From 66d6832eaa9165a7fd21380239ea79942dbc2291 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:19:10 +0200 Subject: [PATCH 01/16] remove pypi-autobuild --- .github/workflows/continuous_integration.yml | 2 +- .github/workflows/{pypi_release.yml => pypi_release.old} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename .github/workflows/{pypi_release.yml => pypi_release.old} (100%) diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 78aa8a4..56de255 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -13,7 +13,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.8", "3.9", "3.10", '3.11'] steps: - name: Checkout repository uses: actions/checkout@v3 diff --git a/.github/workflows/pypi_release.yml b/.github/workflows/pypi_release.old similarity index 100% rename from .github/workflows/pypi_release.yml rename to .github/workflows/pypi_release.old From ac9265d6d040007fad6a162d079579b763ff3b72 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:22:07 +0200 Subject: [PATCH 02/16] correct dependencies --- setup.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 91a2fc1..76c6484 100644 --- a/setup.py +++ b/setup.py @@ -51,7 +51,7 @@ def default_setup_args(*, version): version=version, author="Governance of Emerging Technologies Programme (Oxford Internet Insitute)", url="https://github.com/ChrisMRuss/oxon-fair/", - description="AutoML Framework for evaluating and enforcing ML model fairness", + description="Toolkit for evaluating and enforcing ML model fairness", long_description=long_description, long_description_content_type="text/markdown", license="Apache-2.0", @@ -83,6 +83,7 @@ def default_setup_args(*, version): "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Topic :: Software Development", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", @@ -98,7 +99,7 @@ def default_setup_args(*, version): return setup_args -version = "0.1.5" +version = "0.2" install_requires = [ "numpy>=1.21.4", @@ -107,8 +108,9 @@ def default_setup_args(*, version): extras_require = dict() -test_requirements = ["tox", "pytest", "pytest-cov", 'autogluon', 'sklearn', - 'matplotlib', 'flake8', 'linkcheckmd', 'ucimlrepo', 'fairlearn'] +test_requirements = ["tox", "pytest", "pytest-cov", 'autogluon', 'scikit-learn', + 'matplotlib', 'flake8', 'linkcheckmd', 'ucimlrepo', 'fairlearn', + 'linkcheckmd', 'ipynbcompress', 'pytorch'] test_requirements = list(set(test_requirements)) extras_require["tests"] = test_requirements From 0f4c1006fc0766e594cb3a013fdfdbec186e4163 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:36:39 +0200 Subject: [PATCH 03/16] fix workflow --- setup.py | 6 +++--- src/oxonfair/version.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 76c6484..c0f4c63 100644 --- a/setup.py +++ b/setup.py @@ -108,9 +108,9 @@ def default_setup_args(*, version): extras_require = dict() -test_requirements = ["tox", "pytest", "pytest-cov", 'autogluon', 'scikit-learn', - 'matplotlib', 'flake8', 'linkcheckmd', 'ucimlrepo', 'fairlearn', - 'linkcheckmd', 'ipynbcompress', 'pytorch'] +test_requirements = ["tox", "pytest", "pytest-cov", 'autogluon.tabular', 'scikit-learn', + 'matplotlib', 'flake8', 'ucimlrepo', 'fairlearn', + 'linkcheckmd', 'ipynbcompress', 'torch'] test_requirements = list(set(test_requirements)) extras_require["tests"] = test_requirements diff --git a/src/oxonfair/version.py b/src/oxonfair/version.py index 134337a..0cfbb69 100644 --- a/src/oxonfair/version.py +++ b/src/oxonfair/version.py @@ -1,2 +1,2 @@ """This is the oxonfair version file.""" -__version__ = '0.1.5' +__version__ = '0.2' From a865cb2369fad6bd2c9f78eb53d5121afe406006 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:39:07 +0200 Subject: [PATCH 04/16] fix workflow --- .github/workflows/continuous_integration.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 56de255..c63551c 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -23,6 +23,5 @@ jobs: python-version: ${{ matrix.python-version }} - name: Unit Tests run: | - python3 -m pip install -e . - python3 -m pip install pytest + python3 -m pip install -e .\[tests\] python3 -m pytest tests/unittests From 753ae7bd8736588f76f0875bebeedd84224de372 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:55:27 +0200 Subject: [PATCH 05/16] workflow patch --- README.md | 3 +++ tests/unittests/test_ag.py | 6 +++--- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e3eebbc..3240f15 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,9 @@ To install from source. 3. Download the source of oxonfair and in the source directory run: pip install -e . +To install every single dependency used for testing [autogluon, maplotlib, xgboost, and a range of helper functions] use: + pip install -e . \[tests\] + Now run the [Example Notebook](examples/quickstart_autogluon.ipynb) or try some of the example below. For scikit/XGBoost learn see [sklearn.md](./sklearn.md) and the [Example Notebook](examples/quickstart_xgboost.ipynb) diff --git a/tests/unittests/test_ag.py b/tests/unittests/test_ag.py index 5470c0d..239badd 100644 --- a/tests/unittests/test_ag.py +++ b/tests/unittests/test_ag.py @@ -188,13 +188,13 @@ def test_recall_diff_inferred(use_fast=True): # difference between protected attributes of less than 2.5%) despite not # using sex at run-time - fpredictor.fit(gm.accuracy, gm.recall.diff, 0.025) + fpredictor.fit(gm.accuracy, gm.recall.diff, 0.001) measures = fpredictor.evaluate_fairness(verbose=False) - assert measures["original"]["recall.diff"] > 0.025 + assert measures["original"]["recall.diff"] > 0.001 - assert measures["updated"]["recall.diff"] < 0.025 + assert measures["updated"]["recall.diff"] < 0.001 # Prove that sex isn't being used by dropping it and reevaluating. From 0710ee1d0b40f742eb1a1dc58310a3c06dfec262 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 17:57:58 +0200 Subject: [PATCH 06/16] reduce python versions tested to end points --- .github/workflows/continuous_integration.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index c63551c..20b7a23 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -13,7 +13,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.8", "3.9", "3.10", '3.11'] + python-version: ["3.8", '3.11'] steps: - name: Checkout repository uses: actions/checkout@v3 From 2fa18c4acc7d4e4fa70f071a1d44af397767fc93 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 20:36:58 +0200 Subject: [PATCH 07/16] .gitignore --- .gitignore | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.gitignore b/.gitignore index a1cccce..4209e9c 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,8 @@ __pycache__/ # C extensions *.so +# Autogluon files +AutogluonModels/ # Distribution / packaging .Python @@ -128,3 +130,6 @@ dmypy.json # Pyre type checker .pyre/ /.vscode + +#Mac Preview +.DS_Store \ No newline at end of file From 4ec900fa42df5e0a782c40b408fac65faf5530a7 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 20:38:46 +0200 Subject: [PATCH 08/16] .gitignore --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 4209e9c..fa7d2e4 100644 --- a/.gitignore +++ b/.gitignore @@ -129,7 +129,9 @@ dmypy.json # Pyre type checker .pyre/ -/.vscode + +#VS code project settings +.vscode/ #Mac Preview .DS_Store \ No newline at end of file From 43babec7a7d1728f8c77b6d159fe0fbe5cb797d4 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Mon, 17 Jun 2024 22:17:22 +0200 Subject: [PATCH 09/16] final clean-up --- README.md | 37 ++++++++++++++++--- setup.py | 14 +++++-- src/oxonfair/learners/efficient_compute.py | 1 + src/oxonfair/utils/group_metric_classes.py | 3 +- src/oxonfair/utils/group_metrics.py | 1 + tests/test_check_style.py | 2 +- .../unittests/test_additional_constraints.py | 11 +++--- 7 files changed, 52 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 3240f15..0296e1d 100644 --- a/README.md +++ b/README.md @@ -10,17 +10,42 @@ We support a range of complex classifiers including [pytorch](https://pytorch.or It is a modified version of [autogluon.fair](https://github.com/autogluon/autogluon-fair) and actively maintained. +Compared to autogluon.fair: +We support: + + * scikit-learn, xgboost, and pytorch (for image and NLP tasks) + * a broader range of fairness measures, including conditional metrics. + * improved performance on tabular data + ## Source install -To install from source. +### Standard install + +Download the source of oxonfair and in the source directory run: + + pip install -e .\[full\] + +This will download and install enough code to run any notebooks except those comparing with fairlearn. + +### Compare with Fairlearn - 1. (recomended) Install autogluon (see ) - 2. (Minimal Alternative) Install scikit learn (see ) or XGboost (see ) - 3. Download the source of oxonfair and in the source directory run: +Download the source of oxonfair and in the source directory run: + + pip install -e .\[notebooks\] + +This will download enough supporting libraries to run all the notebooks. + +### Minimal install + +Download the source of oxonfair and in the source directory run: pip install -e . -To install every single dependency used for testing [autogluon, maplotlib, xgboost, and a range of helper functions] use: - pip install -e . \[tests\] +By default this will only install the necissary dependencies sklearn; pandas; and numpy. You will not be able to load datasets, without install `ucimlrepo`, and will have to install `matplotlib` to plot. + +### Full install for running the test suite + +Download the source of oxonfair and in the source directory run: + pip install -e .\[tests\] Now run the [Example Notebook](examples/quickstart_autogluon.ipynb) or try some of the example below. diff --git a/setup.py b/setup.py index c0f4c63..3eb755e 100644 --- a/setup.py +++ b/setup.py @@ -104,17 +104,23 @@ def default_setup_args(*, version): install_requires = [ "numpy>=1.21.4", "pandas>=1.2.5", + "scikit-learn" ] extras_require = dict() +full_requirements = ['matplotlib', 'autogluon.tabular', 'ucimlrepo', 'torch'] +notebook_requirements = full_requirements + ['fairlearn'] +test_requirements = notebook_requirements + ["tox", "pytest", "pytest-cov", 'flake8', + 'linkcheckmd', 'ipynbcompress'] -test_requirements = ["tox", "pytest", "pytest-cov", 'autogluon.tabular', 'scikit-learn', - 'matplotlib', 'flake8', 'ucimlrepo', 'fairlearn', - 'linkcheckmd', 'ipynbcompress', 'torch'] - +full_requirements = list(set(full_requirements)) +notebook_requirements = list(set(notebook_requirements)) test_requirements = list(set(test_requirements)) +extras_require['full'] = full_requirements +extras_require['notebooks'] = notebook_requirements extras_require["tests"] = test_requirements + if __name__ == "__main__": create_version_file(version=version) setup_args = default_setup_args(version=version) diff --git a/src/oxonfair/learners/efficient_compute.py b/src/oxonfair/learners/efficient_compute.py index 1c2fd09..1685d41 100644 --- a/src/oxonfair/learners/efficient_compute.py +++ b/src/oxonfair/learners/efficient_compute.py @@ -83,6 +83,7 @@ def keep_front(solutions: np.ndarray, initial_weights: np.ndarray, directions: n mask = front[:, 2+i] >= val*directions[2+i] front = front[mask] weights = weights[mask] + # drop all points worse than the extrema of the front # NB we have ties so pick the best extrema # This matters for replicability rather than performance diff --git a/src/oxonfair/utils/group_metric_classes.py b/src/oxonfair/utils/group_metric_classes.py index b8a497e..bf193ac 100644 --- a/src/oxonfair/utils/group_metric_classes.py +++ b/src/oxonfair/utils/group_metric_classes.py @@ -86,7 +86,8 @@ def build_array( else: weights = False - assert y_true.size == y_pred.size == groups.size, "Inputs to group_metric are of different sizes. Make sure that all variables are ordinal encoded and not one-hot." + assert y_true.size == y_pred.size == groups.size, ("Inputs to group_metric are of different sizes. " + "Make sure that all variables are ordinal encoded and not one-hot.") t_pos = y_true * y_pred f_pos = (1 - y_true) * y_pred f_neg = y_true * (1 - y_pred) diff --git a/src/oxonfair/utils/group_metrics.py b/src/oxonfair/utils/group_metrics.py index de15ebe..a00acbf 100644 --- a/src/oxonfair/utils/group_metrics.py +++ b/src/oxonfair/utils/group_metrics.py @@ -9,6 +9,7 @@ Utility) # noqa: F401 # N.B. BaseGroupMetric and Utility are needed for type declarations + def ge1(x): """Helper function. Return the elementwise maximum of x or 1. diff --git a/tests/test_check_style.py b/tests/test_check_style.py index 6577811..9f21a46 100755 --- a/tests/test_check_style.py +++ b/tests/test_check_style.py @@ -60,7 +60,7 @@ def test_md_links(): def test_run_notebooks_without_errors(): from ipynbcompress import compress - check_call(['pytest', '--nbmake', '--overwrite', '-n=auto', '--timeout=500', 'examples']) + check_call(['pytest', '--nbmake', '-n=auto', '--timeout=500', 'examples']) # Now compress notebooks because running test makes them too large # This is not really a test, hijacking the test suite to build. for file in glob.glob('./examples/*.ipynb'): diff --git a/tests/unittests/test_additional_constraints.py b/tests/unittests/test_additional_constraints.py index 003e5e9..badfcc9 100644 --- a/tests/unittests/test_additional_constraints.py +++ b/tests/unittests/test_additional_constraints.py @@ -43,12 +43,14 @@ def test_slack_constraints(use_fast=True): cpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast) fpredictor.fit(gm.accuracy, gm.recall.diff, 0.005) - cpredictor.fit(gm.accuracy, gm.recall.diff, 0.005, additional_constraints=((gm.pos_pred_rate, 0),)) + cpredictor.fit(gm.accuracy, gm.recall.diff, 0.005, + additional_constraints=((gm.pos_pred_rate, -1),)) # Evaluate the change in fairness (recall difference corresponds to EO) measures = fpredictor.evaluate_fairness(verbose=False) cmeasures = cpredictor.evaluate_fairness(verbose=False) - assert np.isclose(measures, cmeasures,).all().all() + + assert np.isclose(measures, cmeasures, atol=0.01).all().all() # check fit did something assert measures["original"]["recall.diff"] > 0.005 @@ -59,9 +61,8 @@ def test_slack_constraints_slow(): test_slack_constraints(False) -# def test_slack_constraints_hybrid(): -# 'Warning this consistency fails 50% of the time ' -# test_slack_constraints('hybrid') +def test_slack_constraints_hybrid(): + test_slack_constraints('hybrid') def test_active_constraints(use_fast=True): From aed7a0d156b93b58da413090404b5074ac379fe9 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 09:41:15 +0100 Subject: [PATCH 10/16] robustify test to avoid stocastic failures --- tests/unittests/test_additional_constraints.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/unittests/test_additional_constraints.py b/tests/unittests/test_additional_constraints.py index badfcc9..4415edb 100644 --- a/tests/unittests/test_additional_constraints.py +++ b/tests/unittests/test_additional_constraints.py @@ -42,8 +42,8 @@ def test_slack_constraints(use_fast=True): fpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast) cpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast) - fpredictor.fit(gm.accuracy, gm.recall.diff, 0.005) - cpredictor.fit(gm.accuracy, gm.recall.diff, 0.005, + fpredictor.fit(gm.accuracy, gm.recall.diff, 0.0075) + cpredictor.fit(gm.accuracy, gm.recall.diff, 0.0075, additional_constraints=((gm.pos_pred_rate, -1),)) # Evaluate the change in fairness (recall difference corresponds to EO) @@ -53,8 +53,8 @@ def test_slack_constraints(use_fast=True): assert np.isclose(measures, cmeasures, atol=0.01).all().all() # check fit did something - assert measures["original"]["recall.diff"] > 0.005 - assert measures["updated"]["recall.diff"] < 0.005 + assert measures["original"]["recall.diff"] > 0.0075 + assert measures["updated"]["recall.diff"] < 0.0075 def test_slack_constraints_slow(): From 95a53bb843e6f30a182756f3aba297530c148c69 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 09:41:40 +0100 Subject: [PATCH 11/16] spell check --- README.md | 28 ++++++++++++++-------------- measures.md | 30 +++++++++++++++--------------- using_fit.md | 38 +++++++++++++++++++++++++------------- 3 files changed, 54 insertions(+), 42 deletions(-) diff --git a/README.md b/README.md index 0296e1d..9545e75 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ OxonFair is an expressive toolkit designed to enforce a wide-range of fairness d The toolkit is designed to overcome a range of shortcomings in existing fairness toolkits for high-capacity models that overfit to the training data. It is designed and works for computer vision and NLP problems alongside tabular data. -For low-capacity models (e.g. linear regression over a small number of variables, and decision-trees of limited depth), we recomend [fairlearn](https://github.com/fairlearn/fairlearn). +For low-capacity models (e.g. linear regression over a small number of variables, and decision-trees of limited depth), we recommend [fairlearn](https://github.com/fairlearn/fairlearn). We support a range of complex classifiers including [pytorch](https://pytorch.org/), [scikit learn](https://scikit-learn.org/stable/), and ensembles provided by [autogluon](https://auto.gluon.ai/stable/index.html). @@ -21,7 +21,7 @@ We support: ### Standard install -Download the source of oxonfair and in the source directory run: +Download the source of OxonFair and in the source directory run: pip install -e .\[full\] @@ -29,7 +29,7 @@ This will download and install enough code to run any notebooks except those com ### Compare with Fairlearn -Download the source of oxonfair and in the source directory run: +Download the source of OxonFair and in the source directory run: pip install -e .\[notebooks\] @@ -37,10 +37,10 @@ This will download enough supporting libraries to run all the notebooks. ### Minimal install -Download the source of oxonfair and in the source directory run: +Download the source of OxonFair and in the source directory run: pip install -e . -By default this will only install the necissary dependencies sklearn; pandas; and numpy. You will not be able to load datasets, without install `ucimlrepo`, and will have to install `matplotlib` to plot. +By default, this will only install the necessary dependencies sklearn; pandas; and numpy. You will not be able to load datasets, without install `ucimlrepo`, and will have to install `matplotlib` to plot. ### Full install for running the test suite @@ -49,9 +49,9 @@ Download the source of oxonfair and in the source directory run: Now run the [Example Notebook](examples/quickstart_autogluon.ipynb) or try some of the example below. -For scikit/XGBoost learn see [sklearn.md](./sklearn.md) and the [Example Notebook](examples/quickstart_xgboost.ipynb) +For scikit/XGBoost, see [sklearn.md](./sklearn.md) and the [Example Notebook](examples/quickstart_xgboost.ipynb) -For pytorch see a toy example on [adult](./examples/pytorch_minimal_demo.ipynb) and for computer vision, this [Example Notebook](examples/quickstart_DeepFairPredictor_computer_vision.ipynb) +For pytorch, see a toy example on [adult](./examples/pytorch_minimal_demo.ipynb) and for computer vision, this [Example Notebook](examples/quickstart_DeepFairPredictor_computer_vision.ipynb) More demo notebooks are present in the [examples folder](./examples/README.md). @@ -80,7 +80,7 @@ More demo notebooks are present in the [examples folder](./examples/README.md). ## Overview -Oxonfair is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e. it does not require access to protected attributes at test time. +OxonFair is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e., it does not require access to protected attributes at test time. Under the hood, FairPredictor works by adjusting the decision boundary for each group individually. Where groups are not available, it makes use of inferred group membership to adjust decision boundaries. The key idea underlying this toolkit is that for a wide range of use cases, the most suitable classifier should do more than maximize some form of accuracy. @@ -106,11 +106,11 @@ The full set of constraints and objectives can be seen in the list of measures i ### Why Another Fairness Library? -Fundamentally, most existing fairness methods are not appropriate for use with complex classifiers on high-dimensional data. This classifiers are prone to overfitting on the training data, which means that trying to balance error rates (e.g. when using equal opportunity) on the training data, is unlikely to transfer well to new unseen data. This is a particular problem when using computer vision (see [Zietlow et al.](https://arxiv.org/abs/2203.04913)), but can also occur with tabular data. Moreover, iteratively retraining complex models (a common requirement of many methods for enforcing fairness) is punatively slow when training the model once might take days, or even weeks, if you are trying to maximise performance. +Fundamentally, most existing fairness methods are not appropriate for use with complex classifiers on high-dimensional data. These classifiers are prone to overfitting on the training data, which means that trying to balance error rates (e.g. when using equal opportunity) on the training data, is unlikely to transfer well to new unseen data. This is a particular problem when using computer vision (see [Zietlow et al.](https://arxiv.org/abs/2203.04913)), but can also occur with tabular data. Moreover, iteratively retraining complex models (a common requirement of many methods for enforcing fairness) is punitively slow when training the model once might take days, or even weeks, if you are trying to maximize performance. -At the same time, postprocessing methods which allow you to train once, and then improve fairness on held-out validation data generally requires the protected attributes to be avalible at test time, which is often infeasible, particularly with computer vision. +At the same time, postprocessing methods which allow you to train once, and then improve fairness on held-out validation data generally requires the protected attributes to be available at test time, which is often infeasible, particularly with computer vision. -OxonFair is build from the ground up to avoid these issues. It is a postprocessing approach, explicitly designed to use infered attributes where protected attributes are not avalible to enforce fairness. Fairness can be enforced both on validation, or on the train set, when you are short of data and overfitting is not a concern. When enforcing fairness in deep networks or using provided attributes, a classifier is only trained once, for non network-based approaches, e.g. scikit-learn or xgboost, with infered attributes we require the training of two classifier (one to predict the original task, and a second to estimate groups membership). +OxonFair is build from the ground up to avoid these issues. It is a postprocessing approach, explicitly designed to use inferred attributes where protected attributes are not available to enforce fairness. Fairness can be enforced both on validation, or on the train set, when you are short of data and overfitting is not a concern. When enforcing fairness in deep networks or using provided attributes, a classifier is only trained once, for non network-based approaches, e.g. scikit-learn or xgboost, with inferred attributes we require the training