDaqqwNBVADIdrNxziq3pbkUopGHfkSSYAFYtbQKYglzeRm9/quTmyS+WpmkKhjL2lFaRi/eF+cdXzN8XNpjMY+v2g1VvmGp9LdpjRgghjaUoDKuWJOB0lN/je6KUGy8t2wXnIsiuqjb2TUciZgCAt/9aAWOyk4Y7LlecC4G+/vSUgv3JxrfRjAUh5H2BX8m93D3CieoFTghZOOqqIu7LZDJIpVJlv7d06dKaX+eWW27BT37yE3zxi19EV1cXbr75Zlx77bX4zW9+A8MwJjz+uuuuQz6fx+23345UKoV/+qd/Qi6Xw5e+9KWSx+3cuRMf/OAHwfnEwa3W1yBzUz2pz/5zEjFjwmBc6Tl++vWu/cPY+cIgRjMmOJdf51xAURgUxiasHPvPy5ku0nmZjh6NlA9ix68i+/vCDg1moWtKyefz94UtWxTFzr2Dk04wfORtGyZ9reUdMdpjRgghJ4nx24a4QDBempaLZNaCXVTYS1UYDg5kZmSr0OplzWhpCiGVtSC8/pV8XHDN2NgY7O+briXYX9nZhM7WCA4cyyDqT5p741wqa8LyPmMyayGds6FrChIxI5hkLx7vy3UQIYSQetUVYO/Zswcf//jH8dJLL1V8zPgV40osy8Ktt96Kj33sY7j00ksBAF//+tdx0UUX4YEHHsBVV11V8vgnn3wS27dvx5YtW3DKKacAAG666SZce+21uP7669HZ2QnHcXDzzTfjxz/+MU4//XSMjo5O+TXI3FZP6vNUn+OnXx8cyCCbtyEA6JqCqKEiV7Dhcnlj0tYcgqYowcrxpRuX4rn9w0HaNlMY8qYDTVPQFJ04YTR+FbmWPd+vWrMY923rm3SC4dBAhvaYEULIAlFu21A8IgNrTWEYTpvgAlAZC3py2y6Hk+fYtX+44UGmojBc+spl+OH9e8C5QFGnsrHHeOOPqiqIqUrNdUH29o0gZ7rIm65X44RB1xQYqoJM3vbeX35WAcByXAynCmhLhBEyVNoeRQiZMXUF2DfccANGRkbwiU98Ai0tLdM6gD179iCbzWLz5s3B1xKJBNavX48dO3ZMCLAff/xxdHR0BIExMLY/fOfOnXjDG96AXC6HHTt24Hvf+x6OHDmCT3/601N+DTK31ZP6PJXnFO9XMy0XAjKYdlyOZJYDDNBV2XolnbWxuDWClriB48kCfvk/+xEJqYiFdehRBVwA6ZyFkbQJTWWIhMaOqdIq8mT7wlwuyk4WWLYL1+VwuUDBdPHyoSQuO2f5tPaYVSuiRgghC9VcuzZW2mc9lCwgbzowbRdcAFpxiwsm20dyDjz6fD9ed95KaEpdDWYquuSVS7H1sT4MjORRnFDImBxXuZD1UAxdBReipsC3+LO2NBnI5m3YDodpuTDhylVxAahMpqUz+VHhCoFU1kILM2h7FCFkxtQVYL/wwgv4+te/jssuu2zaB9Df3w8AWLJkScnXFy9eHHyv2LFjxyY81jAMtLS04OjRowBkgH733XcDQPDnVF+jHprW2EFpPlC9ppaqemI/++plzVjaHsPBgQwMXb635fBghjyXd7CyswmrlzUHNzzjn8PAYDkuXC6gMCBXcLFicRw9SxP42k+fgmm5iIU1ZPMONFUO0uCAAwHmbehSGIPtcNguh67K13McgeaYDkNXoDAFhsawqDmMgZE8hpImFrcy6LoqV5HzDiKGijdduGpC0H/WqYtwxint2H80hZcPp8AgcMqyZvQsSaCvPw1NZXBdDlVXUTBdjGZMua+uaHngV3/aj6dfHsJVF/TgE//rbPT1p5HO2WiK6ujumvxmcNf+Ydz7aC+ODmXhuAKayrCkPYarLujB+lXTL/4yW78/8wmdo+ro/FQ3F87PyTg2TnZtZArDSwdH0T+YRjSs1XS9nQ4uBH772AGYlovWprFtQ6qhol0L4fCgI49TYV6hMQEhULIX+ujxHL74oyfwtstObdj1/ekXB/GzB/YgV3BKvqcUBdcKg7fVCXAdDk1laG4KQVEZ+vrTSGUtZPI24hEdiZiBFZ3xCZ81ETVgOS7ypoPRtAXdG7MdVwSr9YwxKELuB8/kbPQsSZTcI8yEufD3b7bROaBzACy8c1BXgL1ixQrk8/mGHID/OuP3WodCISSTybKPL7cvOxQKwTTNmt9zuq8xnqIwtLbG6nruySCROPHF4f728rX4jzufxvGkCcfhcDiHEPLGQdcUbDpzCdrb4uBcYN/hJFJZCxefvRy/fOhlDCVN2A6H47ryOUDwnGTOxbGRPBJxA64rbz7k+MxkkC13kgFgUAC4EDAtF8N5B44jvzOcMpHO2WhLhBGL6GiKGuACGE2byJscBYtDUxWsWtaMq//iNGw4raPsZ3z6xUHc86deHB7IwHHlc5YtjuMtl52KFV0J9B5NQQAYShXgunxCj9GC5aLXKxzzoas34Oz1S8q+T6X3vmPrXuQLDppiOnRVge1yHDqeDV6v0nFP1Wz8/sw3dI6qo/NT3Wydn5NxbBx/bdQUhpzpYH9/Gt+7bxeuevVqPPXC4ITrdrVr/XS9dHA0GLd0bVyGliq7ZaSyNhxevhm1ogAQwLGRfMOu70+/OIj/uPNp5AsOmptCiEZ1jCTNYCUdXCBkaGhNhBANaRBCIGe66FmSANNU/NsvnsH+w0lkCzY4l8cYi+jobIvi2HBuwmeVEzkMyYwFVwi0xkPyv716KcxLiedCwDBU/O3la9HeFp/WZ6wVXZ/oHAB0DoCFcw7qCrCvv/56fPGLX8SiRYvwile8AuFwuO4D8J9rWVbJ65imWbaidzgchmVZE75umiai0WjN7znd1xiPc4FUKlfXc+czVVWQSESQSuXhuie2UurKRVFcsmEp7n7oZdgOhz8JrasKdFXBlj/tQz5n4bl9QyWrDJqqoGA5cLmQaWOMQVNZ8JystxIcCavwd4sJAZlvVkQIAeHtJ0tmTBR/fC4A0+Y4OpRDeyLsFVZRYVka3nhhNzrbYiWryCMj2Qmfb9f+Ydy2ZTcKlotYREMkrMO2Xbx0YARf+/FOnLeuE0cGMzLtzp14w6SpDEIAtuMil7fxs617sLw9UtNsPRcCP9u6B7m8jZYmA4wxcCGgKgzNMR2jaWtKr1fJbP7+zBd0jqqj81NdPecnkYg0bJXhZBsbx18bC5aLwYwsGiYA5PI27rh3FyIhFe0tEShMg+242H84iX//ryfxnjesa8jq8HiHj6WCccsp83NWlerXaS53PiGkKcF4sbQtjIPHMlPKegpeTwj87IE9yBcctMQNgAGqomJJexSprNwyBQa0xHRoKkOuYCObl3VB1q5owX/8/Clk8jYKlgsBGSBzLpDJWSiYsnBoWFfLfC5vzOZyvGpPhDGaMYOibkIIaIqCN12wCisXRcuOvY1E1yc6BwCdA+DkOAdTGRvrCrBXrVoFIQSuueaast9njGHXrl01vZafqj0wMICVK1cGXx8YGMCaNWsmPL6rqwu/+93vSr5mWRZGR0exePHimt6zEa9RjuPMz1+YRnBdfsI/PxcCz7x8HOGQirZECELI1RJDVyGEwNBoAXc99DLChoJ4xEDU2482MJKHywVam+Tst6IwGF4K42jGwo49x6AqDLbNg6rclsOhKQwlQ7kQwX6yMvFtYDhVgKoCqqJAVWXblqBKuSvAJ5R8kZ/t14/sR76oSnjedJDyqr9y08HvnziERc1h+P1H/N6i/r42xSvs4rhyRf/IUBb7DidrKmLT25/CkaEsomENABvXt5QhGtam9HqTmY3fn/mGzlF1dH6qm83zczL9XIqvjXlTFs3iQkBhMqPJ3zls2lwWGdMZdE1Fc1x2d/j1I/tx6vLGpyVHQypUdWzcKiaECIp++e9absgSAFI5C4mogb5jafzzbTuQzFpBsbTiThmT6e1P4ejxLJpiesn4BABNXuuuVM5C1nTATBeqyrCsI4bXn9+N327rQ86Uq+0CMsWbMQZFZXC4ABcyi2wkbSIcKr2N1TUVmpdtxQCEDBWdbdGgPkm24KC7qwmv3rDkhP5e0vWJzgFA5wBYOOegrgD705/+NEZHR/H2t78dixYtmtYBrF27FvF4HI899lgQYKdSKezatQvvfOc7Jzx+06ZN+MpXvoK+vj50d3cDALZv3w4AOOecc2p6z0a8Bpl5kxWQKW7VVa7tlu1yOC5HLBwq+b4Qcs913nTRFDVKqnDHwhpGMyZam0I4njShawqaYwaGUgU4XECBDGCFABwuA3pRJt2u+CZGQAbuIV3FisXxmlpijW9Dljedkhs5FXI2fyRtgnOBWFgLqqiq4wrYwNvjZjui5oqp9VRpJ4SQE+1EFxrzr41qmMnrrxgLAGUQKYLjGkkXsLg1AoBVbAXZKNXaO1q2G+y/hrcvuRx/T3Q6Z3mFNDma46GgWJrfKaOWPtWZnC0nd1UFXEx8v1hUh8MFrrqgG12t0eBn5499IV1DrmBC8c5tcIxM1h7RFMUrauYgZIzdygoh31MAyBbkmOjXACjYHLGITp0zCCEzrq4Ae9euXfjCF77QkGrbhmHgne98J77yla+gra0Ny5Ytw80334yuri687nWvg+u6GB4eRlNTE8LhMDZs2ICzzz4bH/3oR3HjjTcil8vhhhtuwJvf/Oaa22s14jXIzCrXamT87Hm1IFDeUMgZ7OL41y/owpgsimY5HCG9dA8XLwBnn9aBh585GrS2amsKYdRLA2QMCIdUKExWES+YbvB8VWFwuZiwOmDbHLHw2MA+2U1h8WcTXtXTkhs5BjAOREMaLMdCwXbBGMOEewbvQLjAlCqm1lOlnRBCTqRaxolG86+NBdOF7fCSALD4yq8qsgCm5XAY3j7heiYma51AqNbeMZW1wQC0JUJwuMBwqnytGb/atu3K7VOJmBFc//1srtGMVVOf6nhUpn7bLi+bnu54xcxOXdZcMtngj32KziC8wFygaGwLemarSOXkZ2v2gmi//WQsquPKonaZU+2cUYu5VkGeEDK31BVgL168uOz+6Hpdd911cBwHn/3sZ1EoFLBp0yZ8//vfh67rOHToEF7zmtfgC1/4At7ylreAMYZvfetb+NznPodrrrkGoVAIV1xxxYRWXNU04jXIzKnUamT87Hm1IJAHgTSD4g3uBdPBaEb2AIW30jCULKCtKRSkmfmB4/pVbVi1JFFy8xaP6GhpMnD2aR1Yv6oNyxfHsX3XMfz4v19Awesx6mVrTwiwGQNec/YyrOtpq+mmsPizCWDCjZz/BiFDhW4qsGwOQ1PgcB7sKwdkSxK/uvnyjtpWz4HqqyGVWotVQjcihJBG8S8du/bXNk40mn9t7D2ahgBQLsfHD1S5GBuLgKlPTE51AqFSe8fOtgiOJwtQVQWMibJjlHfkEEKmbqoqK5sZ5q/C9/WnwBireF1f2dmEJe0xHDqeRXNMB1DbGBKP6uBCYDQ9Nla74MHWJ3+jlqapiEWAjpYwRjNW2SD6deeunJGxZzYmdggh80tdAfb73vc+/Nu//RtWrVqFnp6eaR+Eqqr4+Mc/jo9//OMTvrd8+XLs3bu35Gvt7e345je/WdNrv+Utb8Fb3vKWCV+fymuQE4cLgfu29cnelvGxViPlZs+rBYF+Gremyf3VBdPBkJdi7X8PAByXYyhVQHsijJChlgz6CmNY091adYA+/4wu/H7nIfT2p8GFQOW6DQyRkFbT5MGa7lYIIQP6oWQB0bA24UbOFQKGpiBkaGiOy/3mfqVzv+2Y66XCa4qKsKFNKS2u2mpItiAL0dTyenQjQghpJNPmMG0Xjz7fD0BMOk40ejLPvzZ+777dyFsOBFgwq8rFWL9lATkO+RO8U52YrHWiebw13a0Ih1TsO5yCYMDqpc1Y2RnHv/38aRwazCIaUoNjcidsbxqrK9IU1UvGVJ+mKUhlLdxx/15k8nbF67rCGK66oEeOIWkL0RrHkJxX2Gx8GrsQMrVdYcKriyLban7kbRtwaCBTdoxWGGt4Kn69PxdCyMJSV4D9wAMP4NChQ3j961+PRCKBeLy0zQFjbEIRMUJqMX7vcbFye9gqBYE504GmKdC8an/JrAUuAE1R4HIeFCVTmLzJkMVSJgaikw3QCmN468Wr8Y27ng2qlAKlq9gKkzdaj+8dAICqkwc//+PLiIZUHBvJw7RkT8+8KfuHCiZLlvuBc7NXKEZTZAuWjpYwBkcLyBWcoOJ3JKRhxeJ4XQFtpdWQWtPsarkROevU6dVwIIQsPPsOJ3FoMIOuthjAgGzeCSpnz/ReZ0BeG9/7hrX49j3PI1dwwIQAY3IyNxLSkMqacF0BQ1ehqQyW7U5pYnIqE83Fr1VtQtMfK3OmI9PXXQ5FQRBQK97EM1Pk+BU2yt8eZvM28qaDoWQBiZhRNcBcv6oNH7p6A362dQ+ODGUnHUO4ENjy2AHoGoMQ3narcXMAXMiCof5YrSnKjPyMy6n350IIWXjqCrA7Ojrwute9rtHHQsiUi2tVDgLjOHNVG/741BEMJQuwbDfY+yzgVdn22n4wCDgux6LmGN568SlTDkTXr2rHJRuW4nc7DwVfKw6uVUVBIqbj8PEcADFh8sCyXS+NUODgsTQiIQ2JmIFYREc2bwepctybvTd0Fc0xA2Gvb2i24JTM5KezFtJ5G/GIjkTMmFZa3LqetklX8cup9UbkjFPa6zouQsjClc3bGEqaEAJojofQEjdgOhzZnA0uxAkpwrh+VTv+4c1n4rb7ZCvFaFhDJKzBcTgsS4PtcoQNFamMPeX9v1OdaAZqm9D0x8qDAxk4eQ4hAF1jiEV0KGAwbReRkIpYxMBI2pyQGcY5RzJjgTGG9uZwTQHmhtM6sLw9gn2Hk5OOIf7nbo6F4HJZe8Tye2YHnx/oao/UNVZPVz0/Fx8XAr39KdoqRcgCUVeA/YUvfKHRx0EIgPqKa1ULArs7m/BfD76EgwMZcMhW1oamIhEzEDZUWI6sNF4oOPirC1dNacAu3lu8uC2CqHdzJVPb5IqGriloS4ShaQzHRwuAwFhFU9NBMjvWP9Xfq6dpY3vfmqIGYmENgyN5OFwgpMtj1zRlwqrITM3k15NmV+uNSF9/Gu1t8QqvQgghE0XDGlQFyJsOHFcgEtIQj+poTYSQNx2Mps0TUoTxjJ42vPfKdcEEbypjQVUZupc04aoLe7CkI4HDx1KIhtQpBVRTnWiudULz+rdvDMbKXfuHsfOFQVmXhANQge6uJlx5vuysUr5YmgUhgJYmY0oBZq1jSPHnNhgLxmjX5V76vUDedHH+uk64XAasJzJQrbe7xtMvDgar+LRVipCFoa4A2/fyyy/jkUcewcDAAN71rnfh4MGDQdstQupRb3GtSgP4up42XHPFGnzjzmegKQoMQ4VR9LohXZXFaAw16M1ZjZyFTuPPzx3F8/uHgzYggIBlu2iK6ggZGjgXUBSGkK5A11TkCraXri7gOBycC29PuGw7IiDgJ5hncjZCuoaIV3hNURS0JsLI5O2qBV3mklpvRNLU5osQMkVd7TEsaomgfziPhKogbzrBym0sLK+dnW3Rmos6TkelCV5DV9HaGkN7XC/p+VpL0cepTjRPdWXV/+eK87srHku5zLD25jCGkgXEIuUnLqabOTD+czPGZJcP7xykcxZM28W92w6AASc8UK1nAcAvxpfL23IfOu3ZnlFUVJXMFXUF2Jxz3HDDDbjrrrsgvL1Hr3/963HLLbfgwIED+NGPfoSurq5GHytZABpVXKtYd1cCyzviODSYRXwaFbF39w7j5398GYcGMiXFYXRvQBVC9rvuaFYQCcsBVhZUE8jm5XsIIXD4eBam5Xp7wuWxFFea5QJIZS2EDTU4Vk1ToDCGv7pwFZpixpwfPGq9EWmiNl+EkCliDLh0w1L88k/7kcrZiIZUqKqCkXQB/SMcHc1h/OWrVshiXuXLZTdUrSu0tRZ9nOpEc70rq9WOu9zEARfALb98dsbaN1b73HnTxkha9sUuvi84kYHqVH8uXAjc+2gv8gUHLU0G/ErqtGd7ZlBRVTKXlL8aT+KWW27Bb37zG3z+85/HI488EvQq/PjHPw7OOb7+9a839CDJwuLvq17eEYNpu0hl5Kz18o5YXYOoH7SHDRWjGX9Pl1xxHs1YNQXtu3uH8d17d+HAsfSEyqu2K5DMWmiKyPmqoZRZ8h5DKTN4jzds7gGErITLICCEAOdjlVsVpbR/qi8ISGMGeroSOHN1O3q6EnN2UPZvRLIFJ7g++EzLQTJjoSVuYEUnZbsQQiaXNx184xdP48s/eQJPvngcq5Y1469fvQpdbRFYtotMzoZlu+hoDuPSDUvRlghhJG3CdnnZatgnmr9H+tBgRm71iRsI6SoODmTwvft2Y8ufe9HbnwIXYspjVvGEZjm1Br7+PuHn9g2htz8FACXjTU9X5eu6H2B2TSNzoNLnNi0HQ0nZu7s9EYKhq1CY3ErVEjdQsFzct60PfHxFtAqfq9Lj6j2+Sj+XA8fSODqURVNs8swCMj2V/n75EzC7e4dn+xDJAlPXCvZdd92F6667Dm9961vhum7w9XXr1uG6667DV77ylYYdIFmY6i2uVe31plIRuzjNKBrRce+fe2U6c3GxFYz9L+dA3nLR2hRCKmvJ1HEAmsrQsySBKzatgOty/HZbHyyv2q3LAXfc8grnAFMEBFiwqu3fuCxbFAUXwHP7hub06jVQPhPBdTlGM3LPOWPA4GgBX/vpU/jby9di5aJo2dehdC9CCCC7MDz98hAAYN+RFP709BFcft5KvOcN63D0uKxQHQ1rWLIoBoUxCAGYlgvb4QjrKmIRHZrKJlSlPhEq7ZF2uYDlcGTyNn75p/34wxOH0dU+tuJW65hV79aqYsWrf47DAQa0xEO4ZMNSXPLKZVAYm5EMs/HKfW4B2V6zNR4KssN8kxUXq2VVcyrjjH989/65F4ePZ+E4AprGsGxRDFdt7in5uWRyNhxXQFeVskH9iSjGtxBQdXcyF9UVYB8/fhzr1q0r+73Ozk6kUqlpHRQhQP09LCsNlrUG7eMHZAFZtdbfV+1yAf8ZxUG25XC0agqiIQ1v3NyNzrYomptC2Li2C39+6lBQ4TUe1mGapaG17CYmX9vlgMLkDYVfyExhQM50ccsvn503qU/FN0oHBzLI5m0IALquoDluQFMUHBzI4D/ufBrXXL4Gp69oKXk+pXsRQnyrlyQQMlSYlpzUPzKUw21b9uDUZc244ryVOG3c9cPHuUDOdGA6LqIhDbGwBuDE3mSX2yOdNx0MpwqypSJjwfgyPuW5ljFruoFvcQVyTVVgOhy2w5HO2fjxf7+Ih585irddKqt2T7d9YzX+2O1ygbdccgoY5Paq/pEc7n20L9j7LYScmPDPmaYyuAUxIVCtpbI6gLrGGflzZLJyKljZLIl4VE7q2C6Hqkz8/nRT6ok0neruhMyUugLs7u5uPPTQQ7jgggsmfG/79u3o7u6e9oERUo/pBmXlBuRs3g7SwgX8PycSAjieLCAS0nDK8mb0dCWCiuH3PtobzK4CwHAKJSspXMi93IAMsLkAcnkbuq6iNW4gmbUwki5UvEmYqwHnup42nLayBV/44U64LkciZgTFawDA0BUkszbufbQXH3nbhuAGsJYbo7n6mQkhjbesI44b37MJd/7xZezcOxh8/aXDSXzr7mex8dRF+MtNy9HaFC77fNeVAVjBchGL6IgY6glbzR6/R1oI2YLKD67BGBiX24Na4saEFbdagoJ6A9/i1b+woWI4ZQZp6owxOFzg0ECm5Lrb6AwzoPrYfeqyZmheCrzfvsvvvuEXOwvpakmgWsuq5s8ffAl5y53SODNhbKqyF3xlZxOWtMdwaDCLSEgB54CiyNT2qdR/IdXVW4OAkJlUV4B9zTXX4IYbboBt27jsssvAGENfXx8ee+wx3HrrrfjUpz7V6OMkZFKTBWWXblyK5/YPVwy+Hc5x18MvI5OzgkAQ8CqNMxkQ8/Jb3AKOK1CwXOTyYxfyfYeTODqUDWZXLdud8DwhZLqg36M7ZKh404WrsHpZAnc9tA8jGXPepj4dGsggmbXQHA9NKIzDGENTRMfRoWwwu0zpXoSQcjpbo3jfG8/AeeuP4/7HDmDfkbFsuadeOo5n9w3h/DM6cdkrlyEanrgqKADYDkcqY6GgK4hFdBiaOmE/caONL/poeSvEfhDrv72isGmtuNUT+Pqrf9GQhtHMWNAfFNhUGFzOkc3bJdfdejPMypls7P67152OrrYoevtTMC0eHKNfw86yZU/vbMGZ8LkqrWpGQyoOH88ipKs19/Se6tikMIYzV7fjhUOjSGbk1igA0FQFuqogFtWnnVJP6qvuTshMqyvA/pu/+RsMDw/j29/+Nn76059CCIHrr78euq7j2muvxTve8Y5GHychVU028A2NFvDL/9mPsKEgHjHKBt879gygr18WGzHtAnRNQXPMQDikIaSrKHipiaoi90+Xo6my9/WWxw5grTeLncpacFyBqDe7yrmsvK8y+d/+rZ0Q8nibYjosi6OzLQrG2LxOfeJC4KXDSRQsF4ZWfnZZ1xQ47lh6H6V7EUKqWbE4jvdeuQ4vHBzF1u0H0T+cAyAnKR95th879w7iko1LccGZS6CXue5wISdCbYcjHNIQj+gyWJuhOHv8Hmn/uu8fmSsEDE0JgoPprLhNNfD1V/+EJrc5yStu0XWXIWiXNRPX3VqC1i2PHcAZ3S3Y3TsSjJeOkFu1GJNjsqEq+O22PqzzgtvJVjUF5O9LqKhbR/CRK4wzUx2bdvcO48EnDkFX5c/VdWVhU9uVq+9XblxKmVgN0IgaBIQ0Wt19sP/+7/8e/+t//S88+eSTGB0dRSKRwIYNG9DS0tLAwyOkNpMNfLbL4bgcsfDYKur44FvzJj4VhYGBwXI4hlIFtCfCaI4ZsOw8uKgcXOuagtamEFSFBYPsqctbkIgZQXqboauydQzkjZCiyhsMIYDWRAixsAbb4XBVgXhUn9epT37K36HBjOxTazowdDWYtPDZDodWNLs8nz8zIeTEYIxhzcpWnLa8BU++OIjfPX4IyawFAChYLrZuP4g/P38Mrz1nOc4+vSO47hZzuUA2b8OyXEQjsn/2TLT1Gr9H2tAUWb9DyOBaYUBzzAgefyJX3GT7LYGh0UJQWNMF9wJXORYCcjtP3nQbft2tJWg9OJDBwYHMhOcK71/N8RDCRukEwGSrmrYtB/JKE7/lxpmpjE1jEwcuutojcFwO0/b2jTO52v7c/mG87tyVtII9TSei+B4hU1VXmy5fPB7HRRddhDe+8Y1ob2/H9u3bqcAZmRXBwFdmsLRsF44rZ+bHddgqCb6jobEBnjGZGscFkMxaCIc0tMRl8Dz+Pk1VGFqbQljSHkUkJC/sbtGK7OplzVjSHgtam/iBvSvG9nMbuuoV30FJq5NGtV850YpbZkRDWnCDY9ouhlIFFEyZyieEQDpvY0n72OzyfP3MhJATT1EYzlmzGNe/fSOuOHclwsZYMJXKWrj74X345l3PYHffSMVUcNvlSGUtjKQKchV3Bm7Ei9tPutzvJCGgqwztiXAw6diIdldTkcvLfelO0eDoB/+OK+ByHqwKzsR1t9rYDcigNW86sGwXTGHQVQZNVaCpsrgZUxjypjNh3K3WKlIIAdN25QRChZ91uXFmKmNTMHEQ0cC8lPuQriIS0hAyZNYEtehqnEa3dyVkuupawR4YGMA//uM/YvPmzfjgBz+IH/3oR/iXf/kXCCHQ0tKCH/7whzjttNMafayEVFRtttqflWeMTVjFKA6+FUWmd1uO66WeMahM9qQ2LQcOF1izsgWbz1iMn/7uJWiqgkhY8/Zoj73u+IFZURiuuqAH379vF4aSBTnIGios24XtFbaJR3XkCg5yBQchQ8UbzpOz2vMx9alcyl9zDLJiLhdwucBoxkSbwpArOIhFdFx1QU8wuzwfPzMhZHbpmoKLNy7Fq9Yuxh+fOow/P9cfFKccGMnjh1v3oqerCVect7LstUMIwLQ5bNdE2FARj+hQlca29SreI71r/zD+8MRhOJxDURi4ECd0xU32hk7j5398GaoCOBhbvC/+yEIAiag+Y9fdyVaa8wUHnAvEIzqcnOxEIYfxsWR22+HIe0XdgnF3klXNaFjDopYIRtJmzePMVMamXfuHJ504oEysxpqJ4nuE1KuuFeybb74Z+/fvx1lnnQXOOb7zne/gggsuwK9+9Suceuqp+OpXv9ro4ySkqmqz1X6BMlVlE9LBioNvVVWQiBlQGIMrhEzdhvAqvtrBTc/mM5eiZ0kC8GakAbkymzcdmJaDTN4uu/oQDeswbRcjGROjWQuMMYR1BYauYCRlYjhVgOnIgH/LYwewu3c4uEkIGypGMxYs2wUXApbtYjRjTXojJm+iUnhu3xB6+1Nle3E2WrmUv0hIQ1siHFQQt7ybnBWL4/jg1RuwftXY7PJ0PzMhZOGKhjW84fxuXP/2jXjlaYtKGnL19qfxnXuex48feAHHR/Nln8+5QK7gYDhlIptvfPDj75F+w+YevPfKdVjeEZ90xa3R1/HdvcP42n89hW/e+QwODmRg2VWqd3rpzOOvu406pslWmnMFR6aKRzS5f12I0sexsceNH3errWq++4q1eNulp0xpnJnK2ESZWLPD//t15up29HQl6D6BzJq6VrD/9Kc/4TOf+QwuuugiPP744zh+/Dj+5V/+BWvXrsW1116Lj33sY40+TkKqqjZbnfPSxzR14nySH3xrmgy+GWNoS4TH2oB4A3lXewRvvfiU4KbHf6/jyQIclwfFSwRkhdAzV7UFF/anXxzEbVt2I2856GiJBD08TcuVqeKuANNlAB4JayXF1951+RrEwhoufsUSPPHiIEbTVs3tV2arj3SlfWqRkIawocK0XaSzNt64uRt/ed5KtLfFMTKSLXnsTPZaJYTMbwwMYpLN0q1NIfzNZafi1a9Ygq3bD+CFg8nge8/3DmN33zA2revEX5y9DE1RY8LzHZcjneco2LKtV1hvfFuvWlbcGn0dH9/zGgIoDgFls8giAmhvDuPtl50avF8jj6mW/bOqysA5kIgZGE4V5L51jKWyC4GKE6+TneOpjjO1jk3+xMHhwazc2198ShuYieX3DqcVW0LmlroC7Fwuh66uLgDAww8/DMMwcP755wMADMOY8ZYXhJRTeeCL48xVbfjjU0eqBt9+4AsAzXEDEAKpnI3WphDeeEEPImEt6A+6rqcNl25cil/+z344zlj7Dd1rv/HHp46gu7MJZ5zSjjv/8CIKlltSITVkAPGIwOFBGVguXRSFosiA1NBVaCrD4EgB37rrGRi6Aga5/60lHsI5p3dg/aq2qgPpbPaRrpbyx7zWJeGQilOWN1e9EaB0L0LIeLrGEAlrKFgOXHfye40l7TG8+/Xr8PLhJO5/7AAOH5fXXC6Ax3Ydw5MvDOLVr1iCi16xFCGj9HolhGwB5bgWCpqCeFQWrGzkLU61qt+Nvo4Xb9+JhjTZ3qrM4/xEL5cDigL83eWnY/XSloYd0/igcE13a8Wg9fXnd+O32/pwaDCLlrhRMgHOIYPVaFjDe65cV/F9q53jesaZWp6jMIbXn9+N27bsRv9wHtGQinBIa+g2gNmaRCeETK6uALunpwePP/44Nm7ciK1bt+Lcc89FKBQCAPz6179GT09PI4+RkKrGD9YfedsGHBrITBj4ujubKgbfD+w4iCPHc+NuNgQYGNI5G7f/dm/J4LWmuxXP7R9GJKQimghBCLnX2tBlT1W/H2YsquPwQCYodFIsWCFnDLYrEPJuavKmg9G0GQT7lsPlfu+QiqFUAQ8/cxSrllROfZrtPtKN3EPdyF6rJxqtLBDSeAwMiaiBaEhDznRqDrRPWdaMD/z1mXj25SE8sOMgRtImAHl9/cMTh/HY7gG85uxl2LRuMVRl4laivOXCcguIGJq8nmNm/y7PxHX8wLE0Dg5kYFousvmJKdmAt3rtTYS6wiv4xpSSY8qbNmJhHS4XEA6HoSloiRs1HVO1oPD6t28se81UgJIV7kUtYRRMN6hZ8t43rMXa7lbsO5rCviNJMAGsXpZAd40pwvWMM5M9Z3fvMH67rQ+OV8MlX7ChKAyRkIYVi+PTDoJncxKdEDK5ugLs973vffjkJz+J73//+8jlcrjhhhsAAFdffTV27dqFr3zlKw09SEIqqTZYn7m6veSxlWad9/aNjD2oqLK3EACDgK4piEX0ksHr8k0rgn3GflBtORx504GiMERDsmXIy4eTcFyOSHjiPiteVLXV/++86WA4VYBTdMMohAzGbYdDVxlcR1S9iZntPtLUMoNWFgiZSUIIqMrUA22FMWw4dRHOWNWG7buP4Q9PHEauIDsaZPM2fv1ILx55rh+v27QCZ65qm3D9dF3Z1sv00sYjRuPTxn1TvY7XMqG3a/8wsnlZKExTFAgG8DLnTAjZZ1phQEhXkfP2ogcBus2RKxQgINO0dU3WL5lsbKk3KByfneYW5DW1Z0kTrjy/GwDwz3c8jsODmaCwnaowLFsUw9uKUttPlOLPGY/qaG8OI523kcvLtPw3nLdyWsc025PohJDJ1RVgX3XVVViyZAl27tyJc889Fxs3bgQAbNq0Cddddx0uvvjiRh4jIWXVM1iPn3X2ByouBJZ1xGQPaq/Kte3I6uK5goN4RC8ZvB56+ggcVyAWkS1Egj3bkDccmqZAUxgEZEsR23YhwGQPTEXu9y6uaK4ozCumZpUE3uPZroDLHRwcyFS8iZkLfaQX8h5qWlkg5MSYEGgXbBRsd9JAW1MVXHDmEpx9egcefvooHnnmKGxXZgwNJQv46e9exIrFcVxx3kqsWlJ6jRWQE57JjImCLquN65ra8K1xU7mO1zKhx4XAzhcGIeD1t2YAxIQd1/Izeu0koyENYAgKcfkBOiDHLMV7tuW4GE4V0JoIlbTKKjbdoLDaBPl3790V9EBXGQOYnLQ+MJDBd+/dhfddtb7qNbeR2UbjP6eiyAKq8YiOaEjDaMbClscOYG1PW93vMduT6ISQydUVYAPAOeecg3POOafka5/85CenfUCE1KJRM7jjBypDl+2zXFfIGX4I2A6H5fCgHVcsrGE0YwJgyOZtGRR7e7P9Gw7bdmFDthNpiunoPSL7w/sBuKGrSMTGBkddlZW1bX8/d2mR1OC58rMjCOrLnZdUTh5P3muBNZ7jcCgKkMpZeG7f0IylLy/EPdS0skDIiRcE2rEQIi5HvsZAO2xoeN2mFThvfSd+v/MQdu4dCFakDw5k8N3f7MLalS143bkr0dUWHfeeQMFyYTscYUNFLGJAVdCwFe3J2lf5VagHk3ls3X5w0gm9A8fSGM3IllR+a0rZn7n0mDWVoTlmQPHaKK7sjGNlZxO4EHjixUHZkUNhwfWLef+4QiCZsRALacHY0twUQnOzPG+NCAorTZCnvYBeLypkyhTZZzyds3Hvn3srXnMbnW10IoLf4skXP3uuePKeWoARMvvqDrAfeeQRPPjgg8jn8+C8tA0BYwz/+q//Ou2DI6SSRg1i5VYJghVkBjCvwmrxqrKmycqrzXFd7tv2bu5Y0Q2HCxlw3//YAQynTBQvSgsApuVi0HYRDcnWI8ms7a14o9yCQgnFm53PjGsh498oHB3KBoNrOmehJR5C2KtiKoRAMmuBAbj7oX1wvANriRu4ZONSXLJxWUMDv/m8h7oetLJAyOwRQkDzAu2oy2te0W6OGXjLxavx6rNkxfHdRduG9hwYxd6Dozj7tA689lXL0RwPlTzX5bKuhGlzRMOaXPVtgFpqWSxbFMXOvYM1TehlcjY4lwU8R1JmUIlbYUDx6RECGMlYEELWIMmZLvb2jSAS1jCatmSAzkVJj1fmTS7bNkeeubjroZfBuQzWV3TtxxWbVoxNXDcws+rAsTQODWa9n3vp6zLGoChyQvrw8WzZa+5MZBudiAwyf/Ilm7eRKzgl2XO6piAa1qgFWBGqh0JmQ10jwa233oovf/nLCIVCaGubuE9p/P8T0miNGsTKrRIEqdtibMW5OJ3bcTg0TcFZq9txeNAvjDYWHLtewB3WVQyMyF6rqiIrsvoEZBP6RS0R/M0lq7HlsQM4NJiRaYZlVq+L+QXVmopWpyfcKGgKhpMmTJvjeDKP9kQYqqogmbVgWq7s380A05KDczpr4ccPvICHnzoyK3vWThZzIT2fkIWueEU7CLQtN9ifW8ni1gjedfka9PancP9jB3DgWMZ7PWDnC4N4+uXjuODMJbhk49IJrZcclyOds+Q1OCKvwdNRSy2LV61ZLItp1jCh5491mqKUtqIEk5O23jK2y+Xea8OrPTKSNnHH1r24+BVL4HKBlriB4bQJh4sgHRtC7k8X3rkPG/JYXZej92gKt23ZjddtWlHTivxUgsJMzobjD6xlBkv/S44zMW19prKNas08mE7wu7KzCU0RHQcGMsH9SZCub7swbRcrF8en3QLsZED1UMhsqSvA/tGPfoQ3vvGN+Jd/+RcYxsT+kYTMtEYNYuVWCfwB1rTdIJ3b8G6Wiqtgn76iBQ89dQSW4924cSGfrylIRHWMZrw9YQqgqkqQPsgF93pvK0hlTUQjOq5/+0b09adwx/17MTiaR8F05c1KmWNmDIiGNTTF5N+9cjcKBlSwZoZk1oJluxhKFRANaXC5LNoWj2gYTsuVddVLhedc4NBgFrffvwfvvmItDT51OBE3V4SQ2pQE2iGOrGnDrCHQ7ulK4O/fdAZ29Y5g6/YDOJ4sAAAcV+Dhp49gx54BXPbKZTj/jE7ZSzp4P6+tl2PBtjkisRCms95QrZbF68/vxtGhLAqWG4xP4xVP6K1f1RaMdS1xA2EjEqQWMwgMjhbAGNCWCENTleD65XfFeOLFQSjeWNaeCCPpBejjJ4Rbm0LBc1VdRSSk4fhoAY/vHWhYdwlfPKqPnX9/NryIf2iaNvGaO1PZRsX3FJrK4HAB03blcSisYf2v/V8s+bFZkHEX9IenhS6qh0JmVV1TrMePH8fVV19NwTWZNf4gli1MbDXiD9ZdbdFJBzF/lSBsqBjNyGCUCxGk+QnIYNafGR7NWEEV7KFkAaaffui121L9/WuqEsysK0Wpa4zJgFZRGFxXwLY5MjkbCmNYtaQZb7/sVDRF5f63csOjrikIe20+/M9W6UYhEtLQ2RpBU1SHEIBpc1iWnN0+niyAc5lOyZg8D/4qfa7gBIXfyNQ06veSENI4Qgjv2hxCW1NYptAq1QMQxhjOWNWG//s3G/BXr15VkjGUNx1s2daHr/3XU3jyxcEJ10ouBPKW7AiRzFrgov54Z11PG65/+0Z8+C1n4b1XrsOH33IW3nDeSmz5cy/u+Z/9yBccHBvJ49hwDgXTKXlu8YTe+LHOdjh0TYGqMGTyDgCG5ngIUa8zRvF5iHnp4S3xELJea6zOtigWt0awqDmMlri8F9R1ZcLEImMMsYiGYyN5nLOmY8JYO35cncpq8crOJizviIExBmfcVkUhhDd5ACxbNDGgDbKNqkxOVCrYVo1/nhUGHDmew8BwPvjnyPEcFIZpd9E4cCyNdM5Ca1MIIV0F9z4rFwIhXUVrPIR0zsKBY+m632O+G7/wYOgqFG8BpSVuoGC5dJ9DZlRdAfb69evx4osvNvpYCKlZpcC4nsHaXyVY3hGDabtIZSyAycF75eI4ACCVsWDaLpZ3xHDN5WsAAPdvPxAUh1EUBpUxOK7AUKqAfMGuWuyGQd4AQCmdWQ+OZXFcFqCB3CMn26DoCBuyam3xZ6t2o1CwXGTyNrjX6stvP8aDf8YO0q+tFtLVYOaeTE0jfy8JIY01IdAOTR5oqwrDees7cf3fbsRrX7Uchj52nR3NWPjFgy/jP+5+Fi8eGi3zfrL913CqgGzBmfD9Wvm1LM5c3Y58wcH37tuNFw4mkTOd4JpesOTEqR9kl5vQKzfWmbaL9uYwIiG1bFFMwAs2ucA5p5cGyJrXDSNvumBM7u8ut0XQD1Y7miNl398fV6e6muhfb5u8MdR2eRBoulyu5TZFdVy1uWfCNbc426icRmUbCSGCfxrFH/NjEb1komNxawSdbVHEonpdkwMnk6lkKBAyE+pKEf/MZz6Dj3zkI4hGo9iwYQMikciExyxdunTaB0ca42Qt8DBZK6g13a3o7U/V9LkrVbwGvNnirIV03kY8oiMcUnHXQ/u8G5OQV8RM7ltTGeB4lUvlarVszTX+Js5P/+5oDk+YWV/X04b//zWteOjJw3jo6SOyYrmQrWXK7R2qlJYshMBI2oQ/sT++mA0g980pWnGqmVyFKJjuvBuc58rv+UJuUUbIfBAE2vEQHIcjU7BlEa4qqeMhXcVfnL0c567rxINPHMZju44FE5RHh3K4bcsenLqsGZeftxLLFsVKnjt+f3ZYr69/NhcCP3/wpaBQpeq1d/S3IbtcYDhVwKKWSLBPu3hCjwuBSFjD5ZtWBONZImaAC+CWXz5bMoZYthtUpgYAVWVYv6oNq5YkgmtbNm8DDEjEdIB5LbLKKA5We7oSDe0usa6nDe+7aj1+/seXx/pgi8n7YNdSRK5aKnel8WZ860+ZySanrzW1MZ0kxo/547MGaCsS1UMhs6+uAPsd73gHOOf4zGc+U7Gg2e7du6d1YKQxTvYCD9V6Y37tv56a0ueuVPE6X3CwdcfB4LUEBHIFB4mYgUhIR1uCBQVjOLzWJxBY3BpFKitn6GXhGLlPSnABx9sL/daLV5cdZBXGcNnZy3HJK5dNeiNS6UbBtGULmeA1FQbuipJ93QJ+hXQBDsDQVHnjNs8G57n2e74QW5QRMt/4gXZLPATbcZEtOJMG2vGIjjde2IMLzurCA9sP4tl9Q8H3XjqcxEt3P4sNp7bjivNWBi2q5HuN7c8u6AriEQOaxqYUaPf1p3D4eDYIrhljgDfe+Cu2tiuQLdhY3hEvuf5Vu0au6W4NxhDX5Ujl7GDsEELWFlnubUtSGMOa7lY89NRhPPTUEYxmLORMF5btYnBUTjpHQmNjhxAC2byDZUXBaqO7S/iT0r39aew7kgQTwOplCXR3JUomF8ZfjycrIlcp26jauYx4K6PFrT81b8uYEGhIJ4npTg4sBFQPhcy2ugLsz3/+840+DjIDFkqBB3+w9gfQ+7f14Q9PHIbD+bQ/d7lzmM3ZcLlAMmNCVxVEQhrChjpWMIbJoPzCM7vwP88cBWMMlu0Ge7KFkCnff33RKqxf1V7TZ5vsMeVuFPJFKYmaKm/GVAVBay6f//+KIlcicqY7rwbnufp7vtBalBEyXwkhVxeLA23TW8GtpD0RxjteexouGliC+7cfwL4jqeB7T780hOf2DeOSs5fj1Wd2ImyM3WrJ/dkuLLeAiKEhFtGgsNoC7X2HU3B5aVtIQE6eMq8SuMuBC87owtWXnRoEh7VcI688vxvfvXcXBr2Cbn6FcOFtJUpmTOztG8G6njbs7Rsp7b2tKdBUhpG0icHRAlqbZPqy63DkTLfmrTHjg+Dli+M4NJCpaZJSYQyrlySwesnEa261gHiq2UaTncuLX7FkxldOa6kwv9C3ItEkBJltdQXYf/3Xf93o4yANNlMtKOaq4h7QqawNzgUMXUHE0ILCFlP93BXPoaEGqWDJrIVwSANjTLa+AoK9aetXteHU5S24f8dBHOhPwbJdMMbQ0RLGX198Cs6YYtBXLQW6XFpyUGTNK2I2GSGAZMZCLKzj9Sd4cOZC1JzOP/55C+n3nBAyc+oJtJcvjuO9V67DCwdHsXW7zHQCZLr2Hx4/iEeeOYJLNizFBWd1wdDGVtJcVyCTl1XNYxEN4ZBWvm1E8fH5l7AyFbMZY2ACYEygrTlcsnJb6RophEAqa+Ouh1/GJ/7X2WiOGUh7gR+HrEpt6CoiIRWmxeXjVjSXfb2mqAFNYRhKmUhlLTheEbWeJQlcsWkFTl/RUvWzjQ+C/cJdisKgeAVEa81KKh4rB0fzuH/7AZi2W3Fy4fq3b6wp26iW8cavtj7TK6e0Fak6moQgs62uABsALMvCnXfeiUcffRSDg4P413/9V2zfvh1nnHEGXvGKVzTyGEkdZqoFxVxUPKPs38CoCoPtFRxrT4SDIHgqn7vSOTQ0BbqmwLLd4B//ZsWyXaSyNjrbIli+OI6woWHzxuV4ak8/kmmz7lThWlKgx6clp3Imbv/t3iDdUQgxYfW6mF9pXFMd3P3Qy+g7msL6VW0zntr89IuD+NnWPTgylJ1yevdC+j0nhJwYxYG25bjITRJoM8awZmUrTlvegqdeOo7/3nEQyaxs02haLh7YcRDbnu/Ha161Amef3lFSk8N2OZJZC3nLRTyiw9DUigWxVi9thqowueWoTJ8J12tLtnppc/C1ctfIgukEbbaEEOg9msZNt+1AOmejoyUMMFk7RAYjNlJZe8Lj4hE9yM7yA+FIWMdiVUG24OCNm7txencrNq7tQjKZq1hMDAB27R/C97fsgWk5iIZ1KDowkrK91XqZLaCqSk1ZScVjpeMK5LyCo+3NY+3Dyk3A1jI+1DLe+NXWh1LmjK+c0lak6mgSgsymugLs4eFhXHPNNdi3bx9Wr16Nl156CYVCAX/84x/xxS9+Ebfffjte+cpXNvpYyRTMpQIPjS4+Vfx60YiOe//cG8woFyzZP1phgMZkD8pk1gpSuF2Xe0GwNelxVTqHjDEkYgaGUwW4roBpuXA4R9JrfcIAHE8W8G8/fxpvunAVLjw7hlVLEnA6Kt9gVDOVFOjitGQuBP778cM4cCwN2+U1twwwbY7e/jT6+tPYuuMgViyOz9hgtGu//Gy5vI1oWJtyevdc+j0nhJxchBDQiwPtvA3T2wpUjqIwnH16B85a3Y7HdvXjj08eQc6r6p3K2fjlw/vwyLNHcfmmFVjb3RoEX0LIQNx2OCKGrOYtC5iVvn5PVxOWdcSDa3pxoO5PpC7riKOnSwZvXAi8fCiJgulC1xQIIceroVQBXMg0cMFkHY6hZCF4/3BIRSZnYzRrwmt24aWgI3icqsiU8OI6H7qmIBHVwQB0tkWxakkiKJJWyfO9w/jOPc8jZzpgjMG0TVl1G4CuMrhCnrvOtuikWUnjx0pDE8jk5ETHcMpEW4Ih4rXgrGcCttbx5pzTO/DwM0cxmrEQ97YAWLaLTL7+ldNK9yu0Fak6moQgs6WuAPvLX/4ystkstmzZgmXLluHMM88EAHzzm9/Ee9/7Xnzzm9/Ebbfd1tADJVMzVwo8NLr41PjXEwByBRuJmGwP4veP9jPoVMZgORxH/cd7dyw/+8NLCD28D+m8Xfa4uBBI5WQQni84E9qXREIaEjEDqayFgu2ikHGCvdUtcSOYbb9ty27Em8JYuSg6/qPUZDop0ApjeNulp+C79+5C2ts3Xgv/fLhc3owdGsxMCHYbMWnChcC9j/YiX3DQ0mTAz3mcSnr3XPk9J4ScvIJAuylcU6CtawoueeUyvOa8Htzzxxfx6HP9cLwWDgMjefzwgRfQ09WEK85bWbKSybnw0tI5omEN0VDpLZp/Tf/2Pc8hk3eC1/TFIxredukpUBgLxspDg1nkLQcF24XhtdviAtCUseCeMYZ4RMdw2sRwyoSm2ShYbvC6xW/jcg4uZIsyvz2lP+haDsdQqoBoWK/pmru7dxi33bdbriwG+8jlP/6xqYzBdniQKVYpKC43Vha8oN3fny47cgioquzXPdUJ2FrHm+Jq68e8lVMhBFriIVyyYSnWdLfW9H7F52kuFfGcb2gSgsyGugLsBx98EJ/5zGfQ3d0N1x27CIdCIfzv//2/8alPfaphB0jqMxcKPDS6+FTZgmNegJzKWtBUBWFDBmeW4waBNucCjpBVvAXkKkP/UA6MAa1NISTiRslxvWJ1O144NIqRdAG5gmxXlc7JtK+wd8MjhIDjCpy6LAHT5jg2nEMiZiBUVMxG1xQkMxbu/MOL+MjfjG2bmEpwOj4lza9w66fkGZqC/qHKM/B+C5P7tvVh3+EUCrZb5l1KMW/PNvNSEaMhDTnTDYLdvX0jDRnsDxxL4+hQFk0x+dmKV2tqXV2YC7/nhJCFYaqBdiyi48oLenDe+i787vGDeOrF48E2697+NL5zz/M4Y1UbLt+0AotaxtqdTtbWS1OVoEWXjzEGTZUrq+PHStNyYHlBKvdaWPmfhwsBQ1MRDWtIZi04roBrVR4nnOJvCQHBACZkEKsCsF055i5fHK96LosDYsYA5k2Os6KN6C4XUL041j/HlYLicunbStEkAueAxTkGRgvyWFWGprA+pQnYqYw3frX1/3nmKP7nmaMYGs0Hk8Y7Xxisebys9z5qrrStJGShqivANk0TLS0tZb+nqipsm9IxZ9tsF3hodPGpSq8X0lU5O81lkB02ImPp295MPeClt0F41VYZGBNgAHIFR+5701W4LsfxZB6/23lInkOFBTcxpi2/5+8F88/hprWduG9bH5rjoQkz2owxxCIaDg9k0NefxoqOOJ7vHcYvH34Zg6MFcCEQ0lR0tVcOTv2UNFfhOJY2g0rlY+8hb5Z27R+uGIT6KVJ/fu4Ivn/f3knPdfDj8GYoiluLPPTU4dLqsdOYNMnkbDiuvGHlYuINai2rC7P9e04IWXimGmi3NoXwN5edile/Ygm2bj+AFw4mg+89v38Yu3uH8aq1i3HZ2cuQydnIFRxEwxqWLIrBcQUKmmzrpagMW7wey0sXRWG7Y5OtusqQzNq498+9cvW2aKyUe4LllibATycX3nYqFmSAyXFVVK21pioI+m5zAXBXvhYwNh4pCsOhgQxOXd5S8XX8gDga1mHavKhwGwteTwT/GguWK2UllUvfNnQVqiKz2IoJATiOwEjGQmvcqHkCdqrjzd6+Edy/rQ+mzRGLaFDVqY2X9d5H0Yo3IbOv1m2ZJc466yz85Cc/Kfu93/zmN0HKOJldfoGH5R0xmLaLVEb2ZF7eEZvx1kVTKT41ndczdFUWGANkGpnDEQlpaEuEg9l8711haCoSMQOcC2iKAkVhwXMKpoPhtAnujcOaIvedOS6HALzCMsBQqgDTGjuHHS0ROahrlfdkydUIG/c/1odv3vkM9h9JI5u3kS84yORt9B1N446te7G7d3jC8+NRHVzIYm1WmSI7QsiU7t8/cbjs830KY1i6KA6lhr/xwfkturHxbyIeeupIMNgbuhpUaG+JGyhYcpXb4Ry9/Sk8t28Ivf2pssGz/9k0lcF2y+9NrzW9ezZ/zwkhC1dxoN3aFELEUKvuOV7SHsO7X78O771yHZYtigVf5wLYvnsAX/7Jk/jevbvw8wdfwo8e2IvbtuzGiwdGkLdcDGcKeOnwKGzO0Z4IQ1EUhHQVkZCGkK5CURTEwhoOH8/i0GB2wkpuU0QvGaeEAAxNRVsijEhIkzVKvDaTmlr5M/AqpUSEAJq8PceTpV37AXEkrEHXFLhiLEgvfneHC+iaEhQSzRYcdLVFJwTFxenbUzGSsbB1+4GaH1/reDMWHLtBgbVy42Wl8RGo7z7KX/E+NJhBSFeRiBsI6WoQ1Fe7TyCENE5dK9j/9//+X7z73e/GX/3VX+GSSy4BYwz33nsv/v3f/x1/+tOf8L3vfa/Rx0nqNFsFHhpdfKra6zXHDBxP5sGFgGXJYi6qt/rMIHs7R8I6DE2B6ae+MdmChGNs9bs0eGUywBTyD1cIKN7s+nnrF+OctZ3I5W2kctaElhzFVVUZZDrf4HAOv/yf/bAdDk2R6dcQ8ubB5Q6QQ9mZ6KUdMdgOR3EMWtypxf+C47qTZgRk8w4ihoa85VS9SfK5QsDwbmws2wUYvFn7yoP9wYEMvvDDnUhmrUlnzld2NmFJewyHjmfRHNNRfFs11fRuKmRCCJkt5Va0nSqB0ynLmvGBvz4Tz748hP/ecRDDadN7HVlk0nbkHuyjQzn88k/78devXoVTlrfg2FAOubyLRa0RCM69fdhjF3M5ESoAJid9i6uFj9ccM9AUMwAApu0iX/DaW2pyxXtgtBA8dmxNubSTmKp4E7Le6rPLBXKmrIZeaWLUT13uH8lBQMC2XTTHDAylCsGE9vgzF9Zl145qWUnl0rct2y05P5Xc+0gv/nLTCmi1zECjtvEmCI4jWpAZ4GNMbu86OJDBtuf7cf4ZXWXHqkzODtqd5U0n2Bbmj7/j76OobSUhc0ddAfarXvUq3HbbbfjqV7+K733vexBC4Pbbb8f69evx//7f/8P555/f6OMk0zAbBR4qFQMRQsi9YJYLAYFYpLZfwWrFRcIhDc2xEFI5Cw7nSGUsGdi1R3E8KQuu+M8JVhfEWEYa50JW/2YM/kY3DjEhCGXeDcR/P34If3q2X/bmVBgsm8O0LcTDGlI5O7iZEUKAKfLc/88zR+C4HJrKxga2oNK5bNUyfi/17t5h3PXwy2OTAv45HHduGICQXrnwy1jbLguGriBkhGSfUrf8DaAQAo5XObY5ZgTBbks8hNGMNWG13p9QsBzX2xPP0RwPTZo+rjCGqy7okel2aUtWEZ9GejcVMiGEzKbiQNsVAoomM6XcMtdahTFsOHUR1vW04lt3PYvjybGAlgsgk3eCllwPPnUYPUsTSOcsDKcLsGwHHa1RtMQNWA5HJmeDC9lWS9PkTuZs3va6ZaCkEBn3UsDzlgum2MgWHNmuy5tg5t7Er6KwYNK50lQBgzeeFW0rsh2OlqbyadcTW2g5yOYdtDeH0BQ1MJo2y75XMitT8LvaonjrxavLZiWVS992XY4KWfslE9UF28X2XcdwwZlLKnzSiSYbb4JFgXHjpT/pYdmy48nP/vASHn2uv+wk9GAyj5zpIFNwgmPWNQWJmIFISJuQ5VXrindffwrMyzKgyWhCZkbdfbA3bdqEn/3sZygUCkgmk4jH44jFYhMe96tf/QqXXXYZmpuby7wKOVmVm03Omw5S3mw69/p13vXQvpr2BU1WXMThAqevaMFbL1mNbN5BPKpj+eI4/u3nT094juqlhjMGL20reKWx1xy3YiwQxN5wvNZcne1ROA6HabkoWA5yBUcWaWFj+5gFFxgczcPxiq6VG8JUxuC4HJbjBjPRfppXJmeNvda4GwWGsQkDhQG2I0oyAibsw/ImA/wZfb/1iiiq2grI/XVyFcOAojCMZmSbs0s2LJUp4N4kR0kvVYwVoQkZatV+o8UD+fpVbfjQ1RuCPtjUp5IQMt8JL/unpSUCbrtIZSfWz/ANjuRhOzKN2LQ4snk7GIlcLguO9R5N45t3PgPLHhtrRrIWFrdEsKglgtbmEPJ5GyNp00s9Z3jh4Ci4F/AHxwURpGALzmVAK+CNhbLCuOMVDVUVOTgUj33FGGSwDjE2RnIhX//s0zombaEViyjQVNnqa3C0EEwCqF4lcYUxNEU15AquV0VctsLc8tgBMMbKjg3j+x6b44p6VgwhBTCcKlT6bl1KFgU0OR4WTCdokeYXXQ0XpW8XT0Lv7h3G1u0HIYTXl12Rj7ccF8OpAtoSIRQsXpLlVUvmYCpr4Y779yJToYMKIaQx6tqDXSwcDqOzs7NscO26Lj796U/j0KFD030bMs/4s8lhQ8VoxpIz7/4+Yi/dOhEzat4XNP71/NexbDcIAK88vxurljRj/So5SOzpHcE5p3cgpCsYGi3g6PEsjg3nxgJCAeiqXGEQQpTcRPj/WZzY5d8bad6qgh9otreES9LnuBgrAtMcM+BygYLlykrZ/uOEvBkRQWEXr1WKt+f6vm19yOZthHQVYAzltvUVr4ZzgZKZ7LL7sAw1SEF0XFlVXVEUqN5+dIXJ8xExVMQjsvBM8d6yS165DF1tUWQLDvIFW+4Ld7isOF40OZHKWsh7vV/9z1Vtz/2G0zrwsf/fK/Hht5yF9165Dh9+y1m4/u0babAnhMxrjDEYuoLWphBa4iGEyuzRzhUcuBzQvRohi9siiIZL1z64AAZHC8ibLuJhTU6o2hxHj2dx8FgKo6kCbFdgWUcMf3XhKrxqbYfMoII/zshq4a6QK9PNcQOukEVCO1rC6GyLoqs9Jot4KnISWZSLqouoCoOmKLIOiFcMU9MUxCJ6MAaPHX9p6rK/H7kpaqCjOSzrnXAvrQwMuibHoXTOgcN5UGxUUdik9wzretpw/ds34srzu9HWFC4JqsennwfjPAPaEuGqn3eq/EWBbF626IIAkl5GgYKxn4WmMjTHdGTzNv7rwZew/2gSDufB+WpPhGQmgxjLGJB9y02EdKUky2uyfejZvI286WAoWaD92YTMsLpXsGs12UWanLz82eR7/9yLFw8l4bpydjukq2iOGQiHNAghat4XNH52utxqZ7nqmbqqoOAF5P6qr195PFtw4HLu/b+cPXfLBNo+VZHtRITXKgsAMnm77Aw/F0AqJ3t05wSCGxen7BAPdLSEsbKzCQ89eViuPnCBvMUqVqb1Vwx0lcFyXCzviGNlZxMcznHnQy8jk7OQiBnB6r2hq0jEdORMJ7jxYt44bGgKmmNyxbpgOXjLJauRiBoT0sf8FLyhpKyCLmfVx6q1K4o8LlnRXa24V2w8Su8mhJysZEExBaGmEEybI1uQW4k4F4iGNVmZ2+VQNBWqoqAlHkIsrCOVtUpWYS2vKGdIV+ByDscFRtNyu09XWxSveeVyrOxqAhjQFDVQsF04DgeHnMf1C33K7U4WEjENkfDYXulwSMOi5ghG0iYcV04g2w4PCpAVc7iA4vV21jQFCgOyBQcrFscnpIf39VdOXY6EdTS7AiNpE7GwLLZm23LF2scU+f6OyxENqcgWnKr3DHv7RvDrR/YjnbMnjOHAxHE9EtJw7vrO8j+8aThnTQeOHM9hYCSPkF/LBIDjHYDrcgyMFADIyfacmcE37nwG7YkwjicLQYeTdsYm7KVnDLji3JUlE9HVMv04lxkAjDG0N4dpfzYhM2zGA2yysK3raUMopOGbdz4DTWUI6WrJHupa+x0Xv16l4iKV+kUOjMgCaC1xA7qmBoVChBAYHMnDFYCmqjC5iwrbkuHVJAMw1ntUURgKpoORlFnxeF0ukMlbYAqDXenFPa86vUPeGDzaK/t/Mhb0BnXLBNmuK6AogKaoCBsarjy/G3v7RnDXwy+jr1+uFJt2oWTPlhB+ajhDa1MoaKni/0y4EOAFIBE1cObq9rLn//JzV+Cnv3sRDP6sutwX5jgcKlNk4ZrgRlC+bq0VwQkh5GRVLtBetjiORS0R9A/nkVDHgiJdU9AU1WAm3ZJMKkBmIQFASJcFPS/ftAIbvLTsZNaCK4ClHVE4jkCmYHtjxViBrHTWAiCPZbxwSEOnoWIkVcBrX7UC2547iv6Rgjc5XbqliAsgnbOwqCWCbMFBJKSVrZuRniR12dDl13OmA3i1TZyiMc/PCEtlLaSygKIy7D+aKlsgjAuBO7buRTJbWwFVhQFXbu6uucBZLXb3DuPeP/fi8PEsTMuVxd8KTslWLD9zv3hsZwA0RcGx4TwKloOIoQK6inBIQzikBfVOGANypouOot7p8rNUbiOWyloQAmhpMiatSE6T3YRMHwXYZMbl8jYYgFhELzszOtWK4uVWOytVzwQQpMrlTRdN0bHBpWC5Xh9RGfxV44+B/ky+oSngXOB4slC1Zygg90ZPNh8c0hU8u38Yz/WOwPHSruHtlVO9FffxRckYA0KGhu6uJlx5fjcABPu2gbH92WN7tsJyX513ThSFIWSosBweVCiFEJMGwh3NEURDGiJhL2D3btwGRvKwHC730GFsT3ZxRfDli+Po7U8FkyOrl1FtBkLIwjI+0L5qczd+8ceXkczaiIZUqKoC1+XI5GS2UUtcB5iCdK60OKUfaPf2p3Hm6kVQNAYh5NYkVVFgCRddbVHkTTfYtiOEgGm73rhSfmTytz8tah6rJu4/VGFyDHa53J9tuwLZgo3lHfGK+3ibyhQptWw3KODpHwcXgK6U3/PtH4PLAe4IOI6LH//3CxMKhD345GEMjORr/llEQhq6a+yDXYvdvcP47r27SlfPx21B01V57se3qBQC0HUFhq4gbzkYzVglGQbF506rME5XyvRrbw5jKFlALFJ+bJ/qfRghpDoKsMmMq1YBHGjM6mal6plytlcGqMWrqkJ4rbm8Ii6VKmpX4nKZ0lath2Wxao9SGNDaFMLh41kADE1RHa5XlVsWTZP7rnSVBQXKZHCtQlP9/eMCWx47gILlIBEzYNoFMLCgoI3rfd7FrRFoqgLb5bAdNyg6V7QVfNLWWPGo7qUEMhjG2M8zaLVSdM6LW6ucuaoN//bzp0vS95e2x/C3l6/FykXRms4jIYScLPxAe+NpHQgbGv7w5GEc8CYgVQVY1BLGSNqEpqnQNRVhQ0XOdJDO2iVjz449g3jxUBJ/uWkFNpy6CApjOG/tYvzqkf3Imw7am8MwdAPJtInRjOzYsKhFpoKXKxrqT4g+vmewZLXaH8gUJqCpTO7r5sAFZ3Th6stOrZha3N01lrrsulzWUSmzT9gfqypNSI/vuGW7HIcGM0GBsDXdrfj942M1f4orhY8XC2toS4SQzNoNS43mQuDnD76EZNYq2Y4GKCX7ohljXvGyicfIMJa2bdscpuUgZIzdqtfSwrJcph8XwC2/fHZG78MIIWMowCYzbrIK4FPpd1xJpeqZ49ty+auqlleVdColAoJWlt7+bZdPvjI94TUwcbDnXsEzv3+prhtIxAwMpwryZiMoVjP23NamEGIRHY7Dcfh4Dt/fsgeOy4M9W7qmwHI4NCZnyhV4Ewy2C11VwIXASNqSqxEKAxPyxkYIYChVwP3b+rB+VVvZ9h2Vfp7hkIa2phCGUiYYA/IFB5qmYHlHDGeuasMfnzoyIX3/4EAG/3Hn07jm8jU4fUXLFM8mIYTMf0IAa1a24vSVLejrT2M4VYCiMHS0RHD7b/eUpI/HwjoiIQ2ZnI1Mfmy1cTRj4RcPvow/PXMUV5y3Eqctb8GbL1yFPz59BEePZxCLGIhENHS1R3HJhqUQAmVTif0J0Y6WMB5++mjZ4+VeNU+FMTAm0NYcrhicci7Q15/G+u5WHBzIYHC0ctYXY4CqyNX7ychK6LIwZyZn466HX8Y7Xns6Ul76e3BuKzw/pKtQFKWhqdF9/SkcPp4tyjzzM9EYFGVsgsBxeTBuFh+f4lVQZ0wWohsaLSCVtdHM2JRbWI7P9ONCzPh9GCFkDAXYZMZV2xdUT7/jciqtkvvBpmm7XtVs+R6yP6a/qxolK7jFA56qyMGOC6CtKQRFYUj5PSwFykfMZc/BWJr5+KqmAFAw3aB/qeNwREIa2hJy9cIeN9Oveil0puXKirAxHUNJE6bjoqUpBKB0NVn1B3IhkMrK2ewYdAwlCyXV0zVvU1i24OCXf9qPPzxxGF3tE9t3VPt5FmyO9uYwLj93BTqaIyXt0san7xu6CkNXkMzauPfRXnzkbRtkhdSi3t3Uo5MQslAwyKBo1ZIECraLXN7Ga89ehl88tA+pXGn6uADQEjewtD2GvQdHg728R4dyuG3LHpy6rBmXn7cS73nDOhw9LlsgNsV0dHclkIgY0DSlYtHQK85bif93z/NVJ6D9iWFNZVi9tPxWn137h3H/jqdxsD8VpJKXe0l/fJQp4gxtiQgyOdmjuxLF6+YxkrEAIdB7NI3v3bur5qyykJd9NT41utz4A6CmMWnf4ZSsn1Im/Z4V3QQoCivZj80YvE4hY/comiIrsne0hDGasabdwvJE3IcRQsZQgE1OiFoqgE+mWuBVbZU8EdUxmPQqsXrtSoqDVlX19pOVGZddLtPhDF0N9i4pTLa7SOcdxMIaCpY7aYq57HkpSlLCipm2i9VLE2CMBZ9BHq4IWnQF+8A5MJI2g2Jluib3bBUsgXzBQSyiIxzS0J4Ij/Wp9j5cZ1sEr37FEtz35z4sbpUFUjgXsF0epMyrXhuQ4pYoxf05gan9PHv7UxUryNoOh64qODiQQW9/GqbpTKgCTz06CSELBRcCB/rTyOUdJOI6NpzeAcPQ8Pudh3B4MAPXdKEqQFdbBJduWIpTlrdgKFXAA9sP4tl9Q8HrvHQ4iZfufhYbTm3HX75qBZZ1xAHIidlht4CIoWFtTyvW9rSir790XN32fD/yphO0pKw2ui3riKOna+Kq5+7eYfxg616YNkc0rELnAulcpc889t+2I1d3m2IG8qY7IWBmRXvAATmeMkWOWcm0TD1XvdXiSsetqWOFPYtTo8t1IWmKGoAQSNfQN1oUz9KPG+iL/zdiyJT/VM4Kxlru9U73C7D6Fdk/8rYNODSQaciEcyPuwwghtaEAm5ww1SqAT6bcwOcPcv5rru9uRf9QDqMZU6YhF62qNscMNMcMpPM28qYLh4/tZWbwBuwKM99cyEIoBdMJAlZ/0M8VHCRiOtI5p2yl73hEBuDcm9V2XIFyC9+RkIarNvcAkGl7I2kTplcx1F/VLXdcmiKLmPntP9I5W1ZrN7Sg8qhpOUhlbXS1R/Cpd56DPb0jQTq9wuQe7uSIDK4Vv7KaV7ysJW5UbN9R68+zXPp+8bkU3j74//z1c7BseW6L08grBfmEEDLbGplxU26cW94Rw1Wbe/Dht56F/UfTGE0VYOgqliyKBe/TngjjHa89DRcNLMH92w9g35FU8JpPvzSE5/YN4/z1nbj07GWIhXW4rkAmb8O0XEQjGnqWJEoGpOFUwW9JLTPDKkwgh3QFb7v0lAmf1+Ecdz38MtI5G62JEHRVQd5rDznZ+jLnApZ3XJrGYNneViwGmY01ruCnP+4qjKE5ruN40oRAadbYeG1epldxanS24OCH47qQZPM2DhyTHTlam0JIxI2qY9Lqpc2yZzX3xtIirOjTp/MOGLyicwC4K+8PmmI67HErypqiNLSq93TuwwghtasrwP70pz+ND37wg1ixYsWE7+3btw9f/vKX8Z3vfAeqquIHP/gBVq1aVfX1OOf41re+hV/84hdIp9PYtGkTbrjhhrKvDwAjIyP4/Oc/j4cffhiMMVx55ZX4xCc+gUhkrGXBb3/7W/z7v/87Dh06hNWrV+OTn/wkNm/eHHx/aGgI//qv/4pHHnkEQghccMEF+NSnPoXOzsb3QiRj6ul3XKn91qHBLL577y40x0NI5yy4rlyd5lzePCiMlczOFg8qqZyF//r9S8iZDlwhwKts+WLwVqyF7PeseF/zbzySWRvxiA7Ldr3gWwbuKxfH8bevOQ13PrwP+w4ng9QxPq6iaNhQce2V64KB+prL1+DOh8ZabQnvvbhT7m6BgUEE7cUsx8Wx4TxUjSEe0qBpKizHRTyq460XnwJNUSak0/v70bkAeNHtz2jGRGs8VHWPWi0/z/HvVzAdDKUK4ELuU/NvMI4nCxAC6GgOB6sL1Xp0Uio5IWQ2VZv4PevURVN+rXLjXG9/Bt+7bzfefcUarO9pk6njBQe2zSdMvC5fHMd7r1yHFw6OYuv2g+gflkvGLhd45Ll+PL53EJdsXIoLzuqCoalB5lLBdBCLGEEB0LZEWIaH3kSrpqJsltdrzlk+YdJzd+9wMH4JAZhDrlwhjuhlt1SV25pluy7SWeEdj0wbd7zMM1FhrPbH1ea4gdG0CUNXg+ys4uNuiugIeS2v/ED2DeetxJZtfcjmbcTC8rZYgZxA948xV3BKapyUG5N6upqwrCOOA8fSsF0OTZFhNocoKXI2/vMCslWZZXG4qpjxFeV67sMIIVNTc4B95MiR4L9/9atf4bWvfS1UdWIlwocffhiPPvpo8P/nnnvupK99yy234Cc/+Qm++MUvoqurCzfffDOuvfZa/OY3v4FhGBMef9111yGfz+P2229HKpXCP/3TPyGXy+FLX/oSAGDbtm34+Mc/jk984hO48MILceedd+L9738/fvWrX+GUU04BAHzkIx+B4zi47bbbIITA5z73OXzoQx/CnXfeWespIdNQa3BUqf2WoatwOcfgaAHpnC2rY3s3JJm8DU1V8Jqzl00o1OUPKlwIPPpcP3r703AcDqtMhK15rbs4F7AcHgTV3Gtx1ZYIA0JgKGUib8qelYauIBbWcUZPGzaftQSnLm/GexIRfPXHO70+lF5am7dQHIvoeP+bzsD6cenXf+VyfPfeXYiFNKiqTBnrH5atR4pvSLjgJZMDsbCGbN6B4wiMOjYYsxENabh049JgsB6fTl8wnbJpeI4rMJQqoK0pJFc86mzfMf79klnLW3330ua53P8tUxEFUjm7pDVJuR6d1W5saZWbEDLTqk383rF1L/63quDC1lhNr1VtnPODuXv/3IfTV7YipKkIN6nBHm3bESXXb8YY1qxsxWnLW/DUS8fx3zsOIukV/jJtFw/sOIhtz/fjNa9agbNP74CqMJg2h+2aCHlboc47ows//d2LyBYc6F5KtqIyrwc2h8vlWPPmi1eXPSfJjFm0pUkWBB22zWC7UzWayvD2vzgViWgI8aiObMHBb7f14eCxDCy78kw4Y0AqZ6OjNQLTctHeHEYmZ8tuHEwG+IamIJ23kcpYJZPv/cM5vHBwFJwLFLzWYYoiu3b4rS2Lu5BU6hutMIa3XXpK0KbLLcqUKx5hNUXeAAgxtgLfEg/hb//iVDTFDJosJuQkUHOA/bnPfQ4PP/xw8P8f/vCHyz5OCIELL7yw5gOwLAu33norPvaxj+HSSy8FAHz961/HRRddhAceeABXXXVVyeOffPJJbN++HVu2bAmC5ZtuugnXXnstrr/+enR2duK73/0uXvva1+Lv/u7vAACf/OQn8eSTT+KOO+7ATTfdhFQqhe3bt+Pb3/421q1bBwB4//vfjw9+8IMYHR1FS0tLzce/UMx0Glyl4KhS+y2/YBfz/huQg5uhq2j1bkh29Y3gijJFO/zPsr67FUeHcuAu9/ZxIajWrfoBNIDRtClbijC/rYqKRMxAJCT/+ixWFWQLDjat6cD+/hRG0xYe3zuIJ186HrSh+vs3nYFfP7IfhwazcFwOTVVKZqnHn994VK4mqKoCQ1dln2pWWkkcwISV91zBkelxGFv5Zgz441NH0N3ZhHU9beOKnZjBLH0xVZGtwRwuMJqxEI/odbfvKH6/oWQBlldwjnv74VWmIBrWkMrZUBkLqp0XF6srLkQz2Y0tpZITQmZSTQHxo73YvHF5Ta9XaZwDyk8wCgEZaCcqB9qKwnD26R04a3U7/vx8P/745OEgeEzlbPzy4X2y4vi5K7C2uxWcA3nTgeW4iBga/uayU/CLB19GtuBA89O2hNzbrCoMV26W6cvjz0k6a1UMhMulbBd/iUFOtuZMF+ef0RaM3eu6W7Ht+X786L9fgOPwkhTx4ordtsORLzgIGSquuWINGGMl9yxcCGzfdQzDqQLaEmGcu74TLx4Yxa8f7ZUZZt7Mt8sFXC9jzH8vhrEuJEDlvtHretrwvqvW475tfUFbMkWRxyEwNnEPMKiKPHbb5RgczSMWNWhlmZCTRM0B9k033YRHH30UQgh85jOfwQc+8AGsXLmy5DGKoiCRSOC8886r+QD27NmDbDZbkr6dSCSwfv167NixY0KA/fjjj6OjoyMIrgG5Ss4Yw86dO3HFFVfgiSeewKc+9amS55133nl44IEHAADhcBixWAy/+tWvghX2e+65B6tWrUIiQRe38U5EGlyl4KhS+y3L4bAdOXAJUTrwVZpdLvdZuBBjNzQCAGMI6UpJAG3bLqyMheaYgXBIgzGuiJqmyaquO18YHNs/rE1sQ3X92zeWnaQod347WyNoihpBn1LFu4FQmUxT///Y+/MoSa76Shy/78Wae9ba1eq9tbTUEkhoQ0ggITYJIQPGGDw2izlH5njGHg/2DMYe8+N4bIPB6AtGeIDBZhM7liwwWpDYJAHaV7S0Wkt3Va+1ZuUe63vv98eLiIzcqrKqq1stFPccqbsrMyNfRGbF530+n/u5t59QauRZCjmDJjiQTWloOqyNzrZjyxAuO38Tfnz/AVQb7RsEVSHRxia09yrm9COy7wjFVb7782exf7YODoAExYrhvAkBgVrTizZxvGMnFgrRpFMa/uOO55bc2K6Vp2mCBAkS9MIgCfHhhQb2HKxgJLt8YbJfnAvRL5kbJNHWVIqLzzwB5506jtsfPoi7n5iOksa5soWv3/Y01o+kcfbJY9i6Pof1oxlw5uH0bSNgQuDnDx7E7GITHpOz0BlTxZtesQWXv3xL1zXZP1OH7bJBjDW6EF5Gx2O48a5JPPLMfFSApoQgn9ZhqApG8gbmFm14jEMJhD4JkaNGggs0bR9b1+ewZSLfFgOemCzhhjufw1zZBhcChqrgrsen0bA8+D6PhEgZ670+AVnADvcFS/lGx+ecmw7DnsM1/Mftz8rXRcUBEdiSESiBaNueQxVsX5/sQRMk+E3AwAn2unXr8Nu//dvRvy+66KI1mVeenp4GAKxfv77t5+Pj49FjcczMzHQ9V9d1FItFHD58GNVqFc1mExMTE32Pp+s6Pv7xj+MjH/kIzj33XBBCMD4+jm984xugtHeAe7HiWNPgOpOjfvZbPFA2JUGsoh3cs14bkn7n0rA9GawpwVDOgB5QwADZHXd9DlUhMHUFhtY9FuG6PmzXh88ohnJGpGLey4aqszrdb00H55vSo5pIf9O0oUBVKbzQHmyg6w1ASMpbvOBg2S2lbsuR3WuFkkhsTAg5000IggIEcPbJY0ecsJ62dRjvvXwHPnPdr6FSCl1XYGgUmqrA85n07vZa9LwQcSEaArGiTs+RIJnxTpAgQS8MmhBXG+5ACXa/OBdiqWQOaCXaRl6B7TJYdneinTJUvPGCLXjFGRP48f378cgz81EifHihiZsWpqAqBBMjaZxz8hiG8yaKaR3veM1J2DdTQ8P2ccJoBi/fua6tcx3iyb0l1PvYcPWDqhCkDBVNW44pEQCapiBtqF1F9/AacQ4UcwZKVTsQ5pQXQASF5152Uz+6dwo3/GIvfJ9HibyncOw5VIXrMRSyOnwu4Lh9susAdctD2lRg6uqyvtHhnLOqUjy1r9z2WDiKLoRMuAOHzGg/kyBBghc+ViVy9vd///f4p3/6J7z+9a8/4gVYlpwr7Zy1NgwDlUql5/N7zWUbhgHHcWDbdt/jOY4DQG7Yd+3ahZe97GW46qqrwBjDpz/9afy3//bf8O1vfxvZbHZV56Kqv1nJORcCt9y7D47LMJRrJcRKMGtcrrm48W5Jg1OU5c997+EqZkpNZFNaV0IsZ6RUzJSaODjfwLagirt9QwEnjGSwf7YOXWt1jhWFBHRuaaFlaEqbDwYLkuJCzoCq0mXPhTEpoNK0fSnqEvpDWlLYZKyYwmLNgaErbYld0/awULGlJYiQM+GaSlHIyg44AUUupeHwQvt5dV7fYlaHx4SkT1OCYlZDpe5hKGcgk9IwvdCASgncmJc2WUIlNY5qw4WhKWBM4KmpMn7+0H7ULR9KYO9lOSwqWAAIBNPkv7RgA/SSk0bX5Pt94sYiNo/nsH+2DkOjoEQekxKKfEbDfJmB0FBvVUSfQUpX8OaLtsFyORgT0NIUvXJdTZMb26bDjmi9T+4t4ca7JnF4oQGfSa/X9SMZXHnhVuzcdmzp5+Hv1iC/Yy9GJNdnaRwP1+c3LTYWcgZUhYAxDqVHQizjj2RCDXLd+8U5QO5XmoFl0/YNhWWLfNkURTalwXEZ6rbXZtMIACMFE7/3upORSWn41a8PtyXEPhM4MNvAgdkGNCVkShEUcwaGCyZqTQ8TQ2ns2DLU9p5cCDz0zHxU/O0lXBZHZD3JBWzXhwgKuZRI5wpTV0EIUGl4uP7OPfjw9uG2a1TM6RgpmKjUA1eP4DqlTRVXvXknztg2Er3XE3sW8P1f7IUX2HeF19bnku/OBdB0GNKGsmyCDQCLVQcpgyFlqHjzRdt6FkTiIJRg977Fjh8iEpETkLR7VSE4aXPxN+53BTg+7kHPN5Jr8OK7BqtKsCcmJlCv19dkAaYp51xd143+DgCO47Spgsef77pu188dx0E6nYZhGNHxOh8Pj3fLLbfgG9/4Bn7+859HyfQXvvAFXHrppbjuuuvwh3/4hys+D0oJhgbs5L5Q8Oz+MmYWLeSzOjS1O4jkMzpmShb2HKzgpE3FZY+3d6YBLiCDJyVwAvsqSgkMXYFJCCyHAVRpu5a/d9mp+L/XPYpKw0MupUFTKRQiZ4uFAIbyRltQEkKg6TBsXZ/HWadOgFKy7LkM501Umy7WDaexWHVgOVIobduGAt7+mpMBoGsNtaaLhUD5GgipapJSXao4GBuSs8WEyAS/87zCNek6xVzZDjZCMnHWVLlJatg+/vs7XwZKCKoNF489N4cf3LkHrEMZdSkIIbvguYyOnz18APMVWWhCbDPRLsACCMiOts8ExobT0XVcC3R+noRQ+IzD9QWGciaKeQPVugvLbf8Mzjx5DM/uL0MPlGV7bUQcV85ub1iXX/Xv46PPzOFrt+6GZfvIZTRoCoXHOA7My47Kn7z9TJx58tiRXoYVI5/vvh8maCG5Pkvj+bo+v4mxsVBIY9PEXkwerspCamdCHMSf7RsKA983e8U5z+eoWR4yKQ2/d9mpGBleWfFfJueeFL5kLY+IXXsXcPfj00t2m70oPAgs1qRtl+X4+M+7p/D7ORNnnNgaD3t2fxnVphetuYcNdARKgOGCiUbTg+0x+EwE+ikUQzkTIMDMogXPl/uDycM1fPwbD+E9V+zsukbrR9KSqWb7SBkK/vQdZ6GQNrB3poFsWgMXAt/48TNSQwUykQ2jXbxO4fscSlobyEbMYwLrsgb++G0vHSgOPLu/LEe9gjgC9B7xmhhJ4+zT1ktPbC6w52AF1YaLfEZf0ffoeEZyj06uAfDiuQarSrDf+c534qMf/Sgefvhh7NixA5lMd/B861vfOtCxQrr37Oxs20z37OwsduzY0fX8iYkJ/OQnP2n7meu6KJfLGB8fR7FYRDqdxuzsbNtzZmdnI0r7Aw88gG3btrV1qguFArZt24apqamB1t0JzgWq1eaqXnu84uCMpE+lTAU+6xYtIYEHc7Xholq1wHo8pw2cgRKgUnclLTtmW6GpUn2bEvm8hVIdU9M11JoecmkN777sFNx811RbR3HjWBaVhoumJf01411nU1dw+XmbUKk0Bz4XCOC3LtyKfEaP3nfLRIsW/N7LdkRdTc/naAb06jDRj/tgCyJQqtrQ1bT0xCQE4AyLi42269u0Pel3LYLnQFqG2Q6D63EYGsXh2RpeeuIIRrIannh2LupGDMomIxRwPQbX87vmrXu/QM5gMyak4IvPsVhurIoezYVo+xy3TOSweTQdu5bN6Nw3jsoO8albh7peQwnB4mIDQxkV64ZSURejW/jOxabxLIYyatu1Xsl6v3PrU4FdiwLXY2CMQ1cVFDIayjUX37n1KWwcSR0zuriiUOTzqcF+x16ESK7P0ljN9cnnU2vWZfhNjI0AcPl5m/CVm3dhvmwjk1K74s8VF2wGpWTg695+X4zFueC+uHk0vap7GgBoBGCco2l7cD2Gb936VFu8Wg6cA9WGA0OlODBTww0/fwbDWQ1pQwPjHA/tOgzL9pBLqVisuZL+3OM4BHIkLGtq0BSCSsMD5wLFnBT1tF2G+bINETh1hJ7SB2bq+Ox3H8b7rjgN73nDKbju9ucwW5bXlRAgY2rYvj6Pr/3wCcxX7EAITXan+51nPMnlXETHGqRwXW+6qNfsgT6P6bkaGOMYLhiYK1s9LUEJgAtPX49KpXlcsafWCsk9OrkGwG/GNVhJbFxVgv3xj38cAPC9732v5+OEkIET7FNPPRXZbBb33ntvlGBXq1U8+eSTeNe73tX1/PPOOw9XX301pqamsGWLFNm47777AADnnHMOCCE4++yzcd999+F3f/d3o9fde++9OPfccwHIJP2mm26C4zhRx7vZbOLAgQN485vfPNC6e6HT5/CFjrShQFEIPK/3XJjncaiUIJ/RwRhf9vw3jGaQTevYNyP9nVVKo2Ekx2NwPIbN63Ko1R380zef6RJVu+Llm5FOaW0zsbunFlsCYZYPRSHYEKhzn7KpGK1pkHNRFDnDu2msVXjhTMAXHPtmanA9hrdevB0EAnc9No2fP3KoL0WbC8D1OGzPh+1ybBzNYMNopu0apXQqk2su6XGMibZNCeOyEzK9UMfOLUPYNVnCjx/YD8ZF3+5ALwjeEmgJEX995ymE69GDWbhq08Weg5VV+ZcvpRb/gXeciYPzDYAqAGfYMJqR6uJMdH0GcX/uN758M752624s1lxkzNjGNvA0fePLN3e9ZlBMTlcxNVOD4zI0rFYxQlMpChkdmkoxNVPDLx89hAtOnzimM9mD/I69mJFcn6XxfF6f38TP5ZRNRbznsh1948+pmyWNeiXX/ZRNRXzgHWf21H440mtIiUwM9xysBGKeChxveUo0IGOE5wvULA+GRrF73yKe3FMCIQQP7J7FnkMVNB0fliPHt+Iq321roEA+rcF2ZbG3mNNhOwwEBLbDZAIaCnWyljtIPiOFOr/902eQNhQs1hzYLouK9A2bYbZsH9H1sRwGSgHe45LEO9sKlWrl//mrvThp4/KU/bSpQlUoFEowVkihXHfamgsKJTANFaduKeKxZ+fbNFnSgSbLvtk6vnzzrhe8Q0Zyj06uAfDiuQarSrB/+tOfrtkCdF3Hu971Llx99dUYHh7Ghg0b8MlPfhITExN4wxveAMYYSqUScrkcTNPEmWeeibPPPht//ud/jr/9279Fs9nERz7yEbz1rW+NOtTve9/78P73vx87d+7ExRdfjOuvvx67du3CRz/6UQCyu/6lL30JH/jAB/A//sf/AAD88z//MwzDwNve9rY1O7cXOjr9izu7hQ3bx+ZgLizsFC+L0AcacsaWBDNIYQCzHB9fu3U3HI91iapde9vTeO9lO3DG9tZ8VVyts58YFRcCQghkUxoWKjZGCmbPc+klWNIrScyldcyXm11K153gQqBa91DMGbjywq097MLke/N+5f4A3//FXkwMpXHzvfvgMwZVoW0BejmoKoFCaTRftlRyDUg/TkNXoGtKcA7uij2wB1WL37ZeUrkXFxsD33BDRfLwc2nafpun6ZFsQJ7cW0LDCuxUYgUg12OYXbSii/ednz2Lux6fTny3EyR4EWOQ+LNShOJYa4l4HPN9DkNXMVIw4foMtYY3cKJda3qwFQpFIXhqqoQHds9B0xSMFEwoCkWpYqPp+JFtFuc8SpgJgKypotyQc9MEQLUhtwQN25eU8B4BiQsBzgVUSrB/po6UoYAGSe5aot/7x5PrcHwrl9YGFtPcMpHDhvEs9h6sIGUoKGSNaDSOEqDp+Ng4lsXG8Sz++XuPrrlDRiLWmSDB84NVJdgbNmxo+7fjOND1drrmSvBnf/Zn8H0fH/7wh2HbNs477zx86UtfgqZpOHDgAF772tfiH//xH/G2t70NhBD8y7/8C/7P//k/eO973wvDMHD55Zfjr//6r6PjvfKVr8THPvYxfO5zn8OnP/1pnHTSSfjCF74QWXuNj4/jW9/6Fj75yU/ive99LyilOPfcc/Gtb30LudzqrYh+09Dul9y7W3jlhVsHng3aN1NDzZLCXU3bj8RJQtpY2lRRqtowgoA9aIBZakMS31g4LoPl+Dg410AhoyOT1to7nxdsaQtETcvDtbc93Z0kztaXTa5DDOUM/Ne3n4nNo+m2BHLXZAnf/fmzPelinWjaPq6/cw8qdRcZU4PtOgO9NyA3NIRKBXTHY5GoSr9PTKFSqTYSgllGvbYXVqIWv1ocjY0tFwIPPj0n/cMpiWb0OERr4xV4pJuakvhuJ0iQ4KgkxGuJzmIn5wJzZQuGriCflmJhjsekf3VH0qpSWQiOhzuPcXgMuOPRwyAEKGoKKnUXukoxnDeQdjVUGzJGFbMm6pYHn3FoCkXN8iGETFKLWSkAV2k4yyb4pZojnS4gkDIUlKqDx8BBIO2yKFiPpF20PUcy9jRNgeWwgQrPlBCcdcoYHn9uHpWGG8VeTaXQFIpMWsM5O8Zwx0MHcWCusaYOGcuxyBIkSHD0sKoEGwD27NmDa665BnfddRfq9Tr+/d//Hddddx22b9+Od7/73Ss6lqIo+OAHP4gPfvCDXY9t3LgRu3fvbvvZyMgIrrnmmiWP+da3vnVJmvqJJ56IL3zhCyta54sRy3ULVzITFFqb5LM6sikNrs+jSq4eWDQxLmBoypoEmM6NRSaloWF5qNRdlOsuHI/B0BVsHMvgjG3DuCUeiCiB4zEICIwWUl0d70HxjteciDNPHmub1QrX1bA8DKKqwgVwaL4BSgk0TZfXbEAFcZ8LGCrFKRuLmK9MR6/r91IpytayKFvOiqQXBvGInS41MTldg6ZS7J1ptFHEB8Vab2z3zdRQrkvfcZ/xQKmddM3wKQpB2lSRBhLf7QQJEhy36FXsDK27HJdhzrVg6gpyGR2jxVTPRFuhcmyncz7ZDhhRnNvIpDRwXQXjHJpKMVpMSestLnDq1mG8/twNuP725zC90EQ+o8PQW1vPfFpH07aWPA+fiUAMTf59BePjA4ELwGdy5I0LqT0SFr8JAQgIVJWgmDWQMlS4HmsrPC/VJb757in8x53PSU0TtGKv63NwIZCBipvunoLtMFiuDyf4rEyjfXvezwu9HwZlkSVIkODoYFUJ9q5du/AHf/AHGBkZwW/91m/hW9/6FgCZKH/sYx9DNptt88xO8MLGWnULO70+Oz2lXU9GNE1b2lt0kADTr4uaS+vImCpKVQcjBRPvufxU2HZ3p9qyfTRtH4RIWpymUqnuyXhwLDGQGErNkiIuew9XUak5yKTUaF0jBRPeQrOra9ALjicFWDwvEIAhLd/qpeAzDrjAo8/OB7ZmSz9fCbyv4539Tk/R5dDLIzb0Ew/nu23Hx7U/egr1pgsv2HiMFkz8zsXbsTNmsbIU1pr6Vm964BwoZHUsVh0wIVs3nZ9zJqYavJa+2wkSJEiwluhV7DQCJlFIsbZdBrsj0XY9hnrTheNxCMjENmWq8FwOJkRb0dHxOBzPAaUOChkdpi7ZP7m0jsvO34wrLz4Rjzw1jXLdRSFrdGmgBPnsQIoZXMgxskEx6HEBWUyllABC6pAoFIH9JgAIeL5Aue4AQsD2eFR4XqpLzAH8xx3PSYuwyFoUkee3zwQWKg7GiiY0lcL2GFyfY6FqYyRvtiXZ/dhknXFw43gW+2dq+O7Pn0XD8rrYgFIM1MP1dz6Hv9pc7OlpniBBgiPHqhLsT3ziEzjjjDPw5S9/GQDwzW9+EwDw4Q9/GI7j4Nprr00S7N8wrEW3cLmZbsdjkprb5/UroSsv1UWlVPqT1i0PBAI337uvKxFXAto7F8BizQEN1qUoQWI7YNT+9TMLeGD3PPZPV+H5HIwL2C5DJiWTNENXBkqwASlS4wcbG95jAdJHVG4KCKSvpoDsVDgYbKNRs3xwkEClvXumeZCktrOQYjk+qsHcndyryA7E4fkGSKASK7hA3fLwmesfw2+/ahsuf/mWJdd5NKhv2bQmaYgcyKU01Cwvut5xkNhIxEq7CgkSJEiw1uh3X+5V7CSEYChnYL5itXVpXY9hoWwhZaoYK6ZQyOioWS6qdReOz9G0ZGLbL45wDizWXKQNhrSpwmcechkV5boUJNNVpae1YnzEbBBmluMNPnu9kkY34yJwtJD/jq8jyLHhehyzZRvZlIo3XbAFu6cW+3SJ6/i3G58EISRKrsPkXSEAFYgKyyLw5jRUGjH5fCawWHNQhLw+mkJ6ssk642A4rx7q2RAAs4sWChkdAFAJ47CQ9mcfu/YBvOqlJ2CsmFpVkXqp/UD8sULOQKGQXsGnkSDBCx+rSrAfeeQRfOpTn4KqqmCsfXbmiiuuwI033rgmi0vwm4XlZrozKQ2jBROLdRd6B018pXTlXhuLOMLEaM/Bas9E3GPtGtSSLiw7wmIJinUnfr1nAaauQqGA43O4HoMQQLXhoWn7AyfqQOjh2R9CAExIpVYKEgXwQRA05cG5wHmnjuGil6zvCraDJrXxQgrjHKWqE1lxUQDh/shjAoQLqAoFFLl+z+e44Rd7sWldDqf3SZSPFvWtaUmxn1Btvd/VqzZcAICmUDAuorn1BAkSJDjWWOq+3FnsDJEyVIwWUlis2fB9AUOj0DUFG0YzuOIVW5ExVVi2j8WGg188ehi1houmKwulYpk41HQYmg7D+JCJ0UIKluPD9RmKOR2UELg+bxMo0zUFCiU9i5nHGmEMXQ6Ww8CEwI96FOc5F7KoHSsEsIAJFdLN4711gdC5Q853z1csmcz7HPNlCyDyFbm01sYm64yDvsJRqjiBLah8h/B6z1fs6L0UQiCIXNO+mTq++eNnkDIU6BpFMWvgnFPGsHPb8LLJ9lLfOwBtj6kKwaaJvbj8vE04ZVNxZR9KggQvUKwqwTYMA7bd2xKhXC5D1/UjWlSC31wsN9MNYElRtUHpyv02FiFV2XUZfMZxcL4uFctTWttz4pZWQNgdloHOJyvIsIVAylQwt2hF1fEwgPezMjkSEAKoCo3o9ks+F63EmlLZmecceOZABf/ldad0JdeDJrVhIeWrP3oKCxUn8jQlIGAdqm5CyK6FELLbTomQSfYdz+G0YK45XglPpzTcePfkmiut7pos4drbnpbK9kt0UcIZwFLViZgWaVNF00o62AkSJDi2WO6+/O7LdvRljZm6AlNXMTpm4i2v3IZ8Ru/qQH7qu49gsWZjJG9AtSkUSnuKofXC7KKNz173a7z9tafghCETQgB1x8dI3oTPOBqWFySessjq894q3muBsDMexrzl1MI7ETHrCCC47BJ/96fPoOn4bcV52/GxULXBRXs3XibuMaXMOASi6yDfIpZ8C4AgyMxj6ByBA4DFRQcCApoiixUiKLarQZINSGE1hF1u0UrxXY/BYxzluoup6RpuvX8/No1n+zLClvre/euNT0ZrDB9jjGPycBVfuXkX3rOKAniihJ7ghYhVJdgXXXQRrrnmGpx99tkYGxsDIGlHjUYDX/7yl3HhhReu6SIT/GZhuZnutbBg6kVHD6nKrtcK5Hc+elh6fHpNDOWksEhYYQ9nsELEDcY6BV/6oZDVUK7JDq4Sblyw/Oz0aqEQdCXXYRjqfE+B1jlw1vLWLtfdtpnilaiCh5/haVuHcfn5m/Htnz4jA7mQtmyhxVh8LYyLqMIfYmqmjjsePoiJ4TRuumcK+2frEbXNZxz5TLdrwWqVVuPnN1pIodb0sFjrVqnttTGjVBYGQgu5RDQmQYIExwKD3JdvuWcKV7x8M6697emeReuUoeLtl5zY8761b6aGfTM12C7D3KINo0MMLRz7WQqHF5r47PcewUkbCjh96xB+8dhhNCwP40MpFLJyTGt20UIuo6FA9DYf7NVCVaSvdCGtoVSzkdI16LoCzjiqTU8mmz2Cd7+3DdXDo+cpBIJxqWxOCLLpFkuu0nDBBaRYGh9MjE0AKNcc1JtuIEAq578FCIoZHYbeHWc7R+AcT3qCUyLt0RQC+EEiHZ+5C8exOpflMQEasLFY0IE/MFfvyQhb7nt3cE4Kup4wmgYN5rsVTUHKUDFftldcAF+OOZck3wmOV6wqwf7gBz+Id77znbj88stx6qmnghCCj3/849i7dy+EEPjUpz611utM8BuGpWa64wl4reGiZnnIpjSkTDWiGg9y/DgdXVUIqg0XjLWCCyWIqriSRmVhtJCSiSdCKlWrkhxWmHWVQlUotq/P47G9pb5rUChAqEwoCWnNcQ2anA8KJbBrZkIGOd9pH9sYuNke/Okz3jZTPKgqeGdSO1ZMIW1qSBtKUE2Xr50pNdvOv1cnn3OB/7hzDwiRNmWdG5XFmgshgEJQvQ+xmpnozvMLBe2iDn+wns5VqiqVRRldSdTEEyRIcEwx6H05ndJ6Fq03jKZx7o5xMC4wOV3tSkxuf+gg6laLydUphjZWTMF2ZaLtB5XosPeqKe0jSs8erODZgxVsX5+HzzkOzdVhGhoKWR1nbB/Gq16yHnsOVXH9HXtWFRylqJoGxgUuO28Tztg+glrDxZdvfgqZtCbPS1OQMjWUKjZqSzCOOufA48k1gPaASlssOTdKcrutzZZce7B+15fjZ2FyrasUuUyLDRqPs50jcGF8ClN9QgmokLRzFruey9mLUkIiXZS0oaLpsK64ttT3LiyCg8jP34hN6BFCkEmpOLzQwD1PTCOf1nsmxPGEea5i4db79vdlaLz6rBPw+N7SirVYkqQ8wbHAqhLs9evX4wc/+AG++tWv4p577sHmzZvRbDZx5ZVX4n3vex/Gx8fXep0JXmSghMCyfdx6//5VC1mFdPQb757EMwcqbYmcqpBIiTukUzEuBc2G8jJp84OK7kjelArigaUYIIXD6rYHTaHw+gxHMx4qU4uog3s0wHgryQ6tUwZFZ3ebQIrdZFKtW8Og8+ydSW02rUFVSCTmFj1foV0Uw851UErQdJaeUS/XXegaRcpo0ftX49vdeX7x5JoSEtHo2kTuQDCSNyK7mURNPEGCBMcSK7kvn7F9pI01Nlex8ODuOdx0z1TP2PrEZAl3PTHd87hhop0yVOTSGsaH2hNtVSHRDb0zWd1zuAqFEpy+bRinbx3CSN7ExnU5pE0Vdz56CPmMhrrlL9sZ7xW3VEqhKsAZ20ewdSKPyelq15iY5fho2EsXX9uTa3QlXiygtKd0FYWsjoWqtHbkAc2bx4rxgyA6h6DwzjigKoiEyULEP8/OEbgwZonw2giZ0IYsgV4jY52UeC4AzjkIpUDAOusV15b63sUT+F7JvM84qg0P3/3Zs6CEdH3v4t1q3+fRHmAk31KfDzvlC2UbN/xiL0ydIpvSB9ZiSbzBExwrrNoHe2hoCO9973vx53/+5wCASqWCubm5JLlOsCZYKyGr07YOwzBUXHPdrwEhULd9UNIKmoQQKDS05aBB99aV3W0QDOfa/SiFECjXXYwWDJSqds+kOT7n1bD8FdiErL6zvZwAWj/Eg3uoQM6FwKGFJrZM5EEJ6TvPHqJfUhvS9Kema8iYKhRFCukUszpmy90aDvG1KJSA+e0XoxfVvVxzYepSkX21vt2d56erFJpK4foseE/S9uEISBZD/FokauIJEiQ4lljpfTlkje2aLC3ZFXzPG07BDb/YE/k294Pl+LAcSTPPZ3SZaDs+6pYLz5cFyaG8Ds4JKnUnSlwZF/j1cwvYva+Ms04eQaXhwvUZDi00sWk8By4EZkpNlOsu/I5xohBRIokWy8jxGLZM5KJ7f+eYGIC2dYToKu4SYKxgolRzpBAnhHSOCOaoCQQ0RcHESLqNfg+s3p+bh7S5APmM3uWDHf88O8+tM2YxIWNUaEt6YLYRaJzIPQ/rwcgCAJ8DSvAIpaRnXFvqe9emCN/R+W/aPhYqDjgXMDQFKVNt+9699MQRPPDULDxfjoBpKkXd9iGEQKnmYISQ6JoQQuAxDp9xZMzu5LsfoyzxBk9wLLEqA7xarYarrroKf/AHfxD97NFHH8WVV16JP/uzP+srgJYgwSDonPHRNQWUkCg5s11JW+plVdULTcsDgRR1kcmvvOHKarOI/KRzGRVpQ8Vl52/GO197cmDl5aNheeBCCoEsVGxQAmxdn4ftsJ70ZoGYuMlKTjwQRjkWkAqjMpENu/lhki448L2fPYtPffcR7JosRcG8EQS7tiUHSe3EcLorqd09tYiG5cFyfMyVbcyUmpheaMBnAmaH13lnct0ZuPtdFo9xOB6D6zGU6+6qfLs7zy9Uc6XBRsTnch4fkIUYSghSpgrbZXA8JlkQq+icJ0iQIMFqsZr78iCx9fo792Bu0Q4EM5e/j1qOj5lSE3XLhWmoGCumUcwZSJkKTE1DNq1hOK9DU0gb3drxGO59chbfuO1p3HDnXkwdrmG23AQhBJvX5XDK5iLWj6ZRzLTuqfH4KGJ/kfTjdpXtcExMjvA4gf5KdyW6I7eFAPCG8zfjty/eDk2VThGMcTDOpWiYosDQFbzxgi3YuW0E771sBzaOZdqEUQl6UMs7ED5KCVDM6BgtmNCDQoDa8drOz7P93OQsfC6jgQTuIQQC+bQG12MoVRyQMH4FgmpLgQVMPV1Tesa1pb53od4NgRwTiK9/sWaDcwFdo8iktOh7Z2oU82ULP3ngAMp1F5bjY7HmwAnYeCol4ELOt4eQVmY88hVvu64dY2sh1npfmSDBclhVgn311Vdj165d+O///b9HP7vgggvw2c9+Fg899BA++9nPrtkCE7z4sJKZ30EQVlyjG7GQiZLP5H8s0DypNXwQKgP1w0/PwQ+St1LVxsG5BmYXrUhM5P5dM7BjwZrE/uvEcoE2RCg6tpQX+CBYLrdUKJBJqVAVGiiHi7agG1a6983U8G837cKP7pnCOaeMwdBkZViKxIklk9qwUrxYd1DMGdB1BQjUTMt1F+tGMnjdORuhUJnch8m+oSkYzptRx6Hn+cX+Lj83aa+1cSyzqgp052bF9RgMXYmE1AgIdFXS8BB0AKp1F/MVG3OLFmYWLVQabs8iQ4IECRIcDfS6by13Xx4kts5VbPg8nKkmwZhP7zWEzKd8RoOpK5guNVFuOEgZCratL8DQFZQqNkpVFx6TccboUVx1g071zIKFycMVzCw2AQFMDKcxMZpFNhhZ6qn+TQg2jmd73vt3bBnCS08cgePJuDNI6mRoCkYLJk7dMoQ3X7QVJ4ymoWsyceRMimx6jOOWe6awa7KE07YO43cu2Y60qcq9BkX/inDHeYcFf0OX8+HZtAZKgIbtL/t5hiNwG8cyssjscqRNFRlTRcpQ0bR9lOsOHJ9BU8iKivcEki7eq0iz3Pcul9aQS2uoNLzosYbtw3YZKEWkeg5I1fVSrcUqUCigUDlCVmu6kqoOqYfjBTanQIt+Tgjp6pQDklHGmFi1lkyCBGuBVVHEf/azn+FDH/oQrrjiiuhnuq7j9a9/PWq1Gj772c/igx/84JotMsGLC0vN+LgeA2PyRluNVTSXQotOVYeqkEhMpBMe4+C2wA137gWlQDYlRViqDRfVhrxRD+V0ZNN6YMvkdx8E3bNNQ3kjoM71fn6bnQcQUKgoTEMNqGcrw3IFWMaBuuVD6r7QSKAmjkrDC/YIPm745V4U0jpyGR1pQ6BmeUuqu/dSGc2mNLg+B2McTdtH2lDwjteehMMLDUzN1pE2FFAqqW4yqR2Map0xVbz1ldtw4sbCEQmV9LOPO2VTAefuGMdYMYVf71nAzx48AM/nUCmJLM4cl0GhBGdsG06EUhIkSHDMsJztZWfCOcjcNriASimYIpNJSsPOs7zhherYI3kDbzx/Ex58Zh4zixbqTT9i8zAm0LA8uD7DUM6AoSuo1F2pUL2EhaQAUGv6qDV9TNMGhgomhrIGsikNnMuuNxeSfm7oCsaKKVxy5gm45GUbuu69uyZL+N7Pn8X+2fqKqNtCCPzgV3tRrktRVC5kIV5VCHJpA6ahgDGB/bN1/NtNu/DaszfIAjIIhnI6MoYq6eUDWJkJtBhboUPGuuEUNFWJYr+6xOfZy5Glbnn4/i8kC8F15fWilEJRCMA5iOjv9U0grb18Lq0oO1kB8fddzm41/hgXLT2bOPU9VF2PO7YQIq3Fwk58aHkGxBNruc9RVRJ1/ePo1XlfrZZMggSrxaoS7Hq9jkKh0POxsbExlEr9lZUTJFgOvWZ8bMdHJbAFCWlJP/jlXmgKXbZjGVcU973eyXUIxgXqlgddJUjpaqAOKlXACQDLYcilAUWhbTPT8ZmwzsOXKnZXlTX+3Hjwz6VUGLoK12fwBwjQhMhCQL3prVhCTQgs6WcaHi8Ud1usObKK/ootGCuk+iqA3vPENPbN1mHGaN6EyO40NAWqQjGzaOHAbB2/ddE2fO3W3WhaHtImDSzTGJoO6yo8dBYuKAFGCia2b8j3Ta5Xoha6lH0cFwI/um8fTF0F41yOBgSLMTQKlVI8vreEN5y/OUmyEyRIcMywnO1lHIPMbWsahampaNieFL4KsjHSoTjieAwPPbuAK16xFRlTxXMHKvjh3VPImDJuTpea8HyObFqDqatYN5xG0/FRa7jL0pQBOQ88t2hjseqikNWxYTwDXaWYL9uoNT2YuoLfvfQknNbj3HdPLeKrP3oKpaq0W1QVOUPtD/C+rscxX7aRSckZ3ZmFZjQKJosNFK7nw/U56paHG365FxlDDcTT5HxxWI6ggSL4UurdPuOoNVw4LoPLeOB97QNEdnv7FRA6Y9vObcPYPbWIb9z2NGzXh6EraNgeFEJiKu8EnVPt4d5GCajYcmROYKRg4p2XntR3f7Xc9y7+WMPxcf0de2SSH11nyQZUgvgarCZ6XKUUjHO55lhi7XoMTceHGri5dKKfFstqtWQSJFgtVpVgn3rqqbj++utxySWXdD32/e9/Hzt27DjihSV48aJTwMNxGRaqtqxkQuY1qkoxX7EHFqYIK67X3fEcJg/Xeiaj8YTOYwILVRv5jB50LCkEhKQp+bKqT4kMVvEkuxOqQiI6eid6Pd80VGltJVTsn6kveU6ADEe5lILaUay6hhZbxayOct3FA0/N4ndefRLqTQ/7ZmpRUA3VOffP1uXste2jbnnIZ3SkYlXreKX4rFPG8CdvPxPfufUpHFpoRJXwTeNZnLFtGDfdPYVGMNvWeb0EgIWKjf97w+M9VUBXoxbazz5u30wN0wtNpE21TVE+7D64HktUxBMkSPC8YCnbyzg6Y2ucKhsmJkM5A+Wa3VXUjN9/C1kNaUPDgbkGvh7E4EvP2YiHn53HgbkGhBCRZVW14aJSd5FJacimNKSH02jaPmrNARNtxrFQsWE7PoYLJsaGUhgumDg038B1tz+HtKFgZtGK7vHrhlJoOiyaiQ5jNQhAxfJCZAJA2lSj+zrjIvK1LtcdOK5kowkhZHIYHJBxoNrwQIgX7QkIZIxwe7xpKIYqIDu5lBJoqmR7hV7l5bqLH923D4AsJtctD7mUhvmqjQd3z3XFtoblRcwxx2WSDUYJlECgjVICCtLWXdeUwG7SUOF6DI7L4HOB91x+KravX/o7tdT3Lv4YVQju3TWLvQcrKGQDuj0PRUNbXWoh5H6KkNY4Vi6lohYw7poOCzr6cn9w+yOHevq79xqPGOS7v1KB1AQJlsKqEuw//uM/xh//8R/jbW97G17/+tdjZGQEpVIJP//5z/HYY4/h85///FqvM8GLCO0e1g5sRwY5WWEVQbK3cv/h07YO4+xDFUxO10CCpDEe4NvsOYis5kadYQIQAXDIjq6pS7XKcCZIUWhAnWsdRFMphnI6FqsuBG/vnPeK8bLiTYJ5KjZQR5oLoFTzurq7aw3O5YZJcIGn91ekKnuAYlbHKRuLePS5eTgeg6EpaATCcq4vZ9iH82aUZHdWis88eQwbR1LYc7DSVQnfsi6H7/7sGRycb7Z5UYezXJmUFqmAfvVHT+Hy8zdjrJjCXNnCj+7bB8djkVqoZfuYPFzDl27ahfe96TScvoJZ7Sf3llBpuhBcRIFZU2lkpZLQyxIkSHC8oz229k5MEIh+jhVNVBsePJ+3xUlNIShmTfn3DsXm8NjVhif1PdBieTUsDw3LQyYlZ3RTZhpN20MtsLJcDg3bR8Ouo5bVsGEsh3VDKRycawIQyGdaNk37ZuqwHIZ0Sm3zhgbQTYPqA9fjMPSY1ZRkV8P1eJtQGg0q/g3bjxht8TjvxwyxSez9BaRvt6YpYD5DzZKWn6OFVBRfdE0B4xwLFQff+skzAFr2X1InRdpw5bM6fJ9jaroGy/FRzBnts8lBwkohz2ekYEhhNI8DAQss7OhqKo0Sza0T7eJ4R+IbTQnB219zMj773Yej7x2JJdQKlYWFuuXJQkCwbiEEPCYwnDei2N65Pxh0PKLfd9/zGGpND6pKcc4pYwOfU4IEy2FVCfYll1yCz33uc/jsZz+La665JlLePe200/C5z32uZ2c7QYKVIOw4X3+n7DgTyMClq0pbR3Ql/sNcCDz0zDwA6WdMQMACf2OgI+4SQAFpUcpEi6ZMKYmUphcqNoQQKGb1yE4CkAFjKGcgbapQFQULFWvJ2bPw/UtVJ/LTHBSuN1gyfiQIPU55tFlyI5XtasPFvpk6KAFGiyZShgyUbkD/YkI+xwy8sHtVintVwrkQSJkqfueSE1Ftumg4Pn7+0EHUmm7gTS63TvGNyLd/+gzSpoamLbsIIwUDnAvMLVrReIHl+PjC9x/Hf33L6di5bWTZc981WcJPHzoordyCjgACev1C1Y580hN6WYIECY5XhEkS4wKXnb8p6oDGE5OzTxnFf/5qEiqloJRirGii6TAsVu2ASiwLz67HoGtKlzhUZ9zulTc3LA9N20MmEPUK79eDJtrlugfbLSOf0VHI6simVDAuO926piBtqmg6PhzXb/eGhiySd4IGXtdCiC67S0oJBNDXsiyijkNEatf9uvIi+p9EzfJAbV9aUnLJXot3VS3Hb+0H0HL+CJfCg7iqKVKvJWNKYbOG7SOb0iLLKtfnUANl75AoXszqmCvbUZLMg9n5Xt3ftfKNPvPkMbzvitPwn7/aK32umWyWCAEM5w2kDLnmajAKGM5tb5nI9X2vlYxHhM+Pz45XGy6coEkCyLnxB5+eSzyxE6wJVu2Dfemll+LSSy+F4zgol8vI5XJIp9NrubYEL3KctnUYb2Ec//rDJ2GaUvVa76D2rKRzuG+mhnLdgabSwOKhfzAXAR9dBDd5j0m6W+iTDEjbLyOw/uJcwA5u1LqmoBArAqRNFT7TMbdoL5sIez5vWaMMmDWv1ntzJegUbGEcYJDXRiEi8M8G5ss2RosEhYyOhaoNJhBR0pq2nFsbxEqrV1AvZnU0bR+FjBEl10BrIxJWwzWlRT9bqDgItydKIHkruEDT8fGlm5/CVW86bclAGgq2+UxuKD2fQQk6AiqRBZhy3YGhK9g4lk3oZQkSJDju0Ot+um4ohTddsCXqCjYtD9+7/TnUmp6kFjdkR9MMHCBowGkWXLQlwp0x+LStw/irzUV87NoHcGi+CY/xqEAdQgigbnlo2B6yKQ3ZlI60qaFheahbyyfatsthuzZ0lSCXKaCYliKa9aYX6KMQ+L6AptHIzimkHccXQoic9QXQxj4zgmKwrtK+qqHxQ9FABI4QgVAPji8joSIEACLg+UFXOvY+IkieuWixtqL55TDDDkbaKg1pjxbqwoRjbEawD1mo2vC57AqHexXX5yhkdBSyBmpNt2/3d619o3duG8ZJGwtRQhwyzWyXQ6HSvaNI9Kij/OYLt/acP49j0PGIEGFSfscjB/HDX00CkL7jIZMj8cROsFZYdYINAJVKBZZlgXOOcrmMcrkcPXbCCScc6doSPI84UkrQWiGX0aHrCjSFHrEwRb3pgXOgkNWxWHWiqnRn+CQIYhgLZ6vkn1zIZFlACnGFleL3vOEUpFManj1YwY13TSJtSKGyEDJYDk4dZlz0i+l9hdSONhSF9Jwj79wIySTbwmjBxEjeRKXhRh1222PYPJ5dtjrcL6hPL1iwXR+moUCH/C7ENyJhNZwFNG5CWh0GTYnZlwWdGCfwvVxqvCC09simdDAuUKraEYWNQNIPXY8jY/ZWW22/NmvzO3W8/G4mSJDg+Ee/++nB+SYWqvvx3st2wLJ9XHvb09FoDwUAIkd8PF/6WApCog4wIYGid5BsU4q2GPzMvjJEUFhdKlYJAdSaMqkOE+1MavBE2/UFDs7WMZQ3MZQzMFQwYDQpFio2PC6gK1R6WMfu2XFEc79oqVhTgsgmsmZ5fQvY8R/LOLPMDFgPxI/dsH3k0tIa0vW5nGFvEyUjbcm+EAAl0h6rYXlIm2rUsWaMA5oC01AxkjdRrjtwPR6MxfEokV6q+9vLDQRA1BlfrDm47o7n8BbGkUtrECBoWsvHpM6EeGI43UXzXqprvVZ4cPccuJBibp3ntpLRwwQJ+mFVCfbU1BQ+9KEP4dFHH+37nF27dq16UQkkZMLQP9k6WlgrStBaYC2FKUIVSZVSZFMaqg2397XtqHKHz1Go3DBU627Pau/G8SzufnwaMyULOanUEb0+vtHQFBIQ00WUEMax1Od9rBNrIKClLUV76wAXwELVxmghhXWB8IrjMbzzNSfhgtMnutVQucDew1VUag7SKQ033j0J2/VRyOjwmIDjMlBKkEursBwflbqLlK62b0QCv2oB0VtZFO3FCQJZLFluvCBu7aETguG82aKwoTUy8JqzNyxbNFiL36nj6XczQYIExzeWS5LCREIIAdv1MVIwMbtotdGKw3s/YwIg8v5arrtRTBNCIG2okRhlmNBX6s7A8SpMtMMZ7ZUk2nXLR92qY3qhgbGitLgKi7uNIGEjAsHoVSAuFsxOC5CoIUwpibRVynUXKiUo150l1x0XRl0NwvnjqPPsMRi6GtMbCea3SfBeHa8PiwKlqhO4nyjwfIFaELd0jYJS6bOdMTW85uwN2LltuC0B7hf7lvKNtl0Gx2OYmq7hCz94PCpkG5pk9a0kJq2U5r0WWIkndiJammC1WFWC/fd///eYnJzEn/7pn2JiYqKNrplgbcC4QKnuQKMEqkKhKBSq0u5HeTQS77WmBB0peglTKAqBHaiEGrqCK14+mDVSmKxPTtcicbJeATJ+XSmRwU1SoAWypobfe+1JyGX0tiAQJj7zFRuW66PpyM1GyErjYWAnJBAoAYJWqlTRXN6Ra9WgJNaVXwHiCelKv2shdc3QFbg+x6bxbM/k+sm9Jfzo/kexf7oKn8lNRdP2kDJUzJXtaBNHILsKYZGjcyMiN4IcqkKhqrRNgC46CSI3WUxIT1NKgWaT4bkDlYFtbVKGCjM4p7B7wzjHzm0r78iv9Hfqyb3H1+9mggQJnj8MwmQZJJE4MNcAIKLnxGnFCpH3ySjhE5AdYYbIvpAGBemv37ob775sB265ZwqVurOs5kgcYRzmR5Boe77AofkmKAGKORNjBQOOp6JUcyVNHXLeenwojYtfMoHH9pZwcL4B3xdQVYINoxlc+YqtAOQs7tP7yzE18DARlh3gcBlrMZ6VNlUoBCg3PJRrDtImD5rVrT1e+Ln23+WISAFcVSk8n2ExFGClBBvGsnjHq0/sig9LfYf6+UbLkSw7Klh4vohYAKE92Epj0kpp3keKxBM7wbHAqhLs+++/Hx/96Edx5ZVXrvV6EsTg+zyyd6AB5ZUQAk0hUFWZdGuUygotOfKke9Bq97GmzcSFKfbP1tG0pUUGCYSlbr53nxTZG8AP+4qXb8Znrn8MPpOiJIQSEC76dmh5wPwKRdbmKhYyaT0KBlwI3PHwQfznXZNwXAYt9BELIDpmz1SFwOexmTCggwY2sNDpQMimVAznJQWq1nRRrTvoZ30dT6gpAS592Qm476lZWA7rS6fvB0mdZlioSD/Rc3aM4cm9pbYgvmuyhGtv3Q3H40ibCtIKRcPy4DMRzAHKzUF4SV2fgciKBKoND4XgdwJCROfkM465RUsWokJZV4QiLq3Ve77A7KINAuCHd0/h4Wfne1bcezEoQk9vIQTKdXdJBsVa/U5xLnDjXZPH3e9mggQJjj0GZbIMkkj4wYy0GmqLBLTiSsDUCZOnsYKBmuUH9k/ynhzO+RqBo8cNdz6HUsVuU9oOb0e99iZhrOsMv2GivRrqOBdAqWoD0KGrCsaKKViOLxW2Ax2P2x89HI129UouTUPBZ677NVRKUbc8qcESqHKrivRn7hREWynCokIjRkO3PQ7bc6LrQgmgUrJscTx+OXzGMV40AULgeRyOx9C0uxPF5b5DvXyj4yNZoWhaqFNDAk2Spu1jfCjVFpOOF4QFhenFprRdDQr1nUg8sROsBVaVYGezWRQKhbVeS4IlwKNMTcBnAIIgR4j0eFQUAi3sckfdbroiivnxTJs5beswOICv3LQLhkaRNjWkTLVnB2+pqmw6pcEMhMkYF1LMDFKwjFISeWd2IryEPhPYc6iC7evzUYDava8cJeh2n/W3LDyEVN+OzYT5HZuFtSQm8ICerqoUhqZA1xVwh0FVKISQwTh8v/j7aqqCwyULI3kTUzN1KbAiutcaR5wNEO49MikNKV3BTXdPtQXxN16wBbfcMwXbZRgtmtHceSggB4QzZq3KffiZUUowPmSi2vRgO370npRIu7RonbEv/lJzdKpC+lbcB7G1WWr2eq1+p/YcrODwQuO4/N1MkCDBscNKGDG9kqQ4fJ8HIzWi7TmmoXb5Ir/5Vdtxwx17kE1psvBJSdsxM6ZkHTkxV4ulkmtg+Vi31Iy243jwWH9f61LVBQDk0ypygeioqcv4Um14uP7OPfjrd58TiZzF0bB8EBBk0ho0lbZ19DvbyCQ4z5V2s5fqgovYcxSp3Q6PCQzCFRVCzqbnMxpMHcgGheB4AXaQ79COLUNdxeX4SBYLPtTQVQWQ9qZe8Jx4TDppY3FlF+coIF5Q8JksBDQsHyMFqWAeIvHETrBWWFWC/Za3vAXf/OY38cpXvrJrs5fg2CFM2HiQdDtoJd1yZidIutVWwh3aU/Xqdh/PtBkuBG65Zwo+58hndJkcBhuCzlmym+/d17cqW296oIRg3UgafkDzDTcKtYbblmDHv9mifTFRgKo23L7d7zYET/GYDHyOx6PuAA0SpJq1ttd1OGcgk1JRabiAaFmTmbqKfEbD7BKq5o7H8OyBCi56yQT2zdTBuIBKKRTaex6bBBmwSglyGR1ESFEzz2M9g/hXbtoFLxBHCdkXrQO1EAqWURLS6yUl8Q/esAOUAF/70W7MlprwAw9yEVTWKQF8LpNnLiQFv9c4ACXoWXGPJ8yd1h7L+W3GsVa/U9ImTSB9HP5uJkiQ4NhgpYyYQTVMhBA4ON/sek7cFzmX0sC4QCatRbEztOsC5D2ol73kWoyydSbauZSOYlZHvemhYblw/P5vUm3KRCqT1qAqRhBjBKama/j4Nx7E71zcTZ2OFyY6O/oQ7XFkNSNUy0FS0mUM85l03vAY77ILC+e3AbSJkNYtL4qtnQXYzYF39CDfoc7iss/knoWL1vhbW8QOQjnnArquHDcxqbOgkEnJPfFizcFc2cZQTiCT0gYunCdIMAhWlWCnUik8+OCDeP3rX4+XvOQlME2z7XFCCD72sY+tyQIT9AcXAofnG2jaPtKmivWjGVCQKMgxCBkQHEQJNyEEqkLkPGtAMacKQAlFIasjZShgjIOqR6bYvdbYN1PD/tk6HJehYbWSYE2lKGR0ZEwV+2fr+NLNT4Fx3rcqu1RFP6SA9UKctp0yFdx0zxTqTQ+2y/q+pv3YMiiLMPgoBCqVnp3nnjqOPQfLeOZAf8XSXpD+mb1foFDAYxyVhgeAoJjTcca2YdzzxAwoJbBc1mZL0guuz3HHo4egqUG3OziBkLodD+gELY9yU1ewWHOCuW/RM4jPB52OYk5ve0/ORdu1ZgFHPwzmlEhaomX7yKal3/ZQ3oz8uOPiY7pGW90ZJjs00XqDcyDoXXGPjwCE3rG/c8n2gZVSQwzSQRrkdyqf0eV4wREeJ0GCBC9crJQRMygDB8Cyz0mZKrgQmFlotsWdMAZTSqCrSqSfAaw88VQVaQPWjxIdF0PLpnXk0hoyKU2OPzXcvvGTBWNFDcvDcN7ASMGE5TAsVJyezKXOwkS8o287PmrBKNPRggjWrAT7hqG8gd+99CRMHa7itvsPwHJ9qJRGyXVnLPcDW8xMSsaDeAG213fIDRThKe3ta37TPVM4vNCA7TApzKYQ5FIaqk2vXUCUyyK35/OAbYbnPSb1K0rl0jpUSrBQdWQBO2D6DVI4T5BgEKwqwb7hhhuQy+XAOe+pJJ50tY8+njtQxu2PHsJ82QLj8kY8Wkzh1WeegBN70HGEQEDpkd1uu5NiToF81sD2DQXMlW1kTBUsmE32fQ7PZ88rbebJvSU0LHkzVymNMl7X51io2hjOGbAcH7pKMVpM9a3KfuAdZ/at6Hd+bcPAEQ9dlBIcmGti7+HqwMm1fB2Vs94QOPPEUeyfq6Ncd9F0fNzxyCE4LkPalCrZg852hXRpwUVXJ1qhck44pNGX6y7ueOQQLJdJy5UB9wacA04wM46wYk0kPTCjUTRsBkqAoZwB01DguBzzZRuKEjynz0YwbaqwXR+Ww2Bo8jZkB+IpvZYWrjeTkpS9bFrrUvjuFB8jEKhZPigBxodSaFg+KnUHhIRigdHURc+K+1IzaoPSsNdKBX/7hgLWj2Swb7Z+xGr6CRIkeGFiNYyYQRk4/Z5zzo4xMC5Ho2xX2naF+iVhDJ6vWDA1FVvW53DZ+Zvwz9/79apGnWTyRoE+M87hXY8LyeqpN13kM4YcRTI01C0XdcvrG98YB+bLDgzNRzGnY/1IFuWGi1vubWcu9SpMMMbb1NOPNuSeTe5Lqk0PhYyOKy/ahoeemce+mTq44CCggYhp+05FQM6h1y0vKn6EBdj4d8h2fGmlGVixIdT4UWibrzkHcMOdz2HOt0GpTJ6bjh8wxQQEkXuQUKy13HC71OWfLyxVlFIUWRxyPIY3XbgFJ28sJraXCdYMq0qwf/azn631OhKsAM8dKOOGX+6F4zGkDRVK4PU4XbJwwy/34rdfua1nkt2JToo5PI6dm4u4+fA+1JoucmkNpqFCoQoEgE3jWbz1lVuhUNkVXC0taqU+vlwIPPj0HATaKVEggBoIa5TqDjgXSC9T2T8wW+9b0W/afsge6zmXHNKhfvHoIVjO4Ml1uBomJN358ckSPI/DZ/K/sOpet2S3gPKAeTAA+om9+ExgseZEAdbUKWYX5UyaSgkEESsSaREAiJD2JowLuFzA9cLONUXT8VGuu5H4nA7px5nSFWlC3YGUqYLWCZqWtOOyHYb5itV3TQqVVO+m7WPHZhkE983U2rrDhMhiQ7VDnCekgRu6Eo1PtJ0YZOEk3gVeK+XvI53hjo5DCa68cCu+fPOuIzpOggQJXrhYLSNmECukzufMlS08sHsWN909JeOj44NzKWjFIUBFi1XkM8BlHG+8YAtO3zqMi89ajzseObyKM5T0435xIN4tBWSiXa47qDYcZNM6smk5o10P6OT9jmF7DLOLFjRNQS6twecCh+YbOGE003aNQlX0/bP1qMCvUNKlRdJZiF9LCCE7w/Wmh91Ti/B8ufeQ1yhQR+9x26fBzHS8+BGPmw3LQ6XeMeImBBwu4Pocc2ULgCw0fz2Ihdm0hpShYKFqt6nEx5v5BKHWDCJ1eU2huGgos7YXZkD0KkqFhYX4PuFXj01j42g2iaEJ1gyrSrATHH20WQzFwIXA7Y8eguMx5NN6S4laVZBXKKpND7c/egjbNhRWdaPYvqGIN54P3P7oIcyWmmAcUBVg/UgGr3rJBApZE4s1O5jrpjCInBEKE5flku7llCt7Jd/7Zmoo1x1ogeJp2EUNQQH4vqQjmUb3pgNor+yfsX2kVa1faKLWdEEIwbqhFBgXmC/b8LkUFQmrugppzThpKgVWkGALIBAGkcHHcRg8xiAgKftEtDrQtis7wkfirxkX+woDrEJpTLF1dQcmpHvjoyjypBxXCqelTS1K/CyHST9sQmAa7bca3+dIGSo0hWKh4qBpe0sm/IzLgC2EwDk7xnrOFzqufD8uECmPa8FaFmsORguGtO/yWfQdYkJAD2y9QkXwjeNZ/PP3Hl0zxe4jmeGOY+e2tTlOggQJXpg4EkbMIFZI4XN2TZZw6/37owKjplLUbT+I7wFzSqBtHMfQFGRMeZ9/7+WnYV0xhRt+uRfeEvPRnWAcIMukqr0eDTvajWB8J5cJE223bweVC2D/TB1pU0U2peGpqUXcet8+TE7L/UckyHn+JvzHL/aCMQ5DV1C3/K7g3EaTHvhsBwelBHMVC7feJz8TtSPJ771XkPouYfHj7FNG8eTeEjIpFeuG0ti9b7HtdfEigRDAHY8cxKvOOqGbXq0pyAcF/N7vCugKxVDOiNTlb7xrEq84a+MaXY2VobMoZTu+3CcE+i4koOctVOwXvN3lSptXCY4uVpVgv+c971n2Oddee+1qDv2ih+36uPo7j2DPoSpMXcFI3sRw3sBw3sRI3gTnHLOlJlJBNy4OQgjShoL5soXD8w1sGMuuag0nbixi24ZC93w3IUHXFZGKuapSMFA06ra0lIg8u6UgVtyze7mu4KvPOgGP7y11Jd87twyBc6CQ1bFYddoUuMOEkQSzuYyJ4H3b0VnZP23rMIQQuP7OPZir2BBcYLHuIJ/WoWkUiiDIqUqU6C7WHIAAIwUjiEIrF+6QiZ/0TRYgQeW/OzLGVbFXk2SLQHWFEAKVEHhMgPlcCn4FQWUgYbZ+66JyIyaEpGJzQqRaOeOoxWaywoS8XHcwEUuww43gpvEsfuvCrbjhl5N4dv9iW4CnFBC80+JMJsJjhVRwfeLdYQe2w8DCDouQm8Bi1gCEwFzFRqnqIp/R4DNJcQsF09KGinLdjbrAB2bra66mP0gH6VgeJ0GCBC88rIYRw4XA5HQNew5VQASwfUMeW4L57F7oNbNaDhhiQKAjwmRhMm3K2WRVIag1vDZq+uUXbMWG8Sw+c92vu+7lS+FIElTGBSp1F/Wmh1xGRyFrIJvWUWu6fR1CmraPpu3jP+7cg6G8gdFCCsN5AwtlG5OHa/jijbsghIBpqHLOu0/sPJq08WJWx4O756LPJKWrmK9YUmwM6EqUCQE4CIiQc+2Mc/znryZBIKniukq69hadbL1DC03c9+R0z1hoOb5UDw/2dgKIFNbDWe6wqJ4xVRxeaGDPwQpGssd+HruzKLVYc1r7rlAJnQikDQW2y16wdpeDWvclOHZYVYLdSymy2WziueeeQzqdxhve8IYjXtiLFQdmG9hzqApAdjMPzjdwcL7R9bya5UNVXCg0Zs0VWGeEFgRHAkrIsgl6KNjFhYDrt7ySQ89uSghUVSqZU4Xg7idnkDalLyXjAj7jUIgUXJuv2LjhF3uRMpSu5Ht6oQkupIr1cJuap+wuqwqFrlGMD6UwX3EGquw/MVnCV25+Crbrt1l+LdbdYKbYRK3pwvNld5kQYCgr7Rxcj606+eXB5qRTJKzfc1cDgfaKemipIYKkO5NSUW2sXtmTc4BQEa3RDejswceBYDQvitiOx9GwvOgaxzeCp28fgZk28P994wGoqmRg0OC7E851haMMGVMFoe30x7A7fP2dz2HycC3yKw8F11JBkB9iAtWmpMMZmgInYIgYmgIQtHWBH9+zcFTU9AfpIB3L4yRIkOCFh5UwYnZNlvC925/Dwbl6VFRVKMGG0QzecelJPTfenTOrluOj3nS7nuczjrrlycI20JOans8aSOsqmo6/LLttLcG4QLnmoN50kUvrKGYN5NI6qg0XltN7b2S7DDMLlixcBlZkKVNFte7AYwKay5b03z4aCJPlM7YN41ePT0OlcvTK0BWMFlLt89OQ41qFrA4uAgtOLlBreoHDB40U4BervbvPBPL7ASKv4XMHq12x0PUYXD9gEsoUW7420PQhVAqHhgrzYbysNtznJcGOF6VmF61ovxKHALBYd5FP60sWz4/XDvFajbQlWFusKsH++te/3vPnlUoFf/RHf4Tt27cf0aJezNi6PofTtgxh19Tiss/1mYDPGJwe+/zv/fxZjA2lMJyTHXDZCTcxkjeQMtSjKkQXenYzCHgMsMBweKGB5w6WoxnplEFBiBp4G3M4HoNl+xjKGqCUggsRo+TK6nnd8pAylDDjCuaCpUL0+pE0fvviE/H1ASr7T+5dwBd+8EQ0c+14PJpVLmZ1lOsOKBG4/PxNQNCpvemuqUiRU9cU6EGSttpNw2o6yCuBCLNdtMTbOAcMncLUVdSaXlfleyXoS+cOgyzkvLYf+Fvbrg8vYBFsGE3j3B3jYFxg7+Eq8lkThi4/a9tlbRTusKvNhJwL2zKR66I/nrZ1GG9hHP/6wydhmqosuHQUWTLBnN2VF27BxFAamZTaVxF8rZS/EyRIkOBoYBAmy67JEv71xielTSPaO4z7Zuv41xufxB9dubNr4x2fWRVCROrcnXPGlJJoBtrQFWwcy7bdm3dNlnDzvfvg+PyoJNfhepaafw61SGpND/mMjqGcgXymf6LNhUxIa00Phk4xnDUwPpyWFmHN/irlRwOhNeVIwcATk4syZhMC0pCjT/mMjnXDaTQsDwsVqdquabJI7QdCbGFBgALQdQWUSEvSlKHADorMqkKiCxnG3fB1hq500asXa05XoSH+WcStuoDQa50gn2l3DAGOXcJ62tZhvPuyHfiX/3isa92hp7vPBRq2B0NXexbPj9cO8Uqt+xIcO6zpDHahUMD73/9+fOxjHxuIRp6gG6pC8cH/8jIs1hw8e7CC+YqFUtVBqWqjVHWwEChDLoeG7aNxuIbJw7Wux0xdwXC+PfEezhsYzpmR4uRao970UGv6ECCRQJgaqFUKSKpxJqUhm5Y2TwLyxhxSfhfrDnxfYK5sS2GVoE3Kgshdabig6K+EGt4Ed02W8KWbn0LT8aVgWkwJdaFqw9QVWI6PasPD4QXZUS9mjWg9uqZE6piuv/oE+2iDcUCIVmcZkAEwZSgBtWv1yfVSIDEJGhLMrjMIvO6cjdh+QgFzFQsP7p7DTfdMyU2cQrBpXR65tI5SzUE+o6FU5fC5ACUiOhdKpIJ4PyGvXEaHrivQFNo3KVYVgpM2FJbt/q6V8neCBAkSHC0sxWQJN921IFHQYnNToVBlrenhxrsnuzbe8QJjqB+iBHGyTdwr0LpwPWmLGb83hx01x2Uo5gyUKhZ4jCZOEcpzrR7xcSK+DAXdZxylqg1VoVGinUtrqDW9vh1tx5XCsWlTQT5jIm1Kivgg7ECFLlGE7kAvNpyqkEgbxPU4Glaw7wEAArg+Q6lqI5fSYLksOvfQwlShAAWJrrFkG3IAMjYahgrUW6wEOVMfMMa4gM/lqN05O8axb6aOA3MNMMZR6pFcA0ERnAuQaCywdcyG7WPzeBbbNxRQqTSj1/RKWNcNpXDujnGMFVMrSrgHSdQzpgpdJVKcNWDJxfV8FCI774bebS12PHeIV2rdl+DY4aiInC0sLByNw76okM/o2DKRw6bxbpr2U5MlfP9Xe+F40i4jpAP5bLBKse0yHJpv4FAP6rlCCYZyRt8EXFN702aXQ9pUZdCJeWyHHXjb9SPKEucCKVOFpirSr1uhKOQM2ZXUFcyVmvAYh+cLeD6DSijyaQ22x3HTPVP4i3ee1beyH246HNePxC3CiisN1lOPeWzbLoPPePCnAGcCmbSkV0uK+qouxTFDrwS6XHOhKp7sPgzo47mUeEu3F3ecnC4LIAol2Lo+D4WSSKQlDFKMcUxOVyM1WtuV4md1q130TNcUvPqsE/oGsbVKisNAvXPLEA4vNLFYc5BNaYlid4IECV4w2DdTw4G5BkQwWhWHtFmU8eHgfKNr4x2/lxoalU4MAAglUNFKskOBM0oJXnP2hujeHMbZhuUhk1KhawpGiylU6m5kbRne2pfqPg8KvoJMPUy0NZUil14+0RYAGjZDw24gl9ZQzEpLsGrdjcaMeoGAQKGDOXVE+5CAdCaETFYLWR26qmCx7mCkYEb0ZjVIDH0usBgkyZ3XMSxKx8+jXHMjGzRNkaN5ns/BmICgcu8VP4amypnkkF69ULHBg3ge3zuoVI6gIVi3gJzPBxBpm1x54da2xk2vhLVheXh6fwW791WQMhQYujJQh3jQznK96YFAdvC9GEuudY0k266Y1dv2Ccd7h3g11n0Jjg1WlWDff//9XT9jjGF6ehqf+9zncPrppx/xwhL0x6lbh/G7Km3zwdZVBaPFLC5+yXqMFFNRtzvsfJdqNhaqNlxv6Ts+4wLzFRvzAeWoE/mMHiXbw3kDY8UUNp9QgKEAhtotvBZi/WgGo8UUpksW8kp7AhTO7GrBLLnr8bZ1+j6Dxzk0RUE+qwcCahQKJVA1mfiEtgv7Z2vYsi7fs1IXVvrSpgbHc6JUkAc0817wmYAQsvtpewzNsh/NRQHtgS08j+O1qw2007YHQdpQkE3rKNecnrNLnVR3LqRyacguoARIGSpyaQ3X37GnK0gpmoKUoWK+bKOYMyA4DzaG8hqrwRwcANz+yCFsWZfrGWwHEf+54uWbl6VUxgM1D0Tc6pYnveITxe4ECRK8AFBvevDD7K5HSA5/5Puia+Mdv5c2ArZcqKnBIeNcNqWB0JCvJLBzW+t+eMfDB/H0/jI4F7BdBkIcqIHfcNqUjg5ChMn54J3efojHX2AwZpbntxLtsKOdTWuoNVpFgE7Umh4alodizsBIwYTtMlQbLcGsNhcVAhDR2h0oNJhVFpJ2Hbf55EIma2ldQcPxo+J9ue7B823kM9ItppDRsVC1ZXc5GF0L3koWzDsS5NAtJOyQuz7HQsUGCXRvTF2JXFk64zglsqP79aA7e9n5m/DtnzwDAtnQCRsWRA5iQ0GQZAfnrSoUjseieBn/fvRKWCVr0I266D7jyGv6sh3ilXSWs2lZKFdVimqDd4vlcnk+l5x1Qtu+4HjvECcjbccvVpVgv/vd7+6ZSAkhsH79evzv//2/j3hhCZbGUkrfADCcN3ESCm2vCTt5cbp5lIBXbdQGoJ5XGy6qDXdp6nnOiOa9h4MOeCGj49VnnoAbfrkX1aaHtKFE/t2WwyRdnNJIiKttzQ5DPq1JASxKImqPGlCowu62EAQNy4fPJaVNCY4XBr2w0pfOqNCsll3TcmJjIU1dCNGiVsdeEgYwGXiO78a24EA+o6EyoMiZ4zEYTBYY3AF080JqOiEEmkKgUgWbxrMQIF1ByvUYuBDQVGnvUmu4KGSlMFnGlP7uYcAQQixbKV5K/OeMbcO4+d59mF5owvEZKCEYK5r47YtPxOnB6ECvQN2wPaiU4jVnb8DObcPHjahJggQJEvRDNq1BDWnh7aSi6EcAoKq9N97hvfTGuyfxzIEKGBMgREBRKAgIGrYfFJPluFRohbVrsoT/vGtSxkwiZ1tDmvlC1cZI3kQ2pUVuE2tZjFYUCkMjyJgaSrVACHUZeEHSqasUuYyO4bwp3TD6JNpcAKWqA1VxMZQ3MT6URsP2UAvm3NtPp/UvQgAWHK7ruALgjKPSZACITJ6JbDjYrkCl7kBTKExDxUgg8hovdqsqRcZUUW16UANKHg8SVYp2Wr8UIpOx1/EYxgom6oGKutRNkdT0QkaPLLZuumcKl523CWlDRcpUIYRM6FngGuIFowQgwIbRDF591oYlKd6dCWtrzl/us4CWAGyoiXP9nc/hLYwjl9G7GIn9O8vtr9s4no2YGcN5I2IihkwMQuRe4ZKzNrSt93jvECcjbccvVpVg97LgIoQgm81ix44doHR1NOIEK8MgSt9xEEKQTWnIprSev2yux1CqyWR7IZZ4l6qOFLZYJhoOQj1PGyp85qLWdAEQqArB+HAKp24q4t5ds13Jd9NhMDSK808dx+2PHIwo5mHQDoOo5zN4PoPjMpSqDgiR76mGKusqRT6rY2zIhEIptBEqKVdL0rxaITIMIkHBVs4xxyvmx3PbOgYBDJxcAzJh7qU4GlbM+yGTkrcWU1fxpgu2oGm1glTINgg/u/CzIoTAZRz5jN5WiXU9qd6qqxTTC0tXinuJ/zRsH1+/dXfU1Qk7O/Wmh2uu+zXe+qpteGJvCbbro5DR4TEBx2WgVHYNKg0PT04t4vKEFp4gQYIXADavy2HjWAa793nwOW+bwQ7tFQmRauL9Nt7hvfSORw7ih7+ahO348Jh0owgTHBoEya/fuhvvvmwHbrlnCr7P5X0yCJYURDKnGMdc2WoLlapCYegKmrZ/RMKf+bSGuuXBzJgglIJzqe8huMAgk1Bur0Tb56jUezO3fCYwt2hB1yiGcybShoZq0406/p1F+/DfveImIYDHwk63AIdkJKZNWbjgQqDScGEaavRfreFiseaAUIKRvAEQEhUtQhcOxniLahe9F2lT/lYohaFSpAtmm7BZqDWjqxT7ZuvYe7gKRZHNDV0Pit6qZBE6HgdjHAICf/jGU7FtfXtjpxOdCWvT9uF6PHCfIS22BJdx2HYYJg/X8K8/fBJ6jDaeCjrHvTrL/V53xrZhzFds2C5DMavLfaQnRXbTpop3XHpSV4w/3jvEq7HuS3BssKoE+/zzzx/oeZxzvP71r8cXvvAFnHzyyat5qwTHELomb0ITw+mux6S/pBPRzWUS7mCxJhPwfrSq+Ov70c7rVg0LZRspQ4Xnc1QbYfINjBVTeM3LNmD7xiIenyz1pJgLIdB0GCaGUxgfTmH/TK2rq0+IPL9MSkO55qKY05E2NWnFxaRlmB8k7PI/1kY1E7E/oyQ7WAMlgBfQsxRKYKoUTWfp6/FCh+AtelrEiiOtfzdtH6dsKkZ06slpGaAblhep0irB5xIWS1pdFRl4OxNxEXhbP7m3tCQVKy7+w4XAp777COpND44nRfYUSgEiz8HzOW64cw/MYFMxV7bbiila4PWaiIQkSJDghYJw031oviHvoYElZqgiLiCT0itfsXXJjTclBJe+bCPWFVP4/A+egOP50T3b0JS2TucNdz6HxZqDXFoD49K6kwSjPoyLKJ6G76YQ2V3ljo98RnYqV1unDqnF8fMTsfccFGGibWjS5nG0mILjMVTqTk+mm+txTJeakRhqNqWhUu+9H1Job/eQ+I8Yl88Li8xS5IxF/4UJnq7JGKkqJPqZprZYeQCBEIDfcUF9JkCp/OzShoq6HXSghd86vwYiIVQW2LD+9MGD4ELA8VyMRvR4N4qVcSbD3sMV7DlYhSDA9hMK2DrRXsAJE9aGJSn3rhcongvpChN+Hz0m94KMS+agaarQFBrRvy9+6fqenWXb8bFQtXu+br5i49VnnYDH95baZra3TOT6jn69EDrEK7HuS3DscFREzkIIIXDw4EG4breHYoIXFhRKIro3YtRzRSHI51M4NFPF3KK1eup500O1g2LjeEBjuo5v/uRZjOQN6JoCxjgWqg5SOoWmScsuy+UwNIpTNhbw1VueiubSFQqMFlN49Zkn4MSNRQDAuSeP4YZf7sV8xUbWVEGIQK3pS1E1lcDU9ajoy4IETHbHZeLNguDtcwEKAYVIFfQwjPlMwGO/Ocm1QluUeEJam4F4wSFEIa3BNDW4LoPPOX7nku3Ytr4g6WpCIJvScHCuDi5iqrYEIEE2SyCZCL7PwbnAQtWOEnGZEMvP5KcPHcS29fmBgsa+mRqmF5pSABAEakxohVAClQCMCTQsX6qxBgGeBufm+gx+g8PQlEQkJEGCBC8YnLZ1GH905c52H2yxvA92L6RTGoxALyO0NYp38zKmirmyDcEFsmkd+YyOUtUONEy6idOtkSsBzgHLkeyhcn11e0Wft4TCqCKHoY/EJcPxGObKFkxdQS6jY3woDduViXavJNlyGDzfQsZUg+43Q7kmac/h8+OXQdKxI8fRNuQz0kpV/l1eRxawqsLOZK3pQVFkEt20PaiqEjlwhElxJ6Tzikw6C4HrxtyiBcdlkU92WKAIi9qBJbacG7d9OB7DTKkZfa5xJoPPBK657tdBvJfvqVDJsvwvrz0ZFw1lAMiENZfWMTXdPWYoAu0WPaBdcxF+VyTbIS4s9tAzc6AUXZ3lSljADwSApVsJUAhG4x7fW8IH3nEmDszWB7IHe6F0iAex7ktwbHFUE+wELw6E1POUri5LPe9MwAehnjsew6GFZtvPJLW7ZUGS0hX87KGDEJCz4IaugEBgumThhl/uxW+/chtO3FjEiRuLeOsrt+G2+/ejVHMgGIcT3ChDKMHmIZzvzqS06CbFhYh1ueV/8cq2QiEpaUHSeCyZ473sPo4UIcss/I+SbtXWUPQtZWrRdavWXTQsv004rGm3lMF9xiMldxGIpoRqrtWGC59xcIEoIRZCgEN6o/uMd81i97PpqDc9OL5Ug1eC0RUpeCpAQNo68JwLqAFVPTovyKTe8VhEe0+QIEGCFwJO2zqM/997hzA5XcOeQxUQAWzfkMeWifyKNt71pgfGBTJprefrVFXqnYBKKm3KUJFNaX0TZtZRqbVdhiNLAwRMQ0XT9pAO5oTXArbLYLsWMikVGVPDuuF0IMjldImz+Uyg0vBQszwM50ysH0mjZnmoBJ15EgQbNbSyCgJMeC2kfgtpc2ox9YAqbsnk1qnI2WkhEBWu5ytOxNBLGWpEU49DDejdPKBeL9YdDOeMIA4HVzA4Xjy+cyHXkElpSJsq5hct2D6DCPYFIZMhZaio1J1o/6FQqaTOuIzL/+8/n0Ama4D7DItVG9VG99hZHIzLwgSFPE9dVSJl8lBYrFxzUcwaWKg6UWfZ9VggEicQMvvLdQc0EHYL2WgHZusrYqO9UDrES1n3JTj2SHaMxzEUGksAgqQAeMGM+0ZYjnpebThYiOa97djfnSWtMEJ4jLfRz6UCuQwylBBYDvC925/D+aetA/MZnjlUkTRlzqEoFCMZDfWm7GCGa7IcH1YsBlAqb9DSm1IJNhBB8gdEFHPGBRiTf7oew7H8qI7W9yLcHPRSew073LomA6DryTl4AWB2sYnbHjgQCYdxxmG78iDxuXVdoxjKSYZCOIvlejzqckj6mKyWFzI6FEraKNtL2XRkYxtCARFU3qMzA4C237FeKqHR790RbgEHQa9CQYIECRKsFpQQbF+fx/b1q994DzKHqqsKClkdC1VHum64LCrKDqIUbrlMdkxJp/Xj8jA0OeIzX7ZhOc6ax13P4yi5NgxdQTalY91wBk3HR63hgBDSVmTnHJiv2FAVguGciXXDmfb5bA50yqGFVleUILKzsoIRKddjUTIrtUEQzUuHRXUh5GOexzBeNFG1fBgaRcP2A/tWIcfYgrd1PY7ZRQuEkCiB76STh0gHLh6EEKRTGuwqQyGrw9AUyWRQKWYWrbbiPiEkYIIReIyj0nDwia8/AD10Y7GXHykMC/eUkkhJPbpeQYf7nFPGcOevD0edZcZ4m5o6pZIBtxZstKRDnGClSBLs4xSqQjGcNyIKEReS9hPOFUV/D+hA4U02tIJoS8pF5+38+IEUPzMxlDOBDS3qORcCh+bqWKw6cBkHoQTlSIAtoJ4PcJPkgT9jrenhpw8eaHtMimtJKxGFEugqgev3vlKh4IbjtjrnlMjPSQuSblUlSBmqrFATAp9xOF77XPdyiuXLIbyV9zoKDSlnWPtku99+R/ptSluOw6UmfJ8HXpsE/3HnHoAAo4UUHJehZvWWIQ9n3wlksn3ShgIe3D0XUc0oIdBVOROXMlRwISLVzuVsOt7zhlMwVjRRa3qRd2fnNQz/JMFMYEhJj1uNGZqC5gCjDkeCfoWCN1+0LaLXJUiQIMGxxqBzqFe8fDOuve1plKpSHEyhS0Wsbohw47ICDOV0CAGpJp3TUWu4kWjYWiHsqPqWj4blI2uqyKR1pIczsqNdd7rE1HwmMFu2YGiKtAFLaag23J5+2y1vcQHPk4yrxaoTOJgAI3kT5bobrEPGqnDMSgQ09NDfWtdVqC5HNq1DUxUsVOz+s99BLA1HtTqfFX8f+QP5HF2VquYAgjnqbmX0MNASIoXdGk0PZsGErgJNDDZGp6oUxWyLMh9dr0BYbOe2YWxbn4/iphNraigUEWttrdhoSYc4wUqQJNjHKeR8EmnzGYyjFd9I9O+QMsS5TKw5b/0sohSFSTkCMZAenfFoZup5SsyfO1Bu8/iOz1K/5uyN0fNcn2Gx6uDxPQv4xWOHoVIqgyDnYAMEWCFa6p6rUTANvSWlyqgMmjIgERiaIlU3qaR4tbqostvt+YGoWqzzHSJUARUQXZX/MG7Fqc0hQosL70iNRfuABP/rTN65kF7R8XWkTRW1hrRVCwVRBDq8QqPXS+sTSmTg3r2/HJ2nQglMXUUmpcIIOidhcM2k1J7e2vE5rZvv3Yc3v3IbPvPvv0bsa93z3Io5A0275UMKyPWkDRUgOKoqoUsVCr5y8y5kcyY2j3YzQI4H9KPnJ0iQ4DcDg86hhlTa7/78WeyflXob0lqzVfztBRLMADPOJROK8cgrO0RniDY0ile9dD0OzjdwcL4RxQBdUzDdMVI2CEI2lqrQyBObBXukzv1Bw/FhuT6yaR2mrmJ8JIOm5aHadLvim+OxQAhNRTGrB9R5p6eNmBDAQsy1w9BkgkkpiZJtxiH3ZkIEY1Yk6mqnA7E4oEXVD1/bCfkaRPPKhITNm/b1EEoiJw8/KIRz0RIhdX3eXYAP9qPh9QNCBxO3PWFvPT16Toh8Roso6u3XqF1YjBISdZafOVDB9372TLBvbH+fY81GS5AgSbBfoOikuYb/JiBQwhmfjvvYUkm5ECHVKJaMhwk5AM5E1EUPZ3QERGRXJW/0oiu4rBTPHSjjhl/ulbYJhvRCdhwfB2cbuO6O5/C2S07EyYFgma4qWDechs84Htg9GyRWLd9kSdcWcDw5v8R5S9F0rTu8YcKrEMD1BTzfDz4ZmXiqFKC0o9sdE1SL08yllZSA5zP02pL0iGWggZjIfMWGb/GjUhghAWeLEiAVzLv1qksIiJZCuBAo1x2ZFFMKQfr7jnMBOB5HOqVBpQQeE+BMJu9N24OuSSEX2+XYOJZB3FvbC4TRQvGdcE5rutREvenB1BVYPZTdIzcZQuC4DONDqbZjhYn60VQJXc7Ps1J3cd3PnsEHfvelR+X9jwRL0fOPl7m0BAkSHDkGnUM9besw3nP5qbjmukcBALqmQnCOxVp/lXAlUNOSlGUNqs8kVZwJpE0VpqGA+RzlhgsC4GUnj+Hc08bRsDw8+PRcm1XTalMnEeydhnMyoUVKWn91dsMl840iZUgrq5lSE5mUhlxaQzqlod50UQ8ss+KQY2c+cmmtTYm7M/lVFIAz+T7FrAHTUGE7YQFf7jQE2prEMukN/yKAYk4KxoX7oF6Iz1ov1WAIRdZEwJSkVDICfcYgIPcDnaWCfnssFuxxOtGrcXDOjnE8vrfUt6Bzxcs3dxV2ZaxXYbv+88pGS5AASBLsFxWWSsrDWanO8NTeiGolTaEwGaUE2ZwJJbDlkL6TIkrImRAQfDDqOhcCtz96CI7HkE/rcD2GSsOOaMe2x/DtHz+NN5y3CSOFVGTDtX40g9FiCtMlCzlKpPVHlCAR2B7BaCGFatNDLq3B95kMGMEJOq7fM/la0bUN/vR5+78R/zlv73YDgKYSUEKhqhSaQqGqBIautXW8WSzhjv8Z+1iwcTyLv373OfjFI4dw6/37sVCxBpp7Wwm4AHRF+plXGq5MtiGDrtx0kKhAU7f9wHMT8P3oiyYVw5cAIZCbjo5NDRdyxmzOYyhk9Mhb23EZGpbXdj00lUYqqU3bR6lqQ1cVZFIqqnUvUBRHQElXkMtoaFp+lExnTBW6rsD3Ocp196irhO6bqfX18yREduoPztYxNV3DphX43h9tLEfPf+9lO5IkO0GCFxCWY6MMOodq29LruGn7IMQDAoGtJRGyhjTJRHv9uRvx5NQipktN1BpSNXu8mAIIwa59i3h8bykaF1JVCh2yuN6Lgj3gySOT0SJryPgsb9syhSyG15qtANsIisDZlIZsSkc2paPWdFDvMRZVa3qoWx4KGQNjQylYjo9aoHxNIJPrYDlYrDmY0JVoLrtzHeF2LfqrkLTqS846Abfetz/QmjmycrsX6qFAxnhFIZE2jqr0Lmj0K6Qst5QWvZvgopdM4JxTxnoWdM7YNoyb793XVdg9Z8cYjEDkth8bzeeyKJKwrRIcbSQJdoK+4EJg33TvQEqC26pCKUxdhaVRhDEg3imPJ+TxeXJZ4WzvkB9caIBzgfUjafi+gOV4UlhMoZF6t+tz/OddU0jpCnSNYrSYwhnbhnHSCXkcnm9gpmS13fEJgExKw/mnjuMnDx7AQtmS805BRFIV8rwOqPu+gADrEnML57tVlfZUM493vDOmit97zUl4dn8Zt96/H7brY3wojbrlotZc5WajD7gQ8AKKu0IIeFAlCeebwsDKfR6ppkbfh46CSth5F0BUEOFcRLPSvUAJQSFrYMeWIdzx8EFYDoOAgEppVLRxPIb5ioV8WoeiSHs5RSHQFAUTI5qktIXdbpXC8zkMXcGbXrEFD+6eO+YqofWm19PPM4SqUliON5DmwLHCcl33ct3tUnpPkCDB8YtB2SjLzaHumizh2tuejhhuQP8uatixZFxIJexgvjucr738gi1RMj9XsXDrffvbCnpWoMVRqjggBclDrzRWZ/XFAVQagb4KVr4tEEImzw3LQzatI5cxkE3rqDZcOefc8dxy3YHSlAXrdcMZ1JouGrbXlpy6vvTZLmYNKJQEBXqJ0I6LBlsYTVXgeAybxrO45KwNmBhK41s/eRpNZ+V0+U5wIb8PWVMDpQQLnqSx80CwLbTrOhKEryYANo5lIqX7zoJOw/bx9T6F3fmKjVxKw2LdxXjRRNOR8+yqIvenC1WpuP7DuyahKATFrIFzThnDzm3DSbKdYM1xVBNsQgjOO+88ZDKJQM8LDUdC/Yx3yju75OE8eVyINLynHV5ogHMgk1ZRdh2kTA35UMiDizYREYUCmqag2nBx56OHpfoopTANeaMPO+jhbdvxfNiu397VFYDXR9TsWKHfu7fPd7dAiQykqkIiqnkhq8EXAvc8Po1CRsP6kbRUMfcZUgbWRFwtWhcXEf1NxD5fHn3O7ecUMhYUhcDnvI0REW6s4sfuB0KAoawBQ1dQa7qYmq7igd2zURLPePscGBPAYt3FlnVZnL9zHe56fBoH5hooBuqn0Rpi81yXnLUBl5y1oaeK9+R0dc1njMNu0UypKQsmSyj0qgpF7ijOgK8Uy3bdA3p+qPSeIEGC4xdrxUaJF95GCyk4nvSDZrw3Qyy8ZUsxSyAfJFDx+dqtE3lwIfCp7z7SVdDLpDTUmi4cT1LQWTC7faQIo65CZUFhJaJpXEgWVt3ykE/rKGQN5NI6qnUncioJwbjAfMWGplIUc0Z0PvGE3PM55stWz/MSQsa6kEGWMtSIbXXa1mH8ziXbcc31jy+7ZiWypwxGBOPdeyLPifuyeNHJOiSQnex8SkfT9rv2LPE9Qa9Z6xChgnwureEdl54Uxdh4Qaff9yBe2E0ZKggEDs43Iz0hEVyrcB+hKASVuoty3cXUdA233r8fm8azy+5vE72RBCvBqhPs+fl5XHvttbjvvvtQqVQwMjKCV7ziFXj3u9+NfF7+MlBK8fWvf33NFpvg2OBYUz/DwGHoCmqWrOIu1qR3oRyhCWnIJJixDarcxAehRHo8Uoq8pgY3aRl4dU0motWGiyemyhgupGSHNFAEjf4UclZbzpc/vwn3cuBBh9bxgJBqXq27ODD7OCilyKV1UMrAhYChKTB1NRKXYaG4WgflPGQehEFoufcPRdRCARvBelPpKAmCP5VCJU3b7/LQXgrxYCyEpA6G9hx7DlYxs2ihkNWXnO0r1Rw8s688kEAPgK7guXtq8ajMGMcLWL7P0XR8NCwPI3kDKbOVSAsh0LB8bNtQwJaJ3JLd/UGxFpuEQbruodJ7ggQJjl+sJRuls/CWMqTA50ypGSVwnAcF1457WdpQYHschkZxziljeHJvKbo/LVXQK2YNzFesnqJhRwoyCLW9DziX+iO1potcRsdQ3kSeC1TqDuyORNvzOeYWLegdiuO2y5YUhwshBDBWMPGOS09qi0tNh7UJi3YWwEMwLqApcvPEeUvDJbQBi86pV5IPwGMC1aaLsaCoUqm7SJsqcmkNjsewWHOj54boXIuuKpgYSeHtl5zYN7YOUtgtVe2WUG/I+AsWToj8XKoNV3blAwE4x2U4MFdfcn+b6I0kWClWlWA/9dRTeM973gPHcfCyl70MGzZswPz8PP7f//t/+N73vodvf/vbOOGEE9Z6rQmOAZ5P6mdoBTJ5uCZvkEHmx0QojCUikTLP5xGNmnMBnwuogeCaIECqoWLdcAqUUDnj1JSK1ppCJV1YpVAobeuohsEkpLCHyXiYeMeT8vDvxwPine6GJefRhBCwHAaFIDhfBWpINY/NeIfJ9WjBxPYTcvjxAweDJFyAB11hSuXsdMish2h5debSGir1lnJqW/dahFV1Bb/32pOxWHNw412TcLzWRogQeax+qu+d3fBQQVwQgDEBTaNLFgVcj+Gme6bwF+88a0mBHgD41HcfaQueubSOSt0BF2JNC02dBaxMSoOqUizWHMxVbAwxgUxaiwoAKV3B219zsqThHeE8w1ptEgbxxVUUclSV1xMkSHDkWEs2Sq/Cm6LQyBcZAAQRkbey5TDZdYZkoA1ldYAQ3HT3FByfgRKCsaKJl5081regZxoq8hk9SuLWEqxP8XhFx+AC5ZqDetNFNq1jOG/CZxyVutMWCwEZr2ZCxfGcAc/nqNRd+D0EVcJmQmjRars+jMDGMrzW2ZSkdHMmejqAhJAjXq3CeYh+zLRe4FwKoqVMVTqnBIKj4Vaus4Cv6wpSOkXTZrI4QoBqw8PN9+4DCTrwnRhsnMqHrlKcMJqGxwRcl6FcdyL182owQhB27UmQZKcNFU2H9dzfJnojCVaDVSXYH//4x7F+/Xr827/9G8bGxqKfz8zM4KqrrsInPvEJfOYzn1mzRSY4dng+qZ+hFciXbtoFy/Fl1ZGgbbYnFNICWkleGAI5BCgCz0XLw0KZwHJZW7U4FHMjhES0c0oJlKgTTqQSuyI3BJQSqJSCEhLz9GyhZ/LNOHz+/HTFO2nlRb3sRwABAABJREFUTADMF3D9FuUsPIuw0KApFCCAzzmKWb2NRs+FgM84mC/gCw7f41BUgotfegLuf2oWjUDEJbT8CEEJgaoSpHUFPhfIpjQ4LuvaKMi5/OWT67DybHkMG8cy2H5CAZQClWU2VZzL0YN9M7W+Aj27pxZ7B8/ZOrgQGCuaURI5aKGpX5e4XwErl9ahKgQLFQfVpisLRkEB4M0XbcOZJ49hcbGx5Lkuh7XcJAzqi3u0lNcTJEiwNlhLNkqvwpuhy3um67EoYas1PRlTFZmEjQ+l8KozT8CP79+PetMLWFY8Wt++mTp0Velb0AtH0IpZHXXLB2PdyeJqsJaR22etRDuX1jFSSMHzGcp1t6v7HlccHyuasALF8fheIqQ8h+FzZtHGp7/7CEYKJi458wRc8rINyKU1GJoCm/uRiFqvcwqdYDSFwPa6k/lBr4PHBLKE4PJXbMbtjxxCue5CV+X3KlQbJwQoZAxoKkGp6oAH8/fFrA5CyJLxqNf3SwgR6aq4nhSwTWc1UEphUNm9DtmP4Dyw8CKtmBVcFCHQc3+b6I0kWC1WlWA/+uijuPrqq9uSawBYt24d/vRP/xQf/vCH12RxCY49wmCrmCSwtmqJQRFCjjr187Stw3jfm07DF77/OJqOL8XUgrt7OA8VBt6WLUeLehbv8C3W3ciGKT7rxUSgfhlwmMNOte/LRHEp9W1K5OhDmIArlEQJerhZkEl6OwTQTk/vTMwZjzrjRxvhO/hMwGcMFhjqlocDszKBC8XHpLJ50P3WKEyqgqSADWNpvPlV23DWSaO45d4pTE7XA6VzDgRWJ44nBdiqwcbhOz97FtW6A8aDrjlpJeSdtPFOShsJbOeato9MSsObLtiCrRM5FLMGykGC3W/j4DEOy2HR97VToKdf8AQQeNHLqrqpq63v2zKFpqW6xKngdb0KWClDw1iRoOn4uPLCLThpQwGb1+V6bihXirXeJAzqi5tsOBIkOL6xlmyUXoU3AmnrGC90B65ccAOF6pefNo6Hn55DteHCDQQ/FSoLv4IL+EyAMVnMHS2aXfdpx2NQKIERWHUuVOxjEktXA58JLNYc1Joe8hkdY8UUXJ8FM+Tta44Ux7M6xodSaNo+Gk0XLBz76oidluNj/2wd3/zxM7jt/v0wdaWtsNHvioQFbHsNrtnZp4zh8pdvwZZ1ORkHF5qRF7ehKchndJi6gtlFK5r1NjQFhqbA9TlMjaJheT3jUef3K7Q683y58xPh7HicmRifgYsgWk8Sref12t8meiMJVotVJdhDQ0Oo1Wo9H2OMwTTNI1pUgucP2bQGLgRmF62IHkUgrY/yGT1KJI8m9fP0rcP4r285HV+6+Sk4LkPKUKQfpc/hMS47y2HVEdIKrB/6PcS46LQJh6qSwCu7v082FwBnHP4yrl5h0k1pqxOuxP6uKgRKn2Q8pKizIPn3w+T7KCbh8UPKwC3AXAapFRrrfhPgtC1F7JpcxFDewMUv24DGPVNwHCa/EwJRB5azYK0EaDQ9CMgNEOM8mr9TSHeXYSSg0IU2G4JLb9BN4xmcd+o6MC67w2efPIrJ6VqXOnknHJdhrmz1fKxf8ORB1ZsQROr1cXG0foWm5brEF790/ZLdIk1TQByGiaH0mgbro7FJGNQXN0GCBMcv1oKNEmfsnLNjDPMVG+W6i2xKFiYbMd/h0A5UJlYUKqX4xWPTmC9bbXPZgvMohqpEJqauL+m+GVNrK+hlUtJbulRzkDFfGOY4PuOBhSRFPqtj3VAatuujXHPa47EAyjUXCvVQzBoYG05HauWdCHVrfCYws2iBECClK/DZ0huWtbT0DA8VZ4w9NVXGzx4+ANeVhRDHY3CDpDgUZ5tZbM3RCyHw9P4y7nj4IC49e2Pb+YWF3fmKDcdlEKKlYh4W5xerUsMnZahREdn1eWyv1fobEwK6SqFrshjRub9N9EYSrBaruhP9yZ/8Ca6++mps3rwZZ599dvTzPXv24DOf+Qz+9E//dOBjcc7xL//yL/j3f/931Go1nHfeefjIRz6CTZs29Xz+4uIi/uEf/gF33nknCCF405vehL/8y79EKpWKnnPLLbfgs5/9LA4cOIDt27fjQx/6EF7xildEj3ueh2uuuQbf//73UavVcMYZZ+Bv/uZvcNppp63iavxmoWH7sF05E6MqJLKrcH0mg4GmYOtE7qhTP3duG8FVbzot2ryrCg3mrgkKGR0N22vdMAfINzvFOoQAOkMOpRTZtAoipDjWkSDsVg+CXl1xRSFQKIWmUamK2ZGFx5PwMGFdSSdcCSyxVpqqCwH89MGD+OmDB6Ofhd1jAml7RiiFoQXrVynSphbYZLSfRCguJwILNhaIq4TzW5pKUWt60BSKl+8cx6GFJm66ZyrqCheCSrjtsCXPgxDggd2zuORlG7rW0C94xqveIfsgjnhXJ9xc1houfvCrvUt2iR96Zg6ULq0YfjQKWEdrk9CPdg90K68D3SJySYc7QYLnF0fKRunF2MmlNKQMFfWmtIr0GYeu0chuKmLGaQpqDRczC81uAaxAeyX0WqZE3qdGCybKdberoDc1U8MNv9gbuVysJQaZQV4tXJ9jvmzD0GV3d2Ikg4bto9poF+9kXGChakNTaCSE1imYFsUtIiL7zE5BtU4E9Y41UV8HgLsfn8Y5J49i57aRiDG2fUMBI8Np3PzLPVisO/B9DhFLbKsNVybKAXVbcHm+/3nXJCaG023F2tO2DuM9bzgFn//BExG9POyCFzI6ysFse6XhwtQVkGDPOF+xwHjIkAsZj/J7VcjofYtJid5IgtViVQn297//fTiOgz/4gz/Axo0bsW7dOiwuLmJychKcc3zxi1/EF7/4RQCyO/KTn/yk77E+97nP4Vvf+hY+/vGPY2JiAp/85Cdx1VVX4Yc//CF0Xe96/p/92Z/Bsix89atfRbVaxd/8zd+g2WziE5/4BADgnnvuwQc/+EH85V/+JS666CJcd911eP/734/vf//7OPHEEwEAf/u3f4vbb78dH//4x3HCCSfgM5/5DP7oj/4It9xyC3K5F87M4FpbBnAhcMs9U9AUGglcKUFFlAbBjhCOK16++ZhsjDs373MVK/Iplgm3kIEEgEqJTJp7JJaUEBSzOsoNp6+KtUKlnVWtwZFLaSsOqJQA+Yy0qfCYLE4MagE2SFecEEmZiyfhqkKjTUraoF1JeEg/F5AJbEpX0LA9+L6AphLULX9NgqqI/ekxATAGN8jTFEowX7bbVEnDdauKFF1TFALTUGHqFJmUDtvx4XoMuqpgtKCAUuDxvSWASMVYEAHPk0qglBAYutI+Z4/2z66Q1TGzaPXszvYLnmFS7HgsYh1E5xsLxE3Lw6e++wgOzDXgetLPXFMoHJ3BNFq317BLXK65KGYNLFSdYzq7fDQ3CZ20+54b7rQOCIGa5SUKrAkSHGdYLRulH2Nnse7C0Ch+68JtYCC44fZnUMwbkvbdgYYtE+Kw+xjeEcP7OOMimplVCcFbLtqGXEbv0tG4/ZFD0BQp8BnvhEuKOkXTWXmbVglGxkisirxUzBzK6nB8BsflK2aaOS7DnGshZUj17fUjGdQtD7UOayyPccyWLRiagmJOR5YLVII5bs/nIMFeKAQXrfPoRHhalMS0bXqwylYC22X40s1P4ao3nYbTtg5j12QJt9y7DzOLVkD/J8hndDDhQnCBWqyoK7iAQuXCKGS86qSKcyFQbrgycc7q0FUKRaFRXCsCmK/I92raPlJBwd7UVLjB3sxxmVROVymKWR2UEpTrbs9iUqI3kmC1WFWCvXHjRmzcuLHtZxMTEzjjjDNAe9xA+8F1XXz5y1/G//pf/wuvfvWrAQCf/vSn8apXvQq33XYbrrzyyrbnP/zww7jvvvtw8803R8ny3/3d3+Gqq67CX/zFX2DdunX413/9V7zuda/De97zHgDAhz70ITz88MP42te+hr/7u7/D/v37cf311+MLX/gCXvWqVwEA/uEf/gFvfetb8fjjj7d1uo9nHA3LgJBGWsjq4FxDJZhtCe/uukph6ArSqWNXqevcvMd9iufKFm5/5CAOzDUiSq+pK9A1SSkP57yYkDdllVK4HRm2qpDIBoMi8Hm2/IGSa4VKlc5QHERRKFxPdmFNXYGpAbUeNK7VQFbzB0nC5TqUcC6cUmiqvC6ZtAZFaaluZ1IGfC5npVlIRWctSnrbscN1rHDdncfhAjERtvaTURUCQurSxoVSZDMaTJWi0vAjqr3P3KDAIDv7cjY6EHML7MdY7JxyaQ3plIZy1enZnV0qeObTGuYqwRqFnJuPd3XO2DaMf7tpF2pND0KI6Hvk+hzzFRujBbMtyQ67xOecMoY7f334mM4uH6tNQq8Nd8PysG9GjhQN5Qzks3qiwJogwXGGfmyUfveiQXQdHtg9i/e9+QzcfNceuU/p2B66nmTLEQKolMILFcWDx8NONudCaoJoFLmM3ldHY7RowvO5pEcHj0uG1MqvR1jUFoJHCWfI6Ov5fACvOWcjTt82jFrDxb1PzuCuJ2ZW/L6hwFkqUEfPpuRerJMS7ngMMyULaVPFSN6E4zHUmm6X/RnQvygQFcdjcVrSzMWqaeOESEXzm+6ZAgfw9Vt3w3EZ8lkdKVOB53GUKhacHg2IkLVAiOxI59Ja2+hSuO/dP1tHw5ZCeeH4YgjTkNejVHNgB98vRSHYsj6HN16wBRlTxZN7S3jomTmUa9JDXeGibzEp0RtJsFqsKsH+x3/8R3zxi1/EAw88EHWq7733XvzFX/wF/viP/xjvfve7BzrOU089hUaj0ZbU5vN57Ny5E/fff39Xgv3AAw9gbGwsSq4B4PzzzwchBA8++CAuv/xyPPTQQ/irv/qrtte9/OUvx2233QYA+NWvfoVcLoeLL7647T1/9rOfrewiPI84WpYBcRopJbKr6MaEzlSVolp3Vz1rshYd986Ee8v6PK657tdQFTnfG1Yx3WDGJ5zVrjVdCBAoQTImjyX/zGd0WI4fzf8wzqGrNLoxExAIiEip1GM86JYDDcePvImFkEnUhWesw1zZxnSpCdOnkSrn0aSZhe8fCpd1ghBgpmSBEmmbogZJeNhB1jWlTSU9pEWHvtmccbiMQ3COQIMmsgkJadSD+HUuhfjGgHGOUmVpmr7clBFQhUYFDpVSmJoCI6NLBVVdhe9zpA0FhawefY40qPIrBHjzRVvxjR8/g7rlw9RlNdz3OWyPo5DRUcjoqFmetD0LujpvvGALrrv9OVQCyw+VUgi0OieMSyGb9bEEO+wS79w2jG3r8wN1i7gQ2Hu4ir0zDYAzbBjNrCqQH4tNQr8Nd9OWu1sS/D0beNQnCqwJEhxf6IyvS2EQXYfDC1I4c/1IBvtm613FPRbEUl1XkE9rEdupM44QImPV+pHuImDnOnRNgR7M3KpU+lj7/ahry1wLPyjcK0HCuVRnVwC46/FpvPGCLdg9tYhnDlbaBDtXijDRzpgqckE8q9QdWE57fG/aPpq2VBwfLaZgOwzVZrvi+HId6fjDoYPFaqO5EHL/tW+mhhvueA4Ny0MmpUY/L9ecnsl154LyGQ2apkQipfF9r6EpaFieFMoLxheH8yZSQbxVFIp8WsfbLtmOfFrv2m9uncjj8gu2DLwfTfRGEqwGq0qwv/zlL+Of//mf8a53vSv62ebNm/HGN74Rn/jEJ2CaJn73d3932eNMT08DANavX9/28/Hx8eixOGZmZrqeq+s6isUiDh8+jGq1imaziYmJib7H27t3LzZt2oTbbrsNX/ziFzEzM4OdO3fir/7qr9oS9+MVR9MyoBeNNE4n7SUAMSgG7bivNAnfOpHDxrEMDsw1kI3Nl4YzNz5D5H+oKq3kWqGyOu1zAcvxsW4oBdfnYIyj6fg488QR3P3EDFjgrx2uTQR0NUOjaNgMvi8C6y9pS6UqBI89V8KlLzsBWydyuOPRQ4DHo07586VsGgZ5LgAeUMl6QYmo2yRKxFOG2pZ8h91uz+eyuh90vbkIKOIAUjqF4/EjopoNck4hJd2nCNaqgFAl8A/1QIiLuu1j/UgauqZgvmIjlJVTqJxZO2Esi9977Um4+/FpLNRscAbAENi6PodLz9qAkzYVcWCujkbTQ9pUsXE8i6npOmYXm/I6EbmJC2fewlN2fS6p7prS1SWmhCzbLQp/Z2ZKTelHToB1R8BSOdqbhF4b7rBDFRYg4oJxiQJrggQvXAyi61Brenhk9xzOOXUcs2WrZ3GPEGmPlDY1FLIc5Xq39aKmEGQDB4lBdDQKGR0LVRt+IGa6miRXJtYy4ZTU81Y869yRhIefXbTw0wf24/ZHDqFUddakot6wfTQdH7m0jmLORD7DUao6XTE8VBzPZ3SMF1No2PLfq0GvLvhKwDhQt3w0bclckiNczoosSymlUVE6k1Jx/R172va9dUvq8CiEgAk5Mmbqcr8axtoLTp/oqf0Sj7s7tw0PtFdeKcMjQYJVJdjf+c538IEPfADvf//7o5+tX78eH/7whzE6OoqvfvWrAyXYliWVfTtnrQ3DQKVS6fn8XnPZhmHAcRzYtt33eI4ju2H1eh1TU1P43Oc+h7/8y79EPp/H5z//efz+7/8+br75ZoyMjCy77n5Q1cHp8avF3sNVzJSayKa0trlQQAaEbErFTKmJg/MNbFu/sg3r9g0FnDCSwf7ZOnStm0batH1sGs9i+4ZCdFNRAs6X0sn9iuHJvSVce+tu2C5DJtUKrgfnGrj21t143xWnYee2YTy5t4Qb75rE4YVGIG5CsH4kgysv3Iqd2/pv/t980TZ85eZdqNTd6PiKQmDqKpzAF5EGlWRNkUk1CeZ8FEoidXJDV+B6gMEVnHHiKB7bU0LTkQItYVgIr0jDbp/5VShB2lBhOT4WbQc3/GJv0CUVke/20e1frw2kQBqD0xGXSTA7rSpSuExVKExdgapocj6Oyo634zJ4voDHODSQiLZ99Nctu96Ox0GpLwX6iBTT0TWK0zYNwfPlZxxKu/kcCNobGCum8OZXbsPhhQaaNkM2rWL9iOwWl+vSt7SQNaAQAsfjODBXw1DOhEIDuzcgEpnzWCCgEnQCFIWg3vSRT2t4yyu3tRWtTtpY7Hk+8d+ZbEqDoStwXNb1O7NSvOSkUZx+4gimpmuoNT3k0hq2TKzNJqHpyN81Ld3SAwg97MN/h77n4b81TdLmmw5b9f1zkHvQixnHw/U5FrHxeMPxcN2PJgo5A6oi7/FKh66D7ciuos84vn/ns9BVSfdNGypqTSlQpioEW9fn0bA8LNYcAALFnAFdoyjX3Khoq1KCEzcW8Vt99gG91pEyVYySFMp1J7L9WgkIkTrnYWFTstkoeKhy3ed1jAv8+8+fgx8kkkqw8TjSECgEUG24UQIdWXtVnbaivRBApe6iFiiOjw9p0fUeBEc6f92J8PqFyuaDQgCwbA8AwabxLBSFdu17i1kD8xUreA+5j2s6PjxP6s28+aJtXXojq91jxtEvZi+H3/T7wSB4sV2DVSXYMzMzeMlLXtLzsTPPPBOf//znBzpOaOflum6btZfjOG2q4PHnu253ddNxHKTTaRiGER2v8/HweKqqol6v49Of/nTUsf70pz+NSy65BDfccAOuuuqqgdbeCUoJhoYyq3rtSrB3pgEuAFNXuxJsQN5oLIcBVFnVen7vslPxf697FJWGh1xKgxZQpWuWh0xKw+9ddipGhrNdr8vnuz8vQCYZP7r/UTgeb/Ov1FUFKUPFQtXBj+7fj3TWwNdu3Q3L9pHLaNAUOY91YF7S3v/k7WfizJPHer7HRUMZZHMmrvvpM5icrsJveFBVipM3DeGlJ4/ihtufjWw9dJXg8EITrscB2tr4S/9mgqbDsHV9HpdfuB0/efAA9h6UhR5JmUKXgIqmEgAEPhMo14P5YEVaRoRdTQFEXfteofmFkHoLgUhExepgbmsqRdpQA39r0qV8Ls+fB/R13v73o2E5xgXc2HFdn+P6O/fg+jv3RDS6saE0xoopjBVTGB2Sf+YzOgqF9EDv0XQ5ZhctaBqBSmmbHVtwGaBpFCAUhqZi09YcXnPuJsksCZ5Lgv9CC7f4+m9/9HEQQnDCaBoC8vskxeCU6HfmFWdt7HkPGAS9foePFBvWeUG3vpVQaaoSqbwCcrZPUxWoQYB1XNnh37Auf8T3z373oAQSz9f1OVax8XjFb+r3slBIY9PEXkwergb3+2AkxPGlYjMT0HUFowUTPpfCVKau4B2v34GJ4QzyGR3bNxTw2HPzbXuObEqHriooN1zoKsU7X78Dl1+wte+9rt86smmKdErF4fkGHJdJrY4VMMW9joTQDPZCy0HE2uU8LOavUZjjXKBck3oi+YyOdcNpOC7DYr29O8y5QKlqQ40rjjdcOMsoigtIZld873KkSbcUcV35ASoND4WMjnN2rsPhkt21782mKahCsFh14Hq+ZNB5HNs2FPD215zctV989Jm5Ve8x1wqcC8xWHFQbbvT9X20MfyHjN/We2IlVJdgbNmzA3Xff3VMQ7P777++iaPdDSPeenZ3F5s2bo5/Pzs5ix44dXc+fmJjoUiR3XRflchnj4+MoFotIp9OYnZ1te87s7CzWrVsXHUNV1TY6uGma2LRpEw4cODDQunuBc4Fqtbnq1w/+Rgw0EJHopQbsevJxcIbFxcaKD795NI33XrYDN941iUMLDbieFCAZK6bw9ktPxObRdNtxFYUin0+hWrXAekSvvYer2D9dRdpUgmSq/UabNhTsm67iaz98Ak3LQzGng5AgoaAEhYyGcs3Fd259ChtHUn07bfWaDc/zITiXytmcw/N8kMAKglKZJHMhZ64XKjZ8ziP1TM9naFgeTF3B5edtQqXahO8xSS0jsjLdi2YmOEApoq5oOOMr/ynPgXEZdBnvnUyvJPTI4x1f6bjnc9S426XQTimB1kE3N3UFiqK1iaaFXt9+jyR8EChUfl8GoQHWmh5qTQ97D1W7HtNUipG8geG8iZGCieGcCQEBXVUwPpzC5vFcVDQYzRuA4LBtQFFYF9vDZ3K++4oLt2Lr+hw2jGZBCLDvUBkIZvmB0CKFBDY0MtmeXmxisWpjfDgFTZHdYKpQCCYV4TOmiqbl4rFnZrBlXR5cCOyflR3pbErDpvHs80JbG8qoWDeUwv7ZevR7rAYjB64nZzV0VYFCgKbtwWeSIrp1Io+hjLqq+xWw/D3oxY7VXJ98PrVmXYZjFhuPM7xQv5dciIEZLpeftwlfuXkX5su2ZI8pFPOLVlRQHsoawfdIRLH8Fw8dwP/6/ZeBEoJKpdm254h3Freuy+HKC7fi1K1DeOSp6SXX07WOkIJu+dBUufdwvcE/g16xZNAucDxsCcg9wloj9NA2tMDaaziNpu2h2nDbkmGfccyFiuNZHX5KUqn7FQqEAAiVCt4pU0Hd8o/qmNdyqDVdXP/TZ0ApYDlyL2rqSqQsr6sK1g1JOrztMvze607GK86QtPB4POFC4Du3PnVEe8wjxVP7FnHzPfuwf7oGP1AzX2n3/IWOF+o9MY6VxMZVJdjveMc78MlPfhKe5+F1r3sdRkZGUCqV8POf/xxf+cpX8D//5/8c6Dinnnoqstks7r333ijBrlarePLJJ9vmu0Ocd955uPrqqzE1NYUtW7YAAO677z4AwDnnnANCCM4++2zcd999bRT1e++9F+eee250DN/38dhjj0VdeNu2sX//frzpTW9azeWI4A9Q3TxSbBjNYF2gBlxUumncdUvOnmwYzax6PadsKuLy8zfh+jv3YK5iQ3CBxZqNG381CcFEz1lNxnjP96vUHPhMIB1Tr45DKm+7mClbyKU0AKTjeQRpU8WhhQb2HKz0nNPsEn0Lguv+uQbmKlPIpjQs1t3oepm6iuG8iUrDlXPllIAxgQ3BLOopm4r46QP7cXC+AbJM9dbnAgpaAVmI2PMJgSCAopCIpr4SMdNeyXjaUFG3vTXzrOz5vqsQZumlIcO5gNODbg7IpFhTaaR6rgY2G2mTtiXf8W532EH3O27MnAusH0njj958OioNF6WqHfznoFS1sVB1sFizl03YPZ9jumRhumT1fFxVCMaLKWwcz2IoZyCX1rFYd2UyTUX0eYXXYt1wGi/fuS6iqrew9Dqm5xo4ONdALq0F1mySns8D9oQQAp7HsX+6hum5Ju7dNY3Fhgsi5HdtOG/ilWesx/YNedkhJwSUok3cLb6GXp/1agUJ3/jyzfjarbuxWGvNWqYNOaqBYERjutQM5vfld63edPHEcwtHPAPe7x6UQOL5vD4v5s/lhfS9XKk7ySmbinhPTNfB9SS1Wws8r01DNgFEoA3eL5afsqmID7zjzK57zu6pRfzTNx9adj2d62CWL1lkXEixsgGv/5GIkj0fcDyGubIFM+ahLSnhXlu33vEYZhYtpI2W4ni14fYs1vNASyST0uD7Utx0FRpxawIugKbLApsxgVLViTRvAFkUz6c1OB7HpvEsztkxhj0HK11xa3K6ikMLDaRNFf32mPtm6/jxvftw4sbCms9W75qUI1+Ox5E2FaQVKb66b7aOL9+860XnpPFCuiceCVaVYP/hH/4hZmZm8PWvfx1f/epXo58rioL3vve9eN/73jfQcXRdx7ve9S5cffXVGB4exoYNG/DJT34SExMTeMMb3gDGGEqlEnK5HEzTxJlnnomzzz4bf/7nf46//du/RbPZxEc+8hG89a1vjTrU73vf+/D+978fO3fuxMUXX4zrr78eu3btwkc/+lEAwLnnnosLL7wQH/rQh/B3f/d3KBaLuOaaa6AoCt7ylres5nIcUxwLNeBdkyVce9vTsF0fuVQrYV2NSvkg/ruEEIjAhqMXQnujXurlg4i+pQwVhkbbrpesfsrN/2vP3oCd24ajm+quyRJ++KvJKKAvR2/6/7P3p1G2XdWVMDj33qe9fbTvPUW8Vn2DBJIFSAJJGKcRQjams11fuSPTw+UameXK9IB0OivLZTud6ayRHthOe/jzl3yAu3IaDIhOHTZGEghLAgkJ1Devb+JFe/vT7r3rx97n3HO7iBvx4jWS7hwDFC/i3tPdc8/aa6255uy3olJdcxCCKObK/msLNKlBrx6UXFNNQduOSrPBgITyvhESKtlWFiRcSCDm8Ac8Y5PON2MknffO2Qao2y20liTbUSxQa4U4erqBS+crmC47OLXcwkzZRc4xsHMqh1PLLSxXfYRcVcHX6gFWG50E3As2Ln3EXOLkShsnV/q7cb3FWMdiuP7SabS8KLVyGxU5xwCjqvtACIHkgBdEqDYDxDoxBYAvPvwq/FBASImcbagZQS5wcrmNV4/X8P5b9uGAnhdTM4Xa054SpSZPqZqPo6RDcScEB0/W8OD3T+D0Wju9D2YqLv7FDfO4bM/Eusc+TEhtz44ivCDWKsFSq/1S5B0Dq40A/+c9z/d9D8cYY4xzh626k2TFn545uIKvP35soOe11FaHfsDx6vFa3/e8V8F8s8eTPY7nDq3iG0+eQAylYSGlRHOEDrSUyoqLUkAg68utvETOgZzIluCHHL720M5ae/V23duBEkwruCZmJlx4QYxGT9dbSigNHqhZ9opp4PRa+7wVHggUIyzbxEho90HEsVTjKOctXLN/En/0uacHFmO4kEMF+fwgVrP6scDd3zoEx2ZnbHmbRWeNyjFdcVI25NhJ4/UPIuXWvzaNRgNPPfUUqtUqSqUSrr32WkxMrL8A6wXnHJ/4xCfwxS9+Eb7v48Ybb8Rv/dZvYX5+HsePH8e73/1u/P7v/z4++MEPAgBWVlbwO7/zO/jWt74F27Zxxx134Dd/8zfT+WsA+NKXvoQ/+7M/w8LCAi655BJ8/OMf76KzN5tN/MEf/AHuv/9++L6P66+/Hv/xP/5HXHLJJVu9FOBcYHV1axTHreBs+GAD6mHwic8+heNLza6EFVABstoMMT+Tx6//zJu1cjbFxEQea2utgRWpzvZaqBSsgdubLttYawRwLGMo7T2IOP7NB9/U18E+vFDHn37xh10WXYPe+76378UTLy31Xa8737YHOddMK57zswX80eeexpGFhkq6yODu7HpglGCqrDQFVmr+tlK6iRYMkaI7AadEVXPDTAK2lW1XCjZqzWCkZD3vMHAB2CZFvR2lM1ujQimzylQQZr3jSqjxvUJrSRdcbY/gbVfO4uWTdSyuthFEAkHIEWrzcKqLKtMVF7dfdxEuzoiVeEGM1bqP5ZqPf/jeMdRbap5eib6d2ednGhSTRUU9n0wo6PrnSsFO55ETCCnxmXufx8Kqh1LORBhz1BohuBbM4wIwdHFDSGCyaMGxO8r+UkrU2xF2Trr46J1Xbhi0if4/AoLDp2q497GjiLlA3lF2WkIqeqVtMdz59j04cFFZe5ETnbira0tIpzsupMSx000028qiZW62gD/87FM4erqpCgiMwjIo/JCnTBJKCco5CzunNvcc2+gZ9EbHVq7P5GR+2yji5zo2Xih4Ld2Xm437w9AbjxNxzEY7RK0Z6vgkUcxZmC7buP7SmYFFtY2PJ8B02cH7b9mPYt7qev+g9wZhPJSZNAhJl9Q0KGyLodGOACjxyjOdSd6qo8iwUSjTUCNpXCo/awnVfS7lLEip7CKDAUJvhKhxOdcy0PQitHTxnhJgqmwjiCSmSjZqzRCUEqzW/XNKF08YYcmsdBbJ3HJyV0yVbQCkj8WYNJ3e89bduOefj/StE/0gxkpdrdEIIZipOKCEpO/bjs5y8p1wLIacYyrRXH0dw4gjCDliIfFrH74WBzYpTPxaw2vpmTgMm4mNZ5Rgj9HB+VhEbIevdC9GTViTZHeUBPuh75/AV75zGHEsUMwpb8Psw+/n33M57nv0yLpJ+LDg/szBFXzqnudRKlgDz11IiXozxL96n1Jdzl6vlh/jvp4ihbL38lFwTazWfQSbmNtKYGqarm0xnFppjySMsllk6eOmQfHuH5nHDZfN4I8+91SXwvlmUNAqr8tVb+RASnVHNNYU+M0EYMugkMDQ66MSN6TCWUHI00VGdj9UL+JMg8IwlFXXTNlFGHE0vFjP3ylfdAiBWKq56h+9fg43Xrmj6745sdTE33z9RV1d1vRGTc3mQtluhZHARdN5tHVSfiYq6QTqus9UXExlEu+WF+HhH5xEGAmEsVLnTsYVKAEKrolGO4LQ3YbpcrdoSBRzhBHHz/345ZibGU3QrJPYt1HK9X8PG16EXVM5/NJ7rwAjVCflSJNzQgGWUNJZ0hFX98ep1Tb+7hsvw6RKmAZSdVNWal664IhjgXLBhh9x2Oboi5vXQ9A+mxgn2OcHr6X7crNxfxh6C+qUEoSRwOJaW2mjQKmCE636TIhKBnfPFrqKausdjx/EWGsEiLlAzjZgWd0dx2HvPbncGjkWM0pQKlgouibCWGBpzUvVwA3aGbWRkH3Pf0VnHrxdAqBStEEIlI3XiEhcMQD0FX0pUfagUSz6iu7FvIW8TupW60HfeBWgns/lggXLoGi2I7SDGJQQFHMmfuyGefzD946jVLCwtOYhjLiaKz+HWYNBSV8RfqJowzSUuKgUAiv1ALbJMFV2hq4fpZQ4sdzuWmOeXm0jiDgI1DpjdkLF0TDiqLci7Jh08Zs/fwMMuvVn4Q9eXcYnv/Yc8o4ByzRgUAIviFHTs/BSqu/F7tkCfuZdl7yuqeKvpWfiMGwmNm6JIj7GhYFeStV2YBRvy2F07V5ku+xxLBBEHEFNLZ5ti3X571JgS7T3USjolAL1dojnDq2ikDNxxb4JfOvpk/jqI4cRxQKlvJXSz06vevDD+IzExGIhsVzz0sDGKGBbxkgiKaMqiidqnwTq3B9/9jTa7Qh+KEbahlK6Vv6SlslQawaQImMpNkIENSggM3TyzV6uJLAM/TuQmV/W3VJKIHsWNEJqz2n9wK41Ad+PUfdiWIaa7bZMipJtpRXvmAs88sNTePVkDbdcvRP7dpUhoYRsuOi2kSCEwDQITKhtNdsR3vWWOVy6uwIhJRqtMJ3zXtFz36NSzyU6wmsHBwivdRUt9Ix1XivlCqmE1OJYIIp5WhAA1PHzgI8szANA0+jVnF4vpZ0QAtdiWFxt4+RSSyXtXR+DBDjQ+1RINnNysYVqPUCxYMEQin7oBTFsy0CBqQW3EBKVgg3LZGi0Qzzy7AIu31vRVNPsZP5ra05yjDEudGxX3O8fYWOoNsI0uab6uSW1wJSQEkHEcWyx2UX7HnY8XR1HAI5jwGS0izY+jA48WbSxtE7xOJsYCyGV2rRrwtKF20SJXECCSGQYO4Ouw/B4WG8FyNndS28VywFk9DooISCUQAiRzkULOYhRJRHGg2epa80QLS9CKW9jdsJViuON7k60EBJr9QAGI6gUbRRylhZC42gHapbd8+PUWzwp8maPg6T/t/3P5t7kmgCwLQZbr/f8UF0Ty1QMPiFkylYjhCDvGFhYbeN9b9+LlfqxdI0ppSq8JwysUt6CH/JUBE5qsb//9jdP4EO3XrylxPf5w6v48rcPwfNjeH6cjmJx/X1gWqdHSMV23OwI5hgXNsYJ9hhdGCVhZYygkDO7fi+kxOGFeld3+K8z81OJYEa9FcI0KN53017c9ua5NGkeNruZTcIHYc+OInZq0TfT6Bd9q7VCEABffOgguJBpgPKDOA1aUSxQKVhwHROlvAkviFFt9tvBbYRyXoltRLGai236ERglmK64WKv7I21jM7FJSFXZJlKi6YV45JlTI9uQ2BZDOW/BthiqzRBzMwW0/Qgtfd03UuUuuAb8kEOeAWdM9OwgcatQ9lXq51go7pttdbrJowTwakstBP1QUbCStySdbstUFOWWF+Pbz55GqeBg92wB0xMupko2CCWqeNFjJcZ1wUSJpahFULlgo1ywAfQXu7wgTme9V+s+Dp2q4+DJ+lBV+v5r1P1vzhX9O/23/nujHcE21XfTYBRCdB/nKBhUXMhiK0l7co6WSeGHMURD2XSFMcdqzU+pmMkCTUgJ2zLAucCRU3U8e2gNu6byYLSjsp7anOn/SqI6DkL7a2ep6tljGGOMMQZjq3F/ELKx/MRSE75OYmyTKccIIWEkD3upnmn5ooF2wNNZ1GHHU9PzwspeWjFhemdZP3jbxWCUwPNVoZxSAstkcGwDlYKN1cbgznGyXaFHnULdFEjGYRglMBlBGAvEQoIQOTCJzsbg5DQLOQthxNO1QTtQLDMCHcMz9PZkm0JKUD0EzgjAdaI96LjXQ8yVZZdlUJQLNnZO5dHSiuPZZ2PMJZarPiwtUAcAD37/BPyIo9GOYBoERVet7cKe7iMhKj6ci66kBBCEcZpgR5FiQLW8GPVWpOX0tACaXuO0/RgzFbdrjenrdYFlqrUQAE2Dl6mNphASCyvelhLfREPAC5RtbMIeSK6doW1bhWbUTZZs1FrReB77dYRxgj1GFzZKWFu+Uinfs6OY/v7pl5fwdw+8gJMrLaWWTYFQP/S6vK81hafaDPHEi0u47c1zXfvOipSMSntfT/Qt8X1MOuacC1397rw/CaSLVR/FHMdEwdrytaOUYHbCVd3UUFHqWDJnvAUK9UaQspNwRvH63eAs1Ky1oklVm9qb9HZlW3fPo0dwbLG5LpWOEEXXFnpuaTMZTLa7zvU8WzpbBjVvlUCI7KC5+uFM6Njq/coCzAs6i5/VRoAHHj+K/+dHrsUVeyawazqPpaqvgi6BtnGTiGOO1YbAruk8dk2P5uvr2gbmbANz03kIKfHKiRpsi6GUU/cZ5xKxEPCDuLPo2twlBZCI3HSPBhiM4L7HjqbU84mSnVLQHav/0Z+Iq3EuQI3+RXZvcWEz2DWdx3TFVXPljHY6WvrvkZAwDdWpDkKVLDfbEaqNADMVF/GAqYfksWAYFDEIGg0Pgndm9gdR1RPv8YT90VFVB8YJ+RhvVGwl7q+HJJY/9NQJ3P3wIRRyBggIFtc8sMy2CZSYmJBIO41HTzcGHk8y5tNxZFCJY9NTfsmWQXFssYknXjgNP+Lw/DgtuJkGRTlvgbH+tUTKjdGJNSEdWnKjFcGxGfbtLOKa/ZN47LnTOL7UGliAThll+vhUUVC5P5TyFhgFgqizNvCCWO+vk1z3xre8a6DpxYi1kKUXxOmo0GafU2GsLLsSIbS8Y6LeCtH0ulkJitKvlMnLBRt5IVHTImBrmcYDQWdWPREdPVcp4VpDCbiV8xbavjr+mAtVfEWyruNYqfnI2QaElDi12sLFcxV88Nb9OHSygZW6j0eeOZVqjSyuecq6i3T0RNScutlV/Bkl8c2K704UHfghV8l7ZgGYrAmT7jmltOs7sN3s1DHOPcYJ9hhd2KxK+XOHVJWu7UVwbQZpAJ4fo+XHoDoRczJ0qCxlZ9BDZCu090Hdb6opV7bJ0iT/dCNYNyg12hE8P95UFzmLtUYIIQQoZTrxlagULKzUA0i5+WR0FPQG5FHo4VIC1WaInG30MQQu3VPBY88u4AsPvop6O9KBRnbNXUkJRDzzj02AMRWMKSF457W7cPneCYASfPJLzwBQgYkA4FKm4nKEAH649cp4P6EuezwUOUZxfKmJIwvqfnznm3bhLx94Ecs1D5W8BVvfv2HEMTPh4q6370U5bylqtrbNGsWHexAF2zAIeChSERpCgMmSDQKgFXAYunPS9CIIMbhjMgwxlzh4sj6Qep5zjFR4LUm6J4o2KkVbFxf6F9ntgGPnpDtycSELSghuv+4i3P3tQ6i3I1hMda2lRCrepmz6FEZJ5pPrLTQbgHNFDQWGU9WTcyLJ3DjpqKpTSsH0GALRXSuqZ8uTheSYqj7G6xFnw52EEoJL5itwbQMEai0gpYTUQYoQknYbKSVdNPRBx+P5UVeCkhTRwohjcc1Lg9/9jx1LxUChtx9EHMs1DybrLxwmx5DETimBct5CyCV+4qa9qW3Ti0fW8OBTJ+FYqmBfb4Vd8Tcp2DFIcKEKyJIqR5GkwG/rbrtf4zB1VzPiAoz0zxlTAri2Cdc2sFIL4IVxJw7LrVuKeUEML4iRd00UcyYKORP1Zoh2zziTH3L4q23kXQNTFQdBKPQ5q1hMiCoghzFPvb7P5eMwiFRjRLEUtAJ8UqwQyfpBaYcAwN994xXdmVbrQsWoAOrtCKWcYjImnWtAxSX12al4vZnE9+jpBhZW28g7ykHEtQ1MlR2sNYIuP3bDUGwBV68xNjOCOcaFj3GCPUYfRqVrCynxte8chufHsC1F0UpmV9TfVXfwop55ozN5iAwTdksq5ocXGjh4sobVmo/vPLuQPuASetZGAWAjReuNUGvFyLpdNz1FE1dMr7MTfrJJNaFkJNo25wKOzfDet+5OP89kZv74UhN1TbE2qLKc4lzC34LgW+8xSp1AEwJcPFdCMWeh4akFHJeKisgzyWrS0N6ocZ1QjDez4JBIroPRdT/23v/VZthRnX/rHly+Z0Iv3ggkZNqF5tqnO0m6YyFwcqmFth8j5yil1kEU7IanxMoMqpVgpZrZNw2mklFK0uR6UAGl6/Mf8PdBaPsx2n6M40uDxae8IIbJ1OwhJaqg4pgU73zTri1T1y6er+AD79iPB58+iaWqUvTlUqnglnIWbN1VP9NkfhA6xaHs1em9Uh3aZiLc1ivkRhMhN9qhqGe748kCbpyQj/Faw1bHtNbD3p1FlAoWDp+sQ6JjV6mSalW4tQwGy6CIemjoXcez0kbD68RVpr9jXGZo05nvVvKdZZSmTCvFkhrMJOv9WraDGDuncpipODi53EKtGeCr3zkMP4wxVXb0OkcxroQU4EKtHQwkRTyZHkelYHd1duNYaDcMAlOoMaV4wLgU0c8Z2zQwUyFotJXKd8g5hOh02cUW1ywtL0Lbj1DMWSgXbRTzZl8CqF4Xo+XFKOYszFQceHpOWQjFbDhXoKRTAEn+naBSsNPkf9jlSIq5BEAArsbOpCq+rHGRNkGyr3MthkAXNjw/xuPPnUa9FaLUo1zfi0EaAq5twGAUp5ZVzJVQhRw3sz7ezCjGGBc+xgn2GAMxCl376OkGTq20YJoEq/UgnV3JZjpRLNBohyjmOtTrrT5ENrIme/HIWme+JuDwwhhByNN5olHnd7cTa40ARdeEbdKRE9RRkyQg6cR1d/NGQcQlTq208T+++Aw+8M792LujmHqOZv26Yy4R862pkmeRroF0u0AIif/fP74MgxAwvbgq5ExYBUupncZCJ9ckTYoS322K/oQ76UIkGPUarjUClITsux83uv+zM75qPo+BmAxJ0v3ysSoefvokVuo+glCACw7bNGAw0kXBjmKuzpWo9yXdHCARFqOoNgKYhqIaRgPEbAyDoJQz0fKUz+a/uutKNFoRVhsBVvTs92pd/bxWD/osTwZBym7hOEB1oP7ygRdRzltdnW9lP+Zgagj1PIuL5yvYP1fGqeUWXjlexaPPnUasRWmElOBcoB1w2CbF7ddddF7m0JKPVvb5zskkB+9Cp1OmE/IkOQeGJuSdnzsJeZKgj+fHxzhf2MqY1np44fAa1hpBn96GRGdEKGGpDKKhJ8fz6LML+LtvvAw/4ohjVWzsFbtMYGj7wIQZM1VxVGIvJBpeOBILKIwFTq208SdffEbFH72kMRlFYGUeAgTKUQEqyRay+5nhWFR1V5PzztDtl6seFquDtVmEVG4klqESNNNkYDRGzjFxerWdWlOe6eNRSqQ08VLewnTZRRRzrdLefaEa7RBNL0S5YGF2wtVF6dGu53aAEAJDi89KCUyUbAguUG2FsAyKYs5Cdch8fRYS6l5Qc9kOVmuBKurr3ycLKkmImvlvdt77wHeP4R+eOI6cbfQp32cxTEPAsZWobDrbnym2b2UUY4wLG+MEe4yh2Iiu3WxHqmPni67ZlUSsKImpzXaEgmumdOOtPEQSwYjU51Crfifqobe/+SI8+NTJ9O+mQeHrma2Vuo9S3to6p2qLMJjqPLaDGHMzBVQbPpraNmoYVIKlvLdHOVLZmwNsApSoAsjd3zqEXZMu/FDR+uvbVJUedLlphgoXhBwRITCEBJfKTqNSsMC5YhrEmuKVhUFVoll0DTTbUTfdmxLVsZASBktmeYefe6IUX2uGuGx3ue9+3Oy4QpIYJfdqxDkqeRuOY4BIBi+IUcyZYIyi4JiIhUSrHaZUQSGVMEt2Dj25DsW8CYMRLNf8VAGe6u8Z5xKEUhTzym92uepjbqaA6Yo74BglGu2ok3inAmwqAd9IwCwZL6g2w8HUc9vo8/pOfi7mzDSJnJspYG6mgPmZAh58+iSWqx54wMEosHOy36f8QkbaHe9KyPUPoyTk6h9dlHWWzI9DJ+O9CTk6Sfo4IR9jFIxq67ld7iQJw01widmKq/zuu2aoVSK5Vg9Qa4Yo5syhNHTPjyEE4JgMzXj9MS4lUKkKsTFXXGrXYuBCYLWu6c1YP25KKZVFp24aJDEryq4ngLSySwmBpEAlr7yn6+0IXEi0fA4vaMMyGfKOiYgLOBbD1fsmcPe3quteP3UMHCYjaHoxQi4wX7JUg0GLxW0WydqiF0JIVBsBmu1IJ9A5+GGMaiPoiqFSAtVGCEYjVAo2dkzm0fRUgn62nz1q3SRTYbK8Y6jRqpYqiHhBvKnuRKjF21zHQBxzTJVsLNcCRLES8lCjaj1vkmoUqeVFOHKqMVT8bJimAYHqWi9qBhf0eNmZjGKMceFinGCPsWUUciYIUQ83qlWXEzDasW+KeUeNcysPkaxgRKVgd4mmKfXQAPf88xFQCkwUnTSRtwyKMOLgulppMILwXJVbgXTWWEqJ5aoH22QbWn8llf1zAVURVouGE8ttFHMGqs3tm/3pT667KfgGVQrQcSxAoDqY1UY4ND5yAYCqv+ZsA65toNpU3W4JpD6Yt163C/t2lfH8oRU89INTWB7QJejtgPzI5bMA0KWEv5XOTe+9KiSwWvXV4lKL3pkGRd2KMFlU1LxEtZYLAccydOVbIuYiVWU1DQouRNrhTo6KQIsECQnLZBuqfBOiBFVKeQv7d/Uvov0wTpPtbOK9WlfnsNEiqh3EaC8Npp6bjGKiZGOyqLrdkzoB/4mb98EPYwShQM4xsGs6/7peYAxOyNVvBmE9yno2ITcNipxjjJPsMfqwEfvrbCBhuBXzJhglcGwDjXbYl7Stp4zVGVtqqRnkEe5tIYTWO+l0KwHVqRXpd6+DhI6dCC8CSQxW/6JUFctjLlOae1srlCfCWslpSKiRH66V0pmeYw8ijjAS2L2jgI/cdgBfeFi5miSsrEHnFXOJ06te1++eO1ztO/6NkMSM5DxjOZxGHXOBlZqfuozsnMqj5WnF8czruJBYqfswGMFE0UHeNdFoR2h5Z3d2mAv1WZTySqSVQK01vSBWHt0jXpjk/KvNML0ub7p4GovV09r+Ug4sRMjM+8OYg4VkoPjZIA0B01RWb37IUc5bKOctNLwIXsDPeBRjjAsT4wR7jC1jz46imn1pRzAkSQNl8vBKRpEkkKpxbuUh0isYkQUhBJbBsNr2MVnqKJYniUSi3BhGHOWChTDa2H6LJnQwgyLiYssL1mSWB1CJR6IAqjwkt7bN7QQBAShA9BxX0zu781TZoM5oR0wERO0/mSnrOkbSXaxRRQuk1iu2SbG05qvZOgpUGwHuffSoEswiBH4wnN6e7MkyVID+xGefOuMFaO+9mvi2CqnmAaWU8II47Q64NoOQyrpqdsJBwbXUzD5Usr5SJTANhpxrIgzjzoGT7h8pJWek8p3AsQxcNG3gogGzzzEXqDaCLVPPI67UaRfXvL6/EYKUep7QzSdLTirE5tpv3FC1LmU9AxcMuXFIH6MHG7G/zpbvbrMdIeZSzynrOOjHAFF2V0qcUKJcsFFwjS6LIgB46KkT+OojhxHFAqWcCS8gXSMywwhpvIf5FMcCHiI0tK5I1u8aSMaOhrtwxFymPtUSgBQSvvbElhLpSJXBCBrtADFXMb6QM2FbLB0ZabRC5GwGxzGxpJlIiQWYlEhtRLeC7ForC0ZV3MlilD0EIcdi6CHnGCjmLORcE/VWgFbPGiHmEktVD5ZBUSnZKLgmmu1wW2azk0Ji7yUp6bnlRKtjbjqPWiuEt06sXw+qMS7x9CvL2jKNpkX79RBxCRcYKn42SNPAMhl2zxbw3rft2dZRjDEuTIyj8RhbBiUE73rLPP7q6y+CC6ntI7rnnA2mEqEPvGN/qsa52YfIIMGI7uPQQaNns65tYLLkpN3DIBKp1cV6D89kDCdRtN6OhlByTQymTCT4tmx160jmPpOZaMgzt8DaDLhO6rN7TBYXasEhU1osABisc3yJ4mwYcdSbISIuYFsUBdeCYVC0vAhHF9XgVMHd+BEXRAL3P34MlCJdgHp+jMOnGvjUPc/jo++7ElePuADtvVcT39as7yshBJNFC17Adcf9Ijzw+FEsrvloeTEMQw3thbFAwTUwVS6gHcSYLORhmQztIIbkEkEUw49EaivT8OJtFQbrhcEopivuaNTzeoDVhv5v3d9wwbUR9dy1jUzXuzsBr5TsbTvHMcZ4PWFj9ld41nx3Czk11hJxAUY7QqNMdx4ToUjbYl0WRQ89dQLfe2ERLx+vqfEXouKFbRqI4k6HdNRcdK0RZFwElHUWYTKd5QW6i7+DaNRCAIQCMjO61bv/7vhJlAtHO4JpUEyWHJQKFk6veTh4sgZo4bUk/KqG+xnE34SqDsC2GPKuieWq39eQEGJzOjRtXzUGCq6JUt5GMWdhre4r+nwGYSywuOrBthgmCjYKOQuNdrjhyNF6KOZNlPMWml6MWitIlcoNfS8lbMifftclWFhr42//4eUNGYLDQIjSQwljiZ1TLto+R7URbPiJeAGHa2GoYG9W06AdcMztKGEib6RFmVFGMUYd7RjjwsM4wR7jjHD7DXP41jOncOhkrevhlswJxlyCEIFdU7ktz3UNE4wA1BxN4v87SD3btQ1QojrId928D5fMldFoh/jrB17ESq37AZpVF5X62CkFDHLm1G2mbSSElCMHgbM5Mp6IWAEAY3rhcI5z/mR3vUUM12bw9fx0Fx1WL0FMg6LeDJXQCwFsi2K67KajAW0/Tre5Xgc7i5YfYabiQAiJpTUvVcP3ghh//qVn8H9//9W4av/UhtvJ3qsA0gVl9pwJAMNgKBkMTS/CgYtK+KU7ruhT70066IxR/OUDL2K15sM2mRYJpCjkbJQpkHdN+KGAaxv4FzfMpx2Rc4lRqeddtHOdgFeb69vnAUrV/PgQ6rnBCKYrLiYKymosm4BPFG1d1BpjjDceNmJ/nU3f3T07itg1lcfx5RbKebMze02QWhsmCuKAchept8K0ay2ksnUkIAi1Q8MgBfBeZONJGkOlEsVq+zHCWMCgSuNAdY67KdPDqMFynfjfG8MI0eNhUFTipTUPEyVLFQw0Oy6IBGIh0gz7TJ7ZElBd5LyFIBK47dpd+Mp3juj4I0EyThSb3rZUFqYtP0Ypb2Gq7CKMOarNMI1zCYKQY2G1Ddc2UClYKOYs1Fuhmo/eJPyAY6JIUcpb6ShgHAu0A9UJzrIhL987gW//4CQOnWpuvOEBICCwLQNBFCIIRco82Ogz4VwAhK0r2JtoGhgGxcREHiurzZFH0c7HaMcY24dxgj3GGYESgl9631X4z596DKEQmkpFUqEzg6rZwHsfO4or9k2OVHnrrdjNzxb6BCP8IE4700nQWGsEYIzAtTsPu4RGND9TwI9eP48Xj6zhgcePgQsJy6RpJVYlcmomOVuJFkLNuCYgAEyTYqpo4/SaN3LAYowiisSmKGCUkJRifjbB+bpjcGcfPauTMBKY0syDKO60DEztJ/ru6+eU6rSQePCpE3BtK108pgsxrQmQzC1veBWlEm/hQkBCzbRCW561gxifuvcF/PL7rtwwqGXFTRyTpuen5uy0tYupFpUSSO3BrjkwNZQyZhgU//rD1+HvHngBJ1daCEKOQI865B0TQgIXTefxL26Yx/6LyoiFmt3erE/32cSG1PNm0J+A6583op7HXGJhpY2FlXbf3wiAcqFDPZ/sScDfyNTzMV7/2Ij9dTZ9dykhuOvmfWoOtaHEHKWUkAIQUAloMksLAFHElWgV1JiLF/K0u2toobHeZ1iipp0tWksAlkmRd02YjMIPYjS8CJZBYeYtrNR9xEKJsmYDH1Xksq0loT3/ViNBnU69EDKdxU10a5LnWpxUALaAZFxKCIlKwVZCn0Li6gNTYIziCw+pWe9eta6tMPMSIbSWp4XQKq4edQr71jVZr+1KQRVeN5tox1wgjDgskynvcZNhbjqP979jf59NFiUEH7ztEvzx3z+9aSYe0yrvhkFBKUHLi1DK64LUBkFTQlmEjSrY+/TLS2kc3yhhPl+jHa8nnO/u/3h1McYZo+BacCwGEKWwKAAQqfwtS3k1UzpqlXxYxe6a/ZNYrvmoNkMYlKDWCtIOp8EIXFv5DC9VfUwUJfKu2afM+OKRta4HlmUyLFX9lKpM5PoV3qR6HkYCC6seTJP2eUYOw6ivy+JcsoDOJ2E9G8OYFl0jBNgxmVMFFD1+UG9FEELiG0+eUIsGqTwsTZPBgmI2JJ+ldvgCgNSXtDdWkoy4DaVq0UOIskfpvEgVjIKQj0SlzIqbtLxIMRZ6rLWEnuFjlHTZg62n3nvdpTOYn3Jx8EQNzXaEnGuCQKLlxX2Bg1EGR1uGAYoxEXOhLNf0wk7oha6UZ0RM3BYYTDEQpstDqOde1KGd9yTgG1LPMRr1fKJLeE39XMxbYyreGK9prMf+As6+7+5V+yc7xcHlltKK0IKMWQ/gZMQEUDO2mcmlNAdmhCAm3YPGvTkQ1RntZNGGrS0DCVHCY2EsUMxZfcXb5O05m6HlnbklpTofPdutYwylElEsYFsGHvjuMQzz494KErFDzgWabY6dUy7mZwvwfOVa0WiHfa4kw2a2R0EUCyxXfeScRAgth5Yfodbs17dpeUr4rJgzMVG0UcyZqLfClHW4HoRUSTagLNxc28CHb7t4aFJ59b5JfPCd+/H3Dx7c1Hml95hmFkSxwGLVH7kgfduIdpLPHVIJc9uLkHOMdRPm8zna8XrBhdD9HyfYY5wx6q0QhBDMTriIudQJi/JwJESpQ49SJV+vYrdc83H7my/CM4dW8dKxqpqJIqrqWCnYcG0DrsWwUg9QbynqkmHQlEZ0+d4JfOKzT8EPY5TzFiKukhzZU/lmlHT5QAOdKnG2MiqxtaR5M9jqPNEwbNc8+dnYNwFQKVgwTIqVqo96K0JZd2/jWKDeDOGH3cmUhFrIrFR9kIoqsiQK26pgol5nGKTPP1qJmShhsCx6g1USfHOboFIm4iafe/BVHFlodJ0jodB2MT4sk2HfzuLI1e/N2OdkrZuoFgK0DIA46u9cZBJvnXQnM4nnwy9+GAghKOUslHIW9u3s/7sfKiaLF0kcW6hhubq91POJHsXzbCd8TD0f40LHMLsgAOfMdzdbHHzu0Cq+8eQJxHouO2tRZGSo4gQq2QnjpItNuhT0B81OM62UzXu60ET/LQg5Cq5am5TyFoSQ4FzAC3lqy7Wdj72YCxVnQBQ1HIDQjioFxxx5fGk9KDcMFcNW62rWfKnq4w8/+xTaAYeQEnMzBTTbEarNII2PsTjzwmrb52j7HvKOgWLeQs42EQQRVgck2o12lHptT5YcRFzF9ISxMAxNL4JrGyOL497x9n2QAL748MGRR/qEAEyDoOUrUT7LoIi5KohsdI0IgNmKs/E+tGWd58eoFC0kZaNhCfP5HO14PeBC6f6PE+wxzhilvAWDqSBib1AlH0bZGKVi98yhVbzlkim8eHSta9v1VggCwLENTBQV7fbma3bixitnsXdnCZQQHF6oY2G1DYNRLFX9dMY2+wAd5imdVN23iq3OUm9nksOYOojtUC/fSuV9vZebjGKyZMOxDYQRR941MVNxUG2GqR0K1+wCJejVkXSNtH/0WiOAYynqtakF0CTU/VjKW1ipeSlLLlF15VpopnssQEJKoj+zzpygYzM0WtHIVMrL904oxViTIYyVpB0jBETTzhNtgjvftgfAmduDjYrknqJEzSCajILYKmhzobrcXHe7o5iDi0S48MJJurNwLAN510S5nMPFuwrgmSJYzAVqzXCg8NpKXT0D1kOikLtUHaB6DujF4mDP7zH1fIwLAYPsgpKi5bn03U2Kg/t2lrB/V6lPb2J+Jo8bLpvBPY8eSbvtiQsIl1IxkmTn+cUoUC7YMJmi9a41AoQ6ISJAap2VjIhdNJXDaiPA8cXmwDi/Xd3kXiQ6LqbBYDACP+LIOybWRhDQGhXJOZsmRblggRGCQ6caCCIO1zZ055yla6qkML1daGkhtGLeQsE1scMyUG0EfcmzlECtGaLRClEp2pgqOwgijnrCJugBAXDHW/fgTRdPdcXEjWi/7337PggJ3P3wwZE/U9UppzAZRSlvaqYiR6053DZUHQvw6ftexC+/j66bsGUt60jPvP2ghPl8jna81nEhdf/Hq4AxzhgH5srYNZXH0cXmulXythcNtUJy9QNmvYrdscUmjp5udOabtHx4GAss1TwYlKZWF488s4Cji8206tlsR3p2VVV10y9WL20Y/QmxlGcWkC6ExETKweItmwHVvOucxdDcgjqoGmvuFCsSOt+OSReGQSGEuld2zxbwb3/6OhxfbKLZjlBrh/iLe5/X9O1MwCEdf9IoFmh5ijqdc4zOLJ9twLbUYm2toSvrUtunSQlJSFdSxgXA9dQ9Ieo+K+Wt9H4dlUp59HQDp9c8TJRsCCFTSmLCmLAMCsOg+P7Ly/jitw7q+e/zQ2NKutYEyj7HZFSPJ1gQUkBwIBIi7XbHF8hc90YwGMVU2cFUub/DIKVE04sGe37rOcP1IKEU4mutEIdONfr+7tpMd7rH1PMxzi8G2QWdT9/drLJyNkkCgCdeWkq77YkLSJKACamSVccyAAkU3M5aoZy3sFzzEHM1f82ooie3/RiUEgSRKigMe16t9xxjWyiwJ7FD2U+qUZRyQSlwSym1YNvWNVaSp4eEGmmaKjswDYqmF2O1FaTH2/ZjtP0YBiPpWNBZAQHaXgTPj1DIqeQ5jDnWGkFXfAVUUrpaD8BoiImijZmKCy+I0WiHXSxB1zHwpounujq0o9J+r9w3iS996yAgB6vCdx06ybIUBZaqPgBdiMDg9xP9ezni+Nggy7osehPm8z3a8VrGhdT9HyfYY5wxKFViJp++9/mhVfJr9k/ir77+0lDKxq3X7tqwYucFsaaBddPFqARiAYRCqYMSALbJuuggeVclXULI1INZPTP752MTmtn5ANVJaK8fNDvDYxJnWJ4nSIKMHJhcK7EZ2VFbJcD/5d0X45tPnsLptbaqsieVRIMi1p1nyyBaUZWj6XU6Kgal6cPvH584pooqXYUbtTcCAkpUd9sLYuVZygj2zBYAQtBoh0pxnBHsmc1jrRkijAQi3VUm6yxwpAQck4ESpTI+P1MYmUqZrUBTQuDYBoIwRhgJhFwgCNXC55+ePAFA0SErBQuM0QtCxCShmBMQMAYwxkAsNdctoTrwXAhwTS+P4k6XOxYCp5ZbaPsxco6BXdP5855QCin7jqmYUyq3e3f2f6ZByLHa8LGSzn13ZsCrzWDDzogXcJxYauHEUOr5AMuxMfV8jLOEYUnt+fpeDht36e222xZDhVhotCMYBsVP3rwPs5M5/HVPR55SAtNgIFyJWp5a1oKHulje9KJUr2WQCJaEogn3jhIRqGLcKMiOQWmDC1CitmkYFLe/ZQ5ffeRQ6t6x0cBW71gVpUpQyzIopFT6I7VWiHLBhpDAwqo3lJVzti04hQBAlUL6al25XZQKFnZM5ND2I9S1XWUWXEgs13yYBsVE0cbsRA7tIEajFUJKib07uuNtlvZrGQyGSSAkcHyp2RUv1bO+CYCAEJkWI7JFiQRF10DOMVBtKsFcAKn4XfK5Z5PrJBFPPxs52vhYr2VdL3pZnlJKFFwTKzUfU2XnvIx2vFZxIXX/xwn2GNuCq/YPr5K/9+17cV+GsgGorrMQUgmL+DGeeGkJjA6v2Hl+DCEkSjkTTS9WCpx6yDZLexZSCajkXRM5KVM6yAduPQAAXYke6QthCd2KgNEOnXq7Z6HXQ6J+Pei4zic22r+Uir+dzBnPz+Txozfswdx0MRX8sk0Gy1QMh3ozRMgFHJOh2gxBCYZ2VJJZ6kSZPutfmj2y6y6dxq7JHCZLDt561Y50lim7oHz+yBr+/EvPIIw7AmeA8tYUA0Tumn6Mpu4ATJccHF5oDBUXy6K3Au0FMeotldxnK9hS7zsWEquNAFMlB5WC1UVjulCQnes2KIFBGUiPmNorx2p4+IcnsVT1EYQcQgiUChZuvmoHDsxVzstxv3q8igefPonlqgcuFL10uuLi9usuwsXzg4/Jthh2TeWxa6pf9ZwLgWozxGrd193vbvG10ajnftopyaJDPVeU8zH1fIztwmY0HDaL7VLrzXbbT620ELUEQJW+y4duPZBaJWbXGvVWZ5aXUZJ+//KugTCWaeIksT4NXEqiu5Xdz+dRm8yUkpSaLoQElx26+vtu3o93vWUXHn9uAYdONba2ppCqI23mLdgWS0fjwpij5cXndJ0yCInjGKCS/6U1D65toJS3sMMx0WyHqZBdFlEssLjmwTKVns6OyRwIgJ+4eV8XLfyeR4+o5EkItLxOkd9gBDxWf5dS4t7HjuLYYjO9HkltYdDVafkcgXbcgEw0UobPp1Pd4Nns+FivZV1Wvj6bMLfaIf7LX30PSzUfcSwQhBx+2EI5byGf6xfuPd+F6wsRF1L3fxytx9g2DKuSZykbftiZuUlmhxgjWKp6mJ1wsVwLBtLM27pr2vJjTVEF4gFPQUIIynkr/Tmhgxw6VYdtMvhh3GXRkU1lCVFUroQGm0ApgZ6ZD/aoUIrZ/Sd2rmInpR0a92bYa4QQ5GwDQkrkHAM//a5LQAnpoyd6AQdjBHt3FfHet+9FKW8BlAGCY25Ip/PAXEnR9Hgn8A2qRj/9ygqeY2tgjOA7zywMTNbzjgHLpHBtR1unBGoemRJQkLSz3ouYS3zrB6fwyDMLIEQxJGyLDaVzZ8WFlKhZMDCBT5Ak2bVWiB12rovGdMmQJPBCQDbpfvHIGv766y+BC4FywUapYAICCGKBf35uEaWCjbnp/Dmd6371eBV3f/sQgogjZxtgjIJz5QJw97cP4QPv2D80yR4GRimmdMJ7ac/fstTzrgRcd8M3Rz3v/7tjsTTpnkq63joBt61+FfYxxjjbWI+2+6ZLprteO0oifuW+SQgAdz/8qnL5kBK1Zoh7HzsKAMi5JriQ+OBtF+PwyRq++s9HAADFXGe2mUAlo1KSLkbaekwuKbWy95C/JyNN2QZksk7I2Qwl3V0GOg0E9T+BN182gxcOr6EdcAQjKGgDParfUHE5iDiWax5MRhFrX+t668KZw+29ul4Qww9jFFwThZyFvGui3gzQHiDwFkYq0XYshp+8ZS8uma/oeWV1zxxbbCKI4o6NZqrDIhHzGIdPNfCpe18AFwK2ydAi6rqsv3aSCKPOyNqGY3RSueQkY4ajjo/1WtblBrA8ZyoO/scXn0GsWRiEKJeRxOItiDhsi5230Y7XCi4EYccE4wR7jG3FoCp5QtmImcCaTjQoIdC2k6ln7/x0AU0vHkgzZ5RAAh2PYzk44S0XLDiZLk9CByFSdaVsi6Htx6lFB8momxAAQRinIiiMAjnHRNs/d9VhkQz+bBN6xVv6e+Pdf2NECYgxXckf9bS5kPDCGJfOl3HXTfv65qHW83iemMhjba2lqsgDsHdnCXPTeRw53Ux/N+iw4lhgquysqxbZbEcQAijkDIQhB9HCYwnW7XDo86QEabAbtp9EXOgv7n8BK7VAzTgPKTZzbUPGiOq+hBF/zYmYJB2GdhChUrARaT9yAICUWK3HeOipk/jXH3yTqv5n5rqjWCh7v21OuoWUePDpkwgijlKu47dLDYYSo6i3Izz49EnsnytvWyeAELIl6vlK3UdtBOq5H3KcWG7hxHI/9TxnG/jF916OG6/YsS3nMsYYG2Ejtd5/yShumcinrx1lfvb5w6v4a73Ngmum64DDCw388Rd+CCcV7FLK30RKzE7mEOnCuKENrZVvtgQb8audjfGMkvRZlKwFSjkLXsi7GCqWkYxzdVxTAFWAlZpBt2e2gKYX4jP3Po9GO9pUeJ8oWDAMikY70vaVROvMCLgWAyC2RP+mGF5I2G5IqVTEW36MUs5CpeigkBOoNoOBTix+yPHID09jx0QeF8+VkXfMNFEXst9G0yAEEZfaY1tiuuIqllwrzNDxByP7vO2NO4PWSlyqa5dY0DqWYuCNkrB1WdattLpYnlfvn8SXvnUIUZwozyeMC6V6b1A1Z/8Ld1yBfTvP32jHawEXirAjME6wxzgHKORMUKoUJIVU3eMOTVv9HxcSx5eb+Pn3XI77emjmc9M5tAOOdhB3BMoIAaNq3jOpOtomRTFnde07oYMcmCulVa3ZCVeJpmg7Mc4FVupB6q1MdYeynFfJeqUgUsrr+aZqbxakR7GNaUEUy6DwAo6mF8EPOSp5E34k0sIDoFQXGVU0qo1QcBgoY/jQ7ZfgwK5+GuKZ0BMpIbj1zRfhrx94aZ3XqHsooQUNU4vM0ocSOl/S8hi1mJC1npupOKi1ooEiJ1fum8Qdb92D//WNl7uE8nrF3pKail4zQujzeC2JmKwnLAKiFp+HTtVx+FQd+3aW+ue6pQTnsk9M7UyS7lPLLSxXPeRsY6DYSc5mWK56OLXcwtxMYesnvwlslXq+Wvc3XCy2gxj3P3Z0nGCPcU4wilrv175zGDe9eT71AN7INmfYNrlQVO9EYJFRolTD9XPh1EobBUcvZwlSbQ6Ve6pZ3FGfIYnoFYf2RY7UfryQ960dADXfbTI6fDF/01584ZuvqOScj9a9Th5XXsixQ9uQhrEA5wLVRoiYC5TyFvyQozrAFms9MAIwRoeytc4UwxTZE8ZY04tQLliYLrvwQ45a0+9rlpxYbuFPvvhDvOnAFN77tj1Ya4aYKefgBTG8IOrbfsJSsExVfPGD0ZoiScxNYzCQzu1nmY16Ag62wVDImXBsBs5VAWUzCVvWsi5pNszPFvDf/uYJZS2baAzpYyFQgqwCSk8m0egZY31cKMKO4wR7jLOOPTuKqBRsVJthKjCWhYAKZNWGClC//jNv7up2Cgn82d0/RLlgKY/rDMWbEoLErTDXM5+YpYPs3Vnqq2pZFkMcC/iRClZBxOFYTM8Kd2Y3gkgt+l9ryTWgLakyObbyDyeglCLnEHiBmi92HRPlIkOoheAoJbBMhlozgFpqbLQfAiEEmu2Ng32WJlgu2iiXcwP/lu10e348VA2UUYBSmlLykuMZpBaZpQ9VCpb2WhUw9PGPAolEvEYg4nJdVcqZioucYyJnM4SRQLXVCZJdKtxZlgHBttKYtms+cj1sVVgkSzFnjPQn3UIqj+7Ur3t0BXPFPFGLyUFgjIIHPB0/Od8YmXre8Pso6E1NPb/xitlzf+BjvCExilrvqZUWXjlexde+c3idRDzAFx5+Fe/nAg0vwsJK9zallFokS7GHYi61GGlHhTuKBWqtUAc7dERQgYGqzetBJWtq+5WCnc54R7FAGIvUijTpUO+eLaQ6M4MW8znXxInFJmyToqkd/waNOHVdP3RiTBhxWKZalwT6fJKY7tgGaCva3DnqnVNKILZZ/IyMUKiOucBKzYdjMZQLNnZM5tHyI9QHWGL98OAKnju8isvmy2h4IYo5C3nXQDuI0fKi1AEj3adUnexqM4CEVPPZQ8bd1FoUaWMF6CTZyd8ApGKsiStF04vQaEXrJmyDYm6C3mbD4YU6lqq+WqvR/u8RBVTxORKvGUbbhYALQdhxnGCPcdZBCcENl83gyIIS9yCJxKZMAhlQKVgI9AOk9wH0zMEVcC5RKlgwGU0tj5KHoqmVQdtBDMZoWl3spYMMrGpRgumyjYum8vj+K8vIu2bXF9ALYuXFuYlAZJtK1dQbMGd0LpF0SpHxeVYK0GrGp6UVlUt5C7VmiFLehG11HglSypHPoeHFoAT48rcPwWTDPSF7aYIGI9i98xDuuHE3OBcDKYTX7J/EPz15AlILVCU3jxCZBYr+gWYC1KCkrpc+lLMNRHGgE7fRrqsQag5LzWzJdOxgUPBL1EMJISjmLbSDGGEsQIkK4tnPhQtl49EOYjiWsS00plFpmWeK7RQW6Uq6KQGj3QrmXCuYJ17dEiTVScgi5xhgFOBcgBr9x8S50GMgF34Y3JB6HnE4FsXOyfwFbZ02xusHoxbVXjqyhlMrrYGJeBBy+AHHoVMN/O9fflbNGcccU8wG9HMk1OMm2YRaPee7SbwSyhWCQ9GGlejmxs/1Xh2WhO5dyltwbQMEwHLNg5ASYchhDqCbXrlvElcOsR576KkT8IIYptHNJlsvuUZmxjg7Ny407T25BqZBYZkUYczVazc4V0PP9CoRts7sMSXKieVMsd4IWi/8kMNfbaPgmijm1LWOQo5YCDTanS41FxLPH60CUNZrhZyJUt5CzmZoeEp1PNlprR2B6PcSfYuYQ7r1wzrcUq9Lk7FESoC8YwIE+IU7rgAlWDdhGxZzf/KW/em4RAIhJV49XkMYc1UsSNbHPddUSEWtfK0w2i4UnE1hx1Fw4a8sxnhd4Kr9k3jgu8cQhLyrYmgZFOW8BUoJmJADHyDZxbtjG3BsI+20RlxX9Yj6ebXuK/qnY2D3bKEvkbh87wQcm+HgiToWVtt49WQNa40Ai6sevCDG6aiNiaINxzb6Kuej5NgEwLtvmIdrMdz97cNnbI91JkjsIChRdDkp1fF5foyQETiWAUqARjuEH8Z6EdCxi6o3Q0TxiJQ2qAXVcs0fajE1aF6Pc4HDp+r4P778TDpz1E0hbOLl4zWYBoFlUkRcwtCLKwKBWKhZfEklLIN2JXfDkrreQotjKQs3KTenGC+k6pwwSoYmj/OzBVQKFhZWPJTyJsp5Cyt1H7HmxCWhlAs9mmAxzM/037dbwUbzkdtpA3a2hUUGJd2WARBHPRvyBQdEKNXVWFuHze8oYG62gBNLbRhM9gsnBhw7J13smu6na7/WYJsMjtVfRBhjjLOFUYpqhp6XjblEricR94MYyzU/feYm4l8SwHLNxwxR9oZCqzoTmc1PO0W15HcJK4hIiXCQAuoAMEpQcI3U4jHvmsi7Ztc8tWMbKOdt1NshYiFQawQAUZZZt113Uer00LuYTxKtE0tNtPxIJ0pIz3EYDEMxzCKtfp4tGlPdoDdZJ9aV8hZW6z4EpJqt7qVPU2C67MK1GFYbSmiRC5kKaGULF4kWSK9N6EYouCZiLlLF9s2g6UVo+xHKeRvFvAnXMpB3gnRsLztjnoi6NdoRSjkLOdeEaxtotMI+n3Mp1X3H6OhaMllwIeFYDKWcGp+bn8lvOP+8Xsz9zL3Po1B0sGc6l772nkeP4PhSC2GkGJKxkDDQ/Zkn9PWZsjO25XqNYZxgj3FOsGdHEbtnCzi+1ETONiAlUhpyQrUatgAftHi3TAY/iFFrBuACsEyKmYqDlqcSRQLgjrft6RNPSZKqIOTwAg5ClDBapWQjXBMII4HlmofpsguiLT+SAMToxkrilBL887MLmCjayNsGGCOotyI1R7rN13Td49DPZ64r3oQAjmWgXLBgMIKVeoClquKrmQZFMW/CCxQNbrnmI++qiq1tUQhJEOoEdOj+tEdnIvrRO5Ocna3L6UVTrOl2jsVwZEEJmM3N5LsohFJKNFoRCKGo5C2sNoLMiECmXi5lqh6v/rl+UtdLH8q5JqSU+ORXn1X2SXK0SnzLjxBzgd2z/R7Zyf22VPXTAgZjpJvSpj+rHRMu3vGmXbhq/+S20JhGmY8cNDe+VZwvYRHVzSEphZIRAuJAiwEJvP/m/fjKdw4jiLjq3ki1sK+3Yzgmxe3XXTSeaRtjjC1glKLantkCLts7ofynexLxtUbQLSzGVPaYqGOv1n1cNFPI+A53ksAkvnWxgJKxkSFf50HdVSEkaq0o3V6zHSGMuIpleuRMSiWYdtnuCq6/bBoPP30K1WaYPkOfeGlpoFBbJ9EyEEYitRLbCOp5DSxVefps4kLADzhaXgRGFV1ZxXUC1zYwWXJQaykvZ4MpR4+cY+LqfRO4+U07sVcn/kdPN/DcoVU8+fIS1uoB6m2VbBsGAecdFxEyZARn0DWcrThwHRN+EGOl7g+lnSez2YqCrQvj+mcCgmozQMOLUCko8bCJko22F+NNB6bw5MvLWsQM+jNRbguNdohi3kK5YKOQs9Boh30jP6O6vxCo9Vu6VpPKtcGP+IaMMiEljizU8dlvvoKWF3V5Vycxt9YM8fl/ehn/9iPX9iXiQRh3JdksORah7j3ToPjQrQfGseo1hnGCPcY5QXYB3mh3PJHDiG+4AB+2eFcBGmlAWakFqf1XGEf43+9+Bne+fQ8mSg7W6j7+6fsnEXOOgmup+R2dRdVbIQymPBhX6z5iLrHWCFAqWHoOVB3HRs9pRgkmyzYMqjq5fsRhCgLboueMLm5QQEDTwHqsxiRURx5AqtitFigCLU9iomiDUVUQKOVMtPwIru2AC4mVmp9WuQchoXgBGDiTnNpshLzLw9I0aNr5lVIi6lmEJeIinEtQpmZU0xEBdAK+aTA1Uyblhkld73zUVfsn09f84nuuwCe/9pyiqK3jh5lc0ygWyDtm336yAbTgKlGUtXqQLgYpAQxGYVssVWvfv6u0bXSmUeYjh82NbxUXirBIMpdHQHDp7gruevte/MMTx7DaCECAlN1y09U7MTed39Rc9xhjvBZwLnQXRimq3XXzPlwyX8GuqTyOLjbTRDyMeJdoX0L4JpTAgEoyIi7R8iI4NgNjJGOnRLrfl1CpM99d0yCI487zO3neCiG7fI6T/xZyJmyLYbUWINBF9qmSA8Zoei7X7J/E1797vNOdNEYTaqOUYKJEsbjWhtzA/rKUN2DofZZ14rha97XlmKIPWwZFxAVWqj5KWmWc6d/nbAPvvn4uLdQCnaQ6uQ/27Cjiin2TOHiyhtMrbTz50hKCiINzDqV90YmrvQl1QplmlCIWAtMVB66t4rdjG5gqOVit+4gGJNnJ55P4hQsISNGhZANqPnut4cOxjJQ6Pl128H/9sUvxjSeO49BCo2+btWaIZjtEKW+jUrBRzCmtnmxCrpn/6z/fiRo3DOLOZ1RrqTGy29980YZjb8eXmmi0FVNhcc1LRXIBHXNdAycWmzh8qt5X/K4UbKzoUUQJ1RhJ2I+mQfGBd+5PPeDHeO1gnGCPcU7hWkwFDP3wY5RgbqaAn7794nUX4L2L90ZbKWlapgoqTS/qKIzrDqEXcnzh4UNdBW3LYgi0Gmli58E1FXx2wsVkyUG1qRL1ejNc18YhC9tSquNJkmkaFDz2EXEBOnhEbdvBKHD79fP43guLqLfDrsgoBNIkP6HVsUSxUlOja60QOydzKBcI6u0IUkgYeQoTKigOok8ngcsPOUq6oj5o9vm5Q6u6qAF13XXkjuKOdRukoglmE+yEKiWlCjhuz4hAzAXafoScY6LWDEGIYjPMz+Tx3rfvhesYeObgCnKuCQKJ5w+v4cmXl1BthClNbudkDjdcPoOZsotCzsQvv+9K3PPoERw+3YSXqYZ30xGTa07w7uvnuu7dQd1jU1LUaZgGUINR7JzKpT6f291R3qro2JniQhAW6cXleydw6Z5K3zExPWowaK47VTAXmxdJGmOM84lzpbsAbFxUu2q/2t8NV8zi+FITKzVfqV8H3V3GrPgppQRUx/B2oCw1le2V0k0QUoLKjI0RASijiGIBkxFMV9y0KAyoAnlSdKNavEpkCucEgGupuELKJO0ErzYClHIW5mfyuPNte3DvY0dHYgQNKm7mHANTZQe1ptq2kIO7wc22Ot99O0u466Z9EAA+c8/zsE2KnKP0PMJYoO1FCLkWdoOKJ/MzBfzIFSqOAcDzR9ZS4bXkPijmLEBKNLwo/V2pYCMIY3ihB84lCFXn5dqKOp8cb/I55RwDpZyJ1UYAmek6+EGMWiscecwqsQrrVwRXn+VKzYdtG3j61WVYpoHlujd0W1woRkS9FaJcsDFRtFHMmWi0I3hBrAoDA653FlICfo9tWF7rczz41Ens3VFcd+zNoDQVJQtjgZW6j6mSkybZhkHhBRFePVHruz+S4kRy70kArm1g56SLD9x6Ma4ee16/JjFOsMc4J8g+iKbLjvK01rSptj/aIj+7eH/m4Aq+/vgxlIsWVmpBav+lqn/d78s+VMNIzWhKLRpBoB6IiUIoAQDdyeIjcosmixYK2h4syKhwF/PKQ7uYt3FiqbnBVtbHMOuLLCyD4uVjVQgp086cH8Ro9CTbSVKXVEmTP4WRwMnlFoo5U82yUUXrk1BUOkaT93ZmlZVCs7JQWa2ryrPBumeShZR44qUlTQfrqHMmSq/ZGatqK0Q7iNPqr2UyGExV7LPaH5bJ4AUR1hrKO9iPfH2dCEo5C9fsm0gXF0HI1eeSdCgl0lnzWEi8eLSKF46swbUN2BbDzskc7rxpH2rNAJ/9p1dgGRSmyTrWLMn9oa9fsohMMGiBFWrbKcbUPcdFp1t/NjrK2yk6tlmcb2GRQRh0TIPmulUzxkSyMI+1gnmk57rHSfcYFzLOpe5CgkFFtfnZAo4vNvG1Rw7jqVdXsFxtI4pVvF+qel0FN0N3NLNIFK5vvXYXrto3iULORMuP8fkHX8Wx003EQhVzTYPBNpVPNKPAZMmBZbJ0Fjjxy07mWIn+HdHffUaRPsu9IAalBLMVB17A4UccH7ztAN5+9c6+Z7qUEmHGritns/T5Pay46dqGUgKPOKqNQHWBuQDXXXVAxXgvUMmZlBL3PXYUsRAouCaqrVDFStnx5465gGmozvyRhTqOLzXTWXQ/5DAZVWNhDsFqI8DqQgMEQKVooVSwEMcCy2seQi18KoC0iqyE3nIIwhi1ZojJko23XbUD3395GWsNH2Ek4Ac+Gu0QOcdEo630agatUxgloBSIYnWuVAuI9SKd/ILqare8CL4fYW62gFLORt4xsdYM03n9XnAhsVr3wSjBRFEl2oWcqZTgh7xnPbR9DtOkaHr9Vpy9hfQwFiAtpN7csW5aJAm258cQuvCj5v27749EXygIY9TbEX7qnfvxo9fPj2nhr2GME+wxzjqGzYM6FlDYZPcuu1B+8KmTCMKOwighJBWPGgYVmFQQoFqxMQksfhCjqQVAVKVbbuhQRQCYJoMfctQ1dTkJ5IZBYVCC9968D5/56rObEtBS59o55lHeKiRwbLGFStECpRQ2BaqNoK/znhVe692soscrO7WZioOWH8M2qUqO9TVOfMvVsYm0oNH0YjQ1/Xt2wu2iqFWbgVJf1YFcZmyYsiCQXdVf22Iwmdp/y4/TDnlLWxap92hBN001W1ht4/MPHYRjMziWAT+Mu4ouSjFVYkW/X0ipEn0hUTQoji+18NcPvIiff8/lWjeghbzbP2M4TDdg0AIrKWRkRNC7Poft7iifbdGx1yuySTclapEJgyLnqL8nojuJV3fEhab4j+nlY5xfnGvdhSyycfn5w6v4o889jWOLzZS1ZDJV0Ey6ioBKDpMRoCwStpLBCG68chb7d5XTv13+CxV86eGD+O4Li2j7sR4LUgn5VLEzN52cc5aGLjM7SyjJibjkWiNI43aiScJ0sZYS0vVM94J4aKxPCgzDipuEqPUGFxKEyKFe1ItrPv78y8+m65Olqj8wIZVSFcY7nwPHZNlGvRVrv26BWhPwwo4AmASw1gghhYRtGfCjOB21S2avg4hjueahnLdVXMxbuOVNu/DgUyfT4o1pdij1QRToGebOsTFtq5lQng3GwKgqKMSxUIJqPcw4RlUBPxade0MCmvLNUSmYmCo5er0VdBXns+BCYrnmw2Qq0Z4tO/AjgZYXor2JcT0hNaMJBMcWm31jb9mii2VQMKZ0e0DUeimKBRrtEC0vQhipova3f3AS7UDNyhdzVt8+CSFwLIZL5srj5Po1jnNEXh3jQoaQEocX6njm4AoOL9S3vTuzmXnQUTE/W0A5r0QtlLiJUhEf5dBJT5BNgqTyzVXJlsEIRhLQJirxW637CGOuVThVYIgiDi+IsVb3dQEAWgF7dGQE1zdE0mFTHWuZzrltVUHTC5T1ViIaIqGVOYXMeEgOfv/Smoe/uu8FHF6oKyV2ocTkCAginaD0JteJQirVCfxaI0C1GSCfM/GBd+7H7tkCgoij1gi0P7e6zgZTVHdKSZqMC6nmtluesuzIWiEnnfSkc2AyZasWxwIgBJWCBT/kuO/RI7jzbXtS4bZQd8HDiKPaDIfOeGcXWOm5pUI9SD/QbNdmuzvKyXzkZo99jMFQFFN1XZNZx3JBzQdOlR1MFG1t66MW9l1MjTHGOAc4G3F2s0g66McWG0rUK5NErepi71TZUQKnBk2LtUJ2dBCSGem56XwqzpVs+48+9zQefe40gpCD6eTp1mt3oZyz+vzuy3kLiSMooJK9xHkke3Uk0BW3w5hjVbPi8q6Bwwt1nF5tqyKvF60b65dqXlrcbOm56SyklGmBIdHeADIz6JnXtvwYLV91M0cN4UJCjT/xjihrO4gHrouqrQgrNT+Nj0JI5BwTpkF1Fxyot1UR+effczmeObSaFm8skyFnm5gqO+pzRGd9AKjtMUpTJxNACbUln0dye6rib1ZgTK1HTUa1+0nnxTEXWK4FWGuoYv3MRA6lvDVM0w4AEHGJ5XqAaisAIUCl6GCm4sLehOMCgXad8LUlmEZadDEo/CDG4pqn2ZGJhoBa41QbqghBKcFMxUXeNSGlKup4PezNpPi9czI3tPh9ttfrY2wfxh3sNziePbyKux9+FUtVH0JK2AbDzqntndfa7nnQZMZsueanyoubk+iWSGw0k2Br6BmuhKJkMYJwhMOhhKDtqRdm58gIAA7VCf/+S0swDQIRoeOfPOR4sxXdVBSEJKrI65+k0PlcFEucWm2nat1bQULdnq44cC2Kl4/XdMFBKY5OFGws1/yh75cAvvWDU3jm0CrKBUstnLjEerJhOddUXUFN1Y+5wHQ5jw/dqubzf/yte3D0dAOvHq/hS98+BC/k6bVJ95vZfBR3Ci7ZQneW4p78O2ExCE09TBajOdfctHDXQNV7g6qOSsQhIbXIH9P7Pzsd5QtFdOz1is48PoFl9NLLdZeby06nW9PLz7WjwBhvDJwv3YUE2Q563jHR9n1lv0loqsRda4XYYedQcE00ocQp20HcFQ8JISjlTPz0uy5JC4C91Pe8jhXVZojvvrCIYt5KE6/sXOtk0U6ZSkkCrwTBCPxQFZ+VD3QnbhOoxCyKOD7/4EGcXmtr4bYIXCAtlPfFekLwxItLuO3Nc13ibwXXANXCbk0vhmFQcCG7VMWTIn8WEsoXOb0uWH+Zk/w95rJLL2Q9JHZcSXxseTFMk6JUMEF0J/lDtx0AIWRg8ca1DUBKLFV9EAIU8xYarbBrnp4hcTPRbDEC7N1ZxDuu3YXpkoMvP3IIyzW/y13GC2JUm2FakVZ2qWqfXqAcORIRtJzTsekaBCGkmm2P1OddLtiYKjlKZ6e9MXW8M6uvZtcTJIX0lhelx5eMkIlM40FIpdFTKVjqvuUCU2UbS1UfK/UAs4yu67iRFSxcqnr43ouLOL3mnXV9hTHOHOME+w2M+x87gru/dSil6wBAxASOnGps67zWZuZBN1I/7VNnNimWasGWjssxGbyAa6ENkYpPAUDTH41GVHDVfE+i9pUk+1wH8nJeqYCaBoVjG2j78boP9EFJtMiWhkdEFAnU+eYXUkklGVDeuo12iEvmZnDwVANcR404VgE1i6T7nD1M1UlWVC0viNHy1B+1G0tfkSGKBWYrTtrh9v0Y779lf3oPJjTEZjtK99O/KOlOnIddtW6avOw79+xi9JoDU5sS7hqmrptzjHRRlbONkRTPzxQXoujY6xXd9HKVdCuvbvX3hDGRpZcnC8/UYmgLEFLi1HILjFJMVSLMTefHn+8bEOdTdwHo7qAnXeg01c9QZsOIw9Bd0n9x4zyePbyKE8stxLEq3s5N53HXTfvS5/4o1PecLVO2TlbN3I8EpsoO3vPW3ZguOWh4Srfj0WcXcHKlDUAlUEIKVSAHAddJdxALHD3dQCmvEiMJiUY71qrXOo5pOjslBOW8lTIELt87gffcuBsPPX0StWagiptUFTdvuGwGn3/o1b7v+3YW3bb6LCEEiGKORktgomSDh4r2vrjmwQ84TKNjD5aAMZquHy1D+XOHMU/n3SkhkERismSj5cXYMeniN3/+Bi0yq9Zcf/nAi2gHHHnHAOcidTsB+mnkCZpehHYQo6TV1vOuEjodZIcm0REwW6p6sE2GYt7cVKItJbBW76x7kkL6i0eraeEmeZ5nQYmyMssyLFzbxERRFQ5avrKVHVT8Xs9aNpmhH6SvcC5cBMbYGOME+w2K5w6t4O5vHUIUC1WN1RlSLCS4iIE2tm1ea9R50JYf4xOffWqo+umwQDuKAFgCNf+kfs7O4giZ2EGJTQW6aw5M4qmXlxFzoawp9IYsg6Kct2DbDI1WhHLBxlozxEzFwamV1mj08zOE3EL3OhH4Tih7TS/GN588nlb6h21y2O9jLlI10vUOh1G1AAxjAUKUwA0dshgs5EwYBgGi/sp/hog9+ucoAQGpEyIVBHsXo5sV7hrWPd4zWwAIQaMdot4Mz0lH+UIUHXsjIdvppkyNMRC749WdzCTGXNMKeYcyu9FC+dXjVTz49EksVz1Ml12EXKDomuOOxhsQ51t3IdtBl7Ho77jqXwh9vzNGcNX+Sdzx9r3rJgOjUN8bXoT33bQXT7y4tC5b5/nDq/jKI4fRbIfpzLDQKuOq8ythUAJJlL923jG0oGYMP+y0JYWQXUVaSoCWH8FgFM8dWsXnH3wVC6tttU0QTJZdvPPaXXjntbvwwuHV1GZyMzjbrJdkJlrqBkGtGcIyqO4wB/DCGH7EtfhZxzElK0TKKEEpb2G1rmw9KZCOYUWxRDFn4sO3XQyD0jQJ5ELiPTfuVp3ZVQ813Q1OPun1mHtCU7BbXoRywcJUWc1nN1qBUo1ntGtGPUEQcQRVvqlEmwB48uVlvPemfZq+TnDD5TN44ciaWjsPmQcXEmj6MUo5E37IEcUchCg19jgW+Imb9mLHZK7v3u9lbQyylnVto09f4cUja+fMRWCM9TFOsN+AEFLiCw8fRMxFOr8KIKN+qKjTCyvbo2o8il/mNfsn8dcbqJ+6mrabDbQJpZeSblrOMBhaaCuhBTM9FCRER5lzIzuHBLZJceXeCr7/8rIKApr6mV0CxLGAwSje9ZZ53PvYESyteeckuTYMqooFm4zKqvig3lRthul1SGaq1Gv6xcmGoeV3hM/Wh1I+Xa0HqWgUowRfeOhgX2DYs6OIi6ZyeOlYTR0vlZBYf+Z1PXqdkIrKX8pbqULsdixGh3WPAQxdUA6qPG83xtXt84+ka01AYFACw0q6QB3xv0grmKtudyfhTmbuXj1exd3fPoQg4sjZBnI5E8SPz6pi9BgXLkaJs2dTd6Grg56MxMQclMquBzAh6Hq+blQAHJX6PlN28es/8+Z1n61Jgb6UtxBEitZsGgSxEGmizTOz0bHWIlmt+0NjXqK3EOoC8f2PHwWlRFPZVRxerfu4/9EjmC47uO+xo2qfQxIyoDOnTDONj41wpgl4Mi+dWk1FSsBxueYj75gIwlido3YMmSw5cG0DUqqihJDK4izvmKgULdRbSthLjeARzE66+MA79uPS3RV88/vH8dBTJxUNXP9952QOl8yX8b0XF0emuKfnLgQE52hGHDnbxMxEDm0/QsuPUjuwQcgm2qW8hamSgzDmaLSivk44YwTVZtC1Jp4pu3AsBi/k3U4tAAjtjO01WiHaWkA3nVNnBLbJcPF8ue/+720mRboA22st61jdLiQPPXUCDzx+7Jy6CIwxHOME+w2Io6cbWKr5faIaCRhRatxhzLdtXmu9edBRPSZ//MbdfYE2ofRmO5frJVOUZAQ2GE1VxUnmTaPWloUEvvLtw6kIGIBUsCOMBZZrHhzLwKV7JnD7DXNoBxG++PDBEbfec9yUjDxPTSlBwTWUbcYWk/nebrWaPZOpBcioWO+l2X0ki5eYi/RzsEyGQ6fq+OTXnsNP3LIPt715DpQQvHhkDV4oUuuVOFMUSJB8nMk+hh1Gcv/nHGXRFUZ8WxejwxaPg343zL/2J2/Zj1sm8md0HBvtY1zdPv/IUswT2zBiMSR3c6JgzoVayD99cAWOxVAp2Mr5QDNnKgWrq6MxxhsH51N3IdtBrxSsTidTSC2IqTqZ7SCGYxkjP183Q31fL1nPdsLNTAFAyuHF+bqeJ1Y2oIoa3htLehPvth9jfrYAoueuhVSd8GY7wt0Pv4q1RoBKwYFtxmmC2QsJwLUYSgUb9UYAHvKz2sFWfYbOiFuS/DNK0zVZpWBjRX+eQs/TUwLU2xEiIcEYRRAJ+KESdTUNohNAgFKK1bqyvWwHEZarfqoSbhoUhmviyKkGvFAx3QxG0ln4URALYLWh1qptP0bRVXP6jm2g3gq71miDkNjH2RZDKac64WHMUW+GqRJ9zCWCsHtNrNh0FAi5FlIletYfXR7hMZepIBohMlV/lxIDZ8d7WRvp2k9/TllrWdtkqbPKQ1rl/Vy7CIwxGOME+w2IZjsCdOe3t+MKdH5ByPbOaw3r6I2qftr0or5Am9pxZKqNjHXS7Zgru4+ia6YPysTWa1TV8WGIYoHFnlnkhF6VKHhGscCH3nUJAOCZQ6uwTQYu4u6H5ggYObnWAiz1VjRSR3/o/gZQwrmQAN36fFcvJBK19u656QRJUPQCjr/9h5fxvRcW8aYDU6lVSNE1UR9QAKJEiYoEkaoqZ8+Dpve2qi6bBkEUSzRaEVpehJxjYvds4ZwnnFk6mGUwGKZScT2+1MRn7n0ehaKDPdO5bdvHuLr92kD/XDcBQLFcbWCl5qNS6FgTAUCIfsXoS+Yr5+HIxzhfOF+6C4M66JMlG7VmlM7k2hbD/Mzmnq9bpb73MnUarTAt0BOiGEsrNa/PCSMJQZQgw6ZSSaLExuwtCcX+8oNY+XVDd6MJwakVDkYICjmKsmnDNCjWGv12U4l4Z8tTyeuGCmdbRM5hKLoWatp2LNmHwdQ8cTHXWZM5toGpkoO1ZqCSu5Cn71Mipp1DFEIiiNTYVcE1kXMMeCHHkdONDnuQkdRVpN4K9Wha5yJudfkihBIjq3sRSjkLlYLyz641gy7btkEIQo6l0INjMRTzFqYrLoKIo9FSibYXcCzVvPT1e3YUUSnYaLQiLRQ3/ENKGgJEs/UYBSxGcd+jR3BlT9Lby9pIHUf0gp2gI8oKQDugQH/vNnYRGI+MnRuME+w3IAo5E6ZJEXKR+hJ3BS1NY5mpOGdEUR1GRe39co9KASu65sBAW85bWK556bNNiXCoGR+DEUwW7VTsZLnmd9EstyNuJVTzRNgFUMm1ZVA4FkPBtXBkQRURSjkTYaQEjkYBpaoSOsoxJkH5TAsHCSzt751A0VrVz6Net/Ve15lN3XiGnguJV45X8cqJOkxGMV1xsLjmgVKSLoSkBEyTYrbioN6OlLiKlCi4ZiowIoREGHOsNVTnIBbQ+gMyPb8737YHl++dwOGF+jlZoCZ0sGY7AhcCrQyl3mAEnEt8/p9exr/9yLVnvI9xdfv1gVozxGo9QKmgEgxGCRijiPT8ydlWjB7jwsb50l3o7aDHsYBjMUwULVy9bxI3vWkX9u3c3LN0K9T3QUydinazSAr0rm2glLex1ugWSTUNmtKyiZ553cwTUeoZ2QSEdFiBEQcci6XHkHNMuLahkzgl2gUAeZ2UEqikL0lKE+r0euGS6CK7BDBIdAtQ423lgg0/5LAthh12TnXbNdug3goRxAKu00kR/CBGTRcpkgOIYqUZAQJFX9Y6NgmpLIg44oZA09PCpMkaDZ3zMfS1ERIwqIrJ2bXUZiEkIHTBYi2dz7YxXXFTD/ONiiR+yOEPSLRb7TBVik/msC/bXcHR082Rji2OhVaxVxo9lJKBSW8vayPr625kGmPK2kzFgErBRrUZqo76AIxjwrnHOMF+A2LPjiJ2TeVxeKEBIWQqRpF2fYWEaVB84NaLt7zY3gwVdSQKGCVoeBGu2juBhZW28kd2FD2HUgLHNBDEXAkECUASAdOgyDsm/EgF+duuuwhffuRw+uDerqKwhEpsDdZRzoYEKkULQagUMRvtCEGoKqEb0Z66Ek6ZVL9VYM27hkrASH/iTSnRVljbg0FiH0nFtGu/pPPvNAEfYSEAQHtwUogRCg6KpCCU57m282JEzb9RRlIrsFgAecdE04tgMAY/EshrKwzPj1HVybUakdA1ZU3Bb/kR/urrL2K67J4zGvXR0w0cW2wiiGJIELBkzkBCK6rHOHSyhiMLDeyeKWx5H6N65I6r2xc+ep+ZSheh830924rRY4wxDEkH/aGnTuDhp0+h1grhBRzff2UZp1bbW3qObob6Poyps1zz4YccXEhMlx0QQpTnM1XkaClVErJrMgc/5Fip+6leipAAkaNrjyQg0Kw2LlWxXP9cawYo5S3YlgE/5Kg1lVdygkY7QtOLurq6EqqQ79oGWl40tCidqJxXChbafqytIZUrSDFn4sYrZvFTtx7Ay0erQ4sWtmUAhKPlRbBNBiEkVuraM5sQEKbF6hIqOSGQUDaEg45LUeV1QphZQySRiOgFQ7IM2M5mfRgLLFU9XVCxMDuZQ8uL0GiHGzYikkTbtQ0UciZ27yjCjziOLTawd0cJQkqcWGqmYnnrIWExTBSV4jmg1oqDkt5BrI1y3sJK3U+tZU2TAVKi2gzT9e09jx45by4CY/RjnGC/AZGtCDeh5l5T5VqoCu4H3rkfV28xmdgsFXUjCli9qWywPv/gK4hjiVhnblKqLiVjBHt3FXHH2/bghSNr+N4Li6qrw1RFNQnCl++dwCM/PNVVXd4uSO2Dmcy1cyg7K4MpKtqhY2vKEmyE1nL2JdlEGwCCUKCUt3DzNTtx2XwZTT/GWiPAky8t4dhis6t4QEboDK9/TqP9junKtRT6s+k5h0FIjm+yaKHlxxjgrDEUMZdoJn6UmVwxS5uyLQZKCN59/RyeO7KGY4tNeEHcTUdHR9QtLWIIYHHNR8tTQji99+573robM2UXedeAhPJAP9MOd70Vpl6wJus+IUOr2bY8VXnHzJZ2cd49csfYXpxvxegxxlgPLx5ZwwOPH0MQcpQKlrJ/is5sHGUU6vtGTB0e+whjgbVGgIJrpnosqtBLMFGwU5EWmhFmyXZFN4tkK4mHdhirTrYXeGBUFYYHxelBBWoVCzrJdW+TIPm30MIjpbwFzgW8kOOn3rkfP3r9fHq9hhUtJoo2pBBotEN4ftwlHmowmvqJU0rSa7JR4SF5bZezic6wRaZwcRZY8CnCKMZSNUbBVbZrqlChZuy9MEYcD9+7F8SIYw7XYqCUpE2OY7pwXXBNNNvDix5A8nmqgn2CYUnvINaGpefDa60wFZYLY9G1vn3ipaVxTLiAME6w36DIPlxPrbQQRQKgBDNlBx+69QCu2j+1pe1uhYq6HgWs3gzhRxwGI4i4qv4mIhIxF7j56p24/fo5tL0I9z52NKWlJZSw2667CLe9ZS7d1/WXzeDQQmOk7upmIDPl2OTHIOLYv6uEfbtK+D+/9AOM2lte71VhLECDGI8+dxqHFxp439v34uZrduGqfRP448//AJDKH5LqLuhWFwWbARdCBVCouaJsZXpYYzqpxkexhKOr+JuhtXuaSocB151S0mUDs29nEZ+69wVYBoVBAX+QT6au+idI/DwBde9yLrBS8/G//vFlWJqqBaiugG2xM+pwN70IUkjVuR6AROAuLSpsAefbI3eM7cX5VoweY4xhyK4BJoo2TIMh5mLDcZRR3A02or4PY+ok9GfXMUAjjumyg1orRBwLzcBSo2SObcAP4lTMi5IzY4b1vUeqQm4pZ6IVxIjWSeqGYdSiebUZpK4YlCpmVO/17C1aLFU93P+4EpzN2QaaPZ1yLkRnjngTawuiixXd70jWcdu/RhnETuRCjVy1PKW3Uspb2L2jAEopGq0Q1WaAemt4jCWEoNlW9PIw4lir+zh4og6DUfjB+sl1gkTbBtg46R1WALl8TwU3XD6DmbLb9z0Zx4QLC+ME+w2MsyGGslUq6sCHibbQSgQ3hJTp3AuFejB/59kFmAbBEy8tI4w4co6JUsEC54o688B3j2HnZC5NfK7aP4kHvnusr5t5plAWOiQVQSFQSdqdN+/F4VN1LKy24drGhmqWI+0LKrHLdgO4rli7Wkwk5gId/eGzC0WfAyxDWV3EXKDaM9c28DwkMD9bwG3Xz+PTX3kG/gC/ymHgeowh5lLNJOmqumUwmIyg1oowP5PH/GwBf/S5p8GFmsE/sdwaafvZe9cPYqw2AggpQfRcWVKg8cMYtsXOqDNTdM1UqZZI9FWek25Bwd168jvueL7+cD4Vo8cYYxi2sgbYLneDXqZOMjec+E4nyeb1l83g6v2TKqmsedraiIMxjmozSGM4pQSTJQcAUG1sLJK1EYjWSXEdE7bFcHrN3/hN66A3tnd1s/X4VKItcv/jR7vWQn3bkhKPPHMKLT9CFMuBXt2JtkwsxMgiZMnEU3Is2Y7+9g20dWPYVpUvuRaiawQouAYmSw6myy5ytgFKWqg2ByfZYSyw2ghRzqsO+HOHVvHAd48ChGC67CCIOKoZ1fFhEBJoeRHaOul97zpJ72bX6OOYcGFhnGC/wbHdYihnQkXtfZjU2yE+/+AriDi0TQZJE5GOrZPEN548qc8FCCIB06Mo5a0+uxpKCPbsKGL3bAHHFpswGcFqY3vo4oyR1F8bUMEkFhL3fucIbrhqB2IuYRsU7W3YVzLDnT2/6y+bRjuI0fTjVLArlp3Z7e1A8kgftLlCzkRZ+0iruSLlgTkIBiVwbAOUAh9+18UwTGOk2bZssUBKwDYoOI8R8c5iKOcYqLWitFp7fLGZLvYiPppXOqDUbhPUWmFKIUwCtEHVvRgLNUM1O+FuWSismLeQc1TxpVcPQQgJEOWXqboJcksFsHHH8/WJ86UYPcYYw7DZNcB2uhtkmTq9c8OJbgkXEv/05Akc2FXCNQcUU2/nRA73PHoEx5daCGMBQpQQVSlvwU1V+iUWN5kQUwCEATxDmjI1O6re2qKHZg+0fudAtlhSnJ0s2fBD0RefsoWNMFQWlRtFYi5GVF3NQI3QaZYYUUXlIORnXLDYCiQ689DVZqiK9AaBlIDrWOBCzcATAJZFla5LphlTa4X4xGefgm0yBKGK2QZjqBQ6Ymj1TFEnO65HACxVPa2to8Yb73v0CCgw9B7f7Bp9HBMuHAx+Ao4xxhaRDXCDsBEVNXmYXLV/Ep4fww8EYq7nm3XyFvPBypgqR1Mq0at1JWiSrZYn23/f2/fCtQ3EQj1YzxRKFZLp7QOlvImLZvKwTYaDp+r48kMHEcUCjTOg+GYhhO6S627AscUm7vnno7pSLbWHrn7tFpLrnM20L6Y6t1GRdCtivUBJfC4Z7fyP6iF112EgIGi2I3zhm6/AMhk23JVmMxRzpvLJpBSOZYBRAkpVEgqomftkUZYu9gyKcIBo2yBQgpRGHUY8FVPLqp4n58oISQXXeu+1UTE/W8B02YHBiBJ8ExJcKtGUJMkOIo6//+Yr+MRnn8Lzh1e73i+kxOGFOp45uILDC/Whc/5JdXt+Jq8WAc0QQcS7rtcYrz0kz8xrDkxh387SeCE1xnnFZtYAvSNlKg4QWKZKWPyQ455Hj4ykXQJ0mDotP0K1GWhlatXJhZQQkLBMRVnPbvfKfZP49Z95M37ipr1wLQMzFQezE24muQYAgk1/tXooZJQqsartACUqWVeuKINfYxkMkyUHrm32xaeksHF8qalGnWzWlzcPOt0RDVAAqHgtpXqPkEpBffdMHqZBN7W2GBWORTWtH2DrbF5KwLUZJos2ml6EE4stnFxqIYo49uwo4JL5MlybIQwFDErg2gxGZoNBJFBvRwhiiZgrMbSFVQ9rjQCMEsxUXEyVHVgGBaHqflaWZ4DF1GeyazqHgmumhaTeuH4mGMeECwPjDvYY24rtoKImVdXjS610XlYAMFhHTGMY9Tn1bxYStVaImYoD7suujnmWRnNssYlG3PmbSkxJGnhHieuMqWq3lAxTZQdhJLBc9RHFSvF6O2jhvQgjDtc2wBhB249hmxRTJRurjQBcAoxQECY3TYNPkksv5KBU2YWMIgpn6IxeSj0rrHfLCOl4OOq/cylR011UFdyaKBcsFFwTq41gIDWNEEWLL+VN+KHApfNlfOj2S9D2IuRcEwRKCKy3WrvRYm/YuSirN5JRTZdd1m6dAwMg1f1mWWzTQmHJvb5c0/cLlN2JZRJ4gaKiMwpMT7igQF9XZ7PUynF1e4wxxthO9M5Oz88W0jWAZXb3cHrXANvtbpAU0D91z/NoRBGYtjGS+jipVmNmA+yRKCG4eL4Mx2ZpQb9r25q1BCnBDLKuKFbn2iCNhQYjmCo5qW9977XZNIjSodE/qllxPTeec00UXFMlePo8ssyBQVo5zXZ/nB92hqMW7vOuAZOpuJhzDfyrO6/EpbsrOHq6gVeP1/Clbx+CpwU+twoCpMJhBB1dn/WKMgSKzZBzTFCqrMUSVXcv4Mi7Ji7fO4nF1TYoBX7i5n2Ynczhvn8+gsdfWBy6LvSCGF4Qw7UNFHMm9uwoKHutiOPw6SYoJZiddECI+uwZxdgm83WMcYI9xrZiFCrqnW/bM3SB30sX84IoFQJJksX15oqzYlVBqKg6BqPIuWaXr/HleyfSROPBJ0/gO88uIOZS03iUQmOyv0reRN2LhtKLw0ggjEJYBk2p7SrRJyN5PG8F9VYIP+Q6sZfKT9MxMUVIOnMmt8gNj2IOW3cSKnkTTX0+wODrToiqHIeRopglybYpJGIhu2gyRM/PR7FApWCj4JqIuYDrmDANgotsA0EYo+VFaHodulo5b8GxGNoBh2Mx3HXTPhzYtfGCK1vwca2NFzSEqKJCcu+q9ZRiTFCiZuy72hhJV1sLq1EK1Nshnjm4smHymr3XC64J12KoNtVnl+iw2SbFRNFBwVHXqcI6wVgA+OstUCvPl0fuGGOM8frCsALfNfsnsVzzUW2EKOUtEKpUxHvHUc6Gu8GV+ybxo9fP4e5vHdKJNbQmCksp31l7pGyBIOea2DHh4sRyu69BYDLtEkIJdk64WFj11i1gEyim1lTRhgBBTet4BJGK2+IMFwZSAiaj6WhaklAKCXh+jDgWKm7qhD7LHOgtbNQaPhre9jcCGq0I5QLBgYtK+Nn3XIE90znEscC+nSXs2VHEky8v4cVj1S0LxTCaqLMTfOAd+/DdF5dwZKEx1P87BQEiLnD0dAMCytYzYa1xIVFvhbBMikrRAhcStmXAYBTLdR+uzSAl4AXDGXFeEMMPYlgGgWFYAJRCvWVRUEq7jm1sk/n6xTjBfoNgFIXO7cJ6QgvX7J9M1b57O26X753oq6pOFGwsVrvnnjbzLK63IsxUHHzhwVdwes0b2OX7pTtL2DHp4qvfOYIg5MrqSQfOYs6EYxuorqMumSDiAmvNThWY9zzhif6/QQ/+7JzOSCBKbMsPOSglcB31VXZsA45toNEOUW9FI/lLJ2BUU6MJgckI/IhjpRYg75potKM+7+vkWC2DYq0ewjAI5qbzuGrfJO5//ChyNkPDixEnCtm6MpIIyNxw2YyywmIqMTQ1zd62DNiWgZyjLMhiLhDHAiHdvFhHtuDjhbEqnPRc5GROXQLYM1vAh991Ce7T927M1QyblMBkyUajHSGMuf68CLiUsAwK06BYrvkgAL740EFwsX43eaDavsngOqqgsVIPYDKKHZO5LgZAEoxPrbRw98Ovbkqtf4wxxhhju7De7PRyzcftb74Izx1ew+k1D2HEB4otnS13g6v2T+IbTx4Ho8rnmlLS1c1NtrtU8/CJzz7VtR4p5ixQgoENgqI+jnpb2TjWmuFA/RBCVEJvMAbKGOJYoB1wtIJY+W9r4dYzgYpJjtJe6fV0lhJhLLBS9zFVcmBbrIs58Nyh1bSwsVr30TgL9ow5h4FRig/ddjHecd1FmJosYG2tIzJKCcFdN+3DqZXnUG1uXguH6gWVZarxsCv2TeLH3roH/+1vnsDJ5Tb8dUbCpFSiZckMeBhyNLWyeDIWEEYCfsj1vUnRbIdo+xHyjqmV8dvrqsATAqw1IzTaESaKDlzbwM6pXNqIyDL1tmqTeS7X9WNsHuME+w2A7VLo3AwGUVFbfrxux+09N+7uo4u5jomJgkC1GW5Zb3Kp6qPphSjnbUhDVdKPLDTwF/e/gF+64woAwENPn4JlUhRcE5SomZp6O4IXxCOLhG3o/UwVLSl5/hGJVImTEEVN5kKOFHi7uulSwvNj5LXKtB8oz+TNVMiTCn+lYKcLCt5SQdphDI7FEIS86zNwTIpywU5VywFFV3/kh6fg+TG85MSkBNfnC6iqu20xXLV/Ent3FjE3W8ChEzWUC90dA9ticGyG6XIe779lP4p5a0sBpHckoJkpFlBd2aBEFVN++l2XqHt3TwWPP7eAV0/U0fQivHy8Bj/kyDkG4pZQFXIt3JKzDSzXfAQhT227kms4rJu8HjXSYFQXMPT1t7oXnoZBEbUElqq+9nI9c2rlGGOMMcaoGMWO85lDq/jY//YW1NocJ07XkbNZ3/P7bLkb7NlRxK6pPI4vtVApWAO3O1GwtHp493pkrRGAEmCiYKGh1Z6zxQEA6XrKsZhylYBKGGMuFA29oJSmW16Eo6cbIAAKrolAa3oEkYpABlWaIk0vSsVLN4Pl2uAuuoCaQeZCYq0RwLEZHMtImQPJ/PvJxSa2sNuR4PkcOYeglLMGxmwhJWzbwPWXz+DRZxbQXqcjDKilhJrTpyCAdpYBAIkg4Gi0QhiU4kO3XoxPfu25dRPs7DahGQ6Jdo+aWe90/YOIa0EzjmLOhmlQlYznLK04P5hVKSQwN53DzVfvRM4x8Lf/8BIoJSjlrdRtpa0T7a0Uks7Hun6MzWGcYL/OsZ0KnZtFlooqpMQnPvvUugH5oadPIuYS+R66WKlgwzTZGdlkBKHACg8ghEz9kv2Q4+++8RIMg6HZVlQ2y2Ra3VE9RIWQ8MPh1Kn16Oq9f5OZQ+/14RZSqXP2JkujQEhgueaj0Q5RKdip6jVZ7+B6YJkUMxNu1+cyXXaw1ggwXXbw/nfsR941sbDSwlojgB9yfO+FJXjJfWVQtZhYbEJKNW8mND1cEEWtK+WUNUk7iDE/U0gXWx/+0UvxJ5/9/pCRAgMfuvXiM75HswWf5w6t4nsvLiqvU460837XTfvSuebPffMVnFhupcUOSghMQ92XtqkWVcnPEqojb5sM0xVnpG7yetTIpGMtpRxYJIlj5VkvpBJvG4StVsTHGGOMMTbCqLPTx043cf1VuzBVMAfqYJwtd4ONtmubFCBk3fVI3jXxC3dcPlDbI9s8yLsGhFTjOis1H1PlTgxo+3HKTgtjgR2TOUSxQNuPUGtFYAZFMW+hkDNxes1DFIt0H4MK7b0jZ3xIdkwTkVGoOe3pcr4rjrY91TzYSnJNhrDweiEBeGGM3AB7yecPr+JzD76KE0vNjencyX6h5pUdy4AXxGi0o65RuC8/cggGo7hy3yRuvmYn7n306IbbNCgFpcqZhWlGWr0VpkXtXr2AWjPAZMnGVNlBzlECq1Xtpz7oHF46VsOhUw3cfM1OTJZdLNU8PYJnIO+aKtGOOU6uRNg5mRu5kHQ+1/VjjI5xgv06xihV5nNFIx0lIFebAYDBdDHXNkAJsNYIEERiXbp1AkY7NCwhARELpWZNiPJh5BLHltppAAwiH2bGmsPSidRWZ6V63yWH/UFDzRKpruhk0cFaU81sZa2/kjngpIPNqOpmq+MXWKp6al6Ybm72mwsJP+RdqqmEKO/lWksVH/btLOHii8qdYknUfV8lYm6JyrY6BiUsw4XqGoSxgGlQ3HD5TLqf6y6dwUfvvBJfeeTQWfVuTAo++3aWcMfb9w6kVj1/eBWf/NpzqLXCjoq6VHT/IOIwDYr3vm0Prtw3AQmCtqdm7r/40EHYFhu5m7weNdIyGQxGEXHRp6yedF9myg5qzXDbqZVjjDHGGBth1NnpUajHZ8u7d73t3nDZDO559MiGBQJCSGrllUWvjsXhhXpKMU62lzpQaE2SSFOSE7eLJEFMflcp2Fit+xBSDlTvBvpjepLsJq9PmVnamosLCd+P8f5b9qfXMVkXblXEezPSLkIAT754Gn7IMbcjwkTe6IqxgLIy6u01U5rIlcnUxzsRsPWCOL1OSsxMseKWa36aXF62u4L7Hz+6oS1n3jVgm0wV2yVAQXQBJE7ZY0mBZ8+OIqbKDo4vtbBj0kXBNTE3W0DZi1Gte2i0Y0xVbLzjTRfhH793DCv1AID63B966iQci4ExilozRM4R8MIYjKrC/Y6JHN5/y76R1uEX0rp+jPVxQSTYQgj86Z/+Kf7+7/8ejUYDN954I37rt34Lu3fvHvj6tbU1/N7v/R4efvhhEELwvve9D//+3/97uK6bvua+++7Dn/zJn+D48eM4cOAAfuM3fgM33XTTwO195Stfwcc//nF84xvfwPz8/Fk5x/OB7VboPBOMEpAhgUpReTsPoou1A465mTwW1zyEsQDnStl50POe0cGWGkSrg0rReZ9Ex44qSxMq5y0s1zwIoRPWAQ/rUWJNNtHfCATA3p1F/OQ79uMz974AkxGsNUNlPUGUImrMu99BKVAuWGi2o7TDn4xej9rEFkL20aOAwZ3QQfdVspgwKNUFAIlSwYKXmTUKY5Feh3v++QieeHEJP3nLftwykcdV+ydxyXz5nM0TDRL6ElLia/98OPXAZIlqLNGBV1O6njuyhjsyXZVnDq6Ai811kzeiRpqMQkJV0CkloIQgjDianurqfOjWA7j3saPbTq0cY4wxxtgIo85OF0cs8J0td4Nh283OIA/CZhlAg9Y3SWE+ewbJ75JkSK1jhNLfsA1MlhzUWiHCaDRLyWHio5yLdNZbWAzFjDXYQ0+dwMvHa2c8Az4qHnj8OL79gwXYloHZCRfNdpgWXkxGlThbTytdSqnFUhVTi3MJgxE0PbXGEaKTXDNKMFG0YWuR0HsePYIP3XYAecdE248g5PCiAKUEjm1gKnPdJQA/4tgzW+gq8GRZEadXPTSdCOWCBddi4AUHlhXjjhtVzlIuWGj5EYJQpJ+Noqxzfc4RKCEwGMF0xcUt1+zAVNlBEHM4Jlu3iHEhrevHWB8XhA/2n/3Zn+Fv//Zv8Z//83/G3/3d30EIgV/+5V9GGA4WPvi1X/s1HDlyBH/xF3+BP/7jP8ZDDz2E3/7t307//uijj+LjH/84fvZnfxZ33303brrpJvzKr/wKXn311b5tnThxAr/7u797tk7tvCLrATwIhkHBuTwnNNJRvDFBCC6dK8OgBNVmgDDiag414qg2FW3nA7dejD07irBNhpmKg+mKA5P1PmTW8W+W2ipqIP2KgBH1QK+3QtgWQzlvgzECxzKGVpVHwajvlQDece0ulPMWDKb8Ey2DdgXSbi0T5Xvd9mNEPYJmlHTsKwbBYCStYtPMeWfVxwd1QgfdV2mXv6NlBpNRzE64uqqv/lzKm5gsO7BNhuNLLXzm3ufx9MtL6TGcT+/Go6cbOLHcUvN0tN+mxaBKsf34UqvL63or3u9JsHb0oiC511tehOWqD9ti+Kl37sfu2QKCkCvmRtjxrL5q/9TA92e/K1uhVo4xxhhjbISO33Tc51aRFPh2Tuawd+foBb7teP4LKXF4oY5nDq7g8EI97XL2bncrz+z1MGh76agPujvLCfKOCaJ1S5LnN6MEDNCdza09u1VeSsC5SD+HpND6/OFVfPWRw+BcbrmDvVlIKZFzDTg2w+GFOo4uNiGEhEH1+mFAMpmoostEFJUAN1+9EzMVN73GEkpgNbE+yyaXEgS7ZwtwTAPGOidab4XwghiObWDHZA6TJQcF18TP/ugl+PWfeXMfeyJhRczP5BFEHKeW2zi53MJM2cHP/YvLkHNNfO3RIzi95qGYszAz6SJndxegIi7gBRyOZeD2t8zho3deiT07Szh4so4nX1jEc0fW4EfxUM/1C2ldP8b6OO8d7DAM8elPfxof+9jHcPvttwMA/vAP/xDvfOc78fWvfx133XVX1+u///3v4/HHH8e9996Liy++GADwu7/7u/jlX/5l/Pqv/zp27NiBT37yk/ixH/sx/MIv/AIA4Dd+4zfw/e9/H3/5l3/ZlUwLIfDxj38cV199NR599NFzc8LnEGdLoXMrWK9j5/kRVuoBCAG++8KistYQyk+ZEtJHF6MA/vKBF9EOOPKOgUrJxkrVT5NQplWfBxZotXhU759iLsGo1NZaiiYURhyxkLhsdwUfuu0Anj+8hideWsTpVU95FI947ok35UzZwVLNTyncUvTHFkKAqZLTdb1KOeUPHYve9ForWQuJeEB7XUhVQUusLLIwmKr+UkpgEKWsnZ63pqwN64QOuq/ShYOeLSeZ33lBDGh6l2MZoISkFfxaM8Tn/+ll/NuPXDvi1Tx7aLaj1Nt0YGzTv4y52FQ3elg3uVd8zQtiCCG1BgDBs4dWcefb9qBYsAHKAMExN51PF55ni1o5xhhjjLEetmt2ejtVkIeJPt35tj3IuWbXPrZbXG3Q9tIudcRBCGAaqlie7CMWErt3FJCzGU6veWj7MYSUiISEZVLkbFNToUe/BpSqNYGUamQr75rp55BQi6NYqXJtwmDkzKCPxzYZ8o6BlrYDk5r+3evqkSBJrBkjcC0Dt18/h3orxCe/9hzytgHGaN+6NmEetL0I73v7XvzF/S/Ar3M9XgcAHTYh0yNs2ZnrMBbYPVvA26/eOfQ+HMaKAIBPfe05FPMWJgo2mr6K55Wig0JOYLUepL7lALBS9/Hlbx/GIz88BUoJ/JBDCHVcc7MF/NgN87hq36Rei3X2fyGt68dYH+c9wX7hhRfQarW66NulUglXXXUVvvvd7/Yl2N/73vcwMzOTJtcA8Na3vhWEEDzxxBO444478OSTT+I//If/0PW+t73tbfj617/e9bs///M/RxRF+Df/5t+8LhPss6XQuRUMC8itdoS1pppVmSjYyLtKDKXpRTAYxbuvn8NV+yfTwCukhOsYuPXaXXjipSWl4sglbIvBD3la8aQEqX1SQkciSLrXg4+RC4BDglIAUll8FXIqQO3fVcb+XWXc8fa9eOipE3jg8aNYrvkbzvik508Jbrh8Bg88fgxM24ZIohN9nZFKLfaRFBaS65XMRmfVrwGVjBOSzCb1208BqnAwKE4IoShYhZwJk1E1g6Tts2KuZtyHLZTWW0wEkbKwSjrvYSyUEAlUgM0GhEgHgsOn6jh8qo49s9t3H25l4VbImTAMAkSdIkEX9OVNrluCM1lsXrlvElJKfOreF2AZFDnHhGMzcK465X/19ZfwL++8Erdcvwtra62+jsvZolaOMcYYY6yHMy3wbacK8jDRp8MLDfzxF34Ix2JpsT7Zx3aKqw2LATnbUIKYEsg7BiSASNs02SbFrdfuwlTZQdOLkHcMfOWRw1iueZgoOopSTAhMmoigauVxRsC5zHTFAeiGQrL+YYxgzw5FcXYdA88cXEG9HWJhpa2ERv3t97weBD1+nhbbDUbTuXGRqoAPhxRqvSYMibaecbdNNjC5BrqTy307S7jjrXvwv77xsnYw0bPKBkUsJKRUa6MoFqi3QoSRmrl+7wif+6ARs8MLdbx4rIpy3kKxaMN1DDS9GC29lp0sWfBDjpmKi1dP1NP3LdfU+pcQoJg3YRkMRxaa+Juvv4SP3HYxLt87gZxjwjaVf/aFtK4fY32c9wR7YWEBALBr166u38/OzqZ/y+L06dN9r7UsC5VKBadOnUK9Xke73cbOnTvX3d4PfvADfPrTn8bnP/95nD59ertO54LC2VLo3Cp6A3LLj9H2VTI5Vbbh2ippsUyGCS3WkJ137QvIlMCxmEqYQw5CCLxAqXbmXQPlgq3PNUoT4VGqtkLbLuyYdPHh27oVrF88sqatPTjKeQtrjdH8G02DYqKoBCmElCBSz4MDWqxNQkD9rqhVN5Pr9blvvqLUufW20rlqmXTeSVeFU3WPO+c6aJ5HSJVI1/S8ezFnoeVFiLmA78cQFhu6UNpoMSEB5PRiIgy5pukBZT0H5gcxaq2wSwH0M/c8j4/cfnFfp2Er9+ZWF257dhQxN53HS8dqqpOsuwEJYqEUXjfqRm9msSmkxL2PHQUXAtOVjoo7o0gFS772ncO46c3DtSEGBfsxxhhjjLONrRb4tlMFeZjoExdqZCbmqnC8YyrXt4/tZAANiwF7dxTBDIpq3Ue9GYIxgomCBRCCex49ksaoSsHCcs1HwbW0SChJY3335SRgtNP5JSDaAlTCNpWl5k/csg+zEzncm4mDXEp4QazmQjcQZ7EMCgKJSGDLIq+MdmjcSefe0l38IBJpcj3sUKTMCrsCf/X1l/ALP37ZppLLmYqLnGMiZ6sucKKjgiS51wupajNU9HxGcN+jR0CBTX/+CXU7FhJr9QA510QxZ8K1DTTaIWQoAXBcNl/B0pqHZmZdmpxvvRnBsQTKeQtNP8Y/Pnkcu3cWEcYClkGRd1UCftdNe/EX918Y6/oxhuO8J9ie5wFQSXIWtm2jVqsNfH3va5PXB0EA3/eHbi8IVKWo3W7jYx/7GD72sY9h375925ZgD5uJOJ940yXT+JeM4mvfOYxTKy20/RgGI9gzW8BdN+/DVfvPjEaaKGQm/x3leK6+eApHFhp45XgVX/n2IeQcA7bVfSsqBWsDp1fbOLHcgufH+Cvdzc27ne736VUPEkp1u1K00GpHqDZDNNsxOJdwLIY9s0XUmgH8iKuqsBxMzc7+3rEY/l+/9COwWKdKKqTEfY8dRRByTBTtlOo00nWiFIxRRZHyY3CddCbBRUiZzhBVSk56LxFGUmVwrbcFRhW9W2ivr955colEzE11xBPBt96KsZCASVUFN4oDOJaB+dkCPnDrARRyJgiAphfj2FITe3d2L5qy99XJlZYSBiPArslc6hNZb4YA1KxVKW/BdQz4AcdKPaHJk1Qh9PSah/+hOw1Ei3/smspv+h597tBq330SxwInllr4qwdexEfvvHLd7b3/HQfwf3z5GdRaoWJC6Ns6WWSU8hZ+8pb9A6vn2Xu70Y5QzJl9160Xh07VcXq1rfzXe2bFku/AqZU2Dp6oYbZsj3wd3kjY7DPojYYL4fpciLHxbONCuO7nCpfMVwb+ftA16I2jaVHRYrBMimojxH2PHcXVF0+NlCQMeoZKTf0VUoljcSHBuYDds4+P/W9v2fQzez0MigEH5sooFV388KVFVJsBlqoe7n/0SF+MWlj14AcxXJuBEAbbpFoIjfccjy7+yg7LSmj22UXTefz0uy4BAHzm3ue79tH2VTd1I2sux6T46XdfivsfPYLlmp/+fr2cnKSVgE5BXwhF7y7nLUSxRBSr9VLeNRHGQfq69Q5HSoAyFQeDkOP+x4/hrlv24S/vfQG1Zth1/VpeDNdiXfG5XLRhMlWsEAJaY0atiShB6iFOAEyVbBgGHXmt0Ity0dbsAgFmMrT9CH4Yo+AYmCzZaHkUcSTwzKFVCAlMFi2s1MK+8/dDjiDykHMMLK+1cXqtjfmZAiIuUG+HsE2Gy/ZO4v/2k1fjy98+hBNLzW1f158tvJGeicAFkGA7jgNAzWInPwNAEARdquDZ1w8SPwuCALlcDrZtp9vr/Xuyvd/7vd/D/v378bM/+7Pbdh6UEkxM5Ldte9uJWybyuOnN8zh4ooa6tlw6MFfuW9BvFUJILNaCTW17arIAUAZKj8C1+5MLQFUuvYBDEIr7v3sMQSRSn2GlKh6nD/Z2EKNSsjFRYigXLCxWfcxO5PCvP3wdLpmv4IevLuPTX30Wh07W+nwcCdCVXFMCWBZDwxO4ZHenM/jKsSpOr3koFSyYBoNpjlbZNQwC22KYmshjeiKHcLkFSIGYy9Sv2jYZKKU4MF/Bm6/YCUoJnn55CZ++5wV4IdeCZCSdT0+uDx8iNxlnouig5DoB18Iqcayqux/9iatBCMHn/+llnFhsIuYCBqOYmy3gwz96Ka67tGOvdctEHrmCjb+573mcXmkrka1YYHYyj/e94wB2TOZRyJn463ufx+GFOiglqOkAZ7DOnDxjBFGs5o8kgKmyk4qJ/eUDL+Jff/i6rv0OgxAS93/36a77BFBVc9c2sFIPcP93j+GmN88PvT9vmcijUHTwma89i6MLjZQuzxjFnp1FfPSuqzc8lqnJwobHmuDQ6RaE1LPpQ78D6rt1ye7KyNt9I6JU6o8XY3Rwvq7PhRwbzwXG92X3NeiNo32vzVs4veZhrRWP9Mwb9Az1Q46YCzCqKMnKL5poZer+fWzmmT0KBm3vuit2QAiJ/88n/3lgjCKE4GQQo9aKUMxZIIRisuRgac1TxfQMhFCim9MVB4wqp5G5mTz+v//6naCUDNyHmaeoNoKutcEgRFzg5IqHD737Mnzl4VdxYqkFYP1EmBBVRBOiI0wmoa5zvR2p+C4B6EbCRNFGtRFsSBFXNqQSa/UQlaL6zHbNlPD/+Jm3pGsUL1AU7P1z5b41Srmcw+6dh3D4ZB1+GKdrDwAIMnorhAANL8ZFM/mR1wq9SPd1qg5Xi64BQDvgMGKBiEscmC/j1EobxbwJrsf3GAWk7HaakRJoeWp9+/yRKq48MNN1HJwAF++ZxH/86CwWV9tYawTbvq4/m3ijPBPPe4Kd0L0XFxexZ8+e9PeLi4u4/PLL+16/c+dO/OM//mPX78IwRLVaxezsLCqVCnK5HBYXF7tes7i4iB07dgAAvvCFL8CyLLzlLW8BAHCuLBHuuusu/Oqv/ip+9Vd/ddPnIYREvd7e9PvOJaYKJqYKin5cq23Psb5wdA33PnoUx3QysqnOo+CgBPDDeGBHMIzU3xeWGji2UEfOYfohpDyJw0iAUAIKZWHk+VG6nVLORK3ho9n0Uau1sWc6h59/z2X4o88+BS6Bthel88u9yXaloPwjT5yup9cLAE6criOMOFyH6cRL6sro+lGCEgI/jPFX9z6HIOQIQyW6kXcN2JaiLoV6zvqOG3ejVlPJ6t898II6Tr2NRKGbUpVAZ487KQgOosBn57X6PgIBQEqYJoVjMrxydK2ruu46aib+0Ika/uSz3++q6j53aLWvSh7HAodO1nBquYmP3nkl9u/I44637sZn7n0ep1fbCEJlO8WlTK02oA5BzafFAourngrYTF23T37ph/ilO6/A/l3rK8seOlXvu0+yyNkMxxbqeOqFBezfNZxSvWc6h//3L/4IDp2q49UTdRBIXDxXxj69/7W11tD3bhojfQeIWqjUPWXpMkYXGKMoldzx9RmCrVyfUsndti7DayE2ng2M78vB16A3jvaCUPXc642/QzHgGRrFXLO+JKRMYoZM97fpfZwBstfglePVToziEmEcq5lpSlTRnilRtJYfwbEMWCbFZNlGrRlChGr8igsJ06CoFJQ7R6MdoeAY+OA7D6DR8IbHwRGZ3lwA3/jeUTz+7CnM7yjAC2KsNYK+dVIfCxDAdMWBlMkYWIQ17QXd9VopUW0GHWbBul8NosfdBOrtCLZBceJ0HddePIV/+5FrBzIPeuPzHTfuxv/86rMIQg7GSKpO3vl8lGNIdg056lqhF3fcqNY6y1W/r7vu2AbedcM8VuvHUSlaqDWCrnNUjZBucV4pgX/87jE88cIi3nHtLtx8zc6uBJpSAttkmJtyYZts29b1Zwuvh2fiZmLjeU+wr7jiChQKBTz22GNpgl2v1/Hcc8/h537u5/pef+ONN+IP/uAPcOTIEezduxcA8PjjjwMAbrjhBhBCcP311+Pxxx/HRz7ykfR9jz32GH7kR34EAPrEzp5++ml8/OMfx//8n/8Tl1122ZbPZZjlw3ZgO9U2twvPH1Z03CASyDkMOWYgjgWOLjbx6Xuf33COam46jx16nqbC+udpmp6ap3EthphL5BhNH4xJ0kgzalScS0h9RzNGEfMYtUaAeEZ9LrtnCpibKeDIQkNTqAFT062hvZstk8G2GIKII2ezrs80ZzPVbY2EFvXSQl4RX7cSG8cCJqNKRdM10fIi1JphOrNjWwzz2nPxst0VLc5Sx8mVFnKOnmvO0MEICCiR4Lr7ndCsGaUAVeeRPZxBgTA7z20YFDMVB41WhG8+eRxezyybaTCUC2oe+CuPHMIl82UAwFceOTTSay/bXcEvvOdyfPabr+CY31Rde6mq9jnHUIuHzEFSqs4niNS1P7rQwB9+9inMzxTWnY2r6ep89j7JYtA9sR72zhaxNyO8JriEGHWVMiJG+Q7smS3gwFwZtVr7rD5jXuvgXIyvzzo4n9fnjfy5jO/L7mvQG0d7EUVKqKo3/g7DoGcoyRRuhZRa8LSjxrzZfWwHOBdpjGKxUpWOMvs2DYqcY6DeClFvRiAFAsOgoITANChc28Cb9k/i2FIT1WaAIBSImcScnhlP1g7D4mCo1xGjwgs4ji40YDCKvK1G2wjpjHZlRdeAhDWndHEScVndtE5BoIobQgDhCLPdSRE+cTixTdr1me2e6TAFhsXny3ZX8KNvmcPd3zrUcZfRL2O0M+MNdNaQm10rZPf1C5k5fO6pOfyLpnN425U7wQjF0lobMxM5TJddxEJiuepDcKFm6YnS50lEexOsNQJ89ZHD+IfvHsOP/8g83n7NrvR4o0jNldumWk9ZBuuzzrvQ8EZ5Jp73BNuyLPzcz/0c/uAP/gCTk5OYm5vDf//v/x07d+7Ej//4j4NzjtXVVRSLRTiOg+uuuw7XX389/t2/+3f47d/+bbTbbfzWb/0WfuqnfirtUH/0ox/Fr/zKr+Cqq67Crbfeii984Qt4/vnn8V/+y38BgDQxT5CIn1100UWoVCrn9PxHwXaqbW4XOsIiHNMVR1GCJFI16WozxD2PHsHleyeGFgJGFWFzHWOgLVQ69qOfJdnK3nrew39x/wvaEkECVCWsXM8El3LmUBXGQeqN5bylZoqFHB68CEHeVV+1MOSq4jidw0o9wFTZwS/ecTn29vh+Jsl3vmCi6UUIYwFj0HWUSkSEUtIJ1hsImKTJNVGzSJxLBKGAhMRKTXkwRz0WEFmPycQDemG1rbw8e46r97X7dpaUAMwdl+OP/v5pEBCtAkohhOifIe+ZK5cAGCEbCuC8Fu0rRvkO3HXzvtcE7WuMMcYYYyNstwrywGcoU4JVYSTAaEdgc6v72C4UciaElGrNAAKWCKtIaMcN5Y+8Y9JFrRWmQmm7ZzvF5Y2aLcPiYMK0G2Tb2YtEEAyRSkgLrgkv5JC6+5tEZzXLTFKR1mQffsDTWWcOpTdDdeVcSoxcqE5yYQL1uVUK9pY+s6v2T+IbTx4Ho2qtxIXEWt3v3Hs9a8gzWSv0Cv8t1Tw88eISvvqdQxASaHkcjXYdOyYdFHM2aEXRyBvNQFH/DdU1yRYjEvghx1e+cwQ/PLSKD952MaZKaqxWCCVgF0Q8tUMzXwOJ9usd5z3BBoBf+7VfQxzH+E//6T/B933ceOON+NSnPgXTNHH8+HG8+93vxu///u/jgx/8IAgh+NM//VP8zu/8Dn7xF38Rtm3jjjvuwG/+5m+m23vHO96B//pf/yv+7M/+DH/4h3+ISy65BH/+53/eZe31WsGoaptno8O93jaPnm6oBMtNZk2yM7/9CdYwjKK+LKTst4XSFlxhxCEhuyygNvIe/qU7rsDnvvkKji029RyMolzlHRO+tmoYpMI4KJBbFkMpZ6GmBVWgr4TBCFzbQMuLIKXE0pqvbbWSTq9S3W56kVIM7dlXNkgmSXwsZFdAVtcamCjacGwDYcTBhaJfRbHoCKAMecYyXaQQgFK5lBJhLNHWlWpFQ1PbBjoek4kHNOcShjuYKtP7WkAFESGAdhClCup9wl5Aeh2zdxVlFEXHWLdw81q1r9joO3ChCpaMMcYYY2wWZ8PdpPcZyn2Z+gdbTCVVQsrzrrQ8P1tIadGMSkiQVNWbAYiUMxd+4+eux8ml1sC110auEcPioOo8664zHWzr2YuYC500S61gTVOb0f9/e3ce31SV/4//de/N3qQrLcWWsknLXspSFhHEDUU+Iy6fEUdlcUFn5gMzLqg4is6MjgsiKA74cx93/InLjAoq4zYjq+zITmmh0AW6Z7+593z/uMklaZK2aZMmhffz8fAzH5L05uY0Pee8z/I+dU0uuL2D+r4g2PcedqdvC57SZ+F5ZZbY09JERBiMKbliOHCYVHheu35nvjI5VmWFyTsAo9UKED3KsaKSd6JCpxUi6iuE6yP7fkf7Smu9p854YDZqlTwBHIfaJicqahxwumWYDRoYdQJ0aSbYHCJkSZl00Ot4pFkMSnIz7zFiPkcrmrB01U4UD8rCxSNyYfaePEOBdmJJiABbEAQsWLAACxYsCHouNzcXBw4cCHgsIyMDL7zwQovXnD59OqZPn96m9x8zZkzQeySCcMdPNJ8lZt6jfiKd4W4pgG5t1tw3wxouO2yoACuc1o76CHsslHf5NACY9Jo2N6ADe6fjkdmj8cOOE/hhx0nUW5WEeB6ZIdWsw6Th56GgV1rYew0VDOX3TEG91Y0Gq5JsQq/TwOnyeANs7w8zZfkzB847Wq1k7g5VRv6NZKpZh4xkw5mjrbwJy3geMGiVfdyA8r1wixI83uBapxVg1AshjxLjeWUZHZMZmMzgls+cl+1b1uUSZZxqcCAzxQiDXhM0qhvJbPG+0lq8/dUB5exJKEeKcDyCEq4w9f8ETsLzXOsDN4l2LF0k6DxrQsi5oqNnaIe7ZvM61Ob0YE0U36Oj/rPzJJxuJZu2MousBE2+I618wevJU7aA9k1mDKWVjW1qG8K1gwDUY0ItSVo0WMWg5GnN+Qa8nS4Jmam6gC1hvoF/0ZsIVKsVAMZQU++E23PmGC5ZXZcdnuDNZRPqVbKs5LrJzTJjUlFOi9cJ50BZHewuCQ6X5E2Oy0HgOYABoswg8EreHrf3jPK29BVa6yM377/z3t+tJUkLgQdqGpXkpR6Pkkj2vEwTLhuZA62Gx+cbypTM8BwHnUZAtxQjnG4PGm2imkdAZgwbf6nCtoOnMLHwPFwwtAf03r4YBdqJISECbBKaOkvcwjLc49VWvPblfkiyHNF5ki1VDgBanTU3m7TgecDu9EDUKH/wWr89pJEusWltVDZcg5yXZQY4Dk12t3rGZFsaUJ7jMLkoF5OG5+CH7Sfww86TqLe6UN/kwhcbyrD1wKmw1wjVkMsMWPHJbqSY9QHL2P3rM/+l2RqOgygpydpMxuAyCtVIZqYZ4XB6YPc2AJeMylUHCHyNqMvtXZolKImxDDoBdpcEUZS8AfSZbJWSJKvHf8l+e7z9yTJQ2+RCD50QNKrb1tliX0NjtYvKe3uXlLU1xwXPnTnWobWBm1h03DoLnWdNCDlXxGJQMVQdOjBBBi73Hq3Fv34qBZO9eUbkM30CSfauGLPo4HbLAe1be7YIhm0Hs8xo8K5way24Bs7ExQxQj66UPE4km3UBq/cYY9DwHGxOD9ySrBxvKbV05ebvo3yugNNPoPRjdBoeZpMOv76oX7t+b/6rQFMtOtgcIkSPDI83i7hvz7hLlCHILGRfoflklN0h4q2vD7bYRzZ6JwNC9d+NBi2yBB42pwf/M64X+uWmqN/LA8fqkJKkhyBwsDk8akBt0Gmg1wqwuzzqcWMA4BZlrPu5HJt+qcLFI3MxakCWMniA4EDbbNBCo+Ep0O4kFGAnMHWWuIVluA6XBzoNj26pxrAz3M2X07a27NyoE1qdNb9iTB7cogy71a1eW6vh1aAuFstxwzXIANrdgB4oq8NXW46fKQtN2wYomjfke0pqWvxdqbxrqXwzuQDAhdmPFK6R7N3DojYAvbtbAp5nOBNcG71Lu1OSdKhtdEJmDDyUgFUZrVWyvludInh4R88RPKssemScrnfAbNKpo7oyYxhZkImTp22oaXAiOUkXdra4tLIRx6utcIkedd+ZoFGC7NaODAGUkXGddwS+LQM3NBtMCCGJrzMGFRNh4FKWGT5fX6ps3eIAgeMBgXlXi3lXpHFKrpHmK7/askUwlHDt4IGyOnz0wxFYHU0RfQaTXgOnKMHtkWF1iOC991qQl4qRBZnISDbgs/8eRWWtDaInsvLxxXvN94cb9ZqA/eeRCrUK1GLSKdvpJKWvktfdjOsuOh92h1JGuVlmlFdbsaekJmgVhCQpGd9d3q2J3VLC97svH92z1f47B6B7uing+6nXCWi0uZCebEC3FANs3vPLZeab0eZhMWowtF83bDt4Gg6XUthNDhGf/fcoftpdgSnFeRjUO029N1+g7RYl6HUCkvQUaHcGCrATWGtJmxxOD2SZwdTGRFNA68vOaxqcqG10oluKocVZ8ze/3A/mbRQYU5bwuEVJTZJlNmpjshw3XGPZnga0rUvwW0rU5hPqdyV7l10H1mFKYyp7y0yvFWBzhG+NWgsWmz9vMmqx+vvDOHHargTxnLIXPN27xNwtSmpikt49LOiRbsK3208o+7G9+6I1AtSkdT7JSTrccrnSmPuPqIseGS5Rwql6B/TeDOzNR4AbbW7YXR7IDNAK/uUYnI0tVAIWk0Gjnn3e1oGbROhUEUIIISUnGlBRY0Nykg6SzJSkpd4cKOA4cN7B5ia7iF7ZloCVXx3pn4Sc0e+djqslGSs/2wO3KLdpTzTPcdDpBJhNWtQ1udAtxYCrJ/RBcpJO7Y+UVjai3uqCJCv9G63gO44z/HU5TulDSrI3sam3T9k93YgLhvTAoD7pHRocD7cKVKcVAK0AQeBRVecAzwFD+mZgX2ktln24Uw2mZcbgdEvQCjxSzDpojLy6ipDnOTi9x6ue+Txn+t1Wh9iupKt53S1ITzag/JQNPTJMSDJqYdQLsDpE2Bwi7C4J2ekmXDm2Fy4ekYsfd57ET7sr1MmK0w1OvPvNQeR1N+PKMb3QK/tMX0mSlRw7LrcEg06AyaCFRqBAO1YowE5grSVtUpJRcTDog/94gdDLaVtbdu5bghLuz00QOO+RAMqsuVuU1cAN8AaVAGZenp+wy3F9y30On2hA+SkrTHpNmwcoml/DF/TmZpmDfle+fc4Cd2Y5tKQkLlePqALQ6jL61oLF5s9PG9c7aP+VwCsjnya9BpeMyFEbrm+3lSs/5MtS4r0eLyjHl8myku/z4hE5anDtP6KeZNRCFCU02UVoNDyuGtsLk4pyAjOiO0QwX4I2P813XGkEzjtow9SlaRyUmfVI9kaRri0RjyQkhJD2arS5laOzjHzYpKUyY2ob6gtYIzmpIxIWkxYanocIZTm3LDcf6g6k0/LQaXiI3iNHaxpdMJt0Ae9rtYsQRRmSpBytxXEcNBwHD+SgIFunVfoiTrcyI85xDCa9BhkpBkwafh4mDc+JSp3fllWgvj5y876NYOBQXedQltPLMmRZq6ze83ZOZMbQYHPDoBMCfj++a1qM2nYlXfXfHlhRY4fZqEGKRQ+TQQOPxGABcJE32ZtRr8GU4jyMHdQd67aWY9vBU+qAybEqK/6/f/6Cgb3SMKU4D1lpRvU9JFl5f6cowaAVYDRooaVAO+oowE5gbUnaJAicd9lK8M+HGiFrrcLRaZXHRVGGQRf8vHL8wplZc4NeQJIpCQ6ncl1JVs5hDrWvuKOi0fH2n311uiXv/hQZKUkIGIkEwu/33Xu0Bqt/LMGpBuV4Lp7nkGbRI79nKk43OIMCW9HDoBE4WJJ00HozmmoFDg02MSZZrcMtLQ+11KrveSnKURoyA99sFzbPKduoBJ5D35zUsCPqep0GOq2AeqsbWw+eCkpEYjFq1eQqHDuTSZ1rNoOdatZBoxEgy0qyuia7sufL6fRADjEzTqIrEQLbRDySkBBCOiI5SQeNdzZT79273OQQIfkt1xJ4Dr8a31ut5yIJDiPhq2Odbk/AmdDhCDxg0PKorLHD483bwgC8tXY/bph8vnq/ZpMW8GYR9283NDwPt1+EzUHZtmYyaGGRlfPAM1IMmHnFAPTOjm6b09ajO01GLT7+4UhA38YlSkr5eycb6ppc4G1uuD2ymozV5VYmF5L9joHzXdOSpAvov5uNSgZxtyjB6mg9Ee8tUwrwyY9HcKreiUabG0a9Bv1ykjFxWA4y04zweM4cdpZi1uO6Sf0wYWgPfLX5GPYfq1evta+sDvuP1WFUQRYuGZkbcK+SxGCTzgTaNKMdXRRgJ7iWkjZdObYX1mwsi2iErLUKx5dd0SVKMHuXGPtf0+70gOM5GA1+y2KgLLlhGqWT3mh1R1zptyYaHe/mI5Q6DQ+nd19KTYMTKWYdLKbgitJ/gGLtpjJ88p+jauIJXz3UZBdRXm1Ft1QD0ix6NNndajIycEoCOINWUAdIGmxiyAo2WkFOW/ch9862ICfTjGNVTRAlGRolzTnAAI+3UczJNKN3tqVNSfdCjahbknQwGTSwOz2QvPvAmy8O5zlAoxHULJhMx+CRZWUp2gV9YPFbikaiLxEC247sNySEkM7W1va6b04KemQkoaSiEZJ3AJkBgLe/JXDKa/wHp9saHEZyVrN/HZti1qO+ydVigm+thodew6PeFtyfq661B9TLed0tyEwxoNQh+i+IA6CsTvMtYea82bR9q9KSjFrcMPl89O0R/S1dbT26kwNDRY0NOo0Ap1tSzsqWlN+Rr7/i9sjKPnmeB+PObKFrsLmg1fAw6jVB/W6e49T+e1WtHQ6XBJ5Dq5MF+0prsWZjGeqaXGDeDfpmoxYThvTAgF5pAJT3sbs8kPzy2HRPN+GCIdlotLtRVetQE9oyBmzZX40dh07jgmE9MLGwBwy6M314CrRjgwLsLqClYIkHIjqWqC0VTk6mGXanGPKaeu+seTQr/daE6ng7nB6UVjThtS/2Yc5VAzG4lY53qNlXpzc5hO+cxdpGF2wOEalmPfQhErX9UlqLT/5zFKJHDrlXmDGgpsGFNAvDVWN7ITPViBSLHpxGwIdfH8DJGlvYrNYyY0HHhmk6GOS0ZR8yz3H49UX98Mrne9FkFyH5jTTzHAeLSatm7/QfUVfOzJbVIzR0Gj7siHpedwt6ZplRWtmkdi5keAdmvMvOOO9m9cCj1jS4bmI/CqpiLBEC22jmQyCEkFiLZFCS5zllj29ZHSRZybjNeUeZRY8MmecwpE96xH21SFbAhapjtQKP2iYlq7iP76gwgVMGx0/XO0NezyMzWB1iQL183cS+eH71bogeGRpeCabBoObrgXfPtcPpgUbDR31VWqgBj5ZWgeq1PEbmZ+K7bSfQaHPDl3LWtzUNDGAc1IzrvPf3JvBnBgyYrGRY5znA7pKC+t2+/vuJ0zaAFwBZQk63pLDtWPP22GxS7rem0YW3vj6otse+c7WtDhEuUYIsMxwpr8cn/z0KlyghzaJT9/X7Am1RkvH99hPYvK8KF4/IQfHA7tD4LX9VA203JUOLBgqwu4hwwVKkxxK1Zdn5ry/qBwAhrzl1TB6+3HQsapV+a5o3Ci63hFPefTGMKZkRX/p0D3579WAM6pMR9jrNZ1+dLg9qGoMbDpco43SDAwatBkkmbUDW7E9+PKKcWcgryTuAwJlYX0ZQh0vC1oOncM8Nw6HTCkhLS0JeNxNKTjSEPXP8w+8O43i1VT02S6vhoTFqOyXIGdg7HXdMG4QvvKshPJJyLmPz75BvRN3mEJXj2byj8ByU+zUZNCEHV/y/cw6XsjfJV3BuUYIlSQeLUYtGuxsOl9RljtU6GyRKYNve1RGEENLZIh2UlGWGPSU10GsFSLKsBGfejoNey0PD89hztBaXF+ep9Wxb+mqR5CMJVcca9Bqcp9fA5VYSd3kkhhsv64+0JB3+8dUB1DQ6w64gl2QGjySjosam1suD+mTgmgv7KKv8PDI4bz9JI/DQaTVIMmhweXFPZKYYo74NqaUBj1B95DSzDuA4fPrfo0qeGO+6b4FX2hzRtxxe8jtiFb7tbUo/jYMyeOAWlfO1czNDZzznOQ59eiQjLS0JdXU2eDyhM79F1B5DWf2QatbDJUposrvx0y+VcIkSkk06JeO4Vtn6aHOKaPRbhWB3evD5+jKs312Jy0b3xNB+GQG/B/9kaBRotx8F2AmmPUuEIz2WqK1BebhrchzXrn0l7eHfKLjcEk43ONWs5TzPg8kMdpcHr325H7dfNTBsQNZ8P1ODzQ2ZKRW/7HdcFMd5z37kgFv8GsljVU04Ve9URp05ZdTyzDFbgUG2XiuowcD5uakAwg+Q7CutxZtr96O20aW8jleuKHpkNNrcSLfo4XRLMQ9y2vIdyutugcWoxbFqKzgoo7m89zO7RQkuUUJeljnk4Erz75zkURrAvO4WzJgyALkZxrADECR2EiWwjdV+Q0IIiab2DEr6soinmHXQaQW4vTOOPM+p/w5Vz0Y6gdKSlupYvU4DrVZAo9WNFJMOg/pk4IriPLz7zcGw12NMWbEoioHndl8xphd6dreo+4cZY9BpBeRlJ+OK0T2R3zO1zffcVv4DHia9Bkyj5BEqq2zCm2v3Y/YVA3DPDcPV/s2pBge+2nwcDpcIlygH9OEkWTlJRcNz8DQ7TcX/FBiB55CebADPAY12EdPG98bFI3KD+i2+Pr3dJSGnu4i0pPBhV3vaY8aYst3RrazI7JFhgtOlnHMuepTvmV4rICOFV74DMtQtjrVNLqz69jD+u6sCU8bk4fyclID3DAi0tYJ6lC0F2m1DAXYC6cg+yEiPJWpLQNWWWfNI9pW0h69RkHgZpxuc6n4hWWLgOAaeUyoeVytBqP9+JkBZluXLas1zHDSCElinJOm8Sb9kJPntM7faRXWZUEt1CwdAq+XhdEmtBgO+htru9KhnYarZM7yfsd7qRkaKIepBTriBnFav7y0zZQxCGcLlmF9G8BaC4lDfub45KchIN6OuzkYzk3GQKIFtLPYbEkJItLUnCPLPIg4gqI5rqZ6NdAIlnEjr2AzfUa1+HR7/d2Q4MxnRvF4e3DsdA/3uOcWix/AB2WhosIedvW2urZNN/gMeBp2SbNV/dZ3TLeHD7w7jkdmj0Ts7GTJjeG7VDnX1gd3pVCcLfBMtksygEXjwHINvizPPKafAKFvbBCQn6WDUa+AWlWXh5+ekBN1f8z69Tiuge5oRV47JC9lP7kh7XNfoQn2TG9ndTNBrlZUF9U0u9XlB4KDheUyf1AellU3Ysq9K7U+fOG3D61/sQ//cFFwxJg89MpICri15J7JcogSdVkCSQQOtRqBAuxUUYCeIeOyD7MhZwZHuK2kvs0kLmTFlJLTZc8reaaXiM7Uy0+a/n8ngzZTuay2Yd++vTiPAYtKCAUGJ2swmLfQaAaIgq6N/oWgEJRd3W4IBX0PN8VxAohH/0VS3R1ayfcqIWpDT3oGcY1VNaLK7kWbRq0vEzyxzE2DSa9Bkd7c4END8OxeuwYx3NutzRaIEttHeb0gIIbHQniDIP4t4e+rZjvTVfCKtY5Vl06GDa3++s7CB8G23RqOcntJWkfRRfP0ojcCjttEF2ZvF3Le6TpYZjldb8cOOE5hclBswQCLJ7EwiM+9EiyQps9b++9J9d24xamD0JsjlOK7Ftql5n15r4sEYcLzaGrZP35H22GzSwu2RcLyyCQzK8v9srQYNNhdcbgmih8EDCU6XB1dP6IMLhmTj6y3HsedorXqNQ+UNOFy+G8P7d8Olo3oizaIPeA9JVrZlur2BtplmtFtEAXYCSJR9kJFq676SjsjNMqujkeEwxmDQC2iyiWGDUP/9TDZvw8HkMwkseI5DcpKyb0UUpaBKLK+7BdkZJpRVNCmJwNiZo6P97y3FrIXdJbUpGGiyueFyK0urfUItO2+yi0g26aIS5HRkIMfXsUj2ZltvvswtGhnkEyGb9bkkUQLbaO83JISQWGhPEOTLIn6s2hr1eratA9KR1rG+4zU5MPVoLt9V/fs84wZ1B89xLbbdQ8/v1ubPE2kfxWoX4fHIcHmUZdGCdxsjfPfLK0HzDztOYtLwnMBkrZ4zy8M5bxlBUGayOW9CNoBDklGDJruIRrsIgeeh1fAQvVnQw50G07xPz3HK4EuqRYe6ptB9+o60x76fPXCsHjJjMLo8SE7SISPFAKfLg7omFySZYffRGowe1B3dUo34zWX5OF7dhDWbjqG0okn93W4/dBq7jtRg3JBsXDQ8ByZDYKjoH2jT0vHwQg/BkU4VyZKjc83xqia4PVKLr2FQzudubabNt7S9V7ZFOYaBKQ2HTiMgPdkQcMxCdropoBLzNU5JJi0MWg00wpml0j4WkxYukbUpGNhXWovPfjoKu8ujLktqfj3fT3skhlSLrsNBTvNKX6cVwHNKcJxq1ql7veUwlWTzZfY6rQCDXqN2Mjo62+lrWMtPWaHXCkg266DXCmrDuq+0tvWLkIj4vte+pXVuUYLMGNyihHqru1MDW9/fZ25mElyihEarGy5RGayiI7oIIYnAF8jYvFu7/IXtP/Acpo3v3e56VmYMpZWN2FNSg9LKRrWN3ldai+dW7cCLH+/Ga1/sw4sf78Zzq3aEbSsjqWN9x2vy3uAQ8CZy9bueUS9gcN+MVtvuvUfb1na3p49iNilJU0WPrOYI8sdBuf96q7K6LmCARKMEyzJj6u/Sd+yVN4k4tBoeqWY9MlMN4DkOjXY3Glppm9rbp+9Ie8xzHEYWZIIxph4rVtPgRG2jE7zAo3uGCT26mVDb6ETFaZv6cz2zLLhj2iDMnFKArDSj+rgkM/x3VwWe/WA7ftxxMmBG3/81dpcHtU1ONNhckCQW9HnPZTSDnQASZR9kIio50age8dDSeY1Wh4i+5yW3GoT6lrb/sP0E/rm+FB6PDItJC6030UhLs2W+xunD74/gxClrwHO+1U9t2YfuP0Lrf9yDT/OPyXHAiP6ZHQ5yOprQKpaznV11FYdPV17WHs1EOtG4l2jsNySEkFho72qbQX3aV8+Gmxke0icd3+84GfFqtLbWsaGO11SCUA4agYNGoyxbz80yY9mHO1tsuz9fX4pxw3NbLdv29FHyuluQatajyS6GDO4kxqDVKH1rq13EoD7paj8m1axDcpIOtY1OSIyBY2o2GaXfyZ9Z2WjUa5GZysHu8mDa+F44PydF7euUVjYGlGVH+vQdaY8zU4ww6jXw+B2H6nBJ8Egu9OiWhFSLDo1NSuDevGwH9EpDfs9UbDt4Cuu2lnuPLVP2sK/dfAwbfqnEpaNyUdQ/M2i5f1DWcTpHGwAF2AkhUfZBJiLm/TvmvUuVpDB/r1qBb/NMG89xmDwiF9npJrUSi+R4KLtTVEZpTQK0WqVRc7gkaAUeV7bys80DSd++oXA0AgejXoNBfToe5HR0ICeWy3jLKhMjm3V7nA3L2hMpsI3GfkNCCImV9gZBkdaz4ZZLH6+24lB5A7QCj26phogHpNtSx7Z0vKZLlGDUa3DV2F4or7a22nZX1NhQcqIBGeaW+7Dt6aPwHIdJhefh3W8OweM9Y9x3nxJTEuEmGbRqMrZQ/Zi0ZD0arG6Iouy9plKWvkRmPlqtAM4lITvNhN7ZyWHb/pEFmR3q07e3PTabtNDrBFg0PMBxZ7bwaXjIMsOpWge0Wh49uiVBp+W9x92e+Xme5zBqQBaGnZ+BDXsq8f32k+oWxgabG6t/KFEzjhf0TA36fauBtijBoBVgNGihPYcDbQqwE0Ci7INMRH3PS/Fm9WbQCjw4mamJKfyNGpgJo0Gj7qdui0grMY8sY/WPR2BziEhO0kGv8/756IAko5Lxe83GMgxsYZa1+QitxaSD1S7C3Wz5jYbnYDFp4ZEZeoY5+ipS0RjIidVsZ1MXXcURj+SEsUKBLSGEtE17g6C21rMtrepKYgxWh+g9jzl2A9IDe6fjlikFgUduaQT0zDpz3vOekpo2td2NNnerAXaoPop/rhcgdALZSUU5+HFXBcqrrZBk+UxZaXgkm7RwinJAHzro2FCJIcmggSlFg9omF5IMWpgMmqCy9e8jtdT2n25wwmLUos7qbnefvj3tsX8skWrWBb1vo11U35fnlPOwbS6PuizeR6cRMGl4DkYPyMJ3209g4y9VkLxLSKvqHHhr7QH06WFRjmTLMgfdhyQx2CQPnN5A2+Sd0T7XUICdACjBT3i9sy3IyTTjWFUTREmGhuchCAAY4JHP7JXZuv8Udh6uiXjmsK2V2L7SWnz0wxGUVSp7ZlyiE1oNj5QkHQx6TZsbtVAjtGkWPWoanZD91sAnJ+ngkZk6StzW331LS5WjNZATi9lOSxdcxdHVl7UTQghpv1gOSra0XFr2HhflkWQ1o7O/aA1I7yutxZqNZahrcoHJyl69FLMOU/2OmWrLwL1GUJZat8a/jyJJMhrtorr3lzFlf29uiAkHnuPw64v6qUls9VoBOq3SxwnXhw7Vj/Etdy8/ZYOp2b3595HasizeqNdAr+XVPr1Wy8PlltBoc0Ov5TEyPxN7j9ZGdbVYpLFEklELvU4Dq0OEyzuQ4c9k0OKqcb0xbnA21v1cjh2HT6vPHa1owspP92BI33RMGZ2HjBRD0P0EBNo6DZKTEqf/1hnOvSGFBEUJfkLzVZwpSTrwnHI+tSwzSN4lJxyAVIseKRZ9zBJi+UYqq2rtyj3xHHiOU5JINDrhdHkAKI2aJLGgRk2WGY5WKAlKGu1uZd+134y1Qa9BRrJBbZwYlNnySH/3rSU8iWZCK1/HYkjfDPTOTu5w49ArO/LEMfFGyQkJIYTEgjoYrwnupvP8mWRezYMiIDoD0v6Jyww6DdJSDLAYtahpdOGtrw+q/Yq2JH3rkZGEvjkprb6nr4/Cc8CpBqd3ebKSWo0xZVC7vsmJA2V1QT/rn8QWnLL3uLU+dPN+jIbn29RHasuy+Ca7G1cU56l9+oYmN5xuCWkWPUwGLb7YWNamxHSRiiSWYAwQeA6pZj1SzXrotTxCdeXSkw349cXn4/+uHYrzm/0e95TUYumHO/HP/x6F1RF6QEeSGGwOUU26pqwyiMrHTWg0g51AEmkfZCIZ2Dsdd0wbhC82lqH8lA0eSVYqXgZkpOhh1CuNSCxmDv1nKZOTdHCJTgBK4jENx8EjMzTY3DDoNSEbtb1Ha7F2y04cr2yER2LgecAtynC5JWT47Z0y6DXQ6wTUNDiRkWLArCsK0CuCwLWtS5VbW+Jd0CstKGFHZ3z/uuIqDkpOSAghJBZamhnWaXgIAgdRZEGBSjS2FUa6Oqu1tnva+N5tPge7oFcaUpJ0aLIrx6lKfrvneABWhwcffncYj8weHTIRbUf70AN7p2Pm5flY/WMJTjU4AZlBq+UDtsG1dVl8ZqoR99wwHMeqmmB3SbC7Jaz+9hAcMd5SFmk5KEv/eegsBjhcHticHnik4Kzh53VLwq1XDcSh8nqs3XQMFTXKpJPMGDburcK2Q6cwsfA8TBjaI+RqBt/xXtYmJ3QaASaDBgLP4Wzdok0BdoI5V/dBtpaF2b/COHyiAZ+vL4VJrzmzD9or2gmx/Gcptd4jHdweSd37JHAcRI8Ml9sTdP71vtJavPXVAbhEGSaDAJOgVKYuURlRrKl3ItmsC2iMkoxa3DD5fPTp0fpor3/ZRdIYhqt8D5TV4blVO+KWrCuRslm3BSUnJIQQEgstbekC4M3SDNhdHnAcF9UB6UizebfWdkeSpPVYVROaHCKSk7RotLnBMaiDCDyvJO46Xm3FDztOYHJRcGbyjvah95XW4stNx9BgdYMxZe93mkUfkMA2krbfdz+8wGHZ/78LTrfUKVvK2lsOJoPSr7Y53HC4g5eNA0D/3FT0y0nBrsM1+Obn46hrUhL1ukUZ634ux6ZfqnDxyFyMGpAJgQ8ehJBkJYeAw+2BUaeByaCFwOOsC7QpwCZx19YszL4Kw2oXwYGDNkTFBkR35tB/lpLjuIAjHXzVBmMMjTYlUPU1ameCXgndUg1KYjamVKbdUgw43aDMhLvcUocDyfYcbdG88k2UZF1daRUHJSckhBASC63NDJuNWlw0/DzsOVob9QHp9qzOilbbbbWLysksbkmdvfYFXkxWsoLLMvDDjpOYNDwnqn2D5v2gJJMWHo+M0w0uvO3XD2pP219W2YQT1VYkGYOTpyXSSSm+Y3GTk/Qw6CVYHSLcohQU/PIch+H9u2FI33Rs/KUK320/AYd3u2STQ8Rn/z2Kn3ZX4PLiPAzunRb6CDXp7A60KcAmcdWewK4zZw6bv5dRr0F6sgGNNrf3iAOlJsjOMOK6if3Ue1WDXrUyPVNjcByHlCQdnG4Prp3UF8kmXYcCyY4uVU60ZF1dZRVHV1zWTgghpGtoy6quy4vzoj4g3d4+VjTabrP39BTRL0+NrwfFGCB5Z7Trre6oBqPRXhbfvO1vsovwSDKMhtD90kTbUsaYcnJPmkUPp8sDm8MDMcSycY3AY8KwHhhZkIkfd57ET7sr4PFmJT/d4MR73xxEXnczrhiTh35h9uGfrYE2Bdgkbtob2HXmzGGo9zLqNTDoBLhFCY02Ed3TjXjw5pHQ+C2FaSlBCaBUprITSDbpMKRvRofusaMDDu2ZASeKrrasnRBCSNfR2sxwLAak47k6KzfLDEkKDK79/5ep/wdRDUajvSy+edtvMSlHVXk8MrSaLrSljEHJiK4TYHN44HAHH+sFKK+ZUpyHsYO6Y93Wcmw7eEoNkI9VWfHyP/diUO80/O+lBTBpQw8AnW2BNgXYJG7aG9h15sxhS+9ld0kwm7S4flK/gOAaaBb0xrgy7WhjSMm6OqYrLWsnhBDStXT2qq54rs4qr7ZCEHhADJ4tVeMs79tGMxiN9bL4XtkW5GSZcfREA1LMXWtLGWMABw4Wkw5GnQZWhxsuUYYcIvJNMetx3aR+mDC0B77afBz7j53J+L63tA5/eW0jRg3IwiUjcsMe3Xa2BNp0TBeJm7bM8oY69gro3GPN2vNe6tEVjtgfO9XR47f8BwNCSdiR1QTS1mPLZMZQWqkc2VZa2RiygSKEEELiKV5Hx1rtIjQ8B623X8j8/uM4QOCVf6SadRH3n1pqf9vbD2pr289zHK6/uH9UjkmNF8aUHEnK0bg671njoV/bPd2EmVcU4I7/GYSeWWa/awBb9lVjyQc78PXmY3C6PWHfzxdo1zQ60GQXIbPgrPmJjGawSdz4KjRRlABOyQ7J8xx03lnY1gK7zpw5jPS9fEHvW18dQE2jCya9AEGI3QhwR5YqU7KuztHWZH6EEELOTa2dqNKZ4rE6y2zSQqPhYdHwaLS5IctKUMWBAzio/540/LyI7qO19rcz+kGF/TMxZ+pA/POno116SxljgF4jQG8RYHd5YA9zrBcA9OmRjLuuHow9R2vxzZbjaoJfUZLx/Y6T2LyvGpNH5GDMoO7QCKEn24JntLvG8V4UYJO4yetugcWoRfkpGwDvCCUArYZHcpIWTrfcaoXWmUunIn2vgb3TMWfqQKzdctx7DnZsK9P2NoaUrCv2EiVLOyGEkMSUiIOwnb083T/QTU/Wo9EmQvTIkAHvkV1KH2rS8Jw2X7Ot7W9n9IMG9UnH+bkpCTOI0lFJBg0M3mO9nG4JUohjvTiOw9C+GRjaNx27Suvx+X9KYHUoK1PtLg++2FCG9XsqcfnonhjaLyNsWXS1QJsCbBI3B8rq0GBzq8t0BGWYEm5Rwql6CSlJui4f2A3qk45xw3OxY38lGppcMa9M29sYUrKu2Em0LO2EEEISCw3CKvwH/JUzo3VgAERRhkuUYDJo8OvJ57e5rYyk/e2sflBXOSmlLUId6yWG2Z8tCDwuGpGLQT1T8MOOk/jPzpNwe5fk1zW5sOrbw/jvrgpMGZOH88NkHAe6TqBNATaJC1+lJzOGzBQDGu3KKKUviwXPcUgx61HQKy2+N+rVkWVbPM+hT49keDJDL6FJFJSsKzYoSzshhJBwaBA2UPNA1zeb3yvboga6be2TtSc7OPWDIuc71ivdoofDLcHmUM4zDxXz6nUCLhmZi+KBWfh22wls2VetBuQnTtvw+hf70D83BVeMyUOPjKSw75noydAowCZx4V/p6bQCjAatkvTBuw8bAJrs0T3nsL0ScdlWrJxNI6uJgrK0E0IICYcGYYO1FOhG0idrT/tL/aD2YwwwaAXotXyLx3oBgMWkw9UT+uCCIdn4estx7Dlaqz53qLwBh8t3Y3j/brh0VE+kWfRh3zNRZ7QpiziJi1AZxHVaAQa9Bjqt0GIG8c7kW7ZVfsoKvVZAslkHvVZQl23tK61t/SLknEZZ2gkhhITTkRNVzmahMnRH2iej9jc+lGO9tEi3GGDUCerEWSjdUo34zWX5+O30weidfSbnEgOw/dBpPLdqB77cUAa7M3zGccA/67gzIbKOU4BN4qIrVHrNl23ptAJ4joNOKyDVrIPTLanL3AkJRz2yzRn7I9sIIYR0LV2hP5QI2tMno/Y3fhgDBN57rJdZB51WaPH1PbMsuON/BmHmlAJkpRnVxyWZ4b+7K/DsB9vxw44TynbSFgQH2lH5OBGjAJvERVeo9CJZtkVIOB09p5wQQsjZqyv0hxJBe/pk1P7Gn+9Yr4xkA8xGZTApHI7jMKBXGuZfNwzXTuyL5CSd+pzTLeGrzcfx3Kod2HqgGnIrkbO6dNwlxmUmmwJsEhddodKjZVskWnxJW3Izk+ASJTRa3XCJEnIzk86Z7LCEEEKCdYX+UCJob5+M2t/EkWLWIyPZAJNB0+KycZ7nMGpAFu65oRBTinvCoDsz+91gc2P1DyVYvnoXDhyrCxqUShSU5IzETaIfDeW/bCvU0hZatkUiQdlJCSGEhJLo/aFE0JE+GbW/iUPgeaQk6WH0HuvlFqWwCcl0GgGThudg9IAsfL/9JDb8UqmetV1V58A/1h5Anx7JuGJMHnpmmTvxU7SOAmwSV4lc6fmWbZWfskGr4QOWJPmWbeVmJp3zy7ZI21F2UkIIIaEkcn8oEXS0T0btb+LwHeuVZtHD6fLA5vS0uLfaZNBi6rheGDekO77ZUo4dh0+rzx2taMTKT/dgSN90TBmdh4wUQ2d8hFbREnESd6EyRSYCWrZFCCGEkM6SqP2hREB9srMQA4x6DdKT9a3uzwaANIsBv774fPzftUNxfk5KwHN7Smqx9MOd+Od/j8LqiP/WTQqwCWkB7d0hhBBCCIk/6pOdfRiL7FgvADivWxJuvWog5kwdgPMyTOrjMmPYuLcKz36wHf/eWg6XKMX69sOiJeKEtIKWbRFCCCGExB/1yc5O/sd6OUUJNocI0SOH3Z8NAP1zU9EvJwW7Dtfgm5+Po67JBQBwizL+vbUcm/ZW4apxvTCluCeAzv1+UIBNSBvQ3h1CCCGEkPijPtnZy3esl94iwO4UYXdJ8Ejh92fzHIfh/bthSN90bNpbhe+2nYDd5QEAWB0iVn17GAzAFcV5nfQJFBRgE0IIIYQQQghJGElGLQx6DawOEU631OLZ1xqBxwVDe2BkQSZ+2HES63dXQvQG5tV19s665TP30+nvSAghhBBCCCGEhMGYMkOtHutld8PdyrJxg06DKcV5GDs4Gz/troDokTFtfO9Ou2cfCrAJIYQQQgghhCQc9VivZAMcLg/sDo86Ox1OSpIOU8f2gsWkhdmobTEojwUKsAkhhBBCCCGEJC4GmPQaGHQCbA4PHC4PpBaWjccTHdNFCCGEEEIIISShnTnWS4d0iwEGnZCQGeQpwCaEEEIIIYQQ0iUwxiAIHNIseiSbddBpeSRSnE1LxAkhhBBCCCGEdCmMAQatAIOWh93pafVYr85CM9iEEEIIIYQQQrooDklGLdKT9TDpNeD5+E5n0ww2IYQQQgghhJAuSz3Wy6yHQZRgc7jjdi8JMYMtyzJeeOEFXHjhhRg+fDjuuOMOHD9+POzr6+rqcO+992L06NEoLi7Gn//8ZzgcjoDXrFmzBlOnTsWwYcMwffp0bNiwIeD5Q4cOYe7cuRgzZgzGjRuH+fPn4+TJkzH5fIQQQgghhBBCYosxBp1GOdbLoNN0+hFdQIIE2CtWrMB7772Hv/71r/jggw8gyzJuv/12uN2hRx7mz5+PsrIyvPnmm3j++efxww8/4LHHHlOf37hxIxYsWIAZM2bgk08+wbhx4zB37lwcOXIEgBKgz5kzBwaDAW+//TZeeeUV1NbW4vbbb4fL5eqMj0wIIYQQQgghJBYYIMRpqXjcA2y3243XX38d8+fPx0UXXYQBAwZg6dKlqKysxNdffx30+u3bt2Pz5s14+umnMXjwYIwbNw5/+ctf8Nlnn6GqqgoA8Morr+DSSy/FzJkz0a9fPzzwwAMYPHgw/vGPfwAA1q1bB7vdjmeeeQb5+fkYMmQIFi9ejCNHjmDbtm2d+vkJIYQQQgghhJwd4h5g79+/HzabDePGjVMfS05OxqBBg7Bly5ag1//888/IzMxEv3791MeKi4vBcRy2bt0KWZaxbdu2gOsBwJgxY9TrjRs3DitWrIDBYFCf53mlKBobG6P6+QghhBBCCCGEnBvinuSssrISANCjR4+Ax7OystTn/FVVVQW9VqfTITU1FRUVFWhsbITdbkd2dnbY6+Xm5iI3Nzfg+ZdffhkGgwGjR49u92fRaOI+XtHpBIEP+F8SiMqnZVQ+raMyahmVT8sSoXyobTw3URlQGQBUBgCVAXDulUHcA2xfcjKdThfwuF6vR0NDQ8jXN3+t7/UulwtOpzPs9cLtr3777bfxzjvv4OGHH0Z6enq7PgfPc0hLS2rXz54NkpON8b6FhEbl0zIqn9ZRGbWMyqdl8Sofahvpe0llQGUAUBkAVAbAuVMGcQ+wfcu03W53wJJtl8sFozH4l2AwGEImP3O5XDCZTNDr9er1mj/f/HqMMTz//PNYuXIlfvvb3+KWW25p9+eQZYbGRnu7f76rEgQeyclGNDY6ICXAwe6JhsqnZVQ+raMyahmVT8vaUz7JycaozTJQ23jufi+pDKgMACoDgMoAODvKIJK2Me4Btm+5d3V1NfLy8tTHq6urUVBQEPT67OxsrFu3LuAxt9uN+vp6ZGVlITU1FSaTCdXV1QGvqa6uRvfu3dV/i6KIhQsX4vPPP8fChQsxe/bsDn8Wj6drfmGiQZLkc/rzt4bKp2VUPq2jMmoZlU/L4lk+5/Lvhb6XVAYAlQFAZQBQGQDnThnEfSH8gAEDYDabsWnTJvWxxsZG7N27N+R+6NGjR6OyshJlZWXqY5s3bwYAjBw5EhzHYcSIEepjPps2bcKoUaPUf99///1Yu3YtlixZEpXgmhBCCCGEEELIuS3uM9g6nQ4333wznn32WaSnpyMnJweLFy9GdnY2Lr/8ckiShNraWlgsFhgMBhQWFmLEiBG4++678dhjj8Fut2PRokWYPn26OkM9Z84czJ07F4MGDcLEiROxevVq7Nu3D0888QQA4OOPP8aXX36J+++/H8XFxTh16pR6P773IYQQQgghhBBCIhH3GWwAmD9/Pq6//no8/PDDuPHGGyEIAl577TVotVpUVFRgwoQJ+PLLLwEAHMfhxRdfRG5uLmbNmoU//vGPmDhxIh577DH1ehMmTMDf/vY3vP/++7jmmmuwceNGvPTSS+rRXp9//jkA4JlnnsGECRMC/vO9DyGEEEIIIYQQEgmOMcbifRNnA0mSUVtri/dtdDqNhkdaWhLq6mznxJ6KSFH5tIzKp3VURi2j8mlZe8onPT0paknOqG08d7+XVAZUBgCVAUBlAJwdZRBJ25gQM9iEEEIIIYQQQkhXRwE2IYQQQgghhBASBRRgE0IIIYQQQgghUUABNiGEEEIIIYQQEgUUYBNCCCGEEEIIIVFAATYhhBBCCCGEEBIFdExXlDDGIMvnZlEKAg9J6pop9zsDlU/LqHxaR2XUMiqflkVaPjzPgeO4qLw3tY3n9veSyoDKAKAyAKgMgK5fBpG0jRRgE0IIIYQQQgghUUBLxAkhhBBCCCGEkCigAJsQQgghhBBCCIkCCrAJIYQQQgghhJAooACbEEIIIYQQQgiJAgqwCSGEEEIIIYSQKKAAmxBCCCGEEEIIiQIKsAkhhBBCCCGEkCigAJsQQgghhBBCCIkCCrAJIYQQQgghhJAooACbEEIIIYQQQgiJAgqwCSGEEEIIIYSQKKAAmxBCCCGEEEIIiQIKsEmLZFnGCy+8gAsvvBDDhw/HHXfcgePHj4d9/aFDhzB37lyMGTMG48aNw/z583Hy5MlOvOPOFWn5/PLLL5g1axaKioowduxYLFq0CE1NTZ14x50r0vLx989//hMFBQUoLy+P8V3GV6Rl5CuX5v+dreUUafmIooglS5aor7/55puxb9++TrzjzhVJ+Sxfvjzkd6egoAALFy7s5DtPXLGot7744gtMmzYNhYWFmDp1Kj799NOA5+vq6nDvvfdi9OjRKC4uxp///Gc4HI5ofaSIxaMMEq1ui3YZyLKMN954A1OmTEFRURFmzpyJPXv2BPxceXk57rzzTowYMQITJkzAsmXLIElSVD9XJOJRBitXrgz5PYinWLTTa9aswdSpUzFs2DBMnz4dGzZsCLhGV68TolEGiVYnRIQR0oLly5ezMWPGsO+++47t27eP3Xrrrezyyy9nLpcr6LW1tbXsggsuYPPmzWMHDhxgu3fvZjfddBO78sormdPpjMPdx14k5XPq1Ck2evRotnDhQlZSUsK2bt3Kpk6dyn73u9/F4c47RyTl46+8vJyNHDmS5efns+PHj3fS3cZHpGX0zDPPsJtvvplVV1cH/OfxeDr5zjtHpOXz0EMPsfHjx7Mff/yRHT58mM2bN49dcMEFrLGxsZPvvHNEUj5WqzXoe/P000+z4cOHs/3798fh7hNTtOutDRs2sEGDBrH333+fHTt2jL3zzjtswIAB7Pvvv1dfc/PNN7PrrruO7dmzh61fv55NnjyZ3X///TH7jK2JRxkkWt0W7TJ46aWX2JAhQ9h7773HSkpK2PLly1lhYSE7cuQIY4wxt9vNLr/8cjZ37lx24MAB9s0337Di4mL2/PPPx/RztqSzy4Axxv7whz+wBQsWBH0P4ina7fSGDRvY4MGD2T/+8Q92+PBh9tRTT7EhQ4aww4cPq9fo6nVCNMog0eqESFCATcJyuVysqKiIvfvuu+pjDQ0NbNiwYexf//pX0Os//PBDVlRUxBwOh/rYyZMnWX5+Plu/fn2n3HNnirR8duzYwe6++24miqL62JtvvskKCws743Y7XaTl4yNJErvxxhvZzJkzz/oAuz1ldPvtt7O//vWvnXWLcRVp+Rw7dowVFBSw7777LuD1kydPpjoohF9++YUNHjyYffzxx7G8zS4lFvXW448/zq655pqA10+fPl39O962bRvLz88P6Fj+5z//YQUFBayysjJaH63N4lEGjCVW3RaLMhg1ahRbvHhxwOtnz57NHnzwQcYYY//617/YkCFDWH19vfr8Bx98wEaMGNFqQBsL8SgDxhi78sor2RtvvBG9D9JBsWinb731VvaHP/wh4LEbbriBPfLII4yxs6NO6GgZtOUaiYyWiJOw9u/fD5vNhnHjxqmPJScnY9CgQdiyZUvQ68eNG4cVK1bAYDCoj/G88hVrbGyM/Q13skjLp7CwEM899xw0Gg0A4MiRI/jss89wwQUXdNo9d6ZIy8fnpZdegiiKuPPOOzvjNuOqPWV04MAB9OvXr7NuMa4iLZ+ffvoJFosFEydODHj9t99+G3CNs0V7/8Z8/vKXv2DUqFG45pprYnmbXUos6q2MjAwcOnQIGzduBGMMmzZtwpEjRzBs2DAAwM8//4zMzMyAv+vi4mJwHIetW7dG8dO1TTzKAEisui3aZVBbW4vGxkaMGjUq4PGBAwdi8+bNAJTvweDBg5GSkqI+P3bsWFit1rhsc4lHGbjdbpSWlqJv375R/CQdE+12WpZlbNu2LahNGjNmjHq9s6FO6GgZtHaNRKeJ9w2QxFVZWQkA6NGjR8DjWVlZ6nP+cnNzkZubG/DYyy+/DIPBgNGjR8fuRuMk0vLxN2XKFJSWliInJwcvvvhizO4xntpTPrt27cLrr7+Ojz76CFVVVTG/x3iLtIwaGhpQVVWFn3/+Ge+99x7q6uowbNgwLFiwAH369OmUe+5MkZbP0aNH0bNnT3z99dd4+eWXUVVVhUGDBuHBBx/sso10SzpSB3333XfYvn170D7Yc10s6q1bbrkFu3btwqxZsyAIAiRJwl133YVf/epXAICqqqqg99PpdEhNTUVFRUU0PlZE4lEGiVa3RbsMUlJSoNPpgnLSnDhxArW1tep7ZmdnB70fAFRUVKCwsLD9H6gd4lEGhw8fhiRJ+Oqrr/DEE0/A5XJh9OjRWLBggVoWnS3a7XRjYyPsdnvI37Xvel29TohGGSRanRApmsEmYfmSKeh0uoDH9Xo9XC5Xqz//9ttv45133sF9992H9PT0mNxjPHWkfJ599lm8/fbbyMjIwMyZM2Gz2WJ2n/ESafnY7Xbcd999uO+++9C7d+/OuMW4i7SMDh06BABgjOHJJ5/EsmXL4HK58Jvf/AanT5+O/Q13skjLx2q1oqysDCtWrMA999yDlStXQqPR4De/+Q1qamo65Z47U0fqoDfeeAOTJ0/GwIEDY3Z/XVEs6q2KigrU1dVh0aJFWL16NR588EG88cYb+Oijj9T3bP5+Lb1nrMWjDBKtbot2GQiCgGnTpmHlypXYtWsXJEnC2rVr8d1330EURQCA0+kM+X4AzorvQVvK4ODBgwAAo9GI559/Hk888QRKSkowc+ZMOJ3OKH/Ctol2O+37HC1dr6vXCdEog0SrEyJFM9gkLN9Sb7fbHbDs2+VywWg0hv05xhief/55rFy5Er/97W9xyy23xPxe46G95QMAQ4cOBQC8+OKLmDRpEr755htMnz49ZvcaD5GWz+OPP44+ffpgxowZnXaP8RZpGY0aNQobNmxAWloaOI4DoHyHLrroInz88ceYO3du59x4J4m0fDQaDaxWK5YuXarOWC9duhSTJk3CJ598gttvv71zbryTtLcOOnnyJDZt2oSXX3455vfY1cSi3po3bx6mTZuGm266CYCyJLahoQGLFy/GtddeC4PBALfbHfRzLpcLJpOpox8pYvEog0Sr22JRBg899BAWLVqEGTNmgDGGoqIizJkzB6tWrVLfs/n3wBdsnC3fg9bKYPr06Zg4cWLApEz//v0xceJEfPvtt5g6dWq0Pl6bRbud/t///V/1ev78r9fV64RolEGi1QmRohlsEpZvKUh1dXXA49XV1ejevXvInxFFEQsWLMBLL72EhQsX4o9//GOsbzNuIi2fkpISfP/99wGPde/eHampqWflcuhIy2f16tVYv349ioqKUFRUhDvuuAMAMG3aNLz00kuxv+E4aM/fWHp6utrYAMpIf25uLn2HAGRnZ0Oj0QQsBzcYDOjZs2fXONYjQu35/gDAunXrkJ6eftbmf+iIaNdbtbW1KCkpUQdVfYYPH476+nrU19cjOzs76P3cbjfq6+vjsiw2HmUAJFbdFov2y2KxYOnSpdi6dSt++uknvPfeexBFEXl5eQAQ8nvg+3dLf8+xEo8yABC04jErKwupqamtbnuJlWi306mpqTCZTC1er6vXCUDHy6C1ayQ6CrBJWAMGDIDZbMamTZvUxxobG7F3796we6rvv/9+rF27FkuWLMHs2bM76U7jI9LyWb9+PebPnx+Q8O3YsWOoq6s7K/eHRlo+X3/9NT7//HN8+umn+PTTT/H4448DUPbxn62z2pGW0apVqzBmzBjY7Xb1MavVitLSUpx//vmdcs+dKdLyGT16NDweD3bv3q0+5nQ6cfz4cfTq1atT7rkztaeOBpQEOsXFxWrCRXJGtOutlJQUGI1GHDhwIODnDhw4gOTkZKSnp2P06NGorKxEWVmZ+rwv6dPIkSNj8TFbFI8ySLS6LRbt10MPPYSPPvoIRqMR6enpkCQJ//73v9WBrtGjR2Pv3r2wWq3qdTdu3IikpCQMGDAglh83pHiUwdKlSzFlyhQwxtTrlpeXo66uLm5tXLTbaY7jMGLECPVv3GfTpk1qAriuXidEowwSrU6IWJyyl5Mu4rnnnmPFxcVs3bp1Aefeud1u5vF4WHV1tXos1+rVq1l+fj579dVXg86s8z+662wSSfnU1dWxCy+8kM2dO5cdPHiQbdmyhV199dXs+uuv7xJn+rVHJOXT3MaNG8/6Y7oYi6yMTp48yUaNGsV+//vfs4MHD7Jdu3ax2bNns0svvfSsPWs+0u/Q7Nmz2ZVXXsm2bNnCDh06xObNm8fGjRvHampq4vgpYqc9f2OXXHIJW7FiRZzuOPFFu95asmQJKyoqYp988gk7duwY++STT1hRURF79dVXGWOMybLMZsyYwa655hq2c+dOtmHDBjZ58uSAo4s6W2eXQSLWbdEug2XLlrELL7yQbd68mZWUlLC7776bXXDBBay2tpYxxpjT6WSXXnopu+2229i+ffvUc7CXL1/eKZ83lM4ug927d7PBgwezRYsWsZKSErZ582Y2ffp0NmPGDCbLcqd85lCi3U7/5z//YQMHDmSvv/46O3z4MHv66afZsGHD1GO5unqdEI0ySMQ6IRIUYJMWeTwe9swzz7CxY8ey4cOHszvuuEOtLI8fP87y8/PZ6tWrGWOMzZkzh+Xn54f8z/eas00k5cMYYyUlJWzu3Lls5MiRrLi4mC1cuJA1NDTE6/ZjLtLy8XeuBNiRltGePXvYnDlz2MiRI9mIESPYvHnz2MmTJ+N1+zEXafk0NTWxRx99lI0ZM4YVFhayOXPmsEOHDsXr9mOuPX9jw4YNY++99148brdLiHa95fF42Ouvv86uuOIKVlhYyK666ir23nvvBQQMp0+fZvPmzWPDhw9nY8aMYY8++mhcO5HxKINEq9uiXQZut5s9+eSTbPz48WzEiBHszjvvZEePHg34udLSUjZnzhw2dOhQNmHCBLZs2TImSVLMPmNr4lEG69evZzfccAMbPny42k/yPxs8HmLRTn/yySfssssuY0OHDmXXXHMNW79+fcDzXb1OiEYZJFqdEAmOMb91GIQQQgghhBBCCGkX2oNNCCGEEEIIIYREAQXYhBBCCCGEEEJIFFCATQghhBBCCCGERAEF2IQQQgghhBBCSBRQgE0IIYQQQgghhEQBBdiEEEIIIYQQQkgUUIBNCCGEEEIIIYREAQXYhBBCCCGEEEJIFFCATQghhBBCSJwtX74cBQUF8b6NuPj4449RUFCA8vLysK/ZtGkTCgoKsGnTpk68M0IiRwE2IYQQQgghhBASBRRgE0IIIYQQQgghUUABNiEkoVx88cV44YUX8PTTT2P8+PEYNmwYbrvtNpSWlgIAHnzwQcyePRurV6/GlClTMGTIEFx99dX48ccf43vjhBBCYo4xhjfffBNXXnklhg0bhssuuwyvvfYaGGN48MEHccstt+Cjjz7C5MmTUVRUhFmzZmH//v0RvUd5eTkKCgrwxRdf4K677kJhYSEuuugi/P3vf4csy+rrLr74Yvztb3/DrFmzMGzYMPzpT38CANTX12PRokUYP348hg4dil//+tfYsGFDwHu4XC48+eSTuOCCC1BUVISFCxfC5XK1q0xOnjyJe+65B8XFxSgsLMSsWbOwd+/eoM+zZs0azJ8/H0VFRSguLsbDDz8Mu92uvm7Pnj2YNWsWRo4ciaKiIsyePRs7duwIeK+ff/4ZN998MwoLC1FcXIwHHngAtbW16vMff/wxhg4dip9//hnXXXcdhg4diilTpuDbb79FSUkJZs2ahcLCQlx22WX44osvgj7Ltm3bMH36dAwZMgTTpk3Dl19+2eJnP3jwIO68806MGDECI0aMwO9//3scP368XeVISLRQgE0ISThvvfUWSkpK8OSTT+Lxxx/Hnj178MADD6jP79mzB6+99hrmz5+Pv//97xAEAfPmzUNDQ0Mc75oQQkisPfPMM3jmmWdw8cUX46WXXsL111+PZ599Fi+//DIAYN++fVi6dCn+7//+D4sXL0ZdXR1uvvlmVFdXR/xejz32GMxmM5YvX46rr74aL774IpYsWRLwmnfffRdDhw7FihUrcP3118PlcmHWrFn497//jbvvvhsvvvgisrOzcfvttwcE2QsWLMCHH36IO++8E8uWLUNDQwPefPPNiO+xtrYWM2bMwC+//IJHHnkES5YsgSzLuOmmm3DkyJGA1z766KPIycnBihUrcNttt+Gjjz7CypUrAQBWqxW333470tLSsHz5cixduhQOhwO33XYbmpqaAABbtmzB7NmzYTAYsGzZMjz00EPYvHkzZs6cCafTqb6Px+PBvffeixkzZmDlypUwGo247777cNddd+Giiy7CSy+9hKysLDzwwAOorKwMuMdFixbhyiuvxIoVK9C/f3/cfffdWLduXcjPfvToUcyYMQM1NTV4+umn8cQTT+D48eO48cYbUVNTE3FZEhI1jBBCEsjkyZPZ5MmTmcfjUR9bvnw5y8/PZ7W1teyBBx5g+fn5rKysTH1+8+bNLD8/n61duzYet0wIIaQTNDQ0sEGDBrEnnngi4PG//vWv7LbbblPbhy1btqjPVVVVsaFDh7LFixe3+X2OHz/O8vPz2axZswIef/zxx9ngwYNZU1MTY0xpry699NKA16xatYrl5+ezHTt2qI/Jssxuuukmdu211zLGGDt48CDLz89n7733nvoaSZLY1KlTWX5+fpvvkzHGnnvuOTZ06FBWXl6uPuZyudgll1zC5s2bF/B57rvvvoCfveWWW9i0adMYY4xt376d5efns61bt6rPl5WVsWeeeYZVVFQwxhi74YYb2LRp0wLa55KSEjZw4ED2zjvvMMYYW716ddBn++KLL1h+fj5btmyZ+tju3btZfn4+++abbwJ+7tVXXw24x+nTp7NrrrmGMcbYxo0bWX5+Ptu4cSNjjLF77rmHjR8/Xv19MMZYXV0dGzlyJHvqqaciKkdCoolmsAkhCWfo0KEQBEH9d3Z2NgDA4XAAANLT05GXlxf2eUIIIWefHTt2wOPx4PLLLw94/OGHH8arr74KAMjNzcWoUaPU57KyslBUVIQtW7ZE/H7Tp08P+PeUKVMgiiK2b9+uPjZw4MCA12zYsAGZmZkYPHgwPB4PPB4PJEnC5MmTsWfPHjQ0NODnn38GoCwx9+F5HlOmTIn4Hjds2ICBAweie/fu6vvxPI+JEydi/fr1Aa8dPnx4wL+zs7PVJeL9+/dHeno67rrrLixatAjffPMNunXrhgULFiA7OxsOhwM7d+7EpEmTwBhT36tnz57o168ffvrpp4BrFxUVqf9/RkYGAKCwsFB9LDU1FQDQ2NgY8HNTp04N+Pell16KvXv3wmazBX32jRs3ori4GAaDQb0fs9mMUaNGBX12QjqTJt43QAghzRmNxoB/87wyFujb+9b8eY7jAp4nhBBy9qmvrwegDLKG071796DHMjIy8Msvv0T8fs2v5Xtf/+1IJpMp6B5PnTqFwYMHh7zmqVOn1J9PS0sLeC4zMzPie6yvr0dZWVnY9/MfeA7VtjLGAABJSUl49913sXLlSqxZswarVq2CwWDA1VdfjYcffhiNjY2QZRmvvPIKXnnllaD30ev1Af82m81Br2n+/qF069Yt4N8ZGRlgjMFqtQa9tr6+Hl9++WXIfdotfUcIiTUKsAkhhBBCSMJLTk4GoOw77tu3r/r4yZMncezYMYiiiLq6uqCfO336tDqLGonm1/Lt623pWhaLBb1798azzz4b8vnc3Fw1sD59+jTOO+889TnfAEIkLBYLiouLcf/994d8XqfTtflaffv2xeLFiyFJEnbt2oXPPvsM77//PvLy8jBjxgxwHIfZs2fjqquuCvrZtgTPbdHQ0BAQZJ8+fRqCICAlJSXotRaLBePHj8ecOXOCntNoKMQh8UNLxAkhhBBCSMIbNmwYtFotvvvuu4DHX3/9ddxzzz0QBAGlpaUByb2qqqqwfft2jBs3LuL3a55c66uvvoLRaAxY6txccXExKioqkJGRgaFDh6r//fTTT3j11VchCALGjh0LAFi7dm3Azzb/XG1RXFyMo0ePok+fPgHv99lnn+Gjjz4K2G7VkrVr12Ls2LE4deoUBEFAUVERHnvsMSQnJ+PkyZMwm80YNGgQSkpKAt6nf//+WL58OTZt2hTxvYfy/fffq/+/LMtYu3YtCgsLYTAYQn72w4cPY+DAger9DBkyBG+++Sa++eabqNwPIe1BwzuEEEIIISThpaenY+bMmXjzzTeh0+lQXFyMnTt34v3338f999+Pffv2gTGGu+66C3fffTcEQcCLL76IlJQU3HLLLRG/35o1a5CRkYFJkyZh8+bNePfdd3H33XcHLQv3d+211+Kdd97BnDlzcNddd6FHjx5Yv349XnnlFdx8883QarXo1asXbj52W/gAAAMPSURBVLjhBixduhQejwcDBw7EZ599hgMHDkR8j7Nnz8Znn32G2bNn49Zbb0VaWhq+/PJLfPjhh1i4cGGbrzNixAjIsozf//73mDt3LpKSkrBmzRo0NTWpe97vuecezJ07F/feey9+9atfQZIkvP7669i5cyd+97vfRXzvoSxbtgySJKFHjx54//33cfToUbzxxhshX/u73/0OM2bMwJ133okbb7wRer0eq1atwrp16/DCCy9E5X4IaQ8KsAkhhBBCSJewYMECZGRk4IMPPsCrr76K3NxcPPLII5gxYwYefPBBnHfeebj11lvxt7/9DQ6HA+PHj8fKlSvVpFqR+MMf/oDNmzdj1apV6NGjBxYtWoQbb7yxxZ8xmUx49913sWTJEixevBhNTU3IycnBvffei1tvvVV93aOPPopu3brhnXfeQUNDAy688ELcddddWLZsWUT32L17d3zwwQdYsmQJHnvsMbhcLvTu3RtPPPEErr/++jZfJysrC6+++iqef/55/OlPf4LD4VBnp30z7hMmTMBrr72GF198EfPnz4dWq8XgwYPxxhtvBCVQa68nn3wSTz31FMrKypCfn49XXnkFxcXFIV87YMAAvPvuu1i6dCnuv/9+MMaQn5+Pv//977jkkkuicj+EtAfHfNkNCCGEEEII6aIefPBBbN68Gd9++22HrlNeXo5LLrkETz75JK699too3R0h5FxBM9iEEEIIIeSsJ0kSWptX8p1KEW8ej6fV1/A8r56yQQhJHBRgE0IIIYSQs97s2bOxefPmFl+Tk5ODt956q5PuKLxwx275u+aaa/DUU091wt0QQiJBS8QJIYQQQshZr6SkBDabrcXX6HQ6FBQUdNIdhbd79+5WX5OWlobc3NxOuBtCSCQowCaEEEIIIYQQQqKANm4QQgghhBBCCCFRQAE2IYQQQgghhBASBRRgE0IIIYQQQgghUUABNiGEEEIIIYQQEgUUYBNCCCGEEEIIIVFAATYhhBBCCCGEEBIFFGATQgghhBBCCCFRQAE2IYQQQgghhBASBf8P+jnFA2dz5xkAAAAASUVORK5CYII=", 