of two classifier (one to predict the original task, and a second to estimate groups membership). That said, we make several additional design decisions which we believe make for a better experience for data scientists: @@ -118,13 +118,13 @@ That said, we make several additional design decisions which we believe make for ##### Wide Choice of performance measure -Unlike other approaches to fairness, FairPredictor allows the optimization of arbitrary performance measures such as F1 or MCC, subject to fairness constraints. This can substantially improve the fairness/performance trade-off with, for example, F1 scores being 3-4% higher when directly optimized for rather than accuracy. +Unlike other approaches to fairness, FairPredictor allows the optimization of arbitrary performance measures such as F1 or MCC, subject to fairness constraints. This can substantially improve the fairness/performance trade-off with, for example, F1 scores frequently being 3-4% higher when directly optimized for rather than accuracy. ##### Wide Choice of Fairness Measures Rather than offering a range of different fairness methods that enforce a small number of fairness definitions through a variety of different methods, we offer one method that can enforce a much wider range of fairness definitions out of the box, alongside support for custom fairness definitions. -Of the set of decision-based group-metrics discussed in [Verma and Rubin](https://fairware.cs.umass.edu/papers/Verma.pdf), and the metrics measured by [Sagemaker Clarify](https://pages.awscloud.com/rs/112-TZM-766/images/Fairness.Measures.for.Machine.Learning.in.Finance.pdf), out of the box FairPredictor offers the ability to both measure and enforce all of the 8 group metrics used to evaluate classifier decision measured in Verma and Rubin, and all 12 group measures used to evaluate dcisions in Clarify. +Of the set of decision-based group-metrics discussed in [Verma and Rubin](https://fairware.cs.umass.edu/papers/Verma.pdf), and the metrics measured by [Sagemaker Clarify](https://pages.awscloud.com/rs/112-TZM-766/images/Fairness.Measures.for.Machine.Learning.in.Finance.pdf), out of the box FairPredictor offers the ability to both measure and enforce all of the 8 group metrics used to evaluate classifier decision measured in Verma and Rubin, and all 12 group measures used to evaluate decisions in Clarify. ##### Direct Remedy of Harms @@ -150,7 +150,7 @@ We provide support for the utility based approach set out in [Fairness On The Gr Utility functions can be defined in one line. -For example, if we have a situation where an ML system identifies potential problems that require intervening, it might be that every intervention has a cost of 1, regardless of if it was needed, but a missed intervention that was needed has a cost of 5. Finally, not making an intervention when one was not needed has a cost of 0. This can be written as: +For example, consider a situation where an ML system identifies potential problems that require intervening. Every intervention may have a cost of 1, regardless of if it was needed, but a missed intervention that was needed has a cost of 5. Finally, not making an intervention when one was unneeded has a cost of 0. This can be written as: my_utility=gm.Utility([1, 1, 5, 0], 'Testing Costs') diff --git a/measures.md b/measures.md index e443173..cc889e3 100644 --- a/measures.md +++ b/measures.md @@ -3,9 +3,9 @@ OxonFair uses a wide range of measures to enforce and measures fairness and performance. These measures can be passed to a `FairPredictor` by calling `FairPredictor.fit(objective, constraint, value)`. -This will optimise the measure `objective` subject to the requirement that the other measure `constraint` is greater or less than `value`, as required. +This will optimize the measure `objective` subject to the requirement that the other measure `constraint` is greater or less than `value`, as required. -These measures can also evaluated by passing to the evaluation functions `evaluate`, `evaluate_groups`, and `evaluate_fairness` as a dict of measures, where the keys of the dict are short-form names using when `verbose=False` and the values are measures. +These measures can also be evaluated by passing to the evaluation functions `evaluate`, `evaluate_groups`, and `evaluate_fairness` as a dict of measures, where the keys of the dict are short-form names using when `verbose=False` and the values are measures. This document lists the standard measures provided by the group_metrics library, which is imported as: @@ -39,7 +39,7 @@ Having defined a metric as above, we have a range of different objects: * `metric.max` reports the maximum value for any group. * `metric.min` reports the minimum value for any group. * `metric.overall` reports the overall value for all groups combined, and is the same as calling `metric` directly -* `metric.ratio` reports the average ratio over pairs of distinct groups, where smallest value is divided by the largest +* `metric.ratio` reports the average ratio over pairs of distinct groups, where the smallest value is divided by the largest * `metric.per_group` reports the value for every group. These can be passed directly to fit, or to the evaluation functions we provide. @@ -77,7 +77,7 @@ gm. | `gm.accuracy` | Proportion of points correctly identified | | `gm.balanced_accuracy` | The average of the proportion of points with a positive label correctly identified and the proportion of points with a negative label correctly identified | | `gm.min_accuracy` | The minimum of the proportion of points with a positive label correctly identified and the proportion of points with a negative label correctly identified (common in min-max fairness) | -| `gm.f1` | F1 Score. Defined as: (2 * TP) / (2 * TP + FP + FN) | +| `gm.f1` | F1 Score. Defined as: (2 * TP) / (2 * TP + FP + FN) | | `gm.precision` | AKA Positive Prediction Rate | | `gm.recall` | AKA True Positive Prediction Rate | | `gm.mcc` | Matthews Correlation Coefficient. See https://en.wikipedia.org/wiki/Phi_coefficient | @@ -123,7 +123,7 @@ These relaxations take value 0 only if the equalities are satisfied for all pair | `gm.predictive_parity` | AKA Rejection Rate Difference. Average difference between groups in Precision | | `gm.false_pos_rate.diff` | AKA Specificity Difference. Average difference between groups in False Positive rate. | | `gm.false_neg_rate.diff` | AKA Equal Opportunity or Recall difference. Average difference between groups in False Negative Rate | -| `gm.equalized_odds` | The average of `true_pos_rate.diff` and `false_neg_rate.diff` | +| `gm.equalized_odds` | The average of `true_pos_rate.diff` and `false_neg_rate.diff` | | `gm.cond_use_accuracy` | The average of `pos_pred_val.diff` and `neg_pred_val.diff` | | `gm.predictive_equality` | Average difference in False Negative Rate | | `gm.accuracy._parity` | Average difference in Accuracy | @@ -132,10 +132,10 @@ These relaxations take value 0 only if the equalities are satisfied for all pair ## Conditional Metrics OxonFair also supports conditional metrics. -These are used to compensate for accetable biases present in the data. +These are used to compensate for acceptable biases present in the data. For example, in one [famous case](https://pubmed.ncbi.nlm.nih.gov/17835295/), Berkley showed a strong gender bias in admissions despite the fact that each department had minimal admissions bias with respect to gender. The cause underlying this was that women were disproportionately applying to departments with higher rejection rates. -To measure this correct for this bias we the follow the method set out in the chapter 1 questions of: [Statistics by Freedman et al.](https://www.goodreads.com/book/show/147358.Statistics), which [Wachter et al.](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547922) applied to algorithmic fairness. +To measure this correct for this bias we follow the method set out in the chapter 1 questions of: [Statistics by Freedman et al.](https://www.goodreads.com/book/show/147358.Statistics), which [Wachter et al.](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547922) applied to algorithmic fairness. This measure compensates for the fact that different selection rates across groups may be driven by an acceptable factor that is correlated with the protected attributes. For example, in the Berkley case, it is acceptable that different departments should have different admissions rates, but the choice of department is correlated with gender. @@ -144,29 +144,29 @@ This is also measured by [Amazon Clarify](https://docs.aws.amazon.com/sagemaker/ However, no other fairness toolkit optimizes it. All of these measures are subtly different, but weight data in the same way. -Freedman et al. considers the weighted proportion of people in a particular group recieving positive decisions vs. the total number of people in the group. +Freedman et al. considers the weighted proportion of people in a particular group receiving positive decisions vs. the total number of people in the group. -Wachter et al. examines the weighted proportion of [members of a protected group] within the set of all people recieving a positive decision; and the same weighted proportion of [members of the protected group] within the set of all people recieving a negative decision. If this proportion is larger for the positive set, than the negative set, the group is doing disproportionately well, and if it smaller, the group is doing disproportionately badly. +Wachter et al. examines the weighted proportion of [members of a protected group] within the set of all people receiving a positive decision; and the same weighted proportion of [members of the protected group] within the set of all people receiving a negative decision. If this proportion is larger for the positive set, than the negative set, the group is doing disproportionately well, and if it is smaller, the group is doing disproportionately badly. Clarify and IBM360 measures the difference of the two measures in Wachter et al. -All methods are broadly equivilent in the sense that the difference between every pair of groups using Freedman's measure is zero if and only if the difference between positives and negatives measures of Wachter et al., for every group is zero. +All methods are broadly equivalent in the sense that the difference between every pair of groups using Freedman's measure is zero if and only if the difference between positives and negatives measures of Wachter et al., for every group is zero. -For simplicity, we implement Freedman's measure. This give nautural extensions to difference in conditional selection rate, corresponding to conditional demographic parity, and average ratio in conditional selection rate, corresponding to disparate impact. Moreover, the levelling-up measures such as minimal conditional selection rate will also work, which is not the case for the measure of Wachter et al. +For simplicity, we implement Freedman's measure. This give natural extensions to difference in conditional selection rate, corresponding to conditional demographic parity, and average ratio in conditional selection rate, corresponding to disparate impact. Moreover, the levelling-up measures such as minimal conditional selection rate will also work, which is not the case for the measure of Wachter et al. -We assign a weight $w_i$ to an individual $i$ belonging to a particular protected group, and conditioning factor e.g. school as: +We assign a weight $w_i$ to an individual $i$ belonging to a particular protected group, and conditioning factor e.g., school as: $$ w_i = \frac{\#\text{individuals with the same conditioning factor}}{\#\text{individuals belonging to the same group and conditioning factor}} $$ -The conditional positive decision rate is then given by: +The conditional positive decision rate is given by: $$ \frac {\text{wTP+ wFP}{wTP +wFP +wFN +wTN}$$ where wTP, wFP, wFN, wTN are the weighted sum of True Positives, False Positives using the weights $w_i$. This can be used for levelling up, by enforcing minimum conditional selection rates, and enforcing conditional demographic parity. The use of conditional metrics is somewhat more involved, as it requires the specification of a conditioning factor, alongside groups. -Here is a quick example using a conditional minimimal selection rate of 0.3. +Here is a quick example using a conditional minimal selection rate of 0.3. import oxonfair import xgboost @@ -186,4 +186,4 @@ We support conditioning on range of linear measures. 4. `cgm.false_neg_rate` conditional false negative rate 5. `cgm.false_pos_rate` conditional false positive rate -For false negative and false positive rate, we normalise by the total number of negatively or positively labelled points rather than the total number of points. +For false negative and false positive rate, we normalize by the total number of negatively or positively labelled points rather than the total number of points. diff --git a/using_fit.md b/using_fit.md index b8801be..c64c1ab 100644 --- a/using_fit.md +++ b/using_fit.md @@ -1,10 +1,10 @@ # Using Fit in OxonFair -OxonFair is a more flexible toolkit than other fairness approaches, and this expressiveness takes some getting used to. To show how it works, we are going to give examples using fit to enforce a range of different fairness definitions. For these examples, we're going to maximise accuracy, but if that doesn't meet your use case feel free to switch it another performance measure like balanced accuracy (`gm.balanced_accuracy`), or F1 (`gm.f1`). +OxonFair is a more flexible toolkit than other fairness approaches, and this expressiveness takes some getting used to. To show how it works, we are going to give examples using fit to enforce a range of different fairness definitions. For these examples, we're going to maximize accuracy, but if that doesn't meet your use case feel free to switch it another performance measure like balanced accuracy (`gm.balanced_accuracy`), or F1 (`gm.f1`). The use of `fit` is generic. The calls we show here work on tabular, image, and NLP data either using inferred protected attributes or explicitly provided ones. -To get started we're just going to use explicit attributes on an XGBoost trained classifier on tabular data. We won't worry about overfitting and will just enforce fairness on the training set. +To get started we're just going to use explicit attributes on an XGBoost trained classifier on tabular data. We won't worry about overfitting and will just enforce fairness on the training set. Here is some sample code to train the base classifier on the adult dataset, and to prepare the fair classifier. @@ -19,7 +19,7 @@ Here is some sample code to train the base classifier on the adult dataset, and To see the trade-offs made by OxonFair, after running fit, you can call: fpredict.evaluate_groups(test_data) -This will show how the classifier behaviour is altered on a group-by-group basis. Calling +This will show how the classifier behaviour varies on a group-by-group basis. Calling fpredict.plot_frontier() Will show the Pareto frontier, and how you can expect the constraint and objective to vary as you alter the number. `fpredict.plot_frontier(test_data)` will reevaluate the frontier on test data and show how much you are overfitting to noise. @@ -34,33 +34,33 @@ Now let's look at some example uses for `.fit`. `fpredict.fit(gm.accuracy, gm.disparate_impact, 0.80)` because disparate impact is just the ratio of positive decisions, we can also write it as: `fpredict.fit(gm.accuracy, gm.positive_decision_rate.ratio, 0.80)` - Note that fit alters its behaviour depending on what you pass it. By default performance measures like `accuracy` are maximised; differences are minimised; and ratios are maximised. If you don't like this behaviour you can override it by setting `obj_greater_is_better` or `const_greater_is_better` to True or False. + Note that fit alters its behaviour depending on what you pass it. By default, performance measures like `accuracy` are maximized; differences are minimized; and ratios are maximized. If you don't like this behaviour you can override it by setting `obj_greater_is_better` or `const_greater_is_better` to True or False. * Enforce Equal Opportunity to within 1%: `fpredict.fit(gm.accuracy, gm.equal_opportunity, 0.01)` - as equal opportunity is just defined as the difference in recall, this is the same as: + as equal opportunity is defined as the difference in recall, this is the same as: `fpredict.fit(gm.accuracy, gm.recall.min, 0.01)` * Enforce recall ratio is within 80% `fpredict.fit(gm.accuracy, gm.recall.ratio, 0.80)` This definition of fairness doesn't even have a name, but using a ratio instead of difference is useful for problems where the selection rate gets very small. * Enforce Equalized Odds to within 2%: `fpredict.fit(gm.accuracy, gm.equalized_odds, 0.02)` - Under the hood we define equalized odds as the average of the group difference in recall (i.e. True Positive Rates) and the group difference in True Negative Rates + Under the hood we define equalized odds as the average of the group difference in recall (i.e., True Positive Rates) and the group difference in True Negative Rates * Enforce that the difference in precision is less than 5% `fpredict.fit(gm.accuracy, gm.precision.diff, 0.05)` We could also look this up in [Verma and Rudin](ww.) and find out that this has the name predictive parity so, this code will also work. `fpredict.fit(gm.accuracy, gm.predictive_parity, 0.05)` -* Enforcing that the recall rate is at least 80% for every group +* Enforcing that the recall rate is at least 80% for every group `fpredict.fit(gm.accuracy, gm.recall.min, 0.8)` - This is useful because a key problem with fairness is that it tends to [level-down](https://arxiv.org/pdf/2302.02404). When you enforce equal opportunity it will typically improve recall rates for disadvantaged groups, but for the groups with high recall, recall and accuracy will also drop. To avoid this, we can simply"level-up" and push-up the recall for every disadvantaged group, while leaving the classifier alone where it already works acceptably well. + This is useful because a key problem with fairness is that it tends to [level-down](https://arxiv.org/pdf/2302.02404). When you enforce equal opportunity it will typically improve recall rates for disadvantaged groups, but for the groups with high recall, recall and accuracy will also drop. To avoid this, we can simply "level-up" and push-up the recall for every disadvantaged group, while leaving the classifier alone where it already works acceptably well. * Enforce that the selection rate is over 40% for every group. `fpredict.fit(gm.accuracy, gm.positive_decision_rate, 0.4)` This is the levelling-up version of demographic parity. * Pareto efficient minimax fairness This enforces that the accuracy over the worst performing pair of (target label, group) is as high as possible, while maximising the overall accuracy (see [Martenez et al.](https://arxiv.org/abs/2011.01821)) `fpredict.fit(gm.min_accuracy.min, gm.accuracy, 0)` - You can also simply maximise the accuracy for the worst performing group while also maximising global accuracy using: + You can also simply maximize the accuracy for the worst performing group while also maximizing global accuracy using: `fpredict.fit(gm.accuracy.min, gm.accuracy, 0)` - But for high-capacity models (see [Singh et al.](https://proceedings.mlr.press/v202/singh23b/singh23b.pdf) ) this is generally indistinguishable from just maximising accuracy. + But for high-capacity models (see [Singh et al.](https://proceedings.mlr.press/v202/singh23b/singh23b.pdf) ) this is generally indistinguishable from just maximizing accuracy. * Enforce demographic parity, subject to the requirement that there is ~40% overall selection rate: `fpredict.fit(gm.positive_decision_rate.min, gm.positive_decision_rate, 0.4)` To understand why this works see the proof in [Goethal's et al.](https://arxiv.org/pdf/2406.01290). @@ -68,9 +68,21 @@ Now let's look at some example uses for `.fit`. `fpredict.fit(gm.recall.min, gm.positive_decision_rate, 0.4)` * Enforce equal precision rates, subject to the requirement that there is ~40% overall selection rate: `fpredict.fit(gm.precision.min, gm.positive_decision_rate, 0.4, const_greater_is_better=True)` - Here we must swap the sign on the constraint because precision is maximised as the selection rate goes to zero. -* Maximise utility: + Here we must swap the sign on the constraint because precision is maximized as the selection rate goes to zero. +* Maximize utility: `utility = gm.utility(1, 1, 4, 0)` `fpredict.fit(utility)` -* Maximise utility while enforcing that the minimum group recall doesn't drop below 60%. +* Maximize utility while enforcing that the minimum group recall doesn't drop below 60%. `fpredict.fit(utility, gm.recall.min, 0.6)` +* Additional constraints + In some cases, you might want to also enforce additional constraints such as: + `gm.precision.min >0.6`; and `gm.positive_prediction_rate>0.5` while optimizing an objective e.g. some utility function. + In this case you can use: + `fpredict.fit(utility, gm.precision.min, 0.6, additional_constraints=((gm.positive_prediction_rate, 0.4, '>'),)` + Here `additional_constraints` is a list of constraints and each constraint takes the form: + `(metric, value, direction[optional])` + direction is optimal and should be either '>' or '<'. + The method simply removes candidates from the possible Pareto frontier that violate the additional constraints. + This comes with some noticeable caveats. + 1. If the additional constraints are too strong, the method can return an empty frontier. + 2. The method is not stable. In particular, swapping the first constraint with any other may return different solutions. From 2429c9d20727d4e38f8d0ad0abb4adad7759efa9 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 12:16:10 +0100 Subject: [PATCH 12/16] typos --- measures.md | 2 +- sklearn.md | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/measures.md b/measures.md index cc889e3..e7858df 100644 --- a/measures.md +++ b/measures.md @@ -135,7 +135,7 @@ OxonFair also supports conditional metrics. These are used to compensate for acceptable biases present in the data. For example, in one [famous case](https://pubmed.ncbi.nlm.nih.gov/17835295/), Berkley showed a strong gender bias in admissions despite the fact that each department had minimal admissions bias with respect to gender. The cause underlying this was that women were disproportionately applying to departments with higher rejection rates. -To measure this correct for this bias we follow the method set out in the chapter 1 questions of: [Statistics by Freedman et al.](https://www.goodreads.com/book/show/147358.Statistics), which [Wachter et al.](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547922) applied to algorithmic fairness. +To measure this correct for this bias we follow the method set out in chapter 2 of: [Statistics by Freedman et al.](https://www.goodreads.com/book/show/147358.Statistics), which [Wachter et al.](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547922) applied to algorithmic