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" ] @@ -3511,10 +3519,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 12:43:19,621] A new study created in memory with name: my_study\n", - "[I 2024-07-01 12:43:19,668] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:01:03,422] A new study created in memory with name: my_study\n", + "[I 2024-07-02 14:01:03,468] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__c73885c5d5a4182168b8b002d321965a': 'ReLU', 'aggregation__c73885c5d5a4182168b8b002d321965a': 'mean', 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50, 'depth__c73885c5d5a4182168b8b002d321965a': 3, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'features_generator__c73885c5d5a4182168b8b002d321965a': 'none', 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a'}\n", - "[I 2024-07-01 12:44:48,173] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", + "[I 2024-07-02 14:02:28,149] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", " \r" ] } @@ -3581,7 +3589,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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tgQE2UYtypMS924eQMSx0RPzQfSoUIaD7VHREdGQMG/duH4Ij5WI3lYiIiIgWEceNrYMBNlGL2jMSx/B4CuGAD6Jk34wQAuGAhuHxFPaMxBephURERETUCuodNw4Nc9w43xhgE7WoRMqEbUtoWuU/U01TYNsSiZS5wC0jIiIiolZS77gxznHjvGOATdSiIiEfVFXAspyKj1uWA1UViIR8C9wyIiIiImol9Y4b2zhunHcMsIla1PreNvR1hZDMWJAl+2WklEhmLPR1hbC+t22RWkhEREREraDeceNAH8eN840BNlGLUoTA+acMIKCrmEwYMEwbjpQwTBuTCQMBXcX5pwzwXEMiIiKiFY7jxtbBAJtWLEdKDA7H8NyuQxgcjrVkVcXNG7pw6Tmb0N8TRta0EUsYyJo2+nvCuPScTTzPkIiIiBbFUhhHrTQcN7YGbbEbQLQYdgyO497tQxgeT8G2JVRVoK8rhPNPGWi5D5/NG7qwaaATe0biSKRMREI+rO9t4wwkERERLYqlNI5aaThuXHwMsGnF2TE4jtvu34mMYSEc8EELKrAsB/vGkrjt/p0tOcOnCIENfdHFbgYRERGtcEtxHLXScNy4uJgiTiuKIyXu3T6EjGGhI+KH7lOhCAHdp6IjoiNj2Lh3+xDTnIiIiIhKcBxFNDMG2LSi7BmJY3g8hXDAB1GSKiOEQDigYXg8hT0j8UVqIREREVFr4jiKaGZMEaemcaRs+f0eiZQJ25bQgpXnljRNQSpjIZEyF7hlRERE1GqWwthmIXEcRTQzBtjUFEul2EUk5IOqCliWA92nlj1uWQ5UVSAS8i1C64iIiKhVLJWxzULiOIpoZkwRpzl7frdb7GLfWAJ+n4poRIffp+aLXewYHF/sJuat721DX1cIyYwFWbI/SEqJZMZCX1cI63vbFqmFREREtNi8Ql5LYWyzkDiOIpoZA2yaE8eRuOeRwSVT7EIRAuefMoCArmIyYcAwbThSwjBtTCYMBHQV558ysKLTv4iIiFYyFvKqjuMoopkxwKY52bV/CgcOJZdUsYvNG7pw6Tmb0N8TRta0EUsYyJo2+nvCPFqCiIhohWMhr9o4jiKqjXuwaU5iSQOWLRFaYsUuNm/owqaBThYuISIioiIs5DUzjqOIqmOATXMSDevQlmixC0UIbOiLLnYziIiIqIWwkFd9OI4iqowp4jQnh69tx5ruMItdEBER0bLAQl5ENBcMsGlOFEXggjdsYLELIiIiWhZYyIuI5oIBNs3Zlo0sdkFERETLBwt5EVGjuAebmoLFLoiIiGg54diGiBrBAJuahsUuiIiIaDnh2IaIZosp4kRERERERERNwACbiIiIiIiIqAkYYBMRERERERE1AQNsIiIiIiIioiZggE1ERERERETUBAywiYiIiIiIiJqAATYRERERERFREzDAJiIiIiIiImoCbbEbQNTKHCmxZySORMpEJOTD+t42KEIsdrOIiIiIGsKxDdH8WvQA23Ec3HDDDfjv//5vxONxnHjiifjUpz6FdevWVXz+xMQEPve5z+Hhhx+GEALnn38+PvaxjyEYDOafc9999+H666/Hvn37cPjhh+PjH/84tm7dmn/8j3/8I774xS/i2Wefhd/vx9lnn42rrroKbW1t8369tHTsGBzHvduHMDyegm1LqKpAX1cI558ygM0buha7eURERESzwrEN0fxb9BTxG2+8Ed/5znfw2c9+Ft/73vfgOA7e9773wTCMis+/4oorMDQ0hFtvvRVf+9rX8NBDD+Hqq6/OP759+3ZcddVVeNe73oW7774bW7duxeWXX46XX34ZAHDw4EFcdtllWLt2LX7wgx/gxhtvxBNPPIF//ud/XojLpSVix+A4brt/J/aNJeD3qYhGdPh9KvaNJXHb/TuxY3B8sZtIREREVDeObYgWxqIG2IZh4JZbbsEVV1yBM888E8cccwy+8pWvYHh4GA888EDZ85966ik89thj+Ld/+ze86lWvwtatW/GZz3wGP/rRjzAyMgIAuPnmm/HmN78Zl1xyCY444gh8/OMfx6te9SrcdtttAID9+/fjtNNOw2c+8xls3LgRJ5xwAt7xjnfgt7/97YJeO7UuR0rcu30IGcNCR8QP3adCEQK6T0VHREfGsHHv9iE4Ui52U4mIiIhmxLEN0cJZ1AD7hRdeQDKZLErfjkaj2LJlCx5//PGy5//+979HT08PjjjiiPzXTjrpJAgh8MQTT8BxHDz55JNFrwcAJ598cv71jjvuOHz5y1+GprnZ8S+//DJ+9KMf4dRTT52PS6QlaM9IHMPjKYQDPoiSPUlCCIQDGobHU9gzEl+kFhIRERHVj2MbooWzqHuwh4eHAQBr1qwp+vrq1avzjxUaGRkpe66u6+jo6MCBAwcQi8WQSqXQ19dX1+udc845GBwcxNq1a3HDDTfM9XJomUikTNi2hBasPP+kaQpSGQuJlLnALSMiIiKaPY5tiBbOogbY6XQagBskF/L7/Ziamqr4/NLnes/PZrPIZDJVXy+bzZZ937XXXot0Oo0vfelLuOSSS/CjH/0I4XC44evRtEXf0r6gVFUp+v/lor3ND00VsG0Hqk8te9y2HGiqQDSiY+9YAvGUibaQDwN9xVU4l+v9aQbem9p4f6rjvaltqdyfSv3lUmn7TJbDdSyHawCWx3U06xrqHdu0t/nnZTy7Et4LR0oMDcerjgtbxXJ4L4DWvo5FDbADgQAAdy+29+8AkM1mi6qCFz6/UvGzbDaLUCgEv9+ff73Sxyu93rHHHgsAuOGGG3DGGWfgZz/7GS688MKGrkVRBDo7Gw/Ol7JotPzeLmXt7SGs69uNwQMxBP1aUSqVlBKprI3u9gDu/vVuvDKWhGU70FQFa1dHcPGbjsJxR/UUvd5yuz/NxHtTG+9Pdbw3tbXy/Zmpv2zlts/GcriO5XANwPK4jrleQz1jmw1rojj+mD4oyvwFhcv1vXjmxTHc+eCL2D+amHFc2CqWw3sBtOZ1LGqA7aV7j46OYv369fmvj46OYtOmTWXP7+vrw89//vOirxmGgcnJSaxevRodHR0IhUIYHR0tes7o6Ch6e3sBALt27cKePXtw5pln5h/v7e1FR0dHvlBaIxxHIhZLNfz9S5GqKohGg4jF0rBtZ7Gb01TnnrgO39q2AwcnMwgHNWiaAstykExbUBSB8ck0Dk6kEQ5qCAZ8sCwHu/dP4frvP4XLztuMLRu7lvX9mSvem9p4f6rjvamtkfsTjQYXdAWgWn+5XN7b5XAdy+EagOVxHc28hlpjm4Cu4twT12Fqan7Gssv5vXh+9zi+tW0HMoZdc1zYKpbDewEs/HXMpq9c1AD7mGOOQSQSwaOPPpoPsGOxGJ5//nm85z3vKXv+iSeeiGuvvRZDQ0MYGBgAADz22GMAgNe97nUQQuCEE07AY489hre//e3573v00Ufx+te/HgDwyCOP4Itf/CJ+85vfIBqNAgD27NmDiYmJouJpjbCspftLOhe27Sy7az96XQcuOWfT9FmRaQuqKrC2J4xk2sREIouOiD8/A+zTVLRHFEwmDPz4t7txZH87vI0Ky/H+NAvvTW28P9Xx3tTW6venVttave31Wg7XsRyuAVge19GMa6g1tjn/lAEcva5j3u/TcnsvHCnx49/uRjpXnb3WuLDV0sWXw3sBtOZ1LGqAres63vOe9+Daa69FV1cX1q5diy996Uvo6+vD2WefDdu2MT4+jra2NgQCARx33HE44YQT8JGPfARXX301UqkUPvWpT+HCCy/Mr1BfdtlluPzyy7FlyxacfvrpuOuuu7Bjxw58/vOfBwBccMEF+MY3voGrrroKH/3oRzE1NYXPfe5zeM1rXoOzzjprMW8HtZjNG7qwaaATe0biSKRMREI+SCnx9bufq6sK55H9HYvTcCIiIqIKKo1t1ve25l7hpWA21dk39EUXqZW00BZ9V/gVV1yBiy++GJ/85Cfx7ne/G6qq4pvf/CZ8Ph8OHDiA0047Ddu2bQPg/qLecMMN6O/vx6WXXoorr7wSp59+Oq6++ur865122mm45ppr8N3vfhcXXXQRtm/fjptuuim/Ot3R0ZE/E/vd7343PvjBD2LLli345je/CVUtL/pAK5siBDb0RfHqw7uxoS+KZNpyq3BWKQCiaQpsW7IKJxEREbWk0rENg+vG5auzc1xIBRZ1BRsAVFXFVVddhauuuqrssf7+fuzcubPoa93d3bjuuutqvuaFF15Ys1jZxo0b8f/+3/9rqL20skVCPqiqgGU50CtU4bQsB6oqEAn5FqF1RERERLRQOC6kShZ9BZtoKVnf24a+rhCSGQtSyqLHpJRIZiz0dYWwvrdtkVpIRERERAuB40KqhAE20SwoQuD8UwYQ0FVMJgwYpg1HShimjcmEgYCu4vxTBphuRURERLTMcVxIlTDAJpqlzRu6cOk5m9DfE0bWtBFLGMiaNvp7wrj0nE3YvKF1jmIgIiIiovnDcSGVamgP9g033IC3v/3t+crdhfbt24dbbrkFn/rUp+bcOKJWxSqcRERERARwXEjFGlrB/vrXv46RkZGKjz3zzDP47//+7zk1imgpWE5VOB0pMTgcw3O7DmFwOAanZB8RERERLQ3s0xfHchoX0tzUvYL9rne9C8888wwAd9P+O9/5zqrPPfbYY+feMiJaEDsGx3Hv9iEMj6dg2xKqKtDXFcJ5J69HKOjjTCwREVETOVLO20pntT79/FMGmKpMtEDqDrA/97nP4ac//SmklPj617+Ov/qrv0JfX1/RcxRFQTQaxdlnn930hhJR8+0YHMdt9+9ExrAQDvigBRVYloPB4Ti+dtcfENBVKEKwgyYiImqC+QyAq/Xp+8aSuO3+ndwPTLRA6g6wjzzySHzoQx8CAAghqu7BJqKlwZES924fQsaw0BHxQ+Rmz23HrX5p2RICQG93iB00ERHRHM1nAFytT9d9KnyagsmEgXu3D2HTQCez0YjmWUN7sD/0oQ8xuCZa4vaMxDE8nkI44Mt3xFJKxJIGHCmhKQK2I2FZDnSfio6Ijoxh497tQ9zPRURENAulAbDuczPEmtW/VurTPUIIhAMahsdT2DMSb8blEFENDVURP+aYY8r+eEvt2LGjoQYR0cJIpEzYtoQWnJ5nMywHpuVAEQJCCEhHwnHczr60g97QF12sphMRES0pswmAG+lfK/XphTRNQSpjIZEyG2o/EdWvoQD7gx/8YNmHQzKZxJNPPok9e/bgox/9aFMaR0TzJxLyQVVFfoUaABxHQiKX2pKbRFeU6b91dtBERESzN98BcKU+vZBlOVBVgUjI19DrE1H9GgqwP/zhD1d97GMf+xiee+45/NVf/VXDjSKi+be+tw19XSHsG0vCpykQQkBRBATc2NqRErqmFHXU7KCJiIhmb74D4Ep9ukdKiWTGQn9PGOt72xq+BiKqT0N7sGu56KKLsG3btma/LBE1mSIEzj9lAAFdxWTCgGHa0FS3Yrhb4EyiPaznn+910H1dIXbQREREs+AFwMmMBVmyz7oZ/WulPt2RbtHSyYSBgK7i/FMGWOCMaAE0PcDes2cPLMtq9ssS0TzYvKELl56zCf09YWRNG/GkCX+u4mjAp0FRBDtoIiKiOVqIALi0T48lDGRNG/09YZ4AQrSAGkoRv+GGG8q+5jgOhoeHsW3bNpx11llzbhgRLYzNG7qwaaATe0biSKRMREI+JDMW7sud05nKWFBVgf6eMM/BJiIiapAXAN87j/1rpT59fW8bJ8aJFlDTAmwAiEQiePOb34xPfOITc2oUES0sRYiyqqWb2UETERE11UIEwJX6dCJaOA0F2C+88EKz20FELYYdNBERUfOxfyVa3hoKsD2xWAxPP/004vE4urq6cOyxxyISiTSrbURERERERERLRsMB9je+8Q3ceOONyGQy+a/puo6///u/xwc/+MGmNI5oJXGkZEo2ERER0TLDMd7K0lCAfdddd+HLX/4yLr74YvzFX/wFVq1ahbGxMfzoRz/CDTfcgMMOOwwXXXRRs9tKVGa5fGDtGBzPFz2xbQlVFejrCrGoGBER0TK1XMYwVBvHeCtPQwH2rbfeine/+93413/91/zXDj/8cJx88skIBAK4/fbbGWDTvFsuH1g7Bsdx2/07kTEshAM+aEEFluVg31gSt92/k0drEBERLTPLZQxDtXGMtzI1dA720NAQ3vzmN1d87M/+7M+wa9euOTWKaCbeB9a+sQT8PhXRiA6/T81/YO0YHF/sJtbFkRL3bh9CxrDQEfFD96lQhIDuU9ER0ZExbNy7fQiOlIvdVCIiImqC5TKGodo4xlu5Ggqwe3t78corr1R8bN++fSx0RvNqOX1g7RmJY3g8hXDAB1GSFiaEQDigYXg8hT0j8UVqIRERETXLchrDUG0c461cDQXYb3rTm/C1r30Nzz77bNHXn3nmGVx//fV405ve1JTGEVWyXD6wHCnx0v4pZAwbskpHqmkKbFsikTIXuHVERETUbMtlDDNbjpR4ae8knn35EAaHYytiAiGRMmHbEppWOdziGG/5amgP9oc//GE88sgjeOc734m1a9di1apVOHjwIPbv348jjjgC//RP/9TsdhLl5T+wgtU/sFIZq6U/sLy9V/vGEkhnLWSyFnSfivawjoB/+s/SshyoqkAk5FvE1taPBVuIiIiqWw5jmNnaMTiO+x7dg5GJNAzTXjH7zSMhH1RVwLIc6D617PH5GuNxLLb4GgqwI5EI7rzzTtx11114/PHHMTU1hWOPPRbvfe978ba3vQ2BQKDZ7STKW6wPrGYpLHgR8mvImg5M00bWtHEolkF3NICAX4OUEsmMhf6eMNb3ti12s2fEgi1ERES1LfUxzGx5Y56sYSMa0REMqDDNlVHka31vG/q6Qtg3loRPU4oyFuZrjMexWGto+Bxsv9+P//E//gcuvvhixGIxtLe3w+dbHh8G1NoW4wOrWUr3Xgkh0B4GxmMZOI6E7UhMJrLoUgSSGQsBXcX5pwy0/Mwjq2QSERHNbCmPYWarcMzT2eaHT1Nh2e7Egk9TMJkwcO/2IWwa6Gz5cU4jFCFw/ikDuO3+nZhMGAgHNGiaOz6ajzEex2Kto6E92ADw8MMP413veheOP/54vPGNb8RrX/taXHrppXjyySeb2T6iMt4HVkBXMZkwYJg2HClhmDYmE0ZLB6WV9l4F/Rq6ogHoPhVCCBi5D97+nvCS+DBkwRYiIqL6LOUxzGyt1P3mhTZv6MKl52xCf08YWdNGLGEga9pNH+NxLNZaGlrBvv/++3HllVfimGOOwYc+9CF0d3djbGwMDzzwAC655BLceuuteP3rX9/sthLleR9YXhpMKmNBVQX6e8ItnQZTbe9V0K8hoKvImjbiSRN/vnUAZ72uf0l0sLPpQDf0RReplURERK1hqY5hZmsl7jevZPOGLmwa6JzXfdGzGYsd2d/RtJ9LlTUUYH/961/HOeecg69+9atFX//Qhz6ED3/4w/j3f/93fPe7321G+4iqWogPrGartfdKCAFFCAT8Ko7ob2/p6yjEDpSIiGh2luIYZrYKxzyqvvz3m9eiCDGviwwci7WWhlLEh4aGcPHFF1d87B3veAd27Ngxp0YRLVfe3qtkxio7msvbe9XXFVpSe68KO9BKVlIHSkREVC8v6Hr14d3Y0BddVsE1UHnMI6VE1rSRypiIJQ30dgaX1JinVXEs1loaWsE+4ogj8Ic//AGnnXZa2WO7d+9Gf3//nBtGNJOlWClxoQteLISVVLCFiIiI6lM05okb0H0KYikTpuVASgkBgVTWxs6hiZYdty0VHIu1loZWsK+++mp8+9vfxk033YTh4WE4joPx8XHccccduO666/D+978fr7zySv4fombzKiXuG0tA1xQE/CoggaHhOG796QvYMTi+2E2saqEKXiyUlVSwhYiIqJkcKTE4HMNzuw5hcDi27IpQeWOezjY/DsWyMAwbkBK6pqCjTcdEPIvb7t/Z0uO2pYBjsdbS0Ar2O97xDgDAV7/6VXzta1/Lf91L/7jqqquKns+UcWqmwkqJ3geJaTmQAASAjGHjjl++hP/ztycu+AeJI2V+P1V7mx/t7aGKz1usvVeF7Wvmz1wpBVuIiIiaZSlm4lUy09hi00AnwkEfwgENQb8KVVHydWiklMv+uK5K5mM8xrFY62gowL7mmmvKKtQRLRSvUqKmKhiPZeFICUUIKAAkAMeR2DuawENP78dZr1247QqlHaWmCqzr241zT1yHo9d1lD1/vgtezNS+ZnfkK6FgCxERUTMslzOL6xlb7BmJY/hQEh1tfqiKQOEi/Uo8bWQ+x2Mci7WGhgLst73tbc1uB1HdEikTluUgazlwpIQqRH7CRwCAAti2xENPv4Izjl+7IB8qlTpK23YweCCGb23bgUsWuaNcqI58oScNiIiIlprSM4u9MYzuU+HTlKIV3VZW79gikTJh2RI+VamYAr+SKlwvxHiMY7HF11CADQDj4+O45ZZb8NhjjyEWi6GzsxOvf/3r8bd/+7fo7u5uZhuJikRCPkAApuVAKQiuPQICQrgpRwsxG1qto1R9KoJ+DQcnM4ua+jSbjpwznERERPNrOZxZPJuxRSTkg6YKmLYDVSkfZ6yUCtccj60cDRU5Gx4exkUXXYTbbrsNfr8fW7ZsgaZp+Na3voULL7wQIyMjzW4nUd763jZ0RPy5CpTlbCnh09xf7cLZ0GYXEvFe78En92HfWAIhv1a5owxOd5SNvP5c2zubjpyIiGilWqiCY/kzi7XqZxbbtmzpFd3ZjC3W97ZhTXcY8ZRZ8YjSWNJAJOiDlLLsni+nInAcj60cDa1gf+lLX4Kmadi2bRvWrVuX//revXvx3ve+F1/5ylfwhS98oWmNJCqkCIEzjjsM//WzF2E5Epoi3Nxw6QbXigDCAXeV25sNbfZ+l8LXyxg20lkLWdNBexgI+ov/rDRNgZ2eXepTM9ub78iD1TvylZKaRUREVMlCFhwrPLPYK/ZVaCms6M5mbKEIgQvesCF/XFcod0RpMmViKmlASolDUxl8/e7niu75cikC5+F4bOVoaAX7N7/5Da644oqi4BoA1q1bhw9+8IN4+OGHm9I4omrOeO1arOuNQBECtuPAcdxZT11T0NXmh+VI9HWFsL63rehIL79PRTSiw+9T8/tdZns0RNnrhXwQAEzTxngsg3TWKnr+bDvKZre3sCOvZCl05ERERPOl2f3uTLwzi5MZq+KKbjJj5ccwrWq2Y4stG7vwwYuPw7rVEWRNG+NTGUwmDABAR5sfXe2Bonv+00eHFvQ9WQgcj60cDQXYtm2js7Ny4YWuri4kEok5NYpoJooQeMeZR6C7PYCgX0N7WEdPRwCdbX5kTCd/3h+Aov0uuk+FIgR0n4qOiI6MYePe7UN1pxyV7p/RfSr8ugbdp+YrmMdys7FArqNM199RVnr92bS3UirVcujIiYiI5sNc+91GzPbMYseR2H2gtdKkGxlbHHdUDz76P16LD1x0bG78puKwVSG0hfSie57OWrj3dwv7niwEjsdWjoZSxDdt2oSf/OQnOP3008se+9GPfoSjjz56zg0jmknpeX/prF123t/gcKzu/S61iqF55xW+vG8K+8aSZa/XHtZxKJaB7UgYloOsaUNVBFJZu6yjrGU2+3NK21srler8Uwbc1KyEgXAuNcuyHCQz1qzaR0REtJzMpd+di3rPLH5+9zh++vgz2Dscg9VCadLeJMFsxxaKEFAEkEibiIZ1KErxWp8QAn6fivFYBl3RQN3vyXycK91sjd4zWnoaCrA/8IEP4H/+z/+JqakpnHfeeejp6cHY2Bjuvfde/OY3v8F1113X7HYSVTTTeX/N2O9StN86ayNtWMjmZlUDuf3WAb+G7mgAk4ksDMtBPGki6FexYU206jnYlTTa3nqOfainI2/UUujYiIiISi3mvtiZxjA7Bsdx+/07kTUdhAIqQurCnJVdb59e7yRBqZnuOQQgAVQoOA6g/D1ZSnu1G71ntLQ0FGCfeuqp+MIXvoBrr722aL/1qlWrcM011+Atb3lL0xpINJNa5/3NtZBIaeDq0xRkTBuG5eBQLIPuaKAoyO5SBJIZC3++dQBHD3Ti+GP6MDWVqrrfphntrffYh3985/E1O/JGLVbHxqCeiIjmarELjlUbw0z37TZWdQRgOxJSzv+RTrPp0x0pEQxoOPvEdUikTbQFfWgL6zP2xzPdc0i3dq1TJQO88D1ZiHOlm22miRVa+hoKsH/3u9/h3HPPxV/+5V9i165dmJqaQnt7Ow4//PCyVA6ixeTtd9k3loRPU4p+P739Lv094Yr7XSoFrjJXSM0wbdiOxFTSyAfYha931uv63T1D1aZfm9je2aa3NTPFbbE6tqU0W01ERK1rLuOE+ZTv24PeEaDT0eZ8pa7Ppk+v1Q/PFCjOdM+zpo2gX4Nh2QhJrep70r86gq/e8cySPFe61uIQLX0NFTn78Ic/jAceeABCCBxxxBE44YQTcMQRRzC4ppYz20IihSoFrkKI3J4hAQHAMG1kDauu15uv9i7WeZrNKAzTyPmWC13tlYiIlo7CfmX3gRicasugOXMZJ8ynhe7bZ9Onz7UfnumeB/0azt86gICu1XxP9o0meK40taSGVrCj0SgCgUCz20I0J9VShpu9Ryjo19AVDWAq6X7ox1ImArratP0zs23vYqW3zbUwzDMvjuF797+AVw4l616FrjcdvhVnq4mIaH6VrqpqqsC6vt0z1kKZ732xjWxpKurbtfnv2+vt04eGY03ph+u55wO9bTUff27XIZ4rTS2poQD77//+7/G5z30Ou3fvxjHHHINQKFT2nBNPPHHOjSOq10wpw43sd6kVuAb9GhQBpLIWLnjDBhy5tr2p+2dm097FSm+rNAFhmDYcR0JRRM2O7fnd7ux3Km0iFNDqTi1frGqvRETU2iqlN9u2g8EDMXxr2w5cMsOWpfnaF9voliavb98/lkTQXzxcn4++PZEyYdkSuiaRyVpQFFE09vH69F37m3M6CzDzPZ/p8cXeP09UTUMB9r/+678CAL7yla8AQNmAXgiBHTt2NKF5RMUqzQLvHJqoa8/QbPe7zBS4prI2+nsieNMJ/Q13wLVmtett72Id+1DYsTm5/ehmQTE3VRHw62pZx+ZIiXseGUQ6Y6GjTYdbyqS+2e/FrPZKRLQSLYWCktWym1SfiqBfw8HJTF2rqs3cF+tIiYee2o8fPzIIy3LQFvLBF1TrrlPi9e23378Th2JZhPwqVHX++vaxyTRSGROJlJG/fz5NQXtYR8Cv5YNVKdDUfnime17r8VbdP0/UUIB9++23N7sdRDOqNgucTJvzkjI8U+Dq9yl43dE9eH73eEODjmYW6lqMYx+8jm3oQBwZ04KEgCqEe7xG7jxwCSCVLu5k94zEceBQEm1hX65w3PRjM81+c7aaiGjhPL97HD/+7e6WLyg5Y3ZTcGGzm7z+/U97J2E7EooQsB2JaFhH0K/VPT7ZvKELl523GT99fG/uHOz56dt3DI7jp4/tyffHblcu8iemdLX5kTEd9PeEcfhh7S3TDy+Vc6WXwiQVNVdDAfZJJ53U7HYQ1VStsuXQcBzprIWONv+8pAxXC1w7IzogBO7dPtTQoGM+qm8v9LEPihB46ykDuO7OZ2E7gLtFzD1GxIG7982nKdj26B4ck8sgAKbT0HyqUrGoWa3Zb85WExEtjGdeHMO3tu1Aegkcf1RPdpOdXpjsJq9/T6ZNOI7MTzwblo3xWAZd0QCCfq3u8cmWjV3Yenw/nn5hGFPxbNP7dm/1P2va6G73YzyWhSMlFAGoArAciUOxLLrbAzj/lAFs6GutfrjVz5XmqScrU90B9g9/+MNZvfCFF144y6YQVVarsFU4oCGVsZDMWIgEy2eum5EyXBq4jk2lcf9jexsOjuezUNdCH/sQDmgI6CpELmXMgZvwrWsqomEdqiLKBhCRkA+aKmDaDtQKx5jVmv1eKrPVRERLmSMl7nzwRWQMe0kUlGyV7KbC/j0c0JAxbIjcqSMCgC0lYkm3CvZsxieKIrBxTRRWjzPjc2ercPVf96noigrEclu+HLiLFUIA55y0Lj+2abV+uFXPlV6KZ3RTc9QdYP/zP/9z0X97H7ZSyrKvAQywqXlqpX6pqgIhANNyYFgO/CUda7M6VS9wdaTEl7//dF3BcSkvReil/VPYN5ZAyK/Ny6r7QkqkTChCYHVnEJYt4Tgyl1oGONK9ZstyigYQ63vbsKY7jH0Hk2gP++DtwQbqm/1u9dlqIqKlbmg4jv2jiYIzmKe1Yj81Y3ZT2sLaGv1Ks1J4C8cr0w0AINz7pmB6vCKAquOTwva0t/nR3l5eTLhZSlf/g3534tzI1VcRAkhnLPS0B/Pf04r9cKudK81TT1a2ugPsX/ziF/l/37FjB6666ip84AMfwFvf+lasXr0aExMTePDBB3H99dfj//7f/zsvjaWVqVbql/dBZVgObNsBCgLsSsHaXDvR2VSxPrK/I/8zf/nkPjz0zCuYTGRhWg4yho2s6aA9jLLqoEupUJe3amDbEn6finTWwlTCnfn2pt4UAYxNpfPfowiBC96wwZ39jhtuFfFZzn636mw1EdFyEE+ZsGwHwUDlyelW66eqZTfZloNU1kZAV/HWUwYq9hlzSeEtHVPEk0Z+vKIIkR+faLm+SQBwANi2g2xuT3Np0N/oUWONqrT6L4TIL1gYpg1NU8omAtgP18ZTT1a2ugPstWvX5v/9wx/+MD7wgQ/g7/7u7/Jf6+3txbvf/W4YhoEvfelLOOOMM5rbUlqxZkr9Cgd8MBMGUhkLmqpUDdaasQ9mtlWsn3lxDP/fD/+APSMJSLgV9jVFABIwzeL9WJ6lVKircNXAdpyCvVvuTL3lSEgI3P/YXvR1hvL3ecvGLnzw4uPy52A3MvvdarPVRETLRVvIBy1Xsdq3AGcwN0OlVVVNFdiwJopj1nXgvgr9/6s3duFXT7/SUApvpTFFR0TPZ27pPhXtYR2HYhlYub3Y3sRzKmMhHPSVTSbP9aixRsyltgn74ep46snK1lCRs5dffhlbtmyp+Njhhx+Offv2zalRRIVm+vC3HIl1vRGE/CpGJtIVg7W57IMpnKGOpQwoCura5+Wd9Tw6noKEhKa4navlOLnXdf/H24/lVtReWoW6vFWDW3/6Ag5NZSGlewa2gIAtJVRFuNVHDbssFeq4o3rQ3x3Erv1TnP0mImohA31tWLs6gt37p9AeWfxCVvUqXVVtb/NDaCq+fsfTZcXa9o4m8OK+KfhUBas6ArNK4a02pjg4lUHGsGE7EqvaAwj4NXRHA5hKGjBMG1K6aeHreyO4YOuGonFHs44am22mHmubzI9WqQtAi6OhAHvDhg34yU9+glNPPbXsse9///s4+uij59wwIk89H/7vOPOIqqlKc9kHUzZDrQgYpoOsaWBVe6DqoKN/dQTX/fezSKQMSCmhKUp+b7IAYAkJSDfINiwHGcOC47iz2n5dxXknr18yndnmDV0496T1+O4vXoSUcP+BhF5wfqaq2hVToTj7TUTUehQhcPGbjsL1339qyQVdhf2Kogp89b+frVisLSwlEmnT7ZdnkcI705jCtjIwLAcT8SwiQR90XUWn4kcsacCnKfjzUzfgjOPXlt27Zhw11mim3kLvqV4Jx1bx1JOVraEA+4Mf/CD+1//6XxgcHMRZZ52Fzs5OHDx4EA888ABeeukl3Hzzzc1uJ60glT546/3wr9TpNLoPptoMddawkTVtHJzKoD2sVxx07BtN4MChJAK6W+W8oI4XhBBQFcBxAFUFLEvi4GTGfUwRUFWBbY/ugRBiyRTs6ukIIhTwIeRXIaVb8bRwxpapUERES8txR/XgsvM258/BboVCVrNVq1ibI3MT3rYDw7TLVhmr9VszjSmiER2JtIlV7e7KtXffBvraat63uR41NteK1Qu1p7rsbHVFoKNNxwlH9WDLxq5lE2wzM2BlayjAPvvss/H1r38dN954I7761a/m0kIVvPa1r8Wtt96K17/+9c1uJ60QM82+NvLh38g+mFoz1N0dARzKBcQZw4KTQdmg47ldh2DZEmF/7mfmqoh6RO5//D4Ntm1C1xREQjqCAW1JHeHgTYaMjKcA5Aqj6EyFIiJaDrZs7MKR/e1LdrWxVrE2RRH5rVmOI8ser9Zv1TOmUITAX562EdGwXvd9m0tK8Wwy9QBUfT/nO6us9Gx1W3EwmTAwEc9i8EAc9z++F+tWR5bMBM5MWrHaOi2MhgJsAHjTm96EN73pTchms5iamkJHRwd0XS973g9/+EOcddZZaG9vn1NDafmrd/Z1th/+jXRa9cxQZw0bbzvjcERDelkn5Z31rCjlVUSB6Xg7lTEhhEBPZxCK4nbWS+UIh8LJEMtykMpaSKZNdEf9RYMZpkIRES1dS3krT61ibbqmQFUFTNM9iqpQrX6r3jFFNKzP6r7N5aixejP1HnpqP57409icir02qvRs9axhYzyehSMBVXG382VNG3tHE0tigaFerLa+MlWefpsFv9+P1atXVwyubdvGJz7xCRY9oxmVzr7qPhWKcNOMOyJ6vkiWI8tnmWfidVrJjFV0bjsw3Yn2dYWKOq38DLVWI1XLkYiGdLz68G5s6IsWfVh6Zz0nUibawzoUkauonTsb2nEkJNz/bo/o+eDaU5q63mq8yZB9Ywn4fSra2/yIht3qqWNTGcSTBhwpYZg2JhMGU6GIiGjBecXakuny/h9A/uSRVNaCYdp19VuNjCnq4aUUB3QVkwmjqD2HYtma/Wg9Y5asYePHjwzm++1oRIffp+YXMXYMjs+qvbNVmq4/lTTgSEBT3MUIRXGP/AwHtDmN+VqRN0lVabxIy9OcA+yZVPpAIyo1m33Ss1Wr06rWiRbOUFcyU8qzd9Zz0O92FNGQDp8qYDsObNv9m1jVHkAooCEcrH7OqG3Lltu3XG0ypC2ko6cjAEUIxFIGphIGsqaN/p7wspmJJiKipcMr1lat/48EfbjojRvR3xNB1rQRq6PfamRMUS8vpbi/JzzdHsPGhjVRXHbe5qr9aD1jlqxpw7Kcpi9i1MtL19c0BYZpw7QcqAX3SMDN7nMkWnqBgageDaeIEzXTfJ8XONt9MM2o/lh61rNf1+DXgY6IjjOOPwwb+trw9bufW3JHONSaDAn6fejpEEhlLVzwhgEcubadqVBERLRo6inWdvZJ62eVwjufe2srHTV2/DF9mJpKVQ2gZxqzxJIGADdlfjbFXpupMF0/v+e9oCne1jlFESyMSkseA2xqCQtxXuBs9sE0q/rjcUf14LCuAB559gDGYxl0RQM4aUsvNEWBI+WSPMJhpskQn0+FyNro6wwt2X17RES0fMxUrK2Rfebzube2sD2apkBRar/mTGMWXy513FdhfAW4hVqNpI3ndh0CgHmZGC88Wz3oz7UjF1VLKeFICV1ToWuKu7rdggsMRPVigE0tYa4rxvWeqTibTrQZM9TPvDiWX8H2Coo88txw/vuX4hEOCzEZQkRE1EzzUaytlQrA1RqzvG5TD+793VDFfjudtTCZyMKyHNz/+F786ulX5qXwWeHZ6qmsBVURMG0nX+BMEW5hOACLvsCwEs7ppvnFAJtawlxWjGc62msu5jJD/fxutxBYKm0iFNCqVkVfakc4NCN9noiIiJqr2pgFAJ7YOVbWb6ezFsZjGVi2hK4p6IoG5vWo0MJ0/b2jCVhpB7Yj4dMUtEd0qIpY9MKotcaUxx65asHbQ0sTA2xqGY0Em/Ue7TUXjcxQO1LinkcGkc5Y6GjT4W00qnQE11I7wqHRyZDCGeH2Nj/a20OLdAVERETLU7UxS2m/rarCXbm2JVQF6Gzz5wufzcdRoY6UeGnvJCxH4q/OOBwSAi8MjuOJP41hMpGFYTiwVbmoCwwzjSnfqyo4tTO84O2ipYcBNrWU2QSbpdWsvRnZVjhHes9IHAcOJdEWdguKFBbmrFRQZC5pZouRyjTbyZDSGWFNFVjXtxvnnrgOR6/rmNe2EhERrXSl/baRdKuK65qCzjY/Av7pkKDZhc92DI7jvkf3YGQiDcO0i1aFzz1loCUWGOoZU97zyCC2Ht8PR0oMDscWvc3UuhhgU8upN9iczdFeC71HKpEyYdkSPlWpeOxFsypkVktlOu/k9QgFfU378K8UxNc7GVJpRti2HQweiOFb23bgEh7hRURENO8K++3ndh3C/Y/vRVc0UHF8MNtxSrXJfm8MkDVsRCM6ggEVpjl/aeiNqmdMeeBQEvdvH8TDT+4rqq0zH3vWK+He8KWjoQD7E5/4BD7wgQ9g3bp1ZY/t2rULX/ziF3HTTTdBVVXcfvvt2Lhx45wbSlRqvo/2motIyAdNnS7gUaqRQmClH6yptInbH/hTWSrT4HAcX7vrDwjo7jmXc/3wn2mPe63Ji2ozwqpPRdCv4eBkZtGyDIiIquFAlparwkWMXz39SlMKllYbJ7z1lAHclxsDdLb54dNUWLbTEpmGpeoZU8aSBr73sz/Btp2atXXmw3zWG6LmqzvAfuWVV/L//sMf/hBvfvOboarlf5APP/wwHnnkkfx/n3TSSXNsIlFlrVzNen1vG9Z0h7HvYBLtYR8KD3tspBBY2QerIpA1bUhIrGoP5gNX25EwTBuWLSEA9HaH5vThP9c97oUzwmbu7EtFEfD7VHdGOLh4WQZERJVwIEsrwcwFS02sag8gnjQwOByrOslUa5zwrXt3wLQdRIKtl2lYaqYxpWnaMEwbiiLQFfWjVm2dZk8WLES9IWquugPsT3/603j44Yfz//2hD32o4vOklDj11FPn3jKiGTS7mnUzVywUIXDBGza4BUXihjvT2eARXJU+WNMZC6mMBSGAeMqET1OgCGAqkYUjJTRFwHZkvqNo5MO/GXvcEykTWcNGMu2mzHt8moKu9gA0TYGdXpwsAyKiUhzIrmzLPXOh9PrOO3k9bn/gT2UFS2MJA4btYGwyg1u2vVB1kmmmccLByQyypo32iF6xPYuZaVhqpjFlPGUCEGhv89dVW6dZWrneEFVXd4D9mc98Bo888giklPiXf/kX/MM//APWr19f9BxFURCNRnHyySc3vaG0vDXSqc3laK/Sn/v87nE8+eIYJuMGbKc5KxZbNnbhgxcflz8Hu5EjuKqmWOfSzh0JTMSzULz/diQUBRCKgHQkHMftARr58G/GHvexyTTSWXelXVMUd8JXAqblYGwijWhI55nZRNQSOJBd2ZZ75kK16zvz+MPw3O7xfMFSR0oYtgOf5vbzEoBpOhgajuNb9+3AW08eQE9HEJGQD1LKmuOEUEBDxrCQydqIhMpTrxcz07DUTGNKTXPbr89zbZ1SrVxviKqrO8Du7e3FRRddlP/vU089Fb29vfPSKFpZ5tKpzeUcae/n7h1NIJk2IaW7stoR0aGqSlNWLI47qgf93UHs2j/V0Ix4tQ9W03ZQ+PHuPuJ+xXEAkft3pWD/92w//Oe6x92REr/fOQohvKZJCAhAAKoQsGwHk8ksNq3rmNOZ2ct9xYGIFgYHsivXcs9cqHV9B6cy+JtzNiEc0BBLGvjRb3bj4FQaQb+GyYQB08qNN3KZgd/5+YsIB906M5GgD1nDRjhYHiBL6Y7nINzxRCigAqpS9PhsMw3nW60x5euO7sG27UNNra1Tj1auN0TVNVTk7LOf/Sy++MUv4i1veUuz20MrTDM6tVrVrGeqapnOmsiaDgB3VdhyJMbjWXRHA+iI6E1ZsZjLEVxeNXJdk8hkLSiKgC/3YVrIbZoCwL0W25HwqcXtne2H/1z3uO8ZiWNkIo32iI5Y0oAtJRTkFrFzTZOOxOs29TR8b5f7igMRLRwOZFemVspcqDRmacZrznR9920fwj++83jsGYljKmnAp6kYj7lbzhQhACnh7fKyHffcbL9PxaGpDNJZC8m0ibbQdBp4OmshljRgWA6kA2Sljf1jKXRF/QgGNJjm7LfLzfUe1DsRX21MCQBPvXiwabV16m1rKNi69YaouoYC7L6+PiQSiWa3hVaYZnZqlYLYeqpahgM+pDIZKIqAIgQUAJYjMZU00OsPLfqKxdhkGqmMiUTKKEoPt2y3g7Od6eeW3iLLlhidSMOnKYiGfMiYzqw+/Oe6x90brEYjOjTVrb5pWg4cAEK6nbOiCPS0B2d1TzzLfcWBiBZWKxfOpPnTKpkL1cYsf3HqRpzaGW74dWdzfYmUCctykLUcOFJCzRdQLX7NZNpCNOxHd3sA+8eSmMqlVCuKgnTWwngsA8eRkAB0n4Jw0IeppIFDsSyCGQt+XZ3Vdrm5aGQivtrCSLNq68ymrb2dQbSFdEzEs02pN0QLo6EA+53vfCc+//nP46mnnsKmTZsQDpf/4V944YVzbRstc/PZqdVb1dLOdQCF6xWqEDAtB4ZpL+qKxY7Bcfz0sT35QhpCAALu0V9SAqoAvCwlKQHHKe4BRW7Lc9awMWbYiIR8eOssPvznuse9cLAa9GsI6CqMXCVxVRVQhUA6a1UdrNaacW6lFQciWh6aXTiTloZamQtSSjhSIpO18fK+qbq3IM1261LNMcu2HYi0BbB+Vajp1wcUZ2ZEQj5AuHVSFCEgcpmA3pY0b8eXlTuxRPepaA+72X7jsSzaQm4g7QXXqiLQEfEj4NfQFvRhPJ5FVzSAS845GgN90Xnvn5s9Ed+M2jqzbev+gykoufFeo/WGaOE1FGB/4QtfAADccccdFR8XQjDAphnNVzrebKpaSkx3GAJuoCohIaWE7UhgjisWjiOx+0AMU/HsrPYHe9eQNW10t/vdVC1H5oJst722A2iqQHc0AEURODSVgWU7EMJ9TDqAVbBTO5kxse13g1CAujuBuexxrzRY9edXhSSmkib6V1UerM4049wqKw5EtHw0o3AmLT3VMheK0pylxE9+N4SnXjpYd32XeldMHSlxz+8GkUybCOWKiglMj1mmEgbufPBFXPn21zT1+jyFmRnre9vQEfEjnjKn+9aCgi9e2yAlsoYbYIdDvtxYJYDJRBaGaUPAzVJrD+sI+N1QQ+SCbS8jbzZ/R43UWpmvifi51tZptK2dER3hoG/WYzFaHA0F2L/4xS+a3Q5ageYrHW+m4KuwqmUooMGnKciaNqySqpAT8Qx0VcXAmraGViye3z2Onz7+DPYOx9yU7lnsDy68Bt2nIhJ03H3MTnEbA7qKgF+DYdqwHXevlAMJn6bAzuV0qUK4kwYQ2DOSmPWsba097rXUGqymMhbCQR8ueMOGstepZ8bZdiT3ShJR081lUpGWpkqTwWVpzpqCcECbceWzkRXTh57ejxf3TcGRQNpwg1OfpiAa1hH0awgHNewfTWBoOI51PZGmXJ+nNDNDEQJnHHcY/utnL8Jy3CM/UdLVS7iLEZNJA6mshZBfg19Xccm5x2DXK1P44a93Ixrywa+Xhxg+TYFly1n1zY3WWpnPifi51NZptK3xtIlLzt0EIQSLui4BlUenM1i7dm3RP6tWrcJhhx1W9LXZcBwH1113Hd74xjfi+OOPx9/93d9h7969VZ8/MTGBf/qnf8KJJ56Ik046CZ/+9KeRTqeLnnPffffhvPPOw2te8xpceOGF+N3vflf0+IsvvojLL78cJ598MrZu3YorrrgCr7zyyqzaTXPjfegnMxZkSXDrfej3dYVmHdzmV8a1yr/ewYAGRRH5QmFBXUXJjwcAmJZE2rCwKhrAnpF4xWMZqtkxOI5vbduBwVdi8OsqohEdfp+a72R3DI7XfQ3prIVE2sylW7n/eKnhybSFeNKAbTv5VLb8h60Q8KlKfn85gNzkgo17tw/N6nq8zuTVh3djwyzSurzBan9PGFnTRixhIGvaWLc6gg9cfBy2bCzuHEtncXWfCkUI6D4VHRE93/ZwUMtPzlTCvZJE1KjNG7rwj+88Hh9627H4n+dvxofediz+8Z3HM7heprzJ4ICuYjJhIGtYFdOcS/uh0j603v6r8Pt2DI7jJ78dhG1LKMLd+iUEYFg2xmNuATFNU2DZTu4c5rlfn2Ha7lFcpo3JhFGWmXHGa9diXW8EihCwHSd/3GchkUtZzpo2JhJZtIV0bOhrw5Fr2xHQ1XyQKKVE1rSRzlrImra79W4WfbM3YbFvLAG/b3ZjqZnGgpqmwJ5lsD9f6m1rMm01NBajhddQgA0Au3btwpVXXomTTjoJr33ta/H888/j05/+NL797W/P+rVuvPFGfOc738FnP/tZfO9734PjOHjf+94HwzAqPv+KK67A0NAQbr31Vnzta1/DQw89hKuvvjr/+Pbt23HVVVfhXe96F+6++25s3boVl19+OV5++WUAboB+2WWXIRAI4Nvf/jZuvvlmjI+P433vex+y2WxD94Nm5kiJweEYntt1CIPDMQCY1Yd+vQpXxivx9gT7dRWTiSySJRW5i9sM/Oa5A7j+zmfx5e8/PWNg7F2n28m66d31dLLVrsE0bcSSBhzpziSripL/R8DdZx1LGUhm3WvQcrPejjNdnASYTutSVaVo1nYhVBqsfvR/vBbHHdVT9tx6Z5wlxLxMztSr9Hd5NpMVRNT6Gp1UpKWpcDI4lbVgmDYAN825OxqYTnMuWfksVE//tW8siV8+sQ+DwzFYjoN7tw/BtJx8jRUvdVrN7X2OJQ2Ypg1NVdA2hwnjapPd/T3hslV1RQi8/YzDEQ3r8GkKIkEfSk+l8v47/+VcH1i4cJLOmhidSGNsIo2DUxmMjqcxPJ5CW0ifsW92pMSuAzF8/5cvIZk20R7WZz2Wqmcs2CoT8UuprVSfhlLEd+zYgb/+679Gd3c3/vzP/xzf+c53AACqquKaa65BJBIpOjO7FsMwcMstt+CjH/0ozjzzTADAV77yFbzxjW/EAw88gAsuuKDo+U899RQee+wxbNu2DUcccQQA4DOf+Qze97734R//8R/R29uLm2++GW9+85txySWXAAA+/vGP46mnnsJtt92Gz3zmM/j5z3+OVCqFL37xiwgEAgCAL33pSzjzzDPx5JNPYuvWrY3cFqqhVopPs9Px6kmHWrc6gvNOXo8f/HoXBg/E852FpipQBJAxpz/kHAdIpC0MDsfrSq/Od7JBLfezpz/8601L8q5haDheVGzEY0sJv+52MqmshQu2DuB3fxzBwamMm9IF5Hs+b2Vb11TomgIJLHj6dGk6VbXBar378lNpc9H2SvJoMCKi5cfbDvXgk/tqpjlX24JUq//KZC13f7Ll4O5f70bA7/bfY5MZRMM6bEfCsBxouT5L5E41MS0H8aSJI9d3YqCvDY7d+GRuvdu9dgyOY9uje2DZjlvMLG3ms/zc4YXInYvt7hEO5dKXvTHN+acM4OZ7nsfYZAYCgKIICOkGzVICU0kDO4cmqvaXXh+7byyJeG6/tnefglUmOiqNpZZS0cKl1FaqT0Mr2P/2b/+GV7/61bjvvvvwiU98Ir+C9MlPfhIXX3wxbr/99rpf64UXXkAymSwKaqPRKLZs2YLHH3+87Pm///3v0dPTkw+uAeCkk06CEAJPPPEEHMepGCSffPLJ+dfbunUrbrzxxnxwDQCK4t6KWCxWd9upPjOl+ABoajpevelQWzZ24y9P3YiQX0NXewCru0Joj+j5c7ELWY5bVTyRNmdcfa6V6lNakbTa63jXoGmK+xw5ve/JctxUsvawDp9PhYBAX1cYF59xBIJ+Lb+qKx33Z9m5tPFoWIcQoqVnQmczizubGflGVFqlnku6GhERtTZFiLI051LV+tBq/Vcma+FQLAPDciCEQFvYB79PxfChNJJpE5bjoD2sQxFu/57r8oFcUKppCi5+01FNmTCulZnhSIlfPrUPN9/zPIaG44gEfThsVRhtQfc6VUWguz2A1V1BrGoPoKcziNWdQYSDvqJU600DnWiP+POv7V2P7lPR2x2C48iq46jCPlZThRugF6TMx1MGMrkMg5lSvGebGr+YllJbqT4NrWA//fTT+PKXvwxN02DbdtFj5513Hu655566X2t4eBgAsGbNmqKvr169Ov9YoZGRkbLn6rqOjo4OHDhwALFYDKlUCn19fVVfr7+/H/39/UWPf+Mb30AgEMCJJ55Yd9tLVds7sVypqlL0/5U4UuK+R/cga9jobJuujKjqKnSfgsm4gfse3YNXHdGNI/s7mta2Y49chfeqCu55ZBAHckcpaKrA+tURXPCGDfm9vx3RAPy6u7Lr0xQciGWmj6PILT57e7AcKWHZDoYPJbH/YBIb11RefW5v80NTBWxLApqb9gUhkc5amErkzoKWEvf8bgjPvHyoqD2l13DRaYfjPx/YCduRELnOyKcp6GwLIOBX8/uZ2tv82LgmiveqCn7yyCD+tGcSluNAgYCuqWiPuDO/Ukqkciv4h69tX7QP62q/O4evbcdh3WHsHU1A95XP4pa2/dgjV+FVR3RjaDiOeMpEW8iHgb65F/14fvd4/nfHsiU0VaCvO4xk2qzrd3muP7+ev62VivemtqVyfyr1l0ul7TNZDtexHK4BaOw6ZtsP1fy+3IqtdwqIrin54L094kP6kDsuWNMdwqr2ICYTWZiW4449pISmKLjojMNx3FE9iMXSlZoLwB1rzaUffH73OH7y2934095JWLbMnUYi0RHxIxLyuXVgpEQ8ZaK3K1h0TwrHIZqmYPeBGBIpA72dQSD3OqriniKiqgoUCIyMp8rGUaXjRTM3ISGEuwJu2RLjsSwU4a5gq4pAQFfzP7eSeseC9ZrPv4tmt7WWlfz3vVAaCrD9fj8ymUzFxyYnJ6Hret2v5RUnK/0ev9+Pqampis+v9Pp+vx/ZbDbfrkqvV21/9be//W3853/+Jz75yU+iq6vBVVNFoLOz/DzwlSAaDVZ97KW9kxiZSCMa0eHTyiuFR8M6RibSmEhaOHJdR1PbdWpnGFuP78eu/VOIJQ1Ew7rbIRZsJmpvD2Fd324MHoghIgSsXPqV9wwJN9BWFQUid3SXaUtAUau+34WvGfCrUFWBVMbG+FQWjnTPsPb7VLRFdOw76K58fvDi4yruST5ioBPBgIZE2p2hFUK4RcsUt3NJZW1sWBPF8cf0QVFE/pp/un0Q3//ZThiWg47c3iXTchBPmwgHfXjXOcegu2v21UibrdLvzrvOOQZfv/MZTCVNtAV98GnKjG1v5rU88+IYbrt/J9IZC21hH3yqAtN2sGckjmTGQne7f8F+l2v9ba10vDe1tfL9mam/bOW2z8ZyuI7lcA3A7K+jkX6o0vfZUsK03IUoVSjoigby/Yca1KH7DBiWDduWiIR87pFXhg3bdpBImzhibQcuPPPomtfwzItjuPPBF7F/NAHLdqCpCtaujuDiNx1VcVxR6ftvu38n4kkDjgRUTUBAwLQcHIpl0NMRhF/XkDUsWLYD2wECuhvQSCnLxiG7R5JwJBDwa0XjLY9fV5HKWmXjqNLxoqap0H0GsoZdtNqtqO7EhWk5gACEVn08BtQ3Fpyt+fq7mI+21rJS/74XQkMB9qmnnorrrrsOJ5xwAnp63D9eIQSSySRuueUWvOENb6j7tbw0bcMwilK2s9ksgsHyGxYIBCoWP8tmswiFQvD7/fnXK3289PWklPja176G//iP/8A//MM/4G/+5m/qbncpx5GIxVINf/9SpKoKotEgYrF0/kioUvtHYjBMG8GACqvCc4Tizn7uH4mhOzI/KcvdEV/+taemyt+jc09ch29t24HxWDa/3UHm/8cNrt1aom7aNQDAsTExkaz6M889cR2+dd8LOBTLIqSrOBTPwnbc61dyFUk1RaA97MNk3MD37n8B/d3Bohnn53e7lcidXEq4lG6wnzVtjIynENBVRII+nHviurLr2rp5NdoDWn4mNJ4yoakC/avCuOANG7B+Vahm++dbrd+d9atCuPScTWUryAvRdkdKfO/+F5BKm+hoc1PqHenOvgf9KhIpE7GkiZBfK0sfbObvcj1/WysV701tjdyfaDS4oCsA1frL5fLeLofrWA7XADR+HY32Q6XflzFsOLm9yh0RHbpPKRoLufuw0xiPZdHR5qaDO457lGXIr+Hck9YhkchUvQZvnJAxbISDGoIBHyzLwe79U7j++0/hsvM211z5LOzzQgEV6azlFlAVbuae7UiMxzJuO00LtiORyphQFDdVPpl2a54UjUMc261lY1hFR7AKCKiqQNZwHy8dR1UaL0bDPoxmiwvRelXNVdU9KeWOB3Zi/arQjCv2M40F67FQfxfNaGstK/3vu1Gz6SsbCrCvuuoqvPOd78S5556LY445BkIIfOELX8Du3bshpcSXv/zlul/LS/ceHR3F+vXr818fHR3Fpk2byp7f19eHn//850VfMwwDk5OTWL16NTo6OhAKhTA6Olr0nNHRUfT29ub/2zRNfOITn8A999yDT3ziE/jbv/3buttcTbV9o8udbTtVrz2UW8E1c/uaHUdCUUT+Q9c03b1MIb+6aPfv6HUduOScTbjzoZcxODxdFdTrYBQvVTyXLr6qPYC1q8I123v0ug5c9tZj8NPH92L3/kkYpnu2pe5T0R7W4c8fDeaey/3KoSR27Z/KF+pwpMSPf7sbacNCd3sAWcPGVNLIp455s7l/c/bROHpdR8W2HL2uA1e+47iKBU1a5Xe12u/OYrV9cDiGVw4lEQpoAETR8W2qokAId9Y8azrwl5zdPh+/y7X+tlY63pvaWv3+1Gpbq7e9Ho4j8dK+SUzFs0v6vNrl8F4AjV1Ho/1Q4fe9tH8K9zwymDsrWis7ElRRBMJBH1a1BzCVNGCn3UKva3OFXo9e15EPHEqvoXCc0BGZ3rbk01S0RxRMJgz8+Le7cWR/9e1ghX2e1zQpp7P4FCHyqdrtYT9iKQOW7WAqbpS102vb2lVh9OYKdnWoBSn2wt1fnkibWNsTLhtHFY4XvTGiIgREboUhf+9yExbRsA5VEWXjp4Wwkv8uWlErXkdDAfaaNWvwox/9CLfeeiu2b9+O9evXI5VK4YILLsBll12G1atX1/1axxxzDCKRCB599NF8gB2LxfD888/jPe95T9nzTzzxRFx77bUYGhrCwMAAAOCxxx4DALzuda+DEAInnHACHnvsMbz97W/Pf9+jjz6K17/+9fn//tjHPoaf/exn+Pd//3ecf/75jdwGqsP63ja0hXTsG01ASlnQASiIhnzImE5LVEbcvKELn1jfgS/85xPYP5ZyU7qEmyYlAUhHwnIkfJqCvzr98LLOypGyrBPesrELW4/vx50/fwF3PbRrVhVJS4/7CPg1BPyaW/jCcVPVHUdiMmnguV2Hqg7gSqt3e4W7alUQXQyV7l9p2xdCrSqwuk+FT1NgWI474CkIsFnlk4g8z+8ex08ffwZ7h2OweNLAklZvP1SpD9vQF8X63jY8/eJB7BtLQvepZfu5vVNNrnzHcdg3mphV31zrWDDTcqApAvvGkhgcjuPwKjVjCvs8AeT6ODu3ii3cSFvmAhhH4uh1HfirMw5HMm3VbOfrNvXglYNJHJpyq39rmgLbcpDK2lULdlWqpO04Ml+J3HEkVFVBV9QPf+5eOrk98c0+FaXamISoXg0F2ADQ2dmJSy+9FB/5yEcAAFNTUxgbG5tVcA24e6Xf85734Nprr0VXVxfWrl2LL33pS+jr68PZZ58N27YxPj6OtrY2BAIBHHfccTjhhBPwkY98BFdffTVSqRQ+9alP4cILL8yvUF922WW4/PLLsWXLFpx++um46667sGPHDnz+858HAPzgBz/Atm3b8LGPfQwnnXQSxsbG8u3xfg41x86hCUwlsnCkzKcdQbppzmNTNtrD+qJURqz04akpCv7q9CNw2/07kUyZMG0nn6YkpdvxXPTGjdiysbvotaod2/QXp27EqSeEcWR/x6wrklYL9LxZ3XTGxGTKwPcefAmqEHUN4Fr1eKlWaldhFVjdV77POhzwwUwYueIjyoIdDUZES8OOwXHcfv9OZE0HoYCKkOp+RngnDTTjhANqLTP1YfUcKakpyqwnlCuNEzJZK5/tJqW7ue32n76Ad551ZMXfu9I+LxrWMR7LuKePAPlia8mMhXDQh/NPGcDGNe113Qs328vG2GQafp+KgK5iw5oozj1xHY6uUKfEq6RdeK+EcH++lLnaDW1+BAoWKpp5Koo3Lnx+9ziefHEMk3HDLdBW8H4ee+SqOf8cWhka2nQVj8fxvve9D3/913+d/9ozzzyDCy64AFdccUXVAmjVXHHFFbj44ovxyU9+Eu9+97uhqiq++c1vwufz4cCBAzjttNOwbds2AO6M2g033ID+/n5ceumluPLKK3H66afj6quvzr/eaaedhmuuuQbf/e53cdFFF2H79u246aab8kd7eVXOv/jFL+K0004r+sf7OTR3jpw+iqGnIwDd56ZFO3A/SBUh0B7WsWmgc0HbtWNwHF/+/tO44Qd/wDfv3YEbfvAHfPn7T2PH4Hj+6KeBNW2IBH0IBTSEgz5sPKwNV1z8Gpx78kDZa1U7tulb23bgmRfHMNDnzsp6x2cV8jquvq5Q0cpnreOqvCM/bEciUOdRUa16vNTzu1urXd4MerX3ynIk1vVGsL43Mi9HgxHR0uX1eRnDRne7H7pPhSJEfv9txrBnPOaRlpZ6+tb5OlKydJxQeByYkiuIKoTAoalM1f60tM8L+jV0RQPQNXe8Zkt3W99AX9uMbS29F13tAaxqD8DvU6FpCv78DRvx6b/bWnNPeOm9SmXt/HV0Rf35c7CB6uOnRnjjwq/c8Qx+8PAu7H4ljkTahN+nFL2fz+/mUZxUn4ZWsK+99lrs2LED//t//+/810455RRcf/31+PSnP43rr78eV111Vd2vp6oqrrrqqorf09/fj507dxZ9rbu7G9ddd13N17zwwgtx4YUXVnzslltuqbtt1LjC9CXdpyKgazAsJ78PG1IinjaxZyS+YKnAXgeQMSyEAz5owcqrC5sGOjE4HMeuV6YgJHD42igGSto4PZgq3v/kpRJPJQzc+eCLuPLtr6lrBrtw5bNSqpRnMpGF7biVOEMBt9iW9zMnEwbu3T6ETQOd+debqZ2VvmchOI7EPY8MtlS7Ks2gl75X7zjzCGwa6GT6GBEVyfd5Qa8I4nQgLYRAOKBheDy1oH0ezZ/Z9K3euKKZ/UbpOGEqVwVcUwSklHCkhK6p6Ir6MZU0K/anlfo8v66iQ+hucVRNwV+8YQPOeO3amm2tdi/8ugbd557t/Pudo7joz46e8bpK79XYZBo/fWwPMoYDVbGbnjnmjQvTWRPZXL0gVRGwHInxeBbd0QA6IjomEwbueWQQW4/vn+EVZ8YU9OWvoQD7wQcfxMc//nGcd955+a/puo63vOUtiMfjsw6waXkqTV8SQhQVhnKkRDprN33vTDWz6Qx3Dk3MmLZca/+TEALhoIb9owkMDcfzs7Lea6YybiGT/lyBkNJZ4WqBXjpjwTAdqIq7+l/4c6sN4GZsZ52DvmZ3CLv2T+HAoeSc29Vs9b5XHCATUaF8n1flTN5K9TZo6Zpt3zrTfu7Z9rGF44RDUxkYpg0lty/ZkRKKEIiGdSiKUrM/rdbnDfS11b1Vq557cSBXjKyeUzZK71VfV6ju8dNsFI4LwwEfUpmMexSqEFAAWI7EVNJArz8062uoppW2xdH8aSjATiQSaG+vvAejp6cH4+NMoaCZ97M2c+9MPertDB96ej/uf2zvjKvciZTpnpstLcRTBhThVgT3CplomoJ01kQ8N5ia7Qx2pU7PyaVrdbUXp0p5Kg3gahXuAtw0q4xh46X9U1XbMx8dQixpwLIlQlXatZiD0flYbSCi5a2oz9MWv8+j+jQ6eTxT36qqAkbSxnO7DgFAzdet1cfW2vfrjRO+/8uXsHc0AQeAkICuufupvXHCTP3pXPu8me6F9/NjSaOh4HS++uTCcaHtuHvWC69AEYBhOUimTQQD2qyvofR3K5mx8O06sihp6WsowD7mmGNw11134Ywzzih77Ic//GHF47Vo5amV5rwYVZfr6QCSaRMPPf1KXavcz+46hFiy+Lz1WO686a5oAJoqoKkK2goGU7OtjF3aqcRSBu566GVoSuVrqDSAqzbR4RVDMUwbEsA9jwzi6RcPlgXN9abVz1Y0rENroQmYUotRxZyIli6vz9s/liybAOVJA61pLpPHtRYR0lkLk4ksLMvB/Y/vxa+efqXq687Ux75XVXBqZ7hqOzZv6MKl527C1+58FpqiQNdV6CVjrnr607n0efUsqGiqu6LeqPnokwvHhdJyvKLpkNI9rcUrlzAez0JLGgjoat3XUPq7pSiAYTqQEljVEWiJbXE0fxoqcvb+978fP/vZz/C2t70N//Ef/4E77rgDN910E975znfivvvuwwc/+MFmt5OWIC99KaC7+28M04YjJQzTxmTCWPCqy7UKhwG5c1kFcinZtVe5v/eLF/GL3++r/Dq2xNhEGpPxLNaujmCgry1/PNZzuw5hcDhWd5Gb0tnPk7b0Yk13eFYF0yoV7vKKoWRNG4D7AR/ya2XFxUrT6ptZtOfwte2zvpbSe9PIPSUimg+Ffd6hWHbR+zyqrd7in9X6Gq9vjSUNpDMmjFx/ms5aGI9lYJgONFVBVzRQtXhnPX3sPY8MwnFq928DfVGsXeUWBrNtB0auijjQ3GJg1cxUIDSZsbCmO4zD11avQL4YirNOFPg0BbYjYdkF524DEJAwLAcZw0Y8bVR/wZxKv1uqoiCVtZA1LWQNu+j5pVsKLMfBI88dwD2P7MYjzx2A5bTWGc80s4ZWsM844wzceOONuP7663HdddflzzfevHkzbrzxxoor27QyzXbv8XwqXVEHMF10TQDJjIXONj8mE0bNPXTJjIVfP/MKanV3EkAqa+P4o3vwwuAEfvzb3bOeIa82s/7qjV04OJXBRDzr7mkvOPos6NfKBnCV9nO7hdKmz5dsD+tFxUi8WdRm7d+uRFEELnjDBtyybUfdxd9mujfcw0REi2nzhi5cdt5m/PTxvblzsBevz6Pq6q3JIqXEtkf3VOxrACCZNpHOWkhlLAgBaKqAI92JdlUBOtv8+YC50ipls/Yu7xyaQCprI521kcpaEELAp7l7ry1bNmVyp1Yqfe0CoSY0RcGWDV3YtX8KneGGTwhuusJxYUdERzSsY2wiXfY8Kd331qcp+MEvX8KVb39N1des9rvlVUN3pLuvO1CS5eKl0f/qyf144k9jSGctSLhDvO/+/EWcv3Wg7CQbal0N/5afddZZOOuss5DNZjE5OYm2tjaEQqFmto2WiVbZz1rYARycysCyHdi2zJ8VqakKjl7XgSf/NFYzzcm2nXylyZn898//BF1TYEs5q/TqWiljB6cy2DzQgd+/MIbxWCb/ARzwa3jN4d2wHXe2vfAeF0507BtLwrAct/q4phTt0yoNmuvdV9XoPuktG2c/ATNfKetERM2wZWMXth7fj6dfGMZUPMsaDi2onsB272gC39z2AmzHKetrbr7neQBuMNXR5kcyY8G0HBiWO/WuqQLd0UBREFVpUroZe5cL+8SONh3JtOm2xbBhmjbWrY7gHVXOwa5XPZPa1erGOI6ElA7uf2wPfvnUfvR2BvHWk9dj84auRa+mXToxoFdYXHEnSJT8tjaveO26nkjF16z2u6Uowl3UEMJ9f0y7aJxpWQ4My8Zv/nAgXw3eW0BJZizc9dAuAGCQvUTMaRppamoK6XQajuNgcnISk5OT+ccOO+ywubaNlpFW2c+6eUMXzjz+MNz9692wLAfeZ59PVeBTFTzz0kG0hXRMxLMV943HkgY0tf6dFcmMhYwicNiqEJTcvmlvJvvQVAbf/+VLuPTcTRjIVRgFZp5ZPzSZwSPPjSCgu6lnigAypo1k2sKDT+7D7/44DN2noCPix+uO7sGWjV1Y39uWn+j45RP7cPevd6Mt7IM/V5CtUGHQvBCF6mYzAdOqR44RERVSFIGNa6Kwepja2YrqCWzTWQu6pmBVR7Csr9k/lgSAfN8eCfpgWA7SGROxpAlVEWUrlN7rFk5Kz3XvcqU+sS2kw8iliiczFsJBHzYNdDZ0n4DZTWoX9ufP7x7HL57cDws2IkEffJqCrGlj14EYvnnvDrz5dWvx3OBE0zLRqgXrMwXxhRMDe0cTgHD3z2qqgmBAQ9Cv5fe0S8ii4rWVVPvd8lLQva0EhWn/Ukok0u756I4EfKqYHpsJwCcA05a493dDePOJ66rW4aHW0VCAPTQ0hI9//ON45plnqj5nx44dDTeKaL44UuK53eMI+lWEon5I6Q6EdJ8KKSUmEwZCfomAT8WhqQz8PhW6T4FhOphKGpBSQq2SPl6NlBKmLeHPfVthcbG9owl87c5n0d8TyXcqM82sm7YDy3YQDvjh1zVkculpjnRTvg3LgWk5mEwYGBqO4/7H92Ld6unXP6K/HQG/u8+r9PWB4qB5oQrV1TsBM58p60REtDLMFNimMxYcRyJUoa8xvf3NQuT79sJjSONpq+IKJVA+KV1PH7t+dQSHr23H1FQq/5gXNL60fwr7xhII+bWi79V9KuBToarKnPrERia1FSGwvrcNd/7qZdiOg862ALKGjdGJNCzb3ZaXylj471/tQsCvoCMSmHMmWq0tdc/tHp8xiPcmBrb/cRjfe/AlBHwqQgGt7L13JzyKi9eWqva7JXLHph2aysBx3CJqjpT5bXECwk1FV8rHZkIIaIq7v/+x50fwhlevqfve0OJoKMD+7Gc/i8HBQXzoQx9CX19ffmWOqNUVBmilHZ8XoI3Hs4gENWRNdy8TpLunWlGAjjY/QgEN+0aTM/4srxolMD1T6RUXc6TbCTkANEUp6lRsR+ZnPw3TdveI5yYBDNOGZbuVLh3pdsATcXc/taoI2I6EaTlQBPL/nTVs7BtL5F9/00Bn3UFz7X1VtfdJz4f5TlknIqLlb6bA1t1TLRDwlwffhSuPpcXH8quUhruCjIJxRqVJ6Xr62AvesAGKMt2+wmAyY9hIZy1kTQftYZRVr59rn9jopHbh92UNG4dimfyChqYKtwgb3KratiOhV9mn7r1W4epz6deqHX01OBzHzj2T8PtUtEf0GYN4RQic8qo+PPLcMPaNJVG66VVKiWTawsa17W7xWrtyJZ5av1sBXYVfV90xnCMRSxj5bXFdbX789rlhd/BYiXD3go/HMnW9d7S4GgqwH3/8cXz+85/HBRdc0Oz2EM2rmQI0y3HPO7RtB6vaA5AADk1mYOUKgmmqAlVR0BnRMZGoXUlSyQW43r8DwFTSyO+tcaR0z6vUVUQKOpW3nXEEHCkxciiVP5cRcL/Hr6tusJ9byZ4cz8LIVUW3Cj7shXCfIxQBy3bgU31IpEzc+dDL+MTfvG5WQXMrFaprtbPViYho6aknsFVV4a56lgwXCoPdwn8HpoNO07SRzFhQVWXGSemZ+tgtGysfm6lrKvyagnQGMAwbh6wMutv9CPqn+7+59omNTmoXft/YRDo/7vGKfHkcCcRyx1+JXFadF7Q/9PR+PLFzrGj1uS2kA1IinjZrHn3lVQN3/5keL8y0nWym34ugruLiNx2VWyCpHGDP9BqRoA+XnH00QkFf0cTB9j8O45HnhpEvrFNKul/uigbqfwNp0TQUYEciEbS3t1apfVrZ6i2UUStAk1JiKmFASveMZr+u5c+I1hT3w9TrCKIRPwzLRjJjl/0MwF099ma2hRDwqQKGacO0HKhCQEo3NUjXps+r9DqVwVemkDHc54rcMrgEYDgyH0xLIRFPGvkAvpTE9H5uRwITcQOAxNBwHF/4zyfwV6cfUVT0zLLdtKdqQXPpPulQ0AcBdza3tKDafKo1M5w1LMSSJvq6g+hfXbn4CBFRq6in31rsIlBz1crtrxXYnnvyevzw17swfCiNaNgHvz49XC7se3xq8bVI6R7xtG51BKoiMDaVgcz19bUmpeupReKlaydSJmzHQSJtFa2g247EwckMutuBUMBX9zauWu9RpTGTlDJ/AoubYYeyAN77vnSu+JsqRD5oLDzGSxHIFYdz8in2mqYgljTwk98OwikoEJtMm9gzEgfgVmePRnSkMxZSWQOKALKGnd/3bliOm86tCFi2zKfre1mBuqZg+FDl1Plavxd/cepGHHdUDyYmamcxNrIwcdKWXnz35y8imbHgEyjLqrAciXBAw0lbemv+bGoNDQXYf/mXf4n/+q//wmmnnVZxDyfRQprNkU21AjQvAPZpSr4zzQfJioAi3Y4gkTIRz1XpLOWlhXuBr8j9Mz6VhV9XczO3Eo7jrjIX7vHxjgB7+NkDUPIdUeVrdhw3yFaFgJXbe134VOlI2JCwc01UBADhBv3Dh9K47f6dOPP4w9wK6tKBY0tY0kYqY5adYenx9knvGBzHDx56eVGOyKo0M2zb7n5zb0JibDKDr97xTNX2tPKAj4hWhnr6raV+HOFs2r9Yn8uVAttU2sS2R/dgbDKDjGEhnbXg0xR0RHSoqttPe3twp5Jm2Qql139PJLKQ7n4wtEd0nJernF2Nt3fZa8uekXhRYDw0HMfe0QSypgUp3dXfUo4EDk1lkMm6Z7CHg76a27hmeo/6V0fQHtYxMu5ONNiORDzljn8k3MAv5NeQSJsYHI7l72H/6gj6ukIYPBB3xxRK+c/3xkcOilPtTdNGNlcIrLt9elU6lbHy35fKrQRXO/rKyWX/eYsUmayFiXg2P26T0t169/zu8Yp706tNeFTKnKtmtifoaIqC87cO4K6HdsG0JTQF+UGlldsGeP7WARY4WyKErDaaruH666/H7bffjvb2dhx77LEIBIrTFYQQuOaaa5rWyKXAth2Mj8+8L3c50TQFnZ1hTEwkYVUINhdCWXXLklSsSoUypr/HLuoYpxIGMoaFVe0BBANu52mYbmEOJfcp7aVs1/tX49MUKMINuEXuaIZSuqa4Z2UqAsmMBct2kDXsqqvThTTVTUOfqT26puQ6ZImejgDiSROG7UBTAdvB9HFlElBUgbOOX4t3/NmR2DeaKOowf/30K/jxI4OwLAdtIR98PnXG+12x3XP83fEGBXtHE0imTUi497o9okNTlKrtWSoD1lb422pVvDe1NXJ/urrCUGdxOsJcVesvl8t7O9N11NNvAZh137aQ1zCTeq6xsNr0ky+OYTLuZmU183NZ0xS0t4fqPjKttN2W444NTMutfRIO+vJFQwGU9SdtIR1Tiez0yuss3rdq/dNfnLoRp56wDr96fAhf/e9n8sGolNOZxJWGAKoC9PdUP6Kr2nuUSJvQVAXHbuzCvoMJjE1m3H5WTv8cVQB2Lm72qQocuPuLFSGKCoz9/Pf7MBHPQlUEhAIA7rY1b8yiKu51rO4KwZ8rNntoKoOsaaOnI5gPaAvHYhLuWKWnMwgAGJtI5xcZVne635M17fzXHUwvfngr6dKRsKVER8SP952/ue7fs4X4jPrpo0O493dDRedgB/1aU8/BXimftc02m76yoRXsu+++G21tbXAcp2Ilca5q00Kop7rlPb8bhN+vIZWeTmu2HYlzTlqX39uTyli5AmY6JuKyaFbYey0jVzisUszrZT4VPqYpgBTuQNLKd0qVo2DDcjA2mUZQ19DdEcC+0USVZxb8zNyrORJlq9el1NzMsS3dtCi/T8W47c7kOo7bWRW237Ylfv7EPvzm2Vfg86n5Yy5s20HGsHMF2gDLdtAe8SPo1yruaXKkxNBwDLv2x+AACAdUtAV1tIV1HL52bltMNm/owlHrO/B/v/0EbNtBNKxDLzhyrFJ76jlqpBXOayei5aveqsxSyiV7HGE913jHL19COOibniSVKFol9j6XK+1VrfeaHSnxiyf24dfPHsChybS73WuGVfTSdutQEcxtF4slTaxqD+DKdxyXX0Us3Tp1169ewkRczvp9K+2f1IBAJmtj8EAc3/jxHxGK+JFIm5COzE3a1752RRHoyNWKqVTQq9p7ZOe2osVTJh5+9gAE3PclGvJhKjm9z9or+SIlkPVWhSGxpisEy5bYN5bEwakM/uz1/dj2uyGkshaE44a7AsgX7LJz2XyWZcOynNx4zK3IrhWc2OKepS0hBXJb7NzxlTf+KD36Stfcve9Zw3Z/noL8eyalhAMJ3afCsp2af0ulmRVzHbtUe93C3+tzTx7Am09ch8eeH8F4LIOuaAAnbemtunK9UNkfzP6bnYYC7AcffLDZ7SCatZmqW2qqwIv7pnDdnc/Cth1kTds9AkF1i4Ud1u12sumshSdfHMNELAvDdJDJZhBPGeiI+BHwa2gP6zg4lYZVebt15c5OCAi4QW09HAkYtoNXb+zE3pFE2eOls9Te5XaEdRi5GedqvEIfqiLQnnu+t5LuyOqddcZ090W1hX1IpsyiCQRHAlnTwcGpNFa1BxH0a0XVRNMZC3f88iXsP5h0V8dz36coQDjgw0BvG951zjFYv6q0Tmf99o0mMJU00B7xV60I77VnfW/bzAO+X72MkF/FyES6bHWbgTcRNUM9VZn3jSUByCV7HGE9ffPe0QQCugrLnk7ltWwH4/EsuqMBdER0HJzK4D9+9Ef4fWrFle1aA/4dg+O445cvYe9oArkTteDTFGhBX9Uq0tXaLYSAX9fQLgSmkgb2jSby973wiMnB4RhGJtII+bX8HmVFEWV1Vkrft9JgN2PY+XRmCSBtWPjSt3+Pc05aV1QkrFoP5GVjq6qCcFCtGNhXutZ01sJ4LFOUPacobnpyPG2VzeaXTu5blsTIRBqdET86IjomEwb+uHsc7/+LLbjlvp1IZUxYthuIq0LAltMZgQensm7KuALomgrTcjCVyKKzzc2QNW0HjkRR5e6ppFHz6CtVEfmFERVu7RsJb9zjjodURVT9W6qUUXBYd3jOY5damXSFY43DVoVxyqv6ao41Fiorb6lk/7WShgJsolZQq7plOmshljRg2xJwHKSz1vQKbW6G9sV0DHtH3b3Y+XQun4LxqWw+eOyOBqCqCgI+DVnLLqvUrSrC3WNVwq5yfEMtPk0gbVSuS1laVFJRRC5wlUjWCK4rKdzrVLgqXynVzJHAVKL66zsOMBHPIqCr+Wqiz+8ex8+f2IeppJEv0Fb4/GTaxO4DMXz9zmdw6TmbcPS6jlm13zOb6qYzDvgUgb0jCQT9KqLh4uM8br7nebSH9XzVUnYsRNSoej63LNsBJIpW8Eqf08rHEda6Rq/wlswdVWnkZq69fsKx3aMnO9r8yBpuQaqgX0M45CvKODrz+MOqnm8MALf+9AWMx7IAAE1zd/uatluotKvNDWJLg8563pt4ysBzuw4BQNlEayJlImu41cOtXHCcXwEO6/DrasX3rbB/yhg2xmOZfBCoAJBCIJE28cDj+6D7FGRNJx8sVuL1cd7+5EqBfem1SuneG0dKqAKw5PRrqaI4rTv/Xlb42abl4FAsg+5oALqmYO9oAlMpE+996ybc9OPnYVhWPjD36yqCfi2XUu+2V1VFfjEkljSRzu21jlcY51iWk1vh9Vc8+mpDXxtWRQP47XPDRRl/uub280G/Bid3JFvpe1It423vaGJOY5damXSzHWvUk5XXjDHKQv2c5aahAPuSSy6Z8Tm33357Iy9NVLdqFcHzHYUjIQSQyqU1F5LSXV32CpL0r47k08FEuztLbZg2xuNZREM6Bta04YSjV+Huh3cjnXXPx/QqhVeKpesNr1XhBuq2dIPy3+8Yrfrc/CqwmD7uIpY06vpZmircYDlpoCOiFxdEK/iP2U8LuB1q1rCgKAoUBXjiT6OIp8z83iegOHh3pHscWipj4p5HBnHlO45raDV4Nkd2zTRwSmZMSEiEAlrRcR627WBsKoN4ykRPRwC+oMqOhYgaVs/nlqYqAOSSPY6w1jUWZlClslb+64WroUYucJJS5ieylYJzkg9OZXD3r3cj6FcrDviDuUAWcGuKKEKBhHQrSjsSsZSJzjZ/WdBZq91ekSzLdvDAY3vxq6dfKVt1fPrlg24qNNxgUcldk2G5QXM0rFd837z+SQ0ITMSzuSBX5ANlty91fx8UIaBrAlmzeu0VKd30Zz03QVNpQqb0Wr33RREi90Z4q+S5NghRV/EZJVdMdWwyDe9VvvfgS1jdEYCiCKzO7Zt2r0PByEQaEAKqmF59VhV3tdl2ANOWmEgYuaNHpxcEVEVACDegPjSVRVfUj0vP2VS2nWDPSBx/2H0IqqK470lBRgFQ+W+p1hYH3adgKtnY2KXW6852rFHvVpO5biNZqJ+zHDVU1cQthlT8TzKZxLPPPouXXnoJhx9+eLPbSVTGqwjuzoZPf/B7HYWEtxpQXNHb452u4UgUFR8L+jX0dgbRHQ0g4NfwtjMOxz++83ic+dp+rO91A3EpJRzHqasQWS1CiHy+t2W7nYvf5xZGq/ZRJYRAW1jHGccdhmrPKv2qlBIK3EIh8ZQJTRVFz5lpH/dMDsWyueDdj0NT2fxZ3ZXaAgCmJRHwqThwKJk/dmO2qr3/wPQqSV9XCOt724oGE6W86vFCiLLiFbHCWW0xPcjriOj5FRCnjkEHERFQ3+dWf08Ya1eF6/psa0W1rtG23dRpiPI+p7CvsGx3BVcIUXbWtGW7RzCF/O6EaOHncjJtYv/BpFvYE+X9j5orNiqlhG3LoqCzWrszWQsHp9IwLAeapqAj6oeuKRgajuPGHz6H/3Pzo7j+zmfx0FOvFFX3Frk+Q80FnVMJA72dwbL3zeufMlk7H+QWHdEEN2APBzUoikDQ78sVFHOvr/D2COE+NxrWawaRpdear7pd8kZ4zZhN7OTk7oEbFAsEfCqGD6WRTJtQhEAw975NB/UoOvFEEQKqouTrxwC5gm65oNybOJD5+wyce9J6bNnYjQ19Ubz6cPf/varsa7rDMCwHAV2Fv6BWS7W/pZky3tqCvobGLrVed7ZjjXq2mngTSHOxUD9nOWoowP72t79d9s9dd92Fhx56CP39/QywaUF4RzYFdHePkWG6x1IYhp3fW+yvMPvvKezcs0bxBmshBIIBDaoQiIZ0N1VLCFywdQPaQr7cUVxzC0qB3P5oe3om2q+rCHtHT+RmW0sD4f6eMP723GPwmiO64dcVCLizuYWxYWm7JASc3Ne72wO46I0boWlKvlOb63VYtkTGsLGuJ5Kf0KiUwJa/FulWVbdKBjizUfX9N+181dItuZUF78iQagM+r8COXpCSWXRuOYpT6+ezY3GkxO4DMTz5wihefmUKuw7E8NyuQxgcjjGYJ1oiHMf9Oy792631uTWZMBDQVZx/ygAu2Lphxue06opRrWtMZaz8ZHfhRHK+vkjB69iOdD+XC/pxw3Jg2+7KdunHoRAiv19b5PrOsk/MXJEs7/SJULB4Nfl1R/dACPeoq6xhwZFuyrrtuJlgHRE/DNM9GjKdtZDMWDgwnkI87R5x6cWElj196ggg8nuNX79pddn75gW7qYxVcVLAcSR8mptOrQiBPzthLTauiebHCt59E3BXFruiAQRzx1VVCyJL3yPH8YqdyvwWNyFQ1l/OpLCPcnL9aiigIRr2QUpgPJ7Nv6ZTZYHCya1kF757Au7Z12tWhbFmVRirO4NY1R7A6s4gQgEfejqCFV9LEQLnnbweqiJwMFcN3Xacsr8lwN1D/9yuQ3hp/xQsW1bdouHLLdy8tH9qVn1zPpOu5HUbGWtUey2PpillE0iNWKifsxw1dQ92e3s7Lr/8clxzzTV1pZETzdXmDV249JxN+eILbgflphhFw3rNyFHMsGxbOuvrSIlgQMObX9eP3zz7CoYnMnNuf+GPtx2JiVjW3YeUK85h50p2e/u8j+pvx+uO6UHA7w44Aj4NliVhORJVhhNQFaCnIwDDdGA5Di49dxM2rmmHEAJ3/3p3xaPDGr2aP+2fgqYKwETF9uT/K5cFoM0xzdF7/+/53SD2H0zCsiQgvCGKg5/9fh8efGp//siQg1OZ/PnZhcenCAGEC84kBwo6OeG+ZOkqynzsg/QKiYyMp5A13dR7CXeiyK+r3PtNtAQ8v3scP338GewdjsGqsJeyUr+lqgL9PeGiv+96ntOqql3j+t4IDsWyGJtMu2nQuSJaQIUVbQG0h/Wir3nVpIHyz2QA0H251c1ccG5YNhTFXTF3pMwfaxlPW9BUgbt+9RIu2LoBwPSRW5blFkXNTtnQVHf/se5T0BHxA0C+EFhhTOUV81QVdwtXYYDt7vtVoKlKxUDQC3b/v3t3IG1Y7rkeue7TzgXtnVE/LNsdk2zZ2IVzTxnAnpE44kkD8bSJiVgGDz71ivscReQLfXnHg5138vqygnCF79GekXhRRp43PnLPX84dNVrhPSpVeE8UMb2S7tenq31nTRt+n5Z//woDSnfSv/ynSMDdq+24wZ6iiPyediklYqksdh2IIZUuLni3Y3Ac2x7d4x5/atnIxNwtfqGAVnTc2pe//3R+P7+Euy9byx27ViqeNpHKWLjnkUEICPcUmogfrzu6B1s2dlUtglptC0IjY43ZbJGbi4X6OcvRvBQ5O3To0Hy8LFFFmzd0FVVeDAc13PXQLuwbSyLkL/5AKJwlL/wI8zrl/PMKUvXW97YVVVDMGDYypSvecDukOWaM59tn5Y7iaI/okLk9Y7Yj8eL+Kby4fwqqIrB2VRiRoOZ2MI6DamGy47gDC8uR6O+JYCC33+zckwewrrcN3/nZnzB8KDWnVWxNcSuCjhxKobcziGTGys/oVpqR92kCGdNd8W5GmqMbGAvYjp07Uk2gPaIjHJwujHNwKlNUGMcb8A30tSGZNjGRMHJ7/tzWevvBIFG0n83T7I6lsJCIT1WQMax82l7GsODXVe79JmpxOwbHcfv9O5E1HYQCKkJq5YJApf1WpdMJ6nlOoxbiyJ1q7X/oqf34r5+9CMuR0BQFqoL8Zx0wvWVJz00sFlJyqeU+Vak44PfqoxiWg2jYh/GYt5VLFp2Woalu8Lf/YAo33/N8/p6EA758vxFLGgAAXQNWdQahCIHRiXRZITDArbZtO+4/PlXAp7q1VTpyxc0AIGvaVfuLzRu68N63bsLXf/hHZAwboqACeUfEj6Cu4uBkBmtzY5LC6uWeww9rrzgh8+qNXdj26J6qFaCllPjmthfg9zkwbQnkCqxZuf7PyxgI+DWYtgOf5t5f25JQFHeMIYGirAJFuNly3ko64K5Aj01mEEuYaI8IaJqSe6/qG32YtsR4PJvLEpg+D1tRgG9teyGfweBNRr96Yxd+9fQryBhuobSONj/SGQupjNvHvjUXXJcW8DJNG8m0u+deUwSCgen3LJ2xcGgy7Qbpfg22dFP/JxMGhobjuP/xvfnAvbSP9jIV9o0loalu0T3Hmf5HiPrHGoWv5SvYUw6Uj1/nYqF+znLUUID9+OOPl33Ntm0MDw/jxhtvxKte9ao5N4xoNko7m/NPGcBt9+9EKmvlZqCLE5aVXEAIuB1tKle4rHBV00sd2jk0kf8AtmyZL55SyEv/UhXkz4me6ZxKwO2I7dz+aC/l3Hbc86otRyKeMvNHTwDu/jHkCnvsGU3kO7Ra3ZMEcHAyg+72QFla4as2dOGz7zsZdz30Mu5/dE9DEwSal8qem+12INEW8rlHaHj3puD5bpE2BeGADxe8YcOcBnWFQWnIryFr2vk0t1jSgE9VECg4p/u53eO48h3HYd9oomjA573HkwkDmiKQzJi5fXruz3FsB/G0CZ/qzpz7VFFXx1LvILawkEh7WMfYZMb9fVIFBNzCPKmMhdWdQRYVIWpR03/HNlZ1BPKrnNUKAlUKkkrV85zZWsgjdyq1/4zXrsXDzx7AvtEEbMetfyGEGzQH/e7RXd1RPzK5VOzSjCNNVeBTlaIJUWB6wL+2J4JUxkTGcNAe1pHMWEWT4rqmoKPNj6Bfg5QS+8eSAIDDVoWg5M4a1n0qutsDODSVgeW4q8HI7d8uLQQGFPf3pu0g11XDpyn5975Wf7FjcBz3PbYXPlVBVrhVtIUCBP0qDMtGcsJEyK/V3BpQaUIjmbHw7RoVoP/mnE2479E9sB0HvV0hZAwbsaThXmeukKtfV/GGV/diTVcYQgGe/NNB9/xy2z2+0+dTEA37YFkSkwkDigBWlQTXgNuftYV9WBUNYCKRRSrjThx7NXPq5ZTce5nfrieRke5r7h1N4MV9U/CpClZ1BGBaDgzDhk9z/3syYeC+KufN+3UN3e3uZMChWBarVSX/+3cw5mYudrf74UhgIpbNF2azHYmsYWPfWKLiRLiXqXDzPc/jlYPFixreGDIa9tUVxHqv5Y1bSv9GmrWNZKF+znLUUID9N3/zN2Wb3QH3F2HNmjX4l3/5lzk3jGguClOf9o4mkEib0wUxkCv6JdwP+7NPXFe2quml4W0a6MSXv/80EikTpmW7s7s12A7ylUpn4lX29gZagMx/n+NICClhFszsaqrIf4hJId1ZYzk9o1+LlMDZJ65DMKDhuV2HioK9nUMTGDwQa6jSmVeVE/BmsAUyho2/PG0jHn76lbrOwa5UeKwepdUtDcstfKPkKs5ajsRU0kDArxXtYyo8w9Tj/b7c8auXsXckAQl34ObLpUaZtpu+79akc5Pf20K+mh3LbAaxhYVETFvmBzdemp5XmMe0nJY/A5dopcr/HQe97SbV91Iu1t9uKxy5owiBd5x5BG67fyeSaRP+XHVmIdyJy3BQwzvfdBQAlK3Irlsdya9MVhvwv+PMI/LfOzKeyhf19LaORULTQYxX8AwAkmk3OPNWxr0zlscm04inTIQCmltwDICcIZ7wJr7ThoW0YdcMRArfk0jIh0BAxUQsC8uSmIi7E9WapqA7Gqjr3nq/W46U+PL3n65ZAfruh1/GRDybL2IV9GsI6Gr+HO9M1kIyY+L3O8cgcBCqKtDbGcSFp21EOmvhiT+NYTKRhWnKXP+uAdJd7S6+HxLJtIWNa9txxcXHYvCVGBIpE7GUge/94kWks1ZdixIV73XBv9uOu2+4K+rPjfskRifSRVvhfJqCkL/2efNBvw+dbe5EfTIzfbSYAqCjw508GBn3shnconQiF2SH/BpS2fJj4MobPt1yJVdyPpG2oCpKXUFsvVtN5mqhfs5y01CAXekILiEEIpEINm3alJ8BJFpMhbO5z+8exxN/GsPBKXfvlKYqRR8OZ59Uvj9JEQKDwzHsHU0ga9b/4e+tjPs0xa2YWiVoDQc0xNNWvtKgogio8FKTizsN75gST+FrVqqtIYB8gOZWCAV+/sQ+d6a6INgrTaGKJavvJ1aAsjR0b6uQlG46tU9TAQn0tAfxf/72RAwNx7BrfwwOgHBARVtQR1tYx+Fr29HdFcHERLLqz5tJaXVLL8XQu59eUGqYNnSfOuOe6U0DnQj5VQT9KkIBDaqqwLEdHIpl8xkEMjfNLCrs/Ss020Fs4TFiXsG9op+Q690dR0KvcpYqES2uegoCLebf7myO3JlvpYP2dNauOGivliI/0Ns244B/00An9h9M4qX9cdz90EvojPrLgpRM1sr3p5OJrDuxqiloD+sI+N3g3V/QfwBli9cAqs9PJ9MWjl7XgQu2Vg5EKr0nTnZ6n7kXXK/uDGAinp3VJEg9FaDHJjOQjkQkpBQ95vep+eDakbmss9xZ5PsPpnAotheXnrMpvxe8dMW80uRHUFdx8ZuOgqYo+UmA53a5R2it6ghibCI9Yxadt1JcscgdvGPRHCRy75VlSziODVVR8k9wq5dn4dPcCvTV/l69rQJ/vnUAvV0hjIyn8JNHBtEW1JE2rPKK77nXlxIVJ9O899qREmt7wjBzkxiK4v7OHZp0V8czhgUng7qC2PncRrIYP2c5aSjAPumkk+p6nuM4eMtb3oKbbroJRx11VCM/imhOvNncDX3Rso6gf3UE+0YTZSu6hWJJA6msBafO4FoAudlLCx0RPZdClCnrNNw927kyYNKtuC3gBtFSSETDvvxeby9Y9jiy+tnVhSnZQrjXb0s38JyKZxEJ6dB87sr53tF4PoWqu91dAU6mrbKjx7yZfweAJpCvEo5c2x3IXNV2t8MG3MIYihDYuKYdG9e0l92nZnwol55t7VVcz+/5LghKgZn3TO8ZiWNkIo1oWM+vYIzE3SPHdE3JVTYFuqJ+hAJa1VTtWoNY94x2E3c9/DL+eX0HtNxkZGEhkaKMAO9FvX1timBREaIWVVQQSFu8gkDVtqbM5sidI/s75rWNQH2D9mop8jN9r3cPUlkbPZ1B6D6lrFBTJmsVHY/k9iHu/uJDsQy6o4F8Ma3zTxnA73eO4sV9U/k9yd72M6B68pfjSKSz1SdUBofjuT25CkzLga4pmEoakHDPu3aktz9XQUebjvFYFnc+9DL+0nYQDes1g5zSPrKUprlp9lAqF7GaShr5LDtdnz4OrXQypvT98SZO9o0lYdlOfkHjL07diOOO6imaWPf+ZkzTTcHXhIS32Fxp0iJf1bzCDS/8UiZrTe8H97LBci+qCXf/s2k7CPnV2mfSawqO6G/Hhr4oIiEffJoCM3fUXOGEfmEDFEVUnEwr/fsr/ZnRiI6sYeNtZxyOaEivO4itdxtJ4edCe5sf7e2hGb+nkZ9DrnkpcuaRUmL//v0wDGM+fwxRXQo/HHYMjuOrdzwzY/puIm1CVpktrcbIz2oCsYSRryxaGBhLAJOJ0r+L3Iy1cAN7p+C5jiMLzoQsCYCrtS0XuHsr76btYDw2XflcUQDbdqutjk1m3NnUCi8khLtyrQigOxrAeCyTT5X3ZvJ1Tc1NCjjo7wmjf3UEg8OxooEPgPyKtqIKHH9MH7ojjQ80S6tb6rm9boZl5wJTkbtOUVcxjtLBSOHRGd598F6vVrpnpUFsJmthKrevTUqJwQNx/N9vP4GLzzgCmzd0FRUSaQ/nOnHLgTvp7k6S6LnBUCxpoq87iP7VkYbvHRE1n/d3vH8sWbb/dKEKAtXamuIdC1kr4FroFfa5DNqrfW/pPfBpbmZQNmujuyOQ/1x2A8jidDChCGi5LUaTiSz8uor+ngjOeO1anPHatXjo6f34yW8HYVoO/D4VE7lJ2FJqLqhzHInRiUzFlecdg+P4/i9fQjxl5Cfd1VzQnu934NVmcWBaElnTxtBwHDff8zz8vtonS4SCPkhITMWz0FSlKP0dQH4iqD2i41AsW1TEyjBtGLmaJqoq4NgODCD//bqmYM9oAr/74zAO6w4hmbaK+nrpHbOVGyRUO+7L+5sZGo4XTZCX1m7Jv0Ul/19N6UJAYUqYlDJf6LY7GsB43KhawGvtqhAc6a60h4Ma+rrD2H8wiUDuiNTCl/b6ad2nwjDtssm0eiY8UhkL0ZCOVx/ePcMVzk7p34SmCqzr241zT1yHo9d1NPVntYqFKORYy7wG2ETN0sw/lNmk77YFc+nHdUbXEu6eLgFgrOAYrxm2bhe/Ru65qoL8WdFeSpR7zcXzum7g5Qb2xUUzZNHKe2mKu5Or+WI5Eo5pu7OuuRXvwu9zJKCrAh0RPVfVVIXtWNB9KkJ+DT6f29Gksu4+s1dv7CqbvGgL6UhnTYwXpFt/7xcvYe2qMN5+5hEN7eGpVN0yGtanj1CBzFfjrOfs2NKAvfDoDPd+oiiboNpgtLQTzWQtHMplMahCQOYGXSPj6aLfN6+QyFTSRDigYSpp5I8MEQAs28HIeBpCAGOTGXz1jme4/4mohXgFgW6/fycOxbII+VWo6sIVBJqpbzvnxHXL/sid0nvgC7n9YzprIWPaODSZQTSiQ+bO5fY+0yWkuwUqF3wpAAzTQThQXGvjrNf2o68zlF+h9U7U8qlK/uQJLTcJ6+SKsEXDvrI9uV47k2kTQojcmeDue+NIQFHd//b6dNNyMBnP5vulsN/dxlRYrCwc0PJjpFTaxB2/fKls25euKehs88Ovq/kJn/NOXo/bH/hTUVp31rDzk/yW5W6VAqYnrL2Mrlu3vQAhpo+SbAv68hMX4YAvnyK+/2AK39q2A5G2ANavml459f5mbv3pC8gY08XdZJWsQW+hwZrFoMrdwjY9cnKkm5bt96l4/abVePjZAxVT2hXhjmtuvPsP+bFMNOSOg1JpK/+3hIJFiPawXnUybbGOvKr0uWDbDgYPxPCtbTtwyTI8lWQhCzlWwwCbWl4z/1BmswfNLYKmIxRwj8Ky6wyyK68zz44QQFc04BbqimfhSLdDUZXiV1UE8mdzevvLp691dj/PG0BoQsCB4wbkAjisO4R01kbWdKA6EgNr2vDqjV35wnCZgr1zhXu6vQ/yZNrEnuF4PkBVVffn2LaDoRF3Jv7vLtgy6/eyUnVLv64iGtYxlTAACWiqgqxp11WMozRgz6fl5zIBHCmha9NHaFTrDEs7US/NTvNSvyUqDroK9ySOjKcQ0DVkDXd/oHd+q8+noD2iQ1MUHtlF1II2b+jCZedtxk8f35s7B3thCgLV07f9fufosj5yp9I9EMKdDF3VEcBYbo9r1rCRMe38UWDeWdte9exc1wdFEXjTCWvL3jMvPX1wOI7bf/oCDk1lEAm6+5lVIdxtX9KBLZEr4KZCCJHPeFqf2z+eMSx0Rd1q1YZlQxXuz3RsN9NAqMjVNlEQS7iT07laWFBzx5R5e3f/4+4/QFUFbNttfNZw68aUplkbloOxyTSCuoZwrlBnpSJWZsGMvLd/2badosKrwPT52FnY0H1uv+RIiZ72wPRqd66dUwkDdz74Iq58+2vK7uffnnsM7vjlS9g7moB0prd5CUyPZRQFiIZ0SLjF3+qhKIBPc9PAvfdV19w6KwCwZWMXNq6Jlu3n74zomEoamIhniiarJuJZqKqCrmgAh2IZWJZ7DJxPU/KLENUm9BfjyKtqnwuqT0XQr+HgZGbZnUrSCoUcAQbY1OKa/Ycymz1oG/qiWN/bhnWrIxgcjuerOFfj7lUGIMSsZlfLXieX8u2tYBYGyqWr0D5Nze/3aQ/r+ZljXVOgqgrSFY4Uq6R07kBRFNjSgaoI/O15m6Eqoix7oLQwXP/qCL56xzNlH+SpjFW2T1wo7lmlpuUgnjJn/QHvZTTYjsQ5J/7/7P13lCXXeR2K73Mq3Xw7TvdMT0QaAAQJECRAgglgMAASlElJFK33s0iKz7KebclakmxJDnpctn62JS1yKVh8WrISRdJPYgKDBIAESIEASBA5keBgEGamZ6Z7pnPfXPGc8/4459Stm7pvh4m4ey2ge7pv162qW1Xf+b5vf3vvwZMvLmB+1Y2D4xV7injjwR0YH0pvaI5JV9KXyx4cywClUEImcuFTyNogZG3KeTKICiFaaOZaDM42Oxdd+ycL8cJtdqkOUAMsCvG5bx7GQmI2XJ/XbgWhAQYY4Nzj6gMjuOm63Xj28JzSvjjz9MR+Ytv8qos7btqHpbJ3UVrurHcOilkbXhDhp26+BA0/wl0/mEbGMeHYcimcVM+W9pgcVx+Q64tuLLpLdhbwz955mSry+nExNImUbcQWoJrxlNxPSmmTeaW0TICmZSchBIyJeE3BoG1A5b/9gMFVhVgqeeZN9hXQHDNK+IxzVe3+SGL9lJxpr9QDfP17R3Fivqb+otmx7ga9r3U3imeTS7Wmg4c+/9m0idmFGo7PVbFnPNdxTv/zx96I7z13Cg8qhXhpwyabCFfsHsKp5bpqtDTFztYCpXJtM5S1QChtCooZBOV6GMdvSkjLPH82beLOB49iteZ3FKtsi8Yss5+77Vocnl7B0y8volQN4iZEr2LaubC8Wve5kD73zgbbiY020c4kBgn2AOctNnOjrEcl73cGRtN+kw9E14+Qdgy4PuuaaBtGcxi6mzhHv9DxOYw4ut3/lADZtIlIBd2aG4Kq2a2De4fwhoPjGCuk8I2Hj+HUYh1euL5Cm9Dz4WrHZaCX1XfXi7rOA7XPv03PVToe5EHIECTOVSzABgAgMAxJVZpZrOP4XAWEdCby7ejJaLhpH8aL/SfUvZBJWVit+mj4UcuHWMjacGw5W7VWMExeM5W6tAkR6tLQYnA6Ue9GM5ficAUMD2fx9KHTqDRCFHNOB6XsfLH96QfnehZqgAHONiiV93E0vknvoQ2i39g2XkxftJY71XqAIGCgRGuhyOM2EmM93JNd0De/ZhLPvryEmcV6XLjU6tlCiBbf6vVYdLdctwtffehox/4QInVcbMuAQUnMeGr/rNKOiZFCKu6g6yK7UPPL7XmkALBS8TBSkMri8TST7vgmXss4YBlSkE1AxSGVaGpRUg0d06fnKqg0QgzlHVTqQexs0g3xvLQaU9MIIo65lQaGck6sR2CaFK4fotoI1zynn/j5G7rGi2QcWVht4P/9zstrauMYauSt2ohQzMn4HUUc5XrYEb+T65lua5nmZ0qQT1uYW2mAEuB9N+3vENBdK76dbcur+J6gJBZsTR6TaVIw9+JxJdloE+1MYpBgX+S4kBe2G71R+qGSb2YGpv2BmLaBtCMDpqyyqr+Nu9a9n/gbSrxFd5o3F4DrM4zkHbgBw1gxhQ+87UCLouj0XAWlWgDbMvpKsA1DzZmr97OVTyRUMt8uWNbtGuq2wOPtkudoPQF6K34Q4bPfehE1N1xzDGAtRsNS2cPHbju46YdmctvjQ2k5oxdxOR7AZSe6Ugv6ts742G0HcedDRzB9uqpUYIkSg7PjBcd6M1fVDRaEzkecD7NQAwxwsWMjsW3/ZOGis9x5YXoF33j4GOp+FFs0AbIgrUeHdCFau1zoQuhq1YdjGXGA9kOGtGPijjfvw4vHV9dk0X301ivw/LEVZBwTbhCBMZm8UsV0YkKgXA9gmxR7duSwdyKPE/PVjs8q7ZgQQqBcC8ATIqKiLV4aytObCYFS1W8p9hOiCeSt4ELAoEqUi0gmFeOiZ9zQsbyQs2EaFKWaj6CPdUQ7gpBjqeyimHWQz1ixovhiycU3VfNkI8zE1iTYQtoxY+u0dkjdEoF0ysTEcBrletB3MrtescoyqWxuJBoxG1l3nC3LK31PNPwIDT+KLegKavQRuDh0F5LYaBPtTGKQYF/EuNAXthu5Ufqlkm92Bqb9gbhYdvEPD0/3rSwev0ePn+vd6GW/Fb8OTVXxSiPEcN5BuR6gkLVbHvD63KVTZosNSS+MFZxWCpViCAznbNz54NG+rqFuCzxKSafMeSKGJCvfy2UPhazd87M7k9SfXtt2bCCXtlCq+bKQ8dYDyK9jjaJx1f4R/Ie9Q/jdzz+F+RUXhazVQvHuZ+Yqf45EUbYL58ss1AADXOzYaGy7mCx39HOm1gg7YjIXkka9HHmwLQP7J/PxOdDd57sfOY6Vihezq9KOiVuu24WD+4bxB198ds2Yc+dDR1GuBcimZcKnBS3lDDEBFZLJlVEJOyWk62fl+pHsRququmUSMC5dPiglcjabNynkFFL4UkPqqABCdCnji87vTYP2jBvJWK4Tfz2/vlFwDskI80KYBsVle4bw5OGFLcfxWiOEqdYq3RiF+pBzaRP/8SNvwMxCre9kdr1iVRhxmFuMu2f6/kvGXtOQ1mKS2cGwUvFASAq5tIW6G2HqAtZdaMe5EpLrhu6ZywAXPPTNNbNYg2MZKORsOJYRL2xfmF4517u4LpI3SjfoGyWbNluSI9tq+jUO5Wx4gRSS0vTcO968DynbQKkWIAgZuFITXU9pWj8Qrz4wgqdeXIxnc7cDhlId7ReEkNjyiTGBSj3A9FwFzx9dxvRcBdm0GVe6tShXL5gGQd2XkuK2LR9IpVoASuQceL/XkF401L0otuTQ9lnxfiOZXwtESvmcEoJRJYrS67PbCKNho1h/2xZKtQB5VcjoN4E3KcWHbr5Uqrqq0YJ+rzcA2DfZeU419KJ5ciRzXgRHLkTLNRhx3vd9OcAAA2wNW4ltFzKSxVHGu8dkLRIaRhzve9Pe+By8ML2CB549BcMgGCmkMF5MYaSQgmEQPPDsKTz4zOy6MWex7MGPGEyTIuWYGC2kYJs09q+W3WOCdyfE0to/Kz+QNo56ftmgMuYAitqdeEZGXDTtPhOPTkqg/r7zeSoSux5xDkLImoXd9lhuGLTnWqffp7cfcngBw97JAuaW61uO43p92B4X23fToDReu11zyWhf8bvbWkZDCIGqG2Ln6PmblLY3DIbzDgxK5Jw+ZKFmtepjqexddM+F9T67s7lmOqMdbEIIbrjhBmSz2TP5NgO04Xwa8t8K+q3IC5ANUcm3OgOjk7FC1gbjAn7A1gwyWg0zmzLhhQxRQoWTEhmgNiqKppvCQSgTtm98/1gsiqa7zPm0hdVagKG8jeWy31UQZChn49Yb9sSK4PpcTI1lUKoFKNf8WFwLWPsa6iXgkXHMlnMkIKvyejFBCJB1DIRtFcf2z+5MUn/O5La3cr2dC1GUzaAbW2YoZ2Ox5CGXPvezUAMM8GrA2Z7vPB+g47FtGqi7kUyoICnQyfW1aRCkbAOZtExce62TACAjTJRqAR587hQiJpBdIy5AdZh1xyzlmEg5pixwcLkfnItYLE0j+VnNLNZiyzBHqZoTSlCphwhV3OaJgj4DlB0F4gxXNrN7q5BxLmInlHzG6ho3kiOFb7hiHIslF6VagIxjwLIMBIk4TsnGnEosQxZXf3x0GRETyGwx1u6dyGMo56BUC2DIjwGJKTe5LYPAD9imYswbDo7j1FK9yaxTcbfhRcimLbz/LfvXjLvncjyzvWGgCz9lNeMPyAbVxM4Mfurtl1xUPtjn05pp0wn20tISPve5z+Hxxx9HuVzG6OgobrrpJnzkIx9BoSAvZEopPv/5z2/bzg7QH86nIf+toN8bpeFuLDmKOMdqzccVe4Zwxe4i9k/mUcg5fT8Ak8lYMWtjMXJ7ejYWshb8kKtuAlBI2xjK2ah5IepuhGzKxHLFh4CIbaHWSrZ1QkpAwAVHw5fzwUtlF7l0K72aKsqY6zNk0yZcnyFiXNlEARPDGbzttZO4ct8w3nPDnphCtVhy8b0fnsL8SgMA4IceLKVSrlVBe11DyUXD6eU6wjoHKMHEaAaMMaxWgzjRp0SqeUYRR82LUPdZy/u0f3ZnkvpzpmlFW5m5Ot8Xzb1o4HPLLrwgQsoxYKPznF4I8+MDDHCh4WzNd54viOOxpY6PyC4dNYgSluQQIBjOOwhCHj9v+lknlWo+gLXjgmVJX+mlst/SCLC7iKW1Q39W9z89g69/7xjyaTMe1Qr8qIPdQ4jyxRYClmlgJG9jseytW6DX6uaUAGNDadx2wx4c3DesficTwUPHVvDUS4tSDZ3LWe98xkbGkR1bkxIEaNqXSTq9WHdMTiubjw2lQQiwUvbic7eVWEsJwRuuGMfxuSq4aPpjS3E4uZ4ayTvwE595P0gWi8OIww8ZFktu7PO9Z0cOP3vbldg7lunJrjzX45ndGgbJwg9jHA2f4f+49UpcOpnreRwXKs6XNdOmEuzDhw/jox/9KHzfx+tf/3pMTU1haWkJ/+t//S986Utfwt/93d9h165d272vA/SJ82nIf6vo50aZnqv0nRx967HjuPuR43D9qGXe6o6b9vVdbEgmYynHxFDWwUrV73jdaMHBcCGFuhug7kV4/1v247KpIvZO5GPhlOWyBy4ETBWwdBBcqzJMIIsElBDlv0gwnE91ZSqkbAM1N4TnR/E2DaUe6gcRvv3kDO5/ZjZ++BuU4N4nTqLWkB6TlBIQkNg7M5e2mhZbIet6DV21fwQcwNceOoLFkjy+IGCYHM3gthv3QnBgvuTi2ZeX4KoCAVVFgyDiWK54GC2kkHLMls/uTHpIng1/yq3MXJ2vi+a12DKFrAXXj1CuBUjbZsci9nyfHx9ggAsV5/N89XZ39nQ8jmNm06ZCFqMJARHaO7r5vOlnnQQBDOVtlGrBmnHhvW/eh89vsmNGCcFlU0UYlGC1JlloWmelHVolPOIA4xy2bcI2DXAe9VwzpB2KiMnXExDU3BDfeHgaDz53ClfsGcKppTpOLtRQd0MISAGvYs6GSSlWq77c/5v2wfUiPPz8HBZLbpzQEyJH0HThnirrrOQUOBeAY8rutTyyCEN5B6tVf8ux9uoDI7j3iZPwA9bC0NPe5pQSGFz0HWPai8XZtIUwZKjUAxACvP6yMbz9ul147aVjKJcbfW3jXOiOrNUwsC0DAQDHEhjKOWd0P84lzoc106YS7N/7vd/Dzp078Zd/+ZcYHx+Pfz4/P49f+IVfwO///u/jj//4j7dtJwfYGLbajTvflMfXu1HWSo78IEKlHmJyNI1D0yv42kPHwLhMaHUUqHsR7nxQWmzc/qZ96+5P+/vJ+dpIUrwUddu2DOQzNoQQaHgMu8dzeNf1u+N9vmr/CG67cQ/+7jsvA0LOVWmFUCEkhbpXZVgLqIwPp1FpyCQ6Sa/W1DRAYKnkIu2YyGUsuF6EkEnKWqURwjRkhdcwaPzwT9sGvCBCIWvDD2WlmRD5plygQzDth0eXOyy8XphewedVgMmlrXixoZW+P3LbQTx3RNLERosOFlY9BBGDQQCTEkRcqq46ttESbM8k9ed8ohWttY/n26J5rS6QY5uxAE0QsthrFti+osUAA7xacL7F5c3gTHT2mvG4BtMgCJmAmRCS5ELAUfTm3UrFG+hvnWSaFDdftwv3Pn4SpZoP2zTiAngQMaRsM973fjtm3T7HuhfBC1gsnkVEq9UWoOy3VOy3LQrTkAlwMWcjzy2U6wH8kMWZrV4+WKaBMIqUK4lAw4tACFCpBzgxX5NMN1XgNyhBxDhWKz5GCikM5Wwslz3c+/gJhJH0BW932PBDeR6CkMUzr7rGofdBz5NHEYdlGrjl9VP45qPHtxxr907ksWdHDjOLNWQcM07y+2EPtKNXsZgLgAnA9SI88NwpPPPKEvZOTuP2G/Z0UKvPl/HMfhoGe3fkcMlUsWeh4GLAuV4zbSrBfu655/CpT32qJbkGgImJCfzyL/8yfvu3f3tbdm6AzWEr3bgzEQC3Y2Gw1o3SLTlijKNUa3pKzq808NWHjoJzOQsUnxMCWAQImcDdjxzHe27YA5OuLQpGCcH73rQXf3XPYSyVPKQdAymbSuoNBwwKFDIWgoihUWc9A8Z4Ma2qv0JVhAUYVx6VSg2sPcnW9Kw3HBzH6eU6XD+Cp+wXNEVKz3wl/1RbTSWDXsQEVqo+RhOBVM4byWBomRRBxJRQS/dzcf9TMwCAn3335bFP5XoB5msPHcFqxYNjm/BDjnTKRFTn0nsbUnkxCBmWyx6y6dZZse2m/rRfmx+57SC+eZ5Ssc9HrNcFGsrZWCp7qNRDFAk574oWAwxwIWArcflMJ+b9bv9MdfaS8Z9FAhGLEEQChkqEdRc7nVDxBprrpJMLNWSFjHGUkliUU6+Tbr5uCn7ApNJ4w5MxmciYduOVozHVup+OWa/Pse6GsAwKznlTgbwNI0UHpiHXc6ZBsFL2pe2VKUW8BIBSzUcUiRZqec0NAXUedBxPriu4ADiTQmyUtFqAESJ1XurKCsuxKNK2pBoLEYGpzrXrRx0iaMlZbdOkcu3pRjgwVcQ7r5/CjmJqy3E8+dk3fBYn63U3REPFmPeqz3y967Rbsdj1I6xUJAvPIFIczKAE06cr+Mw9L+Cjbdfs+TKe2U/D4P1v2R+PJQ5wZrCpBHt4eBjVaneFP8YYUqnUlnZqgK1hs924MxEAewWUNxwcx3gxvW0BP5l4tdCdLEl3CkOOuieVshMMMgDywWdS+TB9/NA83nLNznWP6Z7HTiBiHF7I0PDbfRhJfJ737yx0rXQCwGLZhRfIfdJUbB0YkwmtaahKamzfQfHiyRKiSKqBUi141mOOpu5FcXe8fVaLq25xETb8UNKsStUgtgURfG2VUC6A7zw5g5mFKn7iLQeQVgFEBxjdTddV5WzKxOnlBvyAgbhRvHFKSRzAZKkBGC2m8M/eeVnHNbdd1J9e1+b73rQXmbR1QXeKzhbW6wIZBkU2bWGsmNqQD+kAAwwgsZW4vB0Fc+0O0O152O/2z3RnLxn/j52uwPUZkqHOoATvvH53yz5RQnDNgRG8PFNGzQ2lwwWRPtmmQZFTxd0Xj6/igWdPgVL5vNNaJn7AcP/Ts3h5towP33Iprto/smYjoNfneHyuCteXtOl8Rnai22M5JUDaNlWiKgvQpimr8FHEwbiIE0GZTCY+P72pdWaluRAwQBSlXnaoidJxYUJ+DZnAcsVDPmOj2ghaaNk0VqluvplO/DmX3eS0beBD75LF+PY4nklbIJBJ+PRcZVMaJScXanD9CJyL+LP85qPHcXK+Ggu59rpO24vFQkh3Fp1cgxAQLo9zNGtjqeR1XLPn03jmes2IdtG9AbYfm0qwf+mXfgmf+tSnsHfvXlx//fXxz48ePYo//uM/xi//8i9v2w4OsDlstNN3JgJgt4BSd0O8eKKEw8dXkXZMOLaxbeIPV+0fweXKd5gxHqtfE0JQCpsz0owLUKPtGFSSulLp7vXIhcDxuQp+8KM5PPbCAoQQsC0KAhEn65QSFDIm3EBGtJuu2Yl/8YHXolp1O0QkuBB46sVFEFVZ1edUEdtaXhurdBsEoQrslAKjhRQWS14soLYW2pXKdSebEDn3vFT24gSeUMTz3eupo+vfT8/V8Nl7X8Q7XrcTjAkwyjFf9Vv8KS2TImUb8AIGIeTDh1B53uV8GFDIOvGM+cduP4gDO4td33ur1J+1Fq2fu+8lfOy2gx3U9wE60Q9bZs+OHH71w9duyId0gAEG2Fpc3o6C+XMvL+IL9x7GqeV6R2ICoO/tn43Ontb++MzdLygatTxHABBGAt99egZ7xrPxPmmLLsugMuYw3qL6ffNN+1p8sNOOiZWKH9tuyTglMLNQW/d8rvU5ZlMmGl6EuhdhYjiNCSeDaiPAatWPO81cAAslF8N5B44lR6emxrIghODkQg2Bsn802pLrjUAoFW5CtCI55PYgVAdcsuQiLlCu+YAe6VIz40IImJQi4vKr7n7LuWyO3eNZ/NO3HsC1l49jdbUOoBnHX5hewVcfPLLpQtBV+0cghMBf3XMYtkmRdkxV+BU4eqqCw8dXkbJNOVve4zptLxYHEVdWrLLooD8LbamaTXdes+eTB7M+L/00Iy6G8ZPzEZtKsL/+9a/D933883/+z7F7925MTExgdXUV09PT4Jzjz//8z/Hnf/7nAORN+Z3vfGdbd3qA/rCRTt92B8BuAcXzo7giqINT3qTbKv4ws1CTHVnlu6thGs2KYjKQNH8oE8aRQif74oXpFXzpu69gdqnekqQ2PEiKuaI/ydnhMH4Q3/vYcZxcrOG9N+7t6GDr813M2ajUA0RcVUnbkNjtlvmmtG2CUopCVlK715XybIN+NSEAY5KSrjvckgIn59SDHtG6fU8di8ILGJ56aRFcyCq3AFGVX/mGQSR9MAkAyyTgHCp4IaalNfwItkmxZ0cO+84Qhep8mZO6GNAvW8ak9LybHx9ggPMdm43L2/GMO3RMJugNN0QmZbYkJn/zrcPIpKy+t382OntcCHzz0eOIOFeK1apoTWRSlOw4AojPz9hQCn7AsFrzZXznAowJ3PPIcTgWxdxKAxlH2nbpJFZv21TJY90N1zyfa32OhiGT0TDiCCIOwWXnNFnABmRytlTy4NgGcmkL779pPwDgL+9+ATU3jLusjHVnsq0F/V4CotU0O6ESrnebEimyZqpEk1AhmW5aqRyyWJF2TGRTFt51/RSuPjCCvRP5rknndhSCuBC457ETYJwjl7GkvVnEW8TiwoitaTfaXizWbDp9xTIhYJsUtim3YZoUzG29Zs+GWOpGsV4z4tCxFfz9w8fOmeL5xYxNJdi7d+/G7t27W342OTmJa665BnSd+dUBzi767fS1B8B2au9GA2C3gFKuB+ACctZICNnVJdIvt5+A30+VrVcgzzgGVhIzzQICBHpmmYNxIG0beONVO1r+7oXpFfzFXYdQrgcdOaxQ/+NqaEp7MEpFcFkJf/HEKk7OVfF/vu9KXH2g2RHV+1nI2bAMGvsTirY30ZVhAcSdbibktl0/AqUEubSJcn3jCxPSNuPNuVCBpXUeupvYSjtM04BtUqxWPUVZA3QsFUKea5JYLgznndiyi6qFAVXUt0zbrNx243yZk7pYcL5YYgwwwNnC2er4bDYx3eozjguBu34wDdeLMJS3oZ/6OjFZLntYrfoYyjmKUUXi5KXb9s9GZ2/dY050HAHEr/UDhuWKJ62eCAFMAsFlsffr3zsGIRCLNdJEci03DEB1dk8u1PDoj+fw5tdM9r0uSZ7TIOJgjKPaCMGFbArotQnQtJ8iAD566xXxc/Xd10/ha98/Jtch69h19QKhgOCIxVE1NDWcGlTOW5PWATu9XrFMGnuBrzfilUS/haDL9w6tyYDSn71p0JhlQAlpqVCETKDaCJDP2PKYu1ynuli8WvWlpo0Q4OrcUwIUs3Z8+N2u2XZ9nkzKRMoxwJiIC85vODiOQ8dWzotO8XMvL+Iz97wA9xwqnl/M2FSC/bu/+7v48z//czz55JNxp/qxxx7Dr//6r+Nf/at/hY985CPbupMDnHnoAKjFIdqpvRlFuek3AHZL2MOIx11aApm46VmZ9QJ+v7Ne3QK550co14OWh23EBChpE+8iwP/88g/jbXIhcNcj012T6yTaqdRciDjZDgIGP2D402/8GD/5jkswMSTnzjPpVqsv7U/oBwyleiAf7CpSMXW+bNMApUDDYyjXA1k9huw8bwZCyKqs3Gf5s0LKgG05qKiEP5lct9uHJUVMMo4BEIKoLosNel6r+arWE2gaFOPDaayUPXmtqcWDQQneff3UGX2on09zUhcLzgdLjAEGOBs4mx63m01Mt/qMOzFfxenlOvJZq4UeCyjbKyLZTssVL77HLZOimLWRcsyO7Z+Nzl4/x5zsOOrXLq66MqGlrYkzAUGo5pvtgLZ0MzW4opNXXbnNL9z/Cn7w/Fxf65IksikLYS1AtREiCFksyiUgNViKWRuObSKMGBgTyKSbn/fVB0Zw/9OzMsFlHJV6oL7vP9nWc9rJvyMEcCwDKeXm0cy9RXz+CJF/M5x3kLIlrToI2LojXhr9FIJOLtTwu59/CuV6sOb8dBRx+AmqPNH09gRqjVBajWoGgklRbQR4/ugyAODgvmHcct0u3P3IcdQaYXy4BAL5vIOUI1MmLdg21XbNJvV5/IjBq0gB2kzKlOxIIcV0z4dOMRcCX7n/ZXgBGzD5zhA21W7+67/+a/zRH/0R9u/fH/9s7969eO9734vf//3fx5e//OXt2r8BzhL2TuSRz9hYrfrw1QOeKlVJP5T0qXzG7jsAJgMKkPB0VPeproNqFUPTpGBMdA34mkI0s1iDYxko5Gw4lhFX2V6YXmk5jsmRDOpeBCEEPD/CcsVDEPFYZVsj3iUCFHMWhnJOyzZPzFdx9FRlXfZ1+6+5SCSfalar4UX422+/hP/19z/Gp7/6I9z5wCtwLAPlWgA/kCJptmUgn7VhKU9Px5JJ6FgxhfHhNPJpE66vhNoEYMjGb8d8t8ZmHomlWoiGFyKfsTCUdzCcs+FYtCW5bq1xA4WsDUpl5ROU9MVWZ1wg45iYHMlghzrGkUIKhYx9xsU32q/Ndgz8mTcHzZa55pJR7J8sDILyABcdNhKLtgPt8SwJnZhOjmQ64vJWn3G1RoiICVhG5xLR8yM0PBmndQynSstjueLB86OO7etRkpRtYLXqo+6GqHsh6m7Y9FreImtpI8esX+uqZkL7eJZen+hup6tGm9oL6VrkyyDyHKT6XJe0vJcaL9szkcP4UEqy1aDsPk0DI4UU8lkbmZSJdMoE46KTljyaQRBxpGwjTpTa1zv9gHGhYnIKGcdEPmOi5oZSI6Vtc0LtYy5tIe2YIEQqsEdcYPd4fyNecVHE7J6KMCbp9/MrjTXvt1zGAhTNvpVl0LrTERexgJznR5hfloyr+x4/iU9/9Uf4///NE7jviZMwDILRYgpDWSu2MKvUfbieLIAsVzqv2eSzIZe2sGssi5FCCo4qqJSqHlZr/ll5bvSD43NVzC7UkE2b67JcBtgcNpVgf+ELX8Cv/uqv4j/9p/8U/2znzp347d/+bfzyL/8y/uZv/ma79m+Aswn14JeBRA4qi4SI10bmfNsDSmwHIJrelJZJY0uMXgG/nUJkWwYokXS0oZwNL2C4+9Hjsfp2MpCXaj5Wq76iIQMcAqZBMFqwY2Vu0wB2j2cxlEt1bLNU8+GHG59nSkLOJel/yAe8EAIvnSxjbrkBN4gwv+Li9FI9fniblMKgBIYh7Tcc2wABsFINAABDWQumQcCErLLrJL7jvTe5zzU3wlLZQ6nqo9IIMTmSwU/fcimyqVbCCyXShqmYc+LF3ngxhYjJurFlSDsRk+qvzf2K95fIwoKjqt+To52Lxe3GZhetAwwwwKsXG41F24HWeBYoQUuBIGQo1YKeielWn3G5jKU8pTvjnx71SmwQRAlecSGtoupe2LH9q/aP4JbrdoErxevlsieVr7nALdft6mvOdnquguePLmN6rtJxntc9Zrd5zPq1Df1a0vpavT5JOQYcy4BtyeAlLSvlf7rTa1C5trBMikzK7GNd0v1z/PAtl+Ln33ulLG5nbYwPp7FjOI2004y7vWjJetuVegjO5b4xvonrUABDOQsfvfUgMikTK5Ugbo60sBiaL0e1EaLhh+tek92wXlGkVAsggFiwttf9tncijyG1Dkm+KyXoKAxwLhsvS2UXgfI6Hyo4sJUeULkeIGUZyKYtFPMpjBVTcGwDXAArVR9ewLB/ZwEff99V8TXb69mQTVsYG0pJNocbobjOcZxNVBthbPPWDWs1vQboD5tKsOfn5/Ha17626++uvfZazMzMbGmnBjj7ODFfRdUNMZyXDwehxnmEkAnQcN5B1Q37rma1BxRAUp0izpUfs6TNSPpZ74C/kVkyDT0TOqaSPYLWarBlmVLFmhII0UqlSm7zh68sbfJsNpEMcgYlCEPeFHojkiptmRQhk0reNTfEvp15/PTNl2D/ZB5+yFCpBdJqi8jZ5WI+hdFCCrZJYzo6IfKcr1W07tKM6AktWMKFtPHaP5HHH/7K2/C+N++Vc0W2gfEhWVlPBta3vnYSQDMAJ5kQmtIOyIe7H/S3WNxubHbROsAAA7x6sZlYtB3Q8Wz3eDaOB37IsHs823NGcqvPuL0TeewczaLaCFuS1SBkisLcnLtlKk4IpdsRhBwmpR3b16rdhkEwUkhhXLGWDIPggWdPrdnFe2F6BX/wxWfx6a/+CH919wv49Fd/hD/44rMtf7PWMbd3HJOvFQIQqvDNhZD6I4SgkLXBmIBjG/jAWw9g93gWgKRG67hO1Zivfr2mz6+1Llnrc9w3WcDu8RwiLgW1Oqj0bvd1ki5edCuIbAgEOLXcQCZt4fYb98Y6LTyxWdMgsEwad8gZF1gp+/CCaM1rshvWKor4gWQXWCbtoNW3n2NKCG6+dhcIiGpiNDvs7b1sQqAaL/JYhnIODEpbMvFKIqlMOyYmhtMYLaSQckz8zDsvw3/9lze1MO3WejZobR0BPTbX+zjOJvIZC6ZBB0y+M4hNzWBPTU3hkUcewU033dTxuyeeeAKTk5Nb3rEBzi6Solu5tCVtH7TImSnnjyq1YEPVrHbxIz3LJQAQIVCq+qg1QlgGRTZjdQ34G5klaxee+adv2Y+/vOsFpFImTIPGAcv1I2VRhaZIWZdt9vKV3iwIUTPnaAq9cS4wNiRnc7QK80++4xIc2FnArTfujY9nfqWBv//BNGyTwlPiZhPDaQSRtO0SQsALGa6/fAyHT5SwXJG2ZJrWJhcCBFQrfvaxv5ZJMZS14YUcdz96HL++bxgfuuUyvGb/SPyZerWgRdCKcQHHMqSntlpw6X3gQs4dmZRgbCgtz3HIzokg1kCYa4ABBtgIzqV2w2Y0DrbyjKOE4P1v2S/dAaqBVBE3qYo1UvdjOO8AQIteh6aMv6tNR6OXmBUAZIS5bXZjB/cN47Yb9+DBZ0/FhX3LINi/s4Dbb9jT4uZx1f4RfPyOq/BnX38eDT9SQpyyEF/I2nGivns8i5tfP4WbXz+FB5+dxYPPnsJS2YPry/lay5RuHslOs74WqvWgxUP88r1D+GnHwJHZMlYqPoYLDi6bKsZ06l6uDCziaPisZ2GEC4Hnj60gZVNkCw6WSh6iTXSwhQD8gKHWCDE+lEYmZcEyCFarvnT7IAQ6VdXU6VzKhADw0zdf2lXgbS2s5UJRqYdqfM/uSFr1Oa67IY7MlFFrhNi/M489O7KYWawrSntzpjhlG6g25PZqjQAR47AtiqGcE39uWjXcIHL2PghZi3BfOmUijDjyGbvJyFRY69mQXF+2rzX1cfRaw55JHZN9k3lM7cjh2GwZxdz5oXh+sWFTCfaHP/xhfPKTn0QYhnjPe96D0dFRrKys4Lvf/S4+85nP4N/9u3+33fs5wBlGuwiH01YxDFUitNFqll4YPPjMLP7+B9MAtM+kvIlDRSe+owdFrF+Rl8WSiz/44rMtwjNDORuEElhGawU0plSL5r+7bXPHULpDabsbCICUQ+EHvDnXjdYk1qDN7bQLvXlKCC1kAvOrLv7kzh91LIIWSy5cP0LdDTtmrCIltAIAj/x4HtmUhZ2jGWUfEqHhqxlvJVa3Um16gvc8JoI4+BgGaxGgW2uxNz1XgWNLyrcWy9MLL9s0kFE0859/35UYKmQwO19BxjHiv9+OALPeNvTvGRf46ZsvgQBBwx0Icw0wwAC9ca49bvt1BEliK+KDVx8YwS996NrYB7vhycK0YZCWhFKLW2nBL8Z5h47G2bAbe/H4alxMkA4lMoa96/W78ZPvvgLlcqOjW/ea/SP41x94Df7qnsPwA9ai+rxc9mCZFG84OB6f/3e+fjduvm4Kj/54Dl+4/xWkLCNm4iURKbGtbzx8DKWaFOfSBfWIcbnuEXLtkXFM7NmRi+N9t8KI2aNI0H5+c2k5My5HtKj6XkBw0bfAuB9yHD6xiqG8A0DAC3mbwJmIGXMEQCYltWEKGXtTsbNXIWhyNI3FkgezhzNR3Q3R8CP8wyPHY7HXfNpCPmMhjHhM7SeEoO5FGC2mcNuNe1B3Q9z3+EkMFVTnWiFeF/ZovOj7O9/l/l7r2ZBcX7avNZPbXSx3rmHPpAgaJQQfetfl+JMvPrOmxeZgPbR5bCrB/vmf/3nMz8/j85//fMu8tWEY+NjHPoaPf/zj27V/A5wlnGmVz6deWoQQAjuGMwDQ7JAToO5FeP7YCm69cW/PWbK19ms47+Bbj5+AH7KWCvdS2YMXMDAuMFZMNYOzSeNqvGO1Jt9CCNTcEGPFFPZM5uFYBryArXlsAkAQChSzdjyf1i2WacXupNAbhKyoapqZtOcgLdV5APjW4yda/CiFQNcOuxBAw4/AhEAubeGDbz+Af3xqFqWaj6GcveZxJEFIM4Fv78yslcAmP6/xoRRCJmImhGUQlOshdo9ncWBnAaMjOYzmrHjRsx3qvOttY63fDyy5BhhggF44Hz1u+0GvxLyfYua1l49j92gaR2dllzCTtnDnA69gdqkh510VJdqxDAjFwup2Ds603diDz8zi3idOxl3ubFrGlVItwDcfO47L9o9g71im6znIpC28+/opPPXSotRtqUTww2bMv/sHx/Hwj07j+svHYz/nN79mEj94fg4zi3W0b1WosapQrUGyKQuMyu/bG5iMCdTdEMdPV1u68e2FkWLewXVXTnYtErSf30CtV2Jvk02M9t73xElkUhbqbtQy5qYbB9qBRLMCt1pY6lYI2r0jhz/60nNd7zfXl+J4VF0DOjFcrQWgBBgbSqPaCOD6nQy56bkKHnj2lIz/icvRNuW4XqA++2QynLy/90123t9rPRuS/7baHF/iNWzOxr2Pn9ySF/hmcO3l4/j4+66KfbAvRiaf/ijO8og7gE0m2ADwW7/1W/g3/+bf4Nlnn0WpVEKhUMDrXvc6DA8Pb+f+DXCWsBZVZ6vVrG5BMtkhJ4T0tOjqZ78gBPywu9UAi6SC+GrVRy5txX9rUOldaVL5QNU/TwbGz33zxQ6BjF5gXKDmRRgrprBc8WNhNz3fzbmmVMn5cyGaXeemRQiJrTFy6WZ1XqjjK2StWMSkG/vLpPLvtU92rR7g6987BkLk7Nz8igvS5xy2SZuFh2RnZr0EVn9ef/Otw1ipSMVMy5Lt+3K993W0ERpgL6y3jVuu24UHnj111oPYxYizSWUbYIDzAWcyRp5tbKSY2Z6gv/+m/Rs+B2fSbqzuRXjwuVNxlxtoFvAzjmRSfeX+l/GrP/O6tc8BJUhZJoIwgGMZyGcscC6LBqtVH9Onq7j3iZNxt7nXtVBzQzk7bMjuNOMCq9Wga8wGZPwPIgYjIC00+eR5N9W8ey8kzy+len2xOaFTAiTOXWvBQwuUxrqtQqDuhdg9nluzsKTjRcNnmJoIMZztTD26FYK6neMwZFguSxbeaMGJr6ckqyHjGPjIba/tykxrT4aB5vWSdmRDRTcXuBB93d/rPRt017tcDzt+51hy/rsflsaZeLZcfWAEl+0uXvDxXI8wCGgmjWLLKpZsLm13zPmfaWw6wQaAfD6Pt7/97du1LwOcY5ypudStzq6ttV9vODiOux853rPCXcjZcUe6XA/iv90/mcc1B0bw/LGVeJtcCBUYaZyMu14UW2OthzDiaPgRijk7ni9OIp0yY+EOqh8GQgZ2QkgsbKIDRjZlYmaxjohxRMqPU88JtYPSZsWVAghDjlDVsEcLKaRTJlYrft/emHqhk6zcNtwQn7vvpb4S1EzKwmrVj+npBiWYGsviw++8rOM62ggNsNdDf/1t+Lj7keOgFBjOp856EDvX6JYQA9hUUD2bPsADDHA+4WLQbthqMXMz52Cz3f9+EnNAKpdnHBPVRihtpdSimqru6rHZMo7PVbFnPLfmOVhYdcGFkHZZSjWaCxm/uCp0n1yoxedJnwcdp02DYqyYwvyKi4hxLJY8JZ7W/VzqZDVkAkXT6Nlo4ELglZOljpGqbuc3ZdNY62aj0FRrISSzsNt6Qf+EEiBioqugXRIvTK/grkemMbtURxQJ2JaBqbEM3tfH/dLtWhOKoj6cc5BOtRZkNKthftUFJcA1l4x2bDOZDC+VPUSMgzERC5GZSoTPD9mG7u/17gsA3dewV4zj7kd7r2GT4xN7J/JnJBHezPjJuQBRHH5CAMY5OAMYl4KBpZqPE/M11OoBHNvAjpFMLHCctjufHWcDW0qwB7j4sJWZrV7YaPW6WzLQa78OHVtZN3mnhOADbzuAQtbuOCYtJFatB/jGw8ewVPZaEjSDkhYP6PVQcyMQN4qVL7UVWbkeoO6GLVEv45io+5G07xKSLl/MNmncpqIr+QED1MxTL9CEOQVR5xBQHqWUSAsPI0TE1i8WmIa0lwhCFldu3/vmfbinh0hNypJiI3c/ehwcwOfVwmV8KA0hRCzE5vag2m92Pm8j27BNAysNDyOF1Kbf40JFt4Q4n7YAQlBtBBtKkreDaTDAABcyzkSMPFuIOMdXHjyCWiNAIWvHyW63QuNa2Og52Gz3v5/EfChnY6nkYtn3ESbo0wSAINKyqtoI8OOjK9gznltTcE3bPFXqYZwYmwmLUcYEsnkTDV9aK73vTXtVV0zEM1+1RgA3kIVlk1JwueF1PxvG5Kxze6PhhekVfPOxE5hfdWNR0PZndZI5pru764FCT1Q3IdB0Pqm7Uc+/1d1CQtAhaNe+739x1yGpRq9+5ocMh08EmF2q41++/+q+kuzktTa32sBdPziObLo7Jb0fsUGtuv617x1DFDU9vi2DxrPrd9y0D+PFdN/3NxcC6ZSJ227Yg6obIpe2UMjaLX+72TVsw4tw6NgKvvLAkYu+sK2TaEDrFshkWqv3M8al7pBy0KEUOHSihLu+fxQLqw0wLjWPxobSuOXaXbh099A5O5ZBgj1AB7a7mrWR6vV63bH2/eo3eS9k7a7HpI91eq6iAr9M0IKQxTQTQgiI6N457gYBSTkyqVy4REy0JJsNT841MSHtGzhkJ7WYtZFq87wMI12Jx5q+lhwiTrKbYmry/DIuUG2EHR31Xkg7Jipt6uBplYDq8+P5UUynl+8p8OKJVaxW/I6Fi2MDubTouXirNUJEyo7DVQrpSYuSfgJmO0tCf35UfQbaTqWrYTiaVMNX1KzhhbRoXgvdEuK6G+LEQg2AtH0r5Oy+kuTtYBoMMMDFgAul45PEC9MruPOhIzg+J+2A/NBrUcBuLzRets7CVJ+DiHM8fmgezx9dxkghhRuvnugqTLWZznc/ifkVe4Yws1DrKIILAILLBbgA8OSLC7j1xj09i7Fcx3qi9E2EaBHBIpCipFxIdtnJhRr+6p7DYJzLZ6uiLy+sulJpXTs/9blwYFzANGkLTV4/v/2AoZCzkU4ZCMPuz+qr9o/g9hv34u/+8WWZAKPZGNC6Lfqcao/zbnl//DMi4uPWP9JirYZBUchY4ELgSjXX3M6Omp6r4rP3Hka5HsTq8jp5YlyO433pu6/g//75G9aNF8n7LTcnPdq3IjaoVdfTjoFMwYkF5+yElsBTLy7i1//ZdVtmdSX/vttzo581LBcC9z89i0hfaxdwYTvZhda+8kk6N+MCEReSVQBluaaS6W44OlvB175/DK4XIe0YMAwKxjjmVlx87fvH8JNvO9CVyXA2MEiwBzjj6Ld6/eLx1Q13x/pN3nfvyHUEgeSDTydojHLMV1sr4WIDybXGcM5CpRGh4YWYGsvGSgsy2bRQqvkYK6QQMo6Vio/RYqpj3yv1QHllA1EiN+4WszkHGKTnt2Z1MeUBuVrpFFfpBYMA1xwYwf6d+dg+hBKC548uxwms50dYVts0VCQXXKqZz602MJx31u0SJxdvi2UXDT9CzYvi40su/PoJmDpI1d0wVi/XkGwC2qIc3456I0TDC3HXD6ZBQC6K6nC3hFgIgYYXxddQw4uQz9h9JcnbwTQYYIABzj50olZrSOsqPU4URAwrFcnsSTvmhq3GvvXYcdz9yPGm9SWAv/vOy7jjpn24/U37Ol6/3XZj73vTXtzz2ImWoKi3pB/1XPkdL6w2cNfDx0AIkYKobR1QmuhUQ8V8vcAnIPHxUUrk+JgfwTYpxobSzedh4jgYBygRPXVc2n/MuMDuhMd18vk9nHdgmYayl+r9rNbWWhnHiJNGQFpvlWq6sy2Qtk0EjXVsSEVrYq73mhCZDLkBw/hQGnc+8ArmV90OdtRKxUNVXUcCsmNOiDJDowSMCcwu1XF8roIDO4tr70sC2yE2mIxl63lsrxXLuBCxQ04UceQzFqy0saHkd73jqblSdyci/IIpbHd0oRnABI8Tacb0yGMzedb32kbAhcB3n5mF50coZC3EFnKmgYJBUWmEeOC5U7j6knOzhhsk2AOcFaxXvT64bxh/8MVnN9wd6yd5v+bACP7oS8+tSa3JZSwwzrFUDlTiCAgiqdebsJOEzu+0FZllGi1d1WzKQqke4I4378O9T5zsuu+W8h83KFBtNOlavXaHdYmXWvGzG9oXIoBMyh/98TyeOLyAqfEcfubmS5BJW5hbbUBAIAxZrJRuJkRXBFFe20LaZ+QznYrl3RZvh46t4N7HT8ZVSlN5azYXfg68gK8bMPdO5JHP2DgxL7szJqXxossPGfyQxf7c7bYqrhditSYVSTOOCcvaWIA8X9EtIQ4UK0JakrT6fa63sDiXPsADDDDA5pBM1ApZG37oSc9n1eVkqpibso0NWY1967HjuPPBo7LzSkn8vK17Ee588CgAdE2yt9NuLLanckxU2ijNyUI04wI1N8I3Hp5uzj2HshisWWOWSWFQ0uLO0ZxBll9tNfLV8CIpBNalCw5CQCHp5UzF+25Ixl1KgGzaaul4npiv4vRyHbZpKDcUKNVr0vNZncvI7i4hBI6aO/V8adWZXMfUvPWf0XqGPSkM1WTRCXhBhKWSC8MgXdlR7XOvERMwgVhzhlK5vaOz/SXYydHBN1wxjsWS27Jucr0IjcRI21rJ5nbEskPHVvD3Dx/DSydL8nMmBIyLuDHQb/K73hrWoHK7pkERtnW5z1Vhu3koSlAv2YVOJNFRWxc6nqTYJpxeqmNxtYFs2lINhOQ+EmQcA0slF/MrLkby6W185/4wSLAvYFxoSr7r+Sdvtju2VvJ+zYGRvpSja8pTUccPpoaT+CYfBxHj8Ry06zOsVFq74qZBYBoU40Ppnvu+azSLB56dBevXwHKD6LVVHfiOz1XxR1/5YfxA9wOGWiOEEICZ8LfQD1jToIi4QBBy1N0wnv/WdO+OWXsu8A8/mJYJedpE1Y3AVFecKvrQctnHSMHpT51XJCltAkS0qp4WsnZM/0oGseWKUiQtOnDs5iMxZVHUGgG+8uARfIDxjnmq8x3dFhFaJE+t0zr8PtdaWJxrH+ABBkjiQot/W8FWjrW9W2eZVI4wEZXoAHGhreGzvqzGIs5x9yPHwbiApRI6AAABLCJFu+5+5Djec8Oenj7GG0W3xFw/4zJpCzUv6mmR2aRHI1bXDiKOpbKLsWIaAkClHiDqVqVOIOJcJqxeBEIIUk6n5zEBQJRwi2lQOWaG3vGWEGDPRB4fvuXSlkLuoWMrqNTDlteZRpPZ1e1Z3d4N9QMWs82SWjLdDrM7M6732kNwaQk6PpSKqdUxO0oAjS7isIwJEKIExdSHIvq4jGMK9nIDfsRACUEuZSGdNbBaC+D6UUzxNwyCbz56HBToWRjvFsuSDRAAa8ay515exGfueSHuLmsmXzsjpN/kt9cadjhnww1kU8Pz5TVnmbRlnNA0KaqNAM8fXQYA7N6Rw8xCbcvPxSSVm3GuZqEFOFedaCYQqZ9rludaVO4zgYYXIVIMlW7vaxgUzGdw/d56AmcSgwT7AsWFquTbq3q92YqiXngwLvBTN18KAoG6G7X4KK7XFecA/uaew+DrMKY2gqTy+GrVbwZdQNmKyUT0sUPz+Nj7rsSvtxUeGm6Iz9774oYfVhsRZOsFg1IQLhAJKRATsajDS5AJDkPR5riQ1dtizkapFiDkAisVDyAkQfe2OjrR33p0Gi+dKIFxDo8QpREjwNDsrhMC3H7j3nWv6RPzVVTdEMN5J6aIc7Ud2zKQSZmIGMcdb96Hp15aTCiSoqlI6lgts+Va5ObY6Sr+7Bs/RiZlnvV7jAux5mjDWui2iNALQIF4xK6lw7JWknyh+gAPcPHhQo1/m8FWj7U9thazNpYrHiKdGECPJMlnTD/FzMcPzcP1I2UN2VkQNyng+hEePzSPt1yzc5NHvj70My7Zde4FQmRso0TOeAohE83lsgcQ9HToSEJwYLnio5C1YRhkXS/lkYIDP+Ko1IKu2zcowTuvn8LPvvvylnP+wvQK7n96ViZvlCh7TdKSwBm003+6tRvqw/MZmNoGhyxeE4I11cH7ge6qQgArFR9jRQIQyYgia6xBBNAhRLfetdYcbwilo4qqDtQaIQzVqLBNikzKQsoxwJhYl32WjGWMcVQaYYueDCEEu3d0tx7jQuAr978ML2DIpkx4AQPRhRW0MkI2wupqb0Atll3c+/hJ1N1QFsKIHFUIIo7liofRQgqAXF9GjOO+x0/i20/OxEUCraC/3rOCqOJAyzw0a6Ny68RZNJPp8wGZlAmTyuvZ6MIUYYyDEunOcy4wSLAvQFyMSr6b6Y6ttfDQwmXrdcVPL9fxtYeOwPX7o7UatHv1dz1IwRXR8bOHn5/Ds68stcysRZzjdz//FOpuiELWwmo16Pt9CCXNIexNQIqYyAVI688VpVgdPOdSoZUSqdBdUOrnjOkgJWn2ILIyvFhiyKYsXK0CiB8wfPHbLyLiXAZ9pUKmk+tCxoJjG2j4cs5rPehFZCFnI5e2Yl9LTYOMGEfdjVBzg5ZCzPxKA//wg2lk01bLbDlB6yIhUNTys3mPPffyIr5w72GcWq5vamHdLSFOLgAFBBzLiO+39ZLki8kHeIALFxdj/OuF7TjW9tiackyMFlIthUQAmBhJ40M3X9rXuVupeGuKRkKNC61UvI0d8AbRMhq0TtjTgmWEEBhUJhSUkDjWUSLHnsI14qf+zR037cXTLy11FBsJIShkLSyWWPwcbHiRPMdq+4WsDcugIER2gE8t1VveQ1P6I85hWxQhEzAVLRyKhrxa9WEZBPt3Fjqe1bobeudDRzB9uhp3lJNxenHV3VKSlBRcjZjA3Iorx7vWsCTrBgHgG987ih3DaVxzoFOISp+LWiOEH0YQIPJzJHI9JRsADONDDjLKssugWJeerWPZX9x1CItleY3GejJCvm+55uPF46sd98PxuSpmF2rIps1mA0RR5VoYIRGPbc/6ZXXpBhQXIh6ZHCk4WCx50itdXUORugYY52AcsC2KtGNgperLf5sU48MpOJaJcj3ENx6ehmVSHNhVjLvQCAk4dbFadhGGvC9BsfMJYcTh2AZyGRtLFQ+OScE4Eh7Yyn4NwB99+Yf4mXdehltv2HNW93GQYF9guFiVfDfaHetn4aErce1dca3mHTEOz2dYjDw4tgk36D+R3U4kZ9b2TeRblF670azWRI8HY7+FAYPSmNreDkLkg1t3C4pZCynHiquD86suuAAsg8BQ80J65kYI2dG474mTuP/pWfghi+eWoJJgEMBUCx43YEg5JkwVnNajSLYvIh3LgOtHKNcCBGGTrv+N70/j3idmsH8yjzvevA+XTBUAIkXOam4YzxN2q/A3vAg7htNr3mPbRVs9dExe3w03lFXaTSyseyXEmZQJX3VYMo4JLkTfSfLF4AM8wIWLizX+dcN2HWu32JpyTKQcE34QoVIPMTmaxn/4uTf0TeceKaSaopHd3loVKUdUl+2MQo8GEe220fkS0razRP0wmzFRqYXIpU3kMrbsZq+TenIuMF5M9yw2egFHMWujmHOwWvUQhAwEgNPFKYQQ0uFxfGSmjJlFOXttEIJKI1RuJjJxEALgEUcUSb2TbkngVftH8AHG8ef/cCge0bItCseWn/mZyJ/ai/L9oupG+KMvP4d3vX4Kr7t0rCVunpivYm65gYhxCJAW3ZekRHu5FiLtWC2FjvXo2Qf3DaOYtWMhNg7J6rItI2bcdbu/qqqTnk5ZICAtIxdAU22eMQ4/XF8/phuSYx2UyrGAlYoHJgQo5IgXFwKmaSBnGxgrptHwQowNpWGbVK7DTIpCzgZjHKVaiLsfO46ffdflSrBPdbktE0HIEEYcp5fqaHgRMikTO8ey5+T5KYSA6zNUGwGqbii/NkLUGiEq+ntXfvXabF+9NdbKjAs89OzsIMEeYG1crEq+G+mO9bvw+OmbL+noirt+hEqici+VNjnSTn9G9FulX/cC4wJf+94xZBwphiY2KwbR85m41hRYE1ygR4Ld3LCmobs+Q9qxIAC4XiQ9OqlcVKUc+eD2/AiVRghZHwXSKROMyVktauj5HgGOptoqBYEfMFTqAfZN5tFwQ/zBF59dkyLZvoj0Akml0wuS5PE1vAiHj6/ixHwVo4WUtE1TlU5N8UqeAUIkjTpUAmG97rHtoq1yIXDXD6bhehGG8nZ87jeTRPRKiPfuyMU+2O2WbBv1Jb3Y518HOH9wsca/btiuY10rtjZ8hlzGwk+/49INzUrfePUE/u47L0sxTtW509AMqGzKxI1XT2z8wDeA9tGgXlaUQiiRLUMJd0FFRDVHpBXF15vB1qi6Id5yzc51hVvvf3oGX//eMaRtaR/ULnjW7nE8s1iH60UxU4wSAEpoTLTtWtoxsFjy8Jd3v4CPv+9K5NJWy/N4sezBCxkavpyL1vO7Gef8W/ZzDnznqVl8/4enkUlbcdxkXMCPGCLGWyzTACAZpSMmO8a6AaDZa1HEe9Kz9bUzPpQCCGnaeqqChEFZ1/srn7Gk3kwkxWvbRy70XjW8qEO4rl9oRp6TNWCZBtKOiULWRt2Vx6KvYYMC+awDzuXahHEO1wsRMlk0r7khbNNAGDFMn/Iws1DD1Hiu5b2OzJTxj0/PYKnknjEPacYF6omEudoIUFFNjeTPam7YtbmxVZgGwbvesHvbt7vu+571dxygL8hETsRzQ0L92/UiScu1ja750oWs5Ntvd6zfhYcAaUm6XD/CSpvgh0GbCqP9oF/qjGl074KuhTDiKEdc0rQ39JcSsoJPwLrLvHT8hEJWWlteJUSPWfRm9Z9SAsEEhvMO/JCh7sqKrqbH6Qq9bRlYrfoQkLQmJhR1XPlhCi5iURrRsovNfb3mwAg+d99L61Ikk4vI1aoPXwmW9Pq8uABqboSGX0Mxa6NcC+KiRvtCRqueynMjYNtGxz22nbRVrR6bz3ZXxtxoEtErIdbvtZkk+UL0AR7gwserScl+O491u5knJqW446Z9uPPBo5LCTBHXcCM183vHTfu2TeCsF5KjQdmUiflVV84BA12L1IwLECqLmJZpxPtKVILVDwgBlsoepucqOLhvuOPZqkWmDh1bQanqx4VmvVZJilRFKhm857ET8PyoY5+1o4neNSNBw/YCBhAC15cd4IxjwqA0nkteLrtxN19qbggEIUMQsS72W+cH/IhjyKRx3Lzthj3NmNQWmkiiaSAgmyelquzG6kOjRNqAdoO+dqy00TXu9bq/9k3mMbUjh2OzZRRztGXkQjdHDINg70QO779pf1/3VvPtZXe5mLexazwDyzBgGpJVGDEKkxJ4IUO1LhPSbNpEw4vgBQx1r3MN6wVMMiGU2Fej7TWHp1dw54NH4IVMXj9dPKTXSrKDiMXJsfya6DqrTnOlEaLhhmdsbjubMjGUTyHjGDBoUwRuajyHQtZGPmNhYiiNHSPZFkX8s4FBgn2ewg8i1LwwttMwKJEXj00xNpSCbRqxjRPX0vhCwPMjZNNmPGtzoWG97hgXAq/MltWDY+2FR8MN46RrseTCC1hCpEt93UZhsyS2ch9vZZ+0oAnQKtrSbZvd3ob1WGTo6j9R2zUMgn/x/qtxYr6KB589heWyByEEyrUArs9QzNpx11dWdUXsIapjyXrrGUoJHjs03zdFUi8iv/KgpNi3fwb6fVsWMFxS94byDlYqPtphGkRZlTT3KYo4KAUqSrkzk7Zw1yPT20ZblWIuApbRna6/mSSiV0I8SJIHuJDwalKy3+5j3W7midYMiX2wFS08mzJ7+mBvN5LnSABKdEw+sznv1BIRqgBAiVxPZVMmxofSWK36yDhGN+PnDnABfOeJk3jouVNxp1Wf10PHVvC/73sRpWoAP2RNIU0AhMjvglAqe4/kHdQaIbyQrRkLkzG5Iz5r9WYux8lGizJpX1jtTCr1qJYOgEr25LxKtDXTYEgJpj754gLGiinpXsJFUyhWvrrl21ojiK3FKOTnLEBw7+MnMTmc6Uh0N3t/UULwoXddjj/54jMxI8S2DQxTB5V6AMuk+Im37sfN103F91Yyge7mDc3bVLltSyb9JxZqyDpmy5pFCIGqGyCIOIJqsCY3seGGsJX9nEGlKFjyXH/r0Wn4IUchYzfp9QZFLiVHE+557ATeUg9Qd6NE17mZTPs9GCNbhUEJ8hkL+YzU1NHfFzIWchk78TsTtmWgWMygXG70dNzp9vmeDQwS7PMYjDWfhnopnXEscC5wcrGGoawNw6CyakmJemDKhfTYUCq2M9AdQ3mzJ6p+59GDFeicX736wEiHsubdjx7HzGINrh/B8yN5c7XNNSUfjPsnC7jlul346kNHz+rx9ppjPpMQav55KGcjYkL5VUvaEutDIXU9REzEQXlqLAvfj3Dv4yfhBZLCr+fafbWAyCvanVABxTYN+EGEaiPsi2rPucDsUh1jxVTfFEk9e/YXdx2KPVmBNZjzgAowFmpuGHc/tNCZ/jsmRCwQtlT2QAB8VXnACgANL0Qha/e9n2tBe5mGjHdVxryYkogBBtgIXk1K9mfiWLebeXL7m/bhPTfsweOH5mN16xuvnthS53ojOhbJc+RYtGlBCFXMFSKmR+vklEAmGnt25HDHm2UR4LP3voiGUkUP1glOlkkwVHBiteq/uOsQijkHKxUPdVfaWFomjYXNtA6JLHLLbXMmsFj2trQm6cy1pRJ82GfSQ2mTFt2rsL4ZOJakaW/22LyAwVBU7fkVF2+6egdOLtRkkZ80V7BciFjNXDaaZOeYgICp343kHXgB61rg3sr9de3l4/j4+67C3z98DAurLiImk/Sr94/g1hv24JKpAjgHQmVpxZUad6QS6X68od981QS+9v1jKNUD2Z1V3eVavblOAdZmO3Ihxxlsg2JiJC2FWhdqqDYCzCzVMH2qAgBKMK3pYa3h+k19oO2AYxkqOZYJcj6tEuVMaxKddsyOtdSFiEGCfYGBEoK3v3Ynvvb9Y5hbbsC2aPxwaPgMjkXxzmt3oloPY1VDLSBFIGX7DUJA1VeiOuOUEFCqVBDjjt3GEvGtCDytN7+apOBmHBN+yBGGLE7mRtXcb/uDkQuBx15Y6Jv+tV04m/l1Lm2CgKDhR1K4Rol6ObYBCAEv7E4a7wV9vXR98BOgmLHxoXdehnuUuifjHHUWtahPMiZQ88L4Z9oHezXov+KZDJ7d0KuTW8jacCwDUR/WLQAQRgzzqxEiJlooeno+XEAWFjKOiaWyBz9gcCwDjrLhqLuSalapBzANinTbjNtGO857J/LYOZrFzFIdxayFZHngYksiBhhgI3g1KdlfKMdqUrptVlzJdUAUcYAAQzkHN1+7Cze/fqrjWJPnSM+n6qWLTrJGCylYloGGKztuH3jrfly2Z6hlfaLp8ycXaghZMzmkRMcB9W8KDOdTqqkh/YEXS5589itGnUEJIsblKJqidHeDENtjq5ncnr+B+KpH2AyyfYsVSoBLdhXQ8BlmFqqbYuPVvRCuH8WJ3j8+PRtHwIiJuFlkGhSmIZtLdS9UFlKyoG8naPiG0X2WejP3FyHNbvSV+4dxYFcBpxZrqLkRbJtirJgC58By2Y/XPjqZ3igu3T2En3zbATzw3CksllxEjVCqyEPANgmyaQularDutsOQIwo5jp6q4pN/9+zGd6QPGFSzFGUekU1ZuHLvEA7sKrYk0+eqk3yuMEiwL1CkLAPlmg9XPVANCkyMZPHeG/fg0t1DcZBpfbgLRG3PXwGB+ZUGPF/aEE2OZuUMDyGgBoWhBJ7i/4imrFMkY92LJ1Zxz6PHcWqpLj3pNiDwtN786kdvvULOKCUouMWstADRFbdSzccIJR0Pxu8+PYOT87VNJ7z9VAk1KCXIOgaqfc5zbxWEAENZG4WcAy4ESBX4iZv2wQ8Z/vHpWQAMjmWpinD/1HPpmSlgKCsRTb8TAhgvpvH21+1Euebj5EKtaZ0hox6I+jwEgCgSoAaB4AJCiA27h+lrNww5Ul0mHnp1cnVlWiuxt2yzy/s0vAgguvDUXHjIGX3JDHEsI6a5O5aBsaFmV92xDOn9yZv+l8nqa7f9XKsYRQnB+9+yXwb+aiBVxM/DhfUAA5wLvJqU7C+kY92qg0JyHWAaFL4Slaw2Qvy/334ZD/3wND58S6eNmD5Hdz0yjZdnymBMKm5rdls6ZcKgBNW6wL7JPN71xj0d+5Wkzx86toLvPz+HxdWGFP1UcdOgwGgxHRdQdcdYz3tHTKgRKNXUUJodSbQn1Ge57t8V2zkml01beP9N+wEAn/3Wi1iuuBvevmTJNU9MEHJQIs8/V6ROS9lS7RzN4up9w7jvyRlkHEOJ1pKWRG6tAre+du559DgWyy5YIGBbFAf3DuHWN8pOdGz5lFjbeAwoV1xEkZCK/LakcfvB9pxMLqQQbCZt4dJdBaxUPLhcgHMGJutOCFl/TEBgk4K5kGvMieE0RgopmSTrZFl9XS41cO8TJxFEvGWGu+EzvDRTxmv2j7yqx9AGCfYFhiMzJXzt+8fghwzDeUclMhxByPv2ck5u64HnTq2hHiiz8VhjQnXDiUqyLYuCEYpDR1dw9yPHEUQMUztycvY2ZKi5Eb72/WMwDYor9g51TXL7UQS/86GjKNeCFlGztGNipJBCpa5mUVTykVx4cCHw4HOnICAT/nZF6X5ANlBhNinBcCEF168jUiqWZ7KTPVZMxd6PUcRhmhQHpor46oNHwDjHcD4FP2CSlUAJWHf1sg7Ijq2kj+lEstYI4fohGn6Ibz85I7vWqpBgmc3Fik5IQybFZN5x7S5877lTm1KGJGq8wQ8ZckL0TeFKVqZdP4qF1HofL2BpKrbqKujK+MRIGv/81oNwvQiVRoCvPngUTlsCbSsvaV/ZXQQRh7OGt3Q/auNXHxjBL33o2tgH+3xeWA8wwNnGq0nJ/kI41q06KCTXASnbwErFBxdCFfSldePMQq2nYKQ+Rw8+O4t/eHgaYcRRyNowTYogZGj4bN3CpKbPy//yuPOho5hfcREymeibRivtPVAFAE21lurpwFrR5nxIqDWSyT6lAMTW9+9f/sTV8drrtjftwb2Pn8Riye17HUQV07Kdsq6FaSkBLMvAjuE0Pnr7ldg/mcfxuQrue/IkgpDDtuW6MYn2Arde051aqsX307/56WvwoyMrKNd85NM2Lt8zBECK2bVTuQ2DwEnZYKyzgLIeIsbblLNbVbSlNZW0pVprzFBAisRuBrZFkbZN5DIWqvVAWqLaRnPUVK3hqm6InaMZfPx9V3W9Z7gQ+NZjxxFErTPc1DRQMCgqjRAPPHcKB6aK59Wz6mxikGBfQOBC4IHnTsEPWcsF7VhAJiU2dEHrRN31ItgWRcqmypOxUz1Q3+fNbrju8Mkq29ceOoL51QaGcnIuiSraedox4foR7n92BhNjWZjq5k1S0k8v1+EGDBMjGaWWLuJZEM6lYMqiEtDKtnUq046JlC0Tm2o9xPvfvBeX7B5Cww0xPVeBELKzHd/4RM7mbATJl1OKHgrbErmMhUD5O1uGHBZiitJ0JsTUDBXwk0kcgWhRWKfJxJH2p5TKOECJrNQGIYdJJW2LEoJsyoJlGajWg3gZwdWcWzxVQKTyqRBAUc3TuH604SSbcYGp8RwaXrhhiqSuTH/pgSM4Md8qeEb0HJcOmETZoKhjoZRgKO/AoFLogxL52U7PVeBHrOM6BIBi1sZS2QUXAkHAYPXYz42ojV97+Th2j6ZxdLZ83i6sBxjgXOHVpGR/No9Vd6IbPsPURIjh7NrLxO1wUNDOIBnHRKnW1A7RsdtUBeK6G/YUjKSE4J2v343J4UxLx980CPbvLOD2G/bgCpU4rXc82rmimLMhhMCCUieXc+YOKKWou4FUJUdrSr2W6NT5AlPp8nDN1uJr65T0g5RtIJe2WootYciknkjU3xkh6PTT1ueTQjLruBCoNgJQArx4fBV3P3ocDS+SyvCegbQjRUulhzRQa0ja+s6xLLyQ4ehsGd//0WnMr7qxB3SoxugYl+NgG7GpYpzjxFwVy2UPHLLgXmsTA9NJdMM/O+zGlG3ANAiCiGM47+ADbzuAUsXDEy8tYqUs7UvrjQDFvAPUfDAOpEzSnPP2ZNJ9y7W7eq43Ti/VsVRykekyL00IQcYxsFRycXqp3mEN9mrBIME+z9DwInz1oSNYLHkwDYKUbSLlGEjZBjw/wuklKcigK6dyZnpjFzQXAt98/CRKVV/O7ShRDNOkyKXkDG+/ifrJ+SoWVxty5pUJRKyVgx5GDIenPbx8YhVT4zmV3BA1Hw6cWqyDcYFs2oRJadwdB5qexHbZgwCQckwVaJUKIxcqGZSz5Y++MI9vPXEyrqDn0pbyKqQIIwayhRCiEzJDJdnt4cKgkipcrgUgKiEr1wKZ1Cbm2bcTyyUX+YyNiAukbAPvffM+HJ2twPNZLLJiW80H7UZqCzrGlWt+/P3okAPHlo+MZJU4Yp2LDKA5v0YJwUjBwVLJk5+7wLp0cUok3ezDt1wKAJuiSF61fwT/98eG8eAzs3jg2VnZFeEClkUxNZbFeDGNh5+fU4m1/Ixt00AhayPtmOBCUr4/+60XUXNDBAGD60eYDxsYzjstwnopx0Qx66DSCBBx3tVbul//9oP7hhPn4dWTRAwwwADbj41Qt9s70bZlYGI4jfe+aW/XZ+1GnmlrrSW0ZZIwpV0lTSTXAGI9Gccy1hWMbO/4F/MOrrtyEuVyo6cuBxcC03NVHJkt4btPn0LdDTGaENfUDCXGBZZKXkuntz2UqRrzeZtkUzX2JxkCSbvS5mtILw2WNWBbBg4dW8FDPzzdRvPvbyuUdDqbkLZ/GEojyKQUR2bLePzwAsKI4dKpIupuGMfxiHF4IUMQStvT1x4YQbka4MhsCV996GhsS8UYVwUd+f7FvA2TUswtN/ClB17BG64Yx47hDFKOqXycQ9S8EF7AsVJ2sVzxUFPCdmcahACOSREyERcChEBs1aUbBpYpFbgbPkMubeH9b94HFnF85+lZ+Ak7Ls65bEAJ2SCouwGYL8cJJ0fWLzA0vEgyX9uYHRq9rMFeTRgk2OcZvvX4Cdz/9OzaL/IZmrriTWiF5y/e/wqGcg5StkzMU6rTm7Ll1xNzVZxaqgOQnUYdDcKIo1wPkEtbfVeeao0QEQdSfd5kWvQhtoqgBOWqB9cLYZmGpKCrLrehOq5MCBRzDupuiFTWhkNpixDXqupSMy6wYygNQgnqbohKXVaYZTeRbFjZ22jrWAshAwClKvlPJNqmQeEHDJOjaSyWPESR9Krkm6BG94uQCZRqAfZM5PCmq3bgm48ex8xiHW4QwQuljVnKNhTFaXPvkRR7SYY7w6AtFLNumxeQCt2GsrrSi5R+9mXPjhw+/M7L4kXdZimSlBC88/rduPn1Ux1/f2K+ih8dW4ZBaawxYCfUROuuFFtZLnsoZG1kUiaCVTmOsVR2MVZMx0m2pAcKXLFnCD998yWou1HHfvbr335ivorL+qicDzDAAAOshY1Qt9s70Vaawg8Zjp6u4K/ufgEfv+MqvKbtbzbyTFurUKgtk8KQtyiBx1Axw7YoXJ+tKxiZLEyaJm0yuXqcoy89cASzi7UWllUYNQupkqHk9e3GsdZrpIbNxkfWtitpNw2qvLTl9UB67EdSXXy9985nLJiU4KmXFlto/r1sk9phmQTFrINIjZ5RSmBS2VE1qRTldUwjLgQ4joEfT6/CD5gUdg0YgpCh5oXwfQamigb7JvN453VT2DtZQMQ57n9mFq66voUAKo1mUs4FUKoGkumojvuBZ09v7OT2ibRjIJeWs8wFNdOcU3PN1bqPHzw/B8+P4EcCBkWsjB4yjmzaksKqinVgmRT5jIVqQwq9mVSORegk+cBUEZ+554UO5qthGnAsA6vVACmb4gNvOwjPj5BJmdg5ll13fZVJmfLzYBzU7BQvY4x3WIOdSSQF6OJmGkn88hxgkGCfZxgpOJv+W53sLJU9LJW9vv4m4QQW/7tcD0EpwTe+fwyjxVScmCeT9JRtIJu2EHKAQiCKmEyQ2y7k9W6ynWNZjA2lMbfiomBQgEhRLM4FAiFp75Mjadx45Q584/vHsFjykHEMWKYBIQT8kCFiHI5pIJ9JIWIcrscgOJBJWfGMciGLFhsCxnnTkkCIuCqoKdTa8swwZLAOdOVbzSm1UMIIYJkGijkbH3jbAfzdd17G3EqnD+V2w6BEdqo5xwPPnooXRn4QIYg4/IDBC5jqIm9uviqdkvRuAC0CXtqyyg87OwK6+k0JwbHTVUwMpzG71EA+YyIos67+2xqUAI5t4KO3H8SBncXEz3t3cvvp0CT/Xr++Ug8wlHOwVPYwlLMRMgEvYCq4Q7ERSEsnY0jZsURMYLXqY8I2Oqjgyf1OQndpzPTa/u0b8bceYIABBuiGjVC32zvRfsBi+yHOBVwvwp99/Xn86w+8BlcfGI3fY7ueaUlhSp3MJZ/g2iaRKLZaJi1HdrY6OvPC9Ar+4q5DKNcDAK1xMog4lsoexorSocSgG7ezSuqwEOgkgKiOqaQ8r5dk6/NhKubiht4frWsVKRQm4vl2uQ+dtqJCyHOu9Ugi1psBZ1DZSCgWbSyWXBACmVx3OVc00TwxlM6JaVCYJkHGMZHPWFit+PEai6u1WsQEGJP2VH7IMZK3sVySayxJRefxcVIi7UoJIchnHDz10hIeeO4UViq+ZG0CaHitTMt4+FEd92ZBqRRL5UIgkzLxhoM7UMhayKftFl/n9jlxDS4EPnPPC4i4QD5rI6z4AOR1YyotAi9gGMpJz+2ICfg+g2VR7N+Zx2sPjGKkkGpJkmcXa2tTuVMGlsseKEE8e94P2tfu7Ro5DV8m+TvHsl3/vpWgQtCSE5NmkqzXry0aUNp6GIBpGShkbFDOwTiPX6P/lggCQoFzwSkZJNjnGd5x7S6MF9OYnqtgterDU0mSF0RwfYbZpVr8kD2TtBTOpcfjzGK9vz9Qglf6RtBq44zLB80zLy/hheOrSNkm0o4BRyXqadvA9ZeN4dtPzaBcl3O2SSVCx6IxVUVbFiyVXLiKypLP2Fit+oAFLJVclBSlWQcWg1JpTRY/2GmsMql/lozmQsiHnFABSFLzGapu2KSmM5WgqyrpaCEFw6BYWHHx6a8+37c91FbBhUAQMswuNZCyjTgRzKYthFU/TmS1sqa+YPS3/Vw+fsDihU4QcQQhg2Ob6sFswQ9lANBe63K/ZJc/kzIxs1jD21+3E6eW6liphF3fs/lAlddOEHK8ML3aM1FNYqPiOu2v1zoCdS9E65JOQHBguOC0BA4trleq+dJ2rOLBtoy+KOu6SxNFvKtdxcDfeoABBtgObJS6nexE+4G0vtRxwzQkk6zhR/irew7jF+64Kn7ObdczTQtT/s23DsMLmCx0Jzp3lACFjIW6F2E47+DOB17B/Kq7KUG19nNUVcm/ZdA4vmtoh5Is480ie5/IpkzkszZqjQA1N4qdOKA0SzJpCw2ve0xsh2NR7NtZxMx8tWOOlyTWLnoETyfMHbPhQiY/1CCg6vgciyKbNrFc9juK8KZBMTmSxnLFj68bvS3dMWQccIMIC6suXD+CYVBYpgFHXTsGpfKrQdsirGzAREwgCjlKgY9KzYcXMngBb9GLoWpBx9X3tmlgsY2qr9FkDgo898pSH2d3fejCC1HnhFB5betGkxa508ceRrKj/pr9wxuaP07ONcvCA1We11Lk1SDyXiMEsG0DE3kH77l+N7Jpq2fXuS8qN++Pyp3cvEko3nP9btz92HFETCBtUximAcE5vIBj52gG73vTXmRTZjzyoQtMWgcHiVyBJpNidR3r17fmOq0fumFQ5LM2oiBEFPUusp0NGn87Bgn2eQZKCF5zYASX7CrEVdUkmiriHGmbghqyKu36UkzipqsnMFxIxUl5MkH3fIbVmr8hVceNgqsAknRerjZC/OD5ub7+vuFF8cPbMg0M52w8fngRPzq2gpRt4MDOAqbGsoCQtCQmBB5UNJ5KIwDjsnoZqiAZMd5hTQYAKYvi1hv2YKSQQsML4YYcK2UPz08vo5C2ACLnh2puqCqhdgfLREDOxKQdOX/u+hGgKrKMSS/qzSiX9wNN7WccgJCe134gP9+wy7y1gKZ5q4o05xB9rBdaqtBCYLniYzgvE03TUA/E5q/l9tUb1Rpy8XD/M7PS6qvXiVCUOb0AYFzg/qdncWBnYc0F00bFdbq9vu6GMjgCIER0znwZnQ/stGPCtihKFR+33rAH11wy2lcHRXdpZhbrsXd989QO/K0HGGCA7cFGqdvJTvTiqht3OAFVf1YLXj9gLYn5Vp9p7eyjj912EF9+4AhOLtRiuy3LpMimLHjKrqlc87FaFZsWVEueo5nFWnys0pKrM0b5IUegCskbgR8yWH6Immo+JLvZAkDN7Y+ppBPz/9/tB0EYxw9fXsSDPzyN5bIXM8UMNVMdMdFicTWct3HHTfsALvDtp2exWpWFYcYEOEnYixGKsaGUSrL1Zy8QMY7lio9s2kI2Zaptk2bxhRIVI+WIVTHXZGAKAURcvpcXMDAmmxSyG81bk38i1w2WZcBvS66BTmuzk/02ftaA/jx0gT/JNEiqquczJupeBG0XXsxbYExgtRrA9aVIWnKdpLWMGMeG54+TyTAhUkeoXPPBhGSKAnI/a40ImbSJ2964Z805aQIgl7aQdigsg8QsU0KaxbNAjRMOFx04VlMHKdZBUJ3iODHWVnQEGC1OYKSYwoPaqztkIJRgajyLd79+CpfvGVZ70jw/G1kPxwruFygGCfYFhvZOLgskBXvXWKYv1cPZxRr+930vApAPeCkS1uz46ueEQaVwk7RMkHYUhBCMF1MglMALmKIgb1wdej3ogOKHrO8uupuo6oZr7I9j0Xjh4IUML54o4aWZkjoXMhmvqbmcXnNJ7RQn25V+0Y5tIk07pdRk0qgSbpYUaGv9Kjvn/ZyhTmp/zQ1baFLdjruQsbFc8aRK5yY/Mq2kWsja8AMGSgiG8jYcywDnckaoUteiITLos0gGVYNKinWtEUrqYczLanavmZA+lBHnuPOhI/gA48hn7Y4EdqMdmm6vF6p7rTdrmRRDOakeLoTA/IqLci1A2u6kVjEmYNsGrrlktIW6vp6/tbYP26gq+vmOrXrgDjDAANuHdup2EMqusGZvtVO3dSfa9aJYEDPirUGGEoJMW2K+lWdaL/bRh955GRZWG3jw2VNxxxQE2D2eRd0NsVrztySopp9V9z81g2ojTNCCewfFzYTLiAmUavL8aprrZpdKlABff+AIfvVnXoc94znc9uZ9+L3//RTmll04NkVV2TpptoEW7eJc4Hs/nEO1EcALWMyuy2cs2JaBlbKHSIldjRXT2DWWVYJZsstNiBTqfOOVO/C9H57C3kxBJWUcUcTBBUep6sOP5JhgnEBvkEovhFy3hWz7BbE0zd0yKdKOTJaLWQuUUiyVpTq8QQCtxRYz8QCYJoEX8piVp/VsbMsEgXRSCdXahhASaxmVagHSjrnh+eNs2kTKVutKy0DKMZBJmWj4EVgk4o7urvEM3nndbhzYmU90hFup0/rnhZyNK/YMY6HkIZ+25C8FIIcgCRpuhMmRDK45MJoQAu4cGeiFy3cP4dKpYtf4fyEnx9uBQYJ9AeLS3UM4MFXE6SXpj9uvKAHQOjdRzNmouZF8UCKhBwBgx3CmoxpdaYRIp8zYF88wCIrFDJaWa7Eg1MxCDZV6CEJlp88PGFyViPsBg+snO+tRnKifrdswOTN83xMzXV+zlqUWV8mwDJvKJxydAVhTo7R4lpH4amq/wS6fl547ihPvtmS8/ecaQZdZ6CRcnyGfkXT2cj2IleM3g4hJde2psQzcQFqn5NKyU1JeaUjqPJGLFtOgYFwFfiErtMN5Ofesn9q6oMKEAIGAbZlouBGO1av487//MRzH7KAA9tuhmZ6rghLgyEwZM4v1ltcnfUwJiFzkqcAGIFbr17T4+DPq0Znph66u7cM2o4p+vmKrHrgDDDDA9kInzHU3RMOLWmZ3LZMqJeEmdVt3oo/MVlriSjK2cSEAIgumyZnqzTzT1mIffV51oj/x8ze0LNqFEPh/vvb8lgTV9LPq5ELtrKk/Azq+bf7v/ZDj6EwJx+eqmBrLYmahhusvH8c/VmdlQVuxsNpL7HU3QhA2MDmSBoF0OpENAqCQc2Ar4TAuoJxXbBRyNiLG4fuy2PK2ayaxXPFxarEO06DgKomW7EC+7Q2WbjAoiS1ZBaSaeD+Ufc3aK2QtuAGHbVJwx0DVjZBxDORS0hYuualkXck2Dbh+FPtzCwilfUPi7ROi5sh1kq3o5GnbxNR4NmbBxTPFVBar4iQ4kRQX9g3jir3DWFTJsMrZIdQ4YKUeYrTo4Jd+6rWwDGNN6nTyd295zSQ+d9+LmF9pxEWweAzTpHjH63Yp1fjNfZYDt5PuGCTYFygoIZvylqOE4JZrd8U080LGgoCcJ3EDaWtQyHYPYL1swKSKoY18xsaO4cyG94kLgRenV/D3jxyXdBVL0lgY4/BDBq67vm33vqTykDgpPVfo9s7dLMu6gUCpphOZgFND0q5kAUPOLlGTwqC0qxCinhnXibdWB9VVbD0bJATQ8ELsGE4jnTKxsFyHG/TuePfcXwKkbQN+xLFalSrtrh9hdrGObNpEELLYb5wSgnTKRLURgkIGRZ3QFrJqdl6Bq/klIRDPxAFAwAQcoIMC2I+4TqUe4HPfOoyaG8LzGdwggq862CnHjBclVH8QbRTBoZxUjq3UQxQJWbMzsxG6eruNzIXc8d0OD9wBBhhge7F3Io98xsaJ+SoAqSysn3F+yOCHDHsn8nGBkBKC9755H/74y8+1bKedxluph8imzI6Z6o0807qxiXSHPeMYaPhR3IlOLtqfP7q8oa58Ow4daz6r/GBjBWadqOmZ47MNAal4ff9TJ7FU8rCgBL4IQSzAZii6tkFpPNeqkUpZ8CM5FsY4R8AElsqeTKQVJVuuFWotNmOUAJ/+6vNnpQFCCJBxTIAAQSCpxlnHQMQFOAe8QFpRDeVsGAbFctlbV0uGAErEDcg4BupeiFteP4Xnj61IBigHUpaBgEmGJqXNmWBDJb/FrAPTAASRNnHjxTSY0mjR701J08VHz2qnHANhxDExklH7QmI1+2by20mdfts1k/jcvS9hYVX6wrevO/7JG/bAIHRDa96r9o/go7ce3JI//AAbxyDBfhWig2bOm2JdpaqPdKq7IMmZ9LX7waF5RIy3UL8AIKq4CHo8SAQkbSefsfHGy8fwwHOnELR5Lup5bk3xsU0D3gaD65mEAKRqOoRsiK8zlqWPJxaS091xooOrpEMZFulIygkkjd31QgwX0hjS+6CTdCXuxrmsmOrKqZ6D4XoeRgC2SaVKe9pErRGgVA1QVx0BQZp+0pSSeBabJJLYtGOg2iBq+wS5jPRybK+GM0U5Hyk48BLzf+uJ69QbIVyfxRZblkmlL2bEsVzxMFpIqc61mjFUb5u0czEMimzawlhRdv17dWY24wWrK76arnjo2MoFl2hvlwfuAAMMcAagVuzyGSdABFqSp/b2bTZlIm2bsrOb+LlONkAk/XUo57Qwd9rHQ64+MLLm/d4uqFauBy0ddoMSnFyodXSiN9qVT4Jzgbt+MA0viJBxTNTdCCalsU7KetAvyaUshBHr6p6xFWhv6m6sNz1jTSnBSzMVOCbBztEsqEHheiEqdTnexrhAGHF4nIGxplMK5wLzK40N0babrIVtPcw1kUtboARwAybH/JhAEHKYhnSE0WN7ZeX+YVkUYSSkHg2I7CYnEmRCCCxDfh0uyG59w4uwd0cON79+CourLhp+hId/eBqluo+0bSKIJCNPc+sihnh8j0IWAELO4+uWc64KLnq9JNmLOWWbVa4F2LMjMdqmtrVWIerKfSP4yK1XbDvLbTP+8ANsDYME+1WKbjRzLoC//faLZ93XLqmcmEyuXT+EG6x944eRQM0N8L0fzUn/P0Xb1qqPatQEBpUP8PomiwPdaODnAvohHgvJbaBWQACcWq632h0oMQsZ1PX3FJRKmjelRvzaZFWcABgupmCrueWpcWlbFUaSAZGyTUSRpJBRKj8nKgQ4CIYLKVimpI57PpNWYxDIOKaaLW8SlQyC2JqimLWxXPZwcqGKvRN5SZVbqsOxjfjk6KJAuR6AEGl7R6n0/bRN6Q/JuPz9juE0LPUzAQHHMuJkXdPA9+zI4Vc/fC1mFmo9A+JmvWDXola/9rKx/j/YPnAmZqS3ywN3gAEG2F6cmK+i6oYYzjtxMqrHwGxLznVW3bDl3qw1pD3nWNHBStWPhaw0wTVSFjhvuGK8hbmz0fEQzT5ilGOl6quRoqYQTMg4Ipfj0LGVlufGRrvySRydLeP0shwRijt/pNU3oh+4foixYhpuEKFS76yGJ8WhtLOGTpJpW8Kc/F072kfFdNyKOAdnArOL9Vhhui+K9hniwhOytU3rwgJR7DZCpBBXyjZb1iUGJUg7FixDMhYtk2JyNItyrVP5PLlvjEtKd8YxEaoZ5vHhDIpZB9V6gNmlOo4vVJGyJN2ac1lE0kUfzXLTDagg4giZ9OpOitQRSNZcPi39rMOIgxmio9jT7/1yplhuG/GHH2DrGCTY5ykMg8qbs50C0+Vp1u8Drtt8xWVTRfU7uQi/fM8QFksesikz4f8gE46qG2L3jhz2qBudKo/obkrL8UZ7/zNGEHLYlols2owfIFwIVOrSBiH+W6GPt3kkQsgOcCA4hnM20mkzFvyS8zHydQYlSghk3dPUgowj537OBS1su6HPYVN4YvORkRLpi00NRW83SJyUVlRyK7hQSXxTjVKPMgcRg20ase1FxGWCKwSJFz5aOER30Ys5B34og1sQcbz3Tftw96PHEUQcadtQdhMc9UaIiZEM8lkL2ZQVd21yaUvauKkOfS5twrayKKuFUjFjIZO2EEUcDT/CztEMfvLtB2AaFAd2FjqSSH0a/YAhm7KQy1rQRiTJDlHKMVCpBfB8Fl+PL55YxZceOBJ3fw1DzoAvV3x88buvwLYMvHm4u3/kRnH4+ArueeyE7GKooD4xIi00rty3efp2rSEV2C2Tdl2oDny9Bxjg3EAnsYWc9N0NVLJAKYFtSj3iSi1ouTd1h9g0DYwV0yjXg4QYpYBlUDi2gasPyGfGZsdDchkLBiUo1aQYpplc5BPEftNPvbSI29sF0jbYldfQnsEZtY/ypeuLipo0wRJTybAfMlgGxWgx1Uye2yjZ7WiObUmGWKj8xRnjLWKnminWD8I+RtA2A82Sk/vdShdv3zehmheMo7VYH3+Va9n2Tnz7+Uom6u1jbxCyk50sJHAuIIbk+qJc9ZQwX5Nlp5raoIRgpJBCuRagVAuwezyLcj3AF/7xGcytNOAFDK4fwbYMFLM20o6JlGPCsQ0EqkFQqwdIOSa8kMMw5FjCSsWL90U2I2TDoNIIYBpSGK2bRstG22mHAAAA3iFJREFU7pfBXPOFj3OeYHPO8elPfxpf/vKXUa1WccMNN+ATn/gE9uzZ0/X1q6ur+G//7b/hoYceAiEEd9xxB37zN38T6XQ6fs03v/lN/Mmf/AlmZmZwySWX4Ld+67dw0003dX3vX/zFX8S1116Lf/tv/+0ZO8bNwLEoHKv7fOn2oHs0uPWNe/ClB46g6obIOiYM1Wls+BHSjoH33rgXO4bTsitsEhQLadhUBook2gNX8t86IOpvvB0RxoYcWKYBS3n1BYyvK9zVfjTDeQe2ZUBAUqW0oNZrDwzj4R8vIAyjmEIeJ5sQiQd7ogShHvIZxwCItEVI/k234+JtAZIrOrVWBxdiYwH0fAYXQLWHzYi3RludEmC57MOgQDZjY6yYwvxyA5W2JIwSlWADAFECaX4YW8WUaz6GCw7e+poJPPSj05hbqYNzSc/KpCz4QSQT23arOwJVMBFYKvtwLIqRnA0QgoYXoqH81UeKKbz1NZMo5lJYKslgyiGwsNKA6zOkUwYmRzJy3pwLWBaF4AI0kWwSIq9lwhjyWRuGQVBRaq8PPncalkkxXMgmlDul4metEeGBZ0/hikvGUPdCcNZcMHS9a3us7AgBpk9VcPejJxBEDDtHs7HHvBsw3P3oCRBCsH/X2kGc9PhXPmtj51gGliEp4e0IGUcubUrRHM7VXzeLdl2+7TiU+N8tr1+H1km6fttRIGnH5jsx23dDv8pFVwfYJrSP0DhtYzRhyDro1EnLraGcjUkng4gLhBEDAdDwI+wez2HvRL5jPARAnMTLWdeo53jI3ok8hvJSg8No66BpDRE5YuK3dNg30pXfO5FvocLmcimkbAoK2f1M2UbHzC1ZIwHUTithQmyUEtkcaNpxyjivLTrleuD8YL4Bck2pFeKBVlYeVQkupbKooLVtBDoTY9rWfTcNgpRttji5aLR34sOo9Xzp3/FEUt0Og+rttP48XGpgKO8gmzbhtnliC8jjGC44cTEnZRu45sAIPp9Icm2TwvMjhCHDSsXDSCGFtGJSOpZsAGTSFu64aR+eenERc8sNVBpBfI0ytVMCImbbLVd8jBZTLRotGxmnAnBG9Vk0m63hM0xNhBjOnts08GJ2IDnnCfaf/umf4m//9m/xe7/3e5icnMQnP/lJ/MIv/AL+4R/+AbZtd7z+V37lV+C6Lv7mb/4GlUoF//k//2c0Gg38/u//PgDg0UcfxW/8xm/gN3/zN/HWt74VX/nKV/CLv/iL+PrXv45LL7003k4QBPjEJz6B733ve7j22mvP2vH2izO/0Or+Bpep+exeNJbLdg/Ff0oghbgoIeDty/D2+2ON+2X3eA4EBNOnqxjK2SCEwPUjLJXd5m4S0rYIJ/H3BtFKjgS2pgtD2lVICnyISt0H5yKmiJPENnVw1dsjaHZR656inFPS8rv2QyKJIN3Po0FTmXW1NpmIt/67WZWNk3Qkknl+YSXsXNPbORBUfKxWuvuLcgHwOE+XB7haDeBYBu594iRStoGUbSDtmNizI4+JoTQAuWC0TYpvP3kSpaovBfMgk+qaGyprEblV23Rw/eUTuOHqCQDoqsofqcLRkZlSh2bB2FAat1y7CwemiiAATi7UUci00qW1+v7kSBrZtIW6G2J2sYZDx5ZhW0ZXPYMwYlharePw9AqGMiZYonLfcm2tc6FxIfCNh49hbqWBYsZuLVgJgaVyiG88fAwfue3ghgMagV6AEMwu1ZHvctw1N8KO4ZS0g+nxOa95OF12iUAyZ3xGUKm64JtRsO16/3Z528QPW17b5YfJu77z2UeaL28vIPT4vut7t+2jbRoDit+rDP0uRjfjT91uuZVLm0jZJjjnqLkRUrYZJw3Tc5V4PMQLGCqKUqvrYIbRfY5av8/1l49j+nQ1jnU62dMezMWcjSDgLR32eiOEEALZtIWMY8okSnXctX9vvRHi2KkKvvPkSSxVPAgurZYmhrPYvSOPUtWX40Y5pxlvedMiM5kAJhPDTMpAwwsRqZiUdmQzoHoWlci3iqGcDT/k8BW7T3eWDUqQsigYB2yTxM8ewQWY0GNabWKqXCBU4rMQAnU3jOnqSbHV7Tg3vdiDEeNYKrmyy07kOJhtUjTcEHU/AucCDTeCYxvYPZ7F+960F/c8dqJrkusrwbxKPYiLDYQ0i0o3XzeFm6+bwqM/nsMX738FmZSJlG3A9ZvXPkdzHXjbjXtautH9jlM9+OxsnMj7kRSMHR9K4SffcSlesw1ioe0UddsyMDGcxnvftPeciJFe7A4k5zTBDoIAf/3Xf41//+//PW655RYAwB/+4R/i7W9/O+677z68//3vb3n9M888g8cffxz33HNPnCz/zu/8Dn7hF34Bv/7rv46JiQn8xV/8Bd7znvfgox/9KADgt37rt/DMM8/gs5/9LH7nd34HAPD000/jE5/4BDzPQ6EwoGC042yrHHfz0iQq4sbJY8eTuvnvCJBzuIS0JBFcSMpOxQ1hmxSraq7sTCPuNsYJfHPWWSfh2t4hTtwTrzWV/2RSEGu9U59M2JOJu0gk6noxoZP2XpZfGzpWIil9BDIZPZOnVwjACxheOlnq7w/c3vP2BECpFuBbT5zEifkqdo7l4qSdEIJTS3X1bxOnFmv4+x9Mww+ZEtKRXeC5FRdf+/4x/OTbDsTK/JVGiIxjxK9p+AyORXHLtbviz7PhRTJJN7ozVAyDwvUZao0QQ5nWR7To+Y9OnFqs4/RSHbZlKG/41j8wKcHppTpOLdY37EigWRw3XT2Br33/GBZLXtfjvumqibhYtD0QMKD81RlvKT68WkAAqW6bI3Bop1bGABcnNrIY3aw/ddJya16xdShBh8BSraGLlTIpEUKxjojsfEaR9Elun6PWuOaSUTz43Ck5qyqkkwOlskBeyFiwDIqQcRRzNhiTIqBlN0AubcVrAr3nFiWghCKIGCLO8cBzs4iYHDmilMB3IzxfWgZnHJVGqBLAjZ37Sr01y3N9BncjAihnEO0CaUmLUNNodp5TtgHTYMilZYGAc4FyIwTnQM2TCWmFC1AKQJCY6UWhPdFboYsiBm36Hav6eV/7vJklhx4faH+vsYKDTFo25BzbxJAQWC57GC2m8LHbD2LfZKFnklvM2liueGBMar3Mr7gAaQqW7RrNqn0mKGRsUELkLL8QklKu6OScyxlv14swXky37He/rif/8PC0osPzuLhfa4T4n1/5IX7y7Qdw+5v2bfykKbRT1K2MdG05uVA7J44frwYHknOaYB8+fBj1er2Fvl0oFHD11VfjiSee6Eiwn3zySYyPj7d0om+88UYQQvDUU0/h9ttvx9NPP43/8B/+Q8vfvelNb8J9990X//vBBx/E29/+dvzSL/0S/uk//adn6OgubJzt+Y92L81I2TmJPoIhpfIh2Y5IiWZwxlFPUKP6RZJCtRHEVPJtmHVuB1Utd6qSdC0QkvRj5EJIiwn1Wpmw05YEv1u+HtO5WqhbnT7cSRswnbQ7JkXKkfPLBcVC8P1IKm2KJgVNQKu5U+yZKMRJkhdEWFTV6LMBgaaV2jOvLOOZV5bX/RtK5Lx1UvCtEkS486GjeOOVO3Bw9xCOnC5L1XQhuzijhRRuft1OHFBaB4AUCTQo1hQTNCm6quFuBP0k8lt1BehwJFAU+8kR2d2/dPfQprc9QHfI58urr7BwMaLfjvRmFqOb8afWf3dw3zBml+oANQDOMKUYPRorVQ9cCISBQNqx1EgPgWVQGAaRlychmJ6vImR65hlxx3O0mMKeHTnMr7iSVq2KwASycFZ3I4wNSfbLctXDsdkS7vrBcZQqHiIuQCG7qzq2FFV3ljMRO4u4PgPnTbp2tR52TRTPV7QnztqGq+kYQhMidE3I4qOM2drm1DAIKDjK9RBpRzKnynVffSbd31s2OQiEstggqoASJRJcAHHBfiPrJdL24n7+lhDErIVC1gYEsFrzJbW/LY6GEYdjGSjVgpjm3ivJTTkm8pmmbaiI/yfXCPc/PYuXZ8v48C2XIpexYBoEIeNxYSGpbwAhYJq0I3av53oSKpE+pkYyBOTnCyKZBGHE8bXvHcPeHTlcfWB03fPbjm4UdUJkYi/HNc6u48erxYHknCbYc3NzAICdO3e2/HzHjh3x75KYn5/veK1t2xgaGsLp06dRqVTQaDQwOTm55vZ+7dd+bbsOoQVmlznEixl64d5rAb9RvPayMVx1yQge+/E8lsoeZhdreOrwguq+dcI2peIkIIWkkvehEAINL8L4UBpLpQY8j8EwgH51QQjZ+hrWoHJuaDtDuqZYb7VXrDvqSWVTHdAzjgHHNuAGDCalsQ1YO5LzVZZJ8Y7rduH7z87CMCkMQlFtBLK7rSjh8TFwwAs4js6WkHYsmIpuHTHeDHLq8KTtmAFfzQGaJoVBpG1HIetgueLC9aJ4kaAZARuZ3+8Xkrbe2Qku1QJ858mZrn9Tc2v4/LdfBoFkWeiuuB9K2qVt0dhuTRdJPJ9htOggYtJSzDIo0o4B02ileq6HXFaeW855xwIEALhO5LNWb6HCPnDFvmFcvncYs0s11N0I2bSJqbHcuqyLzaL1uXMRqA9uAoRIFeVuMWe7n8tnCuf7vnMhcHyuimojRD5jYd9k/yyufo7j0LEV3PWDaZxeriNiAqYh7Zfe/5b9sZCY3o9vPnYCfsAwnG8uRg3bgG1RlKoBvvnYCbzm0tGO/XvtZWN4zaWjOD5XRc2Vx5FM4jsPR9GDIXDpVBGZrINqVXb3NBPq6GwZTx5exFDOiZ06koVXoZI7AuDUUh2HT6xg12iuqV0igCOzZRw5VUa5FoJAFl2B5pxtPmPh/Tftk0V2Adz/zCnUGiEyKROlWgAmAKI0OhgHVipSXEozseqe29fntFkYqjsMAvjrOJ20Q8ZZWYiwTRVbiU6Y5X1NuiTOQiC244qYQBBGat4bsVjaWtoUpZp8b9dn8ev0vHXUxsMWAhgrOijVQwShUPstxUxNAjDWVKLRb5lJmQhC1nO91r59nVTrdYdoWye0gxASu5akHAOuH8XniHOOIAI8j6HuhfHxCACf/9Zh/B/vuQLFvAPTININJ5Hkun6EUq37CJNB5VpyZrGGz937Ij72viuxcyyLmcU6HJN2WMwBwJ4dOVwyVWy5Fy+ZKmLXaBYnF2qwrc6RjVpD3gfy+iVS+E+PMqlzHjGZZF9z2diGk85jpyuYX2lIKzSqR5Y0Q5IilzYxv9LA7FIdB3ae+cZat/3RIIRsaH/Op5jRjnOaYLuufAi2z1o7joNyudz19d3msh3Hge/78Dyv5/Z8f/0ZwK2AUoLhbVD95VwGsEo9QCFryxv1PJ+zKxTS678I6x/bcy8v4iv3v4zZhRpcP0JDiWZYJm2ZkzIowTvfuBt3vOUS1NwAf3rnD1Guh8inLVjKuqLqhsimLXz8J16Dz9x1CNVTZUklU9XHtaCp2mKLGfb5rDyuuwbtAVnPZt302gl8/aGjHb+L1UANGncOChkbAsCN1+zC6eUGTi/XkXbMWOWU8dbFQbIjns9YME2KuRUXric/M+l5yUEJBRdczmmr4b6hnANKCcq1AKvVChzbxOhQGqZBEDE5C+YFEdK2idGiDTfgWC570JeZ7ixn01Y8w+6HDLt35GEaFK4ftfy3XY1CAUlvlx7sTeE11+9e8Tm11MAn//dTLT+TViUm0ikTacdExjFb/t3+X8o2MVRIYbHkopilyhpGLaCFgBswTI3ncNUl49vyjBkaymx5GxtBLpc6q+93PoEAyOdsZNOd8VCj3+fyucB68fJc73syFkWMwzQopnbk8KF3XY5rLx/vezu9juO5lxfx2XtfhOtFyGclJTriHKdXGvjf334J//qnX4fXXSbf58hMSdoKjqTh2CYo0BxBIkAubcOPOMpuhH2TBURM4OR8FTU3QDZlYdd4FgcvGZNjQlwzibRvr1S1bup9KMaYauM1Sm6cQEurwgB3PzyNpXIDEAKlWnehS4NKG8eaGyLiFPnEeeBc4MEfHgbnwPhwCuWqVCuPn7Uqzhydq+HZoyuYX67j5EJNJiKJ57Foi6992VVtAyiRXdQdw2l4IcNy6CmrMRk7teq40dZ91sVs/aTVX3Uc1mscL4hgWdKBgUUcIevPq7sfyI5/YryOC1Aqu/5CACmLIojkftQ8hpRtAohkQq3n5QkBISIuCEdMwDKI7OQqWvVan0UhYyHlmKjUfTDe7Lj3+gsCoJC1kc/acGyjORpgGqoRIlCuBfH8t/4bWYAhmC95+Mu7D+GXP3wd9kwWMH26EguZrVZ9LJe9Hu8s1y6mckhp+BHue2IGH/4nB/FHf/cMFksyf6GGbK3r4k7FDTGz7HY8J372tivx/3zlua5rVds2wAQQhBEo7SykUwJwAiyVPazWI1y2Z6jnPnfDsfk6uIC0P2uL9YZBkCKmXItQY1vymK3sDyCLORvdn3MdM7rhnCbYqZRcIAVBEH8PAL7vt6iCJ18fBEHHz33fRyaTgeM48fbaf99te9sJzgUqlcaWttFvRft8gWFQFAppVCpuh4p4O5LHJv0OZbL0ztfvxi1vmMLh6VV85p4X4AUM2ZSJSsTiMqcQMpGTYi1Aw40wt1jHcNbEaM7Cx2472HHedo/J87ZvPIt3vHYnpk9VECmqlEnJmlQxmpjzeTWBQCmJAnjkR6c6ft/SwVVV23zGhB8xlKsBTs1XcNPVE/jMPS9gdrGGMOTxnLleYOivlklhUArTkIljIWtL2hfkAz9t2zANggXljw5IURVKZNWdcRnMI8bk3J8qvKRsimpDACaTix6a9NpsCrXYFoVtGggjBpMSvP8t+7BbzSEfmSnju8/MYmGljiCSiahpyK65ZdBY8VQIoby+BfbtzIOAwAsilUhHaHjRti76GBeouWGL/2a/qKt5dNIsjINSKQr0v776HNK2iZQju+tJATndcU85BtK22VF9PxcwDIpcLoVazVv3uXOxghCAco7A67wWNvJc1igU0me1A9ArXm5m37cbh46tNGNR2kQ6JUdfjs2W8SdffAYff99V68bj5HHwFvljAi447vn+UaRtAztH0y0jO6IIVBohvvnwMQxnLVBCMDNXQcqWNpYxs0onJlyymTw/wuFjy3h5egXf/+FpLJYa8fNytJjGO163EwemCnJ0qc/zoO+zZw7P4f4nZ7C42kAQcvU8JGqWtTvTK+1YIPJBicWVGh6re6g2QlQbAU4t13FE6Wg01OxvyyaEYgU9caLPPe0N2R2XMYdzvumit44hphJ1tUwCy6KwlNCjaVAUMhYipT8BND8fxhLq2YlOs06q298HkOudIOQd3dHtQLsad/LfbqIb7/lRbOf46KF5nJivxdeUZUlhuWojhKEqPozxhNI4Oo5Nvkpe35VGCEqAlG0gYgBD53nQtPOJ4TTcgMVjVQDkEiRi8ZhaELZy+gR0AUbADyL4QYRPfu5JvP+t+3F6sYalkgfDIH2JbzIu1GdOcXKuAjCO4YIjKfZcxIUebfXlBQxfuPcwdo+mWzrNe8cyPdeqbzy4A3c+dAR+oDv7reeDQzJCGOOYna9gNLf2+Fg7+0YI+bl5QRRT1KVIsbQH9cNIXnucYXW1vu452TI469ifJIKQ9b0/ZztmbCRWntMEW9O9FxYWsHfv3vjnCwsLOHjwYMfrJycn8Z3vfKflZ0EQoFQqYceOHRgaGkImk8HCwkLLaxYWFjAxMXEGjqAV0RYehu0zVtqv8cRCDX99zwvn9cA/Y3zNY08em2lQ+JEMHJVGiM/d+yLuf2ZGKoereQytRmlItQ0wRffeMawXIwSnlus4OlvG3ok8bIvin7xxN6puiFzaQiFrxzS4KOJ427U78d1nZzGzUANj689ivxqTa6C58Fqp9s/2ECAIQw7DkNTy/ZMF/NxtB/GF77yEU0uNpgg8ZEW5PbgDgGXIBYoA1JwZRcOMkEtbMClBKmvHC7nhQgq6wSK4QMQ40ilDdbtltHVM+X0QcdimAVNVik1KQIQkFTMmwKlA3WOYHEljYjgDxgSOzJTwte8fiwXN0mmKqOQiVN3xYs5B2pGPTa0QvmcsjY/dfmVLMNXb0d10QuW16KnF6esvH0Mh68ANIiyXPDUvLRTlkMELGXzl0bmd16NILLA5Ezi5UMPJhVrff0/UoqiZiJsqGTfg6O/VVyfxfTJ5N7ecyMmL6NUqcgbIzyHiHEbUu9ix3nP5XGOtfTtX+y7p2MchIDAxkgalNO7UDeelNeEDz87gst3F+JnEVbdXjgMJZXdEIChFpeYjDJks7Kku1+nlOk4uVGGZFNVGFAtP6hnlMGJ4oezi0NFl7BrLwfVCLKzWYdeMeCQqiTBiCEKGU4s1fO+HpzvEGKfnqji9XMdPvu3ABjUROA5Pr+Ar978CT22TUIJGIGnAlYZUXO52C8pCoPz+C//4yiY+ibWhxTV1R1gAeOPBHXhFMeTyGQsGpfDDCHUvUolt7+1RKgvvhkGlSJj+2jbnnM9a8PxIqnH7Uo+Dc46KmmmOVMd5s6Kh+s9K1eCc23txAZxebuCuR6bxf/3E1VgoeXjwuVOSTq2ue10c0qzAbiNUSSp4LFBHgJGiA5NSLKzKC8VQXXS9PrPU+bctCg7pIpJNmWCMo1Rrqtaj4x1bQSH3s+FF+PbjJ/GeN+7G88dW8GKfYqlCAIJI3RjXZ3j55CoqtQATQ+mY3UEpiRNFSpvr03Ydoyv2DOFXP3xth+4CADz0w1nUGmFznjt+f8koMA0Ky6LIOMa66+12McSJ4TRyas58SI+aEUkL5EKOq+0ez2JqLHtWnrtTY1lMaEtAtT+BUnInpKnevpH9OR/j3TlNsK+88krkcjk89thjcYJdqVRw6NAh/NzP/VzH62+44QZ86lOfwvHjx7Fv3z4AwOOPPw4AeMMb3gBCCK6//no8/vjj+Jmf+Zn47x577DG88Y1vPAtHtDmcjwP/2+VNlzy2lC3terQVB1GCGTMLNQAEQ3lbBSz9FFa2PJCiFYESrjBNSZ86dGwFX3ngSFdV1eS+UkLw4VsuxWfvfRGVmg+vy3yuDgIDSPSr8ukHcp5rzw7pj/rC9Aq++ehx1Ns6a/HzvAsE5CKx/dmoqVvaBzOlOqhBJJVrTW0zQgjSKVPNj5vQPdpi3oFlUGRTJlZrPiImRXAYFzApQd2LWhS+uRB44LlT8EOGQsZGxDjCkEl/1UYIxoFqI4hHFrophANo2U4xm7ifTdkVrjRCnFpu4LY3dar4ahgGQbGYQalUj6nlns8SHfK27/2o42eu+pkfbp/arRBKQbcHtb0faN/UZKfc6ZWY2wZSTmsyn0mfc3fJAc4ztN5GpOPnepEqv292xzQlWifGp5dq8EOGiZGsLPrp3wtZ6E1ZFKeXGvjxsRVMjmZaO8Ki2XmictYG1UbQUQRarfhYrQbIZSxQ0hmLuAD8kMesk51jWYwNpTG34qJgdM5vNnyGieEUnj+2Ej+79GuoaaBgUFQaIR547hQOtM2GJrfjBSzuMlcbIWpeiEd/PKesiwhWA7/lWHhnLrVlUKVFAchZ1qJiNr10soRSPUAhbUkadmLURdsgvu+mfTg2W8bXvn8MDZ/BoNKJQSaCgGVQ5LMW/JDHybNOppNnRFOWI8bjOWdtPQV1nsoNaRmpR4x0UmhSsunkOoluW0gKhQLNNcuZXrvU3Ah/9veH8K8+eA0+8fM3xOvCwydW8a3HToKt07GIk2DRLIwIIbVVAJ2kC1im9DKvNaQYHRMyaTq13MAtr5/CqSU5KlBX1miEdo4JdIO2GiNEjmk9f2wFP/mOS/CHX3oODdafwKepxqsMg0Aox5R0ykI3uVi9Pk1azCVBCWnxaT8+V4EAwesvH8eJ+ZrsbJNO6zrToNg52mqt145eYoizSw3JwiCIXQUsi8JXFnu9XAXOFJIuB8slD2FCNV0IeQ6vOTByQQucAec4wbZtGz/3cz+HT33qUxgZGcHU1BQ++clPYnJyErfeeisYY1hZWUE+n0cqlcK1116L66+/Hr/2a7+G//Jf/gsajQY+8YlP4IMf/GDcof74xz+OX/zFX8TVV1+Nd7zjHbjzzjvxwgsv4L//9/9+Lg91TfTrkdfNU/JMYDu96fSxZRwpTtJUuJbHacYCG7JDmM/Yzeqdih4EsmelA5e2B/nHp2fBOO9LVfWq/SO45bpd+GrbXLGGABStaWPn6mJFv2uEci2IH4YvHl9tebhHTHohr7c9OZ/d+/eMy8CQS1tgXIp9NLwoDkC2SeOiCQGwWvXABGBbFFDJWyFjwfUjABQGBbIZC6OFFN7ymknsHs+BcY7ZpTr8gKGQsVD3QgSqeMC4iMXaokigWgtgWbSnUvbppTqWFLW92/2ccQwslVycXlrfHksKuxiwTQOFTY4460Vga3LeTMrdIJLdcpWg+4kE3Ve/306qu7wuNkd110g5BhxLJuIyIW+lt6diynvi507z99YGBeMGOJ8hUHWj2HFCJ8sCTYXjpr6W6JkUA8DsYh2nlhrKKrLz+uBKkKhU9TE+tLmxs35cBAwqXwfIxeh6NoCvPTCKB56d7XjmaJ0Jy5CWfP/45EkYBm1JpEs1v8mg6YnN3/+EACN5B/mMjVzGQj5t4cWTJVTrgYzrav6WQBYWiZDsteWKJ7ubBHBMCgqg7kXy/PWwQbxszxB+5uZLcf/TM1iuBhgdMqVoGJFJVjZjwaAEy2UPobJlYkwldIz3fM5pFoMbRCjVAmXZZMWinFoVe/vsCDuhR54SLOmWr2cSdS/CZ+5+Af/ijqtw1f6RmOnRLGA1y1q99ke0/VKv5wSkOnkQMtWVVg0Y9TvGBJ48vID/846r8LffeVm6dKC/5FpD0rzlPTW30sCx05V4XKqf85dNm7F3/KVTBZiGXG92Y5REkWT09XIBSa6v/UQB3FENNcYYGBOx44tpUJgGRS5trZkE99OoG87ZyKat2FXAtgzs2ZE7Jz7Yek3+te8dQ8R406bWlMf8wLOnsG8if94yd/vBOW8F/Mqv/AqiKMJv//Zvw/M83HDDDfirv/orWJaFmZkZvPvd78bv/u7v4qd+6qdACMGnP/1p/Nf/+l/xsY99DI7j4Pbbb8d//I//Md7e2972NvyP//E/8Kd/+qf4wz/8Q1x22WX4sz/7sxZrr/MN/XjkrVUR6wdn0g6kn2MTpuxC00RyDUCJtchvw4gjCFn8QAgiDpOQ2JaGcYGGF6LhRUqATGA4n+qr48+FwPPHVmCbFFFCSjz5gBV80MneKCxTPvy//eQMTIOg2giQsgxUG2HfXc5+KNAiEZ1tU6on+wGDZZLYjsQ2qVqoSdreUlkqcNe9UM3AyWttx1AKb75qCm+/dgpGTMUimF9tqIIORT5tg2blAx8CCLn2uSS48coduGRXATtGMvHcehL92GNFXoQTc1U01IJxZ5sNznaCanE0Z/OP+4jx1g55l266GzD4QQTX79JhD7ZPMA6AfH+foYxOTY5+QAmJE/Bmkt5JZ0922FO2qRJ5SYc36Jn5vAbYGASkEvB2jAukHRMEsnvZbfHcnvxuBv10pCdH0tg51hT3uXT3ED74tgO474mTWKn6EIzDMCmKWRtX7hmSC2afKcugUM338o7C5nef6dTW2E4QyOKXpqhzLsfBfvbdl7cUE4eem8W9j5+MxcF04A1VodQyOWxQBIr2WRdK5DFlxiJsacfAvok83vraSVy6ewiGUrkeKaQApbkRhRwui5TyNgdbbsCgBNmUgTASuGx3AbNLDTTcEEGP60cyHQCLApW6XIONFp1Y4MvUo2xcFgYco3WWeTtxriQnCGT3V6+rTsxXMb/qopizUakHcZFkrb/Xv5fjfyShZi2LXJJVIuJuMQAlqibj+1/+wyFUtrAGBuS1Wa2HIAJwTAOBsb7quWnINUbKNnHHm/fhwM4CpnbkcGy2jGKu8/7ViXi3TnPHuKSiRcvxkQiZtAXHFAi5tPMyiJz13zm6trUe0F+jruqG+OjtByVl3meYmihgOGsqav/ZhV6Tp2yKbEo6ElAqleIBXBRWXec8wTYMA7/xG7+B3/iN3+j43e7du/Hiiy+2/Gx0dBT/83/+zzW3+cEPfhAf/OAH+3r/+++/v+99PVNYzyNvvYrYeui3I30mqOr62EIlQtGecuiug6GEx6KIxWIRyxVJHdHl7dWKFydJhAA504IXsPimJCp579bxjzvpKQteqJKl9n3Z1Nl99SLtmChkLJRqPlarTaqVdwYWFwLAUtnHaFHamOhsLYoElkpuTN8yDelBecd1u/DYCws4OV+DgEyMHUvSxd2A45uPncSOoUx8/b8wvYyvPHgEMwtSUIMQGVgtU3Y7DSoLPZZJcf3BHdg9npWLWCEk7VwJ2HAuMFxwMJy34/m9pIe4AOB6Uun8/mdPya6Nsik7n32jZQVdVtE3A+0X2ovO7sWJeW8KfLCN81Vc6TpID/DNOUzYJu3ojLfPpvfsptuyAz/oop9f2Ezyu1H06khHEUPDlxoNl08V8dwrS3Gn+fRSHaeWG3JOUQcqZfV3aqkpFhdu3tI+sX/AaCGFXMbC7FIdluqgJYUqg4ih5oYIIxH/jWnK54NUnpbglMALuLrP1M+EwEszZdimASaUjoKmWKtnuGMboAQoZGzYqqMVM5kyFm68cgKX7S5i13gWpxbrODlfjRXTv/HwMeVE0i6dpd6fC5RqsmP28kwFKcvA5GgGu8dy+OHRZQQRh6G61VEicWSKFpHPWAAI/IDJQr+iPEsRUI5o+yZyzhsQSuLu7/G5Co7OVuD5DPmsheG8g3ItWPP53C6GJy3K5GrQUsVyQNHgAZVYS+cRWUBjcLG1EysEUKlJFbEDu/KYHM2oEbeoJ8OOELnO2T2ei9fLlBB86F2X40+++ExMtzZN2Yiqe1FPunVyfV3M2lgsSS95PT7HhWxIycItwVDOxs++63LkE5pCa6HfRl3djXDNJaMwTYrh4SxWV+tbtn3dDPSaPJe2u+Y9Z5u5eyZwzhPsAYC9E3lMqoF/y9xYRWw9bKQjfSao6vrYjs9VW2aGABnotJq3nuNZrvpgAsimLRQyNko1H0oMsmWWRwhZTa42wljhsZC1kXbMrh1//fDJ5iyYjQBBu3LpWYBBZKA6W3YiZxphGGGxHG2IqrUZGIkkdaXsw7YoGBdxgJYWLwJhKIs1t1y3C7feuBc/OrqMtGPElEJdhBFCtBSLNLW91mh2Q4UAwkggjCKYBlF+q1J8hDEWe4ICAMzkDCjBUN7Boz/OYaHkoZCx4wteCKmaGTKOYdNAIWtBQFLjFlZcfPnBI3jn66dww1UTMDrKPxc2ZIFDJpXFbG9rqbXAuIAfMASMwbQsLK3U0PCilgRdU9q7zaNrvYDtQqB0ITbbVSFE0gLTvRL0Lkl53EV3DBRymzuPA/RGP3Tsds2F9cA4R60RouqGLdTsHUNpnFioxbokyQTkW4+f3PZjswyCqR055DO2ZM/4im5NaWwrpen2hZyNf/H+q/G5e1/EzHwNmTbKfIoSBCHHcF7OzDqWLDS1rxvaO/4EwPxKA3U3xORYBinblGNjAEAJqnX5DGYCijYqEEYCrh8iigSCKMJq1QAlBDuGUvjCP77c0jgoZm0slT2kHTNW8waSs/jNfROQllGEECyVfdTcCLfesAePHZpXtmDatpIgbRnwQnm/1xphy+iTiFrTk35ngy8kECjxKTfEZ7/1/7H3p1GSHed1KLojzpRz1tDVA3qq6gbR6AZIgARJABwAUKIJEoREUqQo+V2JsGxJy35+l0vSWrZ837X19K68/Gw/PdqSZVv30pREUrYJioMoAiBBSgQaHDCQIJog0I3G0F09V9ec8xkj3o+IOHkyKzMrs+ZqxF4LQKM6K/Ock+fEF9/37W/v01is+mKdlcyxobyNUGqjdHJoaf9R1jEFq4xxpB1D2leqDn1ipmMNwSEUzE2D4CtPnMXNEyOYlTovXhAtOe6RvIOfecteHJsYWZLg3vKGMfzafUfxN98/G9OtDYNg31gW991+AOmUiRfOzLWwRZP760AqyzfPuQmlDXF1voHZsos3Ht4BYHkW6no36tYayYKAKsDHgnGSpbha5u5mQyfYWwDJgf9uFbH7bj8wsOjYoB3ptaaqqwXh2MFhXJ6tNX2FqZq7FQuaQRH7J3IOLFY8eH4krJQssfiq+Syg1e+ScwCEww8jzJddjBRSUom6dSFJLj7DOQfTC40NT7AJJSAtZKntjZU0FPsVT0tCJbZpx0DDDSWtSvh52paBoYwjZ2qF+uQLZ+dx5MAQri40UMgurY4mi0XnpsrxM5JLC0ZE+/GF0pqEUpEkKvGhJJobNw4CgnfevAefffQ0ZhYbyKct2LYBFjEsVHxQSjBWdJBJ23FxoJAVXuI/Oj2NSzM1vOPm3Rgeygo14oQ68esZhuyi5A0LxWIGxbQxEC2Yc44w4jGdvdlJF9R2NzGH3k1QzvM7d8VWAlFwieLN5aDYUUzhX/wvbxGUWI01w+F9Q/jIuybw+E8uY3axgcgTFkHtmgteEMXJskqcqw2ZQDcC1N0IixXhELCej65tUhzcnQc4cGGmKhSHKYEXNlX2CYCx4Qx+9s17kXJM/OVUGUM5uwMNPqERMVfD++8Yx+cfOdm52GAb+OCd43jiJ5cxNd9ACs05akN2vBsewcHdeRzeNwTLEB3wuYqLQtZBJmXGM/IR46jVfcyVXISMIQiFN7OgXwtwCI2UnGXgwnQVn3nkpSUaLFfnG4J+myjixbPBHb6E2ZKLQtbGUM7GYtXHC2fnkUmJcZqsKsxaBlwvRNUVateMA1RSmtVxJdFJx4WQpkjZepuUiM8iS+3PVoGIATOLIhm9OFPFUM6BLcf4/CDCfJlhpJDC2HAaV+aWt6utuAFKspiykaYtlAhP7YszNcyWXNxz63V44ew8pubq8IIQHGK2/r1v3Y973ry35x772MQIrt9XbNmX19wQj3RhiyrLNjMtOvYsqViHBI1efn9RxHH8xGXcfetenD63sCwLdT0bdesBtSevNcTYp1KFJxCsBtEY2ToFgZVAJ9hbBEfHR/DAvUfihyhZEbt5YgSPPH1+YNGxQTvSa1kBa6elMy6SIc+PWhJkQQkiMKigpTm2gbmSi3zGxi2Hh/G3z16OFUCBph1EEpyJqnHEOBarHhwp3JBcSJKLz1BOiK1UNrgyxqQX4esZrFNA6ef3GJez92LMwDQEl8wLIvhhhKGsg5zsRkzN13HmUrmvYtGZS+X4GQHEc2EQLNmcKPojgL7u//bnuVIPwCHOoZC14YUcpdka5souQAgcg4IYACUUnNXwzWfOY8/OPHYPp+AHUsWWtVLRo2TizbdmEs44x5XZ2obMmi8HQqR/rWkjv1LBOM7hB1F3OnvLz5d20F0/6riGrQSzJRfPvTKLn71t35q8n4aigYfIZmy86017cHG6isWKjyBqCmv+9fcmUWn48Du4UawFDEqQz1jIZ2yYBsGlmRpMk8IyJU2bkJiuLZSuI7zvbfuxdyyH1y4u4htPn8eVuXq8WTVN4a5Qqvn46vfO4m1HxpbViIi8CLVGiLe/cSc+evdh/N2PL2J2sQHmiy7+wV056a1dRMY28O1nLyJkiJNSFonmQDZl4H1v3Y9hybbgXHQvZ0sNOHWjZZ+hGCFcLmJLjk6ubbZFUa77sE2KHUPplr2NY1E0PEhmm+wwJ363HWHEMV/2RIKUtnBxpgaALynMlmp+C8950KSQc9GVN02CgmOi2gg7dnpXCsci8AKe+Kz1CwScAeWaj7RjIoya40+lmo+hbDM2GhSSMRb/ZtytDUMuRa1a35tAiM1CJphrjWLWRj5jxyy2F87O47c+fgsuTldX5JhDCYnZnKcm5/H5HmzRe9++P95fK+s3tYIkP00pkxMijvH4CaFXsBwLtZ9G3UYqhS+HA7vyyKctnJ+uNv3qIR5VPxDibwfa9vHbDTrB3kI4Oj4SC0gkK2K9HtpeomODdqTXqgLWjZZecwMpikWwWBXVSyLp3cWsjZRjwvVEJWt6oY7HnmvEXW7GRWewU9xg8b8APxCzVUcPDOH81Uq8WKrF59MPncSlmVocxDcSjKG7V9UawTJIy9zYIKCESCV1viFVZUoHE21pUSrlyndW/Hy+4qHuhchnLSGqJ6l9yxWLOEH8jKhRAz8UNi5cfQ4XVEI/5ANVgNuf56mFOh76wTlkZaJeqvmCuUFE4YiHHBEXG9ir8w08+Hev4Ld+8U2wDAp1CkkqOiBnwBkS8+AMkZwFjHirN+9GJ+GvXVxsdgHZxs6ar1diT4myGDMBOCt6jyWCcW3d9KRgnEjOWzvsni9mBkcLKdxyeHTV5/R6QBgx2V0OEl1naUfVaP3zWo4RJJGyDeQzFnJpG/mMGIHKZyyhrC3/nE/bSDvN2fxXLizirx5/TVp6Lb1/VTKsZpwn9haRTjW96Q2Dxl1qZWn14uRCRxXzIIxiT+6UTVHIWaAEOHJwGNfvL+LlCyXUGgGKOQdvOjwCyzBAKbBzKI1izsGjP7yAK3M1+IFQgt41LJoA1+8ballz9nfZZ9iWAdNIJIpt5xpxLkVKRUKXkY0D1wtRqklfZC5iXyj9dI0+Y4wfMpRqHixT0M9Ns7lnUurWBiEglPcdszoVkMOQoxQFYq53yMFwLo0r8zVU6gFq7soH6NU12whwiMKEKmKoGOz7Eapy1EAVgLtVNkyTgEWtCv7x+3NBSV9rUAI0/AgFtDaYLk5XVzzjq1ialZqPr33/bEe2qGkQzJc9HD9xGYWMhbmyh6GcLQpRCRogR1NfKGQclimo08dPXO6bhdqrUbcSF6B1hzwfUQwkQmSWJ+6LLVIMWCl0gr3FkKyIMc7xqQdPrFh0bNCO9FpUwPqhpeczNvyQIes0KVgA4Hoh5souIilAZpkG/FAEHaE82d819AKGr37vLL71o4vYvzO3pRaW9U5cI8UUWIn3sSzQJxd+IcIlFr61TrxX0shT3YhOx+EHEeZLDJmUiUPXFfsqFh26rtjyjChxPeGdSWKbuIYXIbuMTUYnJJ/n3JQFU34WgHjTljw3QbGkyJoUl6arODdVwf6E+m6yGwCIoGRQNBWtLUPGJBW4eFyMYFKJX3XClTgba7M3WotE/LWLi/jq987CC6JYUTiKGKbmG/jq987iI++aWLckezMT+36wFoJxYcQwNpxGynp9h3DOOa4u1HHuagWLFb8lWU4m0XVvDdS/OoBA6IWojnNe/rmYs7FrRx4G4cg6JvIZG5bZZwBLYFBLL2URmMtYS+jfyiKwXPcxlHNQrgfIpSkY42gEQizUMgyAAGmpqr9jKI0Ll0t49JnzuCyTZ4DjuyfSuE/GVc6B6/cN4dDeYl9jbL32GSalCCgH5Ey6KqxHkv1VyFix5gGlQjByvuJJNXLJw46EevqgdGzGxJqcTZlouGHMElDFS6Azzbwbur2Uc6F6z5iDmw+N4O43X4eXzi3gsecuww/WbgRlI0AIgUlFkRoEeMO+Il6+WILr9XaNCOUIAOFLr5O6F9YSlAIGoS1ONaud8T15dj6ewfb9CHVPKoPbEVLSsaPhhSjXhADchekq0nL2fK7sIe0Y8Ry2ugaUCLFfSiCsThmTz0j/ukidGnWDdOU3CuevVlCp+xjOOzFFXF0IxxJOBJW6r0XONNYHqxUdW0lHerUVsH6OebHqCWGVRHINNDt6hlTlVDPXCoN4TDImOviTl8v47KOn8av3HsE3njoHxjmu25FBtRFiseptOUrtasEY4LGlyfVylXylDgsuNm1As5rKwEFBYq/PtUDyrQzpP97vW3d7nVAdFQnjgV25vopF47vz2DWcxnkp5GMYFCN5sfn0gyi+Dw/syuH+O8dXVahJPo8pizYvBFSnnEvPawoQoOEFKxpj4G2cyDgJB+nYCSdx8aS5qY2YSsy57EaIZDw5D867bDoZ53j8J5fhBREKGTteB6hpoGBQlOsBHv/JZUzsLa550N/MxH6jQAiBrRXIETGG/+//eA4vXyyt+XubBom7yrl0s8NcUF7O8v+zKaujXZthEBSLGZRK9VVRXQdVNU9aBKo1XSmAi9loAs+L8PajO3Hi1TnBnFCdawY0Qh8R46gaFH/20ClMLXp45Htn0JBsNMcWOhhnr1TwmYdP4dc+eBQ3yTUxWUxcDt32GQf35HHzxEgsNCb8gEU3z7YMzCUE4WYW3Vj8VOi4iGScEMRU08HjO5ez9YFghCfeYy1p15wD56dr+O/ffkUyDQTdf7uByU4rDBF7ry7U5Xzx0te26690mlXnAFIWRZ0xdNjGDITk53EGcIPHxwysTvTrJ6/M4M8fORU/F5QS1D0hYDpXdjFaSIEDmC+70t5TfOcp2wDhgjFBCZWMwebxAkJToZARbjdDOQeLVb+FUZFEtyLBIM/iZkExbAs5Qdv3pWUZpSK+Mc5Rrvpa5ExjfbBa0bGVdqRXUwHr55jBgaG8EBVRib+iYVEg7mC3n9eg8Y1DUIIi7uELf/syam4gF0NhA0JAOlKUrkUsF7sVNVBV7VkkNjHFrC3mRsNWT1VKROem5oZxwBoEyd/oFGhXAs7FRotSgovT1b6KRacm51H3BA237oXxyEI2JXyOLZPi5945jrtv7S140g+Sz2OtEQg6IwM4UfP5BIWsSEiDMIJpUGkJsxT9+tr3ulbyT/GfqezY90zEEx3v2GtXJeDyHyVoOJJ3QAiNk3fxfgkRpdlaizfuarGZib3GxmN20R04uU47xrIU7XzGEhvhLXCPLKdqnnEM/Oyb98KUIo/DBQe7RjNI2wZs04gLeOoZ9fwQDT/EG/YNYc+OLP7rV1+I1z0lLlTI2kjZBhbKLh78lrBJLWQtMMYxs9CIqdgNL8Sf/vUL+CcfugnHJgYfVei1z3jf2w/g+IlLOH7iMharPqKIodYIxTnmHVRqfot/cTO+iZ9ZJsWI3GP4QX8mRKLQCMBA3Llea7TXqBUbJZ+2Y4/tlQiBbhY4pJWWbIzMldyu163fc6o21kYUUNzTip2F+MJTSlYl+sU4x5e+8wpcP0IxayOIuOzgS/0Wjrh5E+sNcXVuAYayNowgwo5iCm++fge+I5kLjmXAtsR+WO3P777lOjz81Lltoww+CNoZtu3nt53PTUEn2FsYayE6ttKO9EorYP0cs2lS3H3rdXj0mQtx4u96zUSNA2KjLyPNaoINh6CMX5qtw6CAZRmwYcAy1rAdu02QFBXpdE1VQEhS8R1LJHl1L0IYRqi5oXwNielNq8VafQumQTGUs+EFLC7O9NrEJbUChvI2ao0AfsiEFZQU2Pj4e65f0/GC5PP48oVFoZAOAts0Yps5zjlqjRATe4s4uDsP1tYB69fXfi3QMxGnBABdkoTPLbrIpizkMnbMeuAQm5CQcQQRQ7niwQuiJf6oq4Gix2Y62AWtZ2KvsTkYG0rjzpt24dmXZ+BYRgtFO06Y4/+K2eeV0LQ3G9fvH8Iv3n0IPzh5FYsVD4RQ2GkLb9iXxjvfuAeH9xZgEApqCGX57z5/BRemq8ilTUSs2bFTwk77xrLYtzOH81crsC2KtJMSAkPSHocQgoYXohFEsRiVG0Tg8VwzFd7PTAjCfeaRl/DrHzzade3pVQzsts+ghOA9b96Hu2/di8mpCj77jVOYWXTjc1ou+WWScUMJiW0de9ljKrYWh0jgG16IUm3tO2ftR0ClbWfImsn3dkmuFVRxgzOOcA2OnclCeT/Ej167uIiJfYpiHoSMwzLEvqXmBkjZ5opEv85NVXBpugrTIJhZdGXzQbK/IAokfrh0xIoQKapX8aQFrY+bDo3i0HXFOJ43vKhlf37k4DCefXlmIBbqaovvG4Xtpnq+EugEewtjrW7AjZzJ6PeY7751L3YPZ/DFx1/DpZmqoKQm3ocAkqa6dscWMWC+5IEUxWbidQciqIPLJcYRa1qKLFZ9UaWOmhRiAMLHMRL2aVuhVEGIpLkbFAbjLUWnTpu4dq0Az49ExVtmfJwDMyV3XcTw1PN4/LlL+JsfTCIMGfIZC5ZlwA9EESNtG/jYz7xB2K0kru4gvvYbhfYk3LYp5souao0A6ZQJmvDZNSkBOMFQwcHesRyKOSfurq1WmC1Jj+2EdkEoje0NSgl+4+duwq+DY7bkrovq8EZBJHiiQEUgqNymfGaoQfGWG3fhLTfuwlVZJE85JsAZqo0QV2Zr2L8rH6sP33PLdfjso6cxveD2ZK1V64GwvcqYLXuBhhdivtzsRFJJv1Z0aSpHhiCP1fOjrlowqy0GUkJwbqqMK3N1MA7MV/y+GFOMccyVvPg9lhsvK2ZMlGohLEuotc+VVslP7hOqwOj662vlBqxvnKZU7tnW6AMYa1KmDUriPQiHJGXI67bcx4mxgebrhGq88Em/59brlr0HQ8bwzMmrsQXs24/tEpoObgBXik1SAtG5lgyIbrencgAIGUfNDeDYwkP+5kOjPffng7BQN7L4vlpsN9XzlUAn2FsYa3kDbtRMxqDHXHcDQY0xKSqNIK4yq0VqLYOCaYgFtlTzUchYACEtfpbXOhyTwuvTWoYQAtMky1b+2/+GQHY2qRAyScKQ1hTt9iSr7WKqwOUHEco1Hwd355ctOiW1Ajw/wlzZbQrlmKI70+ijO7NSUELwnrfsw86RDL76xGuYWXTBGwFs08C+sSx+/p0TuOUNY1hYqMW/M6iv/WYhWWQzTQqSUM5PdtH278zFx7mcMJuimUcRE8+tTD6SSXg2PZgglIbGRqJjEm1QmJSAGiQuQolnohn5kmvjvrEcTk3O44vfeaXrJrpf1lonthnnHOWaH1tKxgzXxDFEjINKlwUC8Tx10oJZi2Lgqcl5fP37k/F59hsnFCXYNISvsSoUd0LaNuCH4vsp5gTlN1yruaUeUCu0ajysN6hU9yZ0ZWNZyTid3JeZFKCUrukMOYcQtKs2AoSMw6QUNKHg3u++MHm/5DMmHNsEuLD3fPzEZRzcle96D37zqUn8zQ/OCcFYebP/z799BTceHGrxtpf6bugWcQmEJkOTtQHpVMKQSTcFhrvtz/t9nrdi8X05bDvV8wGhdxpbHNvxBuznmFWy4AURRospEELAAJSkfZfCWua+ubSNSt2X897Gunde6QoD2XrB63MWDWiKhdkWjTsGVCY0veh5guLGUUzZwjsUiWJJj5zPMigixlZU7GCMgxNRKLFM2lfRKakVMLPQEHZZSVZDH92Z1eLU5Dy+8dQ5LFQ8cFkKL+Zs3Hf7ARybWPpcr1b0cKOwksJg/8JswkKnWEzDJhxBwOIkPJe2cNPEKKYWGkjbRjwvLopEDHUvxM7hTCwIpaGx1lDJM5WJsm0aIKZI9KjRZHKI8QoRgdqTxvZnIYl+N9H9sNY6sc38kAktFCKSseQ8cDMhlKKIEKKMKcdApRa0aKYsXwz08OUnXsOHIoZ81u7IqFPvEYQsvq4rsbks1YKecc8LGHaNpGE0CExK12T2up+iMQd6Fq/XEgSIBaRStiFUzPv4aNukCCIG26TIpqy4QKp+VXhdU2mLRiANR9bkGpbrgRCtA4/nvBUGffuxoRQyqSarLZMyexakP/vNUzh+4krrm3Cg5oZ49vTskveXdd6OUD9vioeK/3f9CF9+/NWu4qntVO9eft3bpfjeCdtF9Xwl0An2NsB2vAGXO+ZOyULaMVGRSuLLYdDkmBKClGPCNinmK15cbV3P7vVWSq6BwYsJan6dEJEAA4DfoUrd/l00E2qpPB7PAC5VgqdEzE4D4r9B2H8RIHmc6m0PXVfAkYPDmJwqt9x3AFruxUxadG8a0h6iuZGMSWg9uzOrRftGOZcRG+W5sofPfetl/EPTwDuHm4kg4xyvXSzB9aLYH7M9yV6t7chaYj0KgyrxUOdOiUxWVBJuGbjrlj148LHXUKr5yKVNOLYBzjlcn2Dvjiw+cMcBFLI2oojJItLK6Ogar1+oZC/ZkTYNMZ5iUiJFNCmGhzOomARhKOjGyfuqVxLdDYNuovthrd12ZAyXZ2uYK7koZG2EEZOOBoIZVMw5WKx4wkEg8XuRFMQUzxJfogXTqxjo+RFcL8LklQo+/fWTsCyKoZyD224Yw7GJkXifoN6jkLURMS6Vlwe6ZPEcbvulzqdNZNJCuK3uhhjK2RjK2bg0W0fGWYPiO+/fg3sjoM6FUhLP0bM+Di5iDLm0hULGwnzFEzZtiYsTMYBT4U9ezIqiut8ST1cOgtYmhWkQFLM2Fmv+QIUJgxKknaWpjm1SnJ+u4qkXp3DHTbvjvekLZ+fwRHtyvUp0Ot5s2sSl2XrH7nIvqvfNh5YKCq5F8X0zZ7e3g+r5SqAT7G2C7XgD9jrmTmrjtklhGhR+H+JZhiGCZjvduBtMUwi4BAAKGRu/cPchLFY8PPLU+XXzSDUpQA0qvUO3F5IbDLWpGSxgiqDgB4KWW8jYqHkheNhbt52uclPy3Muz+H/+X08ilMqehkGQT4txgErdj3+2aziNfMYW3WvG5UyXOjLxX8ukHbszq0U/G+WHfjCJO2/dB6AZbC/O1NDwQ7hBJKw8pCiawlZT3dyMwuAN+4fxsbsOLdmc7BnJ4IN3HMCNB0c609EjkTQwrmbBARYxhMojPJ5B5VDWQBrXLtqTaOE1T4XVldR6oIQIf11KE8Uf8fuG1B3gfO0KNmvJYElu4IOQwQsizCw24mKnZVCMFFKCRk6FYnty/bdMkRSnbCMe+UiO5XRzE3G9EHNyvpsAoAZBpRZgoeLj7JUKMk+fx0FpiRgxHr9HMWvHYzydusOmtPRUc7oKLUrOyeMIGEaKpvxdiqsLDXzwjoOYK19AzQ37mtvuBY5mHKNU7G38kG1I0b1TccCgBLZF4PkMtZAJD+Y+xswj6Q2+WPXgesKj2zQIcmkT5VogC5McGccEB5BxTAShtyaz2OoeV/cdi3jLGOEg8EMGR1KhlDe1Eid78Duv4gcvTMWiYg/+3St9re+qbrOS20Q9PwBaCmMAcPzEJXz9+5MIQoZC1u6L6r1ax6HtNLu9naATbI1NQaf5L0JEcjBf9vp4h8FqzFmZiJRrPkaLKewZzeCOm3Zjoerh7569tIIz6I3hvI1i1kHVDTC76K75+683WroVDGCcdZ0x6vQtlGoBHFskjJQQlOt+LAiS/AXGmxZVqnOy0uq3+r3pBRe7hlPCRqwR4Px0FYBQhy3kbIShUJUXAZK3WHgkETEWd17XMmntZ6N8Za6GM5dKuDpTael0e34IP2TwgygWXomVx7eg6uZmFAaXS+w70tENQUcHAFhGnIQT0vQIT1LOlT94yJqe4SrxXsukSmP90J5EUwqYKolO0LmpEj/sMBMNoC/RrbXAajfRCu3smWzaQiC9nw2DYDjnoOYFcGzxOWnHRCFry9ls0ZndOZxGFAm6cKeRj25uIqX4PQiiiMfWVAp1N8Tp84u4MncSP/fO8fg9Uo6J0UJKdEiDaIkgKnhnn+puu4RQrqG2ZcTXbWwoHbNuXrmw2PMa9oOUTRFFHMMFB45lYHrRRSCz2vW8ZZJvrQTCGOdoeM2/qXv9i7iFYYSICLcIgxAwDlTqQcw4E+JznuiOQ8xjc756hiDnfEmxvZ/mi4IqxMTaHZYRC/gxLoT6CADHMuLk9d6378d8pZ/9p7jOhlSB7+t45L8oEXsRFftVYez4c5fwo9PTeOViSfq/CwZGMWsj5Zg9qd6rcRw6eXb7zW5vF+gEW6Mr1poywjjHuakyzlwqg0MsHLMlF8MJtXGnw+LQGXyJqoQhRTw6LXcR57g0UwPnHHMlF//5qy9g90gG141mVnw+7VDBPG0bcGwTHFwmmP0pXm5lDHr8tmUgbZv4mdv24htPnZciMuILs0yCKGrOW4cRh2mI+8OgBLm0hVLNHyhJaWcBhgxwIDZs6nupuyHyGbtlDpB32AEoahrnQhPghv3FNU1a+90oL1Y9PPSDyZZO91DOiTtAjAnBPkrEhmm7qm6uBzVttYl9e0dSUW4hv7Lm4RG5EWItomxc2gRFkfIJ11T0zUC3TnRyJrq1E905iV4PN4FBsRa2nd3YM45twrZEN9q2KCJuYrEi/lyuB3EiJUAwu9iAZRoYG0rhI3cdXrIB7zjfHURinhocYY/8jsl194kTl7FrOI1Ls3XJJhLb1YWq18IK42hS1lmb80i3b41DdNNty2i5buO7C2AALs/WsNimB9MPksVhzxfjTrMlDyYlCCPWLKiu0/1EiSh+LFQ8hG3OHyuFyGnF9aYUMIlIKtuTXTWzH4Tiu7jtyBiePzOHKOQD66sM52xUGsESi8qVQLmhAKIwwDiHQQgiLvYp2bSFjJwtP37ict/MBVV8HQSOZSxhnpkmRbnmx44ijAuGJoHQRJgruxgtpJByzK4slZU6DjHGl+wxgO0xu70doBNsjY5Ya8rIqcl5fPGxV3Fptta0/yCkpUpnmhSe3xp5e3Yz2xZCsVmSQlsJ/2xCgGo9BCHAUN5BNm3JCl0VL19YXPW8VSFrgUAs3pwDDT+CO1eHYxvIZSw5p8flcTUTuK0yn7XWSNkGdg6nsVj18eOXZ2CZBKNtXqueLyiJMf2LA7ZpIG0bqHvhwJsCZX+hEEasKdhDRbAKEl0LIgWIqvUA+YyJuhs2VcTlpjzkDODAW4/sXNPg0pdXvEFQqfm4Mldr6XS3d3L8IELdC7FvLLct6VzblZrWvD/FBra7KBugSm+skzI6E0l4uz2ZpqIPBkIECyH+HhJ07qRN3FboRK8Ua2Hb2Q97ptII8ME7D+K7P7mC89PV2P/asSgck6LmhfACcXMuVDx846lzoEDL89pJ6LDhBn2LX3EAl2Zr+OWfuR5z5YtYrPowSLMDTgAQCuTSFoKAwTQp3nbjGH50egZBGKHhLR9cy/UAlkXh+iy+bqcm5/H5R0/D9VY2EpQ8PfVHzjh89RdtN53YC5AVi4K1713ufft+HNk/hP/0lReWTa77EWJrRxhxGLT7noxxwDJkbG0EmNidx8WZGrIpBw0/RLnaW3AOEMm1YxtY6FDg6Pd4CUQsj+R6GoQMcyXBJKQyuaYEKGZt8Xp57y9WfeHzjuXvn6Gcg2oj6DgCqJ6s5OHm0iaGC6klz10oRzQAofnS8KX9KZHFDFlITzlmV5bKSh2HzlwqLdljxOcw4NiJxlJ0bqFovK6hKGQXZ6qi4pazW2g0pybnwTjH2Stl/PilaZy9Uu5Z9Ts1OY9PP3QS56erYFJ0xJAzTlHE4QcRqo0A5aovLBkMgnzGQso2mkJZbVB0YkB1KYAgalWaJETQtApZG7ZFcd2ODPIZW6i7WiKZW62KJyWiM1qqBUuCq+tHWCx7orLOm2IdxjXswW1QIUSiFueZRRdhyJFOmUg7JhyZ3KZTJnaNZuL57lzKRD5tolz3+7YSUxCbjNbv0TRozGZIjNy2bKJVMcaxTewopuFYRjxTxbgQbkk7BsaG0gMdD+Mck1NlvHBmDpNTS58NtVGuueGSzpjaKO8ZzSKftUV332xdplOOiV0jGewcTiPtmLj/HeP4nV+6dUsnpJ3QzzqznSES5SZlXCR/BJZBkbIMZBwT+YyFoXwKO4pp7CimMFoQ/wznHRRzNvIZC9mUGY9bmKrreg2vIYOCEEFr3lFMYcdQCiOFNIo5G7m0hZQthC3Vmqu+iy3QkB4YahOt5p79IALjIn4uVn04FsVtN4zh5Nn5jusOkGDPmN3ZM1HEsaMgRmyyKRNjwynsGslgKOeg7kdxgssJ6fm8KqHDfWNZVBsBSgPqWCjF5QfuPYLhnI2FalMAVdBzKVK2ibHhNAghePVSWSQrfn/xg3Phnw5w3HbDWGt3P5/qOhI1KHrdasvdhgRNDZTlft+gBG+9cSeqbthX55pzrGgd6VYMUA0Ow6Ao5GxMzdfx1iM7kXZM1L0IGcfCjiFnyX6u/Qgss1W3ZiXfA6UAg9hrDudsOBaV42jN2K66wgrqmbC6PBudsGs4Hc//t6NljKFLo0jZ4gFAPmPBMOiSoolBms2BXiyV5PPmBRHKVR9eEGHfWLYrzbssBeOWWw+2gnBqLzAm8pFue67Ngu5ga7SgHwGmLz72KrJpC1fn6/HM7K4uXSfGOR56chKVerAkWKj5Fc6BHcUUPvyuCWQzNr78+Ku4NFvH2FAKQSS6PEHEUK37CKS3sprj2llM4YZ9Q3jlYgkzpYZY/LnYjNiG6F6Uaz4IgJlFN55naXghFitiYVvNo8g4etKYGOfCtoU0PUUJJYjCrbEA9IP2Bb8by800SEvQMqXaNWjnbm02ZWE4n0KpJmae1AZKWYP1W/zgbUdJCZBxDAQRj3+q3F2SGwq1UQQHUikTKccUm1ZpZwIIv0wVzPqhMvfTke2n2nz/O8YxlHNg9uh0EyIsV67fW9x29K3tbCuyluhERQcBDKp0KdQrm1R0zpU6sq6PA5CKzST+c28ZxdVhpeMMazUG0U2dfzhnA4Tg4afO9WSC9EszrzQCTM3VMJR3Yr95Fe8tg4JxjjAUnvRDObvr83p0fASH9xfxB3/+Q3i+YAktt67HKzkHuHyrstzgU9JUbo8Yi3UoVPfRk/PZy+l4EMi/54AfMDz81Dl876dXMFtykZPq4ivp8K4EyYRVdS5BIBktnY/BoJAGVjxOlG2T4sylMmpeuCRmd0M7a2OlbD4COe/NRTLIZfMkOdeuYqLoIDc/Rf3JNMR+cK7sIZsyl/z9oEeUFAIt5ByUqh5KNR+FrI2iFBhLIpTHHSyzN1PJutIQSDsmqvXWzjxve/3u0SzqbtAx3quE3rKECohlUvih6mKT+EuJIgY3YD1ZKoMKixayds89xlYTTu2Ek2fn8c0f/gQXpsqCYbGFWHA6wdZowXIUMtMguDBdRdoxUczZSNkmXD/sKohw/moFl2ZrYsEhiGcR4/eEWOTnyh7yWRvjuwu4/85xfPbR0yjVgrh7Y4QEoW0imyJ4yxt2oNIIcP5qBXU3xIlXZ2EYBPt35nDougLAgR+/PIOIc5iUwvUjUCCeZ8mnLTHfs8aUwE7BiXEAEROiLpDCFdHgVlRriUFFxNRGg8roP5J3kE2ZqLlCMIRD3BtjQ+mWRToMGWzTQDEnZu2zSsSMEjiWSL7DiOGG/UO486ZdePCx1+DIYNOf0F0TyU1KPmOCUgqbcFkNj8DB4VhGfHycc/hhhLRjwgsiZFKmoI0n/j6pjttP4tyvRy2wvI3VsYkRFIsZ7BnN4vx0dcWU0K2K7eLpvdnoREUnRKgva2wsVjrOsNZjEO2b6JlSA48+c6GvdadfmnkubSGMeJxMq/lpQ76eQJBomfQ/7va8npqcx5eOv4Yrc/W+z0/d8tQgmNhTwFefOAPPD+PuqDpiAjF7Xa752DGUEsUn+QbLhTfFbCJEFB0sg+LqfAOuH8KgBNXG5nTsVKztp/vM5SiTbVFYJkXDC1vmeAcBJUAxJ+zQ2oXn+gGHbCYQEhctknPt6n49e6WMrxw/g4YcAxPaCGLNF2KnYn/YXnQY5HQIBPVbjecp5DIWKnVB6W63uRT3fiAZlmLfEHQQVDMpkLJNjF9XwEfePYFy1UcuY+HU5Dz++nuTsf2rKDhQmCZBPmPj4/ccBoCO8f62I2N4+MlzcZJbyNqYV0r7BLGDRc0NkU1by+qsDKI/cmhvcVvvMU5NzuNzj56GFzBkUgYyxtYSaNMJ9jbGeogD9RJgUg8c50L50LYMUTm1DAwZnbtO1XqAMORdlXXVjzy/OVfSLfnYvzOHmydG8PSpaVy4WgWHWCQtkyJrmpgre6i580jbBiLOMZRz4IcMpAZAzrMEEUOp5os523UoUXdLsgtpC37IkHFMhBFDqepvWpK9krqCojg5JkE2LaqZmZSJSsOA50ewTdKSXCcX55vGh/HX35tEtRHEVVnDILBMA7mUiQ/ecRBhxBAGETw/El6sKzw3AqDaiGAYvhAvSZnN+SbHjDsvolNs4p5br8PjJy73nFs6fW5h2cT5yMHh5a23npyE45ioN8TzeuTgcM9qM6UE979jHH/2yKmB5qq2A9ZKEVlDYyMwSPEsifVS6FWbaMY5PvXgiYG8sfuZ1UynTJiGiJdKPAxAzNdVCapi+nR6XtU1q9YFU0xpYfTjvwwAY0UxmjM1X0cmZYnRoXjmRyq/Q8zXul4kWGumgSCM+tI3EQQQAtOgMrGx0PBDlKo+CBVjRhHjK56PHgSWQZBNmyj1MaMMiG1LIWsh7ZgIwwhzJQ8ciBPXQWHL5Mr1V25ZGkmRM8Y5Gn6IiT2FODFT9+vJyXnU3OZncK7mpeXMtGT8/fw7DuLvnruMmYVG33sBU86GcwB1L0RezlfHxxdxZFImLLlXbb/3Tal+nkvbyDCOuVIjtoWj8lhD2SC69YYxEEJwbGIkPreDu/L48hNnMFNyAcZhWRR7RrMthbRO8R4Anj09Exe90o6JXNpCueYvuffuufW6ZdeLQfKC7bzHaLLgIllgEznGVmLB6QR7m2K9xIF6UciUaBSRleQkulWxxfu15rHJW139OIw4sunm7diJ6lJzQ3z+0dOYK7ng4DClBUUQRijXGEYKDmqNAPNlFzuKKSlkRVsoNxSCqi1i69oGzl7v1nADRFwol8oi77aCbVIxq0NlR5hz+AEDkeqt6udqca42hI2HYxn4xtPnQYmgFoaRpGEF4gq85y3jAIAvfOcVuAPOXid9ZtX6mctYqDVCLFY8eH4ExzZwYGcOyge7XPVbOsVHx0dwcFe+ayf5yMHhvjawjmMuy/x45WIJf/yl58WGosfzqvQNzl6tIZ0y8av3HsE3uhzfZlOgVoq1UER+vWI9Cqsa3bHScYaNUOhdCROkF3vmvtsPIJ0yUan5KOYczJc9FHNWc7RGUvDFHKsBWzKO2p/X5DUrZG14gSuPqekJ3QsEQM0N8PlHT8PzI4wUU6g2AnhBJMYBVKLNOTgI6m6I3aNCPds0aV+FOcYFfVedg20ZMClFEDEQOau7mnGDTsV20dkE1CZAUbzTKROL1f6LiRxApeaj3ggQJCj3K9WU8QIGN/A66t10Q3t/gvOmVZrnR7g6X8dfPHIKNx4cxtuP7cLf/vACvvrEmaXnwpWTCIn9x7MZGw/cewSffuhkbA/X8RjQvMbFnIOaG8bin0rQVHyGKPjv35nDB+442DGeHjs4jG//6CIMQ+wrcxkbDTdEGDGwhDhiGHL8zRNnloxGHpsYxQ0Hh/HMyavx6MLbj+2CmRjl6dZdTha9TEMwKJpjQyJecg48fuIyDu7Kd437K8kLjk30ZtNt1T1GvPalzRbxSmDrsOB0gr0NsdJqej/oRSGLZHJkm7TjprhTFfvArjxGCylU6sKLuNv6LeJN698mFyNVqa81AnBJ/VbjKU2qWNBUoY7fl8SUmzBh4aHmrzYKARMU5YYXbbvsmnHh27hnNIvJqQquLtTj62hQgtGig7RjoVL3pRo3RxAKD+nnXpmN38cyKYbyDiyDglKg4TE89eIU/rbhY6GyAksUqb5qisE8MMaRtk1hZVVyMVpM4YH3H8FBeQ91qhxPTpURMY6P3n1IbNQarUnL5FS5rw3smculrh3ZhheKarTcRDRV7Jc+rypAtusb3Hf7AWTS1jWTVK2FIvLrEdtVdX07Y6XjDBuh0LtSJki3AvYjiXuLcw43iBCEUTyr6Ycs7lwXpJhlp+c1ec2stiK3sEvrrZxdyFnIOBbmSi4aXoRaQ8R21486JJEctkXxkbsO4xtPncOZS6W+rh0lzXOAPC7bIgii5bVV+kGn31ZOGuLvxciUZVK43uCd47U4RgX1Lv10v5MjZp2+R0rF/Phc2cP3fjqF778whc8/ehpBxGKh105Q76OsOo+Oj+A37j+GBx97FeevVpe8vj36GQZFPmNhviwYDLWG8HWPIt7SjT06PoKjHTrJ569W8OgPL2B6oSHuf/kZpklhGVSoe3PRCMpn7CWjkQCWrM0/eGGqr7VZFb0eenKyxQfbsYxYN0iNrXUryq0mLxh0dnsroB/Bxs1mwWmVlG2G9mq6bRmxKvZQzobrR3j4qXMrVtHrpVQqPIWbFOEYHPCDCDUp9JBJ/D0lBG89srMpjNX2j3iNWEjqPeaeVMB2pLJ4cnUlkvqmLJkAIEh0QxXlZjOFBRkDXK+H+ecaYy3XRQLg6nwDJ16ZxULFi63GcmmhglxrhKi7AT54x0H8vbfuAyDEwdpn3IOQYbEqZqsd20QuY+HSTA3VhthcGAOsRrm0KX03STz3BUgaotw4VRsBOAjOX63g5Nl5cM7jJPX4c5fw//vCc/iTr/wUn3n4FP7zV1/AV46/BoOKok5yxKEf1V3CEXdkk1AqoUo0x+nxvLaoatsGhvMOHFuo9H7uWy+j4Ya4+dBoy/FtVyyniLyVqWmbhWtddX2rot81oH0jtxEKvUkmSCf0YoKoAvbNh0bRkOyw5L2Vy1iwTANBgpEkNADE+urYRtfnNXnN1OuVuFXE2LK067obwTIpRgpCdXqx4qHSCLrGNZUk3zwxAr+PpJMQyMJws8fkeqEogK8DlAifaLoLBkDERfzKpKyWLvRmwepT14FDCJKZkkGWhGPSJUk056JDrvYNXd9Xzp7bloFcRtC7j46P4IH3H0EmZUqBu+Y+oXUPKcYZlFUqAFQaAa7M1lFtBEuUtJP3voqnNTeE60eCmUcEbZ0QsW+puSEYE4WcbMpqjkbKGP7Fx17FZ795GuemKgCAtGP0vTYr15GIcbzjjXuQdkwMFxzsGslg10gmFo1tL8q1v8dq84JO12Q5R5TNxGrWvo2C7mBvM2yEOFA3CtmBXTnUvQgLFS8Wiah7IeZLrqQNiyTjy4+/ivvvHI8Xs2MTI3j0hxfQ8MIl1WfbpMilLUDSYLpBBey0IzvniVksoCm6wrkIZl4QISePseEGsRVCEsI+a3k7i9VCkVc2amkiWNsP6/RWjAOVehirwrt+hOM/uYxsykJdzlgZUhU0Sd5hDFisekireWjGYBiGVFtHPO/X6TNVXWW06IAQiroXxRXCqI1ZYZpCPf5z33xJUAv9KJ7FNg0aB9FizkYhZ3et9PZLZT60t9CxI6vGKjjQIrImzqf5vE5OVVoCZOwZ3kPfYLtjOaE33ZFtYhCassbaYqXjDBuh0LsWTJBu95ZhGdg9YmJmoYHRYgofetcE5koufnR6GlcXGh3HbRTar1naMTFSSKFcE8W0JDpRqYOQodIIUMjYKGQtLFR8sEiJYC3FQsXDf/ziCewoppGyxBx2e9IquueSpm4ZSNmt29/FqhfP3CpRz9WOXxcyFn7+nePYOZLB//m1F1H3RJOCALBNIWjVTZ9moxHKIrDaF3XrNisXA0oIDErBIhZT/5ez2OznNOtuiP/29RfxrjfuwbGJEezflcf47jzOXakgZEzuIZN0YLHXqMhCtoi1FPmMjZrUfTl6cBjplCmF1JbGT8Y5vvHUOSHqx4RInEGkoCQ41B2bTYk5fYsZMGUxP+OIRJpLdT3XF6+2TIpCxoqT225d52TXm3GOuhtipOj0zRIF1icvWC1bar1HmdTad2mm1lIoA7YOC04n2NsMGyUO1I0yogSfFqs+LIOiVPPiTqVhiEr1pdl6S6JyYFce+3fmcHGmioxjxh6Hyts1qdbcDbmMBUrFrA6lBGHE4gUOaObbXhBh71gutkQwKYkDp4IKoN2q6GpGNmJrE/g2OnZu5OcxLqrZEeO4cFV0XhWFn8ujaT8eLxD2KnVfFGVUQOJAC0ct9shOW+AA3v2mPXj65FSc2HPOhfUZkSqoCVGTWj1Aw4swV3Lh2EbcUecAwkh8HoXoMpmGEBaxTIq5kosHH3s1ppb3u4E9uLvQUTzI96NYHbXYJroCNJ/XM5dLr0tV7e1ITdsMDLKBun7f0OYc5DWKlSaxG6HQ269oWbfniXGOp16cwoVp0bnudG/lMlZsb/Smwztw95v3Lvu8drpmacdEyjZQqflYqPotCW+U6G6qCFCtB8inLdjSvkgVbLshYsDVhQYKaRPMoIhY1JogE8FwuufNe/H8a3Mt16vhhkL5mhLkMxaqjWAgcTNKCQwi9ifJ3+Ic+PErs/jgHQfxTz50Ez7zyEvwfOFakXKM2OppK6A5PieTUArwLsVusftsWmkSIp0N1uhUpuYb+PITZ/DoDy/E4razJReuHyJnUHh+JAR3xWGI45bHalCCoZwjlM0hOtl//b2zeOzHl7B7tHOCqNbXYs4GY+J+D4SiWcv5L9aa961pUBSzNvwgEorfED9TFSM/ZJiveChk7K4K++2U7obcu8+VPIQZhpRjLnFm6VSUW+u8YLVjqBsxyqTWvs89ehpzZQ8Zx4BhbC2BNk0R32bYSFpEJ8pI0sxeqRwqe6ORQgr5jL2EktKkg5qoe0J4KiuPr186aE0GwLmyK2fBgSASCp+i2iqWwUzKxMfvOYx/8P4bxTHWWwUylKdnN2QcAyNF4asZe6tqLAMSC84pH9Ll2ujletD1HlZgXPwTMY79O3PIpi3U3AiuH6HuieRceFnzFv9tzjlKNR+EAMN5O54Lb9PBiD1ZFyseGm6A6YUGGl6IC9NV/NGXnsenHjyB0+cW+qYyJ58NL4hQqnhwfeFJ6lgGSAdKnXpeCceKaKjXAjqtMxqtWClNWWP1WOk4g1LoXe8xiPZ1pyz9oNtpse04NTmPTz14Al/4zquoSnHQq/P1JfPA7fdW+/MKYAmNtNs1C0IWz3ETmfBS0vmejhgXDKBAZN/FrN0UW0NTfyX+f/k/5UYIP4xgUDH7rcSz1Dr8pkOjHa8XpQQjRQfFnIORQioWP+sHnPElyTUA1P0Ar10q4dMPncTUfB0/+5a92DWSRsQYKjUh2jZacLbUXoMxERe7MckAIRQbsVaKtrfG9HrOhVja5JUyvvXDC3jT4VEM5xyUaj5q8h5V95HaSziWgdFCChzAfNlFEEYwCAGX+8TJKxV85uFTeLGNsl2uiXsgjMT4QiFrYzjvYLSYatlLK5YdkSOJc2UXpVglv8my45AMCCbsv8K2tbkbpVvdB4xxLFR9XJ2vx8+kGjcT4468ha69lnnBaunmGznKdHR8BL9231GM7ynA8/tf+zYKuoO9zbAVxIGOjo8g5Rj44y89D8uyhYiJ0TyWJm2miu/8+CKu31vEkYPDK6aDnpqcx+e++ZKYdYVKqsQipqrMlAD7xrL4+Huuj98r5Rj4oy89D3Cg6oqup3p9J1oaANS9CA3ZWdXoF1IQRF1U1V1epgPQ3rHoBsukGBtK4SvHz8SiZkoghstAX3dD2LaBUM55M86Rz1gImaAbEqCrdYsfMsyU3DhYi4IBbanW9nvvqo7s8ROX8Ogz5zFXCsG4uK/qXh22FHpLS9ES9bwe2lvQqtoaXaFV1zcHiuYYMY57374fz56e6bkGqNfXvQh7dwW4cXzlcW8QDMoESXaoUpaBhisKgb5MGkYLKaRTYnvYSSE89uBebMSU8TAUifBQzsHdt1yHd996Hd731n349rMXsVjzQTmHbRs4sCuHubKHmcUGmvyzznEgilisHJ5kOHU6KwrEVF4q6b0AQCiBQYEg5PCCCJm0hUN7Ci3Xq1z38eXjr8WKz4JyyjG76PZFE++aiEbqHHz892+/gnTKRMoS+hpvecMYjo4P48vHz8A0XERs4zRaVgqDimurFMNV4SLqUFxYKZJ7MzFiBTS8CN/58UXhLW1QFLKmsBYDMF/x4IdMjHtlHXDOMb3QAONcJNcQRYNyPQA4R8ML8ad//QL+yYduwrGJUZyanMfXvncWdTdErdEsLhEinE/ai+IgAAUBoYjHv8TrBbW+nfnoBQyGwVrW5k6MJNcLMVd2W9TZFStzZrER/+5cycV//uoLLR3htcwLVkM3X6njwmpwbGIEd966DydemkKp4m0pFpxOsLcZVksJWyuIhYigkLGXzDE3vBAlOWv11989i5RtxIvB7/zSrQPRQRnn+OJjr2K+7LX8PLnkObaBX7znEO558774vRjnOHOpjDDkyGVMeCFDEDQT517BQCfXg4FxwaOyZKeDc95fwO0zw3YsAz98aQYR47AMNRZAYABSMEfQwCImuiNKLKUqaeKi2tv7M7iigiU4Z0M5Ow4Iv/NLt/a9gT19bgFf//6k6KJDbEpUcu+HDHMlF8WcjTDi8fN6cHfnGW5xbFtjnkhj87AVCquvN3SiOe4aTuODdxzE2FB6yRrQ/nrbMrBrOI0P3H5g4Li3EnSzAGpH+yYYAKqNQHR9CUHEgVLNbxYBGyH2ynsreY6eH6HhRSBEMMeU3kSlHuAvv/UyHvzOqzHDTB1fIWfjvjvHMT1fx3//9isIonbvEHUu4r91N0QmZWLHUBqzi0s9kePkBu2WUbxF6TP5dzJVX+JS8v2fXsH5q1VkJHutVPVlMrX6WWx1rK4n9mizJQ9PPH8FaZms5DMWvNLWTrANChiyAEGIcDmNOAelFHyVF6jXvLtgLXBEDGCMISKAbVGYlMAwKIbzDq4uNFBrhEjZJjw/gh8yKdLV2lShhjjWuhfiM4+8hL/31n14/MRlVGr+kn0flzRvBXGPib0EBcASd6PZYaSwpVAQRC0ivp0o3SVpRyb811nMEFBNJUAw8nKZzpoxa5UXrIZuvhEaUZ1AKcHEngLCscGsXtcbmiK+DbFSSthaIpexYBpCuTGJhhcKak4gLDkKGauFHnL63MJAdNDjJy7hwnRViqoJeo6gfIk/F7IWso6JQ9cVWzY6n3rwBL7+5Dk0/BCzJW+JorXG2oEx5SlqIJMyl6rMd4FKhLtBULGAUtVHwwtbZu6br6Fojj0ROJLGZFBBCwwjtqLNUakmfLSTAaEfKjPjHA89OYlKPYjpZKLq3uyoRExQvfbuyOCBe4/Eifuxg8OxZoAvZ8a1qrYGoFXXNxrdaI6XZut49IcXlrgNtL++mLeRcgxcmK6uKO6tJ9o3we0q3xTSFcQNMFf24ntL6a+ocwwjFntiV+oB/CAClXoYHCI5UbOphhyPmZpv4DMPncSu4TR2DKUAdBfR5JxjpODg/W8/gI/dcxjZtBUn3rzt9ygFeOKSivEh3lTsZkK7xbGMli6lwulzC4K95kWYLbmYXmgIwa7EDC4lEEldYtdMV0DtrrthTLU9fuIywpDB7FPBe6PQnDNOMAE6JEyUiMQyZRsDuYC0Ixmj2++HZEdXvbZcCzC9KEYaFioeKBEWclPzdSxUxX4vjFpZdOp+EHP/BJ4f4uEnz6HhBbHIai9kU2IemjMRwzkTTQUASFnGkgNP/q9JCR55+nxcbGqndPtBhCBkMBIsUIMSDBdSMOSFpUS4r3Sja69VXrAaurkeZWqF7mBvU2y2ONCBXXnsGc3i4mwNxawFgLRYEnGIhMuRSp0roYcwznH8xOVYnVz9jmTxIuIcrhfBsRE/sO3iDJ4fikAfbY5SZzcq+rWISj3AzuE0fvXeI/gf33oZV+brS17T63ok/840hNaqEDKT312XAE6IpIwzhrGRDAA5sx9GQnhmwC/ApAQMoqI8NpweSBzk/NUKLs3WxAxWoiBACQE1mhQyxzLw0Xuuh+eF+NSDJ1pURBnjcfddjT5oVW0Nrbq+MRiU5tjp9YSIzeRQ3sZCZWs5AHTqUCVVvhXt1fUjHN43hPe/bT+u31fEpx48EZ9jEAo1Z4MmhMc4gA7q02IeVVBqIyaS8YeenETaMeOxIiqLqQSI1b8JISjVAjz81DnsHsngnluvw9OnpmMrJEAmgrT5GRHnMA0Six0xiE6rbVFkHBMcS91KknuGobxQnladS8YRs6ZCJtSyKaEgRM5ct20qlov3lIrZ3SBkyDoGZhYbopC6jB7JRoND2FQlmWhhxGEZrYm26uruHE4jmzJx/moVEWNw/c7nM+h+KNk17gTGAT9o9pJ77fE4h7xnAQKRpJaleF8YRcsenB8x7B7JtDSVOOOYLbmxAnunz7dMiuFCqqVz285IihtApGnjZpsGLEO4qqg9SbJR1KkjvBZ5wWrYUnqUqRU6wd7G6JcStl6fff87xgUlpeIjIy0QfBmcDUpaVJNXQg85f7WCxaovA2+T+p2k4ogqOcP0Qh1nrlh48LFXUWsEGCk4oJQik7LgVzxsVvja7sn1oAFxvuyCcI7/96+/Hf+fzz+Ly7PCQqHmClVWg5AWMZjk+yd/RgC5WRJCNWGSL9UGtckRGzbxgkLWxnzZHUgJFhDJNaVCdCwIGRoyiek3IFTrAcKw+5wgIQREzqy/NDmPJ56/skSpU3nO/txdh3Bodx57d2S3xMZcY/Ox2YXV1wMGpTluFi2yX7Tb5WTTZsdNsFL5rrsh3CDC/+3v3YD73nUYpVIdr15cbDnHTowwnvin5edcJEKUEFAq/nxhuhbTe+tuKJJ62XUmiV/MOELZ/OJMDbMlF5943w048docHvvxpQT1l8AwBLMtCDksk2K04CCQQl2GQZC2DcyVvJjqnrw27cURZe80V3YBAIZBkc9YWKh4cYcfXHUUDZiUIpTe3kp5ejlU6gFqjWBL7w86aZYEEQclXHRVubT2AsHdt1yH3SOZuFARRXxNvL2FUFjv1wz6KREDHJvCMoisCfG+rNJUYcSxDZgGRRBGWKj42DuWw2LFFVoBBFLMVKmxAyN5Zwmtun3U0zbFrDdngnpOJauEJzZFhC9lTHSia682L1jNGKoeZWqFpohrrBjHJkbwTz92C/bvzMELIlRqATgXfsRJVWeFQekh6nWWSRHJql4oO9HJR5tx4AvfeRWfevAELkxX0fAjzCy6KFc9VOp+bAWyXbCVDlXRxMTsj0gyOyeOataY48tPnAElBB+7+zCKstsRRRxUTb/JN6BEUPvaQamYAyTgsAyKA7tySDsmQra0YyDE1MSfk0lw2jExnHcGupYmTQQwWUmuuyF2j2T6Dgi5jAXTFO/RMV7LH5oGxbMvz3RU6hzOO4gYx09emcXB3Tp50miFVl1fXwxKc9zKtEg1LvUnX/kpPvPwKfzJV36KLx8/g3zaEjZHHbIKP2Q4sDOHO2/eHa+H7eeoft5pbrUzWouOYcQQRgzZtIVdIxnsHE5jtCASEUIFY41IMa0kHfaRp8/jl3/2DfjtX3wTJvbkkc1YSNsGsikT47sL+Mi7J5BNmZgve/D9SHbIOeYrnccouhVHsmkx2gaIxMowqFQWN5oCX3Lu8zc/dBMm9hRgGhTLMcZDyVKqJpLrlTy9m/nEMy46kSET4qG7RtJ4963XJSjKOaQcs+O1WEnK3YvxtlJkUia4bNuQxAaRJP5RiMcSuFA1Z5zD8yMsynvq4/ccxs+/cyIW4lPPgGMZ2FFMg1KCWl1835nE+FyS0s3kCEPEOSxTOPKkHRNUjlYwJgpH7cr269URXindXI8ytUJ3sDVWhVveMIZ9o2mcuVTCaxdL+PqT5+JZlXYMuhioOW8zbaFc81s8MNsXXfF30t+YAH4YwfMF7ccyBAUnXGeOuJoZ7uXV2Q92jqSxUPFiv/CNQqduteoypB0TFWl9BYhOb/M3SOxLzTnHTMnF+auVeJF+8LFXcWG6GtP1HMtA2jHg+hH8gIGQZvVYUcJMg8IyhJ3b/XeO49zVCr58/AyCiMOkzY8OGY87CZGkfyWPnRACg/BYeR5oUtziDggRgYFBDIXLPRk4x8AB4cAu0XF++UIJjHEQ2kqpC5kQX9lRcLBQ9bp3vdImLk1XcW6qgv1jub4+ezuhvaumu7AaWwWD0hy3Ki2yl5etmpXut0PVfo6KLu8H/QpztRYdTYPGNG71fh4EJVkoUwuej0rk25kAxyZGceP4yJI15PS5BWRSottcl1ZOJiU4sKeAX7z7MG7YP9RyVL0EnYpZG7MloUbtecKlIm2L79exKN75xj04sn8I2ZSJD9xxEJ9/9DQ4Z2h4veN2uxAWpd0dLrq+B5pNg363NQYRv1fI2mCMoVxfOos+yOcrRlm1EeA/fvEn8ZiKYticPDuPZ1+ewdR8bdlrAiydtQaas9m9RNCSUCMbPUfRiLgnvCBC2jERMQaDik65Ism1M+xMKuYXQsZRqviwLQP7d+bwgdsPxOf87MszODdVERavcs+pxH7VmOOXH38V9985Hieo7dfr7358CWEk/NjVvLZSJy9krQ3tCK+ULaVHmZrQCbbGqsAYx7kp8QBO7C1i744MLs3W14QekqSbpGwD1Q7iJOK95dwsVxRcAgogBBdKl1KRcS1AABSzFqpeGFOBAZHgFbM2wDlmFt1lhTqW+wxTFQXWgGbVLwxDUrETyGesWE3WC6JmACOKtJ/4jiGTSdbs1hwdH8ED7z+CP/rS8zAphW2LuaKQcTi2IYRIQgY3iDCcs1FphJIFYWD3aNOGQi3KQpQkjFkM2ZSJD95xAC9MLiyhJSktAC6T+mLWRikxY6hQyFpwLDOeP2QQ92smZeLXPnh0oIBACcH9d47j03MnUZI+8ZSKixPJGyCfsfCWG8bw7R9d7Nn1angBKl26Xts5Qe2kzpy0HNHQ2EwMSnPcirTIbnPkAJCyKGpuiB1DaWQcA1cXGstugjudYzFrY6bUaMlkOiVBRCbzqhNHCMH+nVlQaYWo3s/3o7ijzoC4W+d6ISglHWm2SSpssqAwNpQGlyNrXhCh7nbeO/QqjqQcIdhZrQco1XzwGuLPBYDvPn8F3//pVLx+3XPrdfjhS1dx9kp1+S9IXRsK9DuI1f4qy6TIpkwsVPy+3iHiojCQdkSsWy1MSjBcdJZYWh4dH8H47gLGdxfw/jsO4txUGX/xjZcws+jCsSjKtc7UeALBgguZuEfdgME0CSImld/b2RKd3kNeiG7XQvQFCOpuiGzawj23XofHT1xGFAFBGC59X3nvOpaBA7tz+Og918P1I+zdVcBw1gST+7Mkrdr1I5gRR6nmxYV9wxCU70uz9ZbrpH5XXa+JPYWlienOHEpVD67PYNBoQ12DVko316NMAjrBfp1iLTboJ8/O45s//AkuTJWFeIQh5pcGqYz3glq0Pv3QSZS7JNcx5MJqUhJ3NSF+tGbJNSWCOsZBUEjbGMrb2Lcjh5+enReUt5ChVPM6/u4gzfOFiodizsFddxzA1757dk1mmfoB40LNNJnUE6IUZYVdSiFrY2quDs44CG3dSDLVebZoS7fm4O4C9o3lcHGmBpMxzFQCMauWwL6xLP73B96Ki9NVVOsBMmkLBMIiZnKqjAO78nj/7Qfx3rftxzMnr2K+7GKkkMLbj+2CSSkO7i4smRliklJOiehGpBwTKceMFbobfhjPTGccgh1DKbie2Iw5toF/dN+NOLaChO/o+Ah+4/5j+OJjr+LSbC3ulBuUYO9YDh+/5zDSKRPfee5Sz66XKWf/2rGdE9ReXbX2jYeGxmZg0BnETq+3LArPj1CubQ4tsp367ErrTLXucs5xaaaKX/6Z63Fob3HZfUCnc2S9MpkECESRmzHxP/m0iZ97xwQA4LOPnsbcoosgYs3Cp/xXGDFMLzStuSgRdPFMB5eKbgUFxwby4CjVAjz0g0n81sdvaTm/ZuGgGs/hUipmzBernlASlzANgpRtoOGFcH2h+jySSDAvz9Zib+Z+IzZTyWMfsEwhdMWZYGRlHDOeBedcHB/n6Ko7ogr3XhBh53AK56/WYlVt1t/oeAuyaRMZx5LH1lnElhKCiT1F/PLPvAGfffQ0yjWv6+cw3izQuAET8/psqWieUFwn4JzF9poxA26ZR4xxMQp2YFcu7iQf3JXHw0+dw+SVSsx6AMT1sgwKy6Axk+7QngJMk2J4OIuFhVqLVVeyc/vyhcVYd8AyKQpZO25U9BL77ZaYnj63sO06wpupEbVVoBPs1yHWYoN+anIen3v0NLyAIZMykJF0L2GZANmNDFa9GBw5OIxiVvgR90IUCTpZLmPFSubd0G8AVPkjk8Hrf/3oG5HP2Es2I+p6xouqFMqKNxUDIow4jh4cwg9PTQ8s0rUaMAaANtUwCQDPE/Qm9f0xAH/8pecRhAwmaV5LIehBYBoUe0az2Lczh8mpcnytPnDHQXzmoZOYWXRjC5D4M4lQ7H7l/CKOjo/g1OQ8vnL8ta735ztu3rPk2DvRkihtbkIcu5nE2pYBzjkafoT9u/JxFydyxWeN78mvOnAdHR/Bv/oHb8O5qTLOXCqDE+DQdUUc2JXDxekqyjVfUhBdDHfqejVCTOwt4uDufFwlB7Z3gjqoOrOGxmZhUJpjp9e300g3Eknqs+uFmCu7YBzCBoggZkf99fcm8Y9//hhuPjS67Hsmz/HKXE10ImVX1LYMeH4U62TwROLHOOKASwkwlBf2XEcODuNNh0bx2IlLLWucQihjuno/BiCMQvyfX/sp3v/2g7j7zXvjdeLcVBkXZ6owKYUfMpHoqrlaQpBPW7gyV1siNEcJwc0TI3jlYgmVWiBiH5bSuA3JkKs1QhAilMUjzlGpCeeMlE0xs+iCynFegyhni+ZeQ1G0E4LRHUXhOkHNClMAEcT1KNf8lt9VCtkGJUv2DZmUiV949wR2DmeQy1io1Hz8X18/CU/OyFKqCiEDFAeS12gZMb+j4yP4xPtuwH/92otAn5Kz+ZSJdMrCYsWDLwvOkRSTM9QehRDBiJOK7ssx/rIpE79w9yHcfWvz3kkmtS+encdTL06hJDv87Uy65XB0fAQpx2hh7LXfi8uJHrb7s5+/WkHEOD569yFwENQbr9+O8HaDTrBfZ1jJBr29271vZ05ulCPsGErFqt7JjXI2beET7z+CWiNc1WJw/moFMyV32ddxIO6gWwZFqebD9ZfOhyWPQO41mgGvOVIcvye4qM5+5N0TeOOhHR0/u31R5QDqbjCwPZSCaRB87/kpMYOzcfk1AJHw2pbwbd49ksGH3jmBfNZu+f4+/K5xfPW7ZxFGvOmXaVKYBkUubeHmiRH8xy/+pCVB3jWchmMZzeud2GSYlCAImPBzBPD5FSaQnaq/NTfE53t0oj5+z+F1ozKp6v3EniIA8ewlrwvjHK4fYWaxgWzKindcXhAh45j42M+8oTkbju2foG51tWUNjSQGpTkmX1/3ltJINxJJ6nOp5svOnbQJTFhWVhsB/uvXXsQ//vDNuKnPBOLIwWE89eIUHvzOq3AsA9lER1mxgyImRorUzxw5v0wpxULFw6cfOolizsGlmaqcuxYjSo5FW0bBOhXKZxY9/OW3XsYTz1/Bx+85DAD4wndeQVkmyAQiZhdzQjsE8v/DDkJzpybn8fiJy7BMAkKEGF37Z8YWoZxDHZlKdgNJQS9VfUAWEwyTIIqEQCfQ9GKmkl2n0O9doTS4VBKp3oJxQaku5hxUaj6CSAl+Nt/ZkiyoX/vg0Zbvd3KqDMc24EjVeDUaNciGo1IPkHbM+Bq3U/jb942plAXHFvP2paq37Ex1pRHCcUzsHMlgtiQK8w4VDAI1apBJmdi/M4ebJ0bw1MmrOH+1Oz2fUmBHMYXx3XmcPDvf8jwnadofuOPgqvYDtUYIAoJsxur4e52UvzuhVyNMx8ftAZ1gv46wkg16p4d8KGdjZtFFPqM2yklKcXOjTAjpqzLe6TjVAndlvoaG158YR0Yu9CnHhGMbmF5owA+jjr6cgKj0qh+kbQN33LQLL04uYHq+Hi/+BgX2jKRxcJn5ObWoGibBQtmLLRoG3VuJICxmxpWO2HppsyXnwimVsjIccaL80bsOL0lmT03O48XJBaQdE3U3lPPYYl7uwK48bp4YweMnLi9JkM9fraLuhULUhcgNCiVSqIwh8kOcuVzCF/725VUlkJ1oSf10otY7YHUrbAVBBNeL4mIQgVBAf89b9uGWN4xhYaEWv8d2T1B7CQoB/W88NDQ2CoPSHNXru9FINwqK+nxuqgI/iGBQGrtwKKi1uO6G+POHT+Ef9ak3QQlBIWODEoJ0SsRclVhTSpByhGXnYtWDbVLsGsm0rFcRY5hZdFGp+7F+Coj4ecPrLVClwAFcnK7i0w+dFHaKvpyflTVpL2CYLTWwo5hGJmUKxlWb0FxyP7SjmAYALFY9lGut608YcZiJKR7VlScQvdhaI2yhkqsCRhCxljgVMuEbnrJNVBvd17h8xoJJCcr1INY7GcraqLlhXLQgEKysQsYSOiaJbFWGVagXvvet+5YUT5Iz9WNDqdjSLC4W9IlS1UPKFlZqSTG/TvvGXNqC50fYNZIBJSS2QesGxoG5kofRopj3d/0QH737MHJpC9VGgHzaain+33BgGJ968IRUroZqccOUzEZw4NJsDX/0pedja7fdIxncdmQMY8V0SzK9mvi5FqKHyzXCPvG+G5CR+gC6m711oRPs1xEG3aB3e8in5hpw/RBpZ+niAaxuo9y+MIcR6zvBrLoBbFOok9bcEIWsjbtv2YOHnzofi5xwKEspIilRIoAd2JXDm6/fgedfm0PaMeFYBixLzFMt1oJlu6e5jAVKgVLVB+PC75kn2+N9QB1XUjxtPRniigZuUDHbpejaO4op/NJ7ru+YXKv7IZe2MJR30HBDQYc0Dbz/9gP45tPnOybIaccQtjCQ4jVE+JdGXHDSGADXZ7g8V4dlUHh21GLztpoEcrMFN7oVtiLGBaUS0got54BzoYD/2I8v4uY3jOHAjkz8PoMkqFtRBG2rqi1raFxrSOqX1F2AYyl91qBEePZGDK4fDcR+Uc9yTY6BJTU1LJPCtoQGRia1VPm4XAtiJhOB8A2WteR4PeyUZCd/ZkjqeFl25zsdMmNCzyRlU1QaIfbtaBWaS+6HgpDB9cKuopKCfp38EC4Zbhx1t/V3DEIQyfn0ZHHFNChG8k7McOsU2ylB3BW2TIq5sie710xYVUYMmZSJXNpCxMT5tzP1chkLKVvYZNXcEC+cncf73n6g5XtNztSXagGyKdGUIOAodbwCnRGELKbkKzE/xRpr3zfOlVw0vAiVRgDTFAV2wnnHJkRy9Kxc87FjKAXGgELG7tq0qTcCWAbFaDGFMGRxwce2DLheKBXhAZOKmepaI8Dp84t46ZxoGDi2sSZaJqsVPVyuETZbcvFfv/YiHMsQdPltpMHyeoP2wX4dYRC/zvaHPOnTW8ha4FwImXXy0VzpRlklcBdnqnAsA4Wc3TI7C/T2PowijtmSi2ojiP36PnDHOP7Jh28W3Xb5+0YiweEQFeP77hzHI0+fhxdEGC2mkM/aSNkmHNuMPTgffupcbJ3QjgO78hiSns+USA/PAbNjzkUgVb+1nsm1omaLzkGrv3Qn/0p1P9QaAVIyOaKEIJu2sGMohZBx/PV3z+DKXK1jAYcn1cYVxU3OAbafZhAxzJVduG3MhdX4yfbrHcw4x+RUGS+cmcPkVLnr9z0IOhW2mptDDpMScCY2YNm0haGcA9eP8KXvvNLy+ckEtRPUczez2FjiffupB0/g1OT8qs9lNVAbj07+u2rjMYjnuIbGRmE91oX1xtHxEfzcO8dhGKSF9qzWfsUgUlRbVbzsBwd25ZHP2FioeLG7BKWCausFESq1ACBAqq0I74dCzIzKpDoeeoZkNcWzqr0/n5BWUaxuX0cQMswtusg4Ju5/x3jLul+tB/D8CPNlF9MLDSxU/Z7F/Ig1oxjnvKM+CiGCDWZSAkJFscGQ882jBQeObQgFbyLEtggEdTkZc0s1H54fwg0YRosp/P33vgG/+aGb8UvvvQHZlImhvIOIccyX3Y42aTVJsXdskYh3+17VXPRowUGlEWCh5MIPGXYOp5a9/gqMA64bxB7H991+AN/osm8cKTggBFgou02v6Q6fkyykUEJk8SNadk/ZXsBNOU27WDUmQQmBbRvwpQCheo5DxmGbTTX01cTK1XpB92qEub6wnxUaM0TskS1jTY5bY+2hO9jXAPrtVg3SQer1kDu2qK4GIYPnRy0J+0ptSbqqgFpGC5W5W/wbzouFplwLsKOYwm99/BaYVBzXTeMj+Mc/fxO++PhrYuaLcXAm6N87Cinc+/YDyDjGqui3lBDcdsMYzk0JQQpCSddg3bE6T5pV+Y0Y2eNAyzwYeHN2ba7sLaEhvXR+QYi4MTE3rLyqMykTKWm9dWWuDnC0zOQpJDcQjDNQ3jqPpkBAhHooF0Ex2cVe7w7neqlzq8KWkRKbTzWj6AeR2GjKYozaBHfzwe6nMj6cd/DNZ0ShaKuJoA2qzqyhsRWwGar9a8VAufvWvfjRS9M4e7kCL4xgyPGcmEXDRWKRTpkoV/3BipfKpxein0t4wkdYHmoYMhh2c3+grBOJTOwNKVBlyuOhBIiwNGFOxkyCzrPZ3ajl+ayN//vHbsGBHZmW4uT0YiO2fKSdqsqdTln+Nx7hQkK7Bc2xs1iQLOKwDAMhYSjXA5g0hOdHgj0nf7eYtWGbRuyZ7AcR6l6IfWO5+B4zTYq5aiD2XUEzOVSfnTx3xjgWKx4ci/ZkE56anMcjT59HSTZKKCUYzjv4yF2H8ZNXZvG3z17s65os1gLsHE7hgXuPIC33SZ32UZRSDOVszFc8LFRVZ777+xKCeMyu7oYY35PvuafsFh/VNVX7G9ukmF4Q3WzLkKMTIQOIGH9cCy2T1XhBd2OqqaK8Un43ZEFru2iwvB6hE+xtjkGC/yDUlZNn53vSUYdyQgF5oeqjmLWEh/IqNsrdEnrbMuBIhdJueadtUhSyDgCgSAhKNR8Xp6stifCRg8P4xL034NVLJbx6sYQL02Im2JXiWmo+qFNyqCyoXC/CaxdLXTc7xyZG8M1nzgsLjx7eYCqYx+InKrnGhmuatR0XMJRzkLKNFhqS8hHlaG4qBJWZwW+b1yIEmF5oYChng1Ia07REIiln5LjYQC7ZRBHANAnCiIFCVK79IIpVv8s1H6PFFBhjOHOlvKZqmuupzp3LWGCcY3qhIebzgFgQh3AO5Sie3OR18sHuJ0EFF3N0W1UEbTUbDw2NjcZmqPavZUJPCcH9d47jvz18Cl41gpjNkXZOCQvDXsVL1b1PJvvnr1ZQaQQYzjstIlkE0korJbyWK/UAtmXEa5HqXEeMwzIpcikL5bov5pMJiWNMe/6c/N9e2iYq0SSk+ef3vnX/Ej2Lk2fn8JXjr8WfsxK3Dh7/S+xBImmfFXdmZZwzDIJiLoW5sou6PHD1aZwDCxUflino44QA5XqA+98xjp95yz4AQpCs7kXYszOP3aNZTF4pww9ZrCwe09UTx+WHDFcXGsilrY7fa/t9nc1YCEOG2ZKHz8vi+sWZKl46v9jXtZgreTh3tYJ9Y7me+0YlGBcEvCsLJDkGwLhshhgE991+oGfc6hYfPV84oSgv6kCyKBSTkQByRI4PPIrWqxC20tG0bo2wmP2hnpPEfmE7aLC8HqET7G2MQYP/IB2k5brdhhTC2r0ji7nFBsJodRvlXvOlxayN2bABxgDHpnB9kbwqetVw3olf26lim9ywuF6Ihqwg5zIWijk7MR8UotYIkM/Y8e82vBDlmg8/ZOCc46vfPYsnT07hI3cdXiIcIqivWNYqQsx9J35ACCIZmG1TqK0uVv0NT7ZzGeHV2PBElZ0xHs99tdDWe/DoCAH8gGF6wZUibUT6b5JYcdyyDAQJapvaDInxAxvzZTfuUEQRQ8WPUJKV2+n5Ov7tf38OgGA39Jqb6rcLtN7q3DW36ZtqGqKzocReIwYwwuBYRstz1s0Hu1eCetuRMTz85LktL4K22TPxGhr9YDNU+9c6oWecI+UYePP1o/juT6cQhAxEzaaaFMWsGMNarPodWWc/eWUGX3j0JVyeq7Uk+8cODiOKOAo5G7m0BV/OvDImknZKCRzLgKlcReJkJ4yT2UAqnItOHGK9FcMg2DeagRcwzCw2liTbyzG8CJohyjAIrt9XXHKNP/PIS2j4Ucdkvhcci2Io7whtlqoPL4iQy1jIpiy4XoiaFyKKRCufS+tKQoCGLzRhFipex/AZhAwzpQaGsqLAff3eYov3cRRxSbcW79UUbu1+8IG0TT2wM9dim5lJW3joycme9/UjT5/HbTfuxOkLi31p4ESM4+Enz+G3P/6mrvvGhhdiruTJRLY740A9Ssl9FOccjzx9HoSQnvd/p/jI0Uyu047ZHD1TzQKovaT4Qb8aQifPzuNvvn+2ZyFsJYJp3Rphiv3BOF+yXxjkuDU2DjrB3qZYafDvt4PUT7f7wK48/uAfvxPPv3wVpYq3qo1yr4Q+5ZgoZh0s1oQfYnwcAAxpi6XQXolPblgMStCQ1UwOoFwL0HAj5NIm8hkLri8UNLMpE5RSNLwQ82U3tiEDRHA7e7mCP/7S8/jIuyfw/tsPxp/z+UdPt4i99IJ6P9sSlWvln608Ey2TYnpxeXuyJAbdLLT/btoxl9CQ1Jy0STtTutuRVGwXvquiQ+sFLP4MSgnStoFyPRDK5SBxJyXlmBgppLBY9RCGglbnBwyECOq5sujgAFw/hGMbHTefg3SB2sVukuIoq01MGef4xlPnBBWNsdiLltJm14RzoJBIpHv5YAPdE9TlWCdbKQCvVqlVQ2O9sdGq/Wud0J+anMcXH3sVl2ZrYq1JcKyzaRPFnIMwZF3nQk+eFbGz3giQSZktyf6VuXpMrbUtA1wKbqn4pyjHd968G5dna5iar6Nc89HwwpZxIQKlti3OM2Ub+Ll3juPuW/cCAI4/dwmPPnO+r1gY09MTP9s7lgMH8OOXpgEWYc9oBg8/dQ6eHwqKOgEoR9fY1u4pncvYcmyNYGyI4tKs8AOv1oP4+zIMgoxtIIjUyI+IbTOLbs9klTGhYn7jwWHUGwE+962X40KLlaFoeBGmFxs936Pl/OUFcf0I/+HBE7iaYFDV3QCFrN3zvuacI+OY8IJo2aYBIGjcV+YbHfeNnHOUan6zuUCEfzUgCitJb3BTXnPVpR0tOjAp7bvI1B4fM2kLX378VVyarcf3pbo+HKKTbpvCrxrobxTtJ6/M4M8fOYXGOjBbOjXCDIPADyJwef2KWXvJ72mR0K0HnWBvU6wm+PfTQeqn233/O8ZhmhQTewoIx/pLLLthuYTelVYjtknhBVGcNIWMYb7sYqSQQso2WmjuyQ1LyjYwV1oa4IKIYaHqS4EWSPEQD/mMJQJCQoTLkFRnzjiCkOGr3z2LAztzuHF8JP6cpZJd3UEApKQvZPt3aMjuJeccYchBqaAI9Qp0qxFFIzK5b6chJf/eWMF8OJdB3jYJKBEiXhlH+GBSAkQRYFtEUNOVzZotxgL27sgijBjmyx6G8zZmS8L+zJBzyyHjqLshdg6nWzafp88tDNQFUmI3tUbQcn0t2eGxpVdot8S0V6dcPafFnA3GxD0VhEI5nVIhPkSgBHt4/HylbWOJD3YSnRJUrdKtobF22GhbubVM6E9NzuPTD51EqebHThGCMSOKxRV5bo5tdGSdMc7x0A8m0XBDDOVtqHafSvYXKqITWXMDRFKUUolIQdo9cg785NXZeDb3s988jbmSi9FiCq4UmQpCBkJIvK7/o/uPtTDD3vOWfXj3rdfh//WZZ3B1vh4n0Uo5XP1ZIblSphwxMvMnX3peHhtQyIrRtkzKghd4cRJngqNTbbxdjHG+5KJcE+w90yDxB8bjU1x25kOGnBw3y6UtBBFvaQ50A+PAm28YwyNtbhwE4n6M7wrS1Afrdv6OLb6rmUUh/FrI2jDTFLWG+O7LNR+mQWMfawV1XxMuGgCmQVCqLX+PcwgBs077xoYbCs0RkrDoVJ9nUISMgYkxaAQRj23IirLrDGCgIlN7fLz/zvHWYzJITLOnlMTFhn5Vvr/0nVfg+us3ipVshF2YrgpLVFl0EALDHoaAeM+0Uu0jjfWFTrC3KVYb/PvpIC3X7T42sXazZ70S+mojQBCy2ILB86NmQIdIUharHhzLQNox40r85FQZU/N1ZBwTi1W/ZwKaFBfLpkzUPREQ1K+YRkLhlBKYktL25SfO4H9JWZiaq4MS0lelNwYRGx0vYC2BRC2W+3fmcN/tB/DI0+cxNV+HaTBwiKrygV155NMWnjx5FQ2vh1JIn8g4ptjotNGQHNsAaojVZgc15c5nxHx+3Q3hh4Lyl7ZNDBccvPXYHjz70lUxcy3nreIE0zHxrjftwcNPnkMhayNkaFFoB0QnWM1Tqc3n5FQlVjvPSo/W5YRAZhYbaHgROLgQxpMbJT8UG8dCxu6amC7XKU8+p5QIj9ikZ2wUMcxXPLiBoJCr5+vn3zmxZG5wOazWHkRDQ6OJjS5YrVVCzzjHQ09OoiITMlWQhKRuq47xaDGFB95/BAc7uCqcv1rBlbka8llLJh7NvyOExF7EBhW2QfEsc9L1IG3CCxgeefo8fuHuw3GSRwhB2hHimElqecRYvGYncXG6Ci8Q/smAKBIsVr2YWRUxhogl5q65SMYoAWZLLgpZC2nHguuHuDovLEZTFoVlUvhhJJMsKpPs1vjWaRZcUa/VFSMQYllx11Vey7RjwPVZPAfcT+wkABhjSwotfhghCCMYBhWaMHJeuH2OmRDx+cWcjbxUEOfgyKSaitpKOJZJ1oHysVZQ9zUI4HoRGn5/+wsCYKSQ6rhvjGTnOJ+24vsyCWFrypFNmag0QuQzwgY0eVyrtepMHpNpUASh6AYX5JiEH0R9aQidm6rg0nQV2bS5rsyWo+MjYAD+/OFTcCwqLO8oMF/yYp/30UIqtqXdSJHQrWgDuhWhE+xtio0K/hs5L9ktod9RTGG25CKXFgEn5ZgYLaSa3UCI8927I4uP3X04rsSrDQs3VXLWfX6Lc6EqzrigIr/3rfvw5eNnBLWZqtfweEEl8p+ZkosnX7iCUt0XyekA+WcuLbwYPT/CXOhiRFKhkovl0fER3Dg+0vH6M85x6vwCGl5jhVe8CdcLUfcCcKZmx6S6qUxM/ZCB9Jq9Rud5KkHF91tp9l6IxkyImcUG3nnzHlxdqHcs4ESMxxtOJXLXsvWUH8oYj7vMT/70SovaOdDsRKccc0nwY5zjR6enE0qwUnSMACYRm9FSzceRA0NLEtN+5iU7PactwiUQ3p6/cPchFDJ2/P12eqaXg1bp1tBYO2x0wWqtYvr5qxVcmq3F3dn2JMCkFBET1PCkNVYS1bpg8yiV5XaYJpVF+jyee8XreBzlmohXU/N1nLlcWlI8IETMaQPS77iLinl7kRIQS/9c2UXIOKh0nsilBJ05YhxhxBBEYk0UtGQaW4w2/BClWoChvCNGwDgHRVO1OtkdV+NknaB+TgkwUnAAWaAW9mccdS8EIL7PflXKAWCh7C25Vip+xm9DCPIZKy5cK1gGxXDeiQu5iiFgGM33suX8rif/3g9Z/D2o+zplU3zxsdf6HnkDRMf57cd2AVi6byzXfXzl+BkYBomd2ZJXRBUmlEhcLyvZlbJG2o9pZrGBH52extWFBspVv28NoUo9QBgxpCR9Ph4pk2vEWjFb1HhZyBh2DKWbe88iiRXn5yseChl7Q0VCN8NVYbtCJ9jbFBsZ/DdyXrJTQl+u+fjzR15qWXRTjhkHkShiqHshPvSuiZYHXG1YgoDFyVm3RBAQgVV0OT0sVv24G80YwKSTsyEjMeciCgd+iB+8MAUW8Thw9AMCMbOkEDGOuZKLbMrCcN7B3bdehyMHhwF0v/7nr1bg+QyWQWIV1n7mpNtBqRAUmVt0kUmZyDgmwBF7kBezNmZKjZ708G5/1fCiluQaEDNqABAEDN//6RX8Pz76RtERaSsgTE6V4w2nUp9tCcxcHb94DeMcT5+6ikheCyJpkaoTPVpILaF7n79awdWFBoo5WxQC1GYr8Vmcc9x2ZKxlI9rvvORvffyWvp7TO27avSaJr1bp1tBYG2x0wWqtYnq1HiAM5fxvpxfIH4YR65oE5DKCAh1ELLaeSkKsyRDWjF3AIZKRjGOAcCxfPKAE5bqPF87MtcSBXMYS+iluGI9pJYvsimkWShVv06DwWSRUoono/s4sNDBSdODI9TmQmiAjhVRMVWfymDOOgbvevBffe/4KHJNiPiFMRhLnlkT7mBfjHJD0+sWE0wpbJmGlVCTr7dfKoM3ENB6lJ0S8f0U2GiQrQf1OJEXjbIvG88UKxayN2VJD+DP7EazEfU0gxuQGYuMB+Ll3HIytUYHWfQvjHD94YQqXZmqye9y8r5Ke4jW5J1qoeKi7YSxMprDaxlH7XuruN+8duHmUz1hgjOPqfHOmnUDsHQtZGwYla9Lc6jYyotgfdTeEG0T4hbsPrdn+YTlshqvCdoZOsLcpruVuVfsimEy02oOzbRnw0VSgTkJtWM5NVaR1BgGhwvah2+cWczbqDZE0t4dRFcQTJhvwGGIzx0G614rKBUpAuRASY5IGvVjx8PCT5/Ds6ZmeSVG1HiBiHMN5B/MVTwhotYmyiPPqPJ9NCDCUtVHIOfD8EOVagJ3DaXz43Yfwl996Ob6vLIt2nQXueY7AkuQ6Pm+i5q0YvvrEGfzvn3jrkns1ueEsZq0WSh8hJPZwVUURwSDgzfeXB2FAfG/zFQ/FrC26HTL4qe5IIWfDNGjLZotAKJ6blGCsmG45tn7nJS9OVzf8OdUq3Roaa4ONLFitVUzPZSyYJgGCpZ1CAHH4Mg3aNQk4sCuPPaNZXJwVa2/yXVSyP1pwMFdqio+1f45a9r0gwsR1vYsH5aoPEODLx18Dk7ZMqivGpfVg3Q1FZ5mQmJW0czgdz3UbFJgre0hZBubKDIQ2nSsY4yhVfewcTqOYszG36KJcC1DM2dgxlILrifd3bAP/6L4bwTjw5E+nYJoUREpxdgdp6QID0gXCpLj71uvw6DMXUKoFSDtGz44wJUAmZeHQ3iHsHplruVa2acAyDbheGB/JYtUDVfPMpElNT45aESJG3tpjlBKOFRZpLO7g7t2RwULV72vmOj57Atx1yx584I7xrtRhdW9/7tHTiBhHEEYIIlEIjxKWnbGiPOOiQyv1dZQA61qzRlbSPKq5ARp+iCAQyT6FskYTx2tbBsZ39/bs7ge9RkYIIUinTAQhQyFjbxgtfKNdFbY7dIK9jfF66VattLKvFvW/+OZLcKXtVLfnXsykOaCEwJMWUqOFVN9K3oMoeFMiNjfxOST+LowYdgylEEV82aqg6tAbBm2hzHc6lpRtIGVRlOoBOBd+09eNZEBkxdmxTRQJwWLVRy5ttdxXfk1cO9sUGzLOOGpeCD9YullQ1HVAbJLYkmRfzPMJir3YusyU3BbKdjJA33f7AXzuWy+jVBNKtmGNyVk3QWNX8/UmpeCcIZuysFj140ScK2EfiOLFXEl06VWlPEnLbJ8LVFQ/P2RLNqKDzEvefGh0w59TrdKtobE22ApjUoOsFQd25bF3RxYvXyiJmEfREjNDJrQseiUqlBDc/w4pDFXxhYp4W7J/2w1j+MYz55c9Hg4AhHYtHpSrPtwggmNTpOzmzy/OVPGnX3sRDByR1OngXDDJvCDCbKmBlGUim7Fw963XxRaFLR9MlKiWWMcr9QAGJcikzFgcM3IFzXV8Tz6+xqqoHwSiM2zImNqxiC4T+PhjE3uSu2/di93DQrl8aq4OSludNhQsQyTR+3fmML47H+9b5koubJOCGoJTlaSlU8nQUjF/h9SnqdQF9X/XcBoR41ioKleQ1n1TyDhu2D+Ej959CLVGiFzGAuPAf/yrn7Qw/bp17QFgbCiFX733CG6eGF2WOnx0fAS/dt9RfPOHF3Dm4iLqyhFEvrESNVP0/0jO5pdqPigB6l606Y0jxjke+cE5WKaBKGq6ghAi1egjDkLYsp7d/WCriZZutKvCtQCdYG9zXEvdquWqnyup7B8dH8E/eP+N+OJjr+L8dLVjcFOCLynbjCvyhawNxjhMoz/hMo5We4kkDCrmuj3phWzQ1qQs2XFWtlhOH1XBZOFhKGdjl5OJBbT8MMJixQchwic8k7ZQk3RAgwKj+VScXCu0J4XqvnrhzBwe/eEFjBRS8THkszaq9QCLNS++poWsBds0UG8ECBiHJdVD2881gih0xOI7jKNaD7oG6HtuvQ4vnJ3H1HwdjpwdA4RYCwiwbyyLYweH8e0fXYSV8NIOOnxvHCIIfl4WLo4cHF5SvEnOo3Xzhx00+F1Lz6mGxusNmz0mNchaQQnB/XeO49NzJ2NrJCrbbEqELJ+xlk1Ujk2M4J9+7JbYB7s92U+nTDz6wwtxMtaxWw7EMaFjoVF2Xx2bYkexOWfKGEfdDRQ5DJQIZw0CNVMtC7kE+NV7j4An9Dqo7HD7IYMp56JVDF+sinnxjGPiw+8+1HE0CVjKfiOEwKJEzmRzuTcQs+xMio4lO8fJPUny+zx5dh5/++xF+GEEk9KY8h5EDCnbbPlOMikL82UvLgYrxAmvSkwtCpNSKYRlYqbkgjOOhaonu5vouW+a2FOM3/uFM3OAbES0FxOSSTcgRNx+5e/dECfX/VCHj02M4M5b9+HES1NYKLuYvFLGt5+9iLRtIpPotKuGgedH8KTi/K6RDD5616FNbRwp8b+RgoMgsLBY9WJXEACwTQrHNpBJrz7p3WqipRvtqnAtQCfY1wCuhW5VP9XPlVb2j46P4BfvOYw//ZuTqLtBbHWgwDng+RFcP4rnvJW6s/Knbu/EAq1da86lurgBGXABIqvVtmXKeTAilc+lkJb8veR8F0ezGr5cVbBX4SFiQDFno5i1UWkEKFW82IMym7bi+eok2pPC5H31+InLLckkIQT5rB1btoQRA4s4mMExfl0BH7jjIDKOgf/8lRcwX/Hi80tecyWiY1kUM4sNPPrDCx0D9GzJxa/eewTZlIlqPUA2bYKDoN5obozOX63gO89dijvRuZSJxQ40N0KAMIpQqwdx4WIlxZuVBL9r4TnV0NBYf6x2rTg6PoLfuP9Yqw82RFFz71gOH7/ncF+Jyi1vGMO+0TTOXCp1FNkcG0qhWg9A1XxwW5g0DYK0Y3QtNJbrPr58/DWk7GZyVa75WKx4Lckc4wAiJj2AHZgmXaI8nix4FrO2EEFLeCwDTTFTcOAvv/UyHrj3CG4+NLrkvDux30AF60rtCYZyNmqNECBAlKBZd9qTqO9zfHcBE3sKLXsdAmDfWC7+HZWsikSFx6NO6tqqQkbGMWQsN1Gp+7g4U0PaMYUdl4xhC1XR/R3OO6jU/WX3TbmMJUbCQgIm5/g7tRdMkyBtm8hn7YGow4C4dhN7Ctg/lgMlBOZzl5Fuo7GnHBMcwCITCSzjQKnq45Gnz4PIosVmICn+Z1CCXXamxRXENGlXsb5BsdXGQLdaR307QCfYGpuOFyfn8ecPn4Lrh8ikLGRzVsfqZ3twzqQtEHDUGiEmp8pdq/yMczzy9HlQCuwdyyKIOCo1EWxU5b1cF7ZOtx/dhR+/PBPbepnSPqrT5JTyxG4GPjn/y6VfIRO0px1DDsKQo+GJSnQUccBQHp7N8EVkAG/xiFymKrhc4eHIwWEcP3EJx09cxmLVAyGkpzVYp4por2TSsQ2kHAM7ill86J0TyGftlg1YLm3GCXanQB1xht0jWfzo9HTPAP2Np87hd37p1q7BpP0Y6x2sy4ik5keMI4gYpuZE4aLXNbzv9gNIp8wlwjtbLfhpaGhoJHF0fAT/6h+8DeemyjhzqQwOjnTKQiFtIZ0ywZRexTLoluxTQvCRuw7jj7/0PIKQxb7QXPpgUyrsEfeMipjSzlA7NjGCk2fnwVhTNdr1wiXJtYLMcVH3QuzKZlqUx49NjLSs/0oEbWax1WHDsSiGcg4c21h2ZjTJfrswXRXJMBHMrGzKghswZDNWS/G3H7ZBL4ZCMlmNmJBWtUxpYZlgZDEOVBoh/IijmBWxm3Mxb510q1DxM+MY+NV734h6o/e+Sc3eu1fKCMPu9lwGIdg9momL2/1Sh6/fN9Ty992StoYXYr7sxmN9Qzlh77bZYlqdxP9aXEGCaE2TzK00BrrVOurbATrB1thUnDw7hz/92ouxgIkXCN/rYtbGUM5eEgRVsD81OY+vHH+t2fGmQlHzLW8Yw7GJkZagkQwAlFKwIIQXRLHgFudNWvfzZ+bg2BQLleYCSpaQowS4FBVLBj5ZaAcgqdiFFAxKQS1gbDiNSzO1eJaLyXeHnCcGhBJlUvGzn6pgr4B9anIejz7T7AwLARi3pzVY++Zg+WTSxEfvWtoROX+1gqorPC0rHQoERF6wiT15PHXy6qpme5LHOFdyO4rJGFL0xiAEYcTgh1FcuOh0DWtuiEfWiVWhoaGhsd6ghGBiTxGuF62Ltc5N4yP4yLsn8NXvnkUYsZhObZpEiKilBRX99LmFjp9/25GxlgRrodo5uVbgcubYl2NCKjZ2ilEqlKg4P5Szkcs0hVD7iSuqSNEsUvvyTbGqdb6XM8jUfB22aaDWCAXzDd0FVIMgwlxJ0NSJpNEnoeLn1YWGoNlT0rpvarsPktcxDDuPWQGCfXbzhHj9aqjDnZI2zoU/N5Pjdo5lwLFFqrLZYlr9iP+tdZK5VcbLdFNhcOgEW2PTcGpyHp955CXUPTGL1clWqVMQbJ/3iajw9FyoeJi8UsGjP7yA/TublKtkAIgXb87jGWAOQQHPpky4vhDSAGR3mS+dK1PUcCZn2whpemZGMrsmcq47kzIRyp9RSjGUd7BQ9mCZFBlHKHTPl0QgMylBIWvHSWZyweacx13UfTtzuDhdXbLYtgfsjtQty8AOQrBY9eAHDPMlD4WstexmYSXJpLruQ3kHKdtAqeojjFhMcXMsQ1iu2EbXAM25mG1zvQivXSz1DCzqGB987FXU3Gr8czXvHf9evPFqLVwkr+GpyXl8vo+Zsq0S/DQ0NDQ6Yb2tdd5/+0Ec2JnDl584g5mSCzAOy6LYMypiA4Cunz9bcpFPW7EQ13J6J4KGLkTP3IC1JDPtMcr1IynoSVHM2TGVXKHfmVFKCN7z5n24+9bBLZ0GRbxXsZqxissZ704QEiaCMWCZpCN1V53nybPzeOL5K33FtE+87wb816+9iCBqnf+mlMAyCExK8cLZeRw5MISr83WhbbIC6nCnpI1JYVEOEbeLCXeYTgX3bto964FO4n+cc/gBgxdEyKaX1zZY6eduhfEy3VQYDDrB1tgUqOTP80MQQuIkFQQwifBzLtV8jA2nW4Jge9Lo+VGLRRWTlh4Xpqtx0EjSkJSaNCWk2S1VFhEGRdYQ3dy0TIyjSNDDFX0ruW4mu9WmIWwTPF90RkVyvTSgKLGz0WIK1UYA14uQckxAelGqc1BVQUqAWiPAf/7qC+JYZLClMmFMVqDbEz3G0ZG6lXJM7HZM1BoBvCDCR+8+3JeP4qDJZPK6Z1IW0o4ZK3QbBoFBCBpeiJFCqitNrFzzRbDlHF9/8hyee3V2+ULA+4/gP/7VT9DwxGxULKamvjdJ6x8bSnWsNA9qR7FVgp+GhoZGEhtlrXNsYhQ3jo8siQ0A8KkHT/T8/LRjwrEoyrWgayKZBIfwS+6UzKgYdfy5S/j2jy6g4YYIpfVltR60+CoPOjO6Eeu8ipnxZeCt3tftUCKhgIjrnRBKlfEfvDiFat1HQWqnEEK63geZtAXHMpB2zFiDhlICQ9L+KzUfL19YxB996XmAC9p+rRFgtOAgndj39NPVXVIY8SJwacE5lHOWnFeyMLKcds96QIn//be//ikuzlRb9A12FFPr8plbCbqp0D90gq2xKVBUqEzKghd4SxRIDUIQhAwNWSFTQbB93qdUa85KAxBKqRFHNm+iLmlxv/XxW2IakmNRcIhZLgXlp2xbRjwTvaOYwkLFQzZvNoXOIoaFitdCm1KFgZBxVOshdo2kUW0EsIzOdKkwZHBsAw+8/wiIpFflMhZqdR9f+e7Zlg7AcN5Bqephoeohm7IQGqLjHDHepJ8bFBdnavj0QydjQTMVaHIymc92UbRciY/iIJuMTvSvplcoR6kWYN+OLN5+bBd+8MJUy+uSM1gcQp0zmzL76roc3F3A/p15TE5V4AcRIs5B0RSRC6XC+UfuOtzxvLUdhYaGxrWAjVzLOsWGyanysp9fqfv44B0H8f2fXsGZK5W+Pufg7nzXJOr0uYVYMNMyqaCuk6ZP8UghhZRtbMmZ0WbMrMpZX47OOwkBzgHboggijjDkHe24SjUfnHEs1nxpcebCMinyGQuGQWMLzitztfg+qNYDRIwjK+n3SbheiFLNk/suimzGginFTmdKLoYj8XuDUIeTSdtrF0v4+pPnWubJk1CFkV7CqBsxp113A0FftwxYFgUBsFD1N3VGfKOgmwr9odezq6GxblBUqJQjKqiC5pSo00pqVN0NsXskEwfBmEJlUviBtL1KLNyxEjdvzlhdnK7ig3ccRMo2UJeWF0rxNGRi/lnRkEIp1nL3Ldch7Ygk3aDC9sMwKBiHVDK1sHs0g31jWewcyWC04AgxEsaQcQyR+Ld5gqlq7u6RDA5KRdGbD42i4Yb4xjMXUFJ+lZRgKOeASwuQoZwDy6So1AJwcFiGoLWXar4sTAg7qQvTVdgmRSFnw7EMzJVcNLwItUZnCtx6qz4q+ldKCsr4QSToX4GwEMs4Ju5/xzhMSlte5/mhvH48pokN5RzYloGhnA3XF4WTpEBcp8/NpS3YlgHToLEfdqSS63dP4KYuATB5j3WCaVJEkbAWY5xjcqqMF87MYXKq3PWYNDQ0NDYa5ZoPL4gQRc25ZS5ZXg0vjNlSa2mtk1wTX7tYEjF1mbV0bCiNv//3bkCnunR7Wva+t+/H7/zSrR0TmPaO/XDekR1YyUJjHItVDwsVb0vOjDZjpgmTUhAIv+rW14iYOJyzsXM4jRE5Sud0iLOzJaG3wuSoG6VEat1EmFl0MT3fwGzJxULFQ7kW4OTZeQCt7LN2qKYGJQS2bYASgnzGxtiQsPEs132UquK+2zeW7TvZVEnbe27bh31jWSnc1nruyT1UUhjVtsRx9LtHWA0Y5/jSd16BFzCMFlPIZ21pGUrgWBS1RoCHnpzUewEN3cHW2ByoBTyKeOxb3NJplEJg7UEwufDHdLIkbVvyqRjjcGRCrXydH7j3CB56chKvXCzFiqDK0iPlmC10prvfvBe7RzItsyYcIrkezjnIJ8RSWBCiVAsQhAxXF9z45xf9EKOFlOgUB52rue3zcaryO73QQMMLMZQXtDpPFhMUtZ1wDi8Qr1PXgXHACxkcW1R+RwoOLs/WUar6yKWtJZXtjajgd5vZ2b8zh1++90Yc2JFBGLKW112cqcIPonhOW30/QP9dl+T7XZmrIQgYQAnGiil89K5DODax1JpFoV87iplSA5968MSG0tM0NDQ0+sGpyXl87Xtn0XBDNFwxikWl0GPEmsKelAAzpUavtxroM5OUXQ5BHzZN2hIzFZJF3mo9QMo24fqh9JtG/F+FlG3g6IHudPb2jn3KMbGjmEap5scFhjBk2Lsji4/d3Z9VWTvWe+Y3GbsuTFdRd4N4FI0QLNmzLFZ97N+Zw323H8AjT5+P4yylTa2TQtbCzKILAinqypvnovy9I8bxnR9fwsSeAo4cHO6oGO0HURyb2wVZ046FsSGCuhfi/nccxPV7iyu6Nv2Iad12ZAwPP3luU1hm56YquDRdRTZtxmy7cs1HIEcQAeCViyUcP3EJ73nzvjX9bI3tBZ1ga2wKkvThoZyNkUIqXqQYRAKYSZn4tQ8ebQmCyd/LODL54QBDq0DKfNmFWaNw7KUenMdPXMLXvz8p6NHSM9IPoiUJcPusydX5Or7+g8kWynXDCzFbaiDZrKbSs5IxYK7kIu2K6nK7EESv+bhsykTdDVFzQ+TSVtzNpYD0/1TeYK1V0nLVh2VQOTslxF0WKx7mSm6LP+ZGqj52mtk5tLeI0ZEcFhZqS173nR9fxF9/9ywKGStWD02iX3Galc4K9WNHMZyzW9TZN5qepqGhodENqnDb8IJ4zeecIwjVvKh0U2AcHASPPnMBu4czq1qzuomp1RoBFiqe9MTuPp97/moFjm3EhXGRsAjKmGkQ5FIWQNCTcdVJ0TrlGMhmsmi4AcKQoe6F+NC7JlZ0rhs185uMXeWaj1cvlfCDF67CC0IUMp33LEfHR1rm4Mt1H185fgaObcSz1l4QLRno5hCOJrZFETIWz2J3SnI9KRxnGK2CrAqWZYB4EXYPZ1bt495LTCtifMXK5atFpR4gjBjSKas5yiYt76TFOqKI4+vfn1z1M6WxvaETbI1NQacq5Y6hFFwvQt0N4dgG/tF9N+JY2+KU/D2lPp6sHAJNOyalRFlPUKSVIuju4UxfSojJWZOcnDVSnU3OOUpVD21McBiUghDI2S+CHUNpfOLeG3Bwd6Elues1H2cY4j2CkMEPWdx5ECJnzdcRQlqSbKWSnrINUcltE1XbLNXH9pmdbkkuJQTX7y3Gx98Jya6HoCNWcOZyCYQDh/YWWq7zSmaFlqugO5aQjm8vjAhleAPlWoAvHX8N/9uBIZhUT+FoaGhsHJKF2+F8Cq4fYa7UiLuggCj+cipi5UjeQa0R4MHHXsUD7z+yJE4N+pntxeLRgoOZkou5koexIQLLMjoWeZOFzZ3DaVFsl+JaSohrOcZVN/YRkcciZpYNFLJLu+nLoV819rXqcCdj11uO7MQdb9qLLzz6Ei7P1brG8eTvvHBmDhFrjjoVszZmSw20C7WrazyUcxAxjgvTVTz14hTuuGn3kiSXo5lcpzuIqq3l2FmvAvnkVLkvltl6jL/lMxZMgyIIoqYjTZtoLiEcQcg2zU5MY2tAJ9gam4b2KmXkiorw+J7uAibtv3dhugo/MSdk0KZ1hWmIwPzI0+dxo/R4TL7HoN3N9s6mH7IlfsuEIFYaFwIiQsGUELLkvXv5R6qKsx8yRBFD2jHjCrSKj0T6e6qfECJmrFRS7siNTCdRtW7nupGWF93QTwd531gW9UaAP/jsj3CpTclz744sPv6e61dVPOhVQb/thjE8/FQrPa2FJsY5zk1V8G//8tmO/uAaGhoa64X2wm3aMVHIOlioePFrOADLIMimLJTrAXzpvPFHX3oe+8aExeUbr9+x4s9MIp2yMBxxlOs+6l4I4kVdk8P2wqZtixi2WPX7Ylz1Ezv27siAccS2l/3EuH7V2BmAb6xTh/uWN4xh32gaZy6VBnbxsC0DKcdEPmM3vbwlTJMim7JiGj0H8IXvvIofvDCFD95xEL/zS7fGe4JM2sKXH38Vl2brHQXV1nrsrFuBvN89wnqMvx3cncfenTm8en5hqSMNpGiuLOJoMdTXN3SCrbGpWCmNV/3eUy9O4b9/+2UEEQOLBN0NHLBNscAZlHRd5AbtbrZvAExKlthnGLR53GqWPIw6i8i0C4moSrKqyGZTFoKqj7obwjQo8lkL/mIzwU5+lvp/AkH3Yoy3BJpkV6JbEr0Zlhed0M8M1s0TI/hvD59CqSY2C4aUc2eM4/x0FZ9+6CR+4/5jq06yO92bJ8/OtxRG2mliRIrpTM01tg1dfCsUVjQ0NFaPToVby6QxC4rIdTLjCAVvJVjFIFShVUf2HxoU7xzOrvgzk8hmLISM4/53HMTu4UzXNWa1PrudYodlCWpzueaDEqDuRfgvX/3pQDGuHzX2C9NV/PnDpxAytm5jQ8k9y3JrdqckNOWYMOoBAB57hA/nHMyV3fg+4ABSltH1uO+/c7xnbF6uCMIYx9krZZQq3opjTT97hA/ccXBdYholBB/7mTfgD//yR2BeCAMEXKrrRrwpmrueNHWN7QGdYGtsOlYq+U8JQSFjwzYNjBZTCKOmR7QtAwqTSuTJRW41yUS7GFdyBNo0WrvUShTNNGhHqtKBXXnkMzYuTleFxzbEL9gmRSFjIWQMu4bTME2hGA4uvC4bbihV1gEOYbERMS4CJERiL9RSl1b9uyXRN0+M4PETl7fMTHGvjdYH7jiIbzx1DhX5nSYt0QgVYi2VulDyXC09q9O9mSyMWCZdQhPjUmm+kLViq7itTBPbKoUVDQ2N1aMTTTqZXBMIJem6F8YWl4xzEA7YtoGc7Mg+9INJ3HlrfyJN/QhDmoYY/1ku1q/WZ7dT7LAtA8N5RyqIu33HOLVXeOHMHHw/QibVectsGESMtlkUO4bS6+Y3rtDvmn3bDWO4NFvDXMkV1GaTwjAI/EBYfQ7nnFgV3CDKstRAJmUiAyw5bsY50ikTd71pD378ygwWK/5ARZCTZ+fxzR/+BBemyghXGWt67RFunhhZNyYBINgEH37XIfzlt0+DMQ4iiYy2SWMBOj+I1tWlRWPrQyfYGtsaSTVyp49ZnLVIJtQG4NxUGX/xjZdwabYm5m7aXhdFYnZ6385cR6rS6XMLmF1sxPRmDgCcw/UjuH4E01DJGgFAMJS3cdcte/Ds6Vmcv1pBNmXCMIR/t+uFWKx68APxmRFjSwJetxmyC9NVvHKxBMug2DGUWvfNQb/ottE6f7WCizM1cM6XzDgLpVyhqH5ptrYu9Kx2ob12mpjyVXdsoTK6lWli/c4VamhobA906lzaJhUjR0EkrB6lPZZBhKo0k4mVKkxnUyauzNVw5lIJo7nlE4S1puyu1mc3GTvqXoQ9O/P4s795AfMVtyfFOxnjknsFZWvmLzAM5Zwl88euF0lh1vVXte5nzQYQH3sYMnhBBK8Uxb7NnAO2IQrzfhCJUTPJwEqKlyWPu+GGLXsnSoGhnIPbbhjDsYmRZYsgpybn8blHT8MLGDIpAxlj9bGm0x6h3gjwuW+9vO4x7Z7b9uKZl67i3FTrXgzYOJcWja0NrcCjsa2hAnu1EcDzQzS8UMwp8yZFWvloq8B0caYqrCukX7RaeE9Nzvf9uZQQTOwp4pd/5g0oZGyhHMm4FCHjCCIhsFbMOfi5d4wvCTyMc3zxsVfFTFqXz4gijlzaQjFnI5sysVj18a0fXsQbD40gm7bgBix+L0oJHFtU6T/y7gn8rx99E37nl27FkYPDmJwq4/nXZvGl46+h4QVLfCOzKRNhxGJRtiTaNwcbDbXRuvnQKMYlzb0qVTzFAS79HfWjIGB47WJpzT2qk/7e5VoQe3V28lVPemZvNbTPFW6kl6iGhsb6ILk+KV9kDiCTMuPxIsekIkaCd0ysTJMijIRg5ko/U3kx9zs/vdZQseNNh0dBCcHUXK0vWycAS/YKI4UUTIPCDxhmSy7KdT+x7guWHKEE6S4d7m5xIOkZ3k+MChnDl46/hmrdR0bqsrSv2V987NXWYy+mMDaUhiOLCR9+9wQ++bE34eCefKzpwiHG6kYKqZbigTruk2eX7p1Stom5socnnr+Chhv2poXHsSbCaHFtY01yj3BgVx6PPH1+Q2Kauufb92Kbec9rbC3oDrbGtgYlBDdPjOCViyVUG4GkwREYBhHU7LSFD95xEAD6EikZtEt7dHwEv3H/MXzxsVdxabbWIra1byyHX//wG2Ov5yTOTZVxabYGAkEvA5TvJ4/VXps+paTlOF84O49fvfdITIFqUqNySzrWLRV4V/iRun7UEkSZ7L6HEYMfREsoflttlignVTwBSB5+69+r6+iHDF9/8lx8jdeSIqboaV86/hrOTVUETYy0UsSA9VUzXS36mSvcyt13DQ2NzuhGnz2wMwcQgoWKJ+yZEnolyZigKN2DqG2vdn56PVGu+Qgjjkwftk6dBM1cL4xfyxjHQtlDrREgl7YQRhyObQysaj0om+4nr8zgz/7mBZybEkUAL3BhmTT+7gghyDgGLs3W4FhibK5Fzb2YwmLVx7OnZ/A7v3QrjkoNmy9851WkLEELb48DYchgUIJnX55Z1d4pjjVp9RnNBHctY81Gx7StfM9rbD50gq2xrXFqch6Pn7gMy6Bxksg5RxAIEY97br0OR8dHMDlVXreF9+j4CP7VP3gbzk2VceZSGZwAh64r4vp9S72eFc5cKiNiXAiTyeNRAmXJ4OP5zYQ3eZzZlNmi7tk+p7aERkYJGm6IMGSYL7stlWpK1eyw6L63Y6sliQd25bFvLIvT5wOEjLXMYHMu/DE5ABC0iJ+sNUXs6PgI/rcDQ/i3f/kspuYaKGRbfbu3Ok1sOWGirVZY0dDQ6B/dRmwAYHKqgs998yXMlVyMFBxQ2rqG1twQB3bmcGhvEaVSfdWfudldvELWhtlnAtyepLleGIuAGZTERXQ/YFgMPezfmcMv3nMYjzx9vm+K/KCjOaqDXJJK8FQKnPph1BLPFZPO6WBz2Wmfc8dNu/GDF6bEuFPbNVHHvaMoFOhXs3eKY425vrFmM2LaVr3nNTYfmiKusW2RrDTvGEphz44sdo1kMDaUxq6RNNKOgRfOzoNx3tcCvxoqr6KM/+xb9+O9t+3HoT29vUS5+qv2fHYZ5lLyODvRp4HO1F/hq01ioZJyrUlxs6XwiRLnajmcNpr9eqMfypyiZuVlwh9Ewi+VcY5QJtcEwGhh/WnPJqX46F2HxeyXF20JamS/aFexb8dWK6xoaGgMhk4xghKCQ3sK+KX3XI9s2kKpFnRct+5/x3icyK32M9cag1KrD+0tYs9oFjU3jOOeQnuMa98rKBEwkxIYlMAyiBwDspB2TGTTFm4cH+mbIj/oaA7jHA/9YBINN0QxJ5JcAvE9Gm3xPJBUZbvPfU4/1P63vGEMjGFVe6eNijWbFdM24p7X2H7QHWyNbYtOdKBkdTopMNWPyulGJhOHrivG1XCa5Di3rcuO3Xqs/Rxnt+uifLUpafXKBoTSOedCWZYQMrD1xlpgEMpcTM1//LWmDzYHRCOGoJizkU61XqP1oj1vV5rYZnqJamhobC6WW7eOTWzNdWslQqWUEtz/jnH82SOnlrWXak/SgpAJG0gJ4Q5CkE5ZIEAcT/qNA4PSmM9freDKXA35rAWDkDiOm1JYk8pj9IMIXhC1sOLa0Wn/sNxxp1MmvvPcpVXtnVSsuTRTWyIQt5axRsc0ja0EnWBrbFsMQgc6NjGypRbe8d157B3L4fzVCoKICTXs1tEkWAZpCWj9Hme361LM2pgru4gYj+n0BEDNDZFLW7jn1uvwwtn5TUkSV6JmfXR8BP/qgWFMTlVw5nIJRNqWPfTkeWTTnYP9etGetyNNrB8v0a3afdfQ0Fg9ttu6tRrXg2MT/SXAySQtZckYKi9Hu+I6B1riST/Xc1AasxD15LAMCsZ5HMdDxuPEn3OOci1ANm1ix1AaCxVvoH1Or+NmnK9676RizecePY25soeMI1h1ax1rdEzT2ErQCbbGtsUgXelBF97VeGX3A0oIPn7PYXz6oZOo1ANEjCX+TvzXNg34QTRwgOh2XVKOidFCCgsVD2HE4LohmG20bDDe9/YDG77Z6iQqA/QnoqLojof2iG705FQZpnFhU5gKq7WW2Qxs1+67hobG2mC7rFuriRMK/STAyb1CrSFcIjgTY12sTXE96OB1vNz1HJRNJ0Q9CYJICI6pOF6q+QhCFlPed42k8bG7DwPAihLMbse92qRV7aUixvGB2w/iuddmcfFqBWG0PrFGxzSNrQKdYGtsWwxKB+p34V0Lr+x+oGjODz91DhdnaggjBtOg2DeWxc0TIyvuJve6Lo5tIOUY2FHM4kPvnEA+a7dsMDZjs7WWyp+aIjY4tlsXS0ND4/WHtYoT/cS45F7h5QuL8ShXUnF9pfFk0Bh1YFcee0azuDhbQzFrARBJdsox4fkhyrUAu0fT+Be/cptgwgFrnmCuNGlt30uZBsH+XQXc/84JjOaddYs1OqZpbAXoBFtj22IlldXlFt7VUNBWgl7Hs9Ju8vLXxcRH7zq8ZSq5a6n8qSliK8N26WJpaGi8PrHRCtEqNh9/7hL+5geTCEOGfMaCZQlm2UrjyaAxihIxP/7ZR09jseIjk3h93YuQy1j46F2H4+Q6eexrmWAO+p6d9lJRxDA5VcaV2So+ce+RdY05OqZpbDZ0gq2xrbGSymq3hXctKGgrQS9q1koDxHaiSa21AN1WO/f1HjfQ0NDQuNaxlnGi3zWZEoL3vGUfdo9k4njS8KIN7wgfmxjBP/3YLfjCoy/h8lxtVfuc1aDf9+y2lzIsA2nHxOyiuy57KQ2NrQSdYGtse6xVtXYtqcpbAduFJrUetO6tcu4bNW6goaGhcS1jreLEStbkrdARvuUNY9g3msaZS6UtHc+BPvZS6e21l9LQWAl0gq1xTWAtqrUbTUHbCGwHmtR60bo3+9w3etxAQ0ND41rFWsSJ1azJm9kRXs9jWA/0s5eKGttrL6WhMSg63/0aGq9DtPtftmOjvbJfT1CUuX1jWXhBhHLVhxdE2DeW3ZaJaDtFzrYMUCJs14ZyNlw/wsNPnQPjfPk309DQ0NBYVZzQa/LGQe+lNDS2QAebMYY/+ZM/wV/91V+hUqngbW97G37v934P+/fv7/j6hYUF/Ot//a/xxBNPgBCCD37wg/jn//yfI51Ox6/5xje+gf/0n/4TLl68iEOHDuF3f/d3ceeddw70HhqvP2gF6s3FVqF1rwWutXEDDQ0Nja2AlcaJc1N6Td4oLLuXaoTYq/dSGtc4Nr2D/V/+y3/B//gf/wN/8Ad/gC984QtgjOHXf/3X4ft+x9d/8pOfxLlz5/AXf/EX+KM/+iMcP34cv//7vx///VNPPYV/9s/+GX75l38ZX/3qV3HnnXfiN3/zN/Haa6/1/R4ar08oClrKNrBY9eEHERjn8IMIi1VfK1BvABQF7uZDoxjfXdi21zqmyJk9KHIR1xQ5DQ0NjQGxkjhR0WvyhqHXXmqu7Om9lMbrApuaYPu+jz/7sz/DJz/5Sdxzzz248cYb8R/+w3/A1NQUvvWtby15/XPPPYdnnnkG/+7f/TvcdNNNuPPOO/F//B//B772ta/h6tWrAIBPf/rTeO9734tPfOITOHz4MH73d38XN910Ez772c/2/R4ar19ca1Rljc2BpshpaGhobB3k9Zq8oei4l/IjjO8p4NfuO6r3UhrXPDaVIv7SSy+hVqu10LcLhQKOHTuGH/7wh7j//vtbXv+jH/0IY2NjOHz4cPyzt7/97SCE4Nlnn8X73/9+/PjHP8a/+Bf/ouX3br/99jhhX+497rvvvvU4VY1thGuJqqyxOdDjBhoaGhpbBwd36zV5o9G+lyrmHdx6426USvWuhQ4NjWsFm9rBnpqaAgDs2bOn5ec7d+6M/y6Jq1evLnmtbdsYGhrClStXUC6XUa/XsXv37q7vt9x7aGgA1w5VWWNzoMcNNDQ0NLYO9Jq8OUjupSb2FECpvr4arw9sage70WgAEAluEo7joFQqdXx9+2vV6z3Pg+u6Xd/P87y+3mM16Dbbc63CMGjLfzVaoa9Pd7wers0br9+Bf2hQPPSDSVyZq6HuhjANggM7c7j/HeM4NtGdIvd6uD4rhb42vbFdrk+neLldjn05XAvncS2cA9B6HqtZkzcT1+J3sV1xLZwDoM9jI7CpCXYqlQIgZrHVnwHA87yOit6pVKqj+JnnechkMnAcJ36/9r9X77fce6wUlBIMD2dX/PvbGYWCVl/vBX19uuNavzbvHM7izlv34cylEso1H4WsjUN7i31X8a/167Ma6GvTG1v5+iwXL7fysQ+Ca+E8roVzAJrnsdo1eTNxrX0X2xnXwjkA+jzWE5uaYCuq9vT0NA4cOBD/fHp6GkeOHFny+t27d+Nv//ZvW37m+z4WFxexc+dODA0NIZPJYHp6uuU109PT2LVrV1/vsVIwxlEu11f8+9sRhkFRKKRRLjcQRXqeph36+nTH6+3ajOYsjOaEeE6ptPw68Xq7PoNAX5veWMn1KRTSG9oB6BYvr5Xv9lo4j2vhHIDu5zHomryZuNa/i+2Ea+EcAH0eK8UgsXJTE+wbb7wRuVwOTz/9dJxgl8tlnDx5Er/yK7+y5PVve9vb8Id/+Ic4d+4cDh48CAB45plnAAC33XYbCCF4y1vegmeeeQa/+Iu/GP/e008/jbe+9a19vcdq8HoVbYgi9ro9936gr0936GvTG/r6dIe+Nr2x1a9Pr2Pb6sfeL66F87gWzgG4Ns7jWjgH4No4j2vhHAB9HuuJTSWt27aNX/mVX8Ef/uEf4u/+7u/w0ksv4bd/+7exe/duvO9970MURZiZmYlnq2+55Ra85S1vwW//9m/j+eefx1NPPYXf+73fw4c//OG4Q/1rv/ZrePjhh/Hnf/7neO211/Dv//2/x6lTp/DAAw/0/R4aGhoaGhoaGhoaGhoaGoNi06fCP/nJT+JjH/sY/uW//Jf4+3//78MwDHzmM5+BZVm4cuUK3vWud+GRRx4BABBC8Cd/8ifYt28fHnjgAfzWb/0W7rrrLvz+7/9+/H7vete78G/+zb/B//yf/xMf+chH8NRTT+FP//RPY1uuft5DQ0NDQ0NDQ0NDQ0NDQ2NQEM453+yDuBYQRQzz87XNPowNhWlSDA9nsbBQ23LUjK0AfX26Q1+b3tDXpzv0temNlVyfkZHshs5gd4uX18p3ey2cx7VwDsC1cR7XwjkA18Z5XAvnAOjzWCkGiZWb3sHW0NDQ0NDQ0NDQ0NDQ0LgWoBNsDQ0NDQ0NDQ0NDQ0NDY01gE6wNTQ0NDQ0NDQ0NDQ0NDTWADrB1tDQ0NDQ0NDQ0NDQ0NBYA+gEW0NDQ0NDQ0NDQ0NDQ0NjDaATbA0NDQ0NDQ0NDQ0NDQ2NNYBOsDU0NDQ0NDQ0NDQ0NDQ01gDaB3uNwDkHY6+/S2kYFFG0fT301hv6+nSHvja9oa9Pd+hr0xuDXh9KCQgh63hEregVL6+V7/ZaOI9r4RyAa+M8roVzAK6N87gWzgHQ57ESDBIrdYKtoaGhoaGhoaGhoaGhobEG0BRxDQ0NDQ0NDQ0NDQ0NDY01gE6wNTQ0NDQ0NDQ0NDQ0NDTWADrB1tDQ0NDQ0NDQ0NDQ0NBYA+gEW0NDQ0NDQ0NDQ0NDQ0NjDaATbA0NDQ0NDQ0NDQ0NDQ2NNYBOsDU0NDQ0NDQ0NDQ0NDQ01gA6wdbQ0NDQ0NDQ0NDQ0NDQWAPoBFtDQ0NDQ0NDQ0NDQ0NDYw2gE2wNDQ0NDQ0NDQ0NDQ0NjTWATrA1NDQ0NDQ0NDQ0NDQ0NNYAOsHW0NDQ0NDQ0NDQ0NDQ0FgD6ARbQ0NDQ0NDQ0NDQ0NDQ2MNoBNsja5gjOGP//iP8e53vxu33norfuM3fgMXLlzo+vpXXnkFv/mbv4nbb78dd955Jz75yU/i8uXLG3jEG4tBr8+LL76IBx54AG9+85txxx134Pd+7/fw/2/v3sNizvc4gL9VWllKZXPL4hxK0tToRj1tCttSdmXPsZaupI2zsbmk7Gr3WZdccqtUkluUyymFzWIdebKk27HCxpYQp9VF6aKkmb7nD9ucpimM/f2my/m8nqeHvn1/v9/n85mZ78x3freamhoFRqw48tampZMnT0JfXx+PHj3iOcqOI299mmvS+qc71kje2jQ2NmLLli2S/i4uLsjLy1NgxIolT33CwsLafN7o6+sjMDCwU8XaWnvjQG5uLubOnQuBQABbW1uEhoaiqamJj/ABcJ9De4+Hvr4+r++XfDwWKSkpcHJygrGxMaZNm4bk5GQeIpfGdR5NTU3Yt28fHBwcIBQK4ebmhps3b/IVvmSbXI//P/74I6ZNmwaBQIAZM2YgPT2d1xz4yqNZTk4ODAwM+AwfAPc5NDU1ISYmBg4ODjAxMYGjoyP++c9/drk8xGIxQkNDYWdnB4FAgJkzZ+LixYtdKoeWXrx4genTpyMgIIDPFKQxQtoRFhbGLC0tWWpqKsvLy2Pz5s1jH374IWtoaJDpW1FRwaytrZmvry+7c+cOu3HjBps7dy6bOnUqe/78eQdEzz956lNWVsbMzc1ZYGAgKywsZDk5OWzatGls0aJFHRA5/+SpTUuPHj1ipqamTE9Pjz18+FBB0SqevPXZtGkTc3FxYaWlpVI/IpFIwZHzT97arFq1illZWbG0tDRWUFDAfH19mbW1NauurlZw5IohT31qa2tlnjMbN25kJiYm7Pbt250q1pbaGwcKCwuZsbExW716Nbt37x47c+YMEwqFLDo6usvk0PrxyM/PZ5aWlszf35+3HPjIIz09nY0ZM4YdPnyYFRUVsUOHDrHRo0ezixcvdqk8oqKi2NixY1l8fDwrLCxkYWFhzNjYmN29e7fT5PC68T89PZ0ZGhqyAwcOsIKCArZhwwY2duxYVlBQwFsOfOTRLDs7m1lYWDA9PT1e4+cjh4iICGZmZsZSUlLYgwcP2JEjR9iYMWNYUlJSl8pjy5YtbPz48Sw1NZUVFRWxiIgIZmBgwG7cuNFlcmhpzZo1TE9Pj61cuZK3+FujCTZpU0NDAxMKhSwuLk7SVlVVxQQCATt16pRM/2PHjjGhUMjq6+slbcXFxUxPT49duXJFITErkrz1+eWXX5ifnx9rbGyUtO3fv58ZGxsrIlyFkrc2zcRiMfv888+Zm5tbt55gv019vLy82Jo1axQVYoeRtzZFRUVMX1+fpaamSvW3s7OjcacNt27dYoaGhuz48eN8hskY42ccWLlyJfv0009ZU1OTpG3Hjh3Mx8eny+TQmq+vL/voo49eO0H8M/jIY+3atczZ2Vmq/4wZM3gdp/jIw8zMjG3evFmqv4eHBwsICOA+AcbP+D9v3jy2ZMkSqbbPPvuMrV69mpOY28JHHo2NjWz9+vXM0NCQOTs78z7B5iMHGxsbFhERIdUWGBjI5syZw03QbeAjjw0bNsgsa2Zmxnbv3s1N0K3w+bkoLS2NWVlZMUdHR4VOsOkQcdKm27dv49mzZ5gwYYKkTV1dHWPGjEFWVpZM/wkTJiAiIgK9evWStCkpvXx6VVdX8x+wgslbH2NjY2zduhUqKioAgLt37+LEiROwtrZWWMyKIm9tmkVFRaGxsRFffPGFIsLsMG9Tnzt37uCvf/2rokLsMPLW5vLly+jbty8++OADqf4XLlyQWkd38bavrWbff/89zMzM4OzszGeYAPgZB37++Wc4OTmhR48ekrbFixcjMjKS2+D/wPdY9vPPP+PcuXNYs2YNVFVVOYu7NT7y0NbWRn5+Pq5evQrGGDIyMnD37l0IBAJecgC4z6OiogLV1dUwMzOTajcwMEBmZia3wf+B6/G/qakJ//73v2XGO0tLyzcaE94WH+9jdXV1yMrKQkxMDFxcXDiPuTU+HouNGzfKjK9KSkq8fg7m47FYuXIlnJycAADPnz/HwYMHUV9fD0tLS26D/wNfn4sqKioQGBiINWvWQFNTk9OYX0dFoVsjXcbjx48BAIMGDZJq19HRkfytJV1dXejq6kq1RUdHo1evXjA3N+cv0A4ib31acnBwwP379zFkyBCEh4fzFmNHeZva5ObmYu/evUhISEBJSQnvMXYkeetTVVWFkpISZGdnIz4+HpWVlRAIBFixYgVGjBihkJgVRd7a3Lt3D0OHDsW5c+cQHR2NkpISjBkzBgEBAd3yC4k/M+6kpqbi2rVrCjlPFuB+HKitrUVZWRn69u2LVatWIS0tDerq6pgxYwbmz58PZWXlTp9Da1u3bsWkSZNkJnhc4yMPV1dX5Obmwt3dHcrKyhCLxfDx8cHHH3/MfQJ/4DoPDQ0NqKqqypz7/p///AcVFRUcRv4/XI//1dXVqKurw8CBA99ofZ01D+DlhOr48eMAIPmXT1znoKSkJPNFR3FxMVJSUjB79uwuk0dLJ0+ehL+/Pxhj8PX1hZGRUZfK4euvv4adnR3s7e2xb98+XmJvD+3BJm2qr68HAJlv1d955x00NDS8dvmDBw/i0KFDWL58ObS0tHiJsSP9mfqEhITg4MGD0NbWhpubG549e8ZbnB1B3trU1dVh+fLlWL58OYYPH66IEDuUvPXJz88HADDGEBwcjO3bt6OhoQFz5sxBeXk5/wErkLy1qa2txYMHDxAREYGlS5ciMjISKioqmDNnDp48eaKQmBXpz4w7+/btg52dnUIuHARwPw7U1tYCADZu3IjBgwdj9+7d8PLywq5duxAWFsZ9AuB3LMvKysKtW7ewaNEizuJtDx95/P7776isrERQUBASExMREBCAffv2ISEhgfP4m3Gdh7KyMpycnBAZGYnc3FyIxWKcOXMGqampaGxs7BQ5vG78f/78uVzr40p3eB/jO4fy8nIsWLAA2traWLhwIQ8ZvMRnHubm5khOToa/vz8iIyMRHx/fZXI4cuQI7t69q5ALeraF9mCTNjUf6v3ixQupw74bGhqgpqbW7nKMMezYsQORkZFYuHAhXF1deY+1I7xtfQBIvgEMDw+Hra0tfvrpJ8yYMYO3WBVN3tqsXbsWI0aM4PUb3s5E3vqYmZkhPT0dmpqakkNjw8PDMXHiRBw/fhze3t6KCVwB5K2NiooKamtrsW3bNske623btsHW1hZJSUnw8vJSTOAK8rbjTnFxMTIyMhAdHc17jM24HgeaT6+xsrLCl19+CeDlobwVFRXYuXMnlixZInXoeGfMoaWkpCQIBAIYGhpyF3A7+MjD19cXTk5OmDt3LoCXj0VVVRU2b96MmTNnSk4R4xIfeaxatQpBQUGYPXs2GGMQCoXw9PTE0aNHOY8f4H78//vf/y5ZX0tv8lnkz+gO72N85lBYWAhvb2+IxWLExsZCXV29S+YxaNAgDBo0CKNHj8aDBw+wZ88ezJkzp9PnMHnyZGzevBl79uxB7969OY/3TdAebNKm5sM0SktLpdpLS0sxYMCANpdpbGzEihUrEBUVhcDAQHz11Vd8h9lh5K1PYWGhzC0OBgwYgH79+nW7Q6LlrU1iYiKuXLkCoVAIoVCIBQsWAACcnJwQFRXFf8AK9javLS0tLanJg5qaGnR1df/vnzsDBw6EioqK1OHgvXr1wtChQ7vlLcze5rkDAOfPn4eWlpZCr/nA9TigqamJd955B3p6elLLjRo1CnV1dbwc0svXWNbU1IQLFy5g+vTpnMfcFq7zqKioQGFhoczhoiYmJnj69CmePn3aJfIAgL59+2Lbtm3IycnB5cuXER8fj8bGRrz//vudIgfg1eN/v3790Lt3b7nHhD+rO7yP8ZVDTk4OZs+eDTU1NRw5cgRDhw7lIfr/4ToPkUiE8+fPy5w6oa+vz9tjxXUOp0+fxrNnz+Dp6Sl5/WdnZ+PUqVMQCoW85NAaTbBJm0aPHo0+ffogIyND0lZdXY1ff/213XOq/f39cebMGWzZsgUeHh4KirRjyFufK1euYPHixVIXuigqKkJlZWW3O1dU3tqcO3cOP/zwA5KTk5GcnIy1a9cCeHkOf3fcqy1vfY4ePQpLS0vU1dVJ2mpra3H//n2MHDlSITEriry1MTc3h0gkwo0bNyRtz58/x8OHDzFs2DCFxKxIbzMuA0B2djYsLCwke4EVgetxQFlZGePGjcP169ellrtz5w7U1dXRr1+/Tp9Ds4KCAlRWVsLKyorzmNvCdR4aGhpQU1PDnTt3pJZrfiz4Oi2Mj8dj1apVSEhIgJqaGrS0tCAWi/Gvf/2Lty+juB7/e/TogXHjxslclC0jI4PXc/u7w/sYHznk5ubCy8sLo0aNQlxcHK9fcvCVh7KyMlavXo3Dhw9LLXf9+nXeHiuuc3BxccHZs2clr/3k5GSMHTsW9vb2CrsOCd2mi7Rr69atzMLCgp0/f17qnnQvXrxgIpGIlZaWSm7LlZiYyPT09FhMTIzMPela3rqrO5GnPpWVlczGxoZ5e3uz3377jWVlZbFPPvmE/e1vf+uW9zKWpzatXb16tVvfposx+epTXFzMzMzM2D/+8Q/222+/sdzcXObh4cEmT57cLe8xL+9zx8PDg02dOpVlZWWx/Px85uvryyZMmMCePHnSgVnw521eW5MmTZK5dUxnjbVZW+PA1atXmYGBAQsNDWUPHjxgKSkpzNTUlIWFhXWZHBhjLCkpiRkaGjKxWMxb3K1xnceWLVuYUChkSUlJrKioiCUlJTGhUMhiYmK6VB7bt29nNjY2LDMzkxUWFjI/Pz9mbW3NKioqOkUObzL+X7p0iRkYGLC9e/eygoICtnHjRiYQCHi/Dzaf72PNnyn5xmUOjY2NbMqUKWzSpEmsqKhI6nMw3+9HXD8W0dHRTCAQsJMnT7J79+6xXbt2MQMDA3b+/Pkuk0NrLi4udB9s0jmIRCK2adMmNn78eGZiYsIWLFggeWN6+PAh09PTY4mJiYwxxjw9PZmenl6bP819uht56sMYY4WFhczb25uZmpoyCwsLFhgYyKqqqjoqfF7JW5uW/h8m2PLW5+bNm8zT05OZmpqycePGMV9fX1ZcXNxR4fNK3trU1NSwb7/9lllaWjJjY2Pm6enJ8vPzOyp83r3Na0sgELD4+PguEWuz9saBtLQ05uzszAwNDdnEiRPZrl27eJ2o8pFDdHQ0s7Ky4i3mtnCdh0gkYnv37mUfffQRMzY2Zo6Ojiw+Pl7qHuVdIY8XL16w4OBgZmVlxcaNG8e++OILdu/evU6Vw5uM/0lJSWzKlCnMyMiIOTs7sytXrvCaA195NFPUBJvLHHJyctr9HGxnZ9dl8mDs5b3j9+3bx6ZMmcLGjh3LPv74Y/bTTz91qRxaU/QEuwdjjClmXzkhhBBCCCGEENJ90TnYhBBCCCGEEEIIB2iCTQghhBBCCCGEcIAm2IQQQgghhBBCCAdogk0IIYQQQgghhHCAJtiEEEIIIYQQQggHaIJNCCGEEEIIIYRwgCbYhBBCCCGEEEIIB2iCTQghhBBCCOlyGGMdHQIhMmiCTQghhBBCSBcRFhYGfX19TtZlb2+PgIAATtalSNXV1fD390d2dnZHh0KIDJpgE0IIIYQQQrqMvLw8nDhxAk1NTR0dCiEyaIJNCCGEEEIIIYRwgCbYhJAOZ29vj9DQUGzcuBFWVlYQCASYP38+7t+/DwAICAiAh4cHEhMT4eDggLFjx+KTTz5BWlpaxwZOCCGkU2KMYf/+/Zg6dSoEAgGmTJmCPXv2gDGGgIAAuLq6IiEhAXZ2dhAKhXB3d8ft27fl2sajR4+gr6+PlJQU+Pj4wNjYGBMnTsTOnTul9qza29tj/fr1cHd3h0AgwNdffw0AePr0KYKCgmBlZQUjIyPMmjUL6enpUttoaGhAcHAwrK2tIRQKERgYiIaGhreqye3bt+Hp6QmhUAg7OzucPHlSpo++vj7Cw8Mxc+ZMCAQChIeHAwDu37+PxYsXw9raGiYmJnB1dUVOTo7ctRCLxYiLi8P06dMhEAgwceJEhISESOXk6uoKV1dXqbgyMjKgr6+PjIwMZGRkwM3NDQDg5uYm05eQjqbS0QEQQggAxMbGwtTUFMHBwaiqqsK6deuwcuVKHD16FABw8+ZNlJaWYvHixejTpw927NgBX19fpKWlQUNDo4OjJ4QQ0pls2rQJBw4cgKenJ6ytrXHjxg2EhIRAJBIBeHmIcWFhIZYuXQoNDQ2EhobCxcUFp0+fho6Ojlzb+u6772Bra4uwsDDk5OQgPDwcdXV1WLFihaRPXFwcPD09sWDBArz77rtoaGiAu7s7ysvL4efnBx0dHSQmJsLLywsxMTGYMGECAGDFihW4dOkS/Pz8MGzYMBw9ehSnTp2Sux4lJSVwcXHB8OHDsXnzZtTW1iIkJARPnjyR6RsVFYVly5ZhxIgRGDJkCAoKCjBr1iwMHz4c33zzDXr27InY2Fi4u7tj7969sLCweONaBAUF4cSJE1iwYAHMzMzw66+/YufOncjLy0NMTAx69Ojx2lwMDQ0RFBSE77//HkFBQbC0tJS7HoTwiSbYhJBOQV1dHREREVBWVgYAFBUVISwsDJWVlQCAmpoaHD9+HO+//z4AoHfv3nBxccHVq1fh4ODQYXETQgjpXKqrqxEbGwsXFxfJxM7KygplZWXIyspC//79UVNTg6ioKJiZmQEABAIBJk+ejNjYWCxfvlyu7RkaGiIkJAQA8MEHH6Curg4HDhzAwoUL0adPHwDA4MGDpdZ77Ngx3L59G8eOHYOxsbFkWVdXV4SEhCAxMRH5+fk4e/YsvvvuO3z++ecAABsbG0yfPh0FBQVyxbh//36IxWJER0dDS0sLADBixAjMmjVLpq+ZmRk8PT0lv3/11VdQVVVFbGysJJ+JEyfCyckJmzZtQkJCwhvV4vHjx0hISMCyZcvg7e0NALC2toaOjg78/f2RlpYGW1vb1+bSp08fjBw5EgAwcuRIyf8J6SzoEHFCSKdgZGQkmVwDwMCBAwEA9fX1AAAtLS3J5LqtvxNCCCEA8Msvv0AkEuHDDz+Uav/mm28QExMDANDV1ZVMrgFAR0cHQqEQWVlZcm9vxowZUr87ODigsbER165dk7QZGBhI9UlPT8d7770HQ0NDiEQiiEQiiMVi2NnZ4ebNm6iqqpJcIdve3l6ynJKS0lt9qZyTkwMTExPJ5BoAjI2NMXjwYJm+rWPNzMyEnZ2dZHINACoqKnB0dMTNmzfx7NkzSfurapGZmQkAcHR0lOrj6OgIZWVlZGRkyJ0XIZ0R7cEmhHQKampqUr8rKb38/q/53K3Wf28+jIyuIEoIIaSlp0+fAoDUZLK1AQMGyLRpa2vj1q1bcm+v9bqat1tVVSVp6927t0yMZWVlMDQ0bHOdZWVlkuU1NTWl/vbee+/JHWNVVRV0dXVl2ttaV+tYq6qq0L9/f5l+/fv3B2MMtbW1krZX1aI5n9bbVFFRgaamJmpqat4wG0I6N5pgE0IIIYSQbkNdXR0AUFFRgb/85S+S9uLiYhQVFaGxsVFy+lFL5eXl0NbWlnt7rdfVfF7zq9bVt29fDB8+XHI4dWu6urqSiXV5ebnUnubmLxDkoampifLycpn2N1mXhoZGm8uWlZVJ1l1aWgrg1bWorq6WLDdkyBBJn+bHo+UXCWKxWGo9dXV1r42TkM6CDhEnhBBCCCHdhkAgQM+ePZGamirVvnfvXixduhTKysq4f/8+7t69K/lbSUkJrl27Jrm4mDzOnz8v9fvZs2ehpqYmObe6LRYWFvj999+hra0NIyMjyc/ly5cRExMDZWVljB8/HgBw5swZqWVb5/Umxo8fj2vXrqGkpETSVlBQgIcPH752WXNzc6SmpkrtqRaLxUhJSYGRkRFUVVUl7a+qRfPF0FJSUqT6pKSkQCwWw9TUFMDLc6wfP34s1aflFcsBSJ1SRkhnQ3uwCSGEEEJIt6GlpQU3Nzfs378fqqqqsLCwwPXr13H48GH4+/sjLy8PjDH4+PjAz88PysrKCA8Ph4aGxlvd8unHH3+EtrY2bG1tkZmZibi4OPj5+ckcat3SzJkzcejQIXh6esLHxweDBg3ClStXsHv3bri4uKBnz54YNmwYPvvsM2zbtg0ikQgGBgY4ceIE7ty5I3eM7u7uSEhIwPz58+Hr6wuxWIxt27ahZ8+er132yy+/RFpaGtzc3ODt7Y2ePXvi0KFDePjwoeSc9jepxciRI+Hs7IzQ0FDU19fD3NwceXl5CA8Ph6WlJWxsbAAAdnZ2uHDhAoKDg2Fvb4/s7GwkJydLbadv374AgIsXL0JDQwOjR4+WuyaE8IUm2IQQQgghpFtZsWIFtLW1ceTIEcTExEBXVxerV6/G7NmzERAQgMGDB2PevHlYv3496uvrYWVlhcjISPTr10/ubS1ZsgSZmZk4evQoBg0ahKCgIMlVv9vTu3dvxMXFYcuWLdi8eTNqamowZMgQLFu2DPPmzZP0+/bbb9G/f38cOnQIVVVVsLGxgY+PD7Zv3y5XjJqamjh8+DDWrVuHgIAAvPvuu/Dy8sLp06dfu+yoUaMQHx+PrVu3IjAwED169IBAIEBsbKzUheLepBbr1q3DsGHDkJiYiN27d0NHRwdubm5YtGiR5Norn376KYqKipCUlIQjR47A3NwcoaGhUusZNWoUnJycEBcXh0uXLuGHH36Qqx6E8KkHY4x1dBCEEEIIIYQoQkBAADIzM3HhwoU/tZ5Hjx5h0qRJCA4OxsyZMzmKrmuiWhDyP7QHmxBCCCGEkD+IxWK8bv9T850sOppIJHptHyUlJcneYUII/2iCTQghhBBCyB88PDwk92xuz5AhQxAbG6ugiNrX3m2+WnJ2dsaGDRsUEA0hBKBDxAkhhBBCCJEoLCzEs2fPXtlHVVUV+vr6CoqofTdu3HhtH01NzTbvgU0I4QdNsAkhhBBCCCGEEA7QCRmEEEIIIYQQQggHaIJNCCGEEEIIIYRwgCbYhBBCCCGEEEIIB2iCTQghhBBCCCGEcIAm2IQQQgghhBBCCAdogk0IIYQQQgghhHCAJtiEEEIIIYQQQggHaIJNCCGEEEIIIYRw4L/dJfk93ijq7AAAAABJRU5ErkJggg==", 