fairness. This measure compensates for the fact that different selection rates across groups may be driven by an acceptable factor that is correlated with the protected attributes. For example, in the Berkley case, it is acceptable that different departments should have different admissions rates, but the choice of department is correlated with gender. diff --git a/sklearn.md b/sklearn.md index 22c02b2..4f6214a 100644 --- a/sklearn.md +++ b/sklearn.md @@ -55,12 +55,12 @@ and prepare to enforce and evaluate fairness with respect to the variable `sex_ ## Fit the object -Here we call fit to maximise accuracy while ensuring that the difference in recall between the groups is less than 2%. -A wide range of possible performance metrics and fairness measures are suported. +Here we call fit to maximize accuracy while ensuring that the difference in recall between the groups is less than 2%. +A wide range of possible performance metrics and fairness measures are supported. fpred.fit(gm.accuracy,gm.recall.diff,0.02) -We can now visualise the space of possible trade-offs +We can now visualize the space of possible trade-offs fpred.plot_frontier() @@ -104,6 +104,6 @@ Evaluate fairness using standard metrics with: | Treatment Equality | 0.172428 | 0.28022 | | Generalized Entropy | 0.102481 | 0.105529 | -call `fpredict.predict( )`, and `fpredict.predict_proba( )` to score new data. +Call `fpredict.predict( )`, and `fpredict.predict_proba( )` to score new data. Once the base predictor has been trained, and the object built, you can use the fair predictor in the same way as with autogluon. See [README.md](./README.md) for details. From c119199163670a5d1847665e047cf7f8ae3b1bfd Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 12:17:11 +0100 Subject: [PATCH 13/16] simplify tests to decrease fragility --- tests/unittests/test_additional_constraints.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tests/unittests/test_additional_constraints.py b/tests/unittests/test_additional_constraints.py index 4415edb..877d2ad 100644 --- a/tests/unittests/test_additional_constraints.py +++ b/tests/unittests/test_additional_constraints.py @@ -36,25 +36,25 @@ def test_slack_constraints(use_fast=True): - """Slack constraints should not alter the solution found. - In practice there seems to be some instability in the slow pathway and occasionally it does. - Rerun and confirm there's a problem before debugging.""" + """Slack constraints should not alter the solution found.""" fpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast) cpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast) - fpredictor.fit(gm.accuracy, gm.recall.diff, 0.0075) - cpredictor.fit(gm.accuracy, gm.recall.diff, 0.0075, + fpredictor.fit(gm.accuracy, gm.recall.min, .99) + cpredictor.fit(gm.accuracy, gm.recall.min, .99, additional_constraints=((gm.pos_pred_rate, -1),)) # Evaluate the change in fairness (recall difference corresponds to EO) measures = fpredictor.evaluate_fairness(verbose=False) cmeasures = cpredictor.evaluate_fairness(verbose=False) - assert np.isclose(measures, cmeasures, atol=0.01).all().all() + assert np.isclose(measures, cmeasures).all().all() # check fit did something - assert measures["original"]["recall.diff"] > 0.0075 - assert measures["updated"]["recall.diff"] < 0.0075 + measures = fpredictor.evaluate_fairness(metrics={'recall.min': gm.recall.min}, verbose=False) + + assert measures["original"]["recall.min"] < 0.99 + assert measures["updated"]["recall.min"] > 0.99 def test_slack_constraints_slow(): From 6312122edceee41853380e64b71f4853182e1361 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 12:17:37 +0100 Subject: [PATCH 14/16] bug in enforcing continious metrics through slow pathway --- src/oxonfair/learners/fair_frontier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/oxonfair/learners/fair_frontier.py b/src/oxonfair/learners/fair_frontier.py index 4ae59ca..33206a1 100644 --- a/src/oxonfair/learners/fair_frontier.py +++ b/src/oxonfair/learners/fair_frontier.py @@ -63,7 +63,7 @@ def compute_metrics(metrics: Sequence[Callable], y_true: np.ndarray, proba: np.n scores[j, i] = metric(y_true, pred)[0] else: np.subtract(tmp[:, 1], tmp[:, 0], diff) - scores[j, i] = metric(y_true, pred) + scores[j, i] = metric(y_true, diff) return scores From 98b1896d91b39e1ff2c1ee7757773e15b39f0c73 Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 12:44:16 +0100 Subject: [PATCH 15/16] spelling --- README.md | 12 +- examples/README.md | 10 +- examples/adult_fairlearn_comparision.ipynb | 784 +++---- examples/building_datasets.ipynb | 448 ++-- examples/compas_autogluon.ipynb | 240 +- examples/conditional_metrics.ipynb | 712 +++--- examples/high-dim_fairlearn_comparision.ipynb | 394 ++-- examples/levelling_up.ipynb | 252 ++- .../multi_group_fairlearn_comparision.ipynb | 114 +- examples/pytorch_minimal_demo.ipynb | 468 ++-- ...rt_DeepFairPredictor_computer_vision.ipynb | 256 +-- examples/quickstart_autogluon.ipynb | 1278 +++++------ examples/quickstart_xgboost.ipynb | 1938 ++++++++--------- 13 files changed, 3495 insertions(+), 3411 deletions(-) diff --git a/README.md b/README.md index 9545e75..e64b35b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ OxonFair is an expressive toolkit designed to enforce a wide-range of fairness d The toolkit is designed to overcome a range of shortcomings in existing fairness toolkits for high-capacity models that overfit to the training data. It is designed and works for computer vision and NLP problems alongside tabular data. -For low-capacity models (e.g. linear regression over a small number of variables, and decision-trees of limited depth), we recommend [fairlearn](https://github.com/fairlearn/fairlearn). +For low-capacity models (, linear regression over a small number of variables, and decision-trees of limited depth), we recommend [fairlearn](https://github.com/fairlearn/fairlearn). We support a range of complex classifiers including [pytorch](https://pytorch.org/), [scikit learn](https://scikit-learn.org/stable/), and ensembles provided by [autogluon](https://auto.gluon.ai/stable/index.html). @@ -44,14 +44,14 @@ By default, this will only install the necessary dependencies sklearn; pandas; a ### Full install for running the test suite -Download the source of oxonfair and in the source directory run: +Download the source of OxonFair and in the source directory run: pip install -e .\[tests\] Now run the [Example Notebook](examples/quickstart_autogluon.ipynb) or try some of the example below. For scikit/XGBoost, see [sklearn.md](./sklearn.md) and the [Example Notebook](examples/quickstart_xgboost.ipynb) -For pytorch, see a toy example on [adult](./examples/pytorch_minimal_demo.ipynb) and for computer vision, this [Example Notebook](examples/quickstart_DeepFairPredictor_computer_vision.ipynb) +For pytorch, see a toy example on [adult](./examples/pytorch_minimal_demo.ipynb) and for computer vision, this [Example Notebook](examples/quickstart_DeepFairPredictor_computer_vision.ipynb) More demo notebooks are present in the [examples folder](./examples/README.md). @@ -106,11 +106,11 @@ The full set of constraints and objectives can be seen in the list of measures i ### Why Another Fairness Library? -Fundamentally, most existing fairness methods are not appropriate for use with complex classifiers on high-dimensional data. These classifiers are prone to overfitting on the training data, which means that trying to balance error rates (e.g. when using equal opportunity) on the training data, is unlikely to transfer well to new unseen data. This is a particular problem when using computer vision (see [Zietlow et al.](https://arxiv.org/abs/2203.04913)), but can also occur with tabular data. Moreover, iteratively retraining complex models (a common requirement of many methods for enforcing fairness) is punitively slow when training the model once might take days, or even weeks, if you are trying to maximize performance. +Fundamentally, most existing fairness methods are not appropriate for use with complex classifiers on high-dimensional data. These classifiers are prone to overfitting on the training data, which means that trying to balance error rates (e.g., when using equal opportunity) on the training data, is unlikely to transfer well to new unseen data. This is a particular problem when using computer vision (see [Zietlow et al.](https://arxiv.org/abs/2203.04913)), but can also occur with tabular data. Moreover, iteratively retraining complex models (a common requirement of many methods for enforcing fairness) is punitively slow when training the model once might take days, or even weeks, if you are trying to maximize performance. At the same time, postprocessing methods which allow you to train once, and then improve fairness on held-out validation data generally requires the protected attributes to be available at test time, which is often infeasible, particularly with computer vision. -OxonFair is build from the ground up to avoid these issues. It is a postprocessing approach, explicitly designed to use inferred attributes where protected attributes are not available to enforce fairness. Fairness can be enforced both on validation, or on the train set, when you are short of data and overfitting is not a concern. When enforcing fairness in deep networks or using provided attributes, a classifier is only trained once, for non network-based approaches, e.g. scikit-learn or xgboost, with inferred attributes we require the training of two classifier (one to predict the original task, and a second to estimate groups membership). +OxonFair is build from the ground up to avoid these issues. It is a postprocessing approach, explicitly designed to use inferred attributes where protected attributes are not available to enforce fairness. Fairness can be enforced both on validation, or on the train set, when you are short of data and overfitting is not a concern. When enforcing fairness in deep networks or using provided attributes, a classifier is only trained once, for non network-based approaches, e.g., scikit-learn or xgboost, with inferred attributes we require the training of two classifier (one to predict the original task, and a second to estimate groups membership). That said, we make several additional design decisions which we believe make for a better experience for data scientists: @@ -245,7 +245,7 @@ See this [notebook](./examples/compas_autogluon.ipynb) for details. ### Best Practices -It is common for machine learning algorithms to overfit training data. Therefore, if you want your fairness constraints to carry over to unseen data we recommend that they are enforced on a large validation set, rather than the training set. For low-dimensional datasets, many classifiers, with a careful choice of hyperparameter, are robust to overfitting and fairness constraints enforced on training data can carry over to unseen test data. In fact, given the choice between enforcing fairness constraints on a large training set, vs. using a significantly smaller validation set, reusing the training set may result in better generalization of the desired behavior to unseen data. However, this behavior is not guaranteed, and should always be empirically validated. +It is common for machine learning algorithms to overfit training data. Therefore, if you want your fairness constraints to carry over to unseen data we recommend that they are enforced on a large validation set, rather than the training set. For low-dimensional datasets, many classifiers, with a careful choice of hyperparameter, are robust to overfitting and fairness constraints enforced on training data can carry over to unseen test data. In fact, given the choice between enforcing fairness constraints on a large training set, vs. using a significantly smaller validation set, reusing the training set may result in better generalization of the desired behavior to unseen data. However, this behavior is not guaranteed, and should always be empirically validated. #### Challenges with unbalanced data diff --git a/examples/README.md b/examples/README.md index b648578..21c0480 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,13 +1,13 @@ # Tutorial Notebooks -This folder contains a collection of example ipython notebooks illustating different use cases. +This folder contains a collection of example ipython notebooks illustrating different use cases. 