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", 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sxPrNu1Fd74PNIiHbbYXNIqG63o/1m3ejorIx00MkIiIiIhrUGFwTDXC6YeDNLVUIKipy3TZYLRJEQYDVIiHXbUVQ0fDmlirohpHpoRIRERERDVoMrokGuH21XtQ0BuCyWyC0218tCAJcdhk1jQHsq/VmaIRERERERIMfg2uiAc4XCEPTDMhy4h9nWRahaQZ8gXAfj4yIiIiIaOhgcE00wLmdFkiSAFXVE96vqjokSYDbaenjkRERERERDR0MrokGuLLiLJTkO+EPqjDa7as2DAP+oIqSfCfKirMyNEIiIiIiosGPwTXRACcKAubMLIfdKqHZp0AJa9ANA0pYQ7NPgd0qYc7Mcva7JiIiIiLqRQyuiQaBiaPzcd0F41Fa6EIorMHjUxAKaygtdOG6C8azzzURERERUS+TMz0AIkqPiaPzMb48D/tqvfAFwnA7LSgrzuKKNRERERFRH2BwTTSIiIKA0SXZmR4GEREREdGQw7RwIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohQxuCYiIiIiIiJKEYNrIiIiIiIiohTJmR4AEVFv0g0D+2q98AXCcDstKCvOgigImR4WEREREQ0yDK6JaNCqqGzEm1uqUNMYgKYZkCQBJflOzJlZjomj8zM9PCIiIiIaRJgWTkSDUkVlI9Zv3o3qeh9sFgnZbitsFgnV9X6s37wbFZWNmR4iEREREQ0iDK6JaNDRDQNvbqlCUFGR67bBapEgCgKsFgm5biuCioY3t1RBN4xMD5WIiIiIBgkG10Q06Oyr9aKmMQCX3QKh3f5qQRDgssuoaQygqsaboRESERER0WDD4JqIBh1fIAxNMyDLif+Jk2URmmbAGwj38ciIiIiIaLBicE1Eg47baYEkCVBVPeH9qqpDkgRkOS19PDIiIiIiGqwYXBPRoFNWnIWSfCf8QRVGu33VhmHAH1RRku9EeUlWhkZIRERERIMNg2siGnREQcCcmeWwWyU0+xQoYQ26YUAJa2j2KbBbJcyZWc5+10RERESUNgyuiWhQmjg6H9ddMB6lhS6Ewho8PgWhsIbSQheuu2A8+1z3Ad0wUFnjwb/2NKCyxsPq7ERERDSoyZkeABFRb5k4Oh/jy/Owr9YLXyAMt9OCsuIsrlj3gYrKRry5pQo1jQFomgFJElCS78ScmeWc2CAiIqJBicE1EQ1qoiBgdEl2pocxpFRUNmL95t0IKipcdgtkhwhV1VFd78f6zbuZOUBERESDEtPCiYgobXTDwJtbqhBUVOS6bbBaJIiCAKtFQq7biqCi4c0tVUwRJyIiokGHwTUREaXNvlovahoDcNktENql3wuCAJddRk1jAPtqvRkaIREREVHvYFo4DRq6YXBvLVGG+QJhaJoB2ZF47laWRQSCKnyBcB+PjIiIiKh3MbimQYHFk4j6B7fTAkkSoKo6rBapw/2qqkOSBLidlgyMjoiIiKj3MC2cBrxo8aTqeh9sFgnZbitsFilWPKmisjHTQyQaMsqKs1CS74Q/qMJot6/aMAz4gypK8p0oK87K0AgpHUKhEBYvXoxZs2ZhypQp+NGPfoTGxq7/ra2ursatt96KqVOn4vTTT8fKlSuhaVrcMS+++CLOPfdcnHjiibjqqqvw+eefd3q+X/7ylxg/fnxaroeIiCgdGFzTgMbiSUT9iygImDOzHHarhGafAiWsQTcMKGENzT4FdquEOTPLuWVjgFu0aBH+/ve/Y/Xq1Vi/fj327NmD+fPnd3p8OBzGjTfeCAB46aWXsGjRIvzud7/D008/HTtmw4YNWLZsGe666y689tprKC0txfXXX58waP/ss8/w1FNPpf/CiIiIUsDgmgY0Fk8i6n8mjs7HdReMR2mhC6GwBo9PQSisobTQxTZcg0BtbS02btyIhQsXYtq0aTjxxBOxYsUKbNu2DTt37kz4mM2bN+PgwYNYtmwZjjvuOMyePRv33HMP1q9fD0VRAABr167F1VdfjUsvvRTHHHMMli5dCofDgVdeeSXuXIFAAAsWLMC0adN6/VqJiIh6gnuuaUBj8SSi/mni6HyML89jkcFBaMeOHQCAmTNnxm4bM2YMiouLsW3bNkyZMqXDY7Zv347JkycjJycndtvMmTPh8/lQUVGB0tJSVFZWYtasWbH7ZVnGtGnTsG3bNtx6662x2x999FEcd9xxOPvss7Fly5beuMSUNDQchs/HCd2eOnToQNyf1DNudxYKCoZlehhEQx6DaxrQWDyJqP8SBQGjS7IzPQxKs9raWuTl5cFms8XdXlRUhJqamoSPqampQUlJSYfjAeDQoUOQZfPXkeHDh3c4ZteuXbG/v/3223jvvffwpz/9CX/9619TvpZ0a2g4jJ/+9F6Ew0qmhzJgrVu3JtNDGJAsFiuWLl3OAJsowxhc04AWLZ5UXe+HRRbjUsOjxZNKC10snkRE1E3V1dU499xzO73/rrvugtVq7XC7zWZDKBRK+JhgMIjs7OwOxwNmcbTW1lYA6HDetuesra3Fgw8+iGXLliEvL6/7F9QNspyeXXKtrX6EwwrsI2ZCtHJiifqGrngQPLgFra1+yHJRpodDNKQxuKYBLVo8af3m3Wj2KXDZZciyCFXV4Q+qLJ5ERNRDxcXF2LRpU6f3v/fee7F90m2FQiE4HI6Ej7Hb7R0eEw2anU4n7HY7ACQ8xuFwwDAM/OQnP8FFF12EM844o0fXczSiKCAvz5WWczU0mNchWrMhOVhbgPpWVpY9bZ9lIkoOg2sa8KLFk6J9rgNBFZIkoLTQxT7XREQ9ZLFYMG7cuE7v3717N5qbm6EoStxKc11dHYqLixM+pqSkBF988UXcbXV1dQDMYD6aDl5XVxf33NFzHjx4EB988AE+/vhjbNy4EQCgqioAYMqUKVi8eDEuvfTSnl8sAF034PEEknpse15vMC3nIUqG1xtEU5M/08MgGpSysx2QpKNnOTG4pkGBxZOIiPrGKaecAl3XsWPHjlgBsr1796K2thbTp09P+Jjp06dj48aN8Pl8cLvdAIAtW7bA5XJhwoQJsFqtGDNmDLZu3Ro7p6qq2L59O6666ioUFxfj7bffjjvn22+/jeXLl2Pjxo0oKChI6ZpUVU/p8VGalp7zECVD0/S0fZaJKDkMrmnQYPEkIqLeV1xcjDlz5mDhwoWxdlkPPfQQZsyYgZNPPhmAmd7d0tKCnJwcWK1WzJ49GytXrsTdd9+Ne++9F9XV1VixYgVuuOGG2Or3DTfcgEcffRTl5eU44YQTsG7dOgSDQVxxxRWQZRnl5eVx44gG1O1vJyIiyhT2uSYiIqIeWbJkCWbNmoV58+bhxhtvxNixY7Fq1arY/Tt37sTpp58e63tts9nwq1/9Crqu48orr8TixYtx1VVX4fbbb4895sorr8T8+fOxcuVKfPvb38aBAwfw3HPPIT+fW3uIiGhgEAzDMDI9iMFA03Q0Ng7efS6yLCIvz4WmJv+QSTniNQ/+ax5q1wsMvWvuj9ebn+/q1r4t6hvp/P6uqtqLxYsfgHP0+SxoRn1Ga21EoPJtPPTQoygvH5Pp4RANSt397ua3OxEREREREVGKGFwTERERERERpYjBNREREREREVGKWC2ciIiIiIiGpLq6WrS2pqfX/VDkcDhRVFSc6WH0GwyuiYiIiIhoyPF6Pbj//nvA+s7JE0URTzyxBllZbIcLMLgmIiIiIqIhKCsrGz/72Yp+u3J96NABrFu3BrfccjuGDx+Z6eEk5HA4GVi3weCaqBO6YeCr/c04UOuB0yahrDgLoiBkelhERERElCYDIaV5+PCRbLM2QGQ8uNZ1HU899RReeeUVeL1eTJ8+HQ8++CBGjRqV8PimpiY88sgjeP/99yEIAubMmYMf//jHcDgcsWPeeustrF69GtXV1Rg7dizuu+8+zJo1K3b/v//9byxbtgyfffYZbDYbzj//fCxYsABZWVm9fr00MFRUNuKtrftQ29QKJaxBkgSU5DsxZ2Y5Jo5m71IiIiIiIoqX8Wrha9aswW9/+1ssWbIEL730EnRdx0033QRFURIeP3/+fFRVVeH555/Hk08+iffeew+LFi2K3b9lyxYsWLAA3/3ud7FhwwbMmjULt9xyC77++msAwOHDh3H99ddj5MiReO2117BmzRrs2LEDP/nJT/ricmkAqKhsxPrNu7G/zge7TUJOlhU2i4Tqej/Wb96NisrGTA+RiIiIiIj6mYwG14qi4Nlnn8X8+fNx1llnYcKECXjiiSdQU1ODt99+u8PxO3fuxEcffYSf//znmDx5MmbNmoWHH34Yr7/+OmprawEAzzzzDGbPno1rr70W48aNw3333YfJkydj/fr1AIADBw7g9NNPx8MPP4wxY8Zg6tSpuPLKK/GPf/yjT6+d+ifdMPDmlioEFRW5kaBaFARYLRJy3VYEFQ1vbqmCzsIXRERERETURkaD6127dsHv98elbGdnZ2PSpEnYtm1bh+O3b9+OwsJCjBs3LnbbjBkzIAgCduzYAV3X8fHHH8edDwBOPfXU2PlOOukkrFixArJsZsR//fXXeP3113Haaaf1xiXSALOv1ouaxgBcdguEdvurBUGAyy6jpjGAfbXeDI2QiIiIiIj6o4zuua6pqQEADB8+PO72oqKi2H1t1dbWdjjWarUiNzcXhw4dgsfjQSAQQElJSbfOd8EFF6CyshIjR47EU089lerl0CDgC4ShaQZkR+J5J1kWEQiq8AXCfTwyIiIiIiLqzzIaXLe2tgIwA+S2bDYbWlpaEh7f/tjo8aFQCMFgsNPzhUKhDo9bvnw5Wltb8fjjj+Paa6/F66+/DpfLlfT1yHLGt7D3GkkS4/4crHKybJAlAZqmQxbNHw8BAiCYaeCaqkOWBPO4Qfh+D5X3Oao/XK9uGKiq8cIbCCPLaUF5Se9Wpe8P19yXhtr1EhERUeZkNLi22+0AzL3X0f8HgFAoFFf9u+3xiQqdhUIhOJ1O2Gy22Pna35/ofCeccAIA4KmnnsKZZ56J//3f/8XcuXOTuhZRFJCXl3xgPlBkZ3d8HQeTnBwnRpXsReUhDxw288dDkgQAAgzDQCCkYfTwbJw8oQSiOHjbcg3297m9TF3vp1/W4w/vfokDdT6omg5ZEjGyyI0rzjkWJx1b2KvPzfeYiIiIKL0yGlxHU7zr6upQVlYWu72urg7jx4/vcHxJSQneeeeduNsURUFzczOKioqQm5sLp9OJurq6uGPq6upQXGz2sNuzZw/27duHs846K3Z/cXExcnNzY0XRkqHrBjye/tmAPh0kSUR2tgMeTys0Tc/0cHrVhdNH4blNFTjcHES2ywpRFBBWNfhbVditEi6cPgotLYPzvR5K7zOQ2ev9fG8jnttUgaCiweWQ4bBboKo69h5owerf78T1F0/EpDHpb/vG9zjzsrMdXEknIiIahDIaXE+YMAFutxtbt26NBdcejweff/45rr766g7HT58+HcuXL0dVVRXKy8sBAB999BEA4JRTToEgCJg6dSo++ugj/Od//mfscVu3bsW0adMAAB988AGWLVuGv//978jOzgYA7Nu3D01NTXGF0pKhqv3jF7fepGn6oL/O40bl4toLxnfocz2y0IU5M8tx3KjcQf8aDIX3ua2+vl7dMPDHf+xFq6Ii122LFc+zyBJy3CKafQr++I+9OKY0p9dSxPkeExEREaVXRoNrq9WKq6++GsuXL0d+fj5GjhyJxx9/HCUlJTj//POhaRoaGxuRlZUFu92Ok046CVOnTsUPf/hDLFq0CIFAAA8++CDmzp0bW5m+/vrrccstt2DSpEk444wz8Oqrr6KiogKPPvooAOCSSy7BunXrsGDBAtx7771oaWnBI488ghNPPBFnn312Jl8O6kcmjs7H5HEFaPKrOFDrgdMmoay4d/fC0tDRk6r0o0uyMzRKIiIiIuqJjOelzZ8/H1dccQUWLlyI733ve5AkCb/+9a9hsVhw6NAhnH766di0aRMA85fOp556CqWlpbjuuutw991344wzzsCiRYti5zv99NOxdOlS/O53v8Nll12GLVu2YO3atbFV6dzc3FjP6+9973u44447MGnSJPz617+GJEl9fv3Uf4mCgGNG5eLEcQUYXZLNwJrSJlaVvpOieLIsQtMMVqUnIiIiGkAyunINAJIkYcGCBViwYEGH+0pLS7F79+642woKCrBq1aouzzl37twuC5ONGTMG//3f/53UeImIUuV2WiBJAlRVh9XScVJPVXVIkgC305KB0RERERFRMjK+ck1ENNSUFWehJN8Jf1CFYRhx9xmGAX9QRUm+E2XFWRkaIRERERH1FINrIqI+JgoC5swsh90qodmnQAlr0A0DSlhDs0+B3SphzsxybkUgIiIiGkAYXBMRZcDE0fm47oLxKC10IRTW4PEpCIU1lBa6cN0F4zFxdPrbcBERERFR70lqz/VTTz2F//zP/4xV6G6ruroazz77LB588MGUB0dENJhNHJ2P8eV52FfrhS8QhttpYVV6IiIiogEqqZXrp59+GrW1tQnv+/TTT/HKK6+kNCgi6j7dMFBZ48G/9jSgssYDvd0eXurfREHA6JJsHD+WVemJiIiIBrJur1x/97vfxaeffgrALLjzne98p9NjTzjhhNRHRkRHVVHZiDe3VKGmMQBNMyBJAkrynZgzs5xpxQnohhG3Sjx2ZE6mh0REREREg0S3g+tHHnkEf/7zn2EYBp5++ml8+9vfRklJSdwxoigiOzsb559/ftoHSkTxKiobsX7zbgQVFS67BbJDhKrqqK73Y/3m3dy3206iiYgRBS5894IJKBvmzPTwiIiIiGiA63Zwfcwxx2DevHkAAEEQOt1zTUS9TzcMvLmlCkFFRa7bBiGSSmy1SLDIIpp9Ct7cUoXx5XlMM0bnExH763x4+g+f4roLxuO4UbmZHiYRERERDWBJ7bmeN28eA2uiDNpX60VNYwAuuyUWWEcJggCXXUZNYwD7ar0ZGmH/0X4iwmqRIAoCrBYJuVlWtIZUvPFBJfeqExEREVFKkqoWPmHChA6/0LdXUVGR1ICI6Oh8gTA0zYDsSDw/JssiAkEVvkC4j0fW/xxtIiLLYcGhBj/21XoxuiQ7Q6MkosFED3kyPQQaQvh5I+o/kgqu77jjjg6/pPr9fnz88cfYt28f7r333rQMjogSczstkCQBqqrDapE63K+qOiRJgNtpycDo+pejTURYZBGqZnAigojSJnhoS6aHQNSvNDQchs/HbLqeOnToQNyf1DNudxYKCob16XMmFVzfeeednd734x//GP/617/w7W9/O+lBEVHXyoqzUJLvRHW9HxZZjJvsMgwD/qCK0kIXyoqzMjjK/uFoExFhVYfMiQgiSiP78JkQbcyEob6hhzz9ekKnoeEwfvrTexEOK5keyoC1bt2aTA9hQLJYrFi6dHmfBthJBdddueyyy3D33XfjoYceSvepiShCFATMmVmO9Zt3o9mnwGWXIctmkS5/UIXdKmHOzHIWM8PRJyK8rWGUDuNEBBGlj2jLhuRgtwYiAPD5vAiHFdhHzIRo5aQT9Q1d8SB4cAt8Pu/ADq737dsHVVXTfVoiamfi6Hxcd8H4WHupQFCFJAkoLXSxz3UbXU1EBIIqXA4LLvnGaE5EEBER9SLRykknGvySCq6feuqpDrfpuo6amhps2rQJZ599dsoDI6Kjmzg6H+PL87Cv1gtfIAy304Ky4iwGiu10NhExqsgd63Otqnqmh0mUklAohN27d0NRFBiR6ve6rqO1tRXbt29nPRQiIqJelrbgGgDcbjdmz56N+++/P6VBEVH3iYLAKtfdkGgiYuzIHBTku9HU5M/08IhSsnXrVtx1111oaWlJeL/L5WJwTURE1MuSCq537dqV7nEQEfW69hMRXOGnweKJJ55AXl4elixZgj/+8Y8QRRGXX3453n//ffzud7/DM888k+khEhERDXop7bn2eDz45JNP4PV6kZ+fjxNOOAFutztdYyMiIqJu2L17Nx555BGcd9558Hq9eOmll3DmmWfizDPPRDgcxi9/+UusW7cu08MkIiIa1JIOrtetW4c1a9YgGAzGbrNarbj11ltxxx13pGVwREREmaYbRr+va6DrOoqLiwEA5eXl+PLLL2P3XXDBBbjvvvsyNTQiIqIhI6ng+tVXX8WKFStwxRVX4NJLL8WwYcNQX1+P119/HU899RRGjBiByy67LN1jJaI+oOsG9h7yoMUb6reBBFFfqahsjBXC0zQDkiSgJN/Z7yryl5WVYffu3Zg2bRrGjBmD1tZW7NmzB2PHjoWqqvD7WVeAiIiotyUVXD///PP43ve+F9fLeuzYsTj11FNht9vxwgsvMLgmGoA+39uIP2/7FPtrPFD7cSBB1BcqKhuxfvNuBBUVLrsFssNs4VZd78f6zbtx3QXj+83Pxbe+9S0sX74chmHg6quvxvHHH48lS5bgmmuuwdq1a3HMMcdkeohERESDnpjMg6qqqjB79uyE95177rnYs2dPSoMior5XUdmI5zZVoPKgBzarhGy3FTaLFAskKiobMz1Eoj6jGwbe3FKFoKIi122D1SJBFARYLRJy3VYEFQ1vbqmCHml5lWk33XQTvvvd7+LTTz8FADz00EOoqKjA7bffjj179uDHP/5xhkdIREQ0+CW1cl1cXIyDBw8mvK+6uppFzYgGmCOBhIZhuXZougHDAKwWCRZZRLNPwZtbqjC+PI8p4jQk7Kv1oqYxAJfdAqHdZ14QBLjsMmoaA9hX6+0XrfBEUYzbV33CCSfgnXfeiaWG83uZiIio9yW1cn3OOefgySefxGeffRZ3+6efforVq1fjnHPOScvgiAYz3TBQWePBv/Y0oLLGk9EVsFgg4ZCPGkgQDQW+QBiaZkCWE39NyrIITTPgC4T7eGSJXXvttfj666/jbnO73TjxxBNRXV2Nb33rWxkaGRER0dCR1Mr1nXfeiQ8++ADf+c53MHLkSAwbNgyHDx/GgQMHMG7cOPzoRz9K9ziJBpX+ViSpO4FEIKj2m0CCjhgIlawHIrfTAkkSoKo6rBapw/2qqkOSBLidlgyMzrR9+3YYkUm5jz76CNu2bUNjY8ftG3/961+xf//+vh4eERHRkJNUcO12u/GHP/wBr776KrZt24aWlhaccMIJuOGGG3D55ZfDbrene5xEg0Z/LJIUF0jI/TOQoI762yTNYFJWnIWSfCeq6/2wyGJcRodhGPAHVZQWulBWnJWxMb7yyit4/fXXIQgCBEHA4sWLOxwTDb4vueSSvh4eERHRkJN0n2ubzYarrroKV1xxBTweD3JycmCx8Bdvoq60L5IU/YU903ubo4HEgXo/HLb4fxb6SyBB8frjJM1gIgoC5swsx/rNu9HsU+Cyy5Bl8zX2B1XYrRLmzCzPaJbAwoUL8e1vfxuGYeC6667Dgw8+2KEquCiKyM7OxrHHHpuhURIREQ0dSQfX77//PtasWYPPPvsMhmFAkiSccsopuOuuuzB16tR0jpFo0OivRZKigcQLm3ejwROC0yZBkvpXIEFH9NdJmsFm4uh8XHfB+Fh2QCCoQpIElBa6+kV2QFZWFmbMmAEAeOGFFzB58mS4XK6MjomIiGgoSyq43rx5M+6++25MmDAB8+bNQ0FBAerr6/H222/j2muvxfPPP49p06ale6xEA15sb7Oj/+1tnjg6H9dfPBF/3rY/0ue6fwUSdER/naQZjCaOzsf48rx+ua9948aNcX/vrItH1Ny5c3tvMERERJRccP3000/jggsuwMqVK+NunzdvHu6880784he/wO9+97t0jI9oUOnvRZImjcnHrJNL8cmuGrR4Q/0qkKAj+vMkzWAkCkK/nKT4yU9+0u1jBUFgcE1EGaWHPJkeAg0hmfq8JRVcV1VV4cc//nHC+6688krceeedKQ2KaLAaCEWSRFHAmOHZUAv1PntOVrzumf4+SUN94y9/+Uumh0BE1G3BQ1syPQSiXpdUcD1u3Dj885//xOmnn97hvr1796K0tDTlgRENRgOhSFJfY8XrnhsIkzTU+0aOHJnwdq/Xi7q6OowaNQqSJEGSOk7AEBH1NfvwmRBt/S8LiAYnPeTJyIROUsH1okWLcNttt8XSzIqKitDc3Ix33nkHq1atwqJFi+L2fo0YMSJtAyYa6Pp7kaS+1JcVrxOtjg9UnKShRLZu3Yrly5fjX//6FwRBwCuvvIJnnnkGJSUlPUohJyLqDaItG5Jj6PyOQ0NTUsH1lVdeCQBYuXIlnnzyydjt0X6aCxYsiDu+oqIi2fERDUr9uUhSqrqb4t2XFa87Wx2/9LQxOC1vYFZXnjg6H9dcMB4b3v8a9c1BGIYBqywNyUkaAj788EPcfPPNmDJlCu69914sX74cADBhwgSsWrUKxcXFuP766zM8SiIiosEtqeB66dKlHSrUElHP9NciSanoSYp3X1W87mp1/LlNFXBn2VE2zJn0+TOlorIRb22pQpM3BEM3AFFAjtuKi08tY2A9BK1cuRLnnnsunnzySaiqiscffxwAcNtttyEQCOCVV15hcE1ERNTLkgquL7/88nSPg4gGuJ6mePdFxeujrY63+BT84d0vcfd/npj0c2RC+9fa7TRf6wZPCC+8/UVa0+lpYKioqMAdd9wBAB0mq0477TSsX78+E8MiIiIaUpIKrgGgsbERzz77LD766CN4PB7k5eVh2rRp+K//+i8UFBSkc4xE1M8lk+LdFxWvj7o67pBxoM6HqhovRhW6k36evtSX6fQ0cGRlZaG+vj7hfYcOHUJW1sCtMUBERDRQJF4yOoqamhpcdtllWL9+PWw2GyZNmgRZlvHcc89h7ty5qK2tTfc4ifod3TBQWePBv/Y0oLLGAz1Sc2AojqUnKd5R0YrX/qAaq9cQFa14XZLvjBUeS+YaY6vjcuer46qmwzuA+kEn81rT4HfuuefiiSeewD//+c/YbYIgoKamBmvXrsVZZ52VucERERENEUmtXD/++OOQZRmbNm3CqFGjYrfv378fN9xwA5544gk89thjaRskUX/Tn9pH9YexJJPi3ZOK18leY3dWx2VJRNYA6gfdF+n0NPD86Ec/wqeffoorr7wSw4YNAwDcc889qKmpwfDhw3HPPfdkeIRERESDX1Ir13//+98xf/78uMAaAEaNGoU77rgD77//floGR9QfRfe7Vtf7YLNIyHZbYbNIsb3FFZWNQ24sbYPYRDpL8Y62JSstdCEU1uDxKQiFNZQWumL7hlO5xqOujreqGFnkRnnJwEmZTfa1psEtJycHr7zyChYvXozp06fjG9/4BsaPH48FCxbgtddeQ34+9+ATERH1tqRWrjVNQ15eXsL78vPz4fP5UhoUDU3dbeGUSf1pv2t/Gks0iK2u98Mii3HpytEU79JCV8Le0l21JUv1Go+2Ou6wSrjinGPN50Lm0vp7IpXXmgY3q9WKK6+8MtYuk4iIiPpWUsH1+PHj8ac//QlnnHFGh/tef/11HHfccSkPjIaW/pDa3B191T5qoI2lJynenT0+0RjTcY3R1fHo5ysQVCFJAkoLXbj0tDE46dhCNDX5O722/jbpk+prTYPHxo0be3T83Llz0/bcoVAIjz32GP785z8jGAzinHPOwQMPPNDlCnl1dTWWLFmCbdu2wel04oorrsCdd94JSTqyZePFF1/Es88+i/r6ehx//PFYuHAhJk2aFLvf5/Ph8ccfx+bNmxEOhzF9+nQ88MADHTLpiIiIMiGp4Pr222/HjTfeiJaWFlx88cUoLCxEfX093nzzTfz973/HqlWr0j1OGsR62sIpkzK937VtoFfTFICqGXD1k723XQWxyU6SpOv17mx1PNE+7Lb666RPb7zWNPD85Cc/ift7dAKq7RaItpNS6QyuFy1ahO3bt2P16tWwWq146KGHMH/+fPzP//xPwuPD4TBuvPFGjB49Gi+99BL27duHBx54AKIoYv78+QCADRs2YNmyZViyZAkmTZqEdevW4frrr8dbb70VC9rvvPNOHDp0CE8//TRcLheWLFmCH/zgB/jjH/8IUUxqpxsREVHaJBVcn3baaXjsscewfPnyuP3Vw4YNw9KlS3HeeeelbYA0uPWn1ObucDstEEWgNahCFAWIogBrm9Tc3tzv2j7QM2AgEFQhSwKynNYOx2di721XKd7JSGe7rs5WxzvT3yd90v1a08Dzl7/8Jfb/FRUVWLBgAW6//XZcdNFFKCoqQlNTE959912sXr0aP/vZz9L2vLW1tdi4cSPWrl2LadOmAQBWrFiBCy+8EDt37sSUKVM6PGbz5s04ePAgXn75ZeTk5OC4445DQ0MDli1bhttuuw1WqxVr167F1VdfjUsvvRQAsHTpUsyePRuvvPIKbr31VmzduhUffvghXn/9dYwfPx4AsHjxYtx8882orKzE2LFj03aNREREyUgquP7www9x4YUX4j/+4z+wZ88etLS0ICcnB2PHju2QuknUlf6U2twd/qAKJawjEFIgCAIEABZZRLbLCrtV6rX9rokCvXBYg79VRZM3BFkU4LAfCTAzufe2p0FsVzK1v3igTPqk87WmgWfkyJGx/7/zzjtx++234+abb47dVlxcjO9973tQFAWPP/44zjzzzLQ8744dOwAAM2fOjN02ZswYFBcXY9u2bQmD6+3bt2Py5MnIycmJ3TZz5kz4fD5UVFSgtLQUlZWVmDVrVux+WZYxbdo0bNu2Dbfeeiv+/ve/47jjjosF1gBwzDHH4K9//WtarouIiChVSeVQ3XnnnXj77bchCALGjRuHqVOnYty4cQysB4G+7pfcnT7Emmb0i7ZCFZWN+M3m3TAMQBTM4E4AoIQ1NLQEcbgl2Cv7XdsHelaLBFEQYLPKKMixAQAaPCEoYQ26YUAJa2j2KYNi7210f7HdKqHZp/TZNbKXNA00X3/9ddze5LbGjh2L6urqtD1XbW0t8vLyYLPZ4m4vKipCTU1NwsfU1NSgpKSkw/EAcOjQodjjhg8f3uk59+7di/Lycvz2t7/FnDlz8M1vfhN33303amtr03JdREREqUpq5To7Oxt2uz3dY6EM62p/6QnHDOuV50xn2m9vahvgDsu1I6RoaPKFoGoGDONIoH3t+celPVW4q0DPYbMgL8uAx6/AH1QhAINu720m9hdnem89UU+NHj0af/rTn3Daaad1uO/3v/99jwqNVldX49xzz+30/rvuugtWa8etKDabDaFQKOFjgsEgsrOzOxwPmMXRWltbAaDDedue0+fz4d///jeampqwePFiAMDy5ctx7bXX4o9//GOHYL8nOpvg7SlJ4r5vyhxJEtP2WU4n/lxQJvX1z0VSwfWtt96KRx55BHv37sWECRPgdDo7HDN9+vSUB0d952j7S2+QRJyW50rLc7UtyuVyyAOirVDbADekaGjxK9A0AzAMCKIAUQREUYDTkf5JgKMFei6HBaqq41uzylGc7xyUe2/7en/xQJn0IYq64447cNddd6GyshJnn3028vLycPjwYbz99tv46quv8Mwzz3T7XMXFxdi0aVOn97/33ntQFKXD7aFQCA6HI+Fj7HZ7h8dEg2an0xmbsE90TPScsiwjFArh6aefjqWXP/XUU/jmN7+Jd999FxdddFE3rzCeKArIS9P3W0MDFx4oc7Ky7Gn7LKcTfy4ok/r65yKp4Pqhhx4CADzxxBMA0CEgEgQBFRUVaRge9YXu7C9944NKzDq5NOXnSrQ6nuWwQBTQ47ZCfdkiKRrgaqKORm8IugFIghDJDwdUTYe/NYzP9zYedQ9sT8fdnUBPlkWMK80Z1Ptv+3J/MXtJ00Bz/vnn4+mnn8aaNWuwcuVKGIYBURQxZcoUPP/887HCY91hsVgwbty4Tu/fvXs3mpuboShK3EpzXV0diouLEz6mpKQEX3zxRdxtdXV1AMxgPpoOXldXF/fcbc9ZUlKC4uLiuH3bw4YNQ25ubkpp77puwOMJJP34trzeYFrOQ5QMrzfYZWvJTOHPBWVSun4usrMd3crCSCq4fuGFF5J5GPVT3dlfeqjBjz0HWlDgTn6lrrPV8SafAlEA8rJs8AaUbqX99nWLJLfTAkkU0OxToBuALLZ5nQRAEgVouoEdX9Tjwi72AHc27otmlsNllxMG3Az0+h57SdNAdM455+Ccc85BKBRCS0sLcnNzE6Zvb9y4EWeffXZckNoTp5xyCnRdx44dO2IFyPbu3Yva2tpOs9amT5+OjRs3wufzwe12AwC2bNkCl8uFCRMmwGq1YsyYMdi6dWvsnKqqYvv27bjqqqti59iwYQPq6upi+7Xr6urQ1NSE8vLypK4lSlX1lB4fpWnpOQ9RMjRNT9tnOZ34c0GZ1Nc/F0kF1zNmzEj3OCiDuru/1ONXkg6uu7M67rRJuOaCExBo7XpFNxMtksqKs5CbZUWTNwRJjB+TYRjQDSNyHaFOK5t3Nu6qQ16s+sNnsFvNQmXtJwoY6GUGe0nTQGWz2WLBZ3uapuH+++/HH/7wh6SD6+LiYsyZMwcLFy7E0qVL4XA48NBDD2HGjBk4+eSTAZjp3dFOIlarFbNnz8bKlStx9913495770V1dTVWrFiBG264ITYBcMMNN+DRRx9FeXk5TjjhBKxbtw7BYBBXXHEFAOCiiy7CunXrcNddd8V6ZC9duhRjxozBWWedldS1EBERpVO3g+uNGzf26MRz587t4VAoU7qVdiwJyHZ1XAHpru6sjtc2tUIUgOPHFnR6nky1SBIFAVOPLUTlIa9ZQd0ABABGZEyiICDHbYWi6AmLXHU2bl03EAyr0HRAEICiPAc0zegwUcBALzPYS5oGIyMNXSCWLFmCpUuXYt68eQCAM844AwsXLozdv3PnTlx77bV44YUXcOqpp8Jms+FXv/oVFi9ejCuvvBI5OTm46qqrcPvtt8cec+WVV8Lr9WLlypVobm7G8ccfj+eeew75+ea/b1arFc8//zwee+wxXHfddTAMA6eddhp+8YtfJFyhJyIi6mvdDq5/8pOfxP09Ghy0/ZJuGzQxuB44upN2XFbkxtiROWhpSW5fWrqqL3cWpBuGAUXVIQlAVY0X7+7Yj2NKc9MaCE0ak4/N2/YjFNagaQZ0mAG2VZaQ7bKaqeGSkbDIVWfjbvErMCBAlgBNM6BqBmydTBQw0MsM9pIm6sjpdOKRRx7BI488kvD+U089Fbt37467rby8HM8++2yX573xxhtx4403dnp/YWEhfvGLX/R8wH1MVzyZHgINIfy8EfUf3Q6u//KXv8T+v6KiAgsWLMDtt9+Oiy66CEVFRWhqasK7776L1atX42c/+1mvDJZ6R3fSji/5xmiIYvJBXCrVl9sWAKttDEBVdbjaVOVuDZkp62YPZPO2l979Gi67jFFF7rSt7JYVZ2FUkRv763xwZcnQDbPKrDVS3r/JG8KwHDs8fgWVNZ64wDfR5IIS1hBWdbMwGsxgXY9cQPteytHgjoEeEVH/5XZnwWKxInhwS6aHQkOMxWKF283aK0SZ1u3geuTIkbH/v/POO3H77bfj5ptvjt1WXFyM733ve1AUBY8//jjOPPPM9I6UetXR0o4njUktOE22KFf7AmAGgEBIhSyLyHJa0RpS0egJQtPNftOxc+oGAsEwqg5507YPu+0kRCCkxSYhwqqOFr+CsKrjcEsQz23a1WHfdKLJhWggDQEwImnmbScw2EuZiGhgKSgYhqVLl8Pn82Z6KAPOoUMHsG7dGtxyy+0YPnzk0R9AcdzuLBQUDMv0MIiGvKQKmn399deYNGlSwvvGjh2bUksMypzeTDvuaVEu3TDw3icH8Kd/VCKs6sh2WWMFwPyt4VhhMW8gDE0390BHCQJgkUSougFV1xFU1LTtw040CaEbBsKqDoskwu2wxK6r7b7p8eV5HSYXooG0oRvQYcAqS7FVcIC9lImIBqKCgmEMclIwfPhIlJePyfQwiIiSklRwPXr0aPzpT3/Caaed1uG+3//+9zjuuONSHhhlRm+mHXe3KFdFZSPe+LASX1a3QNMMCAKg6QZyXFbYbTIKsm2obwmiwROEYZitprU2wXW0mrckCFA1A1my1CG9OtXriE5CeP0KXv/HXhxuCR61wFqiyQVJFKC0KRjXtpZBb7TY6sve4EREREREQ0lSwfUdd9yBu+66C5WVlTj77LORl5eHw4cP4+2338ZXX32FZ555Jt3jpEHiaKvj0XZV/tYwdAOQJAECzAC0wRNEQbYdDrsFeZqBFr8C3TBi+6wBQJaEI8FipJy3KABh1UhrenV0EqKyxhMJljuvgh4N7BNNLtisEgwAlkigrRtGr7XYiqXYNwQQUjWIgoDCXDsuO2McJrPaOBERERFRSpIKrs8//3w8/fTTWLNmDVauXAnDMCCKIqZMmYLnn38e06ZNS/c4aRDpbHW8bbsqp11Gq6JBgJnmLQsCVN0MqO02GS6nBSFVg6EDsizA16pCFBAfiBrR86LX0qt7WgW9/eSC02FB5cEWvP/ZITT7QoBhPibdLbaikxa+QBiqpkPV9Nj4V/3hM1z2zTG48NTytDwXEREREdFQlFRwDQDnnHMOzjnnHIRCIbS0tCA3Nzdhn8mNGzfi7LPPRk5OTkoDpcGvbbsqA0f6SEfDZUkQEFZ1KGENAGCTJeS4rTjcEoRFFhFWo8G4+QjNMGCRBCiqhtJCd1rTq6OSqYIenVyoqGzEa+99jZrGAFTNvNLcLCvOPHkEzjx5ZNpWrKOTFr5AGKGwCgMCJFE0C6np5n7xDf+3F2VFbpx4bGFanpOI+tb999+P22+/HaNGjepw3549e7Bs2TKsXbsWkiThhRdewJgx3NNKRESUbomX23rAZrOhqKgoYWCtaRruv/9+FjijbomtAssirLIIiyxCN4wjvdQjsaammWnTJQVOfPuMsXDYZEiimT6u6QZ03UBY0yHAgCyKsFvltKZXtxWtgu4PqnE934Ej+6ZL8p0Jq6Cv37wb1fU+2CzmJIHLLqPZp2DzR/uxu6opbWPcV+tFTUMAqqabPbVFAYJwpDq5LAlQNR2vvr8HertrIKL+6+DBg7H/Nm7ciC+++CLutuh/77//Pj744IPY42bMmAGn05nBkRMREQ1OSa9cd1f7gIOoM+1XgbNdVrPNlmGYs0DGkYDV5bDE0qaj+5j31/li1bslUYDDlt4+14n0tAo6EJ/+frQiaOmYEPAFwgipGlRNN1es2xEi/9W3BFFV40VBvjvl5ySi3rd48WK8//77sb/Pmzcv4XGGYSQsQEpERETp1evBNVF3te+F7bDJyM+2wxPpIR0NmstLsuIC5vbVu72tYbgdFmS7rH1SDbu7VdCj2qa/H60IWjqqm7udlvgib+0YkeeFbsDLntpEA8bDDz+MDz74AIZh4Kc//Sl+8IMfoKysLO4YURSRnZ2NU089NUOjJCIiGjoYXFO/kWgV2GaVkCtY4Q2EIcsiLv3GaJw5peN+5N5sIdYdPekR3tMiaKkqK85CYa4dvkAYhm5AEI+MyTAM6IYBWRJhsYjI6ic9tdkyjOjoiouLcdlll8X+ftppp6G4uDiDIyIiIhraGFxTv9LZKnD71er+qLsBfjJF0FId12VnjMOqP3yGsKpDFo4Ui9MNA6IgQJZEDC9wobwk8z21Yy3DGgPQNAOSJKAk39nv33+iTFqyZAmWLVuG8847L9NDISIiGrIYXFO/05NV4IGoffp729Tw6J7y0kJXWqubTx6dj8u+OQYb/m8vVE2PVVWXJRGyJMId2cOe9p7aPQyQo4XegooKl90C2WHuX6+u92P95t247oLxDLCJEigpKYHP58v0MIiIiIY0BtfUL/VlmndfpyAnUwQtHS48tRxlRW68+v4e1LcEAd2AxWKuWPdGT+2eBsh9XeiNaDD5zne+g0cffRQ7d+7E+PHj4XK5Ohwzd+7cvh8YERHREMLgmoa0rlZYTzhmWK89b0+LoKXLpDEFmDA6v9cmE1IJkPu60Ftf4z5y6k2PPfYYAODll19OeL8gCAyuiYiIehmDaxpyokHO53sb8ZePD0DVNLgd1g4rrDdIIk7L67j6ky69mf7eVSDXm1kBqQTIfV3orS9xHzn1tr/85S+ZHgIREdGQl1Rwff/99+P222/HqFGjOty3Z88eLFu2DGvXroUkSXjhhRcwZsyYlAdKlA6xIKchgJaAAl03YLVI0HQDVkGIW2F944NKzDq5FLphoLLG0ysrjr0R6GYykEslQO7rQm99hfvIqS+MHDky7u+hUAhWq7XDJBcRERH1nm4H1wcPHoz9/8aNGzF79mxIUsdfgN9//3188MEHsb/PmDEjxSESpUfbIMcqSzB0A5IgIKxqaPQEkZ9th8Mmx1ZYDzX4sXlLJd7/uBoHG/wDYsWxtwK57qY0pxIgZ6LQW2/jPnLqS3v27MGqVavwwQcfwOfz4ZVXXsEf/vAHjB07Ftdcc02mh0dERDTodTu4Xrx4Md5///3Y3+fNm5fwOMMwcNppp6U+MqJ2Utmz2j7ICSkaBEGAIAqQDEAzDHj8CuxWyayiLYvw+BW89L9fQNN0OO1yyoFqb++57a1Aricr4akEyJkq9NabBvs+cuo/Kioq8P3vfx8FBQX41re+hd/+9rcAAEmSsHTpUrjd7rie2ERERJR+3Q6uH374YXzwwQcwDAM//elP8YMf/ABlZWVxx4iiiOzsbJx66qlpHygNbammOrcPckQxEugYZpAjAgirOhRVh80iIRzWoIQ1iKKA/GwbzM7QyQeqfZGq3RuBXEVlI57/8y4EgipsFgl2mwQB6HSCIdUAOVOF3npLb+0jZ3E0au/nP/85jj/+eDz77LMAgBdffBEAsHDhQoRCIbzwwgsMrokoo3TFk+kh0BCSqc9bt4Pr4uLiuC/m0047DcXFxb0yKKK20pHq3D7IiQbJiqpDFgQIAHQAum7AMAx4A2EAAnKyzBVgwzhyLkEQYJVF7K/zYcu/azBzckmXgU1f7blNdyCnGwZe/utXaPSEAACtigYBgEUWke2yIKhoCScYUg2QB1Of897YR87iaJTIJ598ghUrVkCWZWiaFnffxRdfjDfeeCNDIyOioc7tzoLFYkXw4JZMD4WGGIvFCre7b7cTJlXQbMmSJVi2bBnOO++8dI+HKE66Up1dDhkGDPgDYVitEqyyiByXFQ2eIFTdgAhzbVrXDTT7FMhyJAiXROhtIutgSEWLX4ES1mAAeOndr/DBv2o6DWz6cs9tV4GcEtYQUswxOx3dC+Te++QA9tf5AACiaK7uGwAUVUOjR0e2yxpbCS8rzooLhseX56UUIPdln/PelO595CyOllmCgLiJtv7EZrMhGAwmvK+5uRlWq7WPR0REZCooGIalS5fD5/NmeigDzqFDB7Bu3RrccsvtGD585NEfQHHc7iwUFPRea91EkgquS0pK4PP50j0Wog7SkeocXekLBFVougFRECKrr1YUZNvR7AtBCesQRQGarqO00IVTjivEpi1VCGs6pEgKeTCkosEThG6YwZ8BwG6Rugxs+nLPbaJALm4ywAAkScCrf/sKl8wa3WUQphsG3vvkYOwx0aBYiPynRQJDmyzi872N+MPfvuZKagLp3EfO4miZEX0pw5qOoKIh22mFrve/CPu0007DqlWrMHXqVBQWFgIw/43x+/149tln8Y1vfCPDIySioaygYFifBzmDyfDhI1Fezu5LA0FSwfV3vvMdPProo9i5cyfGjx8Pl6tjL+C5c+emOjailFOd2670ZbusaPGFoBsGlLBZITzbZYXNKsFlt+CcqSMxaUx+bBVx55eHUX3YjxyXBYCAFr8C3QAkwQwurbIEp12GE+g0sOnL3s2iIODiU8vw6027cLg5CFkS4GsNw4AZEEuSgGyXFQcOB466yllV40WzT4EgAAI6TgpE96hLooC/fHwAmq4fdSV1qO4TTtc+chZH61uCIEDVdITCGoKKClUzzIk2Z6ZHltiCBQvwne98BxdeeCEmTJgAQRDw2GOPYe/evTAMAytWrMj0EImIiAa9pILrxx57DADw8ssvJ7xfEAQG15QWqexZTbTSZ5HE2EquppkVwo8tzUm4knvJN0abK45exdyfHdYgCgI0w1z9znYd6SHbWWDTl72b/13ZiA3/twdBRYUS1hEImatromCubma7rHDYZBiGcdRVTm8k2JckAZqmHykABzPYFmCmNWuaDlUE8rLsXa6k7q5qGtL7hNOxj7wvJ2qGKkEQoBs6QmEdwZCKsKqbdRgi97f9Oehvhg8fjtdffx3PP/88tmzZgrKyMgQCAVxyySW4/vrrUVRUlOkhEhERDXpJBdd/+ctf0j0OooRS2bOaaKXPbpNht8mxPciqbuDbZx2DscM7rvRNGpOPO644CS9t3oWqWi8MmHuOrfKRQDWqs8Cmr3o3/3lrFTb8316oqm7uC21znyAIyHZZYuPtzirn4aZWBIJhaLoBwwB0re0Zjch5AEkS4XZYu1xJfe+TA9j80f4hv0841X3kfTlRM5RE91ErqoagokEJ69B0vd/ure5KXl4errvuOvzwhz8EALS0tKC+vp6BNRERUR9JvARyFCNHjoz7b9iwYRgxYkTcbT2h6zpWrVqFb37zmzj55JNx8803Y//+/Z0e39TUhB/96EeYPn06ZsyYgcWLF6O1tTXumLfeegsXX3wxTjzxRMydOxcffvhh3P1ffvklbrnlFpx66qmYNWsW5s+fj4MHD/Zo3NT7ontW7VYJzT5zxTma1t3sM/tSX3xqGfbVevGvPQ2orPHECpDFVvrkjh9zq0WCy2mBACDQ2vlK30nHFuLeq6bgO+ccA5fDgrwsG4ryHHGBNdB5YNOd8afau/nzvQ3Y8H97Y2nakiTGnU83DHj8YRhtogVZFqFpRsJVzk+/rMemLZVmcNFFgCGYy9cJX9/oc6iqjvc+ORjLHrBaJIiCAKtFQq7bGqs4rg/ESKaPRSdq/EE17r0EjkzUlOQ7U56oGQoEwfwvrOnwBsJoaAmi2asgEFShagMzsPZ6vbjpppvw/e9/P3bbp59+iksuuQTz58/vtNgZERERpU9SwTUA7NmzB3fffTdmzJiBKVOm4PPPP8fixYvxm9/8psfnWrNmDX77299iyZIleOmll6DrOm666SYoipLw+Pnz56OqqgrPP/88nnzySbz33ntYtGhR7P4tW7ZgwYIF+O53v4sNGzZg1qxZuOWWW/D1118DMIPz66+/Hna7Hb/5zW/wzDPPoLGxETfddBNCoVBSrwf1nuie1dJCF0JhDR6fglBYQ2mhC2edPAKbtu7DU6/9E79+swJPvfZPrPj9J6iobIxb6Uuks4BYNwxU1njw2dcN+Gp/MwBg5uQSlBW5oSQ419ECm67Gn+qqrW4YePX9PVA1HbJk9u8WgHYr5Ed6eHfn2v/w7pcIhXUU5Ng6fV5RAGRRQDCsIdAa7hDsRZ8DAiKFvI6+T7gvtX+PB0Jw3xcTNYOdIAjQdPPntcETRJM3BF9rGGFNHxCfga4sX74cFRUVuPPOO2O3zZw5E6tXr8bHH3+M1atXZ3B0REREQ0NSaeEVFRX4/ve/j4KCAnzrW9/Cb3/7WwCAJElYunQp3G53XE/sriiKgmeffRb33nsvzjrrLADAE088gW9+85t4++23cckll8Qdv3PnTnz00UfYtGkTxo0bBwB4+OGHcdNNN+Gee+5BcXExnnnmGcyePRvXXnstAOC+++7Dzp07sX79ejz88MN45513EAgEsGzZMtjtdgDA448/jrPOOgsff/wxZs2alczLQr0o0Z5Vf1DFb7poS3Tt+cf1OCW7fQ9hq0VCcZ4DF51allLV597q3byv1ov6lmCsineUKMS3DTIMI1bhuKt09KoaLw7U+czWZUZ0dbrjArYoClB1QNeBRk8IvqCKnDap8tHnyHXb4lqbtZeJfcJdvcf9PT09XcXRhhJBMCeXWhUV/kAYYVWH1g+rfafq3XffxX333YeLL744dpvVasV5550Hr9eL1atXY8GCBRkcIRER0eCXVHD985//HMcffzyeffZZAMCLL74IAFi4cCFCoRBeeOGFbgfXu3btgt/vjwtos7OzMWnSJGzbtq1DcL19+3YUFhbGAmsAmDFjBgRBwI4dO3DhhRfi448/xk9+8pO4x5166ql4++23AQCzZs3CmjVrYoE1AIii+cu/x+Pp7stAvaizytLRPau6YWDF7z/psi3Rpq37cNHMcvymmwFx+x7CFqcIwwD21/lie4NTCWx6o3ezLxAGdANCpDVY2wBbEgWobfZKC4LZ77qryQBvIAxV0+GwWxAKaWZl8GhcbJivu24Amm5WTjYi5w0pGhrUIPJzbJBFMfYcZ540Am9uqerzfcKdfX668x739wC1tyZqBpO2+6j9IRWtKtDiC0FVB19QHeXz+ZCTk5PwvsLCQjQ2NvbxiIiIiIaepILrTz75BCtWrIAsy9A0Le6+iy++GG+88Ua3z1VTUwPArHTaVlFRUey+tmprazsca7VakZubi0OHDsHj8SAQCKCkpKTT85WWlqK0tDTu/nXr1sFut2P69OndHnt7na3ODQaSJMb92Zs+39uINz6oxKEGP1TNgCwJGF7gwiXfGI1JY8zAZ+8hD2obA3A7LB0q+AqCALdDRm1jANkuK264eGLsfIGgClkSUFbkjjufbhh4a+s+hBQNeVlmsC5AgCQLyMuyockbwltb9+Heq6Zg8rgCVNV44Q2EkeW0oLwkc4FNTpYNVosIRdOhanpkL6k5FlEQIOpmMCwIAlqDKiyy2OHa28p12yBLIjTVrOgNmI81gxUDumoeJ4lCLPDOddvga1WghHU0toSQ47JgVKEL0ycUoyDXjjy3DfXNrbBaOmYPBIIqRhW5MXZkTtpew84+P3NmlXfrPZ48rmBABKrHlOYm9bi+/FnOBEXVEQqpCIXNnwlRFGCxGRBFEZKUeItId8VqGoj9L0ifMGECXn31VZx55pkd7tu4cSPGjx+fgVERERENLUkF1zabrdPiKM3NzbBard0+V7QQWfvH2Gw2tLS0JDw+0fltNhtCoVBsXInO19l+6t/85jf4n//5HyxcuBD5+cmtWomigLy8jv2+B5vsbEevnv/TL+uxfvNutAZVZLkssEgiwpqO6sNmqvcdV5yEk44txN5aP3QDsFvlhO1xREFAa0gDRAmnTR2OWSeXYs+BFnj8CrJdVjOYa/O4r/Y3o7apFdluKyxy/AqrLIvIdllR29SKJr+KY0bloiDf3auvQ3fl5DhRNjwHX+5vhq4b0HVAFM0VbANmpW+rRcQN35qM4QXuhNfe/nwji9yoPORBfrZZgEwJa5AlAW0zaUVRiKVU52bbkJttgzegIBTScM70Mny1v8VcsY7sZQ2GNRxuCSE/ywaLLCKs6vC2huFyWPDdCyak7fXs6vPz600VCKt6t9/jwa63f5b7ihHZdx5UNLQqKnRBgGS1wNnua8Lttic+QQ+IooDc3P7Z6Pq2227DbbfdhssvvxznnXceCgoK0NjYiL/+9a/45z//iV/+8peZHiIREdGgl1Rwfdppp2HVqlWYOnUqCgsLAZirW36/H88++yy+8Y1vdPtc0dRsRVHi0rRDoRAcjo6//Nnt9oSFzkKhEJxOJ2w2W+x87e9vfz7DMPDkk0/il7/8JX7wgx/gmmuu6fa429N1Ax5PIOnH93eSJCI72wGPpxWaltrqT2d0w8BLm3ch0BpGbpY10nPWTD/OcVnQ7FXw0uZdKC1wALoGUQCCipow3djsSQ1A19DU5AcAFLgtKHCb6cctLfHv1YFaD5SwBoddghq5PgFCpM+zAUE0z3mg1hM7R39x4fRROFTvAwwDYU2HphkwDLM3r0UWcfkZ4/CNScWx49tfe1uSJOKKc47F6pc/QUNzEE6bDCWsIqwasf3bogDzNRGAHJc19nmwWyX4A2G889E+GAbgcshw2C1QVR2qqiMc1tDiC0EQBMiSgNJhZjZC2TBn7D1KxdE+P/VNQSiqhhyXdcC9x+nUFz/LvU0QAFUz+1G3hswK33on+6glSYTbbYfPF0z5emVJhEMSEhbw64nsbEfaMwfOPPNMrFmzBqtXr8aqVatgGOZ2kYkTJ2LNmjUJV7SJiIgovZIKrhcsWIDvfOc7uPDCCzFhwgQIgoDHHnsMe/fuhWEYWLFiRbfPFU3xrqurQ1lZWez2urq6hGlsJSUleOedd+JuUxQFzc3NKCoqQm5uLpxOJ+rq6uKOqaurQ3HxkQAjHA7j/vvvxxtvvIH7778f//Vf/9XtMXems6rUg4mm6b12nZU1Hhxs8MNplwEI7drhCHDaZRxs8GPPgRaUFWehOFKsLFfqmG7sazULdo0c5urWeJ02CZIkQFE0QBCg62ZatFOUYcBAOGzuDXbapH73Ph83KhfXRvaCH2rwIxzWAVFAYY4d3z5jLCaNKejRmE86thDXXzQBf/zHXtQ0BmC3ygiFtUhRNPMYiywix2WFzSrF3qdwWEcobG4TKcixx94TiyyhIMeOZl8Iw3Ls+I/TxiDLZY3tE07X69mdz0/QoyIQVOFyRIJnwdyp3t/f497Qmz/LvcGcLDED6mBINSt8a0ZX3eIizGvUIhNPqY3BgNZFIJ9pZ599Ns4++2yEQiE0NzcjKysLTmf/XGknIiIajJIKrocPH47XX38dzz//PLZs2YKysjIEAgFccskluP7661FUVNTtc02YMAFutxtbt26NBdcejweff/45rr766g7HT58+HcuXL0dVVRXKy8sBAB999BEA4JRTToEgCJg6dSo++ugj/Od//mfscVu3bsW0adNif//xj3+M//3f/8UvfvELzJkzJ5mXgdIs1pfacfTK0tG2RGb17hCssgRRAPRIESO7Ve5RW6Ky4ixkOSyorjdXUKPFwawWEQ6bhGBIR0mBA6VFR9KXOyualQnpKHKlGwb2HvJgb60fDruMu688CdV1PvgCYTgdFsDQ8cLmL9DQEowLngFzQsPjN7NFspydtd2yoNmnIMtlTXthN+Donx+7TYIgCAgEVTjtcreqx1NmtS1MFgxpUFQdmj4w+1D3lZaWFrS2tkLXdTQ3N6O5uTl234gRIzI3MCIioiEgqeAaAPLy8nDdddfhhz/8IQDzC72+vr5HgTVg7o2++uqrsXz5cuTn52PkyJF4/PHHUVJSgvPPPx+apqGxsRFZWVmw2+046aSTMHXqVPzwhz/EokWLEAgE8OCDD2Lu3Lmxlenrr78et9xyCyZNmoQzzjgDr776KioqKvDoo48CAF577TVs2rQJP/7xjzFjxgzU19fHxhN9Hup7bftSd6ey9MTR+Tjr5BF488MqNAaCsYDYYZNx1skjelT1eXdVE1r8SqzXrSSYq5mtIQ2tIQ0CgPrmIFa+/CnmzDQnddq2c5IkASX5zoy2Q0qlGnm0PVVtYwC6YaZ+F0eu5/ixBbHjvnP2MZ22I7NECvpZErx3QO+33Tra50fTDNisEiRBQENLENkuKywWESFFg8fPPtH9SbR9VlDREFI0aLox4PtQ97aqqircd999+PTTTzs9pqKiog9HRERENPQkFVx7vV788Ic/xIEDB/DWW28BAD799FPccsstOP/88+P6R3fH/PnzoaoqFi5ciGAwiOnTp+PXv/41LBYLqqurce655+JnP/sZLr/8cgiCgKeeegqLFy/GddddB5vNhgsvvBD3339/7Hynn346li5dijVr1uCJJ57AMcccg7Vr18bad0WrmS9btgzLli2LG0v0eajvlRVn9agvdUVlI/72yUGIIpCfbY9W8UIorOFvnxxEeXFWtwJd3TDw5pYq6IaBwhw7PIEwFFWPpUADZmDossuorvfjmTc+jz0uUX/tgdDOqa227ancDgvsVhlBRU14PV31WT5lfCHe/LDv225FdfX5aQ2G0eAJQRAAqyxCUXXUN7fCZpHgtFswqsg9IPpcD2aCIEDTdYQUDUFFRVgz+m36dX+0ZMkSVFZWYt68eSgpKYm1lyQiIqK+k1RwvXz5clRUVOCBBx6I3TZz5kysXr0aixcvxurVq7FgwYJun0+SJCxYsCDhY0pLS7F79+642woKCrBq1aouzzl37lzMnTs34X3R/tzUv8SnenfdlzoaEAcVFXlZ8SnKTruMZp+CN7dUYXx53lFXIvfVelHTGIDLboHVIsFhk3GoMQDVMCBJYmSvsQEIZmGsg4fNgmAjC10J+2t393n7g7avY67bBlEUIIqCWQVcOnI9x5blxlLE3U5LXMp4NAUdAHbsru/25Ei6dfb58QfCaPKZnQLy3Da4HBaEwxq8gTAssogrzj0Wp04ohJ7iflzquWiLt2DYbJ+ldHsfdd8yDAM1jQE0ekOYNbkETlvSSV+9Ztu2bXj00UdxySWXZHooREREQ1ZSvyG8++67uO+++3DxxRfHbrNarTjvvPPg9Xp7HFwTRXW1Mto25bptQJx4f6+MmsYA9tV6j5oq3X6vrqLqkVRv0QzkYfaK1nUDYZj7sWEYCLdboe3p8yYj3fu8u/M67q/z4bH/2YFmn9IhBb5tyjiAbk+O9Jb2nx9/UEUgaO7RL8ixwWEzV81tVhlWi4QWn4IP/3kIp04o7LUxUbzo26+o0VVqrV/uo1Y1HXsOelBR1YRdkW0jAPD+Jwex+IYZGR5dR263Gzk5OZkeBhER0ZCWVHDt8/k6/RIvLCxEY2NjSoOioa07xbl6UvzsaNrv1Y2mokafLbqXWxSFuDTVRCmrvbmvOLovuqYxADWyyprrtuLMk0fgzJNHJhW0Hu11VHUd/tYwNE1HjtsGQzagqDqqarwJU+C7OznSVvsJg9Iid4dV8Z5cW9vPz1cHWvDGB5Vw2mTYrPH/3AmCAJdDxoE6H6pqvBhV2D96lw9WgiBE2meZad9qP0z79rWGsXtfE3ZVNePLA81Qwh2rqTd5Q9B1o9Ne8ZnyH//xH3jxxRdx+umnd5goIyIior6RVHA9YcIEvPrqqwn7Zm7cuDFhCy2injhaca6eFj/rSvu9utFfmqMr1LpuwCpLsb26sTEm+OW6t/YVt90XLUsiQoqKsKrD61fw4ttf4P1PDuLKs4/p8Z7hrl5HwzDQ4lNgGIDNKqHJG0I4cv2GYSCoaHj5b1/j/10XnwLfk8rlbScMNM0sWhUNXERBSLpQXPTz4wuEIUDosshaaygMby8VWRvqYu2zIivUYVWH1o8CasMwUNvUil1VTdi1rwn7a32dpqQLAEYVu/Hdc47td4E1ADgcDuzYsQPnnXceTjjhhA51TwRBwNKlSzM0OiIioqEhqeD6tttuw2233YbLL78c5513HgoKCtDY2Ii//vWv+Oc//4lf/vKX6R4nUZyuileFFBUef7hD66zOJNyrKwkIqzoEwbw/22WFIAiwSIK5oi0IserYUW33FZcWuVFZ40lL+nbbfdF2i4RGbwi6AUiiCANmMFpd78fzf96F/7pwQo+C0K5eRyVsBkOSJMAbCEPXDQiCAEGIFp8ysL/Wh/d2HsDZU0s7vKZHS4tvO2HgslugiToaPEFoOiCJAvJzbJBFMaVCcdHJg3D4SP9yURRgjVyrquqQJRFZKUyG9KeWbP1B+/ZZIVWDrhv9Ju1b1XRUHvKa6d77mtDkDXV6rEUWcczIHEwsz8P4slzkZdtRlOvodyvuALBhwwZkZWVB1/WEFcO5mk1ERNT7kgquzzzzTKxZswarV6/GqlWrYBjmL90TJ07EmjVrEq5oE6VTooBY03Q0+5RYUNy2ddbRgrL26cyyJCKsGhAEIMdlhc0qQQlr8AfVWCDW2b7i48fkY+XLn3bZpqsnAVnbfdFNkcBajqycCRAA0QxmAkG128XU2j7/KeMLcbgliGafArdDhigIUMIaPP6wWYDdMKBFF+wjEZI56WCe571PD+LMKT1LS29fSE0QBNR6QzAgwCIBmmHA6w+jKM+BXLc16UJxnfUvt8gisl0WhBQdY0bmoLwkK6mCZu1X3vtDS7ZMaL+Pur+1z/K1hvHx7nr8u7IRX+5vQSisdXpstsuKCWW5mFieh7EjcjpMovVX7777bqaHQERENOQlXfL07LPPxtlnn41QKITm5mZkZWXB6XSmc2xEXWobEO+v88HfGoYBwGIRkeO29njVs306c6MnhJ1fH0Z1rRcenxK3dxhAwn3Fx4/Jx98+ORhbjU3UpqvtY7uzdzq6L9qQzSJqUvvCYwB0ADaL1K1iaokCwiyHBQ6bDF9AQWtIi/S5duBgQwAhRYt7LgNmjK1F+mE3+0I9LuDWvpBabJU8sjIuwuxzrKg6bBYp6UJxifqXQzBX5eubNeS6rLjinGNjhet6ov3K+0BvyZaM/rqP2jAM1LcEsauqCbv3NaGyxtvlyvnIYS5MKM/DhPI8jChwcpWXiIiIkpJSP5GWlha0trZC13U0Nzejubk5dt+IESNSHRvRUU0cnY9jy3Lxs9/sgKbpyHZZYbVIsV+Oe9oeq206syyLuOzc4/DJrhq0eEMdVpjb7ysuLXJj5cufIqiocNpk6LoR28scHcfLf/sagWAYobDW7b3T0dTm2H7vdpcQW421iAiGtFgxtUSr47urmhIGhE0+BTaLiG99YwzGjMoDdA3F+Q7c9eTfY88jtPnTaPPchm70uIBb+0JqsYBMOPKH3ub2oxWKS3StADr0Lw+remzwoiAgx23DCeOGoaUl0KPxJ1p5BwZuS7ae6K/7qDVdR2WN19w/XdWMBk+w02NlScAxI3PMgLosD9kuax+OtHdce+21Rz3mhRde6IOREBERDV1JBddVVVW47777Eu7riqqoqEh6UEQ9UV3nQ4tfQY7b1qEoV6rtsURRwJjh2VALO1YNbr+vuLLGg/11PoQUDf5WNXa7RRaR47LCZZdxoN4XW4nt7t7p6L7oqhovDMMADCEWhBqGmXprlSUIQKyYWnR1+lCDH+GwDogCCnPs0DS9y4Bw++46XHbucWhpCeCr6mZIbQo3dVroSex5Abf2hdRiBaIiMwVtK7QDXReK6yw1+5TjCuP7l9stUMJaXKVnb0DBngMtKHD3bPzpbAU3EAiR90QJm58fJaz3i/ZZrSEVu/c3Y1dVE77Y34yg0nm6d5bTgvFleZhYnodxI7NhlRMXuRuojARvRiAQwNdffw2n04nzzz8/A6MiIiIaWpIKrpcsWYLKykrMmzcPJSUlEMWBsSeNBqd0tuVKxed7G2Op6bIoxpZ4FdUs1JXltEDTDdgsEjyBcLf3TrfdXx5UNKi6DlkUYMBcQTULrlkQCGkoLXTBH1Txm8274fGFoOpmj24YBioD5tiynJ0HhIca/Piquhk+XxD/2tMAAYAsAmq7uQUz8AV0A8h122Irxd3VvpBaNMBXVB1S5LqiFdrbFopr/zxdpWYfOOyHqupwOY4Ezm0nX3TDQCCowuNXehxc95fPXG8TRQHhsI5gWDX7UWuZ30d9uKU11nu6qsaLrhbNhxc4MWl0HqYfPwI5DglGxzmyQeM3v/lNwttbWlpw8803Y+zYsX08IiIioqEnqeB627ZtePTRR3HJJZekezxERy321f5+l0NOW1uuVMa844t6GDArXQttcqhlQYCqG7F2T4KAHu+dju4vf/lvX2N/rQ9qpHK3RTZXwYOKDrtVwsWnluGVv32Nw82tHYIOMVLF2RsIw2GT4LDFvx6yLMLjV/D0Hz5FizeIYFhDa0iFKAoQBCNyjsi1GWbRMQECzjxpRI9TnxMVpMt2WtDgCSKsma9hlsuCcJtCcXNmlnf4HHSVmt3QEkQorHX5uZAlIamU4HS2gutv2qZ9t0bSvjO5j1rTzZ/3XVVNqKhqwuGWrtO9x47IwYTyXEwoy0Ou2wZJEpCT40RLSwBaD/fVDwY5OTm45ZZbsHTp0m6ljhMREVHykgqu3W43cnJy0j0W6kUDpV3Q0aovJ7q/OM8Jm0VEi09BtssCm/XIx7qrVc+onrw2nR27r9aLZl8IFlmEqukQEN/6RgSgagYkUTiy8idEzwkARwpB6YYBNWzuw25r4uh8/L/r8vDezgN479ODaPaFYrna0UJrNY0B7KvxxoUQ0T3SbeOjZp8Cu1WOG6O/NYzWkIr6pgCynBY47TIOhTWoqgFRACRJjLVUMiIr5qVFbpw5ZWTiN/Mo2ldo1zQDTrsllratKDo0yYhdW/viYEdLzc5yWhBq0cyV6Rx73DHRz0VZkRtjR+b0eM91Vy3MuvOZ62/ap32HwpltnxVUVHyxv8UsSLa/Ga0htdNjXQ4LJozKxYTyPBxTmgNbJz3Nh7qGhoZMD4GIiGjQSyq4/o//+A+8+OKLOP3001lVdQAYKO2CjlZ9+ayTR8QqcVtlCbJFQDCs4Yv9zWbAaph7MC2yiFy3FZIkdrrq2fY5O3ttTjhmWLeP1XQDmmbAZZfh8Yeh6QbaZIbHCj65nTJaQ1pcIN0+gGnxKxAAvP6PvZAlMe49EgUBZ08txZlTRsaCfKfDAgHmyvjb2/cnDKzbU8I6fIEwsiKrtrquo8WnQBAEFOY5EGhV0egJQo+k0eoGYKg6ctwWiIKIUFiDy2HBlWeNS2mSpn2F9mhhuOo631EnO7qTmh1Nu29oCSLbZe3QNu2Sb4w+st+7BxL2Rm937s4+c/1JtNd3f0j7bvQEY72n9x70djmOknwnJpSZAXVpkbvfv859Ydu2bR1u0zQNNTU1WLNmDSZPnpyBUREREQ0tSQXXDocDO3bswHnnnYcTTjgBdrs97n5BELB06dK0DJBSM1DaBR0txbfJG8KbH1bBMAyENR2+VhVGm8BUgBlMAeb99c1B2G0Ssl1WHD86H3abFNufHHW01+YGScRpea5uHXviuAIEQqoZhEbGpWvmim90gAKAkKJDCetxAXd7hmEWJjvcEuz0PRIFAWXFWXjvkwN4772v0exToOk6Aq3xK3xdhUnNvhBkSYAkmenghgHkZdkQVDQcbjHTyiVBgCiZq+4GgBafGfCWl2R1OTnTk2yA9oXhAHSrCFhXqdmtIRXNvpB5n2COv765FTaLBJtViq2GTxqT/Ge//cp725Zs/W3iqq3otoRWxdwTrmao2reuG9hf58OufWa6d11Ta6fHSpHCghPK8zCxPBd5WfZOjx2qrrnmmoST3YZhYPjw4fjpT3+agVERERENLUkF1xs2bEBWVhZ0XU9YMZyr2f1Db7ULSiXFvKu06q5SfG0Wqcu9lgbMfa65bitUXYc3oKI1pCGotOLdJjONeuQwV6zNVXdemzc+qMSsk0s7PRYA7BYzMH3vk4NmGi3MImW6YRYSi1a9NgzAKosozHPA32r20D4am0WCvzWMNz6sjL1H0dfv872N+Me/DqG2sRWGYQZMUqTIWXfpBtDoDSHbaUVBjh0NLUG4HDLqm4Mw2hVbs0jmZIBFFjEsx467rzzJLNrW4ZxGh7R1WRZ7JVOis9Ts1pC56q5qRuQ1dyIc1uANhCHLIubMLMeZUzr2E09GopX3tj8PfbUd42jP0zbtOxBS0aoCLb4QVLVvg+pQWMOX1S3YVdWIXfuaEQh2nu7ttMkYH1mdPrY0B3ZrSp0jB71EbbYEQYDb7cb48eNZeJSIiKgPJPXbyrvvvpvucVAv6I12QZ/vbcQf/7E3qRTz7qRVd5bi6wsoCW9vywDQHFmBjRKFSIEm3cC+Oh+eeeNz3HzJJNhtEqrrfZBFs0K1tU1w1rZy9p4DLfB4W+Nex2BIRYtfQVg1V6CjKd5uh2ym1hqAJIgwDD0WYIuCuSosRiYKRCF+D3R7qmagoSUICAK+rG7Be58cQEmeE29uqcL+SMp09OGSCIiCCE3vXilkAYDVKsFtlxFSdVx+5liUFLiwZsM/0RpSEVa1DqnSBqL7mK1o8SuorvN1+MxUVDbGCq4ZiBZcEyHLYq9kSiRKzZYkwVyx1gxIYpvX3CrDapHQ7FOw44v6pPeJdzaORD8/fbUdo7Pn+dY3RmNCeR5U1YhL+xZEQLZZ+mw/dZM3ZPae3teEPQc9Xa6SF+U5MCHSLmtUkTuplP2hasaMGd06Ttd1nHfeeVi7di2OPfbYXh4VERHR0MKlgEEs3e2CPv2yHs9tqkBrEinmR0urvmDGqIQpvsGQiobIKmR3tA8YDJipzYJorrx6A2H8/t0vAUGANxCGIAgQ/GYv6myXFQ6bHPfaePwKvG1ex+h4oinTBgxEQ9pASEWOy4bWkGr2U06wlzpKEARIggGti3hYEMz/NM3Aa+/vgUUyA+hQJK08StMBiHpsZftoJElAjssKm1WC6lOQ7bRidIm5ClxZ44WuG5Cl+AJd0dZYdpsErz/c4TMTfX8bWoIwYMRahYVVDR6/jvxsM908mUyJrrRPzVb8kergsoi8LBvstiP/xPVl/+m+2o7R/nkcWTJkyZwweuPDKgRCKkYWuuOqfUvo3YBVNwwcqPehosrsP13T2HmxOFEQMHp4FiaW52FCeR4Kspnu3dsMw8CBAwegKEefsCQiIqKeSSq47k47j0QpatS30tkuSDcM/OHdLxFUtB6nmHcnBXvH7noU5zlw4HAgluIbDKk43NLaZQB6NIYOGIK5iipGAuz99X7YLWaIIQKAACiqhkZPEPnZdjhscnybJl2LvY4tfiWuP3XbWNaIFFTLdlnQ0KLH3ykIUMIaDrcEkeO2Rla8ux67KIiRhxoIhlQEIWBYjg3+1o7p8eZrdPTA2iqLyM2ywWGToYS12PsfXQX+9aYKtIZU6IbRpsp4tJe2NbYy2vYzE31//a1hGIYBWRTNiQHzpYVmGPD4w8h1W3slsG2bmv2vPQ3YvG0/8rPtCQP4vug/3VvbMTp7nrCmYWShG3arBEEQEFZ1BEMq9tf7sGlLFa6/eGKvF/xSwhq+OtASWaFuhq+189fXYZNw3KhcTCzPw7GlubEJLSIiIqKBLqnfaowEq2OBQABff/01nE4nzj///JQHRqlLZ7ugqhovDtT54HLIPU4x7256+pyZ5Wjw7I+l+Db7QikF1oAZHMb2PbdpLeR2WmAEAEXVIQtCmyBQgc0ixrVpamqWUZLvRFWN1wxI2+xdbPujIEUC6GafEVm9BdRote3oyqFhoNkb6ubeaANaZM+yquqAAPiDHVfEu0sAkOM2V+cTvf8TR+fjpm9Nwi83/Au+1siqPgCrbBaGs1vNtOr2n5no+2uzSggqGtoujAqCABGIpdBrmtErgW3b1Oy/fXIwo/2ne2M7RiIH6n1QVA2jCrMgigICQQ1BRUU48qGzySION7fi0GE/Rha6U7qmRFp8IezaZ65Of32wpcvskmE5dkwoz8OEsjyUl2RBYro3ERERDUJJBde/+c1vEt7e0tKCm2++GWPHjk1pUJQe0dXI5/+8Cw0tQdgsEiwWEQKAQEjrUbsgbyAMVdPhsCcOSrpaEUyUnm4YBhRVh64bEAQz6CnMdcRSfKvr/VBU3SzElOLeUN0woLXr2dviV+CyW6BqClTdMKtiC2awXd8chN0q4ZQJRQCOvI7PvPE5/EFAMJfDI2MzV3ijy7SGDoTDupmG3m7cifpNm+dPvP9a1c2+2E6bjJawAsMwuuz3257Za/vIuQ0AnkAYkiggENJgs4g45bhCfL63MVYE6/gxBVhwzTSs/O3HCCoanHYZdpsETTMivbE7fmai76/DJh15IiF+HDrM16W3A9v+0H863dsx2hMEAaqmo9mnIKToCGsKwuGOs1CSJEILaV0WDesJ3TBw8LDfXJ2uasLBhq7SvYHykiyzundZHoblOtIyBiIionSrq6tFa2vn32mZdOjQgbg/+yOHw4miouJMD6PfSGs+Xk5ODm655RYsXbq0W6nj1DecdguavCEEIoGZJApxlbO7I8tpgSyZK6gWuWcrgu3T01tD5l7m6GomYP4yXt/SirOnlGJ8eR7+uqMaG/5vL+w2Cc3eULdWazsLxBOlX4fDOjyaAqddRljVEY4E+gbMisYA8Np7X+PDf9fgstPHADDTWZt98SnYFlmEyy7D12oGVNGnNxI8Z6JLsMiiOcGAjv2uJVGA3SrDFzxSvKynLZPaHx5SNARCKvLcNkAQ8OaWqrgiWNMnFmNsaR6+dfoYbKuoRU1jAF5/uMsWU9H3N1rALJoN0Pa6BZiva3lJVtoD2/aVsi+aWY7fZLD/dDq3Y0SZkyQGQoqGVsXcVx4Oa/D4Q5F0847Po2k6JBFw2pP/Zz6s6vj6QEus/7S3iwkBu1XCsaVmuvdxo3JTet7+RBQFiIIASRJglVlxm4hoMPF6Pbj//nsSZuX2J+vWrcn0EDoliiKeeGINsrJ6r5bNQNIrv/00NDT0xmmph9oWOyrMdcRWi6O/oPdEeUkWRha5sfdAC3LcPVsRbLuaqOk6Gj2h2D5eEeYKrQEBmz/aj5I8JyaOzse40hzYbRKssgirRUIorHW5gu12yHDZLQhrOlp8ylGD0GifaW8gDLtVgtMuR/YMm0Gtoupm26D9zfjFS5/AZpUgSQIskoCwZkAUzUBe180Ayg0Dzb6erUQKMNNlERlLtC8zYAbFum50uXf1aBK9AoIAjB2Rg4OH/bEiWJJdgMevYFdVEyoqm8zVaquEojwH5swsR2Guo8s2Um3f32ynBY3eUCwbwIARyU4Q4LTLaQ9sO6uUfdbJI/CvvY1H7T+tGwa+2t+MA7UeOG1SWlplpWv1vG37rJCiIhTWoel67OegZJgLw3IdqGlsRbbU8XkCIQ0l+Q4MH+bq0fg9AQW7I+neX1W3INzF3oz8bBsmlpnFyEYPz4rbMjEQCZHuAqIgwCILsMjmPnlZEiI1EIy44nBERDSwZWVl42c/W9FvV64HAofDycC6jaSC623btnW4TdM01NTUYM2aNZg8eXLKA6PUdFZUyWYF3A6jx0WVREHAFecci9W/39njFcH49PQQDMOAKAoQIEAzzNTn/Kz4atJxAZvLgkaP3iG1GwBE0eyHK4oiRFGAy2oBDKDZ173VbsAsxhRsM9nQfu+oahhQgyqK8uxwWOVYtXBRMHtZN7Qc6TWd67LCH1TjVuWjZEmAFqnELQgCwmEdWptUewFAfXNrbNy98Tu8YQC7q5pgsYjIdZuveUNLEIp6JIBqDamwWUUcOBxAg2c/rrtgfJd7g9u2xAoqGrKdVviDYfM1iLwupYU9y5Tojq4qch9uCeLa84+D02HptPdzRWUj3tq6D7VNrbHibulolZWoRVhPVs/NbRLx7bMSVYEXBQFnnTQCG/6+F55AGE6bZKaCa3os7f+sk0Yc9efbMAwcaghg1z4z3bu63t/psYIAlBUdqe5dmGvvsK98IBEE83UURXNV2iKLsEhSLBOj7UpGf1/VICKi5DClmdIpqeD6mmuuSfgLlWEYGD58OH7605+mPDBKTTqKKrVNt83JsuHkCSW4/uKJsT7XXa0ItjdxdD4unFGG3/3lSxiGGeQZMGCVReS4rLDbZEiSFjemuIAtErQqkRVsAUBxgRNXzz4WQiS9uaYxAI9fQSisQYhucO6G7v7O3OgJYcQwFwqy7WjyhqCo5gOjsbgsCbBaJFgtUsIq57oenUiwI6zpaFZD8AdVSJLZC7rt29R2+GYAYAbbicYavdbu/uofDGtwOSwIKmaF9PaTCboBtPjCKMjpfvus9i2xbFYZNiuQ67bizJNH4MyTR6Z1xbo7Fbk3bd2He75zcsLnjQbmIUVDttsKh11COJy+VlntX4+j/awIgmC2WVMiRcm07q2QjivNxWWnj8HfPj2Iw82t0EIaJBEoyXfgrJNGYFxpbsLHhVUNu6qa8HmlGVC3bRPXns0i4djSHEwoz8P4sly4Oqm7MBAIgpnmLYkCrLIEWRZhkcRY67n46v8MprsSCoXw2GOP4c9//jOCwSDOOeccPPDAA8jP7/znprq6GkuWLMG2bdvgdDpxxRVX4M4774QkHdnW8OKLL+LZZ59FfX09jj/+eCxcuBCTJk2K3b9v3z4sXboU27dvh91uxznnnIMFCxYgK6v36igQERF1V1LBdaI2W4IgwO12Y/z48RAHeGrgYJBqUaX26bayJGBUyV5cOH0U7vnOyXF7XLubSluY64DTboHTJsEwzF9y2+5JbT+mDgGbLMIWWXE986QROHPKkYBtfHke3tt5AH/8oBKA2XKqyde9Pq7d/RVa1Qwokf3YZlp75PGRTcWabqDBE0RBth0F2XYcbgnGrT7LkmhWKY/snR1V5IbLYYkFXwbMFf1clzW2+h7d7wkAgmFA181f+tvuVRdFIRaIdSceMI8x22NFH9f23Yu23+pp+6y2LbF6+tnoqVQmj9oG5nlZNlhkCaqmp71V1tFej9g+6rCOUEiFounQ2+zb765xpbkYMzIHhw77EQiqcNplDB/m6jB2X2sYu6qasHt/E76q9sRqCySS67aaxcjK8zBmeDZkaWD+my4I5jYPWRKR5bRCFMyfQ1kSzZ+jI0X8hwxBEDB9+nS4XD3bLtDeokWLsH37dqxevRpWqxUPPfQQ5s+fj//5n/9JeHw4HMaNN96I0aNH46WXXsK+ffvwwAMPQBRFzJ8/HwCwYcMGLFu2DEuWLMGkSZOwbt06XH/99XjrrbeQn5+PcDiMm2++Gcceeyx+//vfo6mpCQ888AAWLlyIJ598MqXrISIiSoekgusZM2Z06zhd13Heeedh7dq1OPbYY5N5KkpSKkWVEqXbapqOykMePLepAtcmuarndlpiKdE269HHpBsGHHYZF0wfBW9rGG6HBW6nFQIM+FtV7Kv1xgUrO76oh2EYKMixm5XGfUqPA5WjCYV1sw+0bgbCMAAdZu5zdHW5xa8gL8uGbJcVnkAYRizwjbThiqRKA8DFp5bFUpdrmgJ444MquBwWqKoeaYV1JECKViUXBSFuT7lhmCu22S4LPD4FwQSVo6NBhqGb7b2CioZwpBp72xcp+r+SKCTVPqttS6zelMrkUV+1ygKOvB7RLJAv9jUjy2VBQa4DYUXrsI86ledp327LMAzUNrWae+mrmlBd5+v050EAUFrkjqV7F+c5BmS6tygIEESzD310ssRmlTEs34mmJuNITYNBum/68OHDeOGFF/DRRx+hpaUFBQUFmDVrFq655hpkZ5ufZVEUO+340V21tbXYuHEj1q5di2nTpgEAVqxYgQsvvBA7d+7ElClTOjxm8+bNOHjwIF5++WXk5OTguOOOQ0NDA5YtW4bbbrsNVqsVa9euxdVXX41LL70UALB06VLMnj0br7zyCm699VZ89dVXqKysxKpVqzBu3DgAwPe//32sXLkypeshIiJKl14t52oYBg4cOABF6d4KIqVPskWVOku3lSwSHDYZh5uDna7qta/a3H7VsjtjGjnMCd0ANn1YiR1f1Jt7p3VAkgRkOa2AYcAbqczddo+sIxIQRQOm6P7JtnuJOyOLAtRu/rIdbLM/W2+TTm2RzLReRALX2qbWI721I8eomgFBFGC1mBXGm3wKXnj7C1x3wXgcP7YA7hpz8iE6IdK+8nasEnkkErPKInKzbJEg31yFs1gkBMM6BJivGQAIEGLBvBJ5bDRwFtEuukakqBP6pn3W0T4znUll8qi3W2W1t3tfE/72yQG0+BVoGhBWVTjtFsyaVNxp2nayVE3H3kMe7Kpqxq59TWjyhjo91iqLOKY0BxPL8zC+LA9ux8BK9xYACJHMDjlSyVuWJVjkI8XHogUKh4Jdu3bh2muvRSgUwpQpUzBy5EgcPnwY//3f/42XX34Zv/vd7zBixIi0PNeOHTsAADNnzozdNmbMGBQXF2Pbtm0Jg+vt27dj8uTJyMnJid02c+ZM+Hw+VFRUoLS0FJWVlZg1a1bsflmWMW3aNGzbtg233nor8vLyIIoiXn75Zdx3333w+Xz485//jJNOOikt10VERJSqwdErhTpItqjSUVf1HPGretHg6PO9jR2C4fbFoY42JlEw+28/+cqnZuVumK2qctxWaJr5PADMVWG3NVa8av3m3TjjxOFmwG0XEAprCIbMomLd0ZNE3NZOegarmhnQ6kciYAhitBe2eVO22wKHzQJrZGLBMOILy7WffMhxWdHgCULVDYiIpKKLAlQdkETzdQCAZm8odq1tV+R0A5AikbJhIFI8LrIvWRKhqGFAiB8jgFjKLAAEgipGFDpRWhS/KpoOnVX67k5BsVQqcrcNzKVuZFAkSxAEfLm/CW98WAVfqznBqKg6VFVHfUsINY0BXHb6mJQDbH8wHKvu/WV1S5fp3jkuKyaOzsO0SSUoybX3akuydGtbydsqi7BYxEiKd3wwDQzN/dKPPfYYhg8fjl/96lcoLCyM3V5bW4ubbroJP//5z9OWOl1bW4u8vDzYbLa424uKilBTU5PwMTU1NSgpKelwPAAcOnQIsmz+OjJ8+PAOx+zatQsAUFJSgoULF2L58uX47W9/C13Xcdxxx+Hpp59Oy3URERGlisH1INbTokpA91b1tFZzVS8aHO2v83UIhmVRTFgcqrMx5bmtaPEraPIGEVI0GDBXnFRNR5MnFAueBJgBn9thidsju+OLeui6gbqmVqhq4urKiQhAZHUrste4e/F4B0a7gmJGpJWWKArQNcPs86zoyHEdCQQTpSC3n3zIz7Kh2afEUritFhGAjmyXFQBilcujQTQMM+3bACBH9vRGB2aRBMiihPLhWbjw1DL89+v/RqDdZIG5f1uPFWML6Rrqm4NY+fKnKVfRbqurSt/dKSiWSkXutoG5+XoeYRhm+7NhOXZ4/Aoqazw92jcuCAJ0Q0corCMQDOOND6uwv94Ll80SS/O3yBKyJRGeQBh/+/QgxozM6VGQaxgG6prNdO9dVc3YV+ftMq28tNCFCeV5mFCWh+EFTsiyiJwcJ1pazEmN/qpDJe/IpJAkCbGf26ihGEy39+mnn2L58uVxgTUAFBcXY968eVi4cGG3z1VdXY1zzz230/vvuusuWK3WDrfbbDaEQomzJYLBYCw1ve3xgFkcrbW1FQA6nLftORVFwe7du3H++efj+9//PpqamrBs2TLcfffdePbZZ+MKo/WUzB7mGSdFajtEi3wSEQ1EDK4HuZ4Wmepuum19Sys2f7QfQUVNGAznZ9uR67YmLA7VfkxOu4QX//dLKGENDpsMv65CFsVYerKmm8GyLJo3hFUdiqrDZpFiAWp9cyuCitah8vXRRF8Gs9AY4PEnTgWO7qfu8Hh0LIgmiwIE0dwXHSs0BrPlV3TcsWOPUsRN0wy4HRbkZlkx9dhCTBydh1ff24PqenNCQzfM5wMihc4EwCKJUDUdumGubkfHrqga7FY5FiTfNvd4rPvjv+FvDceqjbdd+ZYlAXnZtk4nSpLVnUrfXRUUi2ZLaLqBC6aPwvbddahtau129fq4wNyrINtlhSCaKfAtfnMi43BLEM9t2tWt1fToyr+imlsGlMg+6uo6H6pqPLHPafxjBDhtEg43t+LQYX+H/dLtabqOykNec//0viY0ejpP97ZIIsaNzMHE0WZ172xnxyCoP+p2Je8eVMYfSvLy8uD1ehPep2ka7HZ7t89VXFyMTZs2dXr/e++9l3C7VygUgsPhSPgYu93e4THRoNnpdMbGl+iY6Dmff/55bN26FZs2bYoF0qNHj8b555+Pv/71r5g9e3Y3rzCeKArIy0utwBulrqHB/AxkZdn5fhDRgMXgegjoSZGpo6bbtqoYMcyJHbvrEVRUOG0y/K3tgmHDgMevwG51HLU41L5aLz78dw0ONQQAAMGwDl03IEjRvcICBCHy23SbvcBtg0BJEtAa0iBLInRd61Z/6GhQ7HJY4GsNo8UXRqL5BlEwV+MtsgRfqxkAS+KRgmIGOgbYZpCqRwJDIbaKrhtAMKTGBdeJUpCPNiEyZ2Y5fv1mBbzhsFmkLFI93KxgLiAvywZV0+HxK2Zht8iYh+XYMfXYQjjssnls5HYh8sYJbV5XAUCW0wIBAgwAOS4LWvzhtFTRTqWgWKep5LPKUZjj6Pa+7egkRts+17phIKzqsEgi3A5LbCU8OrHQtm92ttuKUUVuaJqBoKIhFO7YjzoQVKHpR1Zj2pMkEVpI65A90PbxX+xvRkVVE76sbo7rxd5ettOC8WVmde+xI7NhlZNfwesrYpv90pZIjYShXsk7FXfccQeWL1+OsrIyTJ06NXb7nj178OSTT2LevHndPpfFYokVDEtk9+7daG5uhqIocSvNdXV1KC5O3C+2pKQEX3zxRdxtdXV1AMxgPpoOXldXF/fcbc+5Y8cOTJo0KW6Fury8HHl5eaisrOz29bWn6wY8nkDSj6f08HqDsT+bmvwZHg0RUbzsbEenv9O1xeCa4nSWbqupOgIhDXarhGnji/Dmliq47JYjQW4klhEEASLMVVp/UIUgAIqiwdumj277dPLoKSQx0p4IZvEvWYpU/42Er0abAFtsU6QoGNJgGAaynBZomtRlC65oIBxdbfS3Hlmpjt93LMSqk0ti/Iq2KAgQJHNFvX06uCiae7gloU3Q2iZQCIRUc6U0sue6s73BXU2ITBydj3OmjsSG/9sbCarN67LKErJdVjhsZvCsqjq+NascobCGHV/Uo8kbwv9ur8a7Ow+gJN8Zee0NjBjmRFgzoCgamn0hGJHXosmrQBTNV98ii3AmWUW7fdEyj19JqqBYV6nkh1uCuO6C8T0a18TR+Zg8rgBNfhXVNS3Y8P7XONwSTLiafrgliF+9WYFctw0W2UxNdjssmHZcIco6eU6nXYYkApqmQ0wQ7GqaDkk0j4s63NyKisjq9L4ab5cTRSOGuTChLBcTy/MwYpirX1f3brtf2iKLkeJjifdLD9ZK3r1t48aNCIVC+P73v4/S0lIUFxejqakJlZWV0HUd69atw7p16wCY78U777yT9HOdcsop0HUdO3bsiBUg27t3L2prazF9+vSEj5k+fTo2btwIn88Ht9vM1NiyZQtcLhcmTJgAq9WKMWPGYOvWrbFzqqqK7du346qrrgJgBuEff/wxDMOIfd5ra2vR3NyM0aNHJ3095nMluSeI0kaL7EfSNJ3vBxENWAyuqYNE+6JlScDo4dm4cPooKJFVumhwA+DIEi7MFW7dAJo8IURDz9f/sTfWK3f95t1oDYURirSMkgRAMwBNB2TpSOCr6QZESYidV9PN3tJWiwRrZD+WYRgIBFUIogCHXTZXggPh2Bjai96kt1kVkyUxUvTLiKWVCwKQ47Ye6YEst2mJJZir6qIkRM5z5HFGpJhb20DHTJc371c1A6GwBlEQjro3uCuTxuTjLx9XQxJFcwUwsi81+ryqqkOWRYiSgPd3HDoSkEZWY6tqvGgNqZFq4yJsItq0DDvyPELkehVVg+o3U9p7UkU70UpzjssaC/67W+k71VTyzoiCgGNG5cLjbY1MJlk6vHeAgGyX1QwMLSIMwywit7/Ojz0HPZ0WJRs+zIVhuQ7UNLYiW+qYBRIIaSjOsyMY1rBpSxV2VTXhcEuw07HKkoBxI3Ii+6dzkeO2dXpsprVN8bbI5nt0ZL+0ELdHmvul06O0tBSlpaVxt5WUlOD444+HKKZ3/2pxcTHmzJmDhQsXYunSpXA4HHjooYcwY8YMnHzyyQDM9O6Wlhbk5OTAarVi9uzZWLlyJe6++27ce++9qK6uxooVK3DDDTfEVr9vuOEGPProoygvL8cJJ5yAdevWIRgM4oorrgBgtt3asGED/t//+3+4/vrr4fV68bOf/QwTJkzAmWeemdZrJCIiSgaDa0qofWpyTpYNJ08oQUtLAF9VN8fty27bMkrXjVgxLEEwA1yLZK78rd+8Gw6rFAv0AsFgbGVU18zUZnPF2gxGzQBbjwTAkdtgrvQZAMKR1XGbVUo4nu7kk8qyGOskLQgCZMkcQ1g14AsosFstaFVUBBUNkmgWPAtrOmRRjK2iq7oBQQCyXVZ4/Qo03awUHl0m1yPtgETBPNbrD8Nuk466N7grZcVZGF7gQnW9H7lua6dtzdqm7+v6kYDWZZcRCKrwR4rDCZGq4e0nJKJFpaJ730NhDS5H9/7Z6Gyl+XBLEEFFg64ZKMi1d6vSd2/3pva2KeQnRvqw2ywiJFFEg6cVoZCGQDCMkGKBzSpDFEVkOy1dFiUTBQFnnTQCG/6+F55AGE6bBEkSEQ5r8LWqMAwD1fV+/PqNik7H5XZYMD6yOn3MyJyEkxH9Qay/tCTG2uDJkvT/2Xv3OLuq+u7/s9bal3M/c00mmSQzIZAbYALITQW8tSJYW62Cj1XQeqk/tNbSVtunPj5t9UGt1uJjpa1a5dL2EQRBBQRFBFTu4aJILkAySSbJ3Ofcz76u9ftj7b3nnJlzZs5MJskkrPfrBWHO2WfvffY+Gc5nfb/fzwcaI3Ut3nJeWonpI8HnP/95fOMb38CTTz4ZVagfe+wxXH311fjIRz6C9773vYt6vM9+9rO45ppronbzCy+8sM407emnn8YVV1yBG2+8Eeeeey5M08S3vvUt/P3f/z0uu+wyZLNZvPvd78ZVV10Vveayyy5DsVjEtddei1wuh9NOOw3f+c530NEhf0du2LABN910E77yla/g8ssvRzwex2te8xr81V/9FXT9+IqSUygUCsWJyREV14QQnH322UgmlTHF8Uhta7Km0agVu1lklOvzegdfkCgyyjQYxvMWJgoWurIx8KCdmkJ+ThjFVNZ04N7tc/kPJUDc1NCRiUU514WSE5lXXXLuGtz92L4Z54MmJmS1eD4PhC+J3jMNFgUmiw4EZIu5nFmOo1R1YTm+zLSuuU7phI7fe1U/7vjlHtiOL2eyg2NrjCBpamCMwvF8/N75/TipNwMBgkrVnbcjdXjMudyyX7lhWXQ+5erUXG/Y4k2mmcM1HDrHlLN5KIoE5j7P2SrN7UGbteNx5Ep2XUW9WTX/SGdTpxM60kkdqZiGREwHF3KWulC2cGisHI0T1I4jtGJKtm5VG972mrX46bZBjExW4HoimtdvRk9HAhv72rGprw293aklGZdVOy8dLmipFu9jy7e//W1ce+21eM973hM9tmbNGrz5zW/GF7/4RcRiMbzzne9ctOMlEgl87nOfw+c+97mGz5977rnYuXNn3WN9fX349re/Pet+P/CBD+ADH/hA0+e3bt2KG2+8cf4nrFAoFArFUWDB4npsbAw33ngjHn/8ceTzeXR2duL888/He9/73ihug1KKm266adFOVrE0aCTsOtImJop21P4ctilnkwZipvyYmTpDxfYikRLNPwc/M0w5g4dz0V3ZGC7cshKnru2IKpmNjL4IIQ0jrJwGc1uhcAdkcbt2vruWcJY5rN6WKi50naIrG8NkyYHnc2iMRhXoDX3t2LZrFIOjJSRMTYpa24PvC+TLDoSQVXfX83Hbg7sXlO88fX75it9dj7sf29cwam3PoUIUkRZW2iGCrOWyI9vVuZBzbjqT0WE1CxJyTnYqI5uS4B5W5xawc1Was0kjirzKlZw5nb5bdbFPJfQZ12iuhQvOBXo6E1jWHsfQWBXFiuw+cAOzsvB6GLo0tqulmSkZ5wL7R0pyfnrvJEZz1abHZ5TgpJWZKC4rzC9fKtTPS085eat86aXFd7/7XXziE5/Ahz/84eixFStW4NOf/jS6urpw/fXXL6q4VigUCoVCMZMFiesdO3bgiiuugG3bOOOMM9Db24uxsTH8+7//O2655Rb8v//3/7By5crFPlfFEqJRZFTM0FC1PaTjGuIxfYYICjOFXZcjldCD9m0/ELDyyzuHzIb2fAFKpWjavncSJ63IRAKpUdvvbBFWW0/uwmPPD2OyaCOTNOB6HOPToozC+e4wwgqQIpwxOWPrcQGfewA0JOM6rrh4A8pVr6GT9w337kSx7MJyvSiDOlxQcD0ftz64G7pGkU4YSCQ1+L5oKeqqmVP2m8/rQzKm1YlJALj1wZeiiLRIWxJAIwSuz0EDsV2suNE2hBAQEc6dk6jyaGgUCVMDCOpmoZvRSqWZEoLff/VapJPGnEJ4Thf7oJW8bHn4ys3PzLlwQYJrYFkunt93EHf9cnewGOFFTcvTXeBjDUR9rSmZ5Xh4YTCPHXsnsXN/rqkLOCC337imDRvXtOOUVW0wjaXT7j0jX1qj0BkD09S89FJmeHgYp59+esPntmzZgn/91389ymekUCgUCsXLjwWJ6y984QtYsWIFvvWtb6G7uzt6fHh4GB/84AfxxS9+EV/96lcX7SQVS5MNfe0wTQ27D+ZBBBAzGW5/aA9MgzWsLsr2bwLb9ZGCjkzSwETBklVRoM70ixIgmzBgGqzljOVmEVb7hot44JmDiJkaciWZYzydcL67touVUTrVDkzkvLXt+9g/XMKh8QrOO7VnhhDc1N+B975pA/7tjudk5TsQ1oYuZ3gLFRdCALbL4RVt6FWKTNJAW8rAeN7CzT9/EVdevAF9PZm6fT+/Zxz/cfcO2I6HREyvE+U3BdfmtJM6o+0HhgrIlWzomsy8DhcwAFlVldVo+b48X85BGxoFJQBhBB0ZE4xSWc2mJDINa+Rs3ohWK83ppNHSjHQrbfCnre3ATU3cxP/zp7vwvos34uTVWdiOzKN2PY7dB/P4/i/2oGp5iJsaCCEoBq3l02Vj2XKhaRQxQ/7aFEKgVPWQjGm45/F9GDhUnLXle1l7HJuC6vTqZam6FvNjSTQvHZqP6YH5WJSfHmyo5qWXNL29vXjkkUcip+1annjiCfT09ByDs1IoFAqF4uXFgsT1s88+iy9/+ct1whqQDqIf+9jH6kxNFCcmYRV1cLQctUf3diWRTuiYLDlNq4u93SlULDcSSO0ZE/mSA8edKXgLFRe64yOT1GE5/pxu0LXtwIm4DiEEnt8zgaHJiswidjwISGOx2qzqqddP/Tejcn++L+rmyLknUPRc/OdPd+Fn2wbxhxeehM1rO+v2k4xpMHSKuBmLnLw55xjP2zOcuB3Px3heGrv5Psf+kRK+euuvsao7FVVbfzswgX/7wW9RsT0QQmC79aK8kVN2WDlOxDQUyg48LkCJPLhfc6kzCR3xmIzH0jWK12xZid/snkCl6iIRozAMBs/jyJWclpzNw3tQKDvIJg2M5S20z1JpbkWohzRysZ8+d187400AJJIGuttiqDocDzxzEFXHQ7HiImYw9HQmcP9TB2DZHjJJHQBBvuxEee1cALpGkI7ryJUc+BwolGVUmW17qDo+hACqttfQ5ZsSgrUr09gY5E93ZGItv9cjCQ26REyDgQXRWCpf+vjnsssuw5e+9CW4ros3vvGN6OzsxMTEBH7+85/jO9/5Dv7iL/7iWJ+iQqFQKBQnPAsS1+3t7SgWiw2f830fsdjS+BKpOHxCsVSxffQud9Ge1LB9YALfvPN5FIPIq5Bd+13ETQaN0abVxcteuw4A6tqbkzEN6TjBRMEGAuEbVowdz8dEgSOTNGZ1g65tmbYdH7brA5AzwpQCVcsL5rgFmsVnGoHLeCgDw0p6Iyzbx8BQEV+97Td42wVrcfG5fdFzxbID1+VgMWkCpzOC0aLMlK6DACSIHPO5AAsGvDVKo2r9a7euxH1PDkohGbRuT10XCx2ZWEOn7NF8FRVbtqVDIKpU10Ipidr3OzImJgo2du6dxO9feBJ+8fQBHBovzzkL3ewe+L6ITMHG8haySWNO07JWmK07IZzxNnSGuKnJ2CwO2K6PQsnC7gM5/Hr3GEhgtJdOGJgs2kgHueOO68uKeuCcToLFFUIJEjENZcuD54cRc42Jmxo2rG7Dxr52rF+djarcxwoCgATmYzoj0HWGmKmhsz2OkiY7CJT52InB+973PgwPD+Omm27C9ddfHz3OGMOVV16J97///cfu5BQKhUKheJmwoG9+H/3oR/HlL38Za9aswZlnnhk9vnv3bnz1q1+NojkUxzfTxZKhMyxri2EsbyFfli7atUZZHpfCaVlbHB0ZE8OT1abirFYgJeMabrhnJyYKdp1zNwn+8YNKp8EIXhrMz5gtfvCZA/jRrwbgehxmEPUVmpHZro+EqUXNrEKgzus6fJwS4OJzVuOhZw+hbHmREdr0udtaaOC2ffsv9mD18jRO7e/A9oEJ/OBXe1CxvajSHOZcU0Lg1whsgvqfw2gvw2BIaRS5ko27HtkLzrkUe8GiQ+11KZQddLXF4FsicsrePjCBex/fDyFklVijBBRSxIugFRxEztMaOkPV9lAoS/O3fSMlfPenu9DbKeeUu9viLZmCNYvd4r6A43GUqi4oIS0L9dmodbEPKVddxE0N3W3xYLado1ByYNk+qraLyaINLmTrczymw/c5xnIWbNdHPK6BUTYlLoPMcxHEqI3nm4tpQJrubeprx8a+dqxZno7aqY8F9eZjNDBho9Co7B4QQgRmZDT4fByzU1UcAT71qU/hqquuwjPPPINcLodMJoNXvOIVaG9vP9anplAoFArFy4IFies77rgDtm3jj/7oj7Bq1SosX74ck5OTGBgYAOcc3/jGN6KcTUII7rvvvkU9acWRZ7pY0hPyy/jeoSJKlgcCGekUQWSetetzjBcsfPD3NoNR0tSoqlYgyfngsB13prM0BYKqIsGPHtkLAsh53YQBwTkOjkvxT4hs0QXkuQgh4AuBquPX7bOhniBA/4oMXjhQwO4D+dm3rTk3LTBdu/2hl0CEwI0/2QXL8aAF14ISRBFlrMZ9W+Zj10eX+RyBG7MUQobGMFGxkEkacDx3ylq95rq4Hodl+3VO2WEEVmdGOrj7gakaJQJ+mLtNBLJJA1Xbw0TBAhdS/Ie55IOjZYzmLVz5pg1zzkTPFrvV2RZDrmSjKxuLzMvmGzs2G+H1TMZ1JIL2ds8X8Gp634tVFzxYVNB1JtuiNYZUQsDK+8gXHXRnY4FTvcxcn+uY/T2ZQFC3oSsbX5T3shCU+ZhiOul0GhdccMGxPg2FQqFQKF6WLEhcr1q1CqtWrap7rKenB6eddhoobewOrFh85hs5NJ/9ThdLhEiHZ0OngNVcdDIiZ5l3HyzglFXZlo4XVlx1jcL1BbRp7yGsIgqIqNW8XHWxb3hqNIEx+ZqwlTsUixTS0bkZYdQWBFC2PFx6Xh++8aPfwmowAz4dP4iuEgCGxqu49cGXomtmGz7GC5YUdQB8SPEcvjUR/WvaPn2OYsWFrlH4Nc7l0kGdQ6MkqEBOXZeK5aJ/RaZhe3QnkXPErlcv5LNJE4TIFmfORVBtlffZNJjMb57FYK2WuWK3kjE5s9yqedlchIdwPC5HABwfMUMD5xzDk1VkEvI8XM+H4/pwXNnqr2nSqCtEYxSMygWKoYnKrJnohMh70JmJ4UO/txnJ2NyO6UeC8DwYnYrE0pmMxQKU+ZhCoVAoFArFsWRB4vrzn/88vvGNb+DJJ5+MKtSPPfYYrr76anzkIx/Be9/73kU9ScVMmsUyHU67bchsYgk1P4fVwPrnAXDg508N4t7H97V0bqmEDo0RaHE9Mt9igauU4CIy4MqmjMh5ujbmSIjgXzXnFkZrNZKDjAYVciJfF7p6p+NSMMVjOvLlxlnOtW3itXPMFdvDvuES2tJyMSJmaujMxJAvO3Dcqcq5RqV7eKN57tBEa7Jog9KpqmM+qOoLAbi+wHRV7ngcp63tiLK4ayOwYqaGmKnBcX34XGCyaMH1RJTlHApKwQUIZOs/5wJjBQuO6zc0WJtOoezAdn1olMABonvkuD44lx0Fnj/Vtr5QCJELDLbrw3I8eL6omxN+7ZaVuP2Xe5Ar2PA4h+dPyUsBGaXFuYDlerDsqbl8AE2FtWlQxHQG1xeIGQy/d37fURXWkZM3o1FlWpmPKRQKhUKhUCxNFlRm/va3v41rr70W/f390WNr1qzBm9/8Znzxi1/E9773vcU6P0UDwpbtwdESTJ0hkzJg6lORVdsHJg5r/5FA02Z+PEx96jEh6qu7InTXhhRcrZ5bmGHs+QIdaROGRqVTNxfwAtWjUYJMwgAgRZvrcWiURtVU2TFd60gtBZMAAFIvsgmmcp85pGhLxDSMFSzccO9OlKsODJ0GruL15zqbhuECyJWcqDU9ZmpY3pHA8o4E2pI6CJG52bJSjLrZXDp937XCCWH778xjEiIr/g88cxDbBybqIrBqCU2+QoMtd5q4D6+XzihGc1W4HpfCbprBWu3940Lg508N4rs/ewEVy8No3sLIZBWHxso4NFbGyGQVY8FjFcvFaK46y9VrDAla1auOh4mihYmCJefDXT7DgGvdqjacu2kZXM5nvD9Aus8PT1aRLzl1wrqWMHbM1CkSJoPO5ELBis4E3vaatVi3qm3e76FVwqq0xijiBkMmaaA9Y6IrG0NHOoZUXIehybb2ME5NoVAoFAqFQrF0WFDl+rvf/S4+8YlP4MMf/nD02IoVK/DpT38aXV1duP766/HOd75z0U5SMcVs861hFvFckVVzMVtGsalr0CiBx0PDJxFVczkXUSZ0ZzYWjQjMdW61GcaW46M9bcIKIpNCiegLgeHJKrJJY+pkyFTLtayii6jCC0jxLwDoGoMQQrYHB23coeGZrjEwKk22tu0cheV4aE/HYDnSjVsE+53LSLn2GuTLMraq9t4AgOMLmDoNhJ0U/EaQQz19/1E7OJH/3aiznRKgqy2GmKFF1/YTl21BT0cCg6PlhnFoFcsDDSqhtQI83KxsuZEJGxcCRNQarE3dv517J3HLAy9h/3CpzgWdQ8D35M+Ru3lgnHXP4/vQ05GYs7OCEPm5sl0O2/bg+LwlMcmFwK7BPAyNIGEy2C6H6828ttNJJ3RcfO4aZOI6uAASMQ09nQkMjcu4r0RMw4qu5KLNiYc0nZdmJLhvtZVppaQVCoVCoVAoljoLEtfDw8M4/fTTGz63ZcsW/Ou//uthnZSiOXPPt86MZZovYSW5oUCDgKkziKDyVydcgmiptrQ5Y/Z+rnOrzTDeP1JCuepCANA0Ah44bTueNEvLhAI7qEzLCqs0L6NkqlWbCynwkjENluPD9wVMjSIR1yM1bLs+4qaGV25Yhrse3Rtd17ipoTMbw2TRhuPOLWwYI1HV3vU4HI/DDER11XIxXrClgOUcCNrpM0kDhi7nx4tVb+Y+g9lan/OG4jqbMhE3ZYtyeG0HR0rRQsX0OLRC2YEQAm1pE6m4Dtv1MVGw4fscrKZdXaPyPnMhYGgsMlgLj/HgMwdw7+P7MZ63ICCgM+l6znl9e3I4k04pQUfGhOXwpgs/oQh3PD+IUuPwpxm+zUax4uDR3w5h71AxaLlvPjNPiYxoEwASJsMVl56KFe0x+NOq3b3dqdYO3iKhkGZMiulwXjrsYKh9r0pMKxQKhUKhUBx/LEhc9/b24pFHHsH5558/47knnngCPT09h31iisZMn6mdjqZRVCzvsOZbayvJoUDTdQrb8VEoO0gmdFy6tQ+/2T2OwdESXFeAUlkBLFZcJOONZ1Jrz62RGdum/g6csqYNn79pG3xfZlvrGsVozoLj+dIsTch5a12TFWACWRkOj+16HARTlVONSlG+vD2Oszd047mBSTmn7slZ8NXL5Cyxz8WM6xozGCgJ442kLm74vlgQH8YQzQE7jg+NERRKDgrBvWhPG0jGdYxMVuG4HBNFOzJTCwmd0LmQojS8H4LIKiZjgZkZ6t3aGSNwyj6e2z2O007qxHvftAE/Dmbywzi0zmwMY7kqdI3CcnxQStCeNqTADjoQAEQdCZQQZIIM6PD+lasuHnzmoFz8ECKIeAI0QuCJmVViTaNoS5mImxoY9esWV8IuAtfjsAJjMs7FzDzwBgghMDRRwY69OWzfO4HB0fKs22vBNeVcRBFVXW1xvOHMVdjY34F8vjLnMedLKKY1RqLuDflZkUMAal5aoVAoFAqF4sRiQeL6sssuw5e+9CW4ros3vvGN6OzsxMTEBH7+85/jO9/5Dv7iL/5isc9TETBbyzYgY6HCWKbDobaSHAo0Q2dYvSyFN5+7Bpv6O7B6eRq3P/QSRnMyyqnq+HA8jnLVRTox1b4thMw6dhwfAgIjuQruuXlfQzO2eExDvuwgmzKj95dJGpgoWLIyDQLH9ZFKyMqrgGzjjZkaGCUoVlxoGsXZG5fhpQM5jOVt+JxjPG/h0e0juPAVK7B2ZQblqlfnsD4wVJhxXR1PmmIxRkAEAUjjFuNwolvOcktRark+8mVpGgbIamnV9qFrDElTg+M6wbWp35eAzA53fR5Fb03NjQsZrwT5nOtxUOrD9znyZQeex3HvE/vxwDMH0dORwCXnrkEirkcLGLsP5vHdn72I0ZwVnK8U6Km4DsvxI+M1AcDQ5Mxv3Jz6FeF5HCByrlzmift1oeGUkijGKnRRz9bsQ9dknJvt+PB8DsuVgtrnYsb8dCM8n2PPoQK2D0xix75J5EpO020JpOt5zGAwDfnZcFwPVcfH687oRd/yNFZ0Jevj5A6TRmJaZwRkhphWalqhUCgUCoXiRGRB4vp973sfhoeHcdNNN+H666+PHmeM4corr8T73//+xTo/xTRmbdkWAmXLw6ruJNYsTx/2sTb1d2BDXzv2DRdRsX30Ls+gPamB+wLbByZwU5CDnYrrUetxuephsmhDYwRxU0fV9lAIoqC4kDPRN9//EnRGkU0Z0OLydaFh1oWvWDGjghw3NXRkYtF+QnPwNcvTgBAoVl0USg4YI+jrSeO0tR144JmDsBxZ4XYsLme4qy7++74XsXp5Cpe9dh36ezLgQmBgqIBC2UE2aWAsb6E9uK6ykipnrn1MVYrtaTFdIhC9HpcmYL3dSRQDYc1dP6h+EzienOOe4cA+Dcv1gzZpEc0+G8HceO2x82UHKCGq9BoaRUcmFl3PG3+yC1e+aQNOO6kT2wcm8JMnBqMIr9C13PGk0G1PGyhbBL7PoWsMHRkjEIXBeww+W20pE7mSAyMUpcECABAuMojwB1AQMCarxDFDA6k53m/3TKBQdhA3GQQILLvxbHOp6mLX/hy2753EC4M5OLNEpGVTRnSN2lNG3WiCEAJVh6OnI4HzTu1ZlPlpSmXHgha0ees6a1KZVmJaoVAoFAqF4uXAgsQ1AHzqU5/CVVddhWeeeQa5XA6ZTAaveMUr0N7evpjnp5hGo5btSNhaHmIGw6Xn9S2a+RIlBP09GWgaRXt7EpOTZXiCNzVV68yYGM1bGM/byCRlZnNodIYg8sr1OHyfI811UELqDM+27RoFozMr89LpmqFiebBcH5e//mScd6ocP6htL1+1LIVrb3kWliOvxUTBjlqcAcDjAvuHi/jWXdvxO2f1TrWJ+7Id2XJ8jASZx6GQl+ZnAomgTX0sX61rEedcznsDQDqpg0AasGUTOsYLHJRK2UmAIP6qlbbnwCDNl3PL6aQOy/IjcU0D93CvpuKbjGkzruddj+7FKWvaovvVmTExUbThC5lJTok8/4mCg46MiTectRoPPnsQ+ZKLRIPP1kVbVuKuR/eCEOmq7Xg8yiWv/chplKItbWJFZ0IuLDg+hvMWDEZwxy/3YCwnW+MdT1bLDY3B0Ck6szFsXdeFsu1h+95J7B8uzerQvqo7iY197djU146ejgR2H8jj9l/uQbHqIWEyMCbzwiu2D1OneO2WlQv6u0GCBRJKCHRtKmNaiWmFQqFQKBQKRciCxTUApNNpXHDBBYt1LooWadSyzZh0vG6WRdxoxnmhAnw2U7V4TEe7L1CoOCiUHXAuhQkLcpvDNmkugNFcFd1tccRMLTLMypVstKdNjOXtGZV5QLZqr1mWqqs+1pqjDQwVMDRRQcKUDtpcyMxsUeO4HWZJf+/B3YgbWlRBr1oy/9gKXLSnX51ixUFnJoaubByTRRtusF2obxkliJsME0UbqZq5c8EFxLTZ6vlSrrhwPA5KpSgO54dDCAGqjo+0ECBBhFZoQPb488PR/TJ0hk5CZP62Jx3IQof3i89Zg985Zw1OO6Ub3713Bw6MleCWOUAJurMx/OGFJ2Fjfwe27RrF4GgZmYSOiaId5ZJTCmQSOuIxHYwSJOIaihUX5aqDQsWTrfEAeEkEs/B+6O8mDee4wJ5DRew+WGx6HXRGcfKqLDb1tWPDmra68QNAxnG97TVr8cCzBzGWq8K3fTAK9HTE8dotK1uO0gpjsShp7ORdK6CVmFYoFAqFQqFQAIcprhXHjtqW7bkE8/aBiUiIT59xnisWqRFzmaolEzpsz4fggEaBiuMHDs71cAGM5S1kUwZ0Jo2xfA6ceUo3Hvr1oQVV5sNzExqirGYh6iu8IUIAluPBdBiqgVgOxT+BnBfWdIpc0ZbVaS6QK9noyMSgMQLPk27mcUNDzJT5w/mSK6vmJkPC1EEpiUT44cCFgKEzZJMGDJ3Bcf2o5Z4xCiHEDJfy0EBuomDNer9CwnzuLad0o1Cs4tafvxjN0+dLDu5+bB/ItNi0bNIAFwIao9A1BgjANGSW9+hEBT6XWeHL22OwHI582UY6rmO8YMHnUwsYskW98XXKJA1sXNOGjX3tWLcyO+ec9LpVbVjbm8WhsXLLUVqhIZ3GaCSmtRPIybt2cS2bNpHNJo71KSkUCoVCoVCccChxfRwTtmzPxvaBiUAIeUjG9Bkzzle+acO8BXYrpmqUELiCo2T5s+YM+1xgomBHPzMqXbrnW5mffm6uK1u6iRB1MVbTC8gCQK5og1DUiS8BoFh1sDyRRGc2hnxJVnodj6NUdUGDuK6utlhddT0DoGJ7mCzY8BK8JaOuuRAAbIeDUI7ObAzAVHZ2seLKdnvI8Kna44Xmdh2ZWHS/OBcYy1sQQsh2dUohgoWDnz11ACevakNqrIIb7t6B6rR5+trPzB+/eSN+/swBaaTmy0UKnRG8Yl0nzt60HADqxC0XwE337gCCBZVwsaXZ1WGU4Mz13Thn83KsDFrL54OcfW8cpVXb4m2aTL5HCBCIE9LJe/rimsYIVvfswcVnr8b61W3H+vQUCoVCoVAoThiUuD6B4UI0nY2uncltlDs8G6uWpdCWMjA0XkUmqcM0pj5GofFVVzaGfcPFWYV1I3wO3Pzzl/C2C9bi6su3zlmZn97uvmpZCj0dCewdKkZCerqYriUUT6zB+xcCmCzYWN4eh9nOYLs+imUXrz6tB49vH4FpsDrRZ9meNBmDjOSaLMr/Dp2zFyrUGCHwIXOkK1UXqaAVOryPjselaRqm4rtqze3O2bwcDz83hIFDRVRtL7oG0tlbmswZOoPnc/zoV3ug6zIXfPpnJhHT4HgcD/92CJe94RS8+hUrMXCoAM/xsWp5Ciu7U3X3p6dT3odnXxzHr18aQ6lBlvd0kjENiZiGqu3jtLUd6O1KLuyi1UDIlJP39BZvXafIpkxw14Pn8eOyKj0bjRbXfJ9j4FAB37l7O65YwOKaQqFQKBQKhaIxSlyfwMw2G107kxvmDrfC83sm8MNf7cFozoLleKja0pG7LWWAMRq1br/69B7sHW4+Ozsbrsdx+y/2YPXyNE6d5Yt/s3b309Z2YDRXRdX2Gop7StBE9NfXtWVLtw/HkxFalBDETIaObAw+F9Bq2pMt28N4wZL51NP2z0Uwcw45D51NGajaHqq2P+e1iASzkIK9XPWQjMv76bg+4oZsEfc4YOjSYMtx/boWeo1SnLa2Azv2TjasFAsBJAyGmKlhcLQEQimScTkHrzGCmKHB0OX8e7HiYMe+SXzhpidRsbyo7burTc40r+xORe7eu/bnZFzXbO8vMGZDYOCWiMlfSYxO/XctXAgcHC1JozMCrF6WRm93fcs3JQSEAhol0APjMZ3RIOtaLsYUyy4ScdnlQIfKAPfRO0freKNzWSwfgyNBs8U1pjPETQ1jOWtBi2sKhUKhUCgUisYocX0CM9dsdDiTW6q4Le3v2RdG8Z27t0ftwjGTIV+S8VhjeQvJuI7Vy1I4a0M3ShXnsAy8XI/j9odewqYmX/xna3cfy1vY1NeGX/56qOG+mwlrSmp/ClqthWxd12oqwSetzM5oi8+XHXAhBR0XgPBF3dv3uUDMkDPTMVPGUrUirsNKdLQzAgyNV8BrzOGEmDKNK5TlvWxLGbho60ps6GsHFwLP7ZmArtEZMWIk+FfV8ZFKGnA9AU0TyKQMxHQNlBL4XGCyYGM0X0W5pvqsM4J0kKe+f6SEG+7dCZ+LWSv0lEjnd9uVc/gsaNH2hIjmnAsVFz0dcayYVrV+aTCHHz+2D8OTlajVX9cIVi1L4+KzV+Pk1W1BZZpB1wgYlbPo4fk8v2dqMcZyfNiuDwi5KBE3tXn5ECy2j8GRYM7Ftfj8F9cUCoVCoVAoFM1R4voEppXZaMYIUgm9wavr4ULg1vtfqGsXNsAQNzQ4ro9C2YGpUxQrDn7wywHpBH2YHbaHxit49LdDM3KJayty2aQB1xewHR+UEmST0izrl78ZkjPXpLV2bC4Drae2j1q55b6LZRemwXDJuWvQ35PG8vY49g2XkIhpEAJwXD8SczwQin7NBRAA2tMmOBcYnqjAnqOiGxLmXIeGbI0qwRqTsViMEsQMBsvxkCvauOuRvdi2cxRnre/G0EQF6YQBt2DJmWPIPvIgRAu+L227l7XHYJoadEphOb7MBy87yJXsutl1AHB9gYmg9X2281/Xm8HGNe2IGww/3TYI2+VIxjSUqi7kYWXcWNxgKFTchpFZLw3mcMsDL8mKvC4N5EydRXnkP3lyP9rTJk5Z3Rbd79r589rFGAigYk0tElRtH67LYdt+Sz4ER8LH4EjQyuKaX219cU2hUCgUCoVCMTtKXJ/ArFmeRk9HAoOj5RmxVrUzuauWpTAwVJi1vXXvUBEHRkpRu3AICdy4HZejYtsAbNCginq4WLaPm+9/EQ8/N1RXEQwrchqjGM1ZUR512ELt+RxCyNZiSkhQ5Z39WFzIGWRDo3Lm1/EjAVwoOyCUgDGCux/bh30jJVRsH1XbRyVw2BYCkLlj8pjZlIFixYXt+rLZXMjqttWkVb0Zns/n3F4IAVOnyJddVG0fy9rjdSZkB8bK8DyO9owpncY9X+ZkU9nybRpMtk1rFB1pEx4X2H0gj1TQfl4MRHCrxA2Gzf0d2NjXjpNXZSP3cgBIxfUoJsvQWF3ONTAzMosAEAR4ctcoknEdmYQBRqX7u+PyoAXeBSDzs6++fGvDufxwMSamM4zmrJnXmQtwR97L2Vqlj5SPwZFgMRfXFAqFQqFQKBRzo8T1CQytiU1qFmt12toOXHvLs3O2txYrLjyfIx6r/yJu2R7G8tVIfElBS+Hxw4+fIgQwdVZXEdzQ144XD+RRrrrwfAEhpCEXhZxpdr3a7GeZSSxzrhuL1OmV7TC+iwRGXzqTYjud0OH5UnTu2DsJQ2dIxhiKNW3SPHBPS8QYNEahUQJLTHV011ZLW6X2nMOJ8NrJcAKp6QtlN5hbls9QQiLBN563YLs+fF8gkzRQrrqImXLulgKwHR+TJRuGRvA7Z61CImlicLiIXMkBhBSxc0GInJHmvsBlrz8ZG9a0N9xuekxW3GQQILBsDzFTA6NyoWasUMWq7jRMnWIkV0Wh7MByfHieL13Pa64LJRQ+lwsJjVqca9ujxwtW3bVDzf0XAFyfY2i8eav0kfAxOFLMubhW9dDbncSa5eljeJYKhUKhUCgUJw5KXJ/gbOrvaBprddraDjzwzMGW2lvTCR0ak8/r2lQVLJw1DiFE5lVrlMBtkG3dKjRwsE7GdSSEQK7k4JYHXkLCZBgcLdXPDovoX3UIAXCIpnPAGiNRZbs2GmqiaEei2/U4CmUHFcuTWdL+VHv2zPqnpGR5KC1ASDeD0XCumtTlcIdO6KGBWtgsEDqCG4GgSid0iIp8TW93Eo7LkSvZKJQclG0Pvi8QNxj+4IJ10DSKHXsnwMVU7nUriGDeHJQgFZ+9Elobk0WCCvr+4RK27RrFZFFmcvtCoCNt4g1nroLrcQxPVGG5fjSjXUfwo+fzhi3OU9nnU/e5UU2ZEtn14Hh+01bpxfYxOJI0W1zzPY6K7c+ZGa9QKBQKhUKhmB9KXL8M2NTfgQ197TMiq6695dmW21v7etLoXZbCngN5ZFM0cqt2PV5XSQ0FHiEEjKJu7rhVQsGVTRrRvrRAgMVNBtpY18yg1WNTQqAzuT0LFgVqq5lCAI53+JX4hRJ1BTTRQNGMcfBnoeygWHGRiGnobo+jpzOJZMyBgJxjJxCRg7lGCAyTorcriZt/9iKqLc6CN6JYcbF2ZQYrupLgQtTlXK8InLjDe8sogaEx6DrF/qEi7n1iH4oVBzpjAJEty7tyFvYNl/Cmc1ZD0wjgTlXu6y+A/ENjtGGLc9ge7Xg8uliN9hMuqBDSvFX6eGu1brS4pjGC/hUZlXOtUCgUCoVCscgocX2CMz0uaPPaDlBCMDBUmFd7KyUE73j9KfjazU9HVTDf53VuzGya6KWUgHPRoKY8OwRAOiFdtUPKlgsBgURMk+3Ki4DPBWigWLkQ0dz1UiNcvIjcwac9X/uzxigyCR2mwUCpbLeuWDIybcu6TgyOljA6WYXtcXAeVHI94MUDhYbH7m6LoWJ5KLdQiecCOLW/HU88P4zHd4ygWHWitvoVHUlcuHUlTlndFsRiSfM3nwvc8cs9GJ6oNF3k2bZzFCs7E3hhsADOBQitr157XLbyr2rS4hy2R+8dktFwtaMA4Z+EACLocOhuizVtlW7Vx2AptVpPX1zLpk1s3diDfL4C7xguGikUCoVCoVCcaChxfQIzW1yQz8W821u3nNKN91+yCT/81R4MTVTguH6wrQyu8jkPZqDr46OmqtmY1RhLGpIBAjJP2dAoYqY2VSEPzNP8+bhrzYIQUhAKwedl2HW0mUvuM0oQNzUkYhp0JtvyLcdHqerWuYs/+vww0EIV3tAoEERmvfVV/RgrWPjBLwfmPE9DAx7fMYqq5cHQGbJJE5pGEdMZRvIWbntwNy5/3Tps7OuInLxbnWG+9Lw+DE1UkS87clGEygvjB+o4ndCbtjjXtkdbjg/RYJEi/GzqGsXbLlzXtFW6FR+DpdhqTQmJZsA1jU5FvCkUCoVCoVAoFg0lrk9Q5ooLetPZqxfU3rp5bQdOXpXFvuEiCmUHP/jlHozlLcRNhomCDV8IUEih7HEBSqeykHkghKqWB4/XVrwJOjIGihUPjifnav3AXTtmhhVymUccCXUsLEabUmkAFrLUhXWz90kJEAsEtaExCAFUHQ/5sgPXbRyDNpsxWcyQBmcxQ1aMPZ8jX3bxwLMH8b43b8TPnz4QZWhH50AJTJ3B0ClMXTqOIzgvwTmKFQeW44FzoCNjwnI47nxkL9avmXLSbnWGubstjg+9ZTNu+fmLODBWjqr4jMr57cteu27W+KuwPfqWB17C/uESRM0QfljJ1jWKt12wFqfOEaM1m4/BUsq5VigUCoVCoVAcXZS4PgFpJS7oyZ0jC25vra2C6YxGFcFM0kDZ8mQ0lhAgIFi1TAofAJEYMXQG7vhR1VEAKFQ8xA0Gz+eBQJcz3eWqi6rtgRAgGdPAgt7zVvOra9GYNNuqWF5UvV0EU/MjRmgMV9sSHjM0xE0Zn0UgK9STRRuW483reugaxeb+drw4mEPc1GDoWnRM+SdBwmQYy1VxcKyCVExHxfJh6hS6LjOmw7g1n8ss8HLVDboZSLQf2R0gUCi7aEsZM5y05zPD3N+Twf9639nYO1TA7gMFCAKctDKL/p6Z0XGN2NTfgf91ZTsefPoAHnz2ICaLFrgvoGkMXdkY3nbBWmxe29nS9WvkY9Aowk6hUCgUCoVC8fJBiesTkFZabYcnq7j0/D6M5a3Dam+dXsUzNQpTp2hLmbhoy0pcdEZvtI8NfVLYfP+h3VFbMADwQJy5ro9EXIfrcXhBdrXt+ujrSaNcdTFZcqCxQBQvoGzt+wKlqov2tIl8yalz/15qhDnUhBDEDYaYqUELFhZcj6NQslGx5yeoa0mYDKuXpfDSgQI0baaoBSAFPCEYL1SRTBjoj+soWx48j6Nqy0UUx/WnhL9O61q2wyg0GpyzQHAPakYNms0wCyHguD4KZRfLO+JYtSwVXBeCtSuyWLsiu6D3TQnB685chYvO6MW+4SIqto/e5Rm0J7V5z9vXLjIpFAqFQqFQKBRKXJ+AtNxqm40vSnvrbFU8LgQGhgooVVwk4xoe+vUhKQob7IcLee6GRpCMa+ACuPz1J+O8U3uwc+8kvnnn8xjNWQvrBwcicTeakyFahrZ0q4y6zpCJ64iZGghBIDQdVCx3QQsL06nYHvJFWzq6+xxUY9CYzMaOmwyMUfi+gOv5yMR1VKouTJOB+xy5kiWN0ETg7B5EgdmebN/nvgBBkHlOicziBuC6M0cNGs0we5wjX3IiJ/qxvIVrb3l2UVuuQ2GsaRTt7UlMTpbBF/rBUigUCoVCoVAooMT1Ccl8W21PWdOGx58fxkTBQkcmhnM2L4fWat5VQKMq3nRDNS44ipW5XacdT8DxXPR0xJGK69g3XMQpa9qQSRoolJ0oRimUQvOZv67dzltiVWszmHuOG1JQ+z5HqeqgVHHgLjwhqyGuJ/CbPRPQNSmks0kDukaha/K+Vx0PI5NVLGuLY0VXElXHAxfS+Z1RCp9zhFczFPs0mJVH8IzHhfwFQ+Q9CrsQpo8a1HY/7B8poVx1oxnotpQBxmjD7PWjzXTnfdUGrlAoFAqFQqGoRYnrE5D5xAU1chR/+Lmhw64STjdUYzGC0cnqvPYxmrPw7bt3QGPSOGt4sgIQAhLIa12TM9QQwETRnte+GZ3/zPZ8MHUKexYDsantAiMxk4ESAi8Q1BXLW1BG+GwYGoWhMxg6Q8xgSCd1FEsuLNfH/pESPM+HAJBJmXAcH2YwGtDXk0FPRwIDQ0U4rg8uRJBZTera6rmQc+21ixYeF4ELPEEipjUdNdjU34FT1rThC/+5Db7PkUkaMI2pX0+NstePJrM57ysDM4VCoVAoFAoFoMT1CUmrcUE7907OcBR3XR97h4r49x/9Fq85fQXWr5YV45N6Z59xra3qJeI67nxkAJbjIZs0UKp6KFVduPPM1OVCIGEyOB7H0HgFAgClAiww+fJ8jkLZQaxBdX4uBF9wd3lLxA0GP8yRnoZpMMSNKUHtc4Fy1UXZ8upm0Q8XXZMu3qYhBTWBXFBwPB/FioPRXDVqvQZBEHUmMJ63sGZZCu+sceC+5Nw1+Optv4HnC2jBLHid43awb5/L+8Nr8s8BYFV3Epe97uRZhejgSAm5koNsypzRcdEoe/1oES4Ulatu0F0gZ9GXQjVdoVAoFAqFQrF0UOL6BGWuuKANfe34ys3P1DmKV20PhbITVCeBux/dh3uf2I+EqaFveRrvetNGrOlKzDhWWNUbHC3D83nUAhw3NQxNVOctqkOEkG7YtVnNggNgcgEhjPsqWXO3mk/ncEzCG7Whh3PHlACnru1A2XJhexV4vjx3sybqKhTUpaqL6jwr1BqTopY3WBzQGI3En85Y5KhuB/PaYV54ozdEALSnTTAKVGwfyZiGDX3t0SaJuI5Y4FDucwHBRXR8jckqMufyv30eZp3LP193xkr8jzeun7Pa3KpXQK0h2pGGC4FbHngJ43kLQojos6hrFJmEDsvxj1k1XaFQKBQKhUKxtFDi+gRmNqOxgaFCnaN41fYwUbCkcKpRbdwXqFgu9hwq4Ou3Posr37QB61e3Rc9vH5jAN+98HsWKCxHM5EJ6XaG4CCIorOZqjML1peVUeAwujmxr92xMjwLjQgroU/vbMThaRqniQqMUbempGWo3qBhXLT/K/J6LZEyD6/mIGQyGoUFnFI7rI1eyQYk0IDMNDaZO5YIDBRKmhqrtY2SiAruFhQ0R3C/P5zB0DW1JNqNCXKq4oIRgeWcCnsfBuYDPBSaLNki43ECAtrQpK9fB85wLvPr0FS0Jz/l4BRwtHnz6gMzFhpA+BMFbdTyOiaKNTGJmvJhCoVAoFAqF4uWJEtcnOM3igmqrhEIIFMqOrKBO03yMEXAhhVfV8vCjX+3B2y5ah0pVtn/f/PMXkS870bEigb0IhKZewc6lszUPjciOnRmZgMyfDq+Xxgh0jYISYMe+HEydIZsyQCmB6/nIlSxYjj/vhQCdyZIyF9LkLZ2goIwiZjC0Z2KwHQ8el6K9YkkTsJipgVEOSgk6MibGCnbLnQOFshO4tVNQSuoqxM2Eb6nqwvE4aFD9plQKfiEEciWnaVZ6I+bjFXA04ELgwWcPBsJ6KrsbBNAIgccFypYL09COajVdoVAoFAqFQrE0UeL6BGUuZ+NasSQgc4hr3Z7D1mcCAkakoPU5x459Ofzzzc8ARLZoV2zZkj3dyGoxCCugIGEldFF3f1jwQFgLAHFTQ1dbHIwSVCwPjscxWbRhu/MX1CEEQDymwdQZEoYGxqTY5FygFGSC264Px/WhMYr2tJxTrs2Jnm+7vBAAobKNHABG81MGdM2EbzZpYCxfhecDhk6hMQLH9eeVlR7SqlfAbPtbTEfvfcNF5Eq2nC+HvCe1MELgehymgaNaTVeu5QqFQqFQKBRLEyWuTxBqv3CP5qp4cucIhierTZ2Na8WSqdMZ4kFAVo4Jkfv2uYjavMsNRFsorOcTizUbjEphL3OUCdwlpKwJAUxDQ9yQLdkak1XfYsWNhOlCYDRs82YwdQZG5R1xPY6K7cFxfTieD15zKQgADwLFsotMitSJUSEEvAVeN0IJtu0cxUVbe0EJaSp8KSWI6Rocn8PUGYpld0FZ6SFzeQXMtr/FdvQuVVwgiARzPR8EqK+mQ45QtKWMo1ZNV67lCoVCoVAoFEsXJa5PAGq/cNuOj6rtgxAgmzKQSRnwPD7D2bhWLJWrjVtaQ9fno5EHTYkU5cmYDsuRc9ae4DA0uuiRVAuBECBmaIibGkyDAUKgYnsYyy/csI0QGcUVM+Q+IzHtc1QDMd2o+h0uOBAiK+gCArbno1SVc9GMEfR2JTBesBsuhMwG5wKM0RmzxFwIxGMaLnzFCjz1wihyRScSvn0r0njzeX1IxrRFqabO5hXQjOnRb1qcNvzcz4dUQoemUWgaRaHM4QsBiqkFJM4FCAEu2rryqFSOj8R7VCgUCoVCoVAsHkpcH+dM/8JdrroQQb9yoexAYxRxU2uYExxWCe98ZAAvDObh1RRdGW1eMQ5lRCPJu1AZzIWc103GNRg6Rb7kSI8sQsA5BwtnnI8ijBLEDIaYqck54mDGNl+24S9wwcHQpKN3zJD3BJDz7JbjwXb8yKkdaN4FIATgCxGIbLmloTN0ZWP4/desRSZpgAvg/976LAghoKT1lnpdo+hsi0NjBPminMGeXi2lFGhLmThrfTc2r+04Im3JzbwCGsGFwF2P7q1zvgcAQ2eHlY891d1RQjqho1z14PGpDg1CZEX9oq29835/8+VIvUeFQqFQKBQKxeKhxPVxzPQv3K7HgxxiCkDAD4zKYgZrmhMcVgkffOYAfvSrAVi2B9cXEELOWB9JN+7p4pFzgWLZQczUsH51Fq/csAxly8Xdj+6LIq2ONLpGo+q0HjiUl6suJgsWFqLtNUaCrGk5Px222duOL+O6HL/hokEr8kiKbIASgZjOkC87yCQN9Pdk8Nzu8bqW5laQ3Q4mEqaGiiXbu0dzVdz7xP4Z1dLxgo2Hfn0Ia1dkjrmY2zdcrHO+r+Vw8rEpIThtbYdceKrpTmCUQGMU6aSOy1538lF5/0fqPSoUCsVSgHOOgYHdAICBgd1YvboPlDaOZVQoFIqlzDEX15xz/Mu//Au+973voVgs4uyzz8ZnPvMZrF69uuH2k5OT+NznPoeHHnoIhBBceuml+OQnP4l4PB5t8+Mf/xhf+9rXMDg4iJNOOgmf+tSncP755zc89oc//GFs2bIFf/qnf3rE3uORYvoXbl5T8iQgoJDzuo4n52Gb5QRTQvC6M1ahpz2Bux7di/0jJVQsL4qLqhXBtV/rD3e+ekZOMyWglOKS8/rw2jPkrO/PnxqE4/IFCdtWMXWGmCmryZQEudAlmQs938PSYB7bDGanGZVmWI4rY7jsZlnTDWj12FwAXHAIPuXwXdvSnC/x1vYmgImCBcYIKlUPvd1JPLlzpOVqaTj3Xyw7KFZdpOI6MknjiBtuHal87O0DE3jgmYPQNQJCKPxw0UkIECHwujN6j1ob9lLMAFcoFIrFYNu2x3Hzzf+FsbFRAMANN/wH7rrrh7j88j/CWWedc4zPTqFQKObHMRfX1113Hf77v/8bX/jCF9DT04MvfelL+OAHP4gf/ehHMAxjxvYf//jHUa1Wcf3116NQKOBv//ZvUalU8MUvfhEA8Oijj+Kv/uqv8MlPfhKvfvWrceutt+LDH/4w7rjjDqxbty7aj+M4+MxnPoNf/OIX2LJly1F7v4vJ9C/cNJjZDd3JCAAORKI7zAlOxHUMDBVmzLPWzroWyw72HCrgvm2D0qiq6sJxeZ3xWa3gXgzt63OBctXF7Q/tRk97HBv7O/DkzpFF2vsU0ayzqSFmMEBA5nznrQUZp+majMea3updtb3I0XuuDgACYGVXAgfGKgDm/47zJXkvQ9fqVctSaEsZODBagWix/YBS+VkZnayiI23irA3duOuRvS1VS6uWV7cwI4QAoQQJU8PqZakjarh1JPKxa7tCurJy4c4J8r0pkaZ+z+2ZwO+es+aoVK6XYga4QqE4/hgZGUa1WjnWpxGxfftvccst/w/r12/Aq199AX7wg+/j93//7di+/Xl8/etfxWWX/Q9s2nTqsT5NAEA8nsCyZcuP9WkoFIolzjEV147j4Nvf/jb+8i//Eq997WsBAP/8z/+MCy64AD/5yU/wlre8pW77p59+Go8//jjuvvvuSCj/wz/8Az74wQ/i6quvxvLly/HNb34Tb3zjG3HFFVcAAD71qU/h6aefxg033IB/+Id/AAA89dRT+MxnPgPLspDJHL8tlNO/cIcVRcfj0GrigyglUU5we9rEbQ+82NRJvHbW9dSTOrFrMI/B0TLaUgZGJy00SphmQc6z4/iYzygyDTKca/cDIeO9/uPuHXjLq/owOFo+zKsk0RiBaUgxbegMvs9RsTyMhvne8yCcxW7W6m05/lQXwTyYLNrzfk1IaLC1alkqmpE+NF6Zl3t5qMEJIbjkvH50ZMyWqqXP75nAQ78+hFLVge1wCCFAiZyRr1gu9h4qtmS4tdCIqSORj92oDdusEbWEkKPahr3UMsAVCsXxR7FYwN/8zdUtL7geTXbt2oFdu3YAAH7wg+9Hj99yy38fq1OaAaUU//zP1yGdPn6/NyoUiiPPMRXXO3bsQLlcrmvZzmQy2Lx5M5544okZ4vrJJ59Ed3d3XQX6nHPOASEE27Ztw8UXX4ynnnoKf/3Xf133unPPPRc/+clPop8ffPBBXHDBBfjoRz+Kt771rUfo3R15Gn3hziYNjBdkBZYA0HXpbJ0rOaAEyJdsTBZFS27DtY7ituMjkzKk0VgNGpNmWhojqNrzm4uu1Z8k+CdsabcdHw88fSCogra+z1CwR+3ZQawVJbLyWLZcTBSsec+SSxMyuS+N0SAbfP6t3gSIWsWFENE10BhFe8aENV6pi9qaD0IAv3jmYDQj3YrpWpRnToCutjgoBWyHo6s9joTJ5q6WUoJtu0ZRtV34XMZTMUqkkRoIPC7gcWnYNpvh1uFETC1GPvZ0lkIb9vTFhkvOXYMbf7Ireo+MEVi2j4rlwTQYLjn36FTRFQrF8Uk6ncHnP/+VJVO5HhjYjRtu+A984AN/glWr1sx4fv/+ffj2t/8dV175AfT3n3QMzrCeeDyhhLVCoZiTYyquh4aGAAArVqyoe3zZsmXRc7UMDw/P2NYwDLS1teHQoUMoFAqoVCro6emZdX9//ud/vlhvoQ5NO/rmG2999Vp85+7tyJccJOMaTJMhmzSRK9sQXECjBK7HsXpZShpzFW20p6fmZ5nBYOgUuaKDHz+2D6eu66z7gn76yV34Y0Zx5yMDGJ6oIhFjcBwOAQFdY0iYDCs6kxierAJYuNBgTPaxcyGdzg2dYqJgB1FTrWEE7dnd7TFUbdnCW7E9TBSsecdl0dApPBDoBLJt3XZ8FMpOw4isGe+pgcO5AMAhwAiBW3NKHRkTVdtfsLAGAMfz8dCzB2E7PpIxDeWqB52RIKe88Wui1n5CoDECAgKNSTfwVcuSWNmZxP6REgx9ZrW0YnnozMYwWaiCUQrXc4OIMBLNDjBK4PkCGZ1heKKCA2NlrF1R/+Xk+T0TuPHenbAcH8n4lDA+MFrGjffuxPsv2YTNa2cX2NHn9OEBHBovo2J50BjBmmUpvOVV/U1fzxit+zMkmzahMQLf52ANFhZ8j0NjRG53BP7eP79nInovni+gMYIVnUm8/sxVeG73OPYNF6Msc0rlvbvn8f3QNDbntWr2nk9UXm7vV6GYjaXU1nzo0CEAwFlnnQvDMLBr1w7kcjm0tbVh/fqNWLasB9/+9r/DNOPo61t7jM9WoVAoWuOYiutqtQoAM2arTdNEPp9vuH2jOWzTNGHbNizLaro/2154y20rUErQ3p48oseohXOB3QfyiCdMvP31p+DhXx/EwdEyqrYLjVGcdlInTlnVjripobs9jhXdSfzjjU8ikzKgazPFQiZpYHiyismyh5NXt9U99+r2JM7fugq7D+RRKDvRXGep4gaxTwL/698fPrw3JADPnxLSxYoLIWYX1qEADs3DKCFwPR9jeVtWHufZmj3dKRyQgrVYdmA5Prx5zmPXHp8SWVmW7dtSYId0ZEwwRjGWt+a1/xCNAgjasMeDxRNpvgV4Qsy6CECIFB2Cy+0qjof+FRmcvn4ZKCV415s24uu3Pot82UU6rgfu4xzFqotkXMfJq9px/7YiuBDB+wIIEdAYlYs0RB6cMSod3ymr+3vCucA9TzwL2+XoaotNmaZpDHFTw3jBxj1P7Mf5W1dNeQo0YfrnNJM0cFJvds7XAUAmE6/7OZtNYHXPHgwcKiBuajMXFmwf/Ssy2Lqxp6X9z4dnXxjFDffuRNXykE7qkWv94FgZ40UbF5/Xh6HJKnwhkIrrSMR0eMHzN9y7Ex99xxZsOaV73u/5ROfl9n4ViqVOW1sbAOBnP7sXDz54f2RoBgBdXd246KLX1W2nUCgUxwPHVFzHYjEAcvY6/G8AsG27zv27dnvHcWY8bts2EokETNOM9jf9+Ub7W0w4FygUjk6rVaOqVk9nEpec34futjjGJqt4Yscwfr5tX/R8Kq6jYrmIx1gkEoUQkUkTIYDteDgwXEBnaqYpEmMUJ69uQ6FQhR+8vjstFzF+/dL4YUd2eTVClFEp+hpp2bDNO2YE7dlCoGr7mMhbcOZbna5pHY8F4pwLAcvxUaq4sB1vUVzKaRBxVbVllnXtLgmAiuWhWHEjd/b5wmWPuTSw83lwL/2W9kcAiCByrVBxkIobeMfrT0GpZMH3OdZ0JWQW+rTP26quJE47qRM/eWJflH0dIoR0qdcYiUSpF3zOXtg3AXAffT1ynnrPoQL2DxWQiLFgMaL+nBMmw/6hAp7ZMTSj4t2MzpQefYbz+dn/TjJGkUrF8JtdI8iVbKQTenRuF5+9Gt+5ezvGclZdRb1cla3mF5+9es79zxcuBL577w5Uqi7a0oZMARCy1T6b1JErOrjlvl1glKArKxcjxLTnv3vvDqzqjDdtEWeMIpOJ1/1dPpFZiu83k4mrSrriZc/69RuRTmdw2203Y8uWM/Anf/Ix9PauxoED+3HnnXfgtttuQSaTwfr1G4/1qSoUCkXLHFNxHbZ4j4yMYM2aqXmbkZERbNiwYcb2PT09uO++++oecxwHuVwOy5YtQ1tbGxKJBEZGRuq2GRkZwfLlR74VypunuFsI2wcmcMO9O6Pc4UQwN71/pITRXBWv3boSDzxzcMbzY3kLVVuKxnTCQNX2UCg7cL2pGiolwPBEGV5fe9Pj+z6f8T4TJkPcYLBsr6UW7ulu47VojIASOatLATCNRpVpQ5ft2Z7PUao2z4iejUbO3m5gbmbZ3rwF+lyE+sbUKQzNwJhnSbOzYNiZUQLH9cHF1LbzJbwElMgDuq6cLW/5tcHpdGVi+B9vXI8tp3RjcrIc3ef1q9vwicu21M3/rlqWwrW3PAvP5zB0aaI33TXeDxZtGJFmbYQAP/zFbmgajeapfS7g+QIJRhsu0MiKt4d80YbXvfh/v57fM4F7ntiPfUMF2K4PSgi622J424XrcGp/B65404apWfCqB8YIeruTuPS8Pqxf3bbof+cHhgo4OF5GIqYBINOuiTQOLFYcdGRiDZ9PxDQcHC9j94H8nEZrjf4un8i83N6vQnG8IYSI/pl67BiekEKhUCyAYyquN27ciFQqhcceeywS14VCAc8//zze8573zNj+7LPPxpe//GXs3bsXfX19AIDHH38cAHDWWWeBEIIzzzwTjz/+ON75zndGr3vsscfwyle+8ii8oyNLbTxQ49xhG3c9sheUAu3pWN3zHRkTB8cqyJccaIzIeebA1RlBS6+gwL2P7UdPe2JesUlrlqfR05lA2fJacqee7f+VjFIYBkNGo4ibTJpkBVFZ45UqXJfPO6YqZkzFblEiBYnleIfl7N0qYSv4eN4CAgd3jRH4Qs6ZhwIUNSJ3oWcjhMzTHs+3PmMe6HEYOkPF9ppuV+siD0gRGLppxw0N4wVLLnTUnLwIKupecKD2lIlkXK8z0XvT2auPWcTU9gE5612yPLjuVNt/qeLi/976a7ztgrW4+Ny+KJ5uvi7mC2EuIzVKgkvc5PAq71qhUBwv7Nq1A8ViAX/4h5fjgQd+hmuu+bvouc7OLrz97Zfh+9+/Bbt27cDGjZuP3YkqFArFPDim4towDLznPe/Bl7/8ZXR0dKC3txdf+tKX0NPTg9/93d+F7/uYmJhAOp1GLBbDli1bcOaZZ+LP//zP8Xd/93eoVCr4zGc+gz/4gz+IKtPvf//78eEPfxibN2/GhRdeiNtuuw3bt2/H//k//+dYvtVFoVE8UAghBIbGMFGx0JGJzXieUopsysBkwZazvSJsvZ6ayfU5MJav4qaf7sI/fOAcaLS1tsVat+Z8yZG5zk22nS4edY3KGDGNRnPThEgn7vG8JcXvPNUmIUDM0KL56bDafaSq063g+lI9h07hBEA6riNfdqL3JwBQLFxc08BAzWs115oQGDpFJmkgZjDkSg7ufHgA529dNedra0UgJQSdmRjyZSeqwoeQ4H52Zk3ETSmQpxaDHDy5c2RBEVMLje2qff1dj+6VHRCBOR2jFCCA4AKux3H7L/ZgzbIUNq/tPCpxW8DcedZh00OzD4nKu1YoFMcLuVwOANDR0dXwO01nZ1fddgqFQnE8cEzFNQB8/OMfh+d5+PSnPw3LsnD22WfjP/7jP6DrOgYHB/GGN7wBn//85/H2t78dhBD8y7/8C/7+7/8eV155JUzTxMUXX4y/+Zu/ifb3mte8Btdccw2uu+46/PM//zNOPvlk/Nu//VtdfNfxyuFWtZJxHRXLg+txcIiGztRcAEPjFfzvbz2O9/zu+qYV7FDcFMsOilUXqbiON529Gk/uHMHgaBnlqttQFGsN2rzDWKty1UXVnr9xGNDY3dv1OAplB5btzct1/EgSVqp1RlG23CCSa+r5hcr+0M18PnRkTCRiU2ZdyZiGQ0FLcaO5++gchUCh4oALgarlRe317WnpeeD7HI7H4bhy/jud0GEa9b9qCCFIxjQMT1Zx6fl9GMtbLcdoHU5sV8i+4SKGxityLCJoz4/OjRJoRC7I3PbQbmwM8t+PBnPlWTuej7ipwXb9unsXPq/yrhUKxfFCaFT2zW9eh61b62eu77rrB/jmN6+r206hUCiOB4gQaqJlMfB9jomJ8qLsq1lVbmCogH/5/m9g6qxhVatclRnO7WkTus7AuYzpMYIv6Y7ro1h1Ibj8ku77sgzWSGTTIPc4zL7WNIr29iQmJ8v4zYtjuOvRvRgYKsJyZA41pQQJU0PC1DCar0b7NMLKtC6NyIIudNiuL/9xPHgt5DE3glGCuKkhHtPq3L2rto9qIKhDdEaC6vGxhwAgVP4XC2KrPJ8f9dmyns4EzJrPERcChZKDq999FtYuTzacTw2F7aHxsqy6B5tQIgWzrlFkEjosl6MtZSBXtJFNmw3FaXi8D1y6CYySlgTzdM+B6UJ8elZ7M57bPY5//9FvUbU8MEanWvMDhBDwuUAiruPqy7Yctco1UPse/YaLDVO+Co2fn+sa1P5dfjnMIC/F99vRkVSGZkuIxfz/t6J1PM/DRz7yPqRSaXz5y1+Dpml1z/3lX/4pSqUi/u3frq97TqFQKI4Frf6/W/22WmLMVpXb0Nc+Z1XL0FlgHjXVfqxrFJmkDsvh6M7GMF6w4dseKKnPYa7VFwJSrN/16F5sqDE4e37PBL555/Mo1LQzA4DvC5SrLhyPIxU3YOgUuhZUpkUQaVVxYB2GmAakoI4FIl7XaDQ/3czdO6qMLxFhDSBaYGBU3rdwMJsxIhc8jhKO49eJay9w+M4kZ8bdAfXCltF6My0uAAoB2/UxmveRTRq4aOtK3PXI3pbmqft7MnPONs/tOeBEn9e5Ks2phB5t02hTAblYAC6O+vzypv4OXFljpFaxpJHaqsBIbVN/B/qWp2d9XqFQKJY6L764C5xzFAp5XHfdtbjkkrdGleu77/4hCoV8tJ2auVYoFMcLSlwvIWZU5QKn79D86co3bYhmmxu10FJCopleIURUFXVcH6M5KXj+8MKTcMsDL6FYdiDIVKt0rbAmwT+mzjA0UcG+4SJOXtUGzgW++7MXkCtNRZ0Zgfu2YTAYQX42FwKOK1uyHddv2VyrGRqT5mYxU1aoBWTUVKloozqLCReAKCKKcyAZ11Cqzr790SBcAJi+yCAjrci8DdZCsX44hC3Fa5alcFJvdkbEFBcCdz4ygHLVhcYIckVnRps9r2mvziYNXLBlJbbtHG15nnq6adp05vIcSMa06PM6V6V5zfI0uttiKFXcqMOj9ty4kFnduk5bml8+3Bnw6Wzq75h1sWGu5xUKhWKpE85Sf+hDV+H7379lhqHZhz50Fb75zevUzLVCoTiuUOJ6idBqVe7qy7c2rWqVqy4mSza6szEUKq4UtVFME0E2ZWJjfwcu2lrFf/1kV107eK1QCueABQS4DxTKDvYcKmDwuSGM5KpIJ/So1TusTNuuj1zJhuP6h1WZDtE1Klu+TS2qklaDCnXYit4KtePbjscRNxiqztyO5seKhUxpLFRYUyozlGtbit/yqv46oRny4DMH8MJgHpyLWQ3mknHZVVCsuhgcKc26GNRonno2pnsOOK4/Nfqgs3k5ZVNC8LYL1+Frt/46WvwJF5hCF32NUazonHt+eTFmwJud42yLBHM9r1AoFEuZcJZ6YmK84YLp+PhY3XYKhUJxPKDE9RJhPlW5RlUrIQS+fvtzSMak8I3H9DrxAQDFioN9w0VctLUXDz1zEPtHSrMKs1LVRTpu4GdPDcKyPXhcoC1lLnplOkRjBImYHglqLgSsYH66NuIrMm6bJ467NOYtZ+NozV3HDDmTXyg5dS3Fm9fOFIPbBybwo18NtLRoYtk+MkkjylQ/7aTOusWgctUFCNCWMnHRlpV1IwdzkUroYJQgX7Jh2T68QOWHow8JU5uXU/ap/R14+0Xr8P2HXoIb5HSTQFRrjCIV1+cU/610m6g2bYVCoZjJ+vUbkU5ncNttN2PLlnpDszvvvAPf//4tSKczWL9+47E+VYVCoWgZJa6XCHM5gU+vyk2vWj23e3zG641pZlXh6ykhuOx1J+Pbdz+P8cJUi7fGKExdGpDFDBaJ/ELZhq4xWLaP0Vx1USrTIdNNycJM6+mCupYjGEt93BHmVM/nmqTiGj7y1lORiOsoVVwk4joIBMpVD3sOFZDNJqJtw44K1+MttZ97XC6I1IrccDHowWcO4MFnDiJXcqJOjG27Rluu8FaqLiq2B6um84AERmqhQd6a5el5OWVfcn4fNp/chRt+9FsM56oAF9B1WbGe67wWcwZcoTjesG0bX/jCF3DPPffAsiy8/vWvx9/+7d+io6P535nBwUF89rOfxRNPPIFEIoF3vOMd+NM//VMwNtOT4c4778RXvvIV3H///Qveh+L4QQiBgYE9OHDgAFzXibq41K9OhUJxvKHE9RJhrnzbufJr5/v6Tf0d+L1Xr8Xtv9gNjcl5aRJUhH2fo2J7sB0fjsfRkTYA+MiVLHiL0FFNA0GdiE3NUFu2h2LZqRNOitZoVVhTCqxelsZlr10XicbtAxP4/oMvRS3NGiNY3bMHF5+9GutXt0UdFZmkAdfjc2eEC4FixUFvdxKrlqWih3funcS9j++f4fLdaoV3+8AEbvzJLjjTFlyEAHwhpr6A1aj/Vuegz1i/DGven8DuA/l5zS8v5gy4QnG88Xd/93d48skn8bWvfQ2GYeB//+//jY9//OP4z//8z4bbu66LD3zgA+jv78d3v/td7Nu3D3/7t38LSik+/vGP121733334X/+z/+Jrq6uBe9DsfTZtWsHisUCzjvv1Xj88Ufw618/Ez1HKcW5574ajz32K+zatUMZmikUiuMGJa6XCHPl286VXzvX6y3HR39PGv09GQgIeL5AZyaOzmwCjuNJJ2/bh+PL9lhKSNR2W7E8VOzDE72EADFDCmpTZ5GgLlXcOU3JFM1pSxsolF34XIBRgFEKLkSgMeWfhBAYOsW7f2c9zj+1JxKNjVqafZ9j4FAB37l7O6540wb4XEQdEamEjomCPev5cCHb70dzFq695dnI5f5wKrxhhbhclV0bLDB9q11TIACyKQPFqot9w0VULW9ec9ALmV+eb7fJkWCxjdQUilYYHh7GHXfcgX/7t3/DK1/5SgDAV77yFVx88cV4+umnccYZZ8x4zb333ouDBw/illtuQTabxfr16zE+Po5//Md/xEc+8hEYhoFSqYTPfe5zuPPOO7Fu3ToUi8V570Nx/BAalT366K+wZcsZOP30LdB1A67r4De/eRaPPfaruu0UipcbnHPs2rUDuVwObW1tWL9+IyhVMY5LHSWulwiUkDnNn958Xl/TL9LTX59JaNA0BgLpHr6iM4E3nLUKuZINz5duyLsP5LH3UL7O2Cz8Xu7VlEMPR1ibBpNt34YGQmQkV64kXb5bmS+m82x5PhFgtN6IrRm6RmFotMZdnMD1+Ix5dArpHJ9NGNHnpVlLM9Pl/RrLWbjr0b14+0Xroo6IdMJAqeLOWr0mBOjMmtAZi6rSbzp79WFVeMMKsWkwWI4PSsnU3H1gvCeEFOuW7eP5PRN46NeHjvgc9OF2mxwuR8pITaGYi23btgEAzjvvvOixtWvXYvny5XjiiScaiusnn3wSp556KrLZbPTYeeedh1KphO3bt2PLli0YHBzEoUOH8L3vfQ/33Xcfbr/99nnvQ3H8kMnI3/ennLIBf/qnf1EnGl772jfii1/8B7zwwq5oO4Xi5cS2bY/j5pv/C2Njo9FjXV3duPzyP8JZZ51zDM9MMRdKXC8hZsu3PW1tB3786F4cGi/DdTlACbqzMfzhhSdh89pOAMDmtR344KWbcM/j+zCaq8J2OTyPw/V8gACTBQsdmRgA4KXBHB549kAkcMN52sMVsowAus4QMzTETCYr4D5HseKgant1udqtwMXiRE0dL4Tu67XRaI2gFEgndBQrU1X/ZteWB63TtSJvzpbmuBS8BKKuI6I9bWIsb82oHIdkkzp0Js3SEiZDxfbw4LMH4fkCyQVWeMMKcdwMBGxQjScAEHw2fACuy0Ep8NQLo0dlDvpwu00OB2WkpjiWDA8Po729HaZp1j2+bNkyDA0NNXzN0NAQenp6ZmwPAIcOHcKWLVuwceNG3HDDDQBka/hC9qFQKBTHO9u2PY7rrvvqDKO/u+76Aa677qu46qo/UwJ7CaPE9RKjkRN4perixp/sQqnqwPMFBBfQNIrxgo2bfroLbz53Dc7Z3APX4yhUXIzmLRTK0qhM1yh0jWI0Z+H2X+7B216zFmt7s3jg2YOwHB+mwVC1/cMWr2ZQ8UzGGSBkS3nF8lCxPHitlGEVAKZi0GqZXsmmBIgb8q9uJqmj6nhz2qcLgboZ6FZamv2qh3LVizoixnMWXJ/PukCSL7kolN2pSjglGPMtaIwetp8AIQS6RuF4HFqtkIVcjLBdH8va48gVnaMyB91Kt8l8osZaRRmpKY40g4ODeMMb3tD0+T/7sz9r2IJtmiZsu/HoiGVZMyqQoThv9pojsY9maJpqtTzalMuy7f/FF3fh61//Z7zlLb+PVatWY3BwP+688wd48cUXou3U/VG8XOCc4+ab/wtbt56JP/uzqY6ODRs24JRT/hJf/eo/4ZZb/htnn32OahFfoihxvQSpnf/kQuCr33sWPueImzo0RmBoFASydbtq+7j9F3vAGMXaFRn8+LG9mChYyCSMOnGRYRT5soMfP74P3dk49hwszFmlZpTMKqSmZ1H7XKBY8WDZ3tzGV/Pg5VK1bgQB0JWNg1IC35eibXl7HL//mrVIJXTc+sBLGMlV57xGGiMYHClFn6v5tDT392Tw2q0rcfsv9jRdKAm7C8JWbUoBAgLX53A9ju72OMqW11KFd/oc8aplqahCnEnomCja8LhsdRcQ4FyAEIJETMNZ67vx0ycHm34RW+w56Nm6TY5Ue7YyUlMcaZYvX46777676fMPPvggHMeZ8bht24jH4w1fE4vFZrwmFMSJRKLRS47IPhpBKUF7e3LBr1csjNWrVwAA3vve9+Kee+7BZz/7mei55cuX473vfS9uvPFGrF69Qt0fxcuG3/zmNxgbG8WnPvVJdHbO7Hx797vfhb/6q7/CwYMDOP3004/BGSrmQonrJUr4nfnASAmOz5GM67AcH57ro2p5cFwfPpfCwhfAvY/vw1tf1Y+xXBUJU4u+dHMuYLs+LEdGGFUsDwfHKrMeO5xzTsQ0FKeJEEaliImbGjQmzbOqtqxQux4HDbOhFIuCADBZtNGWNkEZRTqhI1d2kE7KqtHQRAUxnc06F5+Ka2CU1gnKOVuaqx56A8HLhcBzeyYQMygMTcdk0QYlJBLUHhd14j6aACdTCzSUyGztuSq8z+8Zx20P7cZo3qqLxTptbQfG8hYsx0cmYaBsuXK+PBgbWNWdxGWvOxnxmIb7nz5wVOegG3WbHEljsaVgpKY4sdF1HevWrWv6/M6dO5HL5eA4Tl0Fe2RkBMuXL2/4mp6eHuzatavusZGREQBo+pojsY9GcC5QKMz+/0XF4rNyZT+6urrxm9/8Ftdc82Xcf/9PMTIygmXLluH1r/8dfP3rX0V39zKsXNmPycnysT5dheKosH//IQBANtvd8HOfyXRF261addJRPbeXO5lMHIzN3S2gxPUSxHK8KPbo4FgZw2NlFKtT+da1EEpAfIHJgo39wyX4XIqbUtWF5Xhw3NkryBqjiBkMjBEUSg7CDhMiEAlrQhBFZxkak1nUjod8yYmyqAlktfKNZ/Xi4d+OgPv8sB3Ga3k5GpuFOB7HWK4KQgg0jUKjBKWKdMUulN0oD7QRBIChMYCgTlA2a2n2PXnfagXvwFABQxMVpOJGVCUmlEQZ241ujOACnAhwyA4H2/Xxllf1Y9vO0YYV3g197fjvn+7Cz585AO6LKL/a8TkGhooYy1t4xbpO7NqfQ67kwDQ0mAbQljJw0daVuGhrLygh4EIckznohbiNL5RjbaSmUJx11lngnGPbtm04//zzAQB79uzB8PAwzj777IavOfvss3HHHXegVCohlZIjKo8++iiSySQ2btzY0nEXYx/N8Bax20rROpdf/kf4+tevxVVXfbCuK+HWW2+G4zj46Ec/Ac5lq6xC8XIgnZaGjXv37sW6dafMeH7v3r3Rdur31tJENesvMbgQKFZdlINKsMYoHF+AcymUZhC04no+x/N7J1GxXIzl5Mx1M2G9elkKXdkY4qaG7rYYMkkDCVODrlP4HJFAjxkM7WkTPZ1JtKVMcC6rqIfGy8gV7UhYA1LkJ2M6utoSEFzA9V+mSvgIQgjguj6qtodfvzSO+586IMUuZn42GAU0KqvLtuujpyMxQ1CGLc2rupOwXR+FkgPb8dHfk8HF5/XB5wIDQwUUyo6slGoUlAZHqrm9tMEH0xdA+Ds/mzLAOdCdjePqy7fiY28/HR+4dBM+9vbTcfXlWwEAn73+Cdy3bRC+L43SuJBi2PM5bMfHWK6KB54+gMmCBUCgLWXg91/dj8+872y87oxVM1zzwyq54/rgQsBxfeRKzhGbgz6ahF0HZcubsbASLiA0ut8KxWKxfPlyXHrppfj0pz+Nxx57DL/+9a9x9dVX45xzzsHWrVsBAI7jYHR0NBJMb3zjG9Hd3Y1PfOIT2LFjB+677z585StfwR//8R+3HKG1GPtQKBSKpcz69RvR1dWNu+76wYxFJc457r77h+jq6sb69Ye3oKg4cqjK9VKk5vvyiq4kOtImBi0vMm4C5JdogSmjK9vleOlgoeHuKJGRWFwIdGZieMOZvZgs2fjFrw+hUHGRMBkYo9AZBQSQiOmR07frcxTK0umbzzZ/zQhWL0uBECK3XWRtHTNk6/NcLtonKlzIarCcZyZ4+LdDYBQwdArXF2CkPj7N5wCBAKWycnzWhu6G+53e0jxRsPH0S2O481d74AXxTtmkAS5EVCltZCrWDAISxUSlEvqMCu/2gQlcf88OjBeshu85GuKGfP+JuNxHruTg3if2o6cjMWOu+VjMQR9NjpWRmkJRy2c/+1lcc801+NjHPgYAuPDCC/HpT386ev7pp5/GFVdcgRtvvBHnnnsuTNPEt771Lfz93/89LrvsMmSzWbz73e/GVVdd1fIxF2MfiqVDrXHTVVd9Ai++uCvK8z355PW47rprcfPN/4UzznilMm5SvGyglOLyy/8I1133VfzLv3wFl1zy1sgt/O67f4hnn30aV131Z+rvxBKGiNl6ShUt4/scExOHPxPEhcB43qozEntxMIcb790JzxdT4nqO/ZBgxjVuaqAEKFc8uJzD1BkIIWAUSMZ0gBDYjjSaklVJOSMr56hdeHNUoEOxm07o+NClG3H7LwcwcKh4wglgRoFsykS+ZLeUQb0QWl04SMQ0WLaHjkwMjBKMFywZWQbMiMhilCBmMJgGmzMDefvABG68dydslyMRkwsunseDEQMfBqPobIuhVHWRLzkQQn4ep39EGCVBVJaAH7SRr1+dxV+864w6wceFwFdufgZ7h4qotJB7TgjQ3RZH3NQghECu5GBVdxJXX761oZCcbozWaA5a0yja25OYnCwfd+1VC825Pp7f80JYiu+3oyPZ0tyW4uiwWP//VsyPHTuexz/+4+fwt3/79w3bX198cReuuebv8MlPfhobN24+BmeoUBw7VM710qPV/3eryvUShnOBwdESXjpYQMzQUKq6TcUXJcBJK7PY2NeGuMHw1AtjGMtV4bgcQgi4nENj0oyMMQrB5Ux3OqHj/NN60ZYwcO+T+zGRt+Y1Kx2ej+v5uO4Hz8NyFm/OuhUokZU87wgPZIugTflIjH0REgjjFt5C2BouAIAAMVNDZyaGfNmB6/G6THBdI+jMxKDrbM4M5Kl4Jx9dbTH4gUmZoTO0axRjeQu26+PAaFl2TYiZCwHhDDYN35CQbt4QwCs3LJshbEPXa1NnKFse5kLO9ZPgOkhX7EPjZTz62yFkEsYMAT3ddf9IGY4dyX3PxtE2UlMoFIrFJJfLAQB6e1c3fD58PNxOoXg5cdZZ5+CMM16JXbt2RB0d69dvVBXr4wAlrpcYluPhuT3jeH7PJHbsz6Fcbe74SwAk4zrO3tiNC7asRMyYup1bTunGobEySlUXP9s2iMmihbZUDPGY3MYLIpLyZQf3PLoPndkYxnJV2HMYoM04BwIkTJmVfSwMx7gAGDtybmcEsvLl+Rz5stt6eblF2tMmOJdzxVYL157RqYUEETjBCwBtaRMQAlwAE0ULvi+QSRjSzbumnbtZBnIU7xQPnean3iQhsvpdtTwQCiBwCieQCw4QQFvagKGxSOSHLzc0Co1RdLfNjOeJXK9Za2KQBjF0IR7nKJRd3Hz/i6CENK3czlbhPf3krpaO3YyFVo8Xi6NppKZQKBSLSVtbGwDgwIH9WLt23QwRceDA/rrtFIqXG5RS1bVxHKLE9RJBCIH/vu8FPPjMwaZZwgDQ3RbDqm6Z+9vfk0bvslTDShUlBL3dKRwYLcHzOXq7U0jGDTiej+GJCqq2jPMSXFbeRgNhLUTr+jGskpato1OtppREc9+17uGexyG7NGbP5V7oMbNJHVXbhwjiySzHQ6k6d6W1Fo2RqMWeEOnSnoprQUwVQSqhwcrPzI0F6u9HWFHWNYLJog1CSDSLL3PHGTxPtmuHzyN4Lps0mmYgR0J3Wj60CMzAihXZNdGZMqDpGjgX8n74HKM5C8WKi+42Dcvb43A8Hj0PSEO1Rs7Voes1b3EyJVkTMVe1PUzkbXAuYOoM8ZjWsDq/fWACN9y7E5bjIRnTocVp3XZ/zChevcD81Ln23ahDQKFQKBSS0Ljpv/7rBhSLBYyPj0XPdXZ2IZ3OKOMmhUJx3KHE9RJhcLSMn20bnPE4JUD/igw2rmnHpr52dGZjLe2PEgJGCXSNIZM0YRgU5aqDwZESLNcHI3IuFhQAB2I6g+3IKmir8vRoT+tTAIROmbiFopMQIJuUFeBceXGzfYUQsB0fndkYciUHPueotNDCXEtPRxye72OiIMWzgJzxq9o+Mkkd+VJQ7W12DnXnI//0fVmlJkKalhFC4Lh+1JYvAGiURhfJ8TjGCxbaUwYcx8dzu8cBIGojrot30mS8U9X2pOt8IJYBoFD10MbkLH/V9pAvO4FbvYhavLNJA7Fpc9GNnKtD1+u9Q8U5F3QIgHhMD66BQL7swOcCpk6RjGnROSZMhort4a5H9+KUNW1Bq7uHtpQZCfPaKv6dDw/g/K2rZjlyY6ba6GfuWwiBQtnFbQ+9hL9e0ybvg0KhUCjqoJTila88F/fccycymSyuvPKD2LLlDDz77NO4/fbvYWBgNy6++C2qDVahUBxXKHG9ROhui6EzY2K8YCNuMJyyug2b+tqxfnUb4mZrt4kQgFEKTafIFyxYjg/b9TBeqEJn0uG56vjhOGxkgMW5QL7iLv0caSLdpxkN26kFLNfH8vY48iUHpXmK3lbgAtA1hvWr2/DzpwbnbWZGCTBRsOAE1WTGSCQkHc/HaM5f0CJFeK+mHONlNnSIrOTLKjeEPA/PFxjL2yAEuPeJ/XjgmYNRC/OGvnb0dCRwYLQcCeeJggUupkz0ADlOMFGwkIrrKFVd8EDccy6PY7s+xvJVZJMmPC5mda6udb22HF+an6FeZLNgMYVSErS9C1Qt2XXBqHS2H56s1i1OMEqwf6SEx58flq3uMb0u7xqon9nefSCPztT8MqGjNvqafVvBYoPrSZ+DgUNFfP6mbXjHRetUBVuhUCimwTnHk08+hv7+tSiVSrjhhm9Fz3V1daO/fy2efPIxvOMd71ICW6FQHDcocb1EiBka/uED52K8YEGjBGggRhpBiBQeOqOIGQyDIyX85Mn92DtUhONyMEpQsb2oKg1I8cL9+hp1YBS+pAV2GD9maAwxg2Ky6KArG8O7f2c9hsbLuPn+F2G5Pjxvcd6EFMMUp6/rxLMvji1IBNfGpQkgEpkEcmb6cK+3NPkKKtpBn354TA4xoxosIAV+RyY2o4X50vP6cOO9OzGWt6LoNUqm2rYZJaBEtqYXgoq1Fn5wCIem0aCiLlCoOFi/um3O2eMwNuuWB17C/uGSFPNB23zcZBBCHjebNFCsuqjaPnwhW87TcR3FiiPn7qMhcMD1Obwqx469k7LVPd74S5mmUVQsWZ2fr7iO2uiDfVu2F7m2M0IgiFy4Gp6oqhZxhUKhaMCuXTswNjaKP/mTj6Gvby3uv/8nGBkZwbJly/D61/8uBgZ245pr/g67du1Qc6cKheK4QYnrJUTc1LCyKzkjiqsRjBJomhTUps7AKMWOvRP4zo93RDOgMVNDOYhRmmtflBB4SzyVzeeykqkxgkPjVQgBjOct/OsdzyEV1+F5HIJPVTsXAgGQTRkwNCoXIQSwf6QE2/WRTurIl+bXdi4E4NdcV18IKQQBzPcUG7VOS00tRwA8n6NGYzdts44F7uHT26ivvnwr3n/JJnz/F7vx4v5ctH9DZ+BcwAtitUggsFnQju5xAUNnWBbMWzuOD49z/OFFJ6GvJ4OBocKsbtab+jvwv65sx4NPH8CDzx5ErmQDQgrs2sp66IpdqDj4/oO7g8p5KPCnLhKjcvZ+96GCvC6BoZsQon4eXEgztUzSmOedQH0bvS6N3GrPRQh5bTJJHRXbb2giNx+OlSO5QqFQHClCF/CRkWH8+7//S13k0H333Yu3ve2dddspFArF8YAS18cRoSNy3GAwDQ0aowhjyn3O8aOHB2bMgM7mNh7ChVjaJesafA6Uqh4okQ7ZyUBUj+ct2IFLtRR9C5sJ1zQaia18ycHyzgRGJyswNBZkNh/erLnvC/iQwnQ++wkXQNwGqwayshzM0AcxWbNRtjxUa+LWwjbqfcNFbF7bATNu4J/+axsSpsy6NnQWVWY9LqITFwLwuAAlQDZpgBACM5hnLpQcbB+YxG0P7m7JSZsSgteduQoXndHbVETWxmrd/9QgJos2GK0XmCJoHdc1iorloiMTw1jehh84i7sejwzgAGD1shRO6s0in6+0fjMwNS8+GESTuR6PFk3CczA0BiPIlW9kItcqx9qRXKFQKI4EoQv4N795HbZuPRN/8icfQ2/vahw4sB933fUDfPOb19Vtp1AoFMcDaohliUOIbONNxDS0pU10ZmNIxnUwSiJhDdTPgLoeh2V7KFfdGUZZjcaWhMARyW9ebGK6bEsO0RkFJQSGztCZjYESWWn1+MIFcDKuwXF9jOctEAJ0ZGLIlxxMFm3kSvaimbjNt7LucwG/yU3iXM5Ct+qULquqcpwgFOzlqovn90wAANpSJkydRsIamMrTDiv64X4MjaIzE0OsxhfA83gggA9gcLQEU2fIpAyYOova0LcPTDQ8tzBa6rSTOtHfk2k6q33mKd0gRAptLkQkaH0hW9mzKQNCyO0oAUZzFhzXR+ThF2yfLzv4zUvSoZYLgYGhAp7bPY6BocKsLubhvHjMYCiU3WBkof4cMsGCQ9guX6rM32wvdCSf73VUKBSKpc7JJ68HpRSZTBZXXfUJrFt3CmKxGNatOwVXXfUJZDJZUEpx8snrj/WpKhQKRcuoyvUSRLb5yiqqrFIzUEKjTOFGX/lLFRe246NcdaPIp7CtuRZKCCgNIp1qHmdMxlzJlt/Zz682ButoYrlTB+VBS/jK7iQqlgfP52CUNhWgrcAogeP4kQjyucDjvx0GFwCFAAVwdELHGrPY15wE/6JECvRHfjuES17dj5N6s1jRmcS+kRJ0jUZdEDFTg2kwjOdlBZtCIJsywAMjszCDulR1ZRs54U1dug+3TXrz2g7c+8R+2K4v57yD92NoDJmkIVvDmcCm/nY8sXNURonVVPVNnSGT0GG7HLfe/wLe9MpV+NHDA/OqDofz4rc99BIGDhWDvz8kOofQiNDzOBgjDePIZmM2R/LFuo4KhUJxrHjxxV3gnKNQKOC6667FJZe8Napc3333D1Eo5KPt1My1QqE4XlDiegmSiGnyC3RN27eYo2Q6mqvKLGaIKIJJCDFjrpcHztGMygovIKuYnRkT+bILx/PnnFleKh3kHhfYN1w67P1QAnRl43jFyZ14cscIACBhajJmKrjuS+U9LxYE0qgsXIgBgINjFfyf65/E+996Gt7yqn58++7tyJUcJGMaNE1mOJctD8m4jk19bXj4uWEMT1ZBECwIMQKNUZg6gwc+q0v34bRJA7Ite/WyFPaPlJBMa/JzTUkk8MMIMAGCYsXBsvY4AETz1mFFXtN87DmYxzd/VIDH+bzzqjf1d+Cv17Th8zdtw/BEFZmkHrWCA/LvYNnymsaRzUYjR/KQxbqOCoVCcawIZ6k/9KGrcPvtt+Caa/4ueq6rqxsf+tBV+OY3r1Mz1wqF4rhCtYUvMRiVX5q1aW3fs8GFwJM7RxAaJgMy0qhRNYtzKahqxTMhgONyxEwGiCD6KKhovhzoysbw3os34OBYGY7nwxfARMmB6890216K1N6mVu8ZF7xOWNNgTv3QeAVfv/VZAMCVb9qAVd1J2K6PQsmB7fpY1Z3Ea7euxPa9OeiMQpeZX3Lu2OVwPY7T13aABu3QIUII2K4vXciFgOfxGW3SC2nLjpsaKrYfZLpTuB5HruREEWCVauDqrckW95ipRcIakKZp5aoHy/HRljJh6CwaNWhLGbAcaUY227lolOIdF61DKiHNy9ygLd5x/bpzmW91OXIk15q7nS+03VyhUCiONeEs9bJly3DNNf+Ed73rPXj9638X73rXe3DNNf+E7u5lddspFArF8YCqXC8xFjLTu2+4iOHJKrIpA4WyI2c+EcQ0NWnhDh8KxViu7AQ/E+hMOk8fF8pyEfi9V/cjGdOkK7gjA6yOp3WF2tvUaoU9XFwhkAs6CObVMykdVdvDnQ8P4BOXbalz6U4ldKxalsK1tzwLy/HQ1RYDIQSOKzOqfZ+jant46WAehIjISbtqy7ir0EwMkJ+70Xw1Op+FmHaFbdnh6yqWB8YIVnUno9cNDBXqXL2nE0aOJeLaYVWHWzmX+TLdkXw6C203VygUiqXA+vUb0dXVjf/6r+tRLBYxPj4WPffTn96DdDqNrq5urF+/8RiepUKhUMwPJa5PAMIKVyZlQGM0EjJ+C87RIMCyNtky63gctuPD8ThMjSKd0DGat+c8PpN9wS0bai0lGAVScR3FsoOK5UEIEThQE/jzDss6MkyP4CJB3BQPrjdZwLUnROYxh3rSD9ytTZ1BZwyHxsuRoKwVlQNDhRmtyjzIvXY9DiEEDo1XYOgMjucgFdcwUbDBA5MvCtnOL0Bw7+P70dOeAADccO/OKEKulbbsMJrK5wJvv2gdCATKVW+Gw3itq3ft/Dgw1bJNKRA3Z4pXYCoLu5Xq8Kb+jhmLEYcTmdXKuS+k3VyhUCiWApRSvPKV5+Kee+6csbg5MTGO8fExXHzxW0AbObEqFArFEkWJ6xOA2gpX3NQQM1gklPNlByQIH2pPm5EgK1nSSZyAAEGEkmkAhuZheKIKErhFtxI95QscXj7VMYQL4EcPD2DjmnaIQAAS0npLfiMW2/CNUCmENUrQ3R4HoxT7hosQQNSWzRsspNSKchK0+UfjAAIADU3v6t2tdY3Aa9JuHLUqx+Vxw4guLuQ5CiLFthAClu3DcjwgmIcmIDLnmxJ0pM2o5VoIMS/Trtmq3NOry2H7+A337mw4Px4zpLeB7wvQBr8N51sdDt3OF4NWzn0h7eYKhUKxFOCc41e/eggAoGk6XNeJngt//tWvHsI73vEuJbAVCsVxg/ptdQIQVrjKQeW1Nm8YkEJP1yiScR3ppAHTYFKUECm7eY0SDGOafF/Acflx1R49XwikOB3LW3j4uSGAyAouMP+OeEqmBOx8mJ7T3Gi/WhCJlUmZeNfrT8EFW1ZE5x/GUDXaTZ0bPCWRcDV1aXgnRbB02O7IxCJ3a9fj0JoIytqFHADIlx1wAWh0qgpOiFzIMTQGEYh5GaUuouiueExHMqZh71ARe4dLMDQ2Z1s2sLBoqrBlu9H8+Ad/bzPW9mZRrnozFlTC6nBPR6Ll6vB85sZbYbZzn81oTaFQKJY6O3Y8j2KxgFNO2YCvf/1b+OQnP40Pf/hj+OQnP42vf/1bOOWU9SgWC9ix4/ljfaoKhULRMqpyfQLQrMIVVhApAbJJI9o+EtMEIEFVMdoXnXI5Ppzq7VKG1ojAVEJHKq7LXOug1uvx+c1cM0awflUbTj+pA7/ZPY69wyVULK/FcyEArTeYM3XZlk4pASMEuk6xonNqdtfnAnFTg8flXLOMoQoqzjVzzfI9BgJeyHZsXaO46m2n4Y5f7Gnqbl2suljV1bjduLZVWQgB1+NgNa/lQXu5oTMkYhpsz0d72oTGaJ1Lt2V7yJVsOK48X8v2UKq6yCaNuszs2rbsw4mmataybegMmXQcX7v56cOuDi9kbrwVFrvdXKFQKJYCoWj+gz94BzRNmxG39da3/iH+6Z8+jx07nsfmzacdi1NUKBSKeaPE9QlCI0MlSmWkFARgGlMzpTRwIhdCChOjxo1Y16QI8nwpso7DMeoZJEwG25Eu4MBUyzaFQL4kZ60Tpgbb9aEzBgEpGkNq26sZlaKX+xy+kPPab7tgLS46oxeUEPzuOWvw6G+H8N2fvQAryGAOZ9I5FzOuJxc8iJGS1d/lHQm8780bsXp5GoMjpYZiKpXQYRoMaY1G+w1jqEpVF5NFG0IAWpBdHlaodY3ibResxekndUGjFDfcuxMV2wcJnL09j8trEdPwyg3L8PyeiRnHrl3IKZSD7GgyVZmuay/XAydxoE4wh63kfpCrHjrbOx7HeMFCZyYWbV/bln240VTNWra3nNKN91+yCT/81Z4Fm5GFFfX5zI3Ph8VsN1coFIqlhQDnHLt27UAul0NbW5syMVMoFMctSlyfQDSqcJUtDzdNq2gDUoxwIZBJ6jI6iAu4vqzWhRFNtVFNxzMV25/xGMHUHLDj8DGiYwAAR8tJREFUcbieDV1jMA0GIQRScR2FigvP55H4Y5RErthCAMmYho+8dTM2r+2M9ksJwXmn9uDh54YwMFSEI3wpOiGFueD1s9F+Tdu0J+Q13z4wCUJI0+pkbfW4LSWFrGV7GJ6oRJVguU8RVXW722J424XrcGog8Jq5W7enTTCN4kcP74HXpPoavva2h17CwKEiOJejCIbGkEkaUXt56ERuOz5ScREJ4nzZkcIagK4zQMjPHiMEvpDPx0xthmnX83sm6ua9pzMf87HpbF7bgZNXZRdUHT6cirpCoVC8XNm4cTPuvPMO3HTTd+C6bp1beGdnF3Rdj7ZTKBSK4wUlrk8wGlW4GkYELUthLFfFeN6OqtgnhpRuDQEpZDUmK8auL+D5HO941To8tWsUQxMVGBqNqqvZhIFkQkfV8lCxXBBCsPWULuTKDjzOodWYrdRWd0sAPJ/D90U0f0sJkIzpKFkuhJA/Myrn34fGK/j+Q7tx7xP7sXpZqmHldPoYgEYJciW7ripOgmgtRgn+4DVTlfVapi/GjOar+Mnj+2GXOBIxhgRrXn3d1N+Bv17Ths/ftK1pe3nF9tHblUTV8aPFHRHkP4eLG+G4wkTBCiLkZLRXuerC8ThiBsObz+uTcXMTFXnfjlA01UKrw4dbUVcoFIqXIxs3bkY8HsfQ0KEZz4VCOx5PKHGtUCiOK5S4fhnQqKJdqbr41l3bYTk+BGYKa0ZJFPcUmnwhaF/2l0ZC1aLg+QKMTs1Yr12RxmvP6MW+4SIqto+K4+PBpwZxaLyMQknGTVmuD8GBh58bwiPPDeH/3fcCLj2/Dxef2xftt7YyfGi8DNflACXozsbw9gvW4seP78feoSKSMTk7XQiMwRiVHQW262P/SKlpS3Ht/nfsm6wT1owGnQlcoGJ7eOjZg7jojN6G7z8UlB7nuP2m3ShWXHRkTDDZyz5r9VWjFO+4aF3D9vJwXvmy150MANHiTvh5M3SGbE2VuyMTq8vCtl0fq5elcNraDvw4eK3ncVRsD+Wqi86MiXhsSkQfy2iq6Q7q0zmcirpCoVCcyIRfL6andEz9/HJa9lcoFCcCSly/TKitynEh8JWbnwEXAiu7EqjYPiYLtpx/JVJwciFAZYKXnDkOhLUQM3OXjxZH6rjhYoFBCcpVL7pWmkbR3p7EORu7sftAHg88dQC//M2hyB07PKGy5eG2B3cDwAyB3ciIKqx0ZpIGDJ1heKIytU/Iffq+QDKtoWL7s5p0mQbF5/8zBwI5d13rPk4o4HOBA2Nl7B0qYO2KbMP3v31gArc99BL2DklH7qEJHxqjUYv3bNXXZu3l4bxy+P5/9+zVKFVdlC0Xdz2yFwlTg2lM/foJI+QqlgfL9XH5609GW9LAjT/ZFc0xJ+M6NI1ismhjNG+h3RdIJvR5mY+F+djh/Tipt/E1mQ+1DupHoqKuUCgUJyI7djwPy6pixYqVcBynQVu4hkOHDilDM4VCcVyhxPXLkNo2VkopGOVAECMlAsUogvnfOudpKv20ebBVK2ZniyWIGQUEP7Ki3nI5fr17HKed1Fn3OCWyjX7brlEZa8amYq1AAJ0Ari9w1yN78cazV89oEZ/eClxb6XRcv85xO9glOOT1na2lmAuBJ3eMBoZmM2O9CCFBp4HA7gONxXVoxFWqyHxRFmSKOZ6PiYIVRXTNVn1ttoiwc+8kvnLzMzPcs9tTJiZLTl0beYjjcaxZlsI5m5fj2luenTHHnE4Y0BjBeN5GoeLA4wJai+Zjjdy8V3Ym8a43bcSarkTT181F7Qy8rtG693QsK+oKhUKxlAndwt/znvdhw4bNMwzNtm//rXILVygUxx1KXL8Mmd7GSimJRHCtPpsuZMMIr+UdcVy0tRc3/+zFOcXu4olhAn4U6uUPPH0AW07uwqa+duw5VMCe4TLAfewfLqBqe0Ge80wRq1Ggant4/PlhvOq0FbMeo7bSWRuLFhI0DIBS0lTUhkJx4FARArL6zgWXbuZk5k0UDYq5Hue47aGXUKo4iJsabJcDJHh9YN5WKDuIGWzO6uv0RYTZ3LNpsJAzW/TV4Eip6Rxz3NTR3UZQsT285VV9OLk3O6f5WLPz2T9SwtdvfRZXvmkD1q9ua/r62WgWhbeQOC+FQqF4+UFAKVWz1QqF4oRAievjlOntrfPJvZ3RxioEGCXwfA7RRMCGu5bzshouPGMlvv/gbjjekRvArq16+0chE4wS2Y793z/difZ0LGrXpiSMLwPQeKwWCOKoJgrWnMdZszyN5e1x7BsuwdCoNJTjsjOgLitao7KqPU3U1grFRExDxZaZ2mG3gcYQfRb84N6etLK+ar19YAK3PjjVCm67HEII+L4ADaqvlACux+G4Piq233L1tRX37Pa0ibhBcXC8As8T0DSC3q4k3nJ+Pzb1d+C53eOzzjHrOgOxffS0J+Y0CZvtfAydIl92cefDA/jEZVsWLIDnao8/nBguhUKhOBEJ3cLvuONWbNiwCbSm64tzjh/+8LZoO4VCoTheUOJ6iTAfsdyovXV6XNJshG2sew8V4XEuZ6x5M1ktIYTA1BkSpoaJgoX/c/2TiyqsCQHaU6Zs9fUFdI0G+cyyjsuobL0+EnPXYWQUoXLBYWiiikLZRTZlIGZosBwPEwUbArJ6T1mD+xLMondkYnMeb+feSVRsH1XbQznQ4jKuK3QTl1nRAGa0FE8XigBglGndvfC5AOjUgkRvdwr9PVOieHoreBhJ5guZw+14PGoxF0KgUJafydrq62yf11bcsycKFrqyMXn1ibzHtdsu5hzzXOeTjus4NF4+bDfvZu3xqmKtUCgUM9m4cTPS6QxeeGEnvva1f8Kll/4+entX48CB/bjrrh/ghRd2IZ3OKHGtUCiOK5S4XgLMRyzP1m7bzFl6OpQQnLa2Azv35eBzEcxaNycV15FJ6ojHdBTLNspFF1ZQLV0MpJEagev5NRnbUy3OAoAXCMUjUb8mwb+4HzSeB7POhs5AqXTM7m6LYf9IGVwEQnjaXK3HBZIxDedsXj7rsbYPTOCbdz6PYsVt0HYPaIygLW2Ac4GxnAXTYLjk3DWzCte2tInxvBWJaSEQ3FeCdELHZa9dVyeKQ3GeSRqwXanuRU3PQlgBB+S9Wd4RxzsuWhd9rub6vM7lnu1xjnLVhe9zZFNm1EJd+xne0Ne+aHPMc52PrlF4vlgUN++FxnkpFArFyw1KKa644o/x9a9fi+3bf4tnn306es4w5ALzFVf8cV1FW6FQKJY66jfWMSYUy4OjJZg6QyZlwNRZJDS2D0xE206vWho6AyVS/LWlDFiOdJYO85RrXzcwVMBzu8cxMFSAxzme2zMB06AwDdbUmExjBDRoFzc02T6eK8nIqMVs0xZCVoSL1SnBLudyp6qZ4kio6gAOKSYDPSlzp1n9Xw1KKdJxWSUNK/1cyD9dX7ZeX3p+X52Z2YzjCIFbHngJ+bIDLgQ0SqFrFFpNJZxzgcmCg4mCBdvz4fkcdz+2L/ocREJRmzpO3NTQmY0hZsjFgPCxDWva8KG3bK5bbKkV52Gbts+FNK+rucbh7D2jBG+78KQ6YT3X57W26jwdIQTyJQdCIHJLb/QZBoBLz+tDzGDIlRw4rg8eZGTnSs685phnOx9Atr5rys1boVAojjpnnXUOPvrRTyCdrl+UTKcz+OhHP4GzzjrnGJ2ZQqFQLAxVuT6GtDKbWhvDNFd7a8JkGBwt4f6nBiOTp517J2dUGdtSBkZzFrJJE0IIjExWQQiRc9VCINQgQgCMEDl36/mgPoHjchyJJlcCSAEUKFyfAxAchJCoan20YEG1ejptGROu50MQAtf1o1iyZEybkXPdiIGhIg6MlgAAeo14J4TA0Agcj4MLwNSlkI/HtBkV3Wbt0tOjrN71+pNx3qk9M8RnbRWXEIJMUsfopD/jXAXk4oqhM9zz2D5sDsR1K5/XT1y2pWnVOXRH1zVaF8UVXodad/TFmmOey827WHWxqku5eSsUCsWx4KyzzsEZZ7xyhlu4qlgrFIrjESWujyGtzKbWxjDN1t5atT3ky7LCd8cv9iBmMKQTBnJFC54vYOoMMZOBABgar8JyPMRMBhrMuoaO4SAEDDxwnxYgRAoQy/FhB8I6zMMmYnHatBMmQyKmgxBgNDdlCOYLNCxZH+mc7Wb79jyORFzH//cHp2F4ohJFVZ2zefmsFeuQ3Qfz8Lmoi92KjlnzPhMGQzKoks9HuAJTUVaNhDUwc5aZUhrc4/rtdI2iLWWCURJ9BgG09HkdHCk1dc8ulF0QAG0pQ1ajuYha7wHMcEdfjDnm2dy8K5aHZFzHW17Vr2ajFQqF4hih3MIVCsWJghLXx5C5ZkGnC41mVcuq7WGiYIFzafiVSUjxs3+4GLldVx0fBFI0xU2Gqg3kSw460tIUK8p/QpCPTGTrcZh17fsCvd1JDA4XUXV88EU0CQ+FO2tkFNYAxuSQ+GJWtAkBjKBF2vcFbMdDzJz661E757t2RQbrprlvt3SM8HQb+6HVnMvChOtcsU88MCxLxXWM5y10ZEwZBUYINCL/5FxA1xh6OuLyZyHqPoOtfl5PO6mzYdV5eUccwxNV5EpO3WiBrlFkkwYoJTOMyhZjjrlZFXz1slSUc92sbVyhUCgUCoVCoWgFJa6PIfN1RG7U3irdnJ3I7dvUGUxDQ7HiRLPUXCAST2FbLqGA40oxoWvSbVojNVFQOkNX1sRk0UFnNoY/vnQTtmzowV989UHsPlhY1OsQVsJdt0VxIwQopaCBWFwMKAHaUiY4BMZzFgplFzQwVHNcH6Xq4ecVn9SbAaNSwBKKGe3JIaYx87PQinCdrV261oTMdnxUbR8HxypIxrVA6xO5EEMJOttiQSzYzM/gfD6vjarO5YqD//v95+Scc+DQjsChfCxfRUzX0LcifURatBudz0m9WXR2pDA5WV704ykUCoVCoVAoXl4ocX0MmWsWdLojcqP2Vi4EHE+6XDNKkA0inKY7H4cxUmJaq/Vk0UYypsP17CjqilKCRExDoSJbZi9/3ck4aWUWmkbxmlesaElcp2IMJWvmLG8jKKF1btVzEl6nBv3h4RWcr+YWAhgvWMgkDCTjOrrbYsiXHFRtH5RgUfKK+3oy6O1KYt9ISbp506m3EHYChHPO02lFuDZrl57uMJ+M6yhXXeRLTvQ54QIwdIr2dAwJU5OZ5w0+g/N18K6tOnMh8JWbn4GuEQghq+I0mFunBPB8wPE53nwYCxhzMb0KrlrBFQqFQqFQKBSLhRLXx5DZZkGbtfhOb2+1bB9CCBjBjGzM1OC4/oyWab9JUdjzOaqOB11jcD0fMlNaOoT3diXxlvP7saGvHXsOFbBnuAwhBEydwp6lykwIEI/pcH3RdDtKpwQlF2Je7uNCCAR+Z/J4kAsC/hxZ3U3PF9Id3PM58mUHG9a04ROXbcGh8QpAGcB99HYlD1uIUUJw2etOjqK4at8yIXJxxNQYhBDzFq7NaGaal04YUXt4Mq7DdX34QgT3RTSt1i+0JR2Y8hjIJk34XHZcuB4Hh7wHhk5h6gzJmPq1pFAoFAqFQqE4/lDfYo8xC3FErq1avjSYx48e2RvlMgNSHAGYYVTVqKrLhWwPN3QKXWNR5jQCo7O9w0Xc9eheDE9UZMYz5/C5QCquwfE4XI9HxyAACJUHGM9LY7JQiBMAcZNB1ynyJReCT51fM2HNAsEMyIpu6CTOhcyQIwTQGJVt7kRu43PRyANtVmQxX1bthRA4a0M3NEqxdkUG7e1JTE6WF20ed1N/Bz546Sbc+uBLGM5ZED6HrlOs6k7h9JM68cAzBxckXJsxl2mezLr28ZZX9WPbrlEMT1RmrdYfjoN3rceAQQhiBpMO6YGpmcYIimV3UfKmFQqFQqFQKBSKo40S10uAhTgih1XLNcvTePrFsbpW3cj5G3O3RzNC4AsB2/HBhRS0HVkTGqXYe6iIHXsnYegUiZiOhKnB57KiWbY8dGVMMI3Bsj1ULE9WIXl4fkBb2qxrQa7aPqqOP3Ves5xcWMkFEJmM0UBMJ2NS2CdiGras68L9Tw1OCTRKgkp4S5c+wudyzlyjBN3Z+PxePA+2D0zg7sf2yZluAlCdobsthkvO78ep/R3oW54+7OipWlo1zetui+Pqy7fiwFh5zmr9Qh28p3sMEEJg1rTAO64/w8xMoVAoFAqFQqE4XlDieomwEEdkLgT2DRexua8dQ+MV5EpyflrTKBglcDwpSMPC8HQtq1FI4ypPRm7pTArtYtnFsvY4HE8KbsvhcFwbOeJAYwSpuIZCxcVE0cGy9jjSSQNxU0Oh7MB2fWiUIJsyAEghlU4Y0BiRMVtCZjy7c6hfIaaiwML4L0CarxFK0NeTxqXn9WFDXzteGMxhcLQsXwOAgIDMZ4YbQDquIxGI9iMl7mbMPid0eB7HWN7GTUGO9WJET9UyH9M8SkjL1fqFfF7n6zGgUCgUCoVCoVAcTyhxfZxS6/7s+9Lhm3OBUtUFJQSmwaTJGZFOzKGO4UIKV0plriQPeqgpDfKuAbgeR64kDc5CeGCE5nAB1+WImQyux1G2XOl+TQjaUgbGCxY4B8bydvRajUnnaZmjDcRjDG55NuEmjxcekxCgryeN17xiBbqz8RmC87LXnYzr79mBiuXB1BkEBPIlZ8728Fo/NEYJKrZ/xMRds9nn6TnWG/raFyV6KmQpCdqFeAwoFAqFQqFQKBTHC0pcH4dMr4BqcSlQSlUXGqN4w5m92Ly2A5Wqi7sf24dd+3PSnZqQYHaZgwUCZmo+OwxkAvwg23g6oRgVAKq2D4MBCVNDoeyCC47RnAUnqHbqbMoK2wnmshmVe6GEgAbz1jOr6UE8k8+RTeiwXY7lHXH8zXvPgkYbtzZv6u/A+y7eiLse3YtD42UUyi6EkJV4PstMd+2jjseRjOt14o4LgRf353BguICEyQ6rgjx99lkELu9hO3vCZBiaqGDfcHHRhDWw9ATt4cxsKxQKhUKhUCgUSxklro8zZquAtgcV0Of3TuLiQDBt7O/Ag08fwA8fHoDnccQMhlxQ1fUDV2oaqmYS/RGJ7pCw6ixNv+Rjjg+MTFYBhKZgU9sLIcU8iJzr9oI5aEbl3HR0XCKPFR2OACL4IWzRfsdF65oK65CwnfrR3w7h5vtfBCHSqI23YETGKMGa5Sm85fz+SNxtH5jAjx/bh+HJajQL3NORmCEAw9b8uVq4a2efq7YXOWWH11vTKDRKjoiZ11ITtIvd+q5QKBQKhUKhUCwFlLg+zpjL/TkZ0+oqoJQQvO7MVejpSESVXSA08JLxXfmyA8fjYJBikTEKv0VHMB7MUAuIyM0bADwuoIfO4zWnyZg8x1LVheNxaIRI628uoGk0anFnNXPVrYo/SggyCQOcC1iOBwECRggIQ925AWF8FxA3NbztwpNw0dbeSNyFnQG24yOTMhCPMTiOj71DRXzjzufx1lf146IzerFz72Rda34zAQ5MzT6Xqy4KZUdmPAdt+AKA6/pwAYzmqy291/my1ATtYra+KxQKhUKhUCgUSwElro8zZnN/dlwfvs/h/P/t3XlcVXX+P/DXuTs7giAmmmSymSwqIKaWxvg1NHOy0l8uqY04mZniEpm5TI6miXuO+1ZNWmlaTlbjZOXkgpBjpbRqKCm4sXiBu5/fH5d74sJlk+WyvJ6PR4/03HPP+bzPFc59n8/78/kYzSgsMti9Vja5On/xFj7/5neYLBbIZAI8XZW4WaiD0WztxXVzUaBQa6hwfAAV6rgVMqHCkl82FlG0Jo9lXnRVyQFBgJebqvSc1mW6lEo5vN2U0JaYoFDIpAS2quTPUa+xq4sSeqO5NOkXpHOXXVcbAFw1CofJe9nKgDYeaigVctwuNqBAW7oms96Ed/7zM746ewUFpUly2dL87OtF2FVmcjKbTu080K6NC366XACx9OGBXSk+rMl2xo/XpUS/pr3iNcWEloiIiIio4TC5bmYczf6s05tQYCszLk0mD/73IpRymV2CZ0uuOgd4Iqi9p12vq6tGKY3/tZit5eK2ycSA0uTZ0dBloewf7HewiLYycwEKubW33GgWYTCaoVLJ4emqQkGRAaIoQiETYDSLNe6tLj+hm63XuEdwW6k1Zot1abDyzZYJwKiH7kX8fe0rJKvlKwOKdSbcLNBJPc1yCLBYRGRf18IiAn7eGulzKD85mUYtR1GJSUqMe4X448dLBaXtEaTlyMylybanq0qqOijRmWrcK05ERERERM7H5LqZKT/7s95gts7QLUIqMVYoZLhRoHPYg2rjqEw40N8d2de00BYbcS2/GHs//xVGkwUK+R8JqG0stm2MtO0VmYAKPdgeLkpo1ArIBKBIZ4JP6brXZcf9hnTyRs8QP4ezgFemsgndsq8X4cqNIihkAgxVrHUtAijQGqodGy2KIvJuWxNruWDtaRatM77BUjr7eWGRERqV4o9eaMG61vZPl/Ox5v1vIUCQEuPwu9vARS2HyWyxlqmXXiuVQgYvNxVUKjkKtQacv3gLX3171WF8VX2mRERERETkPEyumxn72Z/10OnNMFusPZ8W0drz7O2uliYuK7u8k6NjlS8T/uPvvjAYLfjg2EWYSku3BZkAhUIGGQCd0YLyR5TLBLuxzS5qOWQyAUU6E1zUCjw54N46j/utbkmrmwXWUnOZIMBSrs/aNvxbFIFvfr4uTfpWVtnKAAjWZcmE0onebBOt2SYhk8kEGE0WGEwWqO2qCPSwiIBCJpPWss6+XoSrN4shkwnwcdNYYymtFLD1fBuMZshkQMZP11FUYoSbRiF9TpUt2dVc1HeJOxERERFRU8Pkuhmyzf6876tf8dvV21LCqFLI4emmgova+rGWn9ystgbH3Y1O/u7Y99UF3CjQAbCOYw7wdcONghJcz9dZl/iS/dGDbeu9lsmAEoMFCrlYYVbquoz7rWxCN9vSVmqlHCV6EywWsbTH/Y+p0AXBWpqulAvIv21weF3KVgZoVDJYLGLpkmF/JOrSaUs7n8vOrG4dh12aEKvkdolx3m09LBYRRTqj3YMBW/uLdCa4ahS4fE1bOimb2XrNS3u2NWpFnT9TZ6ishJ8l7kRERETUkjC5bqbCOvvgUbMFWz46D41GAYVcBpVCZpewKRQyFOlM+OX3gjvuMQwP8kVoZx/8fqMIkMkBixkd2rrhx6w8bDl0HreLjSi7apdMEODuqsCw+4Pg513zUu+acjShW/mlrWztMZtLE+zSrmfrWt+At7sKeqMFhUUG/JZTWOHa2CoDCrV6u9jKEkXAVDpWWiazxmYwmmEwmq0TtCmsn4eNIAhwd1Fa1yKXyRyuOS0TgNtFBmslglC63nfpOuE3C3Xw9dRApZKjWGeq8ZJdzu4xrqqEnyXuRERERNSSMLluxjxKx+kq5TKptLisomIjinVGHDr+m93Y39r2GMoEAUHtPdGmjRvy8opgMlkQ1tkHk4aG49CJ3/D7jSKYTCIUCgEd2rrZrRddXl2TPbuybVjLsAuLjRBLZ9sWSnuTbf+ZRRFCubHNZosIg8mMd4/+UtrLjQrXZtygYPzj4DnA+MfAbUGwlr4LsC41BrF0JnTRunyY3mCGKFqP5emmqrBUmkIhg0wQMLBHB5zPyquw5nRRiRE3CnTS+uC2tcUVggCTRURBkQFtZGrI5QLcXZXVXitn9xhXV8LfXEvciYiIiIgcYXLdjJWf3KxsMleiMyJPq4dMEOCqVkCplNd7j2Ft106uj2SvUzsPeLiqkF1aOl22Y1kmBywAVEoZIAjQG8yQy4A2HhrrmHSLiHytHvrShLlEXwyVUgYvdxUUMpndtXF1UUKtkkOllKOwdEZzua3+vXSab0EA/Nu4wGCyoERvhog/EmtbaX5ZJpMFcrmA8CAfDO59t911E0URb3zwPTxclVLyLwDSZyoXBGmJtbsDPNCpnUeV16kp9BjXdk12IiIiIqLmjMl1M2Y/uZl9mfHNQj0AwNdLDbXK+jE3RI+ho0nRHPVOZ2blYce/MqEzmOCqUcLNXXlHyd6PWXko0OphcbCwtslsLdP2dldDBHDTpIPJJFrHRIsibhbq7GYQV8gFmMwW5BXq4eOpgbe7Sro2g2I6wmwW4eulgVopR75WD6PJYjfDt0Iuw6iHusLTTQVtsRGuLkrs++IX/H6jGKIoOhxTHejnJj2AKHvdvr9wE2azCKWLddz8rUIdzKXrhAul19RWot4z2K/Ka9RUeoyrWpMdsPbk16bEnYiIiIioKWNy3czZJjez9QgX60zW6bsEoI27Gi5q+/Lhhu4xdNQ77a5R4Fq+DnqDGYIA6I0WaEuM8HJT2SW01SV7tqTRIorw89Yg77Z1nHVZcpkATWmvsY+XGrcK9NAbzCgxmGARrUmxyWyBXCaTZg83iyIKiwzQqFyka6MtMUIhF2A0W6BRy9FO5QqD0SzN8A0AeqMZnm4qu2s4NL6zw4cdRToTNCo5hjiYoRywL3d3USvg46mRxpGbRVFa4sxssV6DjJ+uV9rj31R6jB2tyV6WrSe/JiXuRERERERNneMuJWpWwjr7IHlkFKY+1h3PDAnDI/F3w1WtgJuL46RFoZDBbBbrvcfQVoqcfV0LtVIOT3cVAODStSLoShNrudw67tg2SZfeYLZL9qpSNml0USvh66mGTCZALgMUchkUcsFaUm20zrKtkMng6abEQz07QKNWwLe0d1qwZdWwJpsy4Y8ltWzXxsNFifa+brhdbLSOq4a151ejVkBZOlFcgI9rhfJs28OOQD836I1mFGoN0BvNCPRzq7J33lbiX6QzQRRFuKgV8G/jAk/3P8ZuqxQy+LVxgVopl3r8M3+7VeFYUo+xovIe44b4/KuLqSxbT76ja0hERERE1Byx57qFKFtm7O6qlHpMG6vH0FEpsiiKKNaZpGHKFrH0H1y5Sbr82rjUqDy4fJmxSimHSiGDwWSGDCJQek7r8ll/lGEHtfeEXLgCF41CmghNWqy69H8WWJfUsl0bDzcVhvYp7YW+bYBrLXqhazsWHXBc4i+TAdoio7W3XADaeKhrtOZ1U+kxrmrYQnXXkIiIiIiouWHPdQvkjB5DR6XIBpMFRpMFsjL/yso2R17aY1xSOmN2dcle+ZnCBcE6eZhMEKyl06VJtdkiIl9rkJI3DzeVXbKpVMhgLtMQW54tE2B3bcKDfPDc45Ho6O9eq15o4I+HHffd44vOAZ41SiDL9nprS4y4eqMYRtsgcUFAQZEBOr1Jir2yHv+m1GN8pz35RERERETNDXuuWyBn9Bg6mrzKNpu3bVIua++1BXKhdB8BEC3W3u3O7aufAdvR7Oi28ckFRQYYjGZpVvBAPzdpTLJFFO3e5+Wmws1CHUwW64RhFlGU1gR3USvsrk1kVz8E+rrgQh3WCq+NsM4+sADY8a9MKBUyGEyW0uW/BLv1rjVqRaUTgjW1HuM76cknIiIiImpu2HPdQjV2j2H5XmUAkJWuCQ1BkCYBE0VrMitKPc2ocbJnSxo1KjnytdZk2iJaZwhXKWTwdlfjgai78HBcJ4x44B6E3N3G4ftkMgE+HmooZNYx2gCgVsrR0d/d4bW5k17oO2URRRw+mQWTxSKVgQPWCeoUMgEWESgoMgCoury7qfUYN+Y1JCIiIiJyBvZct2CN2WPoqFdZpZBZe1+N1jWglQoZ5DJrKbgF1hJlV40CE4aE1TjZczQ7ulwuwMdTA4gizvx8w+Ea2uXfZzaLcHdRwttDhR5d/RAe5NMkelPLltcrbdevzJrXtlJ6vcGEYr1ZWtrLEfYYExERERE1HibXLZyjdagb6jyOSpFdNQroS2fv9nBRwtVFAZ3ejGKdCWqVHM8khiK8lr2o5ZPG6/kl+CTtEvRGM9w0SihcZA7X0G4OyWbZ8nrbmPKya14D1ocShUXW9lfX499Ynz8RERERUWvH5LqJsohik04CHamsV7mTvzsgCLhdbMDtIiPkcgGd23tUuk5zTdiSRosoYuXe/0FvNEuzlAOodEbtpp5slp/pu/ya17YJygJ8XTCifxdOCEZERM2exWLBTz/9gPz8fHh7eyM4OBQyGUcuElHzw+S6Ccr87ZZd+XL5EuemrLLeYQAN8rDA0SzlNuVn1G7KSbVNZZO2aVRyGIxmFBYZ0c7HBSljekLBLx5ERNTMZWSkYe/et3HjxnVpW9u2fhg5cjR69ox1YsuIiGqP386bmMzfbmHXpz8i+7oWaqUcnu4qqJVyqcQ587dbzm5itRxNXtVQE1pJZdQKx/+UFQoZzGax2jW0m4rKJm0zmiwo1pvh7qrE4w90YWJNRE6l1+uxaNEixMfHIzo6GjNnzsStW1Xfn7KzszF58mT06NEDffv2xerVq2E2mx3ue+jQIQwcOLDC9m+++QZjx45Fz5490a9fP7z88svIz8+vj5DICTIy0rBhwxoEBnbEyy8vwoYN2/Hyy4sQGNgRGzasQUZGmrObSERUK/yG3oRYRBH/OpkFncEEb3c1VEo5ZIIAlVIOb3cVdAYz/nUyC5Zyaxe3Zo5mKS+rqhm1m6qmNtM3EVF5CxcuxH//+1+sW7cOu3btwoULFzBt2rRK9zcajXjmmWcAAHv27MHChQvxzjvv4I033qiw75EjRzB37twK2y9evIhnnnkGISEhePfdd7Fq1Sp8++23eOGFF+ovMGo0FosFe/e+jcjIaEydmowuXbpCo9GgS5eumDo1GZGR0di7921YLI7v70RETZHTk2uLxYK1a9eiX79+iIqKwqRJk3D58uVK98/Ly8PMmTMRExOD2NhYLFq0CCUlJXb7HD58GImJiYiIiMDw4cNx4sSJWh/DGWpT4kxWtjLqIp1JGo9sI4oiinQmBPi4VruGdlMT1tkHySOjMPWx7nhmSBimPtYdySOjmFgTkdPl5ubiwIEDmDdvHnr16oWIiAisXLkSp0+fxpkzZxy+59NPP8WVK1ewfPlyBAcHIyEhAcnJydi1axcMBuvyglqtFikpKZg+fTqCgoIqHOPAgQPw9/fHyy+/jC5duqBXr15YsGABTp48WeX3BmqafvrpB9y4cR1DhjxaYXy1TCZDYuIw3LhxHT/99IOTWkhEVHtOT643bNiAf/7zn3j11VexZ88eWCwW/OUvf5FutuVNmzYNWVlZ2LlzJ9asWYMvv/wSCxculF4/efIkZs+ejVGjRuGDDz5AfHw8kpKS8Ouvv9b4GM7S0kqcG0NlZdQGoxn5WkON19Buirg2NBE1RRkZGQCA3r17S9uCgoLQrl07nD592uF70tPT0a1bN3h5eUnbevfuDa1Wi8zMTADWsvGrV6/ivffeQ0JCQoVjDBs2DMuWLbN7+Gz7c0FBQd0Do0ZlK+fv0KGjw9dt21n2T0TNiVOTa4PBgO3bt2PatGl48MEHERoailWrViEnJwefffZZhf3PnDmDtLQ0LFu2DN26dUN8fDz+9re/4eDBg8jNzQUAbNmyBQkJCRg3bhy6dOmCF198Ed26dcOuXbtqfAxnaYklzo2BZdRERI0nNzcXbdq0gVqtttvu7++PnJwch+/JyclBQEBAhf0B4OrVqwCA0NBQ7Nq1C2FhYQ6P0aVLF0RFRdlt27JlC/z8/BASEnInoZATeXt7AwB+/91x1YFtu20/IqLmwKmzhf/www8oKipCfHy8tM3T0xPh4eE4ffo0hg4dard/eno6/Pz80KVLF2lbbGwsBEFARkYGBg8ejG+++QYpKSl274uLi5OS9eqOkZiY2BCh1oijmaJtbCXOgX5uza7EuTE0hzWsiYiag+zsbDz00EOVvv7CCy9ApVJV2K5Wq6HX6x2+R6fTwdPTs8L+ACp9T3WWLVuGL774AuvXr4dSWbeHzpVVjFHDCQ8PR9u2fvj44w/xwgsz7UrDLRYLDh/+CH5+/ggPD+eyXETUbDg1ubY94W7fvr3d9sqefufm5lbYV6VSwdvbG1evXkVhYSGKi4sdPh23Ha+6YziTrcR516c/Il9rgJtGAYVCBpPJgiKdqVmXODeGpr6GNRFRc9CuXTt8/PHHlb7+5ZdfOhy6pdfr4eLi4vA9Go2mwntsSbWrq2ut2mc0GjF//nwcOHAAr776qsMS8tqQyQS0aeNWp2PQnZk06S947bXXsGHDajzxxBPo1KkTLl26hPfeew//+5+1s8TXlx0KRNR8ODW5tk0iVv4JuFqtdjh+qqSkpMqn5TqdrtLj2W7i1R2jLurjyXf3e9tiolyGQ8d/w9WbRSjWmaCQC+jk746hfTojPMg5Jc5yuczu/60BY275Wlu8QOuLubXFWx+USqVddVd5P/74I/Lz82EwGOzup9euXUO7du0cvicgIAA//fST3bZr164BQKXvcUSr1WLq1KlIT0/HypUr8fDDD9f4vZWxWEQUFhbX+ThUe2FhkZg6dQbeeedNzJ49W9ru5+ePqVNnICwsEnl5RU5sIRGRlaenS42+Szg1udZoNACsY69tfwYqf/rt6Mm3bX9XV1epxMzR03Hb8ao7xp2qzyff97dxQ3xUIC78XoDCIgM83VS4p4MXZDLn91h7ejrulWjJGHPL19riBVpfzK0t3obUs2dPWCwWZGRkSMO6Ll68iNzcXMTExDh8T0xMDA4cOACtVgt3d3cA1glI3dzcEBoaWqPzGgwGTJ48GZmZmdi2bRvi4uLqJyCg0rlOqOFFR/dCZGQP/PTTD8jPz4e3tzeCg0Mhk8n4uRBRs+PU5NpWnn3t2jV06tRJ2n7t2jWHk5MEBATgyJEjdtsMBgPy8/Ph7+8Pb29vuLq6Sk/Dyx7P9mS8umPcqYZ48u3rroSvu3UcWUGBc5+qy+UyeHq6oLCwBGZz67jZMeaWH3NrixdofTE3xXhr+vS7qWrXrh2GDBmCefPmYcmSJXBxccGCBQsQGxsrTThmMBhQUFAALy8vqFQqJCQkYPXq1Zg+fTpmzZqF7OxsrFy5EhMnTnRYTebIpk2bkJGRgdTUVNxzzz24fv269JrtPNQ8yWQyhIaGO7sZRER15tTkOjQ0FO7u7jh16pSUXBcWFuL8+fMYM2ZMhf1jYmKwYsUKZGVl4e677wYApKWlAbA+SRcEAT169EBaWhqeeOIJ6X2nTp1Cr169anSMumgNT1jNZkuriLMsxtzytbZ4gdYXc2uLt6G9+uqrWLJkCaZOnQoA6N+/P+bNmye9fubMGYwbNw67d+9GXFwc1Go1tm7dikWLFuHJJ5+El5cXnnrqKUyZMqXG5zx06BBEUURycnKF12znISIiciZBFEXRmQ1YtWoV9uzZgyVLlqBDhw54/fXXkZ2djUOHDkEmk+HWrVvw8PCARqOBKIp46qmnoNfrsXDhQhQXF2Pu3LmIi4vD0qVLAQD//e9/kZSUhNmzZ6N///7Yt28f3n77bezfvx9dunSp0THuhNlswa1bLXdckEIhQ5s2bsjLK2o1X1AZc8uPubXFC7S+mJtivD4+bs2657qlaen3byIiqrua3rudnlybzWasXLkS+/fvh06nQ0xMDObPn4/AwEBpOZClS5fiscceAwDcvHkTixYtwrFjx6BWqzF48GC89NJLduttHjhwABs2bEBOTg7uvfdezJ492265r5oco/ZxtOybc1P8gtrQGHPLj7m1xQu0vpibYrxMrpuWln7/JiKiums2yXVL0dJvzk3xC2pDY8wtP+bWFi/Q+mJuivEyuW5aWvr9m4iI6q6m927e3YmIiIiIiIjqiMk1ERERERERUR0xuSYiIiIiIiKqIybXRERERERERHXE5JqIiIiIiIiojphcExEREREREdURk2siIiIiIiKiOuI61/VEFEVYLC37UsrlMpjNTWOd2MbCmFu+1hYv0PpibmrxymQCBEFwdjOoVGu4fxMRUd3U9N7N5JqIiIiIiIiojlgWTkRERERERFRHTK6JiIiIiIiI6ojJNREREREREVEdMbkmIiIiIiIiqiMm10RERERERER1xOSaiIiIiIiIqI6YXBMRERERERHVEZNrIiIiIiIiojpick1ERERERERUR0yuiYiIiIiIiOqIyTURERERERFRHTG5JiIiIiIiIqojJtethMViwdq1a9GvXz9ERUVh0qRJuHz5cqX75+XlYebMmYiJiUFsbCwWLVqEkpISu30OHz6MxMREREREYPjw4Thx4kSlx/vwww8REhKC7OzseoupKs6I12g0IjU1VTrnmDFjkJmZ2SDxOeKMmG/evImZM2eid+/eiIuLw4wZM5Cbm9sg8TnSEDHbZGRkICwsrE7HqG/OiPfnn39GUlIS4uLiEB8fj2nTpuHKlSv1FlN1nBFzWY39u4uotantzzhRa7Np0yaMHTvW2c2gmhKpVVi3bp0YFxcnHj16VMzMzBQnTpwoDho0SNTr9Q73HzNmjDhixAjx+++/F48fPy4OGDBAnDNnjvT6iRMnxG7duom7du0Sf/nlF/G1114T77vvPvGXX36pcKzs7GyxZ8+eYnBwsHj58uUGi7EsZ8Q7d+5csU+fPuJXX30l/vLLL+Lzzz8v3n///WJhYWGDxyuKzol5zJgx4qhRo8Tz58+L586dE5988klxxIgRDR6rTX3HbJOeni7GxsaKwcHBd3yMhtDY8d66dUu8//77xeeff1788ccfxe+++04cPXq0+PDDD4s6na5BYizPGZ+xjTN+dxG1NrX9GSdqTd566y0xNDRUHDNmjLObQjXE5LoV0Ov1YnR0tPj2229L2woKCsSIiAjxo48+qrD/N998IwYHB9slUceOHRNDQkLEnJwcURRFceLEieILL7xg976RI0eKr7zyit02s9ks/r//9//EcePGNdoXVGfEe+nSJTEkJEQ8evSo3TkHDBggHj9+vB6jc8wZMRcUFIjBwcHif/7zH+n1I0eOiMHBwWJeXl49RudYQ8RsNBrFJUuWiN26dRP//Oc/V0i8anKMhuKMeN99910xOjpaLCkpkbZduXJFDA4Obrb/rquL2cYZv7uIWpva/owTtRY5OTni5MmTxaioKHHw4MFMrpsRloW3Aj/88AOKiooQHx8vbfP09ER4eDhOnz5dYf/09HT4+fmhS5cu0rbY2FgIgoCMjAxYLBZ88803dscDgLi4uArH27hxI4xGIyZPnlzPUVXOGfF+/fXX8PDwQP/+/e3O+fnnn1d4X0NwRswajQZubm44cOAAtFottFotDh48iKCgIHh6ejZQpH+o75gBoLi4GKdPn8bWrVsxZsyYOzpGQ3FGvPHx8diwYQM0Go20TSaz3jYKCwvrLbbKOCNmG2f87iJqbWr7M07UWpw7dw5KpRIffvghIiMjnd0cqgWFsxtADS8nJwcA0L59e7vt/v7+0mtl5ebmVthXpVLB29sbV69eRWFhIYqLixEQEFDl8b799lts374d77//fqOOw3VGvBcvXkTHjh3x2WefYfPmzcjNzUV4eDhSUlLsvug3FGfErFKp8Nprr2H+/Pno1asXBEGAv78/3nrrLSkBa0j1HTNg/VK3f/9+AJD+X9tjNBRnxBsYGIjAwEC7bZs3b4ZGo0FMTMydB1NDzogZcN7vLqLWprY/40StxcCBAzFw4EBnN4PuAHuuWwHbZD4qlcpuu1qthl6vd7h/+X3L7q/T6ao9XnFxMWbNmoVZs2ahc+fO9RFGjTkjXq1Wi6ysLGzYsAHJycn4xz/+AYVCgaeeego3b96sl7iq4oyYRVFEZmYmoqOj8fbbb2PXrl246667MGXKFGi12nqJqyr1HXNNz1nXY9wpZ8Rb3ptvvom33noLs2bNgo+Pzx0dozacEbMzf3cRtTa1/RknImrqmFy3AraSToPBYLddr9fDxcXF4f7l97Xt7+rqCrVaXe3xFi9ejKCgIIwaNapeYqgNZ8SrUCig1WqxatUq9O3bFxEREVi1ahUA4IMPPqh7UNVwRsyHDx/GW2+9hddffx09e/ZEbGwsNm7ciN9//x3vv/9+vcRVlfqOuabnrOsx7pQz4rURRRGrV6/G4sWL8eyzzzbarKXOiNmZv7uIWpva/owTETV1TK5bAVu51bVr1+y2X7t2De3atauwf0BAQIV9DQYD8vPz4e/vD29vb7i6ulZ5vH379uH48eOIjo5GdHQ0Jk2aBAAYOnQoNm7cWG+xOeKMeAMCAqBQKOxKwDUaDTp27NgoS/g4I+b09HQEBQXB3d1det3LywtBQUHIysqql7iqUt8x10R9HONOOSNewLrE3OzZs7Fx40a89NJLmD59eu0bf4ecEbMzf3cRtTa1/RknImrqmFy3AqGhoXB3d8epU6ekbYWFhTh//rzDcZMxMTHIycmxS5DS0tIAAD179oQgCOjRo4e0zebUqVPo1asXAOCzzz7DoUOHcODAARw4cACLFy8GYB2v2dA9Qs6INyYmBiaTCd999530uk6nw+XLl3H33XfXa3yOOCPmgIAAZGVl2ZXuFRcXIzs7u1HKaes75pqoj2PcKWfECwBz5szBJ598gtTUVIwfP/7OA7gDzojZmb+7iFqb2v6MExE1dZzQrBVQqVQYM2YMVqxYAR8fH3To0AGvv/46AgICMGjQIJjNZty6dQseHh7QaDSIjIxEjx49MGPGDCxcuBDFxcWYP38+hg8fLj1JnjBhApKSkhAeHo7+/ftj3759yMzMxN///ncAqJBQ2iYmueuuu+Dt7d3i4u3Vqxf69OmDF198EX/729/g7e2NtWvXQi6X49FHH23QeJ0V8/Dhw7Ft2zZMnz4dL7zwAgBg9erVUKvVeOyxx5plzNWpj2M0p3j379+Pjz/+GHPmzEFsbCyuX78uvWY7T0NyRszO/N1F1NpU9zNORNTsOHstMGocJpNJXL58udi7d28xKipKnDRpkrRu6+XLl8Xg4GBx37590v43btwQn3/+eTEqKkqMi4sTFyxYIOp0OrtjfvDBB+Kf/vQnsXv37uKf//znKte9PXnyZKOuFeuMeG/fvi0uWLBAjIuLEyMjI8UJEyaIP//8c8MHW8oZMf/yyy/i5MmTxdjYWLF3797i1KlTG3U94IaI2Wbfvn0O10CuzTHqW2PHO2HCBDE4ONjhf2XP05Cc8RmX1di/u4ham6p+xolIFF988UWuc92MCKIois5O8ImIiIiIiIiaM465JiIiIiIiIqojJtdEREREREREdcTkmoiIiIiIiKiOmFwTERERERER1RGTayIiIiIiIqI6YnJNREREREREVEdMromIiIiIiIjqiMk1EdWZKIrObgIRERHVUn3fv/l9gFo7JtdEdMdycnKQlJSE33//3dlNcYqUlBQMHDiwyn3WrVuHkJCQRmoRERFR9QoLCzFnzhykp6dL28aOHYuxY8fW+Bjl74H/+c9/8OKLL9ZrO4maG4WzG0BEzdfx48fx5ZdfOrsZREREVAuZmZk4ePAgRowYIW1bsGBBrY4xZcoUjBs3Tvr7zp0766t5RM0Wk2siIiIiolbu3nvvrdX+nTp1aqCWEDVfLAsnqiVRFLFz5048/PDDiIiIwJ/+9Cds27YNoigiJSUFY8eOxfvvv48BAwYgOjoaTz/9NH744YdanePUqVMICQnBqVOn7LaXL9kaOHAg1q5di2XLlqFPnz6IiIjAM888g99++83ufV9++SVGjRqFqKgo9O3bF/Pnz0dhYaH0+pUrV5CcnIzY2FhERkbi6aefxvnz56XXs7OzERISgh07dmDw4MGIjIzEvn378NJLLwEAHnroIaSkpNQqxvT0dIwZMwaRkZGIjY3Fiy++iFu3bkmv79+/H+Hh4Th79ixGjhyJ7t27Y8CAAdi2bZvdcQ4dOoRhw4YhIiICvXv3xqxZs5Cbm2u3z3vvvYchQ4bgvvvuw4MPPoh169bBbDZLr6ekpOCZZ57B3r17kZCQgIiICIwaNQoXL17E0aNH8cgjjyAyMhJPPPEEMjMzK8Syd+9ePPjgg4iIiKhw7Rw5cuQIHnvsMXTv3h33338/Fi9ejOLi4lpdPyIiar10Oh1SU1MxaNAg3HfffejRowcmTJhgd4+q7N5/6tQpqcd53Lhx0veKst8xJk6ciMcee6zCeadMmYJhw4YBsC8LHzt2LNLS0pCWloaQkBAcP34cffv2xcyZMyscY9CgQZg3b179XhCiJoLJNVEtLV++HMuXL8fAgQOxceNGPP7441ixYgU2b94MwFpqtWrVKkydOhWvv/468vLyMGbMGFy7dq1B2rN7925cuHABS5cuxeLFi/H999/bjXk6evQoJk+eDF9fX6xevRqzZs3CkSNHMGPGDADArVu3MGrUKJw7dw6vvPIKUlNTYbFYMHr0aPz6669251q3bh0mTZqE5cuXo0+fPnj22WcBAOvXr8eUKVNq3ObTp09j/Pjx0Gg0WL16NebOnYu0tDSMGzcOOp1O2s9isWD69OlITEzE5s2b0aNHDyxfvhzHjh0DAGRkZGDOnDkYNGgQtmzZgpdeegknT560u5lv2rQJr7zyCuLj47Fx40aMHj0aW7ZswSuvvGLXpjNnzuCtt95CSkoKli5dil9//RVJSUlYunQpJk+ejJUrV+Lq1auYNWuW3ftycnKwfv16TJ8+HStXrkRBQQHGjh2LK1euOIz9o48+wnPPPYd77rkHb7zxBqZOnYoPP/wQU6ZM4UQwRERUI3PmzMG+ffuQlJSE7du346WXXsLPP/+MmTNnQhTFKu/93bp1w/z58wEA8+fPd1gOPmzYMJw7dw5ZWVnStsLCQnz11Vd49NFHK+y/YMEChIeHIzw8HHv37kVERASGDx+OI0eOQKvVSvtlZGQgKyvLYeJO1BKwLJyoFgoLC7F7926MGTMGs2fPBgD06dMH169fx+nTp9G2bVvcvn0bGzduRK9evQAAERERSEhIwO7duyskZvXB09MTGzZsgFwuBwBcunQJ69atQ15eHtq0aYN169YhLCwM69evhyAIAACVSoU1a9bgxo0bePPNN5Gfn4933nkHHTp0AAD0798fiYmJWLNmDdauXSud6+GHH7Ybn2UrCQsLC0NgYGCN25yamoqgoCBs2rRJandkZCSGDBmCffv2YfTo0QCsVQJTpkzBE088AQDo2bMn/v3vf+OLL75Av379kJGRAY1Gg6SkJKhUKgCAt7c3vvvuO4iiCK1Wiw0bNmDkyJHSU/K+ffvC29sb8+bNw4QJE9C1a1cAQFFREVavXo0uXboAANLS0rBnzx7s3LkT8fHxAICsrCwsW7YMhYWF8PT0BACYzWa88cYbiIiIkOJISEjAm2++WWFiF1EUsWLFCvTr1w8rVqyQtnfu3Bnjx4/Hl19+iQcffLDG15GIiFofg8GAoqIizJs3D4mJiQCA2NhYaLVavPbaa7hx40aV936dTieVgN97770Oy8EHDRqERYsW4dChQ3juuecAAJ999hnMZjOGDh1aYf97770X7u7uAICoqCgAwIgRI7BlyxZ8+umn0neHAwcOoHPnzujRo0f9XhSiJoI910S18L///Q8mkwmDBg2y2z5v3jxs3boVABAYGCgl1gDg7++P6OhonD59ukHa1L17dylBBYCAgAAAQElJCXQ6Hc6fP4+EhATp5goAiYmJ+PTTT9G2bVucOHECYWFhaNeuHUwmE0wmE2QyGfr374/jx4/bnSssLKzO7S0pKcHZs2fxwAMPQBRF6ZwdO3ZEly5d8PXXX9vtHx0dLf1ZpVLBx8dHKqGOiYlBSUkJhg4ditTUVKSnp6Nv376YOnUqBEHAmTNnoNPpMHDgQOk8JpNJKmMrey4vLy8psQaAtm3bArAmyzbe3t4AYFdS37FjRymxBgA/Pz9ERUU5/LwvXLiAnJycCu2JiYmBu7t7hdiJiIjKU6lU2LZtGxITE5Gbm4uTJ09iz549OHr0KABr8l3dvb86rq6uSEhIwMcffyxt+9e//oX4+Hi0a9euRu0MCgpCz549cfDgQQDWUvbDhw+z15paNPZcE9VCfn4+AMDHx6fSfRzddHx9fXHu3LkGaZOLi4vd32Uy6zMzi8WCgoICiKIIX1/fSt+fn5+PrKwsdOvWzeHrJSUl0p9dXV3r3N7CwkJYLBZs2bIFW7ZsqfC6Wq22+7tGo7H7u0wmk8qno6OjsXnzZuzcuRM7duzA5s2b0bZtW/z1r3/F2LFjpc8rKSnJYVvKlurbnriXV13Mjr6k+Pr64urVqxW229qzaNEiLFq0qMr2EBERVebYsWNYsmQJLly4ADc3N4SGhkr3q5ycnGrv/TXx6KOP4sMPP8QPP/yAtm3b4tSpU1iyZEmtjvH4449j7ty5uHr1KjIyMlBUVIThw4fXqV1ETRmTa6JasJUC37p1C/fcc4+0/cqVK7h06RKMRiPy8vIqvO/GjRu1usnZnjRbLBa77UVFRXBzc6vxcdzd3SEIgt1EYQCg1+tx8uRJREZGwsPDA7GxsZgzZ47DY9jKreuLm5sbBEHA+PHjMWTIkAqvl39YUJ1+/fqhX79+KCkpwcmTJ7F7924sXrwYkZGR0ue1YsUKdO7cucJ7a/L0vjoFBQUVtl2/ft3hAxhbe+bMmYPY2NgKr3t5edW5PURE1LJdunQJzz33HBISErBp0yZ07NgRgiDg7bffxrFjx+Dh4VHtvb8m4uPj4efnh8OHD8PPzw9qtbpC5V51Bg8ejMWLF+OTTz5Beno67r///hr3fBM1RywLJ6qFiIgIKJVKqfTKZvv27UhOToZcLsdvv/1mNxFYbm4uzpw5I43brQlbL2pOTo60raCgoMIEY9Vxc3NDWFhYhfZ+9dVXSEpKwrVr1xAbG4uLFy8iKCgI3bt3l/47ePAg3n//fbuS8/JsveS14e7ujvDwcFy4cMHufF27dsW6desqzJBelWXLlmHEiBEQRREuLi4YMGCANM75ypUriIyMhFKpRG5urt25FAoFVq5ciezs7Fq3v7yLFy/i0qVL0t+vXr2KM2fOIC4ursK+99xzD3x9fZGdnW3Xnnbt2iE1NbXaWcaJiIi+//576PV6JCUloVOnTtIDedtkny4uLtXe+6u6t9vI5XI88sgjOHr0KD755BMkJCRUWc3l6DuBq6srEhMTcejQIXz99dcsCacWjz3XRLXg4+ODcePGYefOnVCpVIiNjcXZs2fxzjvvYM6cOcjMzIQoivjrX/+KGTNmQC6XY/369fDy8rJbQqs6ISEhaN++Pd544w2p93nTpk217tUFgGnTpuHZZ59FcnIyhg8fjhs3bmDlypVISEhAcHAwxo8fj4MHD2L8+PGYOHEi2rRpg48//hjvvvuutNRWZWw9sf/+97/Rv39/uzHLVUlOTkZSUhJmzpyJYcOGwWw2Y/v27Th79mytZh3v3bs3duzYgZSUFAwbNgxGoxFbt26Ft7c3evfuDW9vb/zlL3/BmjVroNVqERcXh9zcXKxZswaCICA0NLTG56qMWq3Gs88+ixkzZsBsNmPNmjXw9vbG008/XWFfuVyOGTNmYP78+ZDL5RgwYAAKCwuxYcMG5ObmVlqaT0REZNOtWzcoFAq8/vrrmDhxIgwGA/bv348vvvgCAFBcXFztvf/HH38EAHzxxRfw8vKq9H746KOPYvv27ZDJZA6HcpXl6emJM2fO4MSJEwgPD5eqsR5//HGMHDkSXl5eSEhIqL8LQdQEMbkmqqXZs2fD19cXe/bswdatWxEYGIhXXnkFo0aNQkpKCu666y5MnDgRS5YsQUlJCfr06YN//OMf0mRYNSGXy7F27VosWbIEycnJaNu2LZ5++mlcuHABFy9erFV7BwwYgI0bN2L9+vV47rnn4OPjg0ceeQTPP/88AOsY8T179iA1NRULFy6EXq9H586d8fe//x2PP/54lceOi4tDnz59kJqaihMnTkjLkVWnb9++2LZtG9avX49p06ZBqVSiW7du2LFjhzTLaE088MADWLFiBbZv3y5NYtazZ0/s3r1but7Tp0+Hn58f/vnPf2Lr1q3w8vJCfHw8kpOT4eHhUeNzVSY8PBz/93//h4ULF+L27duIj4/H3LlzKx2X/8QTT8DNzQ1bt27F3r174erqih49emDFihXo2LFjndtDREQt2913343U1FSsX78ezz77LLy8vBAVFYU333wTY8eORXp6OkaPHl3lvb9r164YOnSoVEp+6NAhh+cKDQ1FcHAw8vLyqq3AGz16NL7//ntMmjQJS5cuxSOPPALAOnu4t7c3EhMT632oGVFTI4hcWJWo3qSkpCAtLQ2ff/65s5tCRERE5HRnz57Fk08+iYMHD9ZLxRhRU8aea6JGZDabUd3zLEEQajQWqqkRRRFms7na/eRyud3SIERERNTynDp1CqdOncKBAwfQt29fJtbUKjC5JmpE48ePR1paWpX7dOjQoVn2fKelpWHcuHHV7rd06VJOaEJERNTC5eXlYceOHejatSsWL17s7OYQNQqWhRM1ogsXLqCoqKjKfVQqFUJCQhqpRfVHq9XWaDx4YGAg2rRp0wgtIiIiIiJqPEyuiYiIiIiIiOqI61wTERERERER1RGTayIiIiIiIqI6YnJNREREREREVEdMromIiIiIiIjqiMk1ERERERERUR0xuSYiIiIiIiKqIybXRERERERERHXE5JqIiIiIiIiojv4/OeyTlcZ31WEAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -3678,9 +3686,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:05:28,285] A new study created in memory with name: my_study\n", - "[I 2024-07-01 13:05:28,327] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:05:29,145] Trial 0 finished with value: -5314.27743738282 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 2, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5314.27743738282.\n" + "[I 2024-07-02 14:22:47,822] A new study created in memory with name: my_study\n", + "[I 2024-07-02 14:22:47,862] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:22:49,237] Trial 0 finished with value: -4430.271946796234 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 9, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4430.271946796234.\n" ] } ], @@ -3758,7 +3766,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3790,7 +3798,7 @@ "outputs": [ { "data": { - "image/png": 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", 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/3X777fr666/1/PPPKyMjQ99//717nYyMDI0aNUrp6em67bbb9Nvf/laxsbFatGiRamtrdfnll4foVQAAQqGpqUnNzU0h25/ZnKCEBO7NA9A7+HsONZkMcjia/peu7Trkes3NzWppaRucczqd2rTpM5WVlSklJVWjR4/pUHHKH1Zrrex2u6zWWs7R6NPCPrHdoTgcDr311ltqbW3VZZdd1mH5v/71Lw0cOFCLFy/W/PnzdeWVV8pms+n444/XSy+9pMMOOywMrQYABMuOHZUqLy/zaRun0ymbrVnx8Wafv0Dm5xeqoKBnlU4AIFL4ew5taWlWYmKiXC7JdegYXk1NjWpubtLnn3+uDz74QDU1NbLb7Xrllb+qf/8sTZ48WcOGHdvh+X05RzscdtXX12nt2lIVFhZxjkafFXFB/NatW92/f/bZZ92uP2jQIJWUlASzSQCACDB48FBlZw/0aRurtVZr1pRqzJhxsljSfNrWbGaEB0Dv4e85dO3aUv3oRz+S0Rjf7Uj86tUf6NlnF2n//v1KS0tTa2ur4uLitWfP93rjjTc0d+5JGjv2ZI/n9+Uc3b7+2LHF6tdvQLfrA71VxAXxAAB0JiHBv9TJmJgYWSxpSk/PCEKrACA69OQcmp6eLpMpocsJx5xOp1555a9qbm7WwIFHyuFwqL6+TomJSUpJsaiqapf+9KfndOaZZ3uMuvt6jm5fn1T6H7TdvlCm8vJyZWUN1LhxxQG5fQGRi78uAAAAgB4pK9uo7dsrlZ6eIYPB4LHMYDAoLS1D27dXqqxso1/Pf2CgumlTmZxOZjCXpNLSVbr44gt0003X6sUX/6ybbrpWF198gUpLV4W7aQgigngAAAAAPVJTU6PW1lbFx8d3ujw+Pl6tra2qqanx+bkJVDtXWrpKs2ZNU3n5ZiUmJslisSgxMUkVFZs1a9a0Pn98ejOCeAAAAAA9kpmZqdjYWNlstk6X22w2xcbGKjMz06fnJVDtnNPpVEnJQjU0NGjAgGyZzW2TA5rNZmVlZWv//gaVlCwkY6GXIogHAAAA0COFhSOVkzNUtbV75TpoGnuXy6Xa2r3KyRmqwsKRXj8ngeqhBfv2BUQ2gngAAAAAPWI0GjV16kwlJSWrqmqXmpub5HQ61dzcpKqqXUpOTtHUqTN9mnCNQPXQgnn7AiIfQTwAAACAHisunqD58x9TXl6BGhsbZbVa1djYqLy8As2b96iKiyf49HwEqocWrNsXEB0oMQcAAAAgIIqLJ2jcuGKtXl2qlSuXa9KkyX6XPDswUO2spFxfDlTbb1+oqNgssznbY1n77Qt5eQU+3b6A6MFIPAAAAICAMRqNGj68UPn5+Ro+vNDvmuXBuM++twjG7QuIHvxVAQAAAESc3hCoBrO+faBvX0D0IJ0eAAAAQERqD1RLShZq27at2r+/QUlJTuXlFWjq1JkRHaiWlq7yaPeyZa8pN3dYQNsdyNsXED0I4hFSTU1Nam5u6vC4yWSQw9GkurpGORyuTrb0j9mc0Ok9VAAAAIgO0Riotte3b2hokMWSKpPJqPj4BHd9+/nzHwtYIN9++0JV1Tc9un0B0YMgHiG1Y0elysvLOjxuMEgmk1EOh1MH3fIkp9Mpm61Z8fFmn09K+fmFKigY0ZMmAwAAIMyiKVA9uL69w+FQa6tNZrNZSUlJqqrapZKShRF/IQKRiyAeITV48FBlZw/s8LjJZFBqamKnI/FWa63WrCnVmDHjZLGk+bQ/s5lReAAAAISOL/XtR44sClMrEc0I4hFSCQmdp7fHxBiVnp4kk2m/7PaOE37ExMTIYklTenpGKJoJAAAA+MWb+va1tfv6ZH17BAb5GwAAAAAQIAfWt+9MX65vj8BgJB4AAABuh5qENliYhBa9TXt9+4qKzTKbsz2Wtde3z8sr6JP17REYBPEAAABwO9QktF1hElrgB+317WfNmqaqql1KSbG469vX11ujor49IhtBPAB4idEpAH3BoSah7QqT0AKeorm+PSIfQTwAeInRqb7F34s2JpNBDkdTp9U2usJFG0SKQ01C2x0moQU8RWN9e0QHgngA8BKjU32LvxdtWlqalZiYKJdLcnkfw3PRBgB6oWiqb4/oQRAPAF5idKpv8feizdq1pfrRj34kozHe55F4AACA7hDEAwDQiZ5ctElPT5fJlCC73RmElgEAgL6MfA4AAAAAIeV0OrVpU5nKy8u1aVOZnE4uegLeYiQeAAAAQMiUlq7ymLV92bLXlJs7jFnbAS8xEg8AAAAgJEpLV2nWrGkqL9+sxMQkWSwWJSYmqaJis2bNmqbS0lXhbiIQ8QjiAQAAAASd0+lUSclCNTQ0aMCAbJnNbeVXzWazsrKytX9/g0pKFpJaD3SDIB4AAABA0JWVbdT27ZVKT8+QwWDwWGYwGJSWlqHt2ytVVrYxTC0EogP3xAMAAKBPampqUnNzU8j2Zzb7V/Wit6ipqVFra6vi4+M7XR4fH6/a2n2qqakJccuA6EIQDwAAgD5px45KlZeX+bSN0+mUzdas+Pi2VHBf5OcXqqBghE/b9CaZmZmKjY2VzWbr9GKGzWZTbGysMjMzw9A6IHoQxAMAAKBPGjx4qLKzB/q0jdVaqzVrSjVmzDhZLGk+bWs2991ReEkqLBypnJyhqqjYLLM522OZy+VSbe1e5eUVqLBwZJhaCEQHgngAAAD0SQkJ/qW3x8TEyGJJU3p6RhBa1XsZjUZNnTpTs2ZNU1XVLqWkWOR0OtXc3KT6equSk1M0depMnzMcgL6GIB4AAABRL1T3t1uttbLb7bLZmoO+r96ouHiC5s9/zKNOfFKSU3l5BdSJB7xEEA8AAICoF6r72x0Ou+rr6/T1118qKyu7+w3QQXHxBI0bV6zVq0u1cuVyTZo0WePGFTMCD3iJIB4AAABRL1T3t7dvc+SRR/nRyujU1NSklpZGORwur7dpz1iwWmsPuc6gQUcqNzdXgwYdqbq6H9br67P4A90hiAcAAEDUC+X97TExMYqPN/u8r2i1Y8cOlZdXyOV9DC+n06nW1hatW7f6kCPs7VkNa9eWymT6ISzp67P4A90hiAcAAABwSIMHD9bAgcf4NBLvjfashrFjiz0yIfr6LP5AdwjiAQAAABxSQkKCkpMTZLc7A/7czPQP+C6igvidO3fqwgsv1F133aULL7xQklRRUaEHHnhAmzZtUkZGhi6//HL9+te/dm/jdDr1xBNP6G9/+5vq6+s1evRo3X333TryyCPD9TIAAEAPhGqW8XbcfwsAiCYRE8S3trZq1qxZamxsdD+2b98+XXHFFZo0aZLmzp2r//73v5o7d66SkpJ00UUXSZKeeuopLV26VA8//LCysrI0b948XXXVVXrzzTcVFxcXrpcDAAD8FKpZxttx/y0AIJpETBD/+OOPKzk52eOxV199VbGxsbrvvvsUExOjIUOG6Msvv9SiRYt00UUXqaWlRX/84x81a9YsTZw4UZL0yCOPqLi4WMuXL9c555wThlcCAAB6IlSzjLfj/lsAQDSJiCB+/fr1euWVV/T666+7g3FJ2rBhg0488UTFxPzQzDFjxujZZ5/Vnj17tGvXLu3fv19jx451L7dYLMrPz9f69esJ4gEAiEKhnGUcAIBoE/Yg3mq1avbs2ZozZ44GDBjgsayqqkq5ubkej/Xr10+S9N1336mqqkqSOmzXr18/9zJ/xcT4loqHnjGZjB4/PZcZZDC0/eTvgp7oqp8Fb5/033AK9fE3mQwH/B7c/dG3PPW14xGO81lXovX4+9PuaH2t/gj2Oc3XYxmtxz4U7Y7WY9Mu0s5pkS7sQfy9996roqIinXvuuR2WNTc3d7ivPT4+XpJks9nU1NQ26U1n69TV1fndJqPRoPT0JL+3h/8slo4jLw5Hk0wmo1JTE/m7ICA662fBQv8Nr1Af//b9ScHvZ/QtT331eITyfNaVaD3+/rQ7Wl+rP4J9TvP1WEbrsQ9Fu6P12BwsUs5pkS6sQfzrr7+uDRs26M033+x0udlsVktLi8djNptNkpSYmCiz2SxJamlpcf/evk5PZpl1Ol2yWhu7XxEBYzIZZbEkyGptksPhWb6krq5RDodTdXWNMpn2h6mF6A266mfBQv8Nr1Af//b9SQp6P6NveeprxyMc57OuROvx96fd0fpa/RHsc5qvxzJaj30o2h2tx6ZdpJ3TwsFiSfA6EyGsQfxrr72mmpoaj/vgJemee+7RW2+9paysLFVXV3ssa/9///79Zbfb3Y8NGjTIY51hw4b1qG3BqIOJ7jkczg7H3uFwyeVq+8nfBYHQWT8L3r7ov+EU6uPvcLgO+D24/Yy+5amvHo9Qns+6bkd0Hn9/2h2Nr9Xfso1Wa63sdrv27dsno7HR4xzXFW/LNvp6LKPx2EuhaXe0HpuDRco5LdKFNYifP3++mpubPR6bPHmypk6dqvPOO09vvPGGXn75ZTkcDplMJknSunXrdMwxxygzM1MpKSlKTk7WRx995A7irVarysvLNWXKlJC/HgAAAESug4PZ9iDVaq31+jl82cbbYDbY/C3b2NTUqJYWm9577z0ZDEa5vIvhKdsIBFlYg/j+/ft3+nhmZqb69++viy66SM8//7zuvPNOXXXVVfrss8+0ePFizZ07V1LbvfBTpkzR/PnzlZGRoSOOOELz5s1TVlaWJk+eHMqXAgAAgAh3cDDrcNhVX1+ntWtLZTJ1/rXY6XTKZmtWfLxZRqPRq23aRUow62/Zxg8/fF+xsbE6/fTTZTTG+zQSDyB4wj6xXVcyMzP1/PPP64EHHtAFF1ygww8/XLNnz9YFF1zgXmfq1Kmy2+2aM2eOmpubNXr0aL3wwguKjY0NY8sBAOg9/E3F9VekjF6i9zk4mLVaa7VmTanGji2WxZLW6Tbt64wZM04WS5pX27SLlGDW37KN7Zmw6enpMpkSSHMGIkTEBfFbt271+P+IESP0yiuvHHJ9k8mkW2+9VbfeemuwmwYAEY1AC8HibyrugaOXvoiU0Uv0Pp0FszExMbJY0pSennHI7Q5ex5ttACBYIi6IBwD4h0ALweJvKu6Bo5e+iJTRSwAAIhFBPAAEwKFGwf2ZNMkbnY2CE2ghWPxNxWW0EohOTqdTmzaVqaKiQklJSXI6nfpfZj2ACEAQDwABcKhR8K4mQAr0KDiBFgCgp0pLV6mkZKG2bdsqq7VOBoNB//73St1557065ZQJ4W4eABHEA0BAHGoUvKsJkBgFBwBEktLSVZo1a5r27t2rlhabbDabXC6XPv30U11yyc9122136oYbpoa7mUCfRxAPAAHQ1Sh4VyPdjIIDACKB0+lUSclC7d27V42N++V0umQ0GmUwGGQwGNTc3KyHH35ABQWFmjDh1HA3F+jTfA7ir7rqKp166qk69dRTlZ2dHYw2AUDE83Ym+K7uifflfnlmggcABFNZ2UZt316plpYWuVwuxcbGyOFwSGqrBmUwGNTSYtNDD/1OxcUTfL4NDN7zp9pMT+bg4TtG9PE5iI+Li9OCBQt0//33KycnR5MmTdLEiRNVVFQUjPYBQETydib4A++JNxiMHvfAd3W//MGYCR4AEEw1NTVqbGxUa2vL/z6TDB7L20fkv/zyC5WVbdTIkZ7f/UMReB64fm8OPP2tNtPa2qJ161ZTbaYP8DmIf+qpp2S327VhwwZ98MEH+ve//61nn31WGRkZGj9+vE499VSdccYZwWgrAEQMb2eCP/CeeEke98B3db/8wbgHHgAQTJmZmTIajXK5XDIaDXK5PJe3PW6U0+lUTU1Nh+1DEXgeePG7sLCo1wae/lSb6Qm+Y0Qfv+6Jj4mJ0ZgxYzRmzBjNnj1b//3vfzVv3jy9/vrreuONN1RRURHodgJARPFlJvj2+94P/L39HnjuiQcARILCwpE66qijtW/fXjkcTo+g2uVyyeFwKC4uTgkJCcrMzOywfSgCzwMvfvfrNyCo+wonf6vNoO/wK4j/9ttvtX79en388cdav369vvnmGyUkJGj8+PE66aSTAt1GAAB81l7nuLy8XFlZAzVuXHFQ7+E8cH8bN27UccedGLR9AUCgGY1G/fa3d+nSS3+h5uZmxcbGyvW/4Xi73S6j0ai4uDgNHZqrwsKRHbYPVeDZfvGbIBd9mc9B/KRJk/Tdd98pOTlZI0aM0E9/+lOddNJJKiwslMlkCkYbAQDwyYF1jvfvb9CyZa8pN3eYpk6dqeLiwNc5Pnh///d/yzR06DDdfPOMoOwPAIJh/PhTddttd+rhhx9QS4tNUtu98PHx8YqLi1dGRoamTp3JpHZAmPn8Dmy/VyYjI0NHHnmk+x8BPAAgErTXOS4v36zExCRZLBYlJiapomKzZs2aptLSVUHfX1JSksrLg7M/AAimG26YqhdffFnDh49QYmKizGaz0tLSdNxxRZo371EuTAIRwOeR+BUrVujbb7/V2rVrtXbtWj344IOqqanRMcccoxNPPFEnnXSSzjrrrGC0FcAB/JkFtid68yyw6D3a6xw3NDRowIBsORwOtbbaZDablZSUpKqqXSopWRiw1PpD7a8trTRR330X2P0BQChMmHCqhg8v1AsvPK39+/froosu1HHHnSinM9wtAyD5eU/8EUccoZ/+9Kf66U9/KkkqKyvTs88+q5dfflmvvPIKQTwQAv7OAntgiTNfUH4E0aC9znF6eoYMho7lkdLSMrR9e2Wn5ZGiYX8AECpGo1GDBg2SJI0cOdI9Mz2A8PMriHe5XPrss8+0Zs0arVmzRhs3bpTT6dTo0aM1YQIpNkAo+DMLbPusru0lznxB+RFEg5qaGrW2tio+Pr7T5fHx8aqt3ddpeaRo2B/gj1BkbvWV+t0AEAl8DuJvvPFGffzxx2poaFBaWprGjx+vX/7ylyouLlZKSkow2gigE/7OAktJM/RmmZmZio2Nlc1m6/T9YbPZFBsb22l5pGjYH+CPUGRuRWP97lBXsACAQPE5iP/uu+80ZcoUTZw4USNGjOiQPggAQLgUFo5UTs5QVVRsltmc7bHM5XKptnav8vIKOi2PFA37A/wRisytaKvfHeoKFuHmTzaG1Vorh8MhSdq3b5+MxkY5HC6vtiUbAwgun4P4ZcuWBaMdAAD0mNFo1NSpMzVr1jRVVe1SSopFTqdTzc1Nqq+3Kjk5JaDlkQ61v6amJtXW1gV8f4A/QpW5FS31u9srSjQ0NMhiSZXJZFR8fIK7gsX8+Y/1ukDe32yM1tZWtbTY9N5778lgMMrlXQzPPDpAkHkVxP/617/2+gkNBoP+/Oc/+90gAAB6orh4gubPf8xjlC0pyam8vIKgjLJ1tj+Xy6X8/ALqxAMRJtQVLCKFP9kYUtto/Nq1pTr99NNlNMb7NBIPIHi8CuI//vhjGQwGDR06VKmpqV2u6/L2Eh0AAEFSXDxB48YVa/XqUq1cuVyTJk0O6pfyA/f3738v109+cj7lmIAI1FcrSvibjSG1ZVikp6fLZEqQ3c5JDYgEXgXxV111ld5++23t2LFD48aN049//GOddtppSkxMDHb7AADwi9Fo1PDhhaqq+kbDhxcGfVStfX+7d39DOSYgQlFRAkBv4NU3mlmzZulf//qXlixZoqOPPloLFizQySefrOnTp2vFihVqaWkJdjsBAACAHjmwokRnqCgBIBr4NCwxcuRI3XHHHXr//fe1aNEipaWl6e6779bJJ5+sO+64Qx9++CGjDkCEOrCUzqZNZbxXAQB9TntFidravR1uAW2vKJGTM5SKEgAims+z00tt9wydeOKJOvHEE3X33Xdr3bp1euutt3T99dfLYrFo9erVgW4ngB7oa6V0AADoTKgrWASSP2XieoLJ6YDI5VcQf6CNGzdq1apV+vDDD9Xa2qqMDO/KkAAIjb5YSgcAgEMJdQWLQPG3TJzN1qz4eLPPFyby8wv9mtEeQPD5FcR/+umnevvtt7V8+XJVVVXpmGOO0UUXXaSzzz5bQ4YMCXQbAfipr5bSAQCgK6GuYBEI/pSJs1prtWZNqcaMGSeLJc2nbc3mhJCO/APwntdB/MGB+5FHHqnzzz9fZ511lo499thgthGAn/pqKR0AALoT6goWPeVvmbiYmBhZLGlKT/c9W5YgHohMXgXxEydO1O7duzVgwAD9+Mc/1tlnn62CgoJgtw1AD1FKBwAQDAffn2211sput8tqre10fZutWTZbs9/7i4szy2w2e72+2ex/XXQAiHReBfFVVVXuq5PvvPOO3nnnnUOuazAYtGLFisC0DkCPHFhKp7MvM5TSAQD44+D7sx0Ou+rr67R2balMpo5fL5uaGtXU1CSXyymDwaiDksM65XLJvX7bKHSi1+3Lzy9UQcEIr9cHgGjiVRB/wQUXBLsdAIKgvZRORcVmmc3ZHsvaS+nk5RVQSgfoJcIxezWjnX3Twfdnt997PXZscaf3Xttszaqp+V6ffvqJioqOV3JySrf7aGiod6+fkXG4zyPxAHzH50h08CqIf+ihh4LdDgBBEM2ldAD4LhyzVzPa2Td1dn92d/dex8ebVV6+SdnZR3p1f/a+fXt9Wh9Az/E5Eh16XGIOQGSL1lI6AHwXjtmrAcAf/oz4djf3QlcY8fUOnyPRgSAe6AOisZQOAN+FY/ZqAPCHvyO+ra0tWrduNSO+QcLnSHQgiAf6iGgrpQMAAHovf0Z8e4IRX/QmBPEAAEQZp9OpTZvKVF5erqysgX0+s4bjAUQff0d8ARDEAwAQVUpLV3nMcbFs2WvKzR3WZ+e44HgAAPoar4L49evX+/Sko0eP9qsxAIDQYfQy+pSWrtKsWdPU0NAgiyVVJpNR8fEJqqjYrFmzpmn+/Mf6VODqy/GgbJL3ODcAQGTzKoi/9NJLZTAYOl3mcrkkyWN5RUVFAJoGAAgWX0YvCX4ig9PpVEnJQjU0NGjAgGw5HA61ttpkNpuVlJSkqqpdKilZ2GcCLl+PB2WTvENmAwBEPq+C+L/85S/u33ft2qW77rpLF110kc466ywdfvjhqq2t1cqVK/Xyyy/rvvvu86kBNTU1evjhh1VaWiqbzabRo0frtttu05AhQ3TppZfq448/7nS73//+9/rJT34ih8OhoqIi2Ww2j+U33XSTbr75Zp/aAgB9ga+juQQ/kaGsbKO2b69UenpGhwvrBoNBaWkZ2r69UmVlGzVyZJFXzxnNI66+Hg/KJnUvkjI9orlvAkCweRXEn3jiie7fL730Ul1++eW65ZZbPNYZNWqUzGaz/vSnP+nss8/2ugE33nijnE6nFi1apKSkJD322GO6/PLLtXz5cj3++ONqbW11r+tyuTRjxgzV1dXp9NNPlyR98cUXstlseuONN5SZmeleNzEx0es2ANGI+qrwhz+juZEa/HjzHuhJn++sXeF8D9TU1Ki1tVXx8fGdLo+Pj1dt7T7V1NR49XzBHHENRQDm6/GgbFLXIinTI5KzAbi4ACAS+Dyx3Weffabrr7++02VFRUV67rnnvH6uuro6HXHEEbr22muVm5srSbrhhht0/vnnq7KyUiNGeI7EvPTSS/rss8/0xhtvKCkpSZK0detWJScn69hjj/X1pQBRjfqq8Ic/o7mRGvx48x5wOOyqr6/T2rWlMpliojpDIDMzU7GxsbLZbJ3+PWw2m2JjYz0uaB9KMEdcQxWABfJ4IDiZHv6IpGyAg61du0Z/+tNzEXlxAUDf4nMQn5WVpdLSUp188skdlr3zzjsaNGiQ18+VmpqqBQsWuP+/d+9eLV68WFlZWcrJyfFYd+/evXr00Ud1/fXXa/Dgwe7Ht27dqiFDhvj6MroVE8NV1VAymYwePz2XGWQwtP3k7/KD3NxcHXnkkSHbX0JCQtQf/676WfD2+UP/leTRl8PRt2tr96m1tVVmc7wMhrb2tLfLYJDM5njV1e1Tbe2+HrUpFK/Nm/dAXV2tVq/+QOPGjVdqapr7/yeffIpSU9N82p8374H2v3Pb74F93UVFRRo6NFfl5ZuVkGD2+NtJLtXW7lN+foGKioq6vEDhdDr1+OOPaP/+BmVn/zDimpBgVnJykr777js9/vgjmjBhgs8XOj744H3deut0NTTUuwMwszlBFRXluvXW6Vq48DGNHz/R72NwoEAdj65E8udPoM9nPT03eHOsulsnmH3zULxt97ZtW/T662+oqakx6H07kHrah4N5TotWTU2NamryLROyoaFODoddDQ11HsfUG20X0iM7wzgQ58pwfEeLZj4H8VdccYXuvfdeVVdX69RTT1V6err27Nmjd955R++//74WLlzoV0Puuusuvfrqq4qLi9PTTz/dIR3+ueeek9ls1pVXXunx+LZt22S323XllVdqy5Yt6t+/vy677DKdf/75frVDkoxGg9LTk/zeHv6zWDqOpjgcTTKZjEpNTeTvcgCOhf8662fBcmD/leTRl8PRt485ZqDM5njZ7a1KSEiQy/XDh6bJZPzfKHW8jjlmYI/aFIrX5s3z7tmTqA0b4jRoULYOO+ywDv8PtPbXLQWnn91115269tprVVX1nVJTU+V0OtXSYlNdXZ1SUy266647lZmZ0uVzfPLJJ9qxY7sOO+wwxcSYJLVNUGsyGRUTY1JmZoZ27NiuL77YpuOPP97rtjmdTj355GPav79BAwcOlMPhUEuLTUlJibJYUvTtt9/qyScf07nnnhWwACwQx6Mr0fD5E6h+1tNzgzfHqrt1gtU3u+JNu1tb92v58uVqbNyvI488MiR9O1B62oeDfU6LRjt2bNEnn3zi0zZOp1MOh10ff7zG5z5y/PHHB6y/B0sgz5X0M+/4HMT/4he/kN1u19NPP61//vOf7scHDBig+fPn66yzzvKrIZdddpkuvvhiLVmyRDfeeKOWLl2qgoICSVJDQ4NeffVV3XTTTR3ufausrJTT6dTUqVOVlZWlVatW6Y477lBra6t++tOf+tUWp9Mlq7XRr23hH5PJKIslQVZrkxwOp8eyurpGORxO1dU1ymTaH6YWojfoqp8Fy4H9V5JHXw5H3z766FwNHpyj8vLNGjBggPs4tP10qKZmr/LzC3T00bnat8//NkXK+/bgdgS7Xe3PLyko/ayo6CTNn/+oHn10obZu3aL9+xvkcDiVl1eg6dNnqqjopG7/bjt3fqPmZpvS0tLlcDg9+oDB4FRsbJxstr3aufMbDR7s/a1q//3vp6qoqFBaWrqcTtdBz2tUWlq6KioqtGrVGh13XGDSsQNxPLoSKf24M4E+n/X03ODNsepunWD1za540+61a9erqqpKmZmHhaxvB0pP+3Cwz2nRaMCAQfrRjw4P2f4SEhJ6dB4LhUCcK8PxHS3SWCwJXmci+BzES9KUKVM0ZcoU7dixQ3V1dUpPT9fRRx/tz1O5tafPP/DAA9q4caNeeuklPfTQQ5KkFStWqKWlRRdddFGH7f7f//t/cjgc7nvkjz32WO3atUsvvPCC30G8JNntfbPzhJvD4exw7B0Ol1yutp/8XRAInfWz4O3rh/4ryf17S4tdGzd+ps2by9W/f2gnR7r55hmaNWuavvtul1JSLHI6nWpqalJ9vVXJySm6+eYZcjrbRg78FSnv24PbEex2tf+d234PTj87+eTxGjPmFK1eXaqVK5dr0qTJ7v7jzf7S0tIVGxur5mbb/0Zc2x53udr+NTfbFBMTq7S0dJ/aX139vVpaWpWeHu9+rgOfNy4uXvv27VN19fcBPS49PR5diZR+3JVA9rOenBu8OVbdrROsvtkVb9q9Z0+NHA6HYmPjQtq3A6GnfTgU57RoExtrVmysucPjwSrH2tDQqIaGjoOL4Z5s9UCBPFfSz7zjVxAvtU1Kt3PnTlVXV+uMM87Qjh07dMwxxxyynnxn9u7dq7Vr1+qMM85QTExbU4xGo3JyclRdXe1eb8WKFZowYYIsFkuH5zCbO76JcnNz9Y9//MOPVwUAwRfuyZGKiydo/vzHPCYfS0pqG71kgqboYDQaNXx4oaqqvtHw4YU+XQAqLBypnJyhqqjYLLM522OZy+VSbe1e5eUVqLBwpE9tCudEcz05HvhBuM8NweqbPZWeniGTyaSWlhbFxcV1WM4kipAox4rQ8iuIf/rpp/Xss8+qublZBoNBI0aM0KOPPqp9+/bpj3/8Y6fBdmf27NmjmTNn6vnnn1dxcbEkqbW1VeXl5Zo0aZJ7vQ0bNnRa891qteq0007T7bffrgsvvND9eFlZmYYOHerPSwOAoNq6dYveeOMNNTY2hnXm5eLiCRo3rrjT0Uv0bkajUVOnztSsWdNUVfXDiGtz8w8jrlOnzvS5L0RqAAbfhPPcEKy+2VP5+QXq3z9Le/Z87878bEffRrtILceK3snns+BLL72kxx9/XFdccYVeffVVuf6XUzRlyhR9/fXXeuyxx7x+rtzcXI0fP17333+/1q9fr23btun222+X1WrV5ZdfLkn67rvvtG/fvk5LyFksFo0ZM0aPPPKIVq1apS+++EKLFi3SP/7xj06DfgAIJ6fTqeXLl7vrMJvNbVfezWazsrKytX9/g0pKFvYojd0X7aOX+fn5jF72Me0jrnl5BWpsbJTValVjY6Py8go0b96jfl1Iag/AkpKSVVW1S83NTe4ArKpqV9gCMPgunOeGYPTNnjIajZo8eTJ9G11KSEhQenqGT/8sljSPcqy+/IuUVHqEh88j8S+++KKuueYaTZs2TQ6Hw/34hAkTNH36dC1atEh33XWX18+3cOFCLViwQDNmzFB9fb1OOOEELVmyRNnZbVfxv//+e0lSWlpap9s/+OCDevzxx3XPPfeopqZGQ4YMUUlJiXtkHwAiRXn5Zu3eXaWMjMPCWocZkIIz4hrudGz0DpGYKTRs2LGaO/ckj1uh6NsAwsXnIH7Xrl068cQTO102ePBg7dmzx6fnS0lJ0b333qt777230+UjRozQ1q1bD7l9cnKy7rjjDt1xxx0+7RcAQm3fvr1yOByd3lMpSfHx8aqt3aeampoQtwx9VTDuJY/EAAzRJxLnORg79mSdeebZ9G0AYefzWWfAgAH69NNPO122adMmDRgwoMeNAoDe6MDJkTrD5EjoLbhVo29xOp3atKlM5eXl2rSpLGS3BIUDfRtAJPB5JP6nP/2pHn/8cZnNZk2cOFGS1NjYqHfffVfPPvusrrjiikC3EX3YgV8MsrJCW4YLCDQmRwLQ25SWrvK4fSLU1TYAhBff1cPD5yD+6quv1jfffKP58+dr/vz5kqRf//rXkqRzzz1X1157bWBbiD6LLwbobdonR3rjjTciauZl9D0H1jO2Wmtlt9tltdYGdB8HPm8k1TNG4JSWrtKsWdPU0NAQ1mobQG8QrDrzhxKI8zLf1cPH5yDeYDDovvvu029+8xutW7dOtbW1SklJ0ejRo5WbmxuMNqIP4osBeismR0IkOLCescNhV319ndauLZXJdOivBb7WMz7weQsLi6KunjGjS11zOp0qKVnorrbhcDjU2mqT2WxWUlKSqqp2qaRkYdQft876ARAM0VZnnu/q4eVzEP/EE0/oZz/7mY4++mgdffTRHsu++eYb/fGPf9Tdd98dqPahD+orXwzQdzE5EsLtwHrG7XWKx44t7rJOsa/1jA983n79vJ8vx9/RKH8zCjobjWJ0qXtlZRu1fXul0tMzem21jUP1gyuuuDrcTUMv5GudeafTqfXr12nNmg918smnaPToMT59j+hJnXm+q4efz0H8k08+qfHjx6t///4dlm3cuFF/+9vfCOLRI33hiwEQiTMvI/pFWjpme/1jX1I2/R2NampqlM1m6zaj4GAHj0YxuuSdmpoatba2Kj4+vtPl0V5to6t+cM89v9X555+v0047K9zN7BLZJNElIcH79PaDLzC98cbrIb3QyHf18PPqU+4Xv/iFNm7cKKlt8qWLL774kOsWFhYGpmXos3r7FwMA0cGfgLh9NHjfvn0yGhvlcLi83jYQ9yf6GwC3trZo3brVXX7B7yztvqfpmJ3xdTRKajvuH374vmJiYrvNKDjYgaNRjC55LzMzU7GxsbLZbJ3223BU2+jJe/bADA6n06kFC/4gq9Wq/v2z5HI51dLSrLi4WB12WD/t3v2d3n33Xf3mN9cqPT0jwK8iMMgm6b0CdaGxJxd9v/zyC9lsNlksqbLb7XI6HXK5XHI6HbLb9b9KPDZ9+eUXGjToKEmB+YzDD7wK4u+//3698847crlcevLJJ3XRRRcpKyvLYx2j0SiLxaLJkycHpaHoOyLxiwGAvqcnAfG//vUvuVySy/sYPiABsT8BsLc6S7vvSTrmofgyGnUgk8kkl0uyWNL8DqwYXfJeYeFI5eQMVUXFZpnN2R7LwlVtI1AXsb766ktVVGxSfHy8Ghqscrlccjgcamiol8FgUExMjKqqvtP776/Ur37162C8lB4hm6T3CuSFxp7cg797d7UcDrv27atRXFxch/dIS0uL7Ha7KisrZLM1SgrORd++zKsgPicnRzfddJOktg+xn/3sZx7p9Ha7XTExPmfmA52KxC8GALzXW1I4/Q2ITSaDUlMTVVfn+0h8T/kbAHurPT0+Ukcfe4pMMO8ZjUZNnTpTs2ZN86raRlNTk8/zFvi6fkbGYTrxxLGSpPh4s+Ljzf68NJWWrpLJFKP09EwZjUY5nW3BSXJyioxGkxISWrVr165D9pNwIpukdwvkhUZ/s57WrCnVT396sVavXqNt2yqUkXGYXC6n+z1iMBhVXV2lvLzhuvzya9z9LBgXffsynyPvm266SYsWLdKGDRu0aNEiSdInn3yiW265Rdddd52mTJkS8Eaib/H1iwGAyNGbUjj9DYhjYoxKT0+SybRfdrszCC1DsJAJ5pvi4gmaP/8xj/f8oapt7NhRqbKyT72qhNDO21s9OtOTUb+jjjpa8fHxcjgciouLk93eFiAZjSbFxMSoublJMTEm9euX1f2ThRjZJL1bIC80+v8ZF6O0tAzdcstszZo1TXv2VCslxSKXy6WWlhbV11tlsaTqlltmKzPzMJ+fH97xOYj/4x//qEcffdQjWB80aJDOPPNMPfzww4qPj9fPfvazgDYSfY8vXwyASHXwiHR+fkG4mxRUpHAi2pEJ5rvi4gkaN66422obgwcPVXJysleVEAKhJ6N+3fWDurpa9e+fFZHndLJJerdIutDId/Xw8jmIf/nllzV9+nRdc8017scGDBigOXPm6LDDDtPixYsJ4hEQ3n4xACJRZyPSxxwzRCecMCriZzT2Bymc6A3IBPOPN9U2EhISZLGkRcUtGd31g6SkZE2ePDki+0EkBXkIvEi70Mh39fDx+Qjv3r37kDPQjxw5Ut98802PGwW0a/9ikJ+fTxkuRI32Eeny8s1KTEySxWJRYmKStm2r0F//ulRr164JdxMDzpcUTiCStY8u5eUVqLGxUVarVY2NjcrLK9C8eY8yutRHdNUP5s59UMOGHRvuJnaqPcirrd0r10Eza7YHeTk5Q8kmiVLtF5iSkpJVVbVLzc1N7gtMVVW7wnKhke/q4eHzSPwRRxyhtWvXauzYsR2WrV+/vsOs9QDQl3Q1Ih0fn6Wvv/5KixY9rTPPPDvcTQ0oUjgRDbwtqTR8eKGeeeYFrV+/TmvWfKiTTz5Fo0ePkdFo1L59e73eHyWVotuhRhnr6mq1YsXb4W5ep8gm6f1IY4fkRxD/85//XPPmzVNra6tOO+00ZWZmau/evfr3v/+tP/3pT7rllluC0U4AiArdjUgnJSVq587PVVa20V07tTcghRPRwNeSSg6HXUccka2qqm+1YsXbIZ1cDZHBm1sFIg1BXnQ68CKjzdYsm635kOsOGnSk/vCHBfr00//oP/9Zr1GjRquoaJSMRqO+/HKHV/uLizPLbG6r4MAFx+jjcxB/+eWXa/fu3XrxxRe1ePFi9+Mmk0mXXXaZrrjiikC2DwCiSncj0jExMbLZbKqpqelVQXyk3acHdMbXkkpWa61KS9+XwSCNGTPO58nYKKmEcOksi2DUqBPU0mLzKZukvczfvn37ZDT6XjaTwNB7B15kbGpq9CpryOWSBg48Qt9/X6Xly9+Sy+WUwWDUQWMInWr7+yRK4oJjNPKruPttt92mG264Qf/9739VW1sri8WiESNGKD09PdDtA4Co0t2ItN1uV0xM7xuRJoUT0cCfkkomk0kGgyJ+MjbgYAdnEXzxxec+ZaJIP5T5+9e//iWXqy1o9BaBoW8OvMjY3Uh8Zxoa6vXpp5+oqOh4JSendLv+wSPxiC5+BfGSlJSUpMMPP1wul0ujRo2S3W4PZLsAICp1NyK9f3+jCgtHqrBwpOrqasPTyCAhhbP3OrhcIrMPRxdv5wI4UPsIrNVa6/P6jMBGJl8zUdqZTAalpiaqrs73kfi+zJv33aHeZ/HxZsXHm33aX9s28crOPpILjn2AX0H8G2+8oQULFuj777+XwWDQ3/72Nz3++OOKjY3VggULFBcXF+h2AkBU6GpEuq6uVmZzvK655vpeGwBRbqb36axcYm7uMC7MRBFf5wKQfhiBXbdutVfvX4fDrvr6Oq1dW6rCwiJGYCOQP5kokhQTY1R6epJMpv2y251BaFnv5M377sD3jckUI6fTKZutWfHxZp8/N48+enBPmoso43MQ/9Zbb+m2227Teeedp1NPPVUzZsyQJJ1++umaO3eunnrqKU2fPj3Q7QSAqHGoEenc3DydcMIojR17cribGFTROBEUOtdeLrGhoUEWS6pMJqPi4xNUUbFZs2ZN0/z5j4U9kG/PEqioqFBSUpKcToKMg/k7AusLq7VWa9aUauzYYvXrNyCo+wKigTfvuwPfNxZLmvv//szBYbM164svvJvUDtHP5yD+mWee0S9+8Qvde++9cjgc7scvuugi7d27V6+++ipBPIA+r7MR6fz8Aq1c+W64mwZ4patyiUlJSaqq2qWSkoVhzbQ4MEugoaFeMTExWrNmnW65ZXbYLy5EEn9HYH0VExMjiyWNVHpA3r/v2t837SnwB//fW75MWIjo5/On7s6dO3X66ad3umzkyJHavXt3jxsFAL1B+4h0fn4+I9KIOt2VS0xLy9D27ZUqK9sYlva1ZwmUl29WYmKSLBaL4uLitG1bhWbNmqbS0lVhaRcAAMHm80h8ZmamPv/8c40bN67Dss8//7zXzbgMdMefCYN6ggmDAIRCd+US4+PjVVu7TzU1NSFuWedZAi0tzYqLi1NKSqr27KkOe5YAAADB4nMQf/bZZ6ukpET9+vXThAltqWoGg0GbNm3SU089pXPOOSfgjQQimb8TBvk7cQklWwCEQnflEm02m2Jjw1Mu0ZcsgZEji0LePgAAgsnnIH769Onatm2bpk+f7g4+Lr30UjU2NuqEE07QtGnTAt5IIJL5M2FQTyYu6eslW3qqsbFRe/fW+FQmpyd8rfMKRIruyiXW1u5VXl6BCgtHhrxtkZwlgM5RphAAAsfnID4uLk7PP/+8Vq9erbVr16qurk4pKSk68cQTNWHChA5XxIHezv+SLf5NXIKeqaio0Mcfr5fLhxieki/oi7oql1hfb1VycoqmTp0ZlkAskrME0BFlCgEgsPyqEy9J48aN0+jRo2W1WpWamqrY2NhAtgsAgiIvL09paYf7NBJPyRf0VYcql5iXVxDWACySswTgKRrKFALh0Fl2CuAtv4L4Dz74QE899ZQ+++wzuVwumUwmHX/88Zo2bZpGjRoV6DYCQMAkJiYqIyNTdrtvtaQp+YK+qrNyieFOhT5UloDdbld1dZUsltSwZQngB9FQphAIh0Nlp1xxxdXhbhqihM9B/Lvvvqvp06fr2GOP1U033aTMzEx9//33eu+99/TrX/9aixcv1gknnBCMtgIAgDBoL5dYVfVNxJRL7CxLwGQyKS9veKd14v2pJGK11srhcMhgaPvdF1QSYQJCoDNdZafcc89vdf755+u0084KdzMR4XwO4p988kmdccYZevTRRz0ev+mmm3TzzTdrwYIF+utf/xqo9gEAIMn/IMxut7sDsIP/3xWCsMh3YJbAihVvKykpSVdeeb0yMw/rsK6/lUTs9lbZbDatXVsqk8n7r01UEmECQuBg3WWn7Nr1jZYvX67rr2eicHTN5yD+yy+/1OzZsztd9vOf/1w333xzjxsFAPBdIIJcX4Q6yPU3CGttbdG6datlNBrlcNhVX1/nVUBGEBYd2rMEvv32C7lcOmSWgD+VRKQf5sQYO7bYpzkxqCTCBITAwbrLTklNTdPu3VUqL98ctrkiuvoucajvDNH0XaK38DmIHzJkiMrKynTKKad0WLZz504NHOj7ByQAoOcCEeT6ItRBrr9B2IF8CcgIwnoXfyuJSFQT8RcTEAKeustOiYuLl8PhCOt8Ol19lzjUhfBo+i7RW/gcxN9777267rrrZDAY9JOf/ET9+vVTbW2tVqxYoZKSEt17773atWuXe/3s7Owung0AECiBCHJ9EeogtydB2IEIyIDQiOQyhUA4dJed0tJik8lkCuvnU1ffJfzNTOoKF8z943MQ//Of/1yS9Oijj+qxxx5zP+76X9HlW2+91WP9ioqKnrQPAKJWU1NThxQzf+/P9obZnEBgCsCDP7fZ+KP9fGazNXs8HqllCoFw6C47pa6uVv37Zyk/vyBMLez+gjkXwiODz0H8gw8+2OEeDgBARzt2VKqs7FPV19dp9epV+uabb/T999X6+uuvdNRRR8vlcnZIS3M6nbLZmhUfbyYlDUCP+Xubja/nofY026+//lJZWZ7BSSSWKQTCobvslKSkZE2ePJn3BrrlcxB/4YUXdrncarXKYrH43SAA6C0GDx6q5ORkLV78vDZs+H/64osv1Ni4X4mJSRoyZKimTLlUKSmpHmlp7alqY8aM8zlVjZQ0AAfz5zYbf85D7dsceeRRnS6PxDKFQDh0lZ1yxRVXa//+unA3EVHA5yD+yiuv1MMPP6zDDz+8w7L3339fd999tz744IOANA4AollCQoI2by7Xq6++IqfTpbS0dMXGxig+PkHbt2/V73//oM4//3ydeea5HmlppKoBCBR/55Lw5zwUExOj+Hizz/sC+ppDZafU1dVqxYq3w908RAGfL4OWl5fr3HPP1Xvvved+rKGhQXfccYeuu+469e/f36fnq6mp0a233qoxY8aoqKhI11xzjT7//HP38jlz5mjYsGEe/yZNmuRe3lZvsUTFxcU67rjjdPXVV+vrr7/29WUBQMA5nU4tWvS0mpub1b9/lszmttRUs9msrKxs7d/foOXLl8vpdIa7qQAAIITas1Py8/PJToHPfB6J/+c//6m77rpLN998sy688EKdeuqpuv/++1VfX6/f/va3uvTSS316vhtvvPF/X3QXKSkpSY899pguv/xyLV++XAkJCdq6dauuu+46TZkyxb2NyWRy//7UU09p6dKlevjhh5WVlaV58+bpqquu0ptvvqm4uDhfXx4ABExZ2Ubt3Pm5kpKSIrYerDd6e/15AACAaOJzEJ+RkaEnn3xS//d//6c777xT//d//6djjz1Wr776qs+j8HV1dTriiCN07bXXKjc3V5J0ww036Pzzz1dlZaUKCwu1fft2XXPNNZ2m77e0tOiPf/yjZs2apYkTJ0qSHnnkERUXF2v58uU655xzfH15ABAw7fVgzebIrQfrjd5efx7d66zSQncCXX2hu305HA65XOLCEQCg1/M5iJekjz76SM8995yMRqOOPfZYbdq0SU8++aRuvfVWpaSkeP08qampWrBggfv/e/fu1eLFi5WVlaWcnBx99dVXamxs1ODBgzvdfsuWLdq/f7/Gjh3rfsxisSg/P1/r16+PqiA+VCVg2vGFBQi+9nqwdru90+WRUA/WG729/nxf5OtnzvbtW7VtW4UaGur14Yfvy2g0dbuNy+VUS4tNq1evkskU49PFnLS0dNXW7vN6fYfDroaGBsXHx3PhqIecTqc2bSpTeXm5srIGMos8AEQgn4P4O+64Q6+//rpyc3P197//Xccee6xeeeUV/eEPf9DKlSt19913a/LkyT435K677tKrr76quLg4Pf3000pMTNS2bdskSS+++KI++OADGY1GjR8/XjNmzFBKSoqqqqokSQMGDPB4rn79+rmX+SsmJrQfWF9+uV2bNn3m0zY9KUU1fPgIDR8+0qdtgslkMnr89FxmkMHQ9jPUf5dg6Y2vKRp01c+63s6/v1dRUZEGDx6isrKNysx0uVPq2364VFdXp/79s1RYONz9vJHYN1JSkpSSkhTuZgREKI6vv/3M9/34/1p8/cxxOp3u+RyMRqO8qTRrMBjdF2RycoZq6NBhXu/PYJBcLq9XV11drVav/kDjxo1Xamqa9xv+T0JCwiGPYSS+J6Xg9LMPPnhfjz66UFu3btH+/Q1atuw1DRt2rKZPn6nx4yd2057uj1OkHsvuHNzuaHodgWhrqM5pfVEg+1ZoPt+Ctw/6mW98DuLffPNNXXfddbrxxhsVE9O2+cUXX6xTTjlFd955p6ZNm6aKigqfG3LZZZfp4osv1pIlS3TjjTdq6dKl2rZtm4xGo/r166dnnnlGX331lf7whz+osrJSf/7zn9XU1DaKcPC97/Hx8aqr8788g9FoUHp6aL+wHn/8ccrLy/Vpm3379um9997T6aefrvT0dJ+2TUxMVGJiok/bhILF0nEEzuFokslkVGpqYsj/LsHSG19TNOmsn3WlJ3+vW26Zoeuuu07ff1+ttLQ0OZ1to5N1dXWyWFJ01llnKj092f289I3gCuXx9bWf+aonr8Wfz5yeCPZnzp49idqwIU6DBmXrsMMOC+hzR/p7MlD9bOXKlZo1a7rq6+uVmpoqk8moxMREVVRs1qxZ0/Xss896TCx8MG+OU6Qfy0M5uN3R9DoC2dZgn9P6ogP/PqmpCfrPfyq1ZUuFcnOH6phjBvo0SBeKfhmKfdDPvONzEP/KK6+ooKCgw+NHHHGEFi9erKVLl/rVkJycHEnSAw88oI0bN+qll17SAw88oF/96lfuADU3N1eHH364fv7zn6usrExmc1sZk5aWFvfvkmSz2XqUKu50umS1Nvq9vb9MJt/abDQ2ymAwymiM93lbm80lm22/T9sEk8lklMWSIKu1SQ6H50zddXWNcjicqqtrlMkUOW3uid74mqJBV/2sKz35e40Ycbx++ctfasOG/2jHjs+1f3+DHI62erBXXnmV6uvrPJ6XvhFcoTi+/vYzX/X0tfj6udETvnzmNDU1ui/Se6uurlY2W4u++mqX6up8+/xuK8F26AsMkfqeDGQ/czqd+t3vHpDVatWAAdlyOBxqbm5WXFy8srIG6LvvvtPvfveARo4cfcigwpvjFKnHsjsHtzuaXkcg2hqqc1pf1P73eeed9/TCC8+7s2D+9re/e50Fc/BzBbNfBnMf9LO2CxjeZiL4HMR3FsC3s9lsGjVqlNfPtXfvXq1du1ZnnHGGe1TfaDQqJydH1dXVMhqNHUaYhw4dKkmqqqpyp9FXV1dr0KBB7nWqq6s1bJj3aXudsdsjv/M4HC65XG0/o6G93nA4nB1eS+98nb3vNUWTzvpZ1+t3//c61D3GVmutBg/O0a9+dZm2bt2iNWs+1Mknn6LRo8eoocGqNWtKtW/fPjkcLvf6ra12j8c6w7wW/gnle8/Xfub78/fO88i2bdv8mkixpaVFa9Z8GPD74SP9OAein23c+KkqK7cpLS1DksF9K0PbT4PS0tJVWblNn376qUaOLDpEO7o/TpF+LA/l4HaH63X4WynEm8+Uzhz8ORPsc1pf5HC4tGXLFr3xxhtqbGyUxdKWBRMfn6Dy8k2aMWOq5s9/zKsKNqHol6HZB/3MG14F8aeccoqee+455eXluR/705/+pPPPP18ZGT9MyLRlyxb94he/8Dqdfs+ePZo5c6aef/55FRcXS5JaW1tVXl6uSZMmafbs2aqurtbixYvd25SVtX2w5+Tk6Mgjj1RycrI++ugjdxBvtVpVXl7uUZIOAILtUDO4Oxx21dfX6aOPVkuSBg7MVm1tjVaufLfTGdzb11+7tlQm06FP0UzEhd6KiRRDr72SRnx855U04uPjVVu7TzU1NSFuGQ5EpZDex+l0avny5WpoaNARRwyUw+FQa6tNZrNZSUlJqqrapZKShUwwiQ68CuL37Nmj1tZW9/8dDof+8Ic/6MQTT/QI4n2Vm5ur8ePH6/7779f999+v1NRUPfvss7Jarbr88stVUVGhG264QU888YTOO+887dy5U/fdd5/OOeccDRkyRJI0ZcoUzZ8/XxkZGTriiCM0b948ZWVl+TW5HgD461CBh9VaqzVrSjV2bNuFyvbfLZa0Dus6nU6tX79O5eUVOvnkwRo9eswhP7QJPNBbtaW3079Dqb2SxqFuR7TZbIqNjVVmZmYYWod2XODqfcrLN2v37iplZBzmnvy2ncFgUFpahrZvr1RZ2cZDZsGgb/KrxJwkuXyZNrYLCxcu1IIFCzRjxgzV19frhBNO0JIlS5Sdna3s7Gw9+uijWrRokZ577jmlpKTo3HPP1fTp093bT506VXa7XXPmzFFzc7NGjx6tF154QbGxsQFpHwB4o6vAIyYmxh20t/9+cFm50tJVKilZqG3btmr//ga98cbrys0dpqlTZ3qVRgcA/iosHKmcnKGqqNgssznbY5nL5VJt7V7l5RWosDByqtr0RVzg6n327dsrh8PRYZLudmTB4FD8DuIDJSUlRffee6/uvffeTpefddZZOuussw65vclk0q233qpbb701SC0EgOAqLV2lWbOmqaGhweN+uLZZoad5fT8cAPjDaDRq6tSZmjVrmqqqdiklxSKn06nm5ibV11uVnJyiqVNnks4LBFh6eoZMJpNaWlo6DeTJgsGhcDYGgC44nU5t2lSm8vJybdpUJqczsJOtOJ1OlZQsVENDgwYMyHbX4TabzcrKytb+/Q0qKVkY8P0CwIGKiydo/vzHlJdXoMbGRlmtVjU2Niovr0Dz5j3KhUQgCPLzC9S/f5bq6vZ1yHJuz4LJyRlKFgw6CPtIPABEqoNT3Jctey3gKe5lZRu1fXul0tMzuB8OQFgVF0/QuHHFWr26VCtXLtekSZOZUAsIIqPRqMmTJ+uNN94gCwY+6VGPOPgLJwD0Fu0p7uXlm5WYmCSLxaLExCR3intp6aqA7MebWaFbW1u5Hw5ASBiNRg0fXqj8/HwNH15I8AAE2bBhx2ru3Ad7lAUT7KxBRB6vR+JvvPHGDvdqXHfddR4TyLW0tASuZQAQJgenuAez5AuzQgMA0LeNHXuyzjzzbL+yYEKRNYjI41UQf8EFFwS7HQAQMUKZ4s6s0AAAoD0LpqrqG6+zYJgYt+/yKoh/6KGHgt0OAIgY3qS4B6rkC7NCAwAAX4UyaxCRh4ntAOAgoU5xb58V+sB0uKQkp/LyCkiHA9AjTU1Nam5u8mkbq7VWdrtdVmutz9vYbM0+thCAP5gYt28jiAeAg4QjxZ1ZoQEEw44dlSovL/NpG6fTqdbWFq1bt9rrc5DDYVd9fZ2+/vpLZWVld78BgB4JZdYgIg9BPAAcJFwp7v7cDwcAXRk8eKiyswcGfT9Wa63WrCnVkUceFfR9AWBi3L6OID5KHVhKIitrICN2QICR4g6gN3zWJiQkdPoFPxhiYmIUH28Oyb4CxZvbDQ6+vcCf2w3amc2h+3ugd2Ni3L6NID4KUUoicnl772FPvgAciC8DwUWKO9B38VnbN3hzu0H7rQJr15bKZIrx63aDdvn5hSooGNGTJgOSmBi3ryOIjzKUkohs3t57eOAXAoPBKJutWfHxZr4MRCBS3IG+h8/avsOb2w3abxUYO7ZYFktaj/ZnNnPhHYFD1mDfRRAfRSglEfm8vffwwC8EkrRmTanGjBnn85cDvgxEnoOzMQ7OuuguA8PXLA2yMYDA4rO2b/H2doOYmBhZLGlKT88IQasA75E12DcRxEcRSkmEjz8lenqCwCx6HZyNcWDWhSSPlMx2TqfTnY3hcjk7XedQyMYAAovPWgDRhqzBvocgPopQSiJ8/C3Rc6g0+e4COwKz6HVwNkZnWRcHp2S2rzNmzLhDrnMoZGMAgcVnLQB01Bsm+uxNCOKjCKUkwsefEj0HBmYHB2PdBXYEZtGrs9TM9jTMA38/OCXTm3UABB+ftQDgiYk+Iw9BfBShlET4+Fuip6tgjKANACIPn7UA8AMm+oxMBPFRhFISAAAEF5+1APzhz/xJB09m68vktqGYP4mJPiMXQXyUoZRE9GtqavJ5xvKeYJK88PH2bx2o/sDfGggMPmsB+Mrf+ZNaW1u0bt1qGY1GjzmTupvcNhTzJzHRZ+QiiI9ClJKIbjt2VKqs7NNuZyw/UFeT5HWHSfLCx9u/9YEf2gaD0eMD3Rf8rYHA4bO2dwrEaKkvuLjad/gzf9LBDpwzqbvJbUMxfxITfUYugvgoRSmJ6DV48FAlJyd3O2P5gbqaJK87TJIXPt7+rX350O4Kf2sgsPis7X0CMVrqCy6u9h3+zp90sEiaJ4mJPiMXQTwQYgkJCbJY0nye2C6STurwji9/a/6+ABB8gRgt9QUXVxHNmOgzchHEAwD6BGrcAgjUaCnQFzDRZ+TiiAMAer3S0lW6+OILdNNN1+rFF/+sm266VhdffIFKS1eFu2kAAESs9ok+8/IK1NjYKKvVqsbGRuXlFWjevEeZ6DNMGIkHAPRq1LgFAMB/TPQZeTjyAIBe6+Aat2ZzW4UHs9msrKxs7d/foJKShXI6neFuKgAAEat9os/8/Hwm+owAHH0AQK/lS41bAACAaEA6PQCg16LGLQAgHJqamtTc3OTTNlZrrex2u6zWWp/3Z7M1+7wNohdBPBDhmFEb8B81bgEA4bBjR6XKy8t82sbpdKq1tUXr1q32+bve0UcP9ml9RDeCeCCClZauUknJQm3btlX79zdo2bLXlJs7TFOnzmQiLsAL1LgFguPgUUZvRhB7MspoNlMaDtFl8OChys4eGLL92WzN+uKLHSHbH8KLIB6IUMyoDfQcNW6B4Dh4lNHhsKu+vk5r15bKZOr862VPRhnz8wtVUDCiR20GQikhIbQXnvbt2xuyfSH8COKBMHI6nSov39whVf7gGbUdDodaW20ym81KSkpSVdUulZQsJLUe8EJ7jdsDs1qSkpzKyysgqwXw08GjjFZrrdasKdXYscWyWNICvj+zmVF4AGhHEA+EydatW7RkyVLt3Pl5h1R5i8Xi9YzaI0cWhekVANGDGrdAYHU2yhgTEyOLJU3p6RlhahUA9A0E8UAYrF27Rn/961I5nS6lpaV3SJW/5JLLmFE7SBobG7V3b40cDpfX2wRztlgmLgyd9hq3VVXfUOMWAABELYJ4IMScTqcWLXpazc3NOvLIo2QwGDqkyr/11pvMqB0kFRUV+vjj9XJ5H8MHbbZYJi4EAACArwjiI0Soa0kyy2v4lJVt1M6dnyspKemQqfJVVd8pK2uAvvnmK2bUDrC8vDylpR3u00h8TxxqtlgmLgQAAIA/COIjRKhrSTLLa/jU1NSotbVVZnPXqfJnn32uliz5MzNqB1hiYqIyMjJltztDsr/OZotl4kIAAAD4iyA+QoS6liSzvIZPZmamYmNjZbfbO13enio/YcJEFRWNYkbtXqi8fDMTFwIAAMAvERHE19TU6OGHH1ZpaalsNptGjx6t2267TUOGDJEkrVy5Uk8++aR27Nih9PR0nXHGGZo2bZrMZrMk6ZNPPtGvfvWrDs/7l7/8RSeddFJIX4u/Ql1LEsHV1WRlhYUjdcwxQ1RWtlEZGS6PIO7gVHmj0ciM2r3Qvn17mbgQAAAAfomIIP7GG2/832Rfi5SUlKTHHntMl19+uZYvX67Nmzfrpptu0tSpU3XmmWfqyy+/1N13363a2lo99NBDkqStW7dq0KBBWrp0qcfzpqamhuPloI/rbrIyo9Goa665XrfeOk3V1VVKTU3rMlWeGbV7n/T0DCYuBAAAgF/CHg3U1dXpiCOO0P33368RI0ZoyJAhuuGGG1RdXa3Kykq9/PLLOumkk3Tdddfp6KOP1oQJEzRjxgy9+eabamlpkSRt27ZNOTk5Ovzwwz3+xcXFhfnVoa9pn6ysvHyzEhOTZLFYlJiY5J6srLR0lSRp7NiT9ctf/kq5uXlqbGyU1WpVY2Oj8vIKNG/eo6TK93L5+QXKyRmq2tq9ch00TX57NkZOzlAmLgQAAEAHYR+JT01N1YIFC9z/37t3rxYvXqysrCzl5OToN7/5TYeRR6PRqNbWVjU0NCgjI0Nbt27V8ccfH9B2xcSE/fpGn2IyGT1+ei4zyGBo+xnJfxen06nHH39E+/c3KDv7h8nKEhLMSk5O0nfffafHH39EEyZMkMlk0LHHHqubbpqu8vLNWrHiXZ122hk65ZTxnY60R8sxiHRd9bPg7fOHv50kGQxSbKxJM2bcopkzp6mq6jtZLClyOp2y2ZpktdYrOTlFM2bcori4sJ+ie51QvJdC1c84L4RGpB7ncJzPuhKpxwk9F2l9DZ0Lzedb8PZBP/NNRH1DvOuuu/Tqq68qLi5OTz/9tBITE5Wfn++xTmtrqxYvXqzhw4crIyNDklRZWan09HRdeOGF2r17t3JzczVjxgyNGOHf7OtGo0Hp6Uk9fj3wncXSMbXY4WiSyWRUampiRP9dPvnkE+3YsV2HHXaYYmJMktpGWE0mo2JiTMrMzNCOHdv1xRfbdNRRR8lkMio9PVnjxp2k3bu/1bhxJykzM6XT546WYxAtOutnwXLg306S+/fzz/+xUlIS9PDDD6u8vFz19fVyuVwaOXKEbr/9dk2aNClkbexLQvleCnY/47wQGpF+nEN5PutKpB8n9Fyk9DV0LhTvwVDsg37mnYgK4i+77DJdfPHFWrJkiW688UYtXbpUBQUF7uV2u12zZ89WZWWllixZIkn67rvvVF9fr8bGRs2ZM0cmk0kvvfSSpkyZomXLliknJ8fndjidLlmtjQF7XeieyWSUxZIgq7VJDodn6a+6ukY5HE7V1TXKZNofphZ2b+fOb9TcbFNaWrocDqf7dTgcThkMTsXGxslm26udO7/5X53yttfUvk5Xry9ajkGk66qfBcuBfzvJ829dVHSS/vrX1/Thhx90yMbYt4+/czCE4r0Uqn7GeSE0IvU4B7OfNTU1qqmpyadt6upqZbO16KuvdrnPd95qm9w30adtEDrh+OyE70JxrgrmPuhnbRcwvM1EiKggvj3gfuCBB7Rx40a99NJL7snrGhoaNH36dH388cd64okn3KPsAwYM0Pr165WQkKDY2FhJUmFhocrLy/Xiiy9q7ty5frUlVDWk4cnhcHY49g6HSy5X289I/rukpaUrNjZWzc1tk5W13+rscrX9a262KSYm9n9B/g+vqX2drl5ftByDaNFZPwvevrr/W+flDde3336tvLzhcjrbbs1AcITyvRTsfsZ5ITQi/TgHo59t27ZN5eVlPm3jdDrV0tKiNWs+9HkC1vz8QhUU+Jc9idAJ5WcnfBeKc1Vo9kE/80bYg/i9e/dq7dq1OuOMMxQT09Yco9GonJwcVVdXS5Kqq6t19dVX69tvv9ULL7yg0aNHezyHxWLx+L/RaNSQIUO0e/fu0LwIQG2l43JyhqqiYrPM5myPZQeXjqurqw1PIwEA6MbgwUOVnT0wZPszm0mfBQBfhD2I37Nnj2bOnKnnn39excXFktruey8vL9ekSZNUV1enyy67TA0NDVqyZImGDRvmsf0HH3ygadOm6R//+IeOPPJISW1p91u2bNHkyZND/nrQdxmNRk2dOlOzZk1TVdUupaRYOi0dZ7PZZLXWym63y2qtlSSP3ztz8Pq+MJsTOi1jBgBAZ9rS2/ncAMKlqalJzc2+3dLCd8W+JexBfG5ursaPH6/7779f999/v1JTU/Xss8/KarXq8ssv10MPPaSvv/5azz//vDIyMvT999+7t83IyNCoUaOUnp6u2267Tb/97W8VGxurRYsWqba2Vpdffnn4Xhj6pOLiCZo//zGPOvFJSU7l5RW468SXlf1Xb7/9pr7/vlpff/2VBg4cqIaGeq1dWyqTqfO3pNPpVGtri9atW02aIgAAQC+2Y0elX7e08F2x7wh7EC9JCxcu1IIFCzRjxgzV19frhBNO0JIlS9S/f3+99dZbam1t1WWXXdZhu3/9618aOHCgFi9erPnz5+vKK6+UzWbT8ccfr5deekmHHXZYGF4N+rri4gkaN65Yq1eXauXK5Zo0abLGjSuW0WhUaekqPfrofG3btlWNjfuVmJiko48+WieccILOOOMcWSxpAW8PaYoAAADRg1ta0J2ICOJTUlJ077336t577+2w7LPPPut2+0GDBqmkpCQILQP8YzQaNXx4oaqqvtHw4YXuAH7WrGlqaGiQxZKq2NgYxccn6PPPt2vHjs81evTJOvvsc8LddABhQOokAKAdt7SgOxERxAO9ndPpVEnJQjU0NGjAgGw5HA61ttpkNpsVH5+lr7/+SosWPa0zzzzb5xQoANHPn9TJ1tYWWa11Wr16lWJj43zaltRJAACiF0E8QupQo00mk0EOR9P/6k+6PJb1htGmsrKN2r69UunpGTIYDB7LDAaDkpIStXPn5yor26iRI4vC1EoA4eJP6uSuXV/rww/f18iRo5SdfaRP25I6CQBA9CKIR0gdarTJYJBMJqMcDqe7vnq73jBRR01NjVpbWxUfH9/p8piYGNlsNtXU1IS4ZQAigT+pk1ZrrQwGg5KTU5SenhGklgEAgEhDEI+QOtRok8lkUGpqYqcj8T0RKaNNmZmZio2Nlc1m6/SLut1uV0xMrDIzM8PQOgAAAADRgiAeIXWo0aaYGKPS05NkMu2X3e4MQ8uCq7BwpHJyhqqiYrPM5myPZS6XS/v3N6qwcKQKC0eGqYUAAAAAogEzaAEhYDQaNXXqTCUlJauqapeam5vkdDrV3Nyk6uoqmc3xuuaa65nUDgAAAECXiBiAECkunqD58x9TXl6BGhsbZbVa1djYqNzcPP3yl7/S2LEnh7uJAAAAACIc6fRACBUXT9C4ccVavbpUK1cu16RJk5WfX6CVK98Nd9MAAAAARAGCeCDEjEajhg8vVFXVNxo+vDDczQEAAAAQRUinBwAAAAAgSjASDwRQU1OTmpubJLXVcLbb7bJaazusd/CyQ63XHbPZ99rSAAAAAKIXQTwQQDt2VKq8vEyS5HDYVV9fp7VrS2Uyeb7VDlxmMBjV2tqidetW+zw7fX5+oQoKRgSs/QAAAAAiG0E8EECDBw9VdvZASW2j7WvWlGrs2GJZLGke63W1zBdmM6PwoXRgpoW3vMm6OFTWBpkWAAAAOBhBPBBACQmeQVdMTIwsljSlp2d0WLerZYhMB2ZaHIrT6ZTL5fT4f0tLi1avXiWXy6WGhnp9+OH7MplM7nUcDkenjw8ZkqucnGFd7o9AH/BeIC7E+YL3JwAgGAjiAcBLB2ZaHMr27Vu1ffs29/8NBqPi482SJJfL4X7c5dIB6xgUHx8vg8Hg8fiOHdv1xRc7utwft1QA3vPmQtzBnE4ntzwBACIKQTwAeOngTIvODB9+3CFHz9tvozj55J7dRnEgbqkAvOfNhbhA4v0JAAgGgngACKDuAn1uowDCx5sLcQAARDrqxAMAEGWcTqcqKspVWVmpiopyOZ3O7jcCAAC9AiPxAABEkdLSVSopWagtWyq0f3+D3nnnXb3wwnOaOnWmiosnhLt5AAAgyBiJBwAgSpSWrtKsWdNUXr5ZiYmJSk5OVmJioioqNmvWrGkqLV0V7iYCAIAgI4gHACAKOJ1OlZQsVENDgwYMyJbZbJbRaJTZbFZWVrb2729QSclCUusBAOjlCOIBAIgCZWUbtX17pdLTM2QwGDyWGQwGpaVlaPv2SpWVbQxTCwEAQChwTzzghaamJjU3N/m0jdVaK7vdLqu11qdlUltZImZQjmyB7hPdoU+gpqZGra2tio+P73R5fHy8amv3qaamJsQtAwAAoUQQD3hhx45KlZeX+bSN0+lUa2uL1q1bLaPRM+nF4bCrvr5Oa9eWymTq+DbMzy9UQcGIHrUZwRXoPtEd+gQyMzMVGxsrm83W6QUdm82m2NhYZWZmhqF1AAAgVAjiAS8MHjxU2dkDA/Z8Vmut1qwp1dixxbJY0josN5sZcY10ge4T3aFPoLBwpHJyhqqiYrPM5myPZS6XS7W1e5WXV6DCwpFhaiEAAAgFgnjACwkJgU9ljomJkcWSpvT0jIA+L0IjGH0C6IrRaNTUqTM1a9Y0VVXtUlJSspxOp5qbm1VTs0fJySmaOnWmz1keAAAguvBJDwBAlCgunqD58x9TXl6BGhsb1dDQoMbGRuXlFWjevEepEw8AQB/ASDwAAFGkuHiCxo0r1vLlb+lf/3pXP/rRGZo8+WxG4AEA6CMI4gEAiDJGo1F5efn66qsdysvLJ4AHAKAPIYgHAAAAgD6EUrnRjSAeAAAAAPoQSuVGN4J4AAAAAOhDKJUb3QjiAQAAAKAPoVRudGMmHAAAAAAAogRBPAAAAAAAUYIgHgAAAACAKEEQDwAAAABAlAh7EF9TU6Nbb71VY8aMUVFRka655hp9/vnn7uUVFRWaMmWKjjvuOE2aNEl/+ctfPLZ3Op0qKSlRcXGxjjvuOF199dX6+uuvQ/0yAAAAAAAIurAH8TfeeKO+/PJLLVq0SH//+99lNpt1+eWXq6mpSfv27dMVV1yhQYMG6bXXXtONN96o+fPn67XXXnNv/9RTT2np0qX63e9+p5dffllOp1NXXXWVWlpawviqAAAAAAAIvLCWmKurq9MRRxyha6+9Vrm5uZKkG264Qeeff74qKyu1du1axcbG6r777lNMTIyGDBniDvgvuugitbS06I9//KNmzZqliRMnSpIeeeQRFRcXa/ny5TrnnHPC+OoAAAAAAAissI7Ep6amasGCBe4Afu/evVq8eLGysrKUk5OjDRs26MQTT1RMzA/XGsaMGaMvvvhCe/bs0ZYtW7R//36NHTvWvdxisSg/P1/r168P+esBAAAAACCYwjoSf6C77rpLr776quLi4vT0008rMTFRVVVV7gC/Xb9+/SRJ3333naqqqiRJAwYM6LBO+zJ/xcSE/U6DPsVkMnr87O1MJoMMhraf9LXQ6Wv9DB2F4r0Xqn5mNBrcPzmP9D2czxAq9DWEAv3MNxETxF922WW6+OKLtWTJEt14441aunSpmpubFRcX57FefHy8JMlms6mpqUmSOl2nrq7O77YYjQalpyf5vT38Z7EkhLsJIeFwNMlkMio1NZG+FgZ9pZ+ho1C+94Ldz/bsSZDBYFBKSgLnkT6M8xlChb6GUKCfeSdigvicnBxJ0gMPPKCNGzfqpZdektls7jBBnc1mkyQlJibKbDZLklpaWty/t6+TkOB/B3A6XbJaG/3eHr4zmYyyWBJktTbJ4XCGuzlBV1fXKIfDqbq6RplM+8PdnD6jr/UzdBSK954//aypqdF9Ydpbu3ZVy+l0ateuahmN8T5tm5CQoISERJ+2QWThfIZQoa8hFOhnbRcwvM1ECGsQv3fvXq1du1ZnnHGG+753o9GonJwcVVdXKysrS9XV1R7btP+/f//+stvt7scGDRrksc6wYcN61Da7vW92nnBzOJx94tg7HC65XG0/+8LrjTR9pZ+ho1C+93zpZ9u2bVN5eZlPz9/a2iKXy6X//vcTbd7s27b5+YUqKBjh0zaITJzPECr0NYQC/cw7YQ3i9+zZo5kzZ+r5559XcXGxJKm1tVXl5eWaNGmSDjvsML388styOBwymUySpHXr1umYY45RZmamUlJSlJycrI8++sgdxFutVpWXl2vKlClhe10AAPhi8OChys4e6NM2Vmut1qwp1cknF8tiSfNpW7OZdEUAAKJVWIP43NxcjR8/Xvfff7/uv/9+paam6tlnn5XVatXll1+u+Ph4Pf/887rzzjt11VVX6bPPPtPixYs1d+5cSW33wk+ZMkXz589XRkaGjjjiCM2bN09ZWVmaPHlyOF8aAABea0tv9z2wjomJkcWSpvT0jCC0CgAARKKw3xO/cOFCLViwQDNmzFB9fb1OOOEELVmyRNnZ2ZKk559/Xg888IAuuOACHX744Zo9e7YuuOAC9/ZTp06V3W7XnDlz1NzcrNGjR+uFF15QbGxsuF4SAAA+aWpqUnOzb/fEW621stvtslprfd6f2ezfRQMAABB+BpfL5Qp3IyKNw+HU3r1MNhZKMTFGpacnad++/X3iPph9+/ZqxYq3ddppZzGCFkJ9rZ+ho1C89/zpZ5s3f+bzPfFOp1M2W7Pi480yGn0rycM98dGP8xlChb6GUKCfSRkZSdExsR0AAPDvnvie4J54AACiF0E8AABh5u898QAAoO/xLf8OAAAAAACEDUE8AAAAAABRgiAeAAAAAIAoQRAPAAAAAECUIIgHAAAAACBKEMQDAAAAABAlCOIBAAAAAIgSBPEAAAAAAEQJgngAAAAAAKIEQTwAAAAAAFGCIB4AAAAAgCgRE+4GAADgj6amJjU3N/m0jdVaK7vdLqu11uf9mc0JSkhI8Hk7AACAQCKIBwBEpR07KlVeXubTNk6nU62tLVq3brWMRt+S0fLzC1VQMMKnbQAAAAKNIB4AEJUGDx6q7OyBIduf2cwoPAAACD+CeABAVEpIIL0dAAD0PUxsBwAAAABAlCCIBwAAAAAgShDEAwAAAAAQJQjiAQAAAACIEgTxAAAAAABECYJ4AAAAAACiBEE8AAAAAABRgiAeAAAAAIAoQRAPAAAAAECUIIgHAAAAACBKEMQDAAAAABAlCOIBAAAAAIgSBPEAAAAAAESJmHA3AIh2TU1Nam5u8mkbq7VWdrtdVmutz/szmxOUkJDg83YAAAAAoh9BPNBDO3ZUqry8zKdtnE6nWltbtG7dahmNviXE5OcXqqBghE/bAAAAAOgdCOKBHho8eKiysweGbH9mM6PwAAAAQF9FEA/0UEIC6e0AAAAAQoOJ7QAAAAAAiBIE8QAAAAAARAmCeAAAAAAAogRBPAAAAAAAUSLsE9vV1tZq4cKFev/999XQ0KBhw4bplltu0QknnKBJkybp22+/7XS7l156SaNHj9bu3bs1fvz4DssfeughXXjhhcFuPgAAAAAAIRP2IH7mzJn6/vvvtXDhQmVmZurFF1/UlVdeqf/7v//T3//+dzkcDve6LS0t+s1vfqOsrCwVFRVJkrZs2aL4+HitWLFCBoPBvW5KSkrIXwsAAAAAAMEU1iD+yy+/1OrVq7V06VIdf/zxkqS77rpLpaWlevPNNzVt2jSP9X//+9/LarXqr3/9q2Ji2pq+bds2HX300erXr1/I2w8AAAAAQCiF9Z749PR0LVq0SIWFhe7HDAaDDAaDrFarx7rbt2/XX/7yF91+++3KyMhwP75161YNGTIkZG0GAAAAACBcwjoSb7FYNGHCBI/H3n33XX355Zf67W9/6/F4SUmJcnNzdf7553s8vm3bNqWnp+uSSy7Rzp07ddRRR+n666/v9D55X8TEMOdfKJlMRo+fQDDQzxAK9DOEAv0MoUJfQyjQz3wT9nviD/Sf//xHd9xxhyZPnqyJEye6H//666/13nvv6bHHHvNY3263a8eOHcrJydHtt9+u5ORk/fOf/9Q111yjP/3pTxo7dqxf7TAaDUpPT+rJS4GfLJaEcDcBfQD9DKFAP0Mo0M8QKvQ1hAL9zDsRE8SvWLFCs2bN0qhRozR//nyPZf/4xz+UmZmp0047zePxmJgYffTRRzKZTDKbzZKk4cOHq7KyUi+88ILfQbzT6ZLV2ujfC4FfTCajLJYEWa1Ncjic4W4Oein6GUKBfoZQoJ8hVOhrCAX6WdsFDG8zESIiiH/ppZf0wAMP6Mwzz9Tvf/97xcXFeSxfsWKFfvzjH8to7PiikpI6jpgPHTpUH374YY/aZLf3zc4Tbg6Hk2OPoKOfIRToZwgF+hlChb6GUKCfeSfsNx0sXbpUv/vd73TJJZdo4cKFHQL4hoYGVVRU6OSTT+6wbWVlpUaNGqWPPvrI4/FNmzYpJycnqO0GAAAAACDUwjoSv3PnTj344IM6/fTTde2112rPnj3uZWazWSkpKdqyZYtcLpeOPfbYDtsPGTJEgwcP1n333ae5c+cqPT1dr776qv773//qtdde87tdRqNBGRncEx8O3AeDUKCfIRToZwgF+hlChb6GUOjL/cxoNHi9bliD+HfffVetra1677339N5773ksu+CCC/Twww+rurpakpSWltZhe6PRqGeeeUYLFizQ9OnTZbValZ+frz/96U/Kzc31u10Gg0Emk/cHEYHDjJQIBfoZQoF+hlCgnyFU6GsIBfqZdwwul8sV7kYAAAAAAIDucakDAAAAAIAoQRAPAAAAAECUIIgHAAAAACBKEMQDAAAAABAlCOIBAAAAAIgSBPEAAAAAAEQJgngAAAAAAKIEQTwAAAAAAFGCIB4AAAAAgChBEA8AAAAAQJQgiAcAAAAAIEoQxAMAAAAAECUI4hFUtbW1uvvuuzV+/HiNGjVKv/zlL7Vhwwb38iuuuELDhg3z+HfppZe6l9tsNs2dO1djx45VUVGRbrnlFu3duzccLwURrKamRrfeeqvGjBmjoqIiXXPNNfr888/dyysqKjRlyhQdd9xxmjRpkv7yl794bO90OlVSUqLi4mIdd9xxuvrqq/X111+H+mUgwnXXz+bMmdPhfDZp0iT3cvoZfLVz504VFRVp2bJl7sc4nyHQOutnnM8QKLt37+7Ql4YNG+bub5zT/OQCguiKK65wnXPOOa7169e7duzY4Zo7d65rxIgRrs8//9zlcrlcY8eOdS1dutRVXV3t/rdv3z739rfffrvrtNNOc61fv961ceNG109+8hPXJZdcEqZXg0h18cUXu372s5+5Nm7c6Nq+fbvr5ptvdp1yyimuxsZG1969e10nnXSS64477nBt377d9fe//91VWFjo+vvf/+7e/vHHH3eddNJJrn//+9+uiooK129+8xvX5MmTXTabLYyvCpGmq37mcrlcP/3pT10LFy70OJ/V1NS4t6efwRctLS2uCy+80JWbm+t67bXXXC6Xi/MZAq6zfuZycT5D4Lz//vuuwsJC1+7duz36U1NTE+e0HiCIR9B88cUXrtzcXNeGDRvcjzmdTtdpp53mevTRR1179uxx5ebmujZv3tzp9lVVVa5jjz3W9f7777sf27Fjhys3N9f1n//8J+jtR3Sora11zZw507V161b3YxUVFa7c3FzXxo0bXc8884zrlFNOcbW2trqXL1iwwDV58mSXy+Vy2Ww2V1FRkWvJkiXu5XV1da4RI0a43nzzzdC9EES07vqZ0+l0HXfcca7ly5d3uj39DL5asGCB69e//rVHcMX5DIHWWT/jfIZAWrRokevcc8/tdBnnNP+RTo+gSU9P16JFi1RYWOh+zGAwyGAwyGq1auvWrTIYDDrmmGM63f6TTz6RJI0ZM8b92DHHHKP+/ftr/fr1wW08okZqaqoWLFig3NxcSdLevXu1ePFiZWVlKScnRxs2bNCJJ56omJgY9zZjxozRF198oT179mjLli3av3+/xo4d615usViUn59PP4Nbd/3sq6++UmNjowYPHtzp9vQz+GL9+vV65ZVX9PDDD3s8zvkMgXSofsb5DIG0detWDRkypNNlnNP8F9P9KoB/LBaLJkyY4PHYu+++qy+//FK//e1vtW3bNqWkpOi+++7T6tWrlZiYqDPPPFM33HCD4uLitHv3bqWnpys+Pt7jOfr166eqqqpQvhREibvuukuvvvqq4uLi9PTTTysxMVFVVVXuwKtdv379JEnfffeduy8NGDCgwzr0M3Sms362bds2SdKLL76oDz74QEajUePHj9eMGTOUkpJCP4PXrFarZs+erTlz5nToL5zPEChd9TPOZwikbdu2KT09XZdccol27typo446Stdff73Gjx/POa0HGIlHyPznP//RHXfcocmTJ2vixInatm2bbDabRowYoeeff17XX3+9/va3v2nOnDmSpKamJsXFxXV4nvj4eNlstlA3H1Hgsssu02uvvaZzzjlHN954ozZv3qzm5uYO/aj9wpDNZlNTU5MkdboO/Qyd6ayfbdu2TUajUf369dMzzzyj22+/XR9++KFuuOEGOZ1O+hm8du+996qoqEjnnntuh2WczxAoXfUzzmcIFLvdrh07dqiurk4333yzFi1apOOOO07XXHON1q5dyzmtBxiJR0isWLFCs2bN0qhRozR//nxJ0n333afbbrtNqampkqTc3FzFxsZqxowZmj17tsxms1paWjo8l81mU0JCQkjbj+iQk5MjSXrggQe0ceNGvfTSS532o/YTf2JiosxmsySppaXF/Xv7OvQzdKazfvbAAw/oV7/6ldLT0yW1nc8OP/xw/fznP1dZWRn9DF55/fXXtWHDBr355pudLud8hkDorp9df/31nM8QEDExMfroo49kMpncfWX48OGqrKzUCy+8wDmtBxiJR9C99NJLuvnmm3XqqafqmWeecV9hi4mJcQfw7YYOHSqpLWUwKytLtbW1Hd7c1dXV6t+/f2gaj4i3d+9e/fOf/5Tdbnc/ZjQalZOTo+rqamVlZam6utpjm/b/9+/f352i1dk69DO0666fGY1G9xfedgeez+hn8MZrr72mmpoaTZw4UUVFRSoqKpIk3XPPPbrqqqs4nyEguutnnM8QSElJSR4BuNTWn3bv3s05rQcI4hFUS5cu1e9+9ztdcsklWrhwoUc6zKWXXqo77rjDY/2ysjLFxsbq6KOP1vHHHy+n0+me4E5qq2W6e/dujR49OmSvAZFtz549mjlzptauXet+rLW1VeXl5RoyZIhGjx6tTz75RA6Hw7183bp1OuaYY5SZmaljjz1WycnJ+uijj9zLrVarysvL6Wdw666fzZ49W5dffrnHNmVlZZLaRu7pZ/DG/Pnz9dZbb+n11193/5OkqVOn6oEHHuB8hoDorp9xPkOgVFZWatSoUR59RZI2bdqknJwczmk9Ee7p8dF77dixw1VQUOC68cYbPepCVldXu6xWq+vFF1905eXluZYuXer66quvXP/85z9dJ510kmvhwoXu55g5c6Zr0qRJrnXr1rnrxE+ZMiWMrwqR6KqrrnJNnjzZ9fHHH7u2bt3qmjlzpmv06NGub7/91rVnzx7X6NGjXbfddpursrLS9dprr7kKCwtdy5Ytc2+/cOFC14knnuhasWKFRw3SlpaWML4qRJqu+tmKFStcubm5rscff9z15Zdfut5//33XpEmTXDNnznRvTz+DPw4s/cX5DMFyYD/jfIZAcTgcrosuush19tlnu9avX+/avn2768EHH3QNHz7ctXXrVs5pPWBwuVyucF9IQO/0zDPP6JFHHul02QUXXKCHH35YS5Ys0ZIlS/T111+777e65pprZDS2JYk0NjbqwQcf1LvvvitJGj9+vObMmdMhzQt9W319vRYsWKAVK1aovr5eJ5xwgm6//XZ3+t9nn32mBx54QOXl5Tr88MP1m9/8RlOmTHFv73A4tHDhQi1btkzNzc0aPXq07r77bg0cODBcLwkRqLt+9vbbb2vRokXasWOHUlJSdO6552r69OnuW4joZ/DHsGHD9NBDD+nCCy+UxPkMwXFwP+N8hkDZs2ePFixYoNLSUlmtVuXn52vWrFk64YQTJHFO8xdBPAAAAAAAUYJ74gEAAAAAiBIE8QAAAAAARAmCeAAAAAAAogRBPAAAAAAAUYIgHgAAAACAKEEQDwAAAABAlCCIBwAAAAAgShDEAwCAgHG5XOFuAgAAvRpBPAAAEeLSSy/VsGHDPP4NHz5cEydO1Ny5c1VXVxe0fS9btkzDhg3TN998I0l6/PHHNWzYMK+3r6qq0jXXXKNvv/22x2355ptvNGzYMC1btuyQ67S3r7CwUA0NDZ2u89e//lXDhg3TpEmTetwmAAAiRUy4GwAAAH6Qn5+ve+65x/3/1tZWbd68WQsXLlRFRYX++te/ymAwBL0dP/vZz1RcXOz1+mvWrNGqVauC2KLO2e12rVy5Uuedd16HZW+99VbI2wMAQLARxAMAEEGSk5N13HHHeTw2evRo7d+/XyUlJdq4cWOH5cGQlZWlrKysoO+np0aNGqW33367QxC/e/dubdiwQXl5ebJarWFqHQAAgUc6PQAAUWD48OGSpF27dklqS72fNWuWpk6dquOOO05XXHGFJMlms+kPf/iDJkyYoOHDh+vcc8/tMCLtdDr11FNPaeLEiRo5cqRuuOGGDqn6naXTv/7667rgggs0cuRITZw4UQsWLFBLS4uWLVumO+64Q5L0ox/9SLfffrt7m7/97W/68Y9/7L4t4PHHH5fD4fB43uXLl+u8887TiBEjdMEFF2jLli1eH5ezzz5bH374YYeU+nfeeUfHHHOMjj32WPdjN910U4dg/7LLLtPw4cPV3NzsfuyBBx7QGWec4XUbAAAIJYJ4AACiwM6dOyVJRx55pPuxt99+W0lJSXr66ad11VVXyeVy6cYbb9TLL7+sK664Qk8//bSKioo0Y8YMvf766+7t5s2bpyeffFI//elP9cQTTygtLU0LFizocv9LlizRbbfdpoKCAj3xxBO65ppr9OKLL+r+++/XxIkTdf3110uSnnjiCd1www2SpGeffVZ33XWXxo4dq2eeeUaXXHKJnnvuOd11113u5125cqWmTp2qYcOG6cknn9RZZ52lW2+91evjcsYZZ8jhcGjlypUej7/11lv68Y9/7PHYhAkTtG3bNtXU1Ehqu+Dx6aefqrW1Vf/973/d633wwQc69dRTvW4DAAChRDo9AAARxOVyyW63u/9fV1enjz/+2B2Qt4/IS1JsbKzmzp2ruLg4SdLq1atVWlqqRx55RGeffbYkqbi4WE1NTZo/f77OOeccNTY26sUXX9QVV1yhm266yb1OdXW1SktLO22T0+nUk08+qdNOO03333+/+/Gmpib985//VEpKigYNGiRJysvL08CBA1VfX6+nnnpKF198sebMmSNJOuWUU5SWlqY5c+boiiuu0NChQ/Xkk09qxIgRmjdvnrstkrq9qNDusMMO0+jRoz1S6r/99ltt3LhRf/jDH/T000+7150wYYIkae3atTrnnHP0n//8RyaTScccc4zWr1+vMWPG6Ouvv9YXX3xBEA8AiFiMxAMAEEHWr1+vgoIC97+TTz5ZM2fO1PDhw7VgwQKPSe0GDx7sDuCltuDUYDBowoQJstvt7n+TJk3S999/r8rKSv33v/9Va2trhyD1rLPOOmSbdu7cqZqaGp1++ukej1955ZVatmyZYmNjO2zz6aefqrm5WZMmTerQFqntgkNzc7M2b97sU1s6c3BK/T//+U8VFBToqKOO8livX79+ys/P15o1ayS1Ha9Ro0Zp9OjR+vjjjyW1jcJbLBYdf/zxPrUBAIBQYSQeAIAIUlBQoLlz50qSDAaD4uPjNWDAACUnJ3dYNykpyeP/tbW1crlcGjVqVKfPXV1d7Z7kLT093WPZ4Ycffsg21dbWSpIyMzO9fh3t21xzzTWHbEtdXZ1cLleHtvTr18/r/UjS6aefrvvuu889S/3bb7+tc889t9N1J0yYoDfeeENSWxB/+umna8CAAXrjjTfU0tKi0tJSFRcXKyaGr0gAgMjEJxQAABEkKSlJhYWFfm2bkpKixMRE/eUvf+l0+VFHHaXPPvtMklRTU6PBgwe7l7UH3Z2xWCySpL1793o8vm/fPpWXl6uoqOiQ28yfP19HH310h+WHHXaY0tLSZDQatWfPHo9lXbWlMxkZGRozZozeeecdjRgxQlu2bPFIoz/QxIkT9dRTT2nz5s3avHmz7rzzTmVnZ8tms2nDhg366KOP3BdRAACIRKTTAwDQS5x44olqbGyUy+VSYWGh+9+2bdv05JNPym63q6ioSGazWe+8847Htv/+978P+byDBw9Wenp6h3XeeOMNXXPNNWptbZXR6PmVYuTIkYqNjdXu3bs92hITE6OFCxfqm2++UXx8vIqKirR8+XK5XC73tgdPUueN9pT6v//97zr++OMPWR6vsLBQGRkZeuqppxQfH6/hw4erX79+Gjx4sJ544gnZbDaNHz/e5/0DABAqjMQDANBLTJgwQaNHj9YNN9ygG264QUOGDNFnn32mkpISFRcXKyMjQ5J0ww036NFHH1VCQoLGjBmjVatWdRnEm0wm3XzzzbrvvvuUmZmpSZMmaefOnSopKdEll1yi1NRU98j7e++9p/Hjx2vIkCG66qqr9Nhjj6mhoUEnnXSSdu/erccee0wGg8Fd+m3mzJm67LLLdNNNN+niiy/Wzp079cwzz/j82k8//XTdc889Wrx4se68885Drmc0GjV+/Hi9/vrrOuWUU9xp8yeddJL++te/6oQTTlBaWprP+wcAIFQYiQcAoJcwGo1atGiRfvzjH+vZZ5/VlVde6S4398gjj7jXu/baa/Xb3/5W77zzjq6//npt3bpVt912W5fPfckll+jhhx/WRx99pGuvvVaLFy/W1VdfrdmzZ0tqC4JPPvlkLViwQL///e8lSdOnT9ftt9+u9957T1dffbXmzZun448/Xi+99JJSUlIkSSeccIKee+457d69WzfddJNeeeUVPfjggz6/dovFolNOOUUul6vbGu/ts9SfdNJJ7sfaf584caLP+wYAIJQMrgPz1wAAAAAAQMRiJB4AAAAAgChBEA8AAAAAQJQgiAcAAAAAIEoQxAMAAAAAECUI4gEAAAAAiBIE8QAAAAAARAmCeAAAAAAAogRBPAAAAAAAUYIgHgAAAACAKEEQDwAAAABAlCCIBwAAAAAgSvx/J6lzsESVh2MAAAAASUVORK5CYII=", 