1. [Getting started with XGBoost](quickstart_xboost.ipynb) 2. [Getting started with Autogluon](quickstart_autogluon.ipynb) 3. [Getting started with Deep Learning and Computer Vision](quickstart_DeepFairPredictor_computer_vision.ipynb) 4. [Code for training deep models compatible with OxonFair](training_a_two_head_model/two_head_model_demo.py) 5. [Levelling up](levelling_up.ipynb) -6. Comparisions with FairLearn. - a. A comparision using random forests and decision trees on the adult dataset. [Here](adult_fairlearn_comparision.ipynb) - b. A comparision using xgboost on medical data. [Here](high-dim_fairlearn_comparision.ipynb) - c. A comparision of run time using xgboost on multiple groups. [Here](multi_group_fairlearn_comparision.ipynb) +6. Comparisons with FairLearn + a. A comparison using random forests and decision trees on the adult dataset. [Here](adult_fairlearn_comparision.ipynb) + b. A comparison using xgboost on medical data. [Here](high-dim_fairlearn_comparision.ipynb) + c. A comparison of run time using xgboost on multiple groups. [Here](multi_group_fairlearn_comparision.ipynb) diff --git a/examples/adult_fairlearn_comparision.ipynb b/examples/adult_fairlearn_comparision.ipynb index 94f48c7..f85fa8d 100644 --- a/examples/adult_fairlearn_comparision.ipynb +++ b/examples/adult_fairlearn_comparision.ipynb @@ -4,17 +4,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook compares the overfitting of Fairlearn Vs OxonFair using random forests and decision trees on the adult dataset.\n", + "This notebook compares the overfitting of fairlearn Vs OxonFair using random forests and decision trees on the adult dataset.\n", "\n", "We use sex as the protected attribute.\n", "\n", "Even on this low-dimensional data, the default parameters of scikit-learn cause both decision trees and random forests to overfit. \n", "\n", - "The models obtain 0 error on the training set. As a consequence of this, defintions such as equal opportunity are trivially satisfied, and fairness methods such as fairlearn which enforce fairness on the training set do not work.\n", + "The models obtain 0 error on the training set. As a consequence of this, definitions such as equal opportunity are trivially satisfied, and fairness methods such as fairlearn which enforce fairness on the training set do not work.\n", "\n", - "This overfitting, and the consequential failure of fairness methods to work can be avoided by specifying a low maximimal tree depth. The examples in Fairlearn documentation typically use a tree depth of 4 on adult. \n", + "This overfitting, and the consequential failure of fairness methods to work can be avoided by specifying a low maximal tree depth. The examples in fairlearn documentation typically use a tree depth of 4 on adult. \n", "\n", - "Oxonfair allows for the enforcing of fairness on validation data, and this means that it can enforce fairness even when the training error is zero. " + "OxonFair allows for the enforcing of fairness on validation data, and this means that it can enforce fairness even when the training error is zero. " ] }, { @@ -22,10 +22,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:43.084805Z", - "iopub.status.busy": "2024-06-17T14:23:43.084596Z", - "iopub.status.idle": "2024-06-17T14:23:46.909404Z", - "shell.execute_reply": "2024-06-17T14:23:46.908692Z" + "iopub.execute_input": "2024-06-17T19:18:59.785621Z", + "iopub.status.busy": "2024-06-17T19:18:59.785157Z", + "iopub.status.idle": "2024-06-17T19:19:03.255922Z", + "shell.execute_reply": "2024-06-17T19:19:03.255163Z" } }, "outputs": [ @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:46.912617Z", - "iopub.status.busy": "2024-06-17T14:23:46.912383Z", - "iopub.status.idle": "2024-06-17T14:23:57.108426Z", - "shell.execute_reply": "2024-06-17T14:23:57.099791Z" + "iopub.execute_input": "2024-06-17T19:19:03.258522Z", + "iopub.status.busy": "2024-06-17T19:19:03.258314Z", + "iopub.status.idle": "2024-06-17T19:19:11.578845Z", + "shell.execute_reply": "2024-06-17T19:19:11.578103Z" } }, "outputs": [], @@ -70,7 +70,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now specify a fair predictors over the validation set." + "We now specify a fair predictor over the validation set." ] }, { @@ -78,10 +78,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.113718Z", - "iopub.status.busy": "2024-06-17T14:23:57.113364Z", - "iopub.status.idle": "2024-06-17T14:23:57.301951Z", - "shell.execute_reply": "2024-06-17T14:23:57.299918Z" + "iopub.execute_input": "2024-06-17T19:19:11.581954Z", + "iopub.status.busy": "2024-06-17T19:19:11.581777Z", + "iopub.status.idle": "2024-06-17T19:19:11.785277Z", + "shell.execute_reply": "2024-06-17T19:19:11.784642Z" } }, "outputs": [], @@ -103,10 +103,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.306237Z", - "iopub.status.busy": "2024-06-17T14:23:57.305667Z", - "iopub.status.idle": "2024-06-17T14:23:57.322552Z", - "shell.execute_reply": "2024-06-17T14:23:57.322187Z" + "iopub.execute_input": "2024-06-17T19:19:11.787906Z", + "iopub.status.busy": "2024-06-17T19:19:11.787490Z", + "iopub.status.idle": "2024-06-17T19:19:11.801037Z", + "shell.execute_reply": "2024-06-17T19:19:11.800707Z" } }, "outputs": [], @@ -128,10 +128,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.330665Z", - "iopub.status.busy": "2024-06-17T14:23:57.330454Z", - "iopub.status.idle": "2024-06-17T14:23:57.460715Z", - "shell.execute_reply": "2024-06-17T14:23:57.457845Z" + "iopub.execute_input": "2024-06-17T19:19:11.803236Z", + "iopub.status.busy": "2024-06-17T19:19:11.803044Z", + "iopub.status.idle": "2024-06-17T19:19:11.900913Z", + "shell.execute_reply": "2024-06-17T19:19:11.900314Z" } }, "outputs": [ @@ -163,43 +163,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.189422\n", - " 0.162837\n", + " 0.187550\n", + " 0.160686\n", " \n", " \n", " Predictive Parity\n", - " 0.092910\n", - " 0.092894\n", + " 0.114406\n", + " 0.128911\n", " \n", " \n", " Equal Opportunity\n", - " 0.053580\n", - " 0.000976\n", + " 0.061973\n", + " 0.014374\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.053580\n", - " 0.000976\n", + " 0.061973\n", + " 0.014374\n", " \n", " \n", " Equalized Odds\n", - " 0.080858\n", - " 0.047254\n", + " 0.081831\n", + " 0.049741\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.102704\n", - " 0.111073\n", + " 0.111037\n", + " 0.125743\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.130122\n", - " 0.136368\n", + " 0.121397\n", + " 0.123448\n", " \n", " \n", " Treatment Equality\n", - " 0.178016\n", - " 0.382540\n", + " 0.247530\n", + " 0.523846\n", " \n", " \n", "\n", @@ -207,14 +207,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.189422 0.162837\n", - "Predictive Parity 0.092910 0.092894\n", - "Equal Opportunity 0.053580 0.000976\n", - "Average Group Difference in False Negative Rate 0.053580 0.000976\n", - "Equalized Odds 0.080858 0.047254\n", - "Conditional Use Accuracy 0.102704 0.111073\n", - "Average Group Difference in Accuracy 0.130122 0.136368\n", - "Treatment Equality 0.178016 0.382540" + "Statistical Parity 0.187550 0.160686\n", + "Predictive Parity 0.114406 0.128911\n", + "Equal Opportunity 0.061973 0.014374\n", + "Average Group Difference in False Negative Rate 0.061973 0.014374\n", + "Equalized Odds 0.081831 0.049741\n", + "Conditional Use Accuracy 0.111037 0.125743\n", + "Average Group Difference in Accuracy 0.121397 0.123448\n", + "Treatment Equality 0.247530 0.523846" ] }, "execution_count": 5, @@ -238,10 +238,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.474460Z", - "iopub.status.busy": "2024-06-17T14:23:57.473432Z", - "iopub.status.idle": "2024-06-17T14:23:57.574631Z", - "shell.execute_reply": "2024-06-17T14:23:57.574165Z" + "iopub.execute_input": "2024-06-17T19:19:11.902840Z", + "iopub.status.busy": "2024-06-17T19:19:11.902677Z", + "iopub.status.idle": "2024-06-17T19:19:11.993905Z", + "shell.execute_reply": "2024-06-17T19:19:11.993494Z" } }, "outputs": [ @@ -273,43 +273,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.188561\n", - " 0.163937\n", + " 0.184112\n", + " 0.157874\n", " \n", " \n", " Predictive Parity\n", - " 0.103807\n", - " 0.104589\n", + " 0.115583\n", + " 0.128469\n", " \n", " \n", " Equal Opportunity\n", - " 0.072807\n", - " 0.023210\n", + " 0.063914\n", + " 0.016894\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.072807\n", - " 0.023210\n", + " 0.063914\n", + " 0.016894\n", " \n", " \n", " Equalized Odds\n", - " 0.087895\n", - " 0.056234\n", + " 0.081155\n", + " 0.049132\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.106547\n", - " 0.114582\n", + " 0.112489\n", + " 0.125768\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.124948\n", - " 0.130461\n", + " 0.121499\n", + " 0.122951\n", " \n", " \n", " Treatment Equality\n", - " 0.133225\n", - " 0.323895\n", + " 0.232591\n", + " 0.476311\n", " \n", " \n", "\n", @@ -317,14 +317,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.188561 0.163937\n", - "Predictive Parity 0.103807 0.104589\n", - "Equal Opportunity 0.072807 0.023210\n", - "Average Group Difference in False Negative Rate 0.072807 0.023210\n", - "Equalized Odds 0.087895 0.056234\n", - "Conditional Use Accuracy 0.106547 0.114582\n", - "Average Group Difference in Accuracy 0.124948 0.130461\n", - "Treatment Equality 0.133225 0.323895" + "Statistical Parity 0.184112 0.157874\n", + "Predictive Parity 0.115583 0.128469\n", + "Equal Opportunity 0.063914 0.016894\n", + "Average Group Difference in False Negative Rate 0.063914 0.016894\n", + "Equalized Odds 0.081155 0.049132\n", + "Conditional Use Accuracy 0.112489 0.125768\n", + "Average Group Difference in Accuracy 0.121499 0.122951\n", + "Treatment Equality 0.232591 0.476311" ] }, "execution_count": 6, @@ -348,10 +348,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.578067Z", - "iopub.status.busy": "2024-06-17T14:23:57.577909Z", - "iopub.status.idle": "2024-06-17T14:23:57.637404Z", - "shell.execute_reply": "2024-06-17T14:23:57.636386Z" + "iopub.execute_input": "2024-06-17T19:19:11.995733Z", + "iopub.status.busy": "2024-06-17T19:19:11.995603Z", + "iopub.status.idle": "2024-06-17T19:19:12.063702Z", + "shell.execute_reply": "2024-06-17T19:19:12.063207Z" } }, "outputs": [ @@ -383,38 +383,38 @@ " \n", " \n", " Accuracy\n", - " 0.807781\n", - " 0.804177\n", + " 0.810401\n", + " 0.806552\n", " \n", " \n", " Balanced Accuracy\n", - " 0.741476\n", - " 0.724565\n", + " 0.747187\n", + " 0.732342\n", " \n", " \n", " F1 score\n", - " 0.604682\n", - " 0.582941\n", + " 0.612423\n", + " 0.593460\n", " \n", " \n", " MCC\n", - " 0.477844\n", - " 0.455199\n", + " 0.487204\n", + " 0.466558\n", " \n", " \n", " Precision\n", - " 0.595357\n", - " 0.594450\n", + " 0.599476\n", + " 0.596953\n", " \n", " \n", " Recall\n", - " 0.614305\n", - " 0.571869\n", + " 0.625941\n", + " 0.590007\n", " \n", " \n", " ROC AUC\n", - " 0.741476\n", - " 0.707625\n", + " 