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", + "image/png": 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", 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" ] @@ -3856,7 +3864,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3885,36 +3893,6 @@ "plt.ylabel('MAPIE uncertainty (±MW)');" ] }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+iPr6ekycOBHbt2/HsGGdk4/fffddrFmzBnPnzoWHhweWL1+OuXPnGp+/dOlSdHR0YOXKlWhpaUFMTAw2bdoElcq6h53Cx7hjz5FipBVUQZZlSJIkuiQiIiKrIckyF9PpSa83oLq6sU/HUCoV0GicUFPT2C9j0u0dBjzwrx/Q1m7AMwtj4OvlYvLXsFT93Xv6bey9OOy9OOy9OOf3Xq12MOkkbS7aYKFUSgVCfDsna5/ibUeIiIhMigHJgoV33XYkj7cdISIiMiUGJAsW8fNE7dyzdWhu7RBcDRERkfVgQLJgnhpHeGkcoDfIyCisEV0OERGR1WBAsnBdw2yn8jnMRkREZCoMSBaua5gtLb/zcn8iIiLqOwYkCxfkq4HSRoEqXStKq/p2/zgiIiLqxIBk4exUNgjyHQKAw2xERESmwoBkBSL8fhlmIyIior5jQLICXRO1M4tq0MLL/YmIiPqMAckK+Lg7ws3FDnoD8Nx7KZysTURE1EcMSFZAkiSEje687ci5qia0tfNeQERERH3BgGQlLo8cbvyec5GIiIj6hgHJSvgNU2OIsx0AYOf+HLS26wVXREREZLkYkKyEJEl4/u5JcHOxRXV9K3YfLBBdEhERkcViQLIi9nZKzEsIBgDsPVKMkooGwRURERFZJgYkKxM5diiixg6F3iDjvT1ZMPCKNiIioj+MAckK3XZVIOxUNsgtqcNPJ0tFl0NERGRxGJCskJvaHn+Z6gcA+PhALnRNbYIrIiIisiwMSFbqyokjMNLTGY0tHfjo21zR5RAREVkUBiQrZaNQ4PbZQZAAHDpVhozCGtElERERWQwGJCvmP8wV06M6F5B8f08W2ju4wjYREdGlYECycjdcPgZqJ1uUVTfh66RC0eUQERFZBAYkK+dor8LNVwQAAD4/VIjymibBFREREZk/BqRBYFKIF8JGa9ChN2Db3mzIXBuJiIjodzEgDQKSJGHerCAobRQ4XVCNIxla0SURERGZNQakQcJL44g5k0cB6LyZbVNLu+CKiIiIzBcD0iDyp0mj4O3miLrGNuz6IV90OURERGaLAWkQUSkVmD8rCADw3dGzyD+nE1wRERGReWJAGmRCRmkQH+YNGcB7X2dCb+DaSERERD0xIA1CN80MgJO9EkXaBuxPPSu6HCIiIrPDgDQIqZ1s8dfp/gCA//6Yj2pdi+CKiIiIzAsD0iA1dfwwBAx3RWubHh/syxFdDhERkVkRHpBqa2vx1FNPYdq0aYiOjsYtt9yClJQU4/6FCxciKCio29f8+fON+1tbW/Hss88iPj4eUVFRePjhh1FdXS3irVgUhSTh9llBsFFISM2uwPHcStElERERmQ2l6AIeeughVFRUYP369XB3d8f777+Pu+66C//9738xZswYZGVl4ZlnnsGVV15pfI5KpTJ+/8wzzyAlJQUbNmyAra0tnn76aSxduhTbtm0T8XYsyghPZyTEjMRXSUXYvjcbIb4a2NnaiC6LiIhIOKFnkAoLC3Hw4EE888wzmDhxIvz8/PDkk0/C09MTn3/+OaqqqlBVVYXx48fDw8PD+DVkyBAAQHl5OT777DOsXLkSEydOxLhx47B+/XokJyfj2LFjIt+axbhuih/c1fao0rVg98EC0eUQERGZBaEBSaPRYOPGjYiIiDBukyQJkiRBp9MhKysLkiTBz8/vgs9PTU0FAMTFxRm3+fn5wcvLC8nJyf1bvJWws7XBbQmBAIC9ycUo0TYIroiIiEg8oUNsarUal19+ebdte/bsQWFhIR5//HFkZ2fDxcUFq1atwsGDB+Ho6IjZs2fj73//O2xtbVFeXg6NRgM7O7tux/D09ERZWVmfalMq+5YdbWwU3f5rziYGe2JCkAdSsyrw/t4sPHHHRCgkSXRZvWZJvbc27L047L047L04/dl74XOQznf06FGsWLECCQkJmD59Oh5//HG0trZi3LhxWLhwITIyMvDSSy/h3LlzeOmll9Dc3AxbW9tfHcfOzg6tra29rkOhkKDROPXlrRip1Q4mOU5/u//GKPz9pf3IKalDSnYVZsWNEl1Sn1lK760Rey8Oey8Oey9Of/TebALSvn37sGzZMkRHR2Pt2rUAgFWrVuHRRx+Fq6srACAwMBAqlQoPPvggli9fDnt7e7S1tf3qWK2trXBw6H2zDAYZOl1Tr58PdKZZtdoBOl0z9HrzX63aBsDcaWOw45sc/OfzUwgeoYba6dfh0xJYWu+tCXsvDnsvDnsvzvm9d3KyM+mZJLMISNu2bcOaNWswe/Zs/POf/zSeFVIqlcZw1GXs2LEAgLKyMnh7e6O2thZtbW3dziRptVp4eXn1qaaODtN8yPV6g8mO1d9mRA3HTydKUaRtwI5vsrFoTqjokvrEknpvbdh7cdh7cdh7cfojmAofMN2xYwdWr16N2267DevXr+8WdObPn48VK1Z0e3xaWhpUKhVGjx6NCRMmwGAwGCdrA0BBQQHKy8sRExMzYO/BWtgoFLh9djAkAIdOlSGjsEZ0SUREREIIDUgFBQV4/vnncdVVV2Hx4sWorKxERUUFKioqUF9fj1mzZuH//u//8MEHH6C4uBhffvklXnrpJdx1111wdnaGl5cXrrnmGqxcuRJJSUk4efIkHnroIcTGxiIyMlLkW7NYY4apMT16OADg/T1ZaOe/hoiIaBASOsS2Z88etLe345tvvsE333zTbd/cuXPx4osvQpIkvP/++3j++efh4eGBBQsW4J577jE+bvXq1Xj++edx//33AwCmTZuGlStXDuj7sDY3TPPH0awKlFU34aukQlw35cLLLBAREVkrSZZlWXQR5kavN6C6urFPx1AqFdBonFBT02iRY9JJ6eV4Z/dpKG0UWH1XLLzcHEWXdMksvfeWjL0Xh70Xh70X5/zeq9UOJp2kLXwOEpmn2BBPhPm5oUNvwLa9WWCOJiKiwYQBiS5IkiTMSwiE0kaB02dqkJRRLrokIiKiAcOARL/JS+OIayd3Lhj5wb4cNDb/es0pIiIia8SARL9r9qRR8NI4oL6pHSvfPcKhNiIiGhQYkOh3qZQK3HJl5+KcdY1tSMmsEFwRERFR/2NAoosKGqkxfr/x81NIy68SWA0REVH/Y0Cii7JVKRAwXA0nByX0BmDDrjScLqgWXRYREVG/YUCii5IkCSvmTcD6JVMQNXYoOvQGvLbrJG9FQkREVosBiS6JJElQKW1w75/DMc7fHe0dBvzrkxPILq4VXRoREZHJMSDRH6JSKrBkbjjC/NzQ1m7AKx+fQG5JneiyiIiITIoBif4wldIGD1wfgZBRGrS26bH+o+PIP6cTXRYREZHJMCBRr9iqbLD0r+MQNHIIWtr0WPfhcZwpY0giIiLrwIBEvWanssH/+9s4jB3hiubWDqzbeRxF5fWiyyIiIuozBiTqE3tbJf7xt/HwH6ZGY0sH1u48jpKKBtFlERER9QkDEvWZg50SD94YidHeLmhobsfaD47hXGWj6LKIiIh6jQGJTMLRXomHb46Er5czdE3tePmDYyitYkgiIiLLxIBEJuNkr8Kym6MwwsMZdY1tePmDYyivaRJdFhER0R/GgEQm5eygwrJbIjF8qBNqGzpDUkVts+iyiIiI/hAGJDI5taMtlt0SBR93R1TrWvHyB8dQVdciuiwiIqJLxoBE/cLVyRaP3BIFL40DKuta8NIHR1GtY0giIiLLwIBE/WaIsx0euSUKHkPsUVHbgpc/OIaa+lbRZREREV0UAxL1Kze1PR65JQruanuU1zRj7c5jqGtsE10WERHR72JAon431NUBy2+NgpvaDqVVTXj5g2PQNTEkERGR+WJAogHhMcQBj9wShSHOtjhX2Yi1HxxHQ3O76LKIiIguiAGJBoyXxhHLb42Gq5MtSioasHbnMTS2MCQREZH5YUCiAeXt5ohHbomC2lGFovIGrP/wOJpaOkSXRURE1A0DEg24YUOdsOyWKDg7qFBQWo9XPjqOppZ2yLIsujQiIiIADEgkyAgPZyy7ORJO9krkndNh2ZuH8Pz7qQxJRERkFhiQSBhfLxc8fHMk7G1t0NKmR945HSq5mCQREZkBBiQSarS3GktvGGf8+cVtqSgo1QmsiIiIiAGJzECQ7xD4ejlDpVSgpr4NL2w7ih9PnBNdFhERDWIMSCScJEl4ekEM1i+ZgqixQ9GhN+A/X2Xiva8z0d5hEF0eERENQsIDUm1tLZ566ilMmzYN0dHRuOWWW5CSkmLcn5iYiOuvvx7jx4/H7Nmz8cUXX3R7fmtrK5599lnEx8cjKioKDz/8MKqrqwf6bVAfSZIEJwcVllwfgbnTxkAC8N3xc/jnDt7kloiIBp7wgPTQQw/h2LFjWL9+PXbt2oWQkBDcddddyM/PR15eHhYvXoypU6fi008/xd/+9jcsX74ciYmJxuc/88wz+Omnn7BhwwZs3boV+fn5WLp0qcB3RH2hkCRcO3k0/nHjeDjZK5F/TodVW5KRVVQjujQiIhpEJFngddWFhYVISEjAjh07MGHCBACALMtISEjAnDlzUFVVhYyMDHz88cfG5zz88MOora3Fpk2bUF5ejunTp+Ptt9/G5ZdfDgAoKCjA7NmzsXPnTkRFRfWqLr3egOrqxj69N6VSAY3GCTU1jejgMFGvaGub8canaSjWNkAhSbhxhj+uihkJSZJ+93nsvTjsvTjsvTjsvTjn916tdoCNjenO+wg9g6TRaLBx40ZEREQYt0mSBEmSoNPpkJKSgvj4+G7PiYuLQ2pq53o5qampxm1d/Pz84OXlheTk5IF5E9RvPIc44PH5ExAf5gWDLGPnt7nY+Hk6Wtv0oksjIiIrpxT54mq12njmp8uePXtQWFiIxx9/HP/973/h7e3dbb+npyeam5tRU1OD8vJyaDQa2NnZ/eoxZWVlfapNqexbduxKsaZMs4ORUqnAvX8Jh/8IV3zwTQ6S0stxrrIRS/86Dl5ujhd8DnsvDnsvDnsvDnsvTn/2vtcBSafT4fjx45g2bRoAoKSkBN9//z2uu+46uLi49OqYR48exYoVK5CQkIDp06ejpaUFtra23R7T9XNbWxuam5t/tR8A7Ozs0Nra2qsaAEChkKDROPX6+edTqx1McpzB7qaEEIQHeOKf7yWjWNuAZzYfwcO3TUBMqPdvPoe9F4e9F4e9F4e9F6c/et+rgJSXl4cFCxZApVLh22+/BQAUFxfjhRdewNatW7FlyxYMGzbsDx1z3759WLZsGaKjo7F27VoAnUGnra2t2+O6fnZwcIC9vf2v9gOdV7Y5OPS+WQaDDJ2uqdfPBzrTrFrtAJ2uGXo9x6RNYZjGHs/cGYvXd51ETkkdVm9Kwl+mjcGfp/pBcd68JPZeHPZeHPZeHPZenPN77+RkZ9IzSb0KSC+//DK8vLzwxhtvGLfFx8fj+++/x3333YeXXnoJr7766iUfb9u2bVizZg1mz56Nf/7zn8azQj4+PtBqtd0eq9Vq4ejoCBcXF3h7e6O2thZtbW3dziRptVp4eXn15q0ZmWqinV5v4KQ9E3JxUOGRW6Lwwf4cHDh6Fv/9IR/5Z+tw97WhcLRXdXssey8Oey8Oey8Oey9OfwTTXkWto0eP4oEHHvhVCHF3d8e9996Lw4cPX/KxduzYgdWrV+O2227D+vXruwWdiRMn4siRI90ef/jwYURHR0OhUGDChAkwGAzGydpA51Vs5eXliImJ6c1bIwugtFFgfkIQ7rw6BEobBU7kVWHV1hSUaBtEl0ZERFaiVwFJkiQ0NzdfcF9HRwfa29sv6TgFBQV4/vnncdVVV2Hx4sWorKxERUUFKioqUF9fj/nz5+PkyZNYu3Yt8vLysHnzZnz99ddYtGgRAMDLywvXXHMNVq5ciaSkJJw8eRIPPfQQYmNjERkZ2Zu3RhbksnE+eGL+BLir7aGtacZz76cgKb1cdFlERGQFerUO0v3334/CwkJs3boVbm5uxu21tbW488474eXlhbfeeuuix3n77bfxyiuvXHDf3Llz8eKLL+KHH37Ayy+/jDNnzmDEiBF44IEHcPXVVxsf19TUhOeffx579uwBAEybNg0rV66ERqP5o2/LiOsgWZb6pja8s/s00s90LiY5e5Iv7r1hPHS6ZvZ+gPFzLw57Lw57L05/roPUq4BUUFCAG2+8ER0dHYiMjISbmxtqampw/Phx2Nra4oMPPoCfn5/JihxoDEiWx2CQ8ekP+fjycCEAIMJ/KBZfFwpHO6ErWQw6/NyLw96Lw96LY3YLRfr5+eF///sfbr75ZjQ1NeHUqVPQ6XS48cYb8dlnn1l0OCLLpFBI+Ot0fyyZGw57Wxuk5VXiqXeTkHeuTnRpRERkgYTeasRc8QySZSuvbcbru9JwtqIBShsJt10ViGnjO5eduNhtSqhv+LkXh70Xh70Xx+yG2ACgvr4ehw8fRlNTEy50iL/85S99rU0YBiTLplQqYOdgi5feS0ZqVgUAQO2ogofGAY/Pm8CQ1I/4uReHvReHvRenPwNSryZo/Pjjj1i6dOlvXskmSZJFBySyfI72Kiz96zjs/qkAu77Ph66pHbqmdhSc02HMcFfR5RERkZnrVUBat24dxowZgxUrVsDLywsKBe8/Q+ZHkiRcEz8avl7O+NcnaTAYZLywPRXXT/PHrFhfKBQ8k0RERBfW61uNvPnmm5g4caKp6yEyuYgxQ7H2vnhs/ToLJ/Kq8PF3eTiWW4lF14TAU3PhG94SEdHg1qtTP8OGDUNDA1ctJssxxMUeS/86Dgv/FAx7WxvkltTh6c3J+O7Y2QvOoSMiosGtVwFp8eLFeOONN1BSUmLqeoj6jSRJmDp+GFbdGYugkUPQ2q7He3uy8MrHJ1BT3yq6PCIiMiO9GmL7/PPPUV5ejquuugpubm6wt7fvtl+SJOzbt88kBRKZ2tAhDnjk1ijsSy7GJ9/n41R+NZ7alIR5CUGYFNq3mxwTEZF16FVA8vb2hre3t6lrIRowCklCQqwvwsa4493/paOwrB7v7D6NYzkVmJcQBGcHlegSiYhIIC4UeQFcB8my/dHed+gN+N+hM/jfoUIYZBmuzrZY+KcQjPN3H4BqrQs/9+Kw9+Kw9+KY3a1GiKyJ0kaBv0wdgydunwAfd0fUNbTh1Y9PYOvXmWhp6xBdHhERCdCrIbbg4OCLrkackZHRq4KIRPHzUePpBTHY9X0+vkkpxvfHzyH9TDXuuiYUgSOHiC6PiIgGUK8C0pIlS34VkBobG3H06FEUFRVh2bJlJimOaKDZqmxwy5VjETl2KDZ/kY6K2hb8c/tRzJrki7lT/aBS2ogukYiIBoDJ5yAtX74cTk5OePrpp0152AHFOUiWzVS9b2rpwM79OfgprRQAMNzDCXfPCYWvl4upSrU6/NyLw96Lw96LY1FzkObOnYsvv/zS1IclGnCO9krceU0IHrg+AmpHFc5WNGL11hR8fugM9Ab+JUhEZM1MHpCKiorQ0cGJrWQ9ogI9sGrRJEQHekBvkPHfH/LxwrajKKtuEl0aERH1k17NQXr99dd/tc1gMKCsrAxffvklZsyY0efCiMyJ2tEWS+aGI/F0GbZ/k438czo8s/kI/jYjADOih0NxkYsWiIjIspgsIAGAs7MzrrzySqxYsaJPRRGZI0mSMDncB8G+Gmz+MgPpZ2qw/ZtsHM3W4s6rQ+Du6iC6RCIiMhEuFHkBnKRt2Qai9wZZxoGjZ/HRtzlo18tQSMDts4Mwddywiy6BYc34uReHvReHvRfHoiZpEw0GCknCFRNG4InbJwIADDKw5assrP/oBCrrmgVXR0REfcWARNQHIz2dETBcDXdXe9goJJwuqMaT7x7B/tQSGHhylojIYvVqDhIRdZIkCSvmTQAAlNc04z9fZiCnpA7bv8lGckY5FlwdAm83R8FVEhHRH8UzSER9JEkSJEmCt5sjHr0tGrddFQg7lQ2yS+rw9OYj+CqpkOsmERFZGAYkIhPqmpu0+q5YhI3WoL3DgI8P5GHNe6ko0TaILo+IiC4RAxJRPxg6xAEP3RSJhVcHw9FOiTNl9Xh2SzI++zEfHXqeTSIiMncMSET9RJIkTB03DKsXTULU2KHQG2TsPngGq7Yko6BUJ7o8IiL6HQxIRP1M42KH+6+PwL1/DoOLowolFY147r0UfHQgF23tetHlERHRBTAgEQ0ASZIQG+KF5xZNQlyoF2QZ+DqpCE9vPoLs4lrR5RERUQ8MSEQDyMXRFvdcF4alN4zDEGdblNc048XtR7FtbxaaW3mTZyIic8GARCRA5NiheG7RJEwb7wMA+PboWTy1KQmnCqoEV0ZERAADEpEwjvYqLPhTCB6+ORJDXe1RpWvF+g9PYPMXGWhsaRddHhHRoGZWAemdd97B/Pnzu21buXIlgoKCun3NnDnTuN9gMOC1117D1KlTERkZibvvvhvFxcUDXTpRr4WNdsOqu2Jx5YQRkAD8lFaKlf9OwtHsCtGlERENWmYTkLZv345XX331V9uzsrJw77334qeffjJ+ffLJJ8b9b775Jnbs2IHVq1dj586dMBgMWLRoEdra2gaweqK+sbdV4tarAvHYvGh4uzmirrENr3+ahrc+O4W6hlbIvK8bEdGAEh6QysvLce+992Lt2rUYPXp0t32yLCM3Nxfh4eHw8PAwfrm5uQEA2trasHnzZixduhTTp09HcHAwXnnlFZSVlWHv3r0C3g1R34wdMQTP3hmDa+JHQSFJSM7U4uE3D+HxjYdh4O1KiIgGjPCAdPr0aahUKuzevRvjx4/vtq+oqAhNTU0YM2bMBZ+bmZmJxsZGxMfHG7ep1WqEhoYiOTm5X+sm6i8qpQ1uuNwfT94xEcM9nGAwyCivacb6D0+gorZZdHlERIOC8IA0c+ZMbNiwASNHjvzVvuzsbADA+++/j5kzZ+LKK6/EqlWrUF9fDwAoKysDAPj4+HR7nqenp3EfkaUa5e2Cp+6YCDe1HQAgvbAGT76bhC8PF/J2JURE/UwpuoDfk52dDYVCAU9PT7z99tsoKirCSy+9hJycHGzduhXNzZ3/mra1te32PDs7O9TV1fXptZXKvmVHGxtFt//SwLGm3iuVCrzywGUorWrE1q+ykFFYg0++y0NSejkWXh2CgBGuokvsxpp6b2nYe3HYe3H6s/dmHZDuu+8+3HrrrdBoNACAwMBAeHh44MYbb0RaWhrs7e0BdM5F6voeAFpbW+Hg4NDr11UoJGg0Tn0r/mdqde/roL6xpt67uTnjnwGe+DalGJt2n0axtgGrtyZjdvxo3HF1KJwcVKJL7Maaem9p2Htx2Htx+qP3Zh2QFAqFMRx1GTt2LIDO4bWuoTWtVgtfX1/jY7RaLYKCgnr9ugaDDJ2uqdfPBzrTrFrtAJ2uGXoOhwwoa+59dIA7xt4bhw/25eCnk6X46tAZHDp5DvMTghAT4glJkoTWZ829N3fsvTjsvTjn997Jyc6kZ5LMOiAtX74cWq0WW7ZsMW5LS0sDAAQEBGDkyJFwdnZGUlKSMSDpdDqkp6dj3rx5fXrtjg7TfMj1eoPJjkV/jLX23sFWiTuvDkF8mDfe+zoT5TXNeP3TNIzzd8e8hEAMdRX/r1hr7b0lYO/FYe/F6Y9gatYDprNmzUJiYiJef/11FBUV4fvvv8fjjz+OOXPmwN/fH7a2tpg3bx7Wrl2L/fv3IzMzEw8++CC8vb2RkJAgunyifhUySoNVd8XiuimjYaOQcDKvCivfTcLXSUXQc0kAIqI+MeszSFdccQVeffVVbNy4Ef/+97/h4uKCa6+9Fv/4xz+Mj1m6dCk6OjqwcuVKtLS0ICYmBps2bYJKZV5zMoj6g0ppg79MHYPYEC+893Umskvq8NGBXBw+XYY7/hQMPx+16BKJiCySJHOJ3l/R6w2orm7s0zGUSgU0GifU1DTylOsAG6y9N8gyfjpZio8P5KKxpQMSgJkTRuD6aWPgYDcw/xYarL03B+y9OOy9OOf3Xq12MOkcJLMeYiOiS6eQJEwbPwxr7o5DXJgXZAD7U0uw8t0kpGbxvm5ERH8EAxKRlVE72eKea8Pw8E2R8BzigJr6Vrzx3zRs2HUS1boW0eUREVkEBiQiKxXm54ZVd8XimvhRsFFIOJZTiSfeTcI3ycUwGDiyTkT0exiQiKyYrarzvm5PL4xBwHBXtLbp8cH+HKx+LwWFZfWiyyMiMlsMSESDwAgPZzw2Lxq3zwqCg50ShWX1WLU1GTv356C5tR28VoOIqDuzvsyfiExHIUmYHjUcUWOH4oP9OTiSocXe5GJ8e7QEHkMc8NyiScJX4iYiMhc8g0Q0yLg62+HeP4fjH38bDzcXO3ToZZRWNeG1XWmcxE1E9DMGJKJBapy/O55bNAkaZzsAwInczknce49wJW4iIgYkokHM3k6JtUsm49nzJnHv/DYXq7ekIP+cTnR5RETCMCARDXKSJGGklwsemxeNO2YHwcleiSJtA9a8l4L392ahqaVDdIlERAOOAYmIAHRO4r48cjjW3B2H+DBvyAAOHD2LJ/59GEnp5bzSjYgGFQYkIupG7WSLu68NxSM3R8LLzRF1jW14Z/dprP/oBLQ1TaLLIyIaEAxIRHRBIaPdsOrOWPzlMj8obRQ4XVCNJzcdwecHC9DOG3ISkZVjQCKi36RSKnDdZX5YfVcsQkZp0N5hwH9/LMAz/zmCrKIa0eUREfUbBiQiuigvN0csuzkS91wbCrWjCqVVTfjnjmPY9L901De1iS6PiMjkuJI2EV0SSZIQF+aNCH937Po+H98fO4uDp8pwPLcSN84IwJRxPqJLJCIyGZ5BIqI/xMlehdtnBeHx+RMwwsMZjS0d+M9XmXhp+1GUVDSILo+IyCQYkIioV/yHu+LphRNx44wA2KoUyC6pw5P/TsJ7X6ajtV0vujwioj5hQCKiXrNRKDB7ki/WLIpDZMBQ6A0yPt6fg8ffOYyTeVWiyyMi6jUGJCLqM3dXeyz96zj8v7+Nw9AhDqiobcarH5/Am5+dQrWuhYtMEpHF4SRtIjKZCUGemBI1Epv/Lw17jxQjJVOL1Cwt3NX2WHP3JKiUNqJLJCK6JDyDREQm5WCnxK1XBeKpBRMx2tsFsgxU1rVg5b+TkJql5dkkIrIIDEhE1C98vVzwxPwJ8NQ4wEYhoaKuBW/89xT+uf0oCkp1ossjIvpdHGIjon5jY6PAC/fEoaWtA18nFWPPkSJkl9Rh9dYUxId54YbL/eGmthddJhHRr/AMEhH1K0mS4GCnwtxpY/D8PXGYHO4NAEg8XY4VGw/j0x/y0NzaIbhKIqLuGJCIaMC4qe2xaE4onlowEUEjh6C9w4D/HSrEio2H8f3xszAYOD+JiMwDAxIRDbjR3mosvzUK918fAS+NA3SNbdj6dRae+c8RnCrg+klEJB7nIBGREJIkITrQA+P83XHg6FnsPliAkopGrP/wBCLGuOPGGf4Y7uEsukwiGqQYkIhIKKWNAlfFjER8uDf+d+gM9qeWIC2/CqcKqnB55HD85TI/qJ1sRZdJRIMMh9iIyCw4O6hw8xVj8dyiSYgO9IAsA98dO4vH3knEF4ln0N7B+7sR0cBhQCIis+Ll5oj7r4/Ao7dGYZS3C1ra9Nj1fT4e35iEw+llXGiSiAYEAxIRmaUgXw2evGMi7p4TCo2LHap0Ldi4Ox1r3k9Fbkmd6PKIyMoxIBGR2VJIEuLDvfH8PXGYO9UPdiob5J/T4fltqXjzs1Mor2niGSUi6hdmFZDeeecdzJ8/v9u2jIwMzJs3D5GRkZg5cybee++9bvsNBgNee+01TJ06FZGRkbj77rtRXFw8kGUTUT+zU9ng2il+eGFxHKaN94EkASmZWqx45zAefuMQ6pvaRJdIRFbGbALS9u3b8eqrr3bbVlNTg4ULF8LX1xe7du3CkiVLsHbtWuzatcv4mDfffBM7duzA6tWrsXPnThgMBixatAhtbfwLk8jaDHG2w4I/heCZhbEI9h0CAKhtaMUjbx3CJ9/lMSgRkckID0jl5eW49957sXbtWowePbrbvo8++ggqlQqrVq2Cv78/brjhBixYsAAbN24EALS1tWHz5s1YunQppk+fjuDgYLzyyisoKyvD3r17BbwbIhoIIz2dsezmSAxzd4SdSoG2dgO+PFyI5W8l4uMDudAxKBFRHwkPSKdPn4ZKpcLu3bsxfvz4bvtSUlIQGxsLpfKX5Zri4uJw5swZVFZWIjMzE42NjYiPjzfuV6vVCA0NRXJy8oC9ByIaeAqFAqsXTcIbD07DAzdEYJSXC1rb9fgqqQjL3zqEj77NRV0jgxIR9Y7whSJnzpyJmTNnXnBfWVkZAgMDu23z9PQEAJSWlqKsrAwA4OPj86vHdO3rLaWyb9nRxkbR7b80cNh7cUT1PibECxODPXE8txKf/VCAglIdvj5ShG+PlmDmhBG4On4UhjjbDWhNA42fe3HYe3H6s/fCA9LvaWlpga1t9xV07ew6/5JrbW1Fc3MzAFzwMXV1vb8MWKGQoNE49fr551OrHUxyHPrj2HtxRPV+ZqwzZsSMQmqmFh/szUR2US2+TirCt6klmD15NG6YMRZuanshtQ0Ufu7FYe/F6Y/em3VAsre3/9Vk69bWVgCAo6Mj7O07/6Jra2szft/1GAeH3jfLYJCh0zX1+vlAZ5pVqx2g0zVDrzf06Vj0x7D34phL7/29nfHE/AlIy6/Cf38oQN7ZOuz+IR9fHTqDGVHDcc3k0dC4WNcZJXPp/WDE3otzfu+dnOxMeibJrAOSt7c3tFptt21dP3t5eaGjo8O4zdfXt9tjgoKC+vTaHR2m+ZDr9QaTHYv+GPZeHHPpfegoN4TM0+D0mWrs/ukMcs/WYW9yMb49ehaXjx+GP8X5Wt0ZJXPp/WDE3ovTH8HUrAdMY2JikJqaCr3+l3swHT58GH5+fnB3d0dwcDCcnZ2RlJRk3K/T6ZCeno6YmBgRJRORmZEkCeF+7lgxLxoP3xyJsSNc0aE3YP/REjz2TiLe35OFal2L6DKJyMyYdUC64YYb0NDQgCeeeAK5ubn49NNPsWXLFixevBhA59yjefPmYe3atdi/fz8yMzPx4IMPwtvbGwkJCYKrJyJzIkkSwka74bHbovHIzZEIHDkEHXoZB46dxaNvJ+K9rzNRWdcsukwiMhNmPcTm7u6Od999F2vWrMHcuXPh4eGB5cuXY+7cucbHLF26FB0dHVi5ciVaWloQExODTZs2QaVSCayciMyVJEkIGe2GkNFuyCyswe6DBcgsqsV3x8/hx5OlmBLhgznxozB0CCfcEg1mkswbGf2KXm9AdXVjn46hVCqg0TihpqaRY9IDjL0Xx1J7n1VUg90HzyCjsAYAYKOQMDncG1fHj4LnEAdIkiS4wouz1N5bA/ZenPN7r1Y7DJ5J2kREAyHIV4NHfDXILq7F7oMFSD9Tgx9PluLHk6VwcVThwb+Nx2gftegyiWgAMSAREf0scOQQLLs5Crkldfjvj/nIKKxBfVM7Vm1NQcAIV1w5YQSiAz2g5IKARFaPAYmIqIeAEa5YdnMknt58BLUNbWhqaUduSR1yS+rg6myLGZHDcXnkMLha+ercRIMZAxIR0QVIkoRn74wFANQ2tOH742fx3fFzqGtow2c/FeDzQ2cQE+KJKyaMgP8wV8HVEpGpMSAREf2GrsnZGhc7/GXqGMyZPBopmVrsTy1B3jkdDp8ux+HT5fDzccHM6BGIDfGCqo/3cSQi88CARER0iZQ2CsSFeSMuzBsFpTp8m1qCpIxyFJTWY9MXGfjoQC4ujxyG6ZHDrW6FbqLBhpf5XwAv87ds7L04g7H3uqY2/HD8HA4cO4ua+s57RSokCdGBQ3HFhBEIHDlkQJYJGIy9NxfsvTi8zJ+IyEypHW0xZ/Jo/CnOF8eyK7E/tQRZxbVIyapASlYFRng444oJwxEX5g07lY3oconoEjEgERGZgI1CgYnBnpgY7IlibQP2p5bg8OkylFQ0YOvXWfjkuzxMHTcMM6KHw4OrdBOZPQYkIiITG+npjAV/CsbfZvjjxxOl+PZoCSrrWvD1kSLsOVKE8QGdw28hozqH3yxhpW6iwYYBiYionzjZqzB7ki8SYkbiZF4V9h8twemCahzPrcTx3EqolAq4OtniidsnwNWJayoRmRMGJCKifqZQSIgcOxSRY4eitKoR36aexY9p59DWbkBlXQsefv0gxgcMxZQIH4zzd+dK3URmgAGJiGgA+bg74baEQMyd5odntyRD19iO1nY9juVU4lhOJZwdVIgL9cKUCB/4ejlz+I1IEAYkIiIBHO1VeHFxPADgXGUjDp4qQ+KpMtQ1tmFfagn2pZZghIcTJof7ID7Mi7c1IRpgXAfpArgOkmVj78Vh7/tGbzDgdEENDqaV4lhOJTr0nT1USBIixrhhSoQPxgcMveBq3ey9OOy9OFwHiYhoELBRKDDO3x3j/N3R2NKOIxlaHEwrRf45HU7kVeFEXhWc7JWIDfXCZRE+GO3twiE4on7CgEREZIac7FWYETUcM6KGo7SqEQfTypB4ugw19a04cPQsDhw9i2FDnTAlvPPWJx4arq1EZEocYrsADrFZNvZeHPa+fxkMMtILq3EorQyp2RVo/7nHkgREjHHH7Hg/BI5wgQI8qzSQ+LkXh0NsREQEhUJCuJ87wv3c0dTSgZQsLX5KK0VuSR1O5lXhZF4VHO2ViA32xJQIH/j5uHAhSqJeYkAiIrJAjvZKTBs/DNPGD0N5dRMS08tx6FQZKmub8d3xc/ju+DmolAqoHVV49NZoDOXtTYj+EA6xXQCH2Cwbey8Oey+OUqmAq6sjEo+X4Pvj55CSVY72jl/+eg8ZpcHkcG9EB3rAwY7/NjYlfu7F4RAbERFdlEIhIdTPDYEjh+C2q8Zi1dYU1De1oblVj4zCGmQU1uD9vVmYEOiByeE+CBmlgULB4TeiC2FAIiKyQo72KrxwTxwAoErXgsTTnUNw5dVNSDxdjsTT5dC42CEuzAuTw30wfKiT4IqJzAsDEhGRleqanD3U1QHXTh6NOfGjkF+qw6FTZTiSXo6a+lZ8dbgIXx0uwihvF0wO98akUC+oHW0FV04kHgMSEdEgIUkS/Ie5wn+YK26eORYn8ypx6FQZTuZVobCsHoVl9fjo21xEjHHH5HDv31y1m2gwYEAiIhqEVEoFJgR5YkKQJ+qb2oyrdp8pq8fx3Eocz62Ek70SMSFemBzuDf9hai4XQIMKAxIR0SDn4miLKyaMwBUTRuBsZSMST/2yavd3x87iu2Nn4aVxwORwb8SHeXPJABoUeJn/BfAyf8vG3ovD3otj6t4bDDIyi2pw6FQZUrMq0NquN+4LGjkEk8O9MTHYk0sGgJ97kXiZPxERDSiFQkLoaDeEjnbDvIQOpGZV4NCpMmQW1iCruBZZxbXY/k02ogM9MDnCG6Gj3LhkAFkVBiQiIvpd9rZKTInwwZQIH1TrWpB4ugyHTpWhtKoJh9PLcTj9lyUDpoT7YBiXDCArwIBERESXzE1tj2viR+PquFE4U1aPg2mlSOqxZICfjwumRPggNsQLzg4q0SUT9QoDEhER/WGSJMHPRw0/HzVumjkWJ3J/WTKgoLQeBaX12Lk/B+MDhmJKuA/Cx7hBacL5IUT9zSICUnl5OaZNm/ar7S+88AKuv/56ZGRkYM2aNTh16hTc3NywYMEC3H777QIqJSIafFRKBSYGe2JisCd0jW04nF6OQ2mlKNI2IDWrAqlZFVA7qjAp1BtTIrzh6+UiumSii7KIgJSZmQk7Ozvs27ev2zocLi4uqKmpwcKFCzFz5kw8++yzOH78OJ599lk4OTnhhhtuEFg1EdHgo3ayRULMSCTEjERReT0OnSrD4dNl0DW145uUYnyTUowRHs6YEuGNuDBvuDpx1W4yTxYRkLKzszF69Gh4enr+at/WrVuhUqmwatUqKJVK+Pv7o7CwEBs3bmRAIiISyNfLBb5eLvjrdH+cKqjGobRSHM+tRElFAz78NhcfH8hD+Bg3TInwQWSAO1RKG9ElExlZREDKysqCv7//BfelpKQgNjYWSuUvbyUuLg7vvPMOKisrMXTo0IEqk4iILkBpo0BkwFBEBgxFQ3M7kjPKcfBUGfLP6XAyrwon86rgZK9EbIgXJkd4Y4yP2vhcrt5NolhEQMrOzoZGo8Ftt92GgoICjBo1Cvfddx+mTZuGsrIyBAYGdnt815mm0tLSXgckZR/vP9S1WJUpF62iS8Pei8Pei2MpvR/iYoerYn1xVawvzlU24uDJUhxMK0V1fSsOHDuLA8fOwtvNAe0dMtROtnjmzhizD0mW0ntr1J+9N/uA1NHRgfz8fAQEBOCxxx6Ds7MzvvjiC9xzzz34z3/+g5aWFtjadh/DtrOzAwC0trb26jUVCgkajWnW8VCruSS/KOy9OOy9OJbUe43GCWFjPXHX3HFIy63A/pRiHDpZirLqZgBAla4Fa3eewFWTfBEf7gN7M1+125J6b236o/fm/WkDoFQqkZSUBBsbG9jb2wMAwsPDkZOTg02bNsHe3h5tbW3dntMVjBwdHXv1mgaDDJ2uqU9129gooFY7QKdrhl7PpecHEnsvDnsvjqX3fpSHE+78UzBumRmAg2mleO/rLABAWl4l0vIqYW9rg5gQT0wdNwyBvkOgMKOzSpbee0t2fu+dnOwG361GnJx+fTZn7Nix+Omnn+Dt7Q2tVtttX9fPXl5evX5NU91PR6838N48grD34rD34lh671U2Clw+fhgOnSpDe4cB4/3dkXi6DBW1LfjxRCl+PFGKoa72mBzujcnh3vDU9O4fwv3B0ntvyfojmJp9QMrJycFNN92Et956C5MmTTJuP3XqFAICAhASEoKdO3dCr9fDxqbzCojDhw/Dz88P7u7uosomIqJekiQJK26LNn7/58v8kFNSh4NppUjO1KKyrgW7D57B7oNnMHaEK6ZE+GBikCcc7c3+VxpZEEmWZVl0Eb/HYDDgxhtvRHNzM5599lloNBp89NFH2LFjB3bt2gV3d3f86U9/wsyZM7Fo0SKcPHkSzzzzDJ599lnMnTu3V6+p1xtQXd3Yp7p5d2dx2Htx2HtxBkvvW9v1OJZTgUNpZTh9phpdv8FUSgWiAz0wJdwboaMH9sa5g6X35uj83qvVDiYdYjP7gAQAlZWVWLduHX788UfodDqEhoZi2bJlmDhxIgDg5MmTWLNmDdLT0+Hh4YE777wT8+bN6/XrMSBZNvZeHPZenMHY+5r6Vhw+XYaDp8pwrvKXv7OHONsiPswbkyN8MHwAbpw7GHtvLgZ9QBpoDEiWjb0Xh70XZzD3XpZlnCmrx6G0MhxOL0NjS4dx32jvzhvnTgrtvxvnDubei9afAYkDtkREZNG63Tj3igCcyK3CoVOlOJlXhTNl9ThTdv6Nc70R4e8Om5+H4Mx9jSUShwGJiIishtJGgQlBHpgQ5AFdUxuS0stxKK0MheX1OJpdgaPZFXCyV0Jpo4DayRZPL5gIhYILPNKvMSAREZFVUjva4qqJI3HVxJEoqWjAobQyJJ4uQ11j59p5dY1teOydREwK9UZsiBdGeDjxjBIZcQ7SBXAOkmVj78Vh78Vh7y+N3mDAqfxqbPoiA40t7Tj/N6CPuyNiQ7wQG+IJH/dLn9zN3ovDSdoDjAHJsrH34rD34rD3f4wsy2ht0+NkfhWOZGhxMq8KHectNjjS0xmxIZ6IDfGCx5Dfv40Fey8OJ2kTERGZkCRJsLdT/nzGyAvNrR04llOBIxlanC6oRrG2AcXaBuz6Ph9+PmrEhngiJtgTbmp70aXTAGFAIiKiQc/BTonJ4T6YHO6DhuZ2HM2uwJGMcmQU1qCgVIeCUh0+/DYXY0e4IjbECxODPeHqZHvxA5PFYkAiIiI6j7ODCtPGD8O08cNQ19iG1CwtjqSXI7ukDjk/f+3Yl41gX03nMFyoFzSa/l+QkgYW5yBdAOcgWTb2Xhz2Xhz2vv9V61qQkqnFkUwt8s/pjNttFBIiAz0wIXAoxo0ZCkd7Jbp+tfKquP7FOUhERESCuantkRDri4RYX1TUNiM5s/PMUpG2AamZWqRmaqG0USDcT4PSqiY4O6jw+PwJDEkWimeQLoBnkCwbey8Oey8Oey9ORV0zTuTX4LvU4m73hAOAyIChmBTqhXH+7nCw4zkJU+MZJCIiIjPl4+6E0ABPzJo4HIWl9TicXoZvkkvQrjfgeG4ljudWQqVUINzPDTHBnhgfMJRhyQLwT4iIiMgEJEnCCE9n/NUzANdPG4NibQNSsiqQkqlFeU0zjuVU4lhO5c/DcL+EJUd7/io2R/xTISIiMjGFQoFR3mqM8lbj+mljUFLRiORMLVIytSirbjKeWVLaSAgb7YaJwZ6IGjsUjvYq0aXTzxiQiIiI+pEkSRjp6YyRns6YO9UPZysbkZKpRXKmFqVVTTiRV4UTeVWwUUgI83PDxCBPRAUOhRPDklAMSERERANEkiSM8HDGCA9n/GXqGJyt+GUY7mxlI07mVeFkXhVsvpYQMlqDmCBPRAV6wNmBYWmgMSAREREJMtzDGcM9nPHny/xwrrIRKVmdw3AlFY04lV+NU/nVeG9PFoJHaRDz8zBcV1ji8gH9iwGJiIjIDAwb6oTrhvrhuil+KK1qNJ5ZKtY24HRBNU4XVGPrV4C9nQ2cHWyx8vYJcHHk7U76CwMSERGRmfFxd8K1k51w7eTRKKtuQkqmFilZWhSVN6C5VY/m1mY8uOEnhPm5IzbEE1FjPXg1nImxm0RERGbM280RcyaPxpzJo1FW3Yh1O4+jobkdre0GpOVXIS2/CkqbLIzz7wxL4wOGwk5lI7psi8eAREREZCG83Zzw0n2TAQBl1U1IztAiKaMcpVVNOJpdgaPZFbBT2SBy7FDEhngi3M8dKqXpVpceTBiQiIiILEjX5Gwfdydcd5kfrp0yGiUVjTiSUY6k9HJU1rUgKb3zewc7JSYEeiA21BMhozSwUTAsXSoGJCIiIgt2/jpL108bg4LSehzJKMeRjHLUNrThp7RS/JRWChdHFSYGeSI2xBNjRw6BglfB/S4GJCIiIishSRLGDFNjzDA1bpwZgJziWhzJ6FyUsr6pHQeOncWBY2ehcbFDTLAnYkO84OfjwiUDLoABiYiIyAopJAlBvhoE+Wpw61VjkVFYgyPpWqRmV6CmvhV7k4uxN7kYQ13tMSnUC7EhXhjh4WR8/mAPTQxIREREVs5GoUC4nzvC/dwxf1YQThVU4UiGFsdyKlBZ14IvEgvxRWIhvN0c0NZugIuTLZ66Y+KgDkkMSERERIOISqlA1FgPRI31QGubHifyKnEkQ4uTeVUoq24GAFTXt2LDrjRcEz8K/sNdBVcsBgMSERHRIGVna4PYkM7htaaWDhzN1mLn/lw0tXbgeG4ljudWYuwIV8ye5IvxAUMH1cRuBiQiIiKCo70Sl40bhikRPjhb0YC9ySVIPF2GnJI65JSkwcfdEbNifREf5gWV0voXopRkWZZFF2Fu9HoDqqsb+3QMpVIBjcYJNTWN6OgwmKgyuhTsvTjsvTjsvTjW3Pua+lbsSy3Gd8fOobm1AwCgdrLFlRNGYEb0cDjZq4TWd37v1WoH2NiYbp0nnkEiIiKiC9K42OFv0wMwJ340fjhxDnuTi1FT34pPf8jHF4mFmDreBwkxIzHU1UF0qSbHgERERES/y8FOiVmxvrhiwggkZ2jxVVIRSioasC+lBN+mnkVMiCdmx/pilLeL6FJNhgGJiIiILonSRoH4cG/EhXnh9JlqfJ1UhPQzNcZbm4SO1mD2JF+EjXaz+CUCrCIgGQwGvP766/j4449RX1+PmJgYPPXUUxg5cqTo0oiIiKyOJEnGdZUKy+qx50gRjmRokX6mBulnajDS0xmzY30RE+IJpQnnBQ0ky6y6hzfffBM7duzA6tWrsXPnThgMBixatAhtbW2iSyMiIrJqo7xdcM91YXjx3jhcOXEE7FQ2KNY24N//S8dj7yRiz5Ei4wRvAJBlGZZwfZjFX8XW1taGuLg4LFu2DLfeeisAQKfTYerUqVizZg3mzJnzh4/Jq9gsG3svDnsvDnsvDnvfXWNLOw4cPYt9qSXQNXaeqHCwU2J61DBcET0Cb//faUACVtwW3edhOF7F9jsyMzPR2NiI+Ph44za1Wo3Q0FAkJyf3KiARERFR7zjZqzBn8mjMih2JxNPl+DqpCGXVTfjqcBH2JBXB8PNpmbZ2A+xszXc9JYsPSGVlZQAAHx+fbts9PT2N+3pDqexbCu1KsaZMs3Rp2Htx2Htx2Htx2PsLUyoVmDlhBKZHD8fxnEp8mViI7OLabvvN+XetxQek5ubO+8bY2tp2225nZ4e6urpeHVOhkKDROF38gZdArba+tSEsBXsvDnsvDnsvDnv/266Y5IwrJo3GydwKPPHWIQDAkCGOsLczTQzpj95bfECyt7cH0DkXqet7AGhtbYWDQ+8aZjDI0Oma+lSXjY0CarUDdLpm6PUckx5I7L047L047L047P2lG+HmgLEjOm9+29TYguamvs1BOr/3Tk52nIN0vq6hNa1WC19fX+N2rVaLoKCgXh/XVBPt9HoDJ+0Jwt6Lw96Lw96Lw95fmsduiwYA6PUyANNcJ9YfwdTiB0yDg4Ph7OyMpKQk4zadTof09HTExMQIrIyIiIh6kiTJIhaRtPgzSLa2tpg3bx7Wrl0LNzc3DB8+HC+//DK8vb2RkJAgujwiIiKyQBYfkABg6dKl6OjowMqVK9HS0oKYmBhs2rQJKpXYuwwTERGRZbKKgGRjY4NHHnkEjzzyiOhSiIiIyApY/BwkIiIiIlNjQCIiIiLqgQGJiIiIqAcGJCIiIqIeGJCIiIiIemBAIiIiIuqBAYmIiIioBwYkIiIioh4YkIiIiIh6YEAiIiIi6kGSZVkWXYS5kWUZBkPf22Jjo4BebzBBRfRHsffisPfisPfisPfidPVeoZAgSZLJjsuARERERNQDh9iIiIiIemBAIiIiIuqBAYmIiIioBwYkIiIioh4YkIiIiIh6YEAiIiIi6oEBiYiIiKgHBiQiIiKiHhiQiIiIiHpgQCIiIiLqgQGJiIiIqAcGJCIiIqIeGJCIiIiIemBAMjGDwYDXXnsNU6dORWRkJO6++24UFxeLLssq1dbW4qmnnsK0adMQHR2NW265BSkpKcb9iYmJuP766zF+/HjMnj0bX3zxhcBqrVdBQQGioqLw6aefGrdlZGRg3rx5iIyMxMyZM/Hee+8JrND6fPbZZ7j66qsRERGBa665Bl999ZVxX0lJCRYvXozo6GhcdtllePXVV6HX6wVWaz06Ojrwr3/9CzNmzEBUVBRuu+02HD9+3Lifn/v+8c4772D+/Pndtl2s1yb5XSyTSW3YsEGeNGmSfODAATkjI0O+88475YSEBLm1tVV0aVZn4cKF8pw5c+Tk5GQ5Pz9ffvbZZ+Vx48bJeXl5cm5urhwRESGvX79ezs3Nld999105NDRUPnTokOiyrUpbW5t8/fXXy4GBgfKuXbtkWZbl6upqedKkSfKKFSvk3Nxc+ZNPPpEjIiLkTz75RHC11uGzzz6TQ0ND5W3btsmFhYXym2++KQcHB8tHjx6V29ra5ISEBPmee+6Rs7Ky5G+++UaOjY2V//Wvf4ku2yq89tpr8pQpU+Qff/xRPnPmjPzEE0/IEyZMkMvLy/m57yfbtm2Tg4OD5Xnz5hm3XUqvTfG7mAHJhFpbW+WoqCh5+/btxm11dXXyuHHj5M8//1xgZdbnzJkzcmBgoJySkmLcZjAY5CuvvFJ+9dVX5SeffFL+61//2u05Dz30kHznnXcOdKlWbd26dfLtt9/eLSC9/fbb8mWXXSa3t7d3e1xCQoKoMq2GwWCQZ8yYIb/44ovdtt95553y22+/LX/++edyeHi4XFtba9y3c+dOOTo6mv9IM4HrrrtOfuGFF4w/19fXy4GBgfKePXv4uTexsrIyefHixXJkZKQ8e/bsbgHpYr021e9iDrGZUGZmJhobGxEfH2/cplarERoaiuTkZIGVWR+NRoONGzciIiLCuE2SJEiSBJ1Oh5SUlG5/DgAQFxeH1NRUyLI80OVapeTkZHz44Yd48cUXu21PSUlBbGwslEqlcVtcXBzOnDmDysrKgS7TqhQUFODs2bO49tpru23ftGkTFi9ejJSUFISFhcHV1dW4Ly4uDg0NDcjIyBjocq2Ou7s7Dhw4gJKSEuj1enz44YewtbVFcHAwP/cmdvr0aahUKuzevRvjx4/vtu9ivTbV72IGJBMqKysDAPj4+HTb7unpadxHpqFWq3H55ZfD1tbWuG3Pnj0oLCzE1KlTUVZWBm9v727P8fT0RHNzM2pqaga6XKuj0+mwfPlyrFy58lef99/qPQCUlpYOWI3WqKCgAADQ1NSEu+66C/Hx8fjb3/6Gb7/9FgB739+eeOIJqFQqXHHFFYiIiMArr7yC1157Db6+vuy9ic2cORMbNmzAyJEjf7XvYr021e9iBiQTam5uBoBuv7QBwM7ODq2trSJKGjSOHj2KFStWICEhAdOnT0dLS8uv/hy6fm5raxNRolV55plnEBUV9aszGQAu2Hs7OzsA4P8HfdTQ0AAAePTRRzFnzhxs3rwZU6ZMwd///nckJiay9/0sNzcXLi4ueOONN/Dhhx/i+uuvx7Jly5CRkcHeD6CL9dpUv4uVF38IXSp7e3sAnb+Au74HOv/AHBwcRJVl9fbt24dly5YhOjoaa9euBdD5P0LPINT1M/8s+uazzz5DSkoKPv/88wvut7e3/1Xvu/5ScnR07Pf6rJlKpQIA3HXXXZg7dy4AICQkBOnp6fjPf/7D3vej0tJSPPzww9iyZQsmTpwIAIiIiEBubi42bNjA3g+gi/XaVL+LeQbJhLpO52m12m7btVotvLy8RJRk9bZt24YHHngAM2bMwNtvv238V4SPj88F/xwcHR3h4uIiolSrsWvXLlRVVWH69OmIiopCVFQUAODpp5/GokWL4O3tfcHeA+D/B33U1b/AwMBu2wMCAlBSUsLe96MTJ06gvb2927xHABg/fjwKCwvZ+wF0sV6b6ncxA5IJBQcHw9nZGUlJScZtOp0O6enpiImJEViZddqxYwdWr16N2267DevXr+92OnXixIk4cuRIt8cfPnwY0dHRUCj4se+LtWvX4ssvv8Rnn31m/AKApUuXYs2aNYiJiUFqamq3tXcOHz4MPz8/uLu7C6raOoSFhcHJyQknTpzotj07Oxu+vr6IiYlBenq6cSgO6Oy9k5MTgoODB7pcq9I15yUrK6vb9uzsbIwePZqf+wF0sV6b7Hdx3y/Go/OtX79ejo2Nlfft29dt7YW2tjbRpVmV/Px8OSwsTF6yZIms1Wq7fel0Ojk7O1sOCwuTX375ZTk3N1fetGkT10HqR+df5l9ZWSnHxMTIjz76qJyTkyPv2rVLjoiIkD/99FPBVVqHN954Q46KipI///zzbusgHT58WG5paZGvvPJK+a677pIzMjKM6yBt2LBBdNkWT6/Xy7fccos8e/ZsOTExUS4oKJBfeeUVOSQkRD5+/Dg/9/3o0Ucf7XaZ/6X02hS/ixmQTKyjo0N+6aWX5Li4ODkyMlK+++675eLiYtFlWZ233npLDgwMvODXo48+KsuyLH///ffynDlz5PDwcHn27NnyF198Ibhq63V+QJJlWT5x4oR84403yuHh4fKMGTPk999/X2B11mfz5s3yzJkz5bCwMPm6666Tv/nmG+O+M2fOyAsXLpQjIiLkyy67TH711VdlvV4vsFrrUVtbKz/zzDPy9OnT5aioKPmmm26Sk5KSjPv5ue8fPQOSLF+816b4XSzJMheFISIiIjofJ2MQERER9cCARERERNQDAxIRERFRDwxIRERERD0wIBERERH1wIBERERE1AMDEhEREVEPDEhEZBXmz5+P+fPn9/tziGhwYEAiIiIi6oEBiYiIiKgHBiQisggtLS1Yt24dEhISEB4ejujoaCxcuBAZGRkXfHxQUBC2bduGRx99FFFRUZg8eTLWrFmD1tbWbo+TZRn//ve/MX36dIwbNw433XQTTp482e0x+/btw6233oqoqCiEh4dj9uzZ2L59e7+9VyISjwGJiCzC8uXLsWvXLtxzzz3YvHkzVqxYgZycHDz88MP4rVtK/utf/0JVVRVeffVVLFq0CB9++CEeffTRbo9JTU3FN998gyeffBIvv/wytFot7rvvPnR0dAAAvvvuOyxZsgRhYWF48803sWHDBowcORKrVq3CiRMn+v19E5EYStEFEBFdTFtbGxobG7Fy5UpcffXVAIDY2Fg0NDTgxRdfRGVl5QWf5+bmhrfffhtKpRKXX345FAoFXnjhBTzwwAPw9/cHANja2mLjxo0YMmQIAECn02HlypXIzc1FcHAwcnNzMXfuXDzxxBPG40ZFRWHSpElISkrC+PHj+/fNE5EQDEhEZPZsbW2xadMmAEB5eTkKCgpw5swZHDhwAEBngLqQa6+9FkrlL3/NzZo1Cy+88AKSk5ONASkgIMAYjgBgxIgRAID6+noAwKJFiwAAjY2NKCgoQFFREdLS0n73dYnI8jEgEZFF+PHHH/H8888jPz8fTk5OCA4OhqOjIwD85hCbl5dXt5/d3d0BAHV1dcZtXcfoolB0zjwwGAwAgOrqajz99NPYt28fJEnCqFGjMHHixN99XSKyfAxIRGT2ioqKsGTJElx55ZV45513MHLkSEiShO3bt+PHH3/8zefV1NR0+7lrKM7Nze2SX3vZsmXIz8/Hli1bEBUVBVtbWzQ3N+Ojjz7q3ZshIovASdpEZPZOnTqF1tZW3HPPPfD19YUkSQBgDEe/dSbn22+/7fbznj17IEkS4uLiLvm1U1NTkZCQgEmTJsHW1hYA8MMPPwD45SwTEVkfnkEiIrMXFhYGpVKJl19+GXfeeSfa2trw6aef4rvvvgMANDU1XfB5x48fx7Jly/DnP/8ZmZmZ2LBhA2688UaMHDnykl973Lhx+PzzzxEWFgZvb28cPXoUGzduhCRJaG5uNsXbIyIzxIBERGZv1KhRWLduHV5//XXcd999cHV1RWRkJN5//33Mnz8fKSkpF3zeHXfcgfLyctx///3QaDS49957sXjx4j/02i+++CJWr16N1atXAwBGjx6NZ599Frt37/7N1yUiyyfJnGVIRFYoKCgI999/Px544AHRpRCRBeIcJCIiIqIeGJCIiIiIeuAQGxEREVEPPINERERE1AMDEhEREVEPDEhEREREPTAgEREREfXAgERERETUAwMSERERUQ8MSEREREQ9MCARERER9cCARERERNTD/wd+fWFriksc6gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.lineplot(data=alpha_impact[alpha_impact['index']<=20],x='alpha',y='unc',err_style=\"bars\", errorbar=(\"se\", 2))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3967,21 +3945,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:05:34,091] A new study created in memory with name: my_study\n", - "[I 2024-07-01 13:05:34,130] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:550)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:550)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/optuna_az/optunaz/descriptors.py:550)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", + "[I 2024-07-02 14:22:54,504] A new study created in memory with name: my_study\n", + "[I 2024-07-02 14:22:54,540] A new study created in memory with name: study_name_0\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", - "[I 2024-07-01 13:05:35,288] Trial 0 finished with value: -0.3374812160438065 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.107744706236782, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.3374812160438065.\n" + "[I 2024-07-02 14:22:55,559] Trial 0 finished with value: -0.34035600917066766 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.676421027478709, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.34035600917066766.\n" ] } ], @@ -4065,35 +4035,35 @@ " \n", " \n", " 2227\n", - " 2.042199e+01\n", + " 2.042023e+01\n", " UnscaledPhyschemDescriptors\n", " 7.0\n", " MolWt\n", " \n", " \n", " 2229\n", - " 2.025368e+01\n", + " 2.025199e+01\n", " UnscaledPhyschemDescriptors\n", " 9.0\n", " ExactMolWt\n", " \n", " \n", " 2228\n", - " 1.802289e+01\n", + " 1.802158e+01\n", " UnscaledPhyschemDescriptors\n", " 8.0\n", " HeavyAtomMolWt\n", " \n", " \n", " 2267\n", - " 2.386575e+00\n", + " 2.387276e+00\n", " UnscaledPhyschemDescriptors\n", " 47.0\n", " LabuteASA\n", " \n", " \n", " 2230\n", - " 2.106688e+00\n", + " 2.106653e+00\n", " UnscaledPhyschemDescriptors\n", " 10.0\n", " NumValenceElectrons\n", @@ -4106,39 +4076,39 @@ " ...\n", " \n", " \n", - " 1189\n", - " 3.616550e-07\n", + " 1784\n", + " 4.598471e-07\n", " ECFP\n", - " 1190.0\n", - " C1=C(C(=O)N)N(C)SN=C1c(c)c\n", + " 1785.0\n", + " c1(OC)c(OC)ccc(C)c1\n", + " \n", + " \n", + " 583\n", + " 4.598471e-07\n", + " ECFP\n", + " 584.0\n", + " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", " \n", " \n", " 995\n", - " 3.616550e-07\n", + " 4.598471e-07\n", " ECFP\n", " 996.0\n", " C(C(N)=C)(=O)N(C)C\n", " \n", " \n", " 845\n", - " 3.616550e-07\n", + " 4.598471e-07\n", " ECFP\n", " 846.0\n", " c(c(c)C)c(O)c\n", " \n", " \n", - " 583\n", - " 3.616550e-07\n", - " ECFP\n", - " 584.0\n", - " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", - " \n", - " \n", - " 334\n", - " 3.616550e-07\n", + " 1375\n", + " 4.598471e-07\n", " ECFP\n", - " 335.0\n", - " N(C)(C)C\n", + " 1376.0\n", + " S1(=O)(=O)N=C(c)C=C(C)N1C\n", " \n", " \n", "\n", @@ -4147,17 +4117,17 @@ ], "text/plain": [ " shap_value descriptor bit \\\n", - "2227 2.042199e+01 UnscaledPhyschemDescriptors 7.0 \n", - "2229 2.025368e+01 UnscaledPhyschemDescriptors 9.0 \n", - "2228 1.802289e+01 UnscaledPhyschemDescriptors 8.0 \n", - "2267 2.386575e+00 UnscaledPhyschemDescriptors 47.0 \n", - "2230 2.106688e+00 UnscaledPhyschemDescriptors 10.0 \n", + "2227 2.042023e+01 UnscaledPhyschemDescriptors 7.0 \n", + "2229 2.025199e+01 UnscaledPhyschemDescriptors 9.0 \n", + "2228 1.802158e+01 UnscaledPhyschemDescriptors 8.0 \n", + "2267 2.387276e+00 UnscaledPhyschemDescriptors 47.0 \n", + "2230 2.106653e+00 UnscaledPhyschemDescriptors 10.0 \n", "... ... ... ... \n", - "1189 3.616550e-07 ECFP 1190.0 \n", - "995 3.616550e-07 ECFP 996.0 \n", - "845 3.616550e-07 ECFP 846.0 \n", - "583 3.616550e-07 ECFP 584.0 \n", - "334 3.616550e-07 ECFP 335.0 \n", + "1784 4.598471e-07 ECFP 1785.0 \n", + "583 4.598471e-07 ECFP 584.0 \n", + "995 4.598471e-07 ECFP 996.0 \n", + "845 4.598471e-07 ECFP 846.0 \n", + "1375 4.598471e-07 ECFP 1376.0 \n", "\n", " info \n", "2227 MolWt \n", @@ -4166,11 +4136,11 @@ "2267 LabuteASA \n", "2230 NumValenceElectrons \n", "... ... \n", - "1189 C1=C(C(=O)N)N(C)SN=C1c(c)c \n", + "1784 c1(OC)c(OC)ccc(C)c1 \n", + "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", "995 C(C(N)=C)(=O)N(C)C \n", "845 c(c(c)C)c(O)c \n", - "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", - "334 N(C)(C)C \n", + "1375 S1(=O)(=O)N=C(c)C=C(C)N1C \n", "\n", "[1570 rows x 4 columns]" ] @@ -4222,10 +4192,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:05:39,444] A new study created in memory with name: my_study\n", - "[I 2024-07-01 13:05:39,502] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:22:59,978] A new study created in memory with name: my_study\n", + "[I 2024-07-02 14:23:00,032] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-01 13:06:23,104] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + "[I 2024-07-02 14:23:43,818] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", " \r" ] } @@ -4290,178 +4260,178 @@ "name": "stderr", "output_type": "stream", "text": [ - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", - "[13:08:32] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n" + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", - "[13:08:33] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", + "[14:25:52] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" ] }, { @@ -4594,41 +4564,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:35,036] A new study created in memory with name: transform_example\n", - "[I 2024-07-01 13:08:35,081] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:08:35,270] Trial 0 finished with value: -0.595949377253611 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.595949377253611.\n", - "[I 2024-07-01 13:08:36,682] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.595949377253611.\n", - "[I 2024-07-01 13:08:36,825] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.595949377253611.\n", - "[I 2024-07-01 13:08:36,868] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.595949377253611.\n", - "[I 2024-07-01 13:08:36,911] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.595949377253611.\n", - "[I 2024-07-01 13:08:36,954] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:36,997] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,026] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,103] Trial 8 finished with value: -0.7803723847416695 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,132] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,163] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,193] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,222] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,254] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-01 13:08:37,284] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,432] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,461] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,491] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,637] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n" + "[I 2024-07-02 14:25:53,892] A new study created in memory with name: transform_example\n", + "[I 2024-07-02 14:25:53,932] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:25:54,028] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-02 14:25:54,127] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-02 14:25:54,169] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-02 14:25:54,259] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-02 14:25:54,288] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-02 14:25:54,329] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,369] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,395] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,469] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,499] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,528] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,558] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,584] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,612] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-02 14:25:54,640] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:54,716] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:54,745] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:54,774] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:54,951] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:37,668] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,696] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,728] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,745] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:37,777] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,855] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,886] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-01 13:08:37,916] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n" + "[I 2024-07-02 14:25:54,980] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,008] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,039] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,054] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:25:55,083] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,160] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,191] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-02 14:25:55,222] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n", + "[I 2024-07-02 14:25:55,250] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n" ] }, { @@ -4642,19 +4613,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:37,948] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,024] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,056] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,131] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,161] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,193] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,225] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-01 13:08:38,241] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:38,271] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,302] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,321] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:38,353] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,431] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-02 14:25:55,329] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,361] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,438] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,469] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,496] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,528] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-02 14:25:55,545] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:25:55,577] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:55,609] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:55,626] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:25:55,657] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:55,735] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4669,10 +4639,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:38,511] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,531] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:38,612] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,644] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-02 14:25:55,817] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:55,836] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:25:55,905] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:55,935] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,003] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,035] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4686,74 +4658,72 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:38,736] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,768] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,802] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,834] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,867] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,902] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,941] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:38,981] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:39,019] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:39,058] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:39,097] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:39,134] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-01 13:08:39,172] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-01 13:08:39,209] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-01 13:08:39,247] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-01 13:08:39,285] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-01 13:08:39,324] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,363] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,399] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,435] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,471] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n" + "[I 2024-07-02 14:25:56,067] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,098] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,129] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,160] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,194] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,232] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,268] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,302] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,337] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,374] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-02 14:25:56,411] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-02 14:25:56,446] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-02 14:25:56,482] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-02 14:25:56,515] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-02 14:25:56,550] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,586] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,623] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,658] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,695] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,729] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-02 14:25:56,766] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:39,511] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,548] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-01 13:08:39,586] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-01 13:08:39,624] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-01 13:08:39,665] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-01 13:08:39,704] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-01 13:08:39,740] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,778] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,818] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,858] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,899] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,939] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:39,979] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,017] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,058] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,099] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,140] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,180] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,220] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,260] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,300] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" + "[I 2024-07-02 14:25:56,802] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-02 14:25:56,839] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-02 14:25:56,880] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-02 14:25:56,918] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-02 14:25:56,956] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:56,995] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,030] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,070] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,109] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,146] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,183] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,221] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,258] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,296] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,333] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,370] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,410] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,449] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,488] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,528] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,566] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:40,341] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,382] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,423] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,467] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,509] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,551] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,592] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,633] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,675] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,716] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-01 13:08:40,758] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-01 13:08:40,801] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-01 13:08:40,847] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-01 13:08:40,889] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" + "[I 2024-07-02 14:25:57,604] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,643] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,681] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,721] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,762] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,799] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,840] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,879] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-02 14:25:57,917] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-02 14:25:57,957] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-02 14:25:58,001] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-02 14:25:58,040] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" ] } ], @@ -4819,43 +4789,43 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:43,256] A new study created in memory with name: non-transform_example\n", - "[I 2024-07-01 13:08:43,257] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:08:43,342] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-01 13:08:43,430] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-01 13:08:43,472] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,513] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,543] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,582] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,625] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,665] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,742] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n", + "[I 2024-07-02 14:26:00,252] A new study created in memory with name: non-transform_example\n", + "[I 2024-07-02 14:26:00,254] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:26:00,332] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-02 14:26:00,422] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-02 14:26:00,459] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,500] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,528] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,570] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,613] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,650] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,726] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 13:08:43,796] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,826] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-01 13:08:43,858] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:43,884] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:43,911] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:43,942] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,019] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,051] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,079] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n" + "[I 2024-07-02 14:26:00,790] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,819] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-02 14:26:00,849] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:00,877] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:00,907] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:00,934] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,008] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,036] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,065] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:44,154] Trial 18 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,205] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,233] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-01 13:08:44,261] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-01 13:08:44,276] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:44,315] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-01 13:08:44,393] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-01 13:08:44,422] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,453] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-02 14:26:01,133] Trial 18 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,173] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,202] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-02 14:26:01,370] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-02 14:26:01,387] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:01,428] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-02 14:26:01,515] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-02 14:26:01,544] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:01,571] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4869,19 +4839,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:44,484] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,562] Trial 28 finished with value: -8873.66926266963 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,591] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,672] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,703] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,745] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,776] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,793] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:44,824] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,854] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,872] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:44,904] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:44,973] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-02 14:26:01,599] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:01,976] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:02,077] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:02,160] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:02,193] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:02,237] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:02,269] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,369] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:03,413] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,537] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,555] Trial 37 pruned. Duplicate parameter set\n" ] }, { @@ -4896,11 +4864,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:45,053] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,073] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:45,140] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,173] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,254] Trial 44 finished with value: -2270.5407991891466 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-02 14:26:03,585] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,665] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,745] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,763] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:03,831] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,864] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:03,944] Trial 44 finished with value: -2270.540799189147 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4914,73 +4884,73 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:45,288] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,321] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,351] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,382] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,415] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,454] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,494] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,535] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,575] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,618] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,659] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,699] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,740] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,781] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,822] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,861] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,901] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,940] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:45,980] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-02 14:26:03,977] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,008] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,040] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,070] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,103] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,140] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,177] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,218] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,256] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,294] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,334] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,374] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,412] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,451] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,491] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,528] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,567] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,607] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,645] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:46,020] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:46,062] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:46,102] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:46,144] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-01 13:08:46,186] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", - "[I 2024-07-01 13:08:46,227] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", - "[I 2024-07-01 13:08:46,269] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", - "[I 2024-07-01 13:08:46,308] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", - "[I 2024-07-01 13:08:46,348] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-01 13:08:46,391] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-01 13:08:46,435] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-01 13:08:46,478] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-01 13:08:46,531] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-01 13:08:46,571] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-01 13:08:46,613] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-01 13:08:46,655] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-01 13:08:46,699] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:46,907] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:46,950] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n" + "[I 2024-07-02 14:26:04,684] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,724] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,763] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,802] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-02 14:26:04,842] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", + "[I 2024-07-02 14:26:04,881] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", + "[I 2024-07-02 14:26:04,921] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", + "[I 2024-07-02 14:26:04,960] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", + "[I 2024-07-02 14:26:04,999] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-02 14:26:05,039] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-02 14:26:05,080] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-02 14:26:05,117] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-02 14:26:05,155] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-02 14:26:05,195] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-02 14:26:05,235] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", + "[I 2024-07-02 14:26:05,276] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", + "[I 2024-07-02 14:26:05,316] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,357] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,399] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:46,991] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,033] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,076] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,118] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,161] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,203] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-01 13:08:47,244] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-01 13:08:47,288] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-01 13:08:47,333] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-01 13:08:47,379] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-01 13:08:47,423] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,467] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,513] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,555] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,596] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,642] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-01 13:08:47,687] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" + "[I 2024-07-02 14:26:05,437] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,478] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,519] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,558] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,599] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,640] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-02 14:26:05,682] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-02 14:26:05,724] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-02 14:26:05,766] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-02 14:26:05,809] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-02 14:26:05,850] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:05,891] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:05,935] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:05,978] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:06,018] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:06,057] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-02 14:26:06,097] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" ] } ], @@ -5039,7 +5009,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 77, @@ -5157,41 +5127,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:52,377] A new study created in memory with name: ptr_and_transform_example\n", - "[I 2024-07-01 13:08:52,416] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:08:52,503] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-01 13:08:52,593] Trial 1 finished with value: -0.002490897902963267 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-01 13:08:52,634] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-01 13:08:52,675] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-01 13:08:52,703] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-01 13:08:52,742] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,782] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,811] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,885] Trial 8 finished with value: -0.0029994624596888664 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,914] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,942] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,970] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:52,998] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:53,027] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-01 13:08:53,058] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,133] Trial 15 finished with value: -0.005505338042993081 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,163] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,192] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,268] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" + "[I 2024-07-02 14:26:10,518] A new study created in memory with name: ptr_and_transform_example\n", + "[I 2024-07-02 14:26:10,558] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:26:10,728] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-02 14:26:10,805] Trial 1 finished with value: -0.0024908979029632677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-02 14:26:10,847] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-02 14:26:10,888] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-02 14:26:10,917] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-02 14:26:10,959] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,000] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,027] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,093] Trial 8 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,120] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,148] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,174] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,201] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,227] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-02 14:26:11,255] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,329] Trial 15 finished with value: -0.00550533804299308 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,359] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,386] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,466] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:53,299] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,329] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,360] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,376] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:53,404] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,479] Trial 24 finished with value: -0.0029994624596888664 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,519] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-01 13:08:53,550] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n" + "[I 2024-07-02 14:26:11,495] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,523] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,550] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,565] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:11,594] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,670] Trial 24 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,699] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-02 14:26:11,730] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n", + "[I 2024-07-02 14:26:11,761] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n" ] }, { @@ -5205,19 +5176,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:53,578] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,655] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,687] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,766] Trial 30 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,796] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,825] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,857] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-01 13:08:53,874] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:53,907] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:53,937] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:53,954] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:53,985] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,066] Trial 39 finished with value: -0.0015694919308122948 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" + "[I 2024-07-02 14:26:11,839] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:11,868] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:11,948] Trial 30 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:11,979] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:12,008] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:12,039] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-02 14:26:12,057] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:12,089] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,120] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,138] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:12,169] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,245] Trial 39 finished with value: -0.0015694919308122948 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" ] }, { @@ -5232,11 +5202,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:54,144] Trial 40 finished with value: -0.001975769419400139 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,163] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:08:54,233] Trial 42 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,267] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,345] Trial 44 finished with value: -0.0034128282598486753 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" + "[I 2024-07-02 14:26:12,326] Trial 40 finished with value: -0.0019757694194001384 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,343] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:26:12,421] Trial 42 finished with value: -0.002341918451736244 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,453] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,521] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" ] }, { @@ -5250,73 +5220,73 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:54,378] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,409] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,441] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,475] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,508] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,546] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,583] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,622] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,661] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,699] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,736] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-01 13:08:54,775] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-01 13:08:54,815] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-01 13:08:54,853] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-01 13:08:54,890] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-01 13:08:54,925] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:54,962] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:55,000] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:55,039] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:55,076] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:55,114] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" + "[I 2024-07-02 14:26:12,551] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,583] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,616] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,647] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,679] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,715] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,750] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,784] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,817] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,853] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,890] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-02 14:26:12,925] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-02 14:26:12,962] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-02 14:26:13,000] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-02 14:26:13,037] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-02 14:26:13,071] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,107] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,142] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,179] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,217] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,254] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:55,153] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-01 13:08:55,192] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-01 13:08:55,229] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-01 13:08:55,268] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-01 13:08:55,306] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-01 13:08:55,346] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,385] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,424] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,462] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,503] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,543] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,580] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,616] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,655] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,695] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,735] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,775] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,818] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,857] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,898] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:55,939] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" + "[I 2024-07-02 14:26:13,291] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-02 14:26:13,328] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-02 14:26:13,364] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-02 14:26:13,402] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-02 14:26:13,439] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-02 14:26:13,475] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,513] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,548] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,587] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,627] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,664] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,704] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,741] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,779] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,819] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,857] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,894] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,932] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:13,969] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,007] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,048] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:55,976] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,014] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,053] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,094] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,135] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,174] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,217] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,259] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,301] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-01 13:08:56,341] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-01 13:08:56,381] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-01 13:08:56,426] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-01 13:08:56,466] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" + "[I 2024-07-02 14:26:14,086] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,124] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,163] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,201] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,240] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,277] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,316] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,353] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,394] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-02 14:26:14,432] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-02 14:26:14,473] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-02 14:26:14,516] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-02 14:26:14,553] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" ] } ], @@ -5472,18 +5442,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:08:59,351] A new study created in memory with name: covariate_example\n", - "[I 2024-07-01 13:08:59,392] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:08:59,513] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", - "[I 2024-07-01 13:08:59,622] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-01 13:08:59,676] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-01 13:08:59,730] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", - "[I 2024-07-01 13:08:59,760] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", - "[I 2024-07-01 13:08:59,812] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-01 13:08:59,865] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-01 13:08:59,919] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-01 13:09:00,008] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-01 13:09:00,060] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" + "[I 2024-07-02 14:26:17,282] A new study created in memory with name: covariate_example\n", + "[I 2024-07-02 14:26:17,323] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:26:17,422] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", + "[I 2024-07-02 14:26:17,522] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-02 14:26:17,575] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-02 14:26:17,628] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", + "[I 2024-07-02 14:26:17,667] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", + "[I 2024-07-02 14:26:17,722] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-02 14:26:17,778] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-02 14:26:17,818] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-02 14:26:17,897] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-02 14:26:17,963] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" ] } ], @@ -5596,22 +5566,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:09:00,351] A new study created in memory with name: vector_aux_example\n", - "[I 2024-07-01 13:09:00,395] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:09:01,955] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-01 13:09:02,029] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-01 13:09:02,087] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-01 13:09:02,142] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", - "[I 2024-07-01 13:09:02,306] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-01 13:09:02,361] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-01 13:09:02,403] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.986e+01, tolerance: 1.914e+01\n", + "[I 2024-07-02 14:26:18,237] A new study created in memory with name: vector_aux_example\n", + "[I 2024-07-02 14:26:18,278] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:26:18,353] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 14:26:18,396] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 14:26:18,454] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 14:26:18,499] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", + "[I 2024-07-02 14:26:18,556] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 14:26:18,614] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 14:26:18,657] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.986e+01, tolerance: 1.914e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 13:09:02,505] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-01 13:09:02,591] Trial 8 finished with value: -2756.40177112848 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-01 13:09:02,648] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" + "[I 2024-07-02 14:26:18,752] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 14:26:18,836] Trial 8 finished with value: -2756.4017711284796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 14:26:18,893] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" ] } ], @@ -5742,18 +5712,14 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Peptide,Class,Smiles\r\n", - "MDLITITWASVMVAFTFSLSLVVWGRSGL,0,N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)O\r\n", - "ARRGGVLNFGQFGLQALECGFVTNR,0,N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O\r\n", - "GWCGDPGATCGKLRLYCCSGACDCYTKTCKDKSSA,1,NCC(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N1[C@@H](CCC1)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)O\r\n", - "NGNLLGGLLRPVLGVVKGLTGGLGKK,1,N[C@@H](CC(=O)N)C(=O)NCC(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)O\r\n" + "head: ../tests/data/peptide/toxinpred3/train.csv: No such file or directory\r\n" ] } ], @@ -5770,16 +5736,16 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:38:38,409] A new study created in memory with name: zscale_aux_example\n", - "[I 2024-07-01 13:38:38,418] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:39:03,804] Trial 0 finished with value: 0.8886986575836505 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8886986575836505.