0.747187\n", + " 0.703610\n", " \n", " \n", "\n", @@ -422,13 +422,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.807781 0.804177\n", - "Balanced Accuracy 0.741476 0.724565\n", - "F1 score 0.604682 0.582941\n", - "MCC 0.477844 0.455199\n", - "Precision 0.595357 0.594450\n", - "Recall 0.614305 0.571869\n", - "ROC AUC 0.741476 0.707625" + "Accuracy 0.810401 0.806552\n", + "Balanced Accuracy 0.747187 0.732342\n", + "F1 score 0.612423 0.593460\n", + "MCC 0.487204 0.466558\n", + "Precision 0.599476 0.596953\n", + "Recall 0.625941 0.590007\n", + "ROC AUC 0.747187 0.703610" ] }, "execution_count": 7, @@ -452,10 +452,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.640490Z", - "iopub.status.busy": "2024-06-17T14:23:57.640177Z", - "iopub.status.idle": "2024-06-17T14:23:57.790458Z", - "shell.execute_reply": "2024-06-17T14:23:57.779493Z" + "iopub.execute_input": "2024-06-17T19:19:12.066164Z", + "iopub.status.busy": "2024-06-17T19:19:12.066048Z", + "iopub.status.idle": "2024-06-17T19:19:12.158076Z", + "shell.execute_reply": "2024-06-17T19:19:12.157484Z" } }, "outputs": [ @@ -487,38 +487,38 @@ " \n", " \n", " Accuracy\n", - " 0.811236\n", - " 0.808779\n", + " 0.811318\n", + " 0.805995\n", " \n", " \n", " Balanced Accuracy\n", - " 0.742806\n", - " 0.727586\n", + " 0.744150\n", + " 0.725404\n", " \n", " \n", " F1 score\n", - " 0.607927\n", - " 0.588691\n", + " 0.609492\n", + " 0.584750\n", " \n", " \n", " MCC\n", - " 0.483642\n", - " 0.464606\n", + " 0.485152\n", + " 0.458510\n", " \n", " \n", " Precision\n", - " 0.604329\n", - " 0.606534\n", + " 0.603761\n", + " 0.599353\n", " \n", " \n", " Recall\n", - " 0.611567\n", - " 0.571869\n", + " 0.615332\n", + " 0.570842\n", " \n", " \n", " ROC AUC\n", - " 0.742806\n", - " 0.710538\n", + " 0.744109\n", + " 0.699059\n", " \n", " \n", "\n", @@ -526,13 +526,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.811236 0.808779\n", - "Balanced Accuracy 0.742806 0.727586\n", - "F1 score 0.607927 0.588691\n", - "MCC 0.483642 0.464606\n", - "Precision 0.604329 0.606534\n", - "Recall 0.611567 0.571869\n", - "ROC AUC 0.742806 0.710538" + "Accuracy 0.811318 0.805995\n", + "Balanced Accuracy 0.744150 0.725404\n", + "F1 score 0.609492 0.584750\n", + "MCC 0.485152 0.458510\n", + "Precision 0.603761 0.599353\n", + "Recall 0.615332 0.570842\n", + "ROC AUC 0.744109 0.699059" ] }, "execution_count": 8, @@ -556,10 +556,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:57.795447Z", - "iopub.status.busy": "2024-06-17T14:23:57.794318Z", - "iopub.status.idle": "2024-06-17T14:24:03.250519Z", - "shell.execute_reply": "2024-06-17T14:24:03.246133Z" + "iopub.execute_input": "2024-06-17T19:19:12.160567Z", + "iopub.status.busy": "2024-06-17T19:19:12.160349Z", + "iopub.status.idle": "2024-06-17T19:19:17.231466Z", + "shell.execute_reply": "2024-06-17T19:19:17.229146Z" } }, "outputs": [ @@ -998,16 +998,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x16dc12710>,\n",
+       "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x30d1093c0>,\n",
        "                      estimator=DecisionTreeClassifier(),\n",
-       "                      nu=2.0474182056426843e-05)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
DecisionTreeClassifier()
" ], "text/plain": [ - "ExponentiatedGradient(constraints=,\n", + "ExponentiatedGradient(constraints=,\n", " estimator=DecisionTreeClassifier(),\n", - " nu=2.0474182056426843e-05)" + " nu=2.8954273085152237e-05)" ] }, "execution_count": 9, @@ -1025,7 +1025,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To evaluate fairlearn, we write a helper function to evaluate performance and fairness on train or test, and concat the outputs together. " + "To evaluate fairlearn, we write a helper function to evaluate performance and fairness on train or test, and concatenate the outputs together. " ] }, { @@ -1033,10 +1033,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:03.255251Z", - "iopub.status.busy": "2024-06-17T14:24:03.254960Z", - "iopub.status.idle": "2024-06-17T14:24:03.419433Z", - "shell.execute_reply": "2024-06-17T14:24:03.417456Z" + "iopub.execute_input": "2024-06-17T19:19:17.276978Z", + "iopub.status.busy": "2024-06-17T19:19:17.275617Z", + "iopub.status.idle": "2024-06-17T19:19:17.575255Z", + "shell.execute_reply": "2024-06-17T19:19:17.573871Z" } }, "outputs": [ @@ -1068,78 +1068,78 @@ " \n", " \n", " Accuracy\n", - " 0.999959\n", - " 0.811563\n", + " 0.999918\n", + " 0.810990\n", " \n", " \n", " Balanced Accuracy\n", - " 0.999973\n", - " 0.742435\n", + " 0.999888\n", + " 0.742996\n", " \n", " \n", " F1 score\n", - " 0.999914\n", - " 0.607673\n", + " 0.999829\n", + " 0.608016\n", " \n", " \n", " MCC\n", - " 0.999888\n", - " 0.483684\n", + " 0.999775\n", + " 0.483521\n", " \n", " \n", " Precision\n", " 0.999829\n", - " 0.605505\n", + " 0.603506\n", " \n", " \n", " Recall\n", - " 1.000000\n", - " 0.609856\n", + " 0.999829\n", + " 0.612594\n", " \n", " \n", " ROC AUC\n", - " 0.999973\n", - " 0.742435\n", + " 0.999888\n", + " 0.742996\n", " \n", " \n", " Statistical Parity\n", - " 0.194639\n", - " 0.182781\n", + " 0.194762\n", + " 0.180055\n", " \n", " \n", " Predictive Parity\n", " 0.000202\n", - " 0.109963\n", + " 0.131554\n", " \n", " \n", " Equal Opportunity\n", - " 0.000000\n", - " 0.060128\n", + " 0.001131\n", + " 0.068685\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.000000\n", - " 0.060128\n", + " 0.001131\n", + " 0.068685\n", " \n", " \n", " Equalized Odds\n", - " 0.000044\n", - " 0.079177\n", + " 0.000610\n", + " 0.080923\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.000101\n", - " 0.110528\n", + " 0.000170\n", + " 0.120165\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.000061\n", - " 0.123349\n", + " 0.000062\n", + " 0.117185\n", " \n", " \n", " Treatment Equality\n", " 1.000000\n", - " 0.219048\n", + " 0.284807\n", " \n", " \n", "\n", @@ -1147,21 +1147,21 @@ ], "text/plain": [ " train test\n", - "Accuracy 0.999959 0.811563\n", - "Balanced Accuracy 0.999973 0.742435\n", - "F1 score 0.999914 0.607673\n", - "MCC 0.999888 0.483684\n", - "Precision 0.999829 0.605505\n", - "Recall 1.000000 0.609856\n", - "ROC AUC 0.999973 0.742435\n", - "Statistical Parity 0.194639 0.182781\n", - "Predictive Parity 0.000202 0.109963\n", - "Equal Opportunity 0.000000 0.060128\n", - "Average Group Difference in False Negative Rate 0.000000 0.060128\n", - "Equalized Odds 0.000044 0.079177\n", - "Conditional Use Accuracy 0.000101 0.110528\n", - "Average Group Difference in Accuracy 0.000061 0.123349\n", - "Treatment Equality 1.000000 0.219048" + "Accuracy 0.999918 0.810990\n", + "Balanced Accuracy 0.999888 0.742996\n", + "F1 score 0.999829 0.608016\n", + "MCC 0.999775 0.483521\n", + "Precision 0.999829 0.603506\n", + "Recall 0.999829 0.612594\n", + "ROC AUC 0.999888 0.742996\n", + "Statistical Parity 0.194762 0.180055\n", + "Predictive Parity 0.000202 0.131554\n", + "Equal Opportunity 0.001131 0.068685\n", + "Average Group Difference in False Negative Rate 0.001131 0.068685\n", + "Equalized Odds 0.000610 0.080923\n", + "Conditional Use Accuracy 0.000170 0.120165\n", + "Average Group Difference in Accuracy 0.000062 0.117185\n", + "Treatment Equality 1.000000 0.284807" ] }, "execution_count": 10, @@ -1191,10 +1191,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:03.424026Z", - "iopub.status.busy": "2024-06-17T14:24:03.422738Z", - "iopub.status.idle": "2024-06-17T14:24:03.603547Z", - "shell.execute_reply": "2024-06-17T14:24:03.602868Z" + "iopub.execute_input": "2024-06-17T19:19:17.582456Z", + "iopub.status.busy": "2024-06-17T19:19:17.582315Z", + "iopub.status.idle": "2024-06-17T19:19:17.779515Z", + "shell.execute_reply": "2024-06-17T19:19:17.778423Z" } }, "outputs": [ @@ -1226,78 +1226,78 @@ " \n", " \n", " Accuracy\n", - " 0.999959\n", - " 0.811236\n", + " 0.999918\n", + " 0.811318\n", " \n", " \n", " Balanced Accuracy\n", - " 0.999914\n", - " 0.742806\n", + " 0.999829\n", + " 0.744150\n", " \n", " \n", " F1 score\n", - " 0.999914\n", - " 0.607927\n", + " 0.999829\n", + " 0.609492\n", " \n", " \n", " MCC\n", - " 0.999888\n", - " 0.483642\n", + " 0.999775\n", + " 0.485152\n", " \n", " \n", " Precision\n", " 1.000000\n", - " 0.604329\n", + " 0.603761\n", " \n", " \n", " Recall\n", - " 0.999829\n", - " 0.611567\n", + " 0.999658\n", + " 0.615332\n", " \n", " \n", " ROC AUC\n", - " 0.999914\n", - " 0.742806\n", + " 0.999829\n", + " 0.744150\n", " \n", " \n", " Statistical Parity\n", - " 0.194516\n", - " 0.188561\n", + " 0.194640\n", + " 0.184112\n", " \n", " \n", " Predictive Parity\n", " 0.000000\n", - " 0.103807\n", + " 0.115583\n", " \n", " \n", " Equal Opportunity\n", - " 0.000202\n", - " 0.072807\n", + " 0.000930\n", + " 0.063914\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.000202\n", - " 0.072807\n", + " 0.000930\n", + " 0.063914\n", " \n", " \n", " Equalized Odds\n", - " 0.000101\n", - " 0.087895\n", + " 0.000465\n", + " 0.081155\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.000044\n", - " 0.106547\n", + " 0.000025\n", + " 0.112489\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.000061\n", - " 0.124948\n", + " 0.000062\n", + " 0.121499\n", " \n", " \n", " Treatment Equality\n", " 0.000000\n", - " 0.133225\n", + " 0.232591\n", " \n", " \n", "\n", @@ -1305,21 +1305,21 @@ ], "text/plain": [ " train test\n", - "Accuracy 0.999959 0.811236\n", - "Balanced Accuracy 0.999914 0.742806\n", - "F1 score 0.999914 0.607927\n", - "MCC 0.999888 0.483642\n", - "Precision 1.000000 0.604329\n", - "Recall 0.999829 0.611567\n", - "ROC AUC 0.999914 0.742806\n", - "Statistical Parity 0.194516 0.188561\n", - "Predictive Parity 0.000000 0.103807\n", - "Equal Opportunity 0.000202 0.072807\n", - "Average Group Difference in False Negative Rate 0.000202 0.072807\n", - "Equalized Odds 0.000101 0.087895\n", - "Conditional Use Accuracy 0.000044 0.106547\n", - "Average Group Difference in Accuracy 0.000061 0.124948\n", - "Treatment Equality 0.000000 0.133225" + "Accuracy 0.999918 0.811318\n", + "Balanced Accuracy 0.999829 0.744150\n", + "F1 score 0.999829 0.609492\n", + "MCC 0.999775 0.485152\n", + "Precision 1.000000 0.603761\n", + "Recall 0.999658 0.615332\n", + "ROC AUC 0.999829 0.744150\n", + "Statistical Parity 0.194640 0.184112\n", + "Predictive Parity 0.000000 0.115583\n", + "Equal Opportunity 0.000930 0.063914\n", + "Average Group Difference in False Negative Rate 0.000930 0.063914\n", + "Equalized Odds 0.000465 0.081155\n", + "Conditional Use Accuracy 0.000025 0.112489\n", + "Average Group Difference in Accuracy 0.000062 0.121499\n", + "Treatment Equality 0.000000 0.232591" ] }, "execution_count": 11, @@ -1345,10 +1345,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:03.621668Z", - "iopub.status.busy": "2024-06-17T14:24:03.620199Z", - "iopub.status.idle": "2024-06-17T14:24:04.349861Z", - "shell.execute_reply": "2024-06-17T14:24:04.348729Z" + "iopub.execute_input": "2024-06-17T19:19:17.784220Z", + "iopub.status.busy": "2024-06-17T19:19:17.784081Z", + "iopub.status.idle": "2024-06-17T19:19:18.373565Z", + "shell.execute_reply": "2024-06-17T19:19:18.372593Z" } }, "outputs": [ @@ -1380,43 +1380,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.170142\n", - " 0.138165\n", + " 0.177003\n", + " 0.141355\n", " \n", " \n", " Predictive Parity\n", - " 0.006499\n", - " 0.031893\n", + " 0.007289\n", + " 0.083214\n", " \n", " \n", " Equal Opportunity\n", - " 0.062601\n", - " 0.012177\n", + " 0.090675\n", + " 0.002777\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.062601\n", - " 0.012177\n", + " 0.090675\n", + " 0.002777\n", " \n", " \n", " Equalized Odds\n", - " 0.066470\n", - " 0.029292\n", + " 0.079664\n", + " 