\n" + "[I 2024-07-02 14:31:29,029] A new study created in memory with name: zscale_aux_example\n", + "[I 2024-07-02 14:31:29,089] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:31:54,458] Trial 0 finished with value: 0.8886986575836505 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8886986575836505.\n" ] } ], @@ -5831,7 +5797,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -5847,7 +5813,7 @@ " (7062, 5))" ] }, - "execution_count": 93, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -5867,7 +5833,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -5876,7 +5842,7 @@ "array([0.2, 0. , 1. , ..., 0.2, 0.8, 0.2])" ] }, - "execution_count": 94, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -5894,7 +5860,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -5946,7 +5912,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 95, "metadata": { "scrolled": true }, @@ -5955,41 +5921,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:39:46,485] A new study created in memory with name: example_multi-parameter_analysis\n", - "[I 2024-07-01 13:39:46,524] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 13:39:46,808] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:47,083] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991905] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:47,232] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:47,333] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:39:47,362] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:47,449] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:47,601] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:39:47,643] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:47,793] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:47,834] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:47,862] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:47,891] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:47,932] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:48,018] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:48,102] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:48,191] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:39:48,230] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:48,271] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:48,419] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:39:48,459] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" + "[I 2024-07-02 14:32:36,740] A new study created in memory with name: example_multi-parameter_analysis\n", + "[I 2024-07-02 14:32:36,779] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 14:32:37,080] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:37,331] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991906] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:37,472] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:37,525] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 14:32:37,603] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:37,657] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:37,746] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 14:32:37,786] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:38,036] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,080] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:38,121] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,152] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:38,180] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:38,209] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,274] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:38,439] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 14:32:38,479] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,508] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,659] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 14:32:38,876] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:39:48,488] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:39:48,530] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:48,560] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-01 13:39:48,601] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-01 13:39:48,681] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-01 13:39:48,735] A new study created in memory with name: study_name_1\n", - "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n" + "[I 2024-07-02 14:32:38,918] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 14:32:38,949] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 14:32:38,977] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 14:32:39,009] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 14:32:39,151] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n" ] }, { @@ -6003,8 +5967,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 13:40:53,451] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n", - "[I 2024-07-01 13:41:58,723] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n" + "[I 2024-07-02 14:32:39,205] A new study created in memory with name: study_name_1\n", + "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-02 14:33:47,802] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 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zWP@d34k!dc0K7{T2hV8u86Kw6!Xqv%d^3iWUFTbkdBJ#KqtmpP)Z4 zxW@4`<{sW4#50vmHyr=T(K!GHu8vO49c}t{L73@_w82F~Vx=)KD&iLvDg<}m|3+z5|{lddk(fP!IrfPEq!sDp8>;cftU&HX-=`ARjk&Rc> ze$n)!;d`F35~e^#wjhXz&KWUGcRuLJ37+B%80Zk#aiOInS2&3{B561+u0kT`acnj3 ziG-h{d3bO#C}96Uug%}3Q1f;JQE+ozqW=8)|pKEN=(`$#3A#m45lVg%?@j*ATXWgisPbPM?7Cm_69}$Ko|CJp#bV z$7>`MPA90ji)N%J>bLL?q6ru;N!nEW<$hZ})45kmpM0#JuS->Cm8V7?N;6ci4F$=me-U_TwgvYS`s z^C!$%m0yP9r}`wm*fS=-zFYq_RR41Eu$@EuAFKb1fct6!&LIyy%HZ@n^?wJz32SA| z14CUh(0{4^mjV4!qUpkzeHVYF{_mkub*f6ftG`l zsNC2ZA3M|dIr>U|HV?_J{!q)FHM|pd(Rlhy<5^aH#un$>e&I~xMFM=n0HDdnr}Ue6 zVCGoHxVlAr6i$nHg2lxB&VNkD$Wj*5;!^&S22bQHfpW}aTo6Of(NC$x*8WdmCAbOMV=ts>3pTu$T-1)0bg(zqj9t&rjY VElH6-TR585sc`(k(b4VN{|8P!BA5UG diff --git a/docs/sphinx-builddir/doctrees/index.doctree b/docs/sphinx-builddir/doctrees/index.doctree index 2e10be919ca667b32984580f04d649f2ce4b58d1..6bf0486b81e0a790fe7f0c4f34e5ae8d9b943505 100644 GIT binary patch delta 194 zcmX?X^T3$3fpzLt$&IWR_$IRmhfP-Fm!DiItTZ{5-)Qn#VP8gv$5R*z&bigGbBN3z{cxj6ldf@%s!IwGvhPF;K}D? L_%?r#31$QUjBPun delta 952 zcmaE0eAtGyfpw~d+(y<5e6|h=!TF^{$*Bs-`6;Oi8t$1T9;Hc|3N{%fB?ZM+`ugda zB^jkjddc~@`i@bdx~^RA`6rifyhEV4UyKI z?7$~7xmloW@+}_jddCFM;^NZOVg(Ja%%VgjXJi4ndPOOEiNz&FiB+k2smY0Yd8sA( zNk#eP#i{!K0U@D&j{0^9@el_&Xf{p(xrsF^kCA}^FcJs_eJMC<_(JAp)L4`*It zZfZ$oL25B5qO(9|c4m}yfQ{?S_|pM4sRL|E2iSxTu, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-01 11:58:08,136] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-01 11:58:08,392] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-01 11:58:08,541] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", - "[I 2024-07-01 11:58:08,591] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:08,653] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:08,801] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:08,986] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,028] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,208] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,248] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,337] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,478] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,531] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,573] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,603] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,656] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:09,795] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" + "[I 2024-07-02 13:17:26,561] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:17:26,714] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:17:27,022] Trial 0 finished with value: -3594.2228073972638 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-02 13:17:27,171] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-02 13:17:27,429] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-02 13:17:27,579] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", + "[I 2024-07-02 13:17:27,644] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:27,698] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:27,853] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,029] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,070] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,336] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,373] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,532] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,680] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,722] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,765] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,793] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,831] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,870] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:09,982] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,013] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,057] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,195] Trial 21 finished with value: -4783.047015479679 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,234] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,380] Trial 23 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,447] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,499] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,630] Trial 26 finished with value: -4454.143979828407 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,658] Trial 27 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:10,688] Trial 28 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:10,757] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,788] Trial 30 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:17:28,932] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:28,974] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,016] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,096] Trial 21 finished with value: -4783.0470154796785 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,135] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,259] Trial 23 finished with value: -4030.45773791647 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,340] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,381] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,474] Trial 26 finished with value: -4454.143979828408 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,503] Trial 27 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:29,533] Trial 28 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:29,600] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,617] Trial 30 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:29,682] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" ] }, { @@ -324,16 +334,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:10,866] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:10,907] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:11,009] Trial 33 finished with value: -4863.581760751052 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-01 11:58:11,041] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,108] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,162] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,205] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,234] Trial 38 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:11,300] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,381] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-02 13:17:29,715] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,794] Trial 33 finished with value: -4863.581760751052 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-02 13:17:29,836] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:29,906] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:29,962] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,005] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,034] Trial 38 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:30,103] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -347,16 +355,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:11,460] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,547] Trial 42 finished with value: -3963.9069546583414 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,593] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,662] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,707] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,738] Trial 46 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:11,770] Trial 47 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:11,799] Trial 48 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:11,878] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:11,928] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-02 13:17:30,240] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,311] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,401] Trial 42 finished with value: -3963.906954658343 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,435] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,501] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,547] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,565] Trial 46 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:30,594] Trial 47 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:30,626] Trial 48 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:30,717] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -372,67 +380,68 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:11,978] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,027] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,078] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,124] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,175] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,224] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,264] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,320] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-01 11:58:12,393] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,446] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,509] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,563] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,622] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,673] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,724] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-01 11:58:12,776] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", - "[I 2024-07-01 11:58:12,828] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", - "[I 2024-07-01 11:58:12,869] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", - "[I 2024-07-01 11:58:12,935] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", - "[I 2024-07-01 11:58:13,008] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-01 11:58:13,073] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" + "[I 2024-07-02 13:17:30,767] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,813] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,848] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,898] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,934] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:30,970] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:31,030] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:31,078] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:31,127] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-02 13:17:31,174] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,222] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,270] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,319] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,356] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,416] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,454] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-02 13:17:31,504] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", + "[I 2024-07-02 13:17:31,542] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", + "[I 2024-07-02 13:17:31,591] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", + "[I 2024-07-02 13:17:31,642] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", + "[I 2024-07-02 13:17:31,706] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:13,136] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-01 11:58:13,199] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-01 11:58:13,263] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-01 11:58:13,327] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-01 11:58:13,379] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-01 11:58:13,419] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-01 11:58:13,469] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", - "[I 2024-07-01 11:58:13,508] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", - "[I 2024-07-01 11:58:13,549] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", - "[I 2024-07-01 11:58:13,600] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,642] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,682] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,734] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,794] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,858] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,910] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:13,965] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:14,026] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:14,092] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:14,144] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-01 11:58:14,197] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n" + "[I 2024-07-02 13:17:31,757] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-02 13:17:31,807] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-02 13:17:31,856] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-02 13:17:31,902] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-02 13:17:31,966] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-02 13:17:32,026] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-02 13:17:32,089] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-02 13:17:32,141] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", + "[I 2024-07-02 13:17:32,193] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", + "[I 2024-07-02 13:17:32,254] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", + "[I 2024-07-02 13:17:32,317] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,367] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,416] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,457] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,497] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,547] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,587] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,639] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,677] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,717] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-02 13:17:32,757] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:14,240] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-01 11:58:14,280] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-01 11:58:14,335] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-01 11:58:14,389] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-01 11:58:14,430] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-01 11:58:14,472] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-01 11:58:14,515] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" + "[I 2024-07-02 13:17:32,797] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-02 13:17:32,848] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-02 13:17:32,898] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-02 13:17:32,935] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-02 13:17:32,986] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-02 13:17:33,026] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-02 13:17:33,066] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-02 13:17:33,117] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" ] } ], @@ -882,151 +891,162 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:17,434] A new study created in memory with name: my_study_stratified_split\n", - "[I 2024-07-01 11:58:17,476] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 11:58:17,601] Trial 0 finished with value: -3057.0737441471406 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 18, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3057.0737441471406.\n", - "[I 2024-07-01 11:58:17,684] Trial 1 finished with value: -254.54445513927885 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.198947293429379, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:17,760] Trial 2 finished with value: -3949.499764716498 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011588664390198248, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.938724970154981e-07, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:17,878] Trial 3 finished with value: -2129.55317061882 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 16, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:17,950] Trial 4 finished with value: -3949.4997740830913 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5362317781163308, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.650598805767507e-05, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,017] Trial 5 finished with value: -1671.9715157777862 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16133633302583372, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,056] Trial 6 finished with value: -269.7614676166147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.8294936868857457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,097] Trial 7 finished with value: -1665.903178404654 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.07664719361338101, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,139] Trial 8 finished with value: -1238.1313914010004 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4856611238698816, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,180] Trial 9 finished with value: -2661.2145086075775 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,281] Trial 10 finished with value: -2983.9453142752113 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,338] Trial 11 finished with value: -2393.982255942199 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.015252051830293623, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 54.06815038159445, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,378] Trial 12 finished with value: -3949.4997740833096 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.292485951890079, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.007632887698169173, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,418] Trial 13 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,457] Trial 14 finished with value: -280.05600853197063 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7820696160095664, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,546] Trial 15 finished with value: -3057.073744147141 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 19, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,589] Trial 16 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,632] Trial 17 finished with value: -271.28451052762006 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.8546679454186392, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,718] Trial 18 finished with value: -3419.9290347326446 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n" + "[I 2024-07-02 13:17:36,922] A new study created in memory with name: my_study_stratified_split\n", + "[I 2024-07-02 13:17:36,963] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:17:37,046] Trial 0 finished with value: -1856.4459752935309 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1856.4459752935309.\n", + "[I 2024-07-02 13:17:37,123] Trial 1 finished with value: -1692.0451328577294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2918844591266672, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -1692.0451328577294.\n", + "[I 2024-07-02 13:17:37,592] Trial 2 finished with value: -1378.9731014410709 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.471164936778079, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -1378.9731014410709.\n", + "[I 2024-07-02 13:17:37,688] Trial 3 finished with value: -2658.13214897931 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -1378.9731014410709.\n", + "[I 2024-07-02 13:17:37,804] Trial 4 finished with value: -2059.3079659969176 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 27, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -1378.9731014410709.\n", + "[I 2024-07-02 13:17:38,330] Trial 5 finished with value: -280.17777558722315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7001901522391756, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,422] Trial 6 finished with value: -3551.475476217507 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 31, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,466] Trial 7 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,509] Trial 8 finished with value: -1686.5737716985532 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.9841058851292832, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,552] Trial 9 finished with value: -1702.174704715547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.861494545249233, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,578] Trial 10 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:38,621] Trial 11 finished with value: -1204.636967895143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5238298142840006, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -280.17777558722315.\n", + "[I 2024-07-02 13:17:38,676] Trial 12 finished with value: -228.44505332657158 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9836853549192415, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:38,729] Trial 13 finished with value: -3949.499774068696 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04535826280986047, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.012999584021838e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-2124.9660426577593]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:18,819] Trial 19 finished with value: -3460.5903729391343 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,908] Trial 20 finished with value: -2067.6829173690335 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,951] Trial 21 finished with value: -1170.5745258675272 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6871013334576017, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:18,997] Trial 22 finished with value: -2641.7637473751115 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,037] Trial 23 finished with value: -280.1817310835568 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.6975554381481632, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,081] Trial 24 finished with value: -1232.7082076294153 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.6441978646496442, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,123] Trial 25 finished with value: -1234.7440636415267 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5782059857124566, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,214] Trial 26 finished with value: -3551.4754762175066 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 29, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,258] Trial 27 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,298] Trial 28 finished with value: -1302.9185102508404 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2656074800868937, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,326] Trial 29 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:19,380] Trial 30 finished with value: -3949.4995316222353 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.000513358273488627, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.404787492717244e-06, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,470] Trial 31 finished with value: -3286.345885718371 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -254.54445513927885.\n", - "[I 2024-07-01 11:58:19,525] Trial 32 finished with value: -221.50343080442565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8463399326459089, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n" + "[I 2024-07-02 13:17:38,829] Trial 14 finished with value: -2856.917927507731 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.306e+01, tolerance: 3.824e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-02 13:17:38,882] Trial 15 finished with value: -2554.2079198900733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10588223712643852, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:38,922] Trial 16 finished with value: -1261.484274761188 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0950442632698256, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:38,965] Trial 17 finished with value: -282.6478019258886 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2920636100136971, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,004] Trial 18 finished with value: -1814.6019641143478 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,048] Trial 19 finished with value: -1284.7430070920798 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1729012287538991, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,103] Trial 20 finished with value: -237.98783693000647 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1721667984096773, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,192] Trial 21 finished with value: -2129.55317061882 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,235] Trial 22 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 3.779895470793612, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.260941957410989e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,279] Trial 23 finished with value: -1740.8894369939983 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.02841448247455669, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+02, tolerance: 3.824e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.280e+02, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+02, tolerance: 3.770e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-02 13:17:39,373] Trial 24 finished with value: -3317.417858905051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.003050380617617421, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,404] Trial 25 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:39,448] Trial 26 finished with value: -1256.7270466276807 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1594144041655936, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,491] Trial 27 finished with value: -1245.1399766270456 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.336730512398918, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,583] Trial 28 finished with value: -2908.3563960057677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 14, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2756.046839500092]\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-2658.13214897931]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:19,567] Trial 33 finished with value: -1814.6019641143478 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:19,655] Trial 34 finished with value: -3537.400387673868 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:19,743] Trial 35 finished with value: -3091.756160298714 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:19,832] Trial 36 finished with value: -3460.5903729391343 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 14, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:19,921] Trial 37 finished with value: -2164.5568965259404 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,037] Trial 38 finished with value: -3473.931670829174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 27, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,139] Trial 39 finished with value: -3551.4754762175057 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 32, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,232] Trial 40 finished with value: -3057.0737441471415 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 27, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,325] Trial 41 finished with value: -2185.7101452604556 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2137259342534783, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,370] Trial 42 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.53941365726222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0013521110946801886, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.073e+01, tolerance: 3.820e+01\n", + "[I 2024-07-02 13:17:39,628] Trial 29 finished with value: -1775.55204856041 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,721] Trial 30 finished with value: -2059.3079659969176 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 19, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,765] Trial 31 finished with value: -1257.9288888831513 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1441514794000534, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,808] Trial 32 finished with value: -280.98174313112844 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1939105579414777, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,900] Trial 33 finished with value: -3054.7066202193805 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,944] Trial 34 finished with value: -1227.082986184029 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.909508127148669, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:39,988] Trial 35 finished with value: -1676.7481962719485 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4307837873914335, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,079] Trial 36 finished with value: -2059.307965996918 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,168] Trial 37 finished with value: -3441.9109103644514 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,211] Trial 38 finished with value: -1670.5213862925175 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.07945856808433427, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,264] Trial 39 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,320] Trial 40 finished with value: -3949.4997735530674 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022099719935614482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.4657380646234507e-08, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,376] Trial 41 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.0862402902634642, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.12519632281925502, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,467] Trial 42 finished with value: -3438.566583971217 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,524] Trial 43 finished with value: -254.4422556954731 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19967589906728334, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.016e+01, tolerance: 3.820e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e+02, tolerance: 3.824e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.179e+01, tolerance: 3.770e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:20,428] Trial 43 finished with value: -4177.323097172766 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0008561544073084626, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,485] Trial 44 finished with value: -3949.49977405752 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.000640296612938773, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.195832309005885e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,572] Trial 45 finished with value: -3460.5903729391357 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,617] Trial 46 finished with value: -265.2551346980534 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.723348820082273, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,663] Trial 47 finished with value: -3949.499774083342 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.1157167467830016, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.1765702159132e-10, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 32 with value: -221.50343080442565.\n" + "[I 2024-07-02 13:17:40,618] Trial 44 finished with value: -359.7639743940817 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.059252880514551576, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,662] Trial 45 finished with value: -1246.7813032646238 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3074782262329858, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,755] Trial 46 finished with value: -2224.3845873049813 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:20,706] Trial 48 finished with value: -2720.793752592223 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,750] Trial 49 finished with value: -1674.9295258477512 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5380013650728686, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,801] Trial 50 finished with value: -279.15926481381143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.980859803766101, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,850] Trial 51 finished with value: -279.61074930369296 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9932257281091896, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,900] Trial 52 finished with value: -278.7093831185512 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.973859558620468, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:20,960] Trial 53 finished with value: -266.5487862405967 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7604405293871064, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,009] Trial 54 finished with value: -263.94628916496316 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.668208882429842, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,057] Trial 55 finished with value: -260.873525093584 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5671663763078958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,118] Trial 56 finished with value: -256.8909655206189 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.483400335050029, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,179] Trial 57 finished with value: -252.8755775763609 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4188303686435595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,242] Trial 58 finished with value: -251.04279976929283 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.387569193115904, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,305] Trial 59 finished with value: -248.92877121211654 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3462231418773571, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,370] Trial 60 finished with value: -242.4510780888153 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.234183343146477, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,431] Trial 61 finished with value: -245.6996084030637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2911941178677253, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,490] Trial 62 finished with value: -242.17350152763916 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2309289484008652, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,551] Trial 63 finished with value: -234.24140824233874 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1055793055454333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,616] Trial 64 finished with value: -234.27793702828032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.106351038051582, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,676] Trial 65 finished with value: -233.27716021851316 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0863259874021596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,737] Trial 66 finished with value: -232.17752595671723 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0594557232454542, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,801] Trial 67 finished with value: -230.10311600188143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0149151156252714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,867] Trial 68 finished with value: -226.1762006075315 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9358117155030716, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n" + "[I 2024-07-02 13:17:40,810] Trial 47 finished with value: -1673.9639799911165 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2737740844660712, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,896] Trial 48 finished with value: -3163.129883232068 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 32, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:40,987] Trial 49 finished with value: -2753.414173913392 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,057] Trial 50 finished with value: -263.1352845182604 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.627030918721665, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,105] Trial 51 finished with value: -271.2979718788249 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.8548903728617034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,165] Trial 52 finished with value: -277.86441431259567 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9605867591283856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,227] Trial 53 finished with value: -277.4329099850367 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9537398361705693, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,274] Trial 54 finished with value: -274.3838070241422 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9045589309769144, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,334] Trial 55 finished with value: -260.4460398258507 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5589021326002044, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,383] Trial 56 finished with value: -257.95032410206767 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5053759377103249, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,444] Trial 57 finished with value: -256.5958038666581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4789082433356577, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,495] Trial 58 finished with value: -253.4269973575198 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4281024602273042, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,560] Trial 59 finished with value: -249.40822811603962 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3546313579812586, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,620] Trial 60 finished with value: -245.71101688809983 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2913960369109012, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,675] Trial 61 finished with value: -247.88538215472033 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3274897484709072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,737] Trial 62 finished with value: -244.23847775159297 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2647865635312279, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,803] Trial 63 finished with value: -247.59033004585282 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3228443521984092, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,863] Trial 64 finished with value: -243.40694430653753 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2489205103047292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", + "[I 2024-07-02 13:17:41,928] Trial 65 finished with value: -223.85145692792733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8934822741396387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -223.85145692792733.\n", + "[I 2024-07-02 13:17:41,990] Trial 66 finished with value: -221.94026043724057 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8552798675517863, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -221.94026043724057.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:21,929] Trial 69 finished with value: -227.10131885883573 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9567040492417759, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 32 with value: -221.50343080442565.\n", - "[I 2024-07-01 11:58:21,992] Trial 70 finished with value: -221.49789694340745 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.846238635688217, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -221.49789694340745.\n", - "[I 2024-07-01 11:58:22,055] Trial 71 finished with value: -222.31601002689078 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8632081482660824, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -221.49789694340745.\n", - "[I 2024-07-01 11:58:22,120] Trial 72 finished with value: -220.52877513881472 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8293301408971474, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: -220.52877513881472.\n", - "[I 2024-07-01 11:58:22,185] Trial 73 finished with value: -220.91270788390565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8360980531311909, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: -220.52877513881472.\n", - "[I 2024-07-01 11:58:22,248] Trial 74 finished with value: -220.5850243118385 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.830333649861826, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: -220.52877513881472.\n", - "[I 2024-07-01 11:58:22,308] Trial 75 finished with value: -219.61564686915472 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8150816129600795, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -219.61564686915472.\n", - "[I 2024-07-01 11:58:22,371] Trial 76 finished with value: -218.4964533127505 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7974981139276134, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -218.4964533127505.\n", - "[I 2024-07-01 11:58:22,425] Trial 77 finished with value: -281.69418903642105 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7872857583898819, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -218.4964533127505.\n", - "[I 2024-07-01 11:58:22,491] Trial 78 finished with value: -216.89257481227185 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7597572292456825, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -216.89257481227185.\n", - "[I 2024-07-01 11:58:22,555] Trial 79 finished with value: -216.387040268309 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7328874336673028, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -216.387040268309.\n", - "[I 2024-07-01 11:58:22,618] Trial 80 finished with value: -215.6659622267538 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6806396875335972, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,680] Trial 81 finished with value: -215.8070616405148 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6530627434318788, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,743] Trial 82 finished with value: -216.17287441162375 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6412003008944885, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,809] Trial 83 finished with value: -217.12449947211158 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6163301674440624, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,872] Trial 84 finished with value: -217.30023741842555 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5839997153821561, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,938] Trial 85 finished with value: -217.298349876422 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.583120843943027, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:22,999] Trial 86 finished with value: -217.50686541362245 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5397495314175259, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,063] Trial 87 finished with value: -217.31869324613135 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5705105918346117, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,116] Trial 88 finished with value: -281.7963118820023 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7317572910979507, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,184] Trial 89 finished with value: -217.44713141162143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5546568764092636, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n" + "[I 2024-07-02 13:17:42,048] Trial 67 finished with value: -219.60947928367543 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8149866573467666, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,108] Trial 68 finished with value: -221.84441955310717 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8531301788095305, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,170] Trial 69 finished with value: -221.24134912135943 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8418420411160932, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,232] Trial 70 finished with value: -223.34805357903284 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.883998932301903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,293] Trial 71 finished with value: -221.99342925522842 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8564564664338091, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,353] Trial 72 finished with value: -222.50886633416462 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8672069097403997, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,415] Trial 73 finished with value: -221.61235541906441 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8482856353268698, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", + "[I 2024-07-02 13:17:42,479] Trial 74 finished with value: -217.7749814513912 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7823980442129331, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 74 with value: -217.7749814513912.\n", + "[I 2024-07-02 13:17:42,538] Trial 75 finished with value: -216.00225784039503 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7113129125761161, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,601] Trial 76 finished with value: -216.8736767409489 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6250904023479531, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,666] Trial 77 finished with value: -216.94414119442342 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6227757503715069, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,731] Trial 78 finished with value: -216.45936690929625 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6343056785694773, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,797] Trial 79 finished with value: -216.63861804615567 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6302707941523814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,860] Trial 80 finished with value: -1969.3749442111905 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00019861806798724335, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.586529041453, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", + "[I 2024-07-02 13:17:42,923] Trial 81 finished with value: -215.82051598778696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6518244359516081, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:42,987] Trial 82 finished with value: -216.06387687700067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6440087841656821, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,041] Trial 83 finished with value: -216.24994687849525 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6393212787552464, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,106] Trial 84 finished with value: -216.92984604804667 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6232144947646524, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,170] Trial 85 finished with value: -217.25506613319246 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.603388647930941, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,223] Trial 86 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,287] Trial 87 finished with value: -217.29854648789728 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5873312673596333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:23,245] Trial 90 finished with value: -216.84517869404704 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6258244370447749, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,311] Trial 91 finished with value: -217.29808737667486 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5868655686599151, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,375] Trial 92 finished with value: -215.71352982450296 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6639283058471219, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,441] Trial 93 finished with value: -215.69366137568082 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6909415122114899, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -215.6659622267538.\n", - "[I 2024-07-01 11:58:23,509] Trial 94 finished with value: -215.66589724827 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6783415593515848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n", - "[I 2024-07-01 11:58:23,571] Trial 95 finished with value: -215.70707085094196 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6934251726272147, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n", - "[I 2024-07-01 11:58:23,626] Trial 96 finished with value: -3955.337261716553 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06215154336061326, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.039444339722642, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n", - "[I 2024-07-01 11:58:23,691] Trial 97 finished with value: -221.49591158739668 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.41499841440951935, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n", - "[I 2024-07-01 11:58:23,744] Trial 98 finished with value: -2726.0476769808097 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n", - "[I 2024-07-01 11:58:23,809] Trial 99 finished with value: -215.91983901269398 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.707169104075229, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 94 with value: -215.66589724827.\n" + "[I 2024-07-02 13:17:43,347] Trial 88 finished with value: -221.16592450348784 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4337907998582289, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", + "[I 2024-07-02 13:17:43,410] Trial 89 finished with value: -215.68514116107337 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6695836226711808, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,475] Trial 90 finished with value: -220.8939514172608 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4420925048614356, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,535] Trial 91 finished with value: -215.72299797702155 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6960582933068138, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,601] Trial 92 finished with value: -215.69285146262294 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.69078828949453, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,665] Trial 93 finished with value: -216.0538787714827 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7144357045239296, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,728] Trial 94 finished with value: -216.4213281391621 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7353090312302926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,794] Trial 95 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 50.74724725664498, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.92653950485437e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,858] Trial 96 finished with value: -216.12287184152592 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7183304951103088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,922] Trial 97 finished with value: -216.22186485689846 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7234233661662641, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:43,977] Trial 98 finished with value: -2720.793752592223 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", + "[I 2024-07-02 13:17:44,042] Trial 99 finished with value: -219.3855763846717 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4726201914486088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n" ] } ], @@ -1153,39 +1173,40 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:24,875] A new study created in memory with name: my_study_r2\n", - "[I 2024-07-01 11:58:24,876] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 11:58:25,001] Trial 0 finished with value: -0.01117186866515977 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-01 11:58:25,127] Trial 1 finished with value: -0.08689402230378147 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-01 11:58:25,216] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-01 11:58:25,302] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-01 11:58:25,354] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-01 11:58:25,445] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-01 11:58:25,523] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-01 11:58:25,588] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-01 11:58:25,702] Trial 8 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-01 11:58:25,782] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:25,858] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:25,926] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:25,968] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,023] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,075] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,176] Trial 15 finished with value: 0.12206148974315852 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,242] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,284] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,366] Trial 18 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-02 13:17:44,945] A new study created in memory with name: my_study_r2\n", + "[I 2024-07-02 13:17:44,947] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 13:17:45,072] Trial 0 finished with value: -0.011171868665159623 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.011171868665159623.\n", + "[I 2024-07-02 13:17:45,197] Trial 1 finished with value: -0.08689402230378174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.011171868665159623.\n", + "[I 2024-07-02 13:17:45,283] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.011171868665159623.\n", + "[I 2024-07-02 13:17:45,358] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-02 13:17:45,410] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-02 13:17:45,485] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-02 13:17:45,558] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-02 13:17:45,611] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-02 13:17:45,705] Trial 8 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-02 13:17:45,773] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:45,838] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:45,892] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:45,934] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:45,976] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,029] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,118] Trial 15 finished with value: 0.12206148974315871 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,174] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,215] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,316] Trial 18 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:26,421] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,476] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,534] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,573] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:26,618] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,730] Trial 24 finished with value: -0.1276979508290983 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-02 13:17:46,381] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,433] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,487] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,586] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:46,629] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,733] Trial 24 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,786] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1199,20 +1220,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:26,783] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,825] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,882] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:26,973] Trial 28 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,041] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,142] Trial 30 finished with value: 0.1193407034334832 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,184] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,254] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,311] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,354] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:27,409] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,454] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,484] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:27,530] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-02 13:17:46,839] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,878] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:46,958] Trial 28 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,013] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,103] Trial 30 finished with value: 0.11934070343348298 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,145] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,213] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,254] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,285] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:47,330] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,372] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,402] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:47,445] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1227,11 +1247,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:27,630] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,721] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,763] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-01 11:58:27,869] Trial 42 finished with value: -0.004614143721600739 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:27,921] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-02 13:17:47,535] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,632] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,671] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-02 13:17:47,763] Trial 42 finished with value: -0.00461414372160085 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,808] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1245,152 +1265,152 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:28,032] Trial 44 finished with value: -0.10220127407364976 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:28,077] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:28,134] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:28,189] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:28,244] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:28,315] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n", + "[I 2024-07-02 13:17:47,919] Trial 44 finished with value: -0.10220127407364991 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:47,975] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:48,030] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:48,076] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:48,120] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:48,175] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:48,276] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:28,416] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", + "[I 2024-07-02 13:17:48,387] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:28,519] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:48,493] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:28,617] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n", - " model = cd_fast.enet_coordinate_descent(\n" + "[I 2024-07-02 13:17:48,600] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:28,723] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.243e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.243e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:28,816] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n", + "[I 2024-07-02 13:17:48,700] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:28,918] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-01 11:58:29,004] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,068] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,130] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,191] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,263] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,334] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,393] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,468] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,538] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,601] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,675] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,739] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-01 11:58:29,836] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n" + "[I 2024-07-02 13:17:48,798] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-02 13:17:48,886] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:48,946] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,009] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,068] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,142] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,217] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,276] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,351] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,424] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,500] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,583] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,644] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-02 13:17:49,742] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n", + "[I 2024-07-02 13:17:49,830] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:29,933] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n", - "[I 2024-07-01 11:58:30,017] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", - "[I 2024-07-01 11:58:30,104] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", - "[I 2024-07-01 11:58:30,201] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:30,301] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:30,401] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:49,926] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", + "[I 2024-07-02 13:17:50,010] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", + "[I 2024-07-02 13:17:50,106] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:50,204] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:50,300] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:30,512] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:30,631] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:50,396] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:50,506] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:30,732] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:50,620] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:30,840] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:50,775] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:30,943] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,005] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,100] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:50,875] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:50,938] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,042] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:31,210] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,271] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,337] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" + "[I 2024-07-02 13:17:51,142] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,208] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,259] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-02 13:17:51,388] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:31,448] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:31,559] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:51,524] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:31,656] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,734] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,795] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:51,625] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,693] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,758] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:31,905] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:31,998] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:32,098] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:32,149] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:32,200] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n", + "[I 2024-07-02 13:17:51,861] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:51,951] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:52,042] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:52,096] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:52,149] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:32,300] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-01 11:58:32,419] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", + "[I 2024-07-02 13:17:52,249] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-02 13:17:52,374] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:32,532] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "[I 2024-07-02 13:17:52,484] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", + "[I 2024-07-02 13:17:52,552] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:32,597] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-_QsKTRFT-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-01 11:58:32,717] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "[I 2024-07-02 13:17:52,664] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] } ], @@ -1560,36 +1580,46 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:58:33,829] A new study created in memory with name: my_study\n", - "[I 2024-07-01 11:58:33,831] A new study created in memory with name: study_name_0\n", - "[I 2024-07-01 11:58:36,572] Trial 0 finished with value: -0.08010977203954363 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08010977203954363.\n", - "[I 2024-07-01 11:58:41,615] Trial 1 finished with value: -0.07268203676328724 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07268203676328724.\n", - "[I 2024-07-01 11:58:45,589] Trial 2 finished with value: -0.08474678334111184 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07268203676328724.\n", - "[I 2024-07-01 11:58:49,734] Trial 3 finished with value: -0.07108254887990631 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.07108254887990631.\n", - "[I 2024-07-01 11:58:54,659] Trial 4 finished with value: -0.07159995006170931 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -0.07108254887990631.\n", - "[I 2024-07-01 11:59:07,383] Trial 5 finished with value: -0.05353323488066142 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:08,819] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-02 13:17:53,733] A new study created in memory with name: my_study\n", + "[I 2024-07-02 13:17:53,734] A new study created in memory with name: study_name_0\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-02 13:18:00,764] Trial 0 finished with value: -0.08099580623289632 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08099580623289632.\n", + "[I 2024-07-02 13:18:05,408] Trial 1 finished with value: -0.07261454017489567 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07261454017489567.\n", + "[I 2024-07-02 13:18:07,780] Trial 2 finished with value: -0.08791063872794351 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07261454017489567.\n", + "[I 2024-07-02 13:18:11,911] Trial 3 finished with value: -0.07114663955819509 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.07114663955819509.\n", + "[I 2024-07-02 13:18:15,879] Trial 4 finished with value: -0.06537440628140882 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.06537440628140882.\n", + "[I 2024-07-02 13:18:28,446] Trial 5 finished with value: -0.05680450487193368 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:29,968] Trial 6 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.07159995006170931]\n" + "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.06537440628140882]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-01 11:59:13,827] Trial 7 finished with value: -0.06438968184448565 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:16,388] Trial 8 finished with value: -0.07840108527918324 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:21,689] Trial 9 finished with value: -0.06353415374693028 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:25,498] Trial 10 finished with value: -0.07689005116035819 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:30,163] Trial 11 finished with value: -0.07248441636315489 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:34,960] Trial 12 finished with value: -0.06358354771558156 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:38,459] Trial 13 finished with value: -0.06919051273910246 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05353323488066142.\n", - "[I 2024-07-01 11:59:50,122] Trial 14 finished with value: -0.05588498230937082 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05353323488066142.\n" + "[I 2024-07-02 13:18:33,543] Trial 7 finished with value: -0.0656836821774901 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:37,333] Trial 8 finished with value: -0.07863564862376404 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:42,329] Trial 9 finished with value: -0.0648840199215795 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:46,014] Trial 10 finished with value: -0.07861037073288182 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:50,608] Trial 11 finished with value: -0.06669924317660021 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:54,997] Trial 12 finished with value: -0.06734611679947522 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:18:59,526] Trial 13 finished with value: -0.06810559387741143 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05680450487193368.\n", + "[I 2024-07-02 13:19:11,856] Trial 14 finished with value: -0.0528189695245453 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0528189695245453.\n" ] } ], @@ -1612,7 +1642,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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