0.022729\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.055173\n", - " 0.074444\n", + " 0.050883\n", + " 0.096031\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.113257\n", - " 0.111909\n", + " 0.103941\n", + " 0.099758\n", " \n", " \n", " Treatment Equality\n", - " 0.138755\n", - " 0.049118\n", + " 0.165954\n", + " 0.228167\n", " \n", " \n", "\n", @@ -1424,14 +1424,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.170142 0.138165\n", - "Predictive Parity 0.006499 0.031893\n", - "Equal Opportunity 0.062601 0.012177\n", - "Average Group Difference in False Negative Rate 0.062601 0.012177\n", - "Equalized Odds 0.066470 0.029292\n", - "Conditional Use Accuracy 0.055173 0.074444\n", - "Average Group Difference in Accuracy 0.113257 0.111909\n", - "Treatment Equality 0.138755 0.049118" + "Statistical Parity 0.177003 0.141355\n", + "Predictive Parity 0.007289 0.083214\n", + "Equal Opportunity 0.090675 0.002777\n", + "Average Group Difference in False Negative Rate 0.090675 0.002777\n", + "Equalized Odds 0.079664 0.022729\n", + "Conditional Use Accuracy 0.050883 0.096031\n", + "Average Group Difference in Accuracy 0.103941 0.099758\n", + "Treatment Equality 0.165954 0.228167" ] }, "execution_count": 12, @@ -1448,10 +1448,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:04.355178Z", - "iopub.status.busy": "2024-06-17T14:24:04.354801Z", - "iopub.status.idle": "2024-06-17T14:24:04.844944Z", - "shell.execute_reply": "2024-06-17T14:24:04.844470Z" + "iopub.execute_input": "2024-06-17T19:19:18.378485Z", + "iopub.status.busy": "2024-06-17T19:19:18.378328Z", + "iopub.status.idle": "2024-06-17T19:19:18.869257Z", + "shell.execute_reply": "2024-06-17T19:19:18.868023Z" } }, "outputs": [ @@ -1483,43 +1483,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.180806\n", - " 0.146137\n", + " 0.177476\n", + " 0.143325\n", " \n", " \n", " Predictive Parity\n", - " 0.005442\n", - " 0.041881\n", + " 0.010084\n", + " 0.070287\n", " \n", " \n", " Equal Opportunity\n", - " 0.114180\n", - " 0.056922\n", + " 0.099217\n", + " 0.013713\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.114180\n", - " 0.056922\n", + " 0.099217\n", + " 0.013713\n", " \n", " \n", " Equalized Odds\n", - " 0.094411\n", - " 0.053377\n", + " 0.084680\n", + " 0.029714\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.051040\n", - " 0.076988\n", + " 0.052987\n", + " 0.090258\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.108786\n", - " 0.108908\n", + " 0.105741\n", + " 0.103289\n", " \n", " \n", " Treatment Equality\n", - " 0.209692\n", - " 0.001891\n", + " 0.172701\n", + " 0.155497\n", " \n", " \n", "\n", @@ -1527,14 +1527,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.180806 0.146137\n", - "Predictive Parity 0.005442 0.041881\n", - "Equal Opportunity 0.114180 0.056922\n", - "Average Group Difference in False Negative Rate 0.114180 0.056922\n", - "Equalized Odds 0.094411 0.053377\n", - "Conditional Use Accuracy 0.051040 0.076988\n", - "Average Group Difference in Accuracy 0.108786 0.108908\n", - "Treatment Equality 0.209692 0.001891" + "Statistical Parity 0.177476 0.143325\n", + "Predictive Parity 0.010084 0.070287\n", + "Equal Opportunity 0.099217 0.013713\n", + "Average Group Difference in False Negative Rate 0.099217 0.013713\n", + "Equalized Odds 0.084680 0.029714\n", + "Conditional Use Accuracy 0.052987 0.090258\n", + "Average Group Difference in Accuracy 0.105741 0.103289\n", + "Treatment Equality 0.172701 0.155497" ] }, "execution_count": 13, @@ -1551,10 +1551,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:04.849241Z", - "iopub.status.busy": "2024-06-17T14:24:04.848858Z", - "iopub.status.idle": "2024-06-17T14:24:05.337686Z", - "shell.execute_reply": "2024-06-17T14:24:05.336940Z" + "iopub.execute_input": "2024-06-17T19:19:18.874390Z", + "iopub.status.busy": "2024-06-17T19:19:18.873293Z", + "iopub.status.idle": "2024-06-17T19:19:19.363648Z", + "shell.execute_reply": "2024-06-17T19:19:19.363084Z" } }, "outputs": [ @@ -1586,38 +1586,38 @@ " \n", " \n", " Accuracy\n", - " 0.854627\n", - " 0.855528\n", + " 0.860360\n", + " 0.860934\n", " \n", " \n", " Balanced Accuracy\n", - " 0.767929\n", - " 0.753861\n", + " 0.779087\n", + " 0.773247\n", " \n", " \n", " F1 score\n", - " 0.664525\n", - " 0.649304\n", + " 0.681130\n", + " 0.675583\n", " \n", " \n", " MCC\n", - " 0.578162\n", - " 0.573177\n", + " 0.596944\n", + " 0.595141\n", " \n", " \n", " Precision\n", - " 0.742085\n", - " 0.774668\n", + " 0.750928\n", + " 0.764706\n", " \n", " \n", " Recall\n", - " 0.601643\n", - " 0.558864\n", + " 0.623203\n", + " 0.605065\n", " \n", " \n", " ROC AUC\n", - " 0.903254\n", - " 0.895934\n", + " 0.908998\n", + " 0.895994\n", " \n", " \n", "\n", @@ -1625,13 +1625,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.854627 0.855528\n", - "Balanced Accuracy 0.767929 0.753861\n", - "F1 score 0.664525 0.649304\n", - "MCC 0.578162 0.573177\n", - "Precision 0.742085 0.774668\n", - "Recall 0.601643 0.558864\n", - "ROC AUC 0.903254 0.895934" + "Accuracy 0.860360 0.860934\n", + "Balanced Accuracy 0.779087 0.773247\n", + "F1 score 0.681130 0.675583\n", + "MCC 0.596944 0.595141\n", + "Precision 0.750928 0.764706\n", + "Recall 0.623203 0.605065\n", + "ROC AUC 0.908998 0.895994" ] }, "execution_count": 14, @@ -1648,10 +1648,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:05.342050Z", - "iopub.status.busy": "2024-06-17T14:24:05.341589Z", - "iopub.status.idle": "2024-06-17T14:24:05.840801Z", - "shell.execute_reply": "2024-06-17T14:24:05.838814Z" + "iopub.execute_input": "2024-06-17T19:19:19.369589Z", + "iopub.status.busy": "2024-06-17T19:19:19.369306Z", + "iopub.status.idle": "2024-06-17T19:19:19.867763Z", + "shell.execute_reply": "2024-06-17T19:19:19.866160Z" } }, "outputs": [ @@ -1683,38 +1683,38 @@ " \n", " \n", " Accuracy\n", - " 0.852919\n", - " 0.852838\n", + " 0.857178\n", + " 0.858570\n", " \n", " \n", " Balanced Accuracy\n", - " 0.767506\n", - " 0.750798\n", + " 0.773707\n", + " 0.767584\n", " \n", " \n", " F1 score\n", - " 0.662660\n", - " 0.643523\n", + " 0.672795\n", + " 0.667437\n", " \n", " \n", " MCC\n", - " 0.574235\n", - " 0.565098\n", + " 0.586992\n", + " 0.586832\n", " \n", " \n", " Precision\n", - " 0.734388\n", - " 0.765455\n", + " 0.744601\n", + " 0.763100\n", " \n", " \n", " Recall\n", - " 0.603696\n", - " 0.555099\n", + " 0.613621\n", + " 0.593087\n", " \n", " \n", " ROC AUC\n", - " 0.904493\n", - " 0.897846\n", + " 0.905340\n", + " 0.891552\n", " \n", " \n", "\n", @@ -1722,13 +1722,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.852919 0.852838\n", - "Balanced Accuracy 0.767506 0.750798\n", - "F1 score 0.662660 0.643523\n", - "MCC 0.574235 0.565098\n", - "Precision 0.734388 0.765455\n", - "Recall 0.603696 0.555099\n", - "ROC AUC 0.904493 0.897846" + "Accuracy 0.857178 0.858570\n", + "Balanced Accuracy 0.773707 0.767584\n", + "F1 score 0.672795 0.667437\n", + "MCC 0.586992 0.586832\n", + "Precision 0.744601 0.763100\n", + "Recall 0.613621 0.593087\n", + "ROC AUC 0.905340 0.891552" ] }, "execution_count": 15, @@ -1745,10 +1745,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:05.847447Z", - "iopub.status.busy": "2024-06-17T14:24:05.846985Z", - "iopub.status.idle": "2024-06-17T14:25:29.311778Z", - "shell.execute_reply": "2024-06-17T14:25:29.309206Z" + "iopub.execute_input": "2024-06-17T19:19:19.872821Z", + "iopub.status.busy": "2024-06-17T19:19:19.872014Z", + "iopub.status.idle": "2024-06-17T19:20:36.544358Z", + "shell.execute_reply": "2024-06-17T19:20:36.539163Z" } }, "outputs": [ @@ -2187,16 +2187,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x16de40220>,\n",
+       "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x30d33eb30>,\n",
        "                      estimator=RandomForestClassifier(),\n",
-       "                      nu=2.8954273085152237e-05)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier()
RandomForestClassifier()
" ], "text/plain": [ - "ExponentiatedGradient(constraints=,\n", + "ExponentiatedGradient(constraints=,\n", " estimator=RandomForestClassifier(),\n", - " nu=2.8954273085152237e-05)" + " nu=3.5460871351809455e-05)" ] }, "execution_count": 16, @@ -2214,10 +2214,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:25:29.331676Z", - "iopub.status.busy": "2024-06-17T14:25:29.331403Z", - "iopub.status.idle": "2024-06-17T14:25:30.284707Z", - "shell.execute_reply": "2024-06-17T14:25:30.284409Z" + "iopub.execute_input": "2024-06-17T19:20:36.584508Z", + "iopub.status.busy": "2024-06-17T19:20:36.584141Z", + "iopub.status.idle": "2024-06-17T19:20:37.862485Z", + "shell.execute_reply": "2024-06-17T19:20:37.862191Z" } }, "outputs": [ @@ -2249,78 +2249,78 @@ " \n", " \n", " Accuracy\n", - " 0.999959\n", - " 0.854803\n", + " 0.999918\n", + " 0.855540\n", " \n", " \n", " Balanced Accuracy\n", - " 0.999914\n", - " 0.769331\n", + " 0.999888\n", + " 0.770050\n", " \n", " \n", " F1 score\n", - " 0.999914\n", - " 0.666165\n", + " 0.999829\n", + " 0.667546\n", " \n", " \n", " MCC\n", - " 0.999888\n", - " 0.579339\n", + " 0.999775\n", + " 0.581346\n", " \n", " \n", " Precision\n", - " 1.000000\n", - " 0.740477\n", + " 0.999829\n", + " 0.742869\n", " \n", " \n", " Recall\n", " 0.999829\n", - " 0.605407\n", + " 0.606092\n", " \n", " \n", " ROC AUC\n", - " 0.999914\n", - " 0.769331\n", + " 0.999888\n", + " 0.770050\n", " \n", " \n", " Statistical Parity\n", - " 0.194516\n", - " 0.180692\n", + " 0.194762\n", + " 0.180079\n", " \n", " \n", " Predictive Parity\n", - " 0.000000\n", - " 0.005158\n", + " 0.000202\n", + " 0.011680\n", " \n", " \n", " Equal Opportunity\n", - " 0.000202\n", - " 0.118862\n", + " 0.001131\n", + " 0.122334\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.000202\n", - " 0.118862\n", + " 0.001131\n", + " 0.122334\n", " \n", " \n", " Equalized Odds\n", - " 0.000101\n", - " 0.095849\n", + " 0.000610\n", + " 0.096742\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.000044\n", - " 0.050187\n", + " 0.000170\n", + " 0.053005\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.000061\n", - " 0.106707\n", + " 0.000062\n", + " 0.104866\n", " \n", " \n", " Treatment Equality\n", - " 0.000000\n", - " 0.212862\n", + " 1.000000\n", + " 0.203247\n", " \n", " \n", "\n", @@ -2328,21 +2328,21 @@ ], "text/plain": [ " train test\n", - "Accuracy 0.999959 0.854803\n", - "Balanced Accuracy 0.999914 0.769331\n", - "F1 score 0.999914 0.666165\n", - "MCC 0.999888 0.579339\n", - "Precision 1.000000 0.740477\n", - "Recall 0.999829 0.605407\n", - "ROC AUC 0.999914 0.769331\n", - "Statistical Parity 0.194516 0.180692\n", - "Predictive Parity 0.000000 0.005158\n", - "Equal Opportunity 0.000202 0.118862\n", - "Average Group Difference in False Negative Rate 0.000202 0.118862\n", - "Equalized Odds 0.000101 0.095849\n", - "Conditional Use Accuracy 0.000044 0.050187\n", - "Average Group Difference in Accuracy 0.000061 0.106707\n", - "Treatment Equality 0.000000 0.212862" + "Accuracy 0.999918 0.855540\n", + "Balanced Accuracy 0.999888 0.770050\n", + "F1 score 0.999829 0.667546\n", + "MCC 0.999775 0.581346\n", + "Precision 0.999829 0.742869\n", + "Recall 0.999829 0.606092\n", + "ROC AUC 0.999888 0.770050\n", + "Statistical Parity 0.194762 0.180079\n", + "Predictive Parity 0.000202 0.011680\n", + "Equal Opportunity 0.001131 0.122334\n", + "Average Group Difference in False Negative Rate 0.001131 0.122334\n", + "Equalized Odds 0.000610 0.096742\n", + "Conditional Use Accuracy 0.000170 0.053005\n", + "Average Group Difference in Accuracy 0.000062 0.104866\n", + "Treatment Equality 1.000000 0.203247" ] }, "execution_count": 17, diff --git a/examples/building_datasets.ipynb b/examples/building_datasets.ipynb index e435c92..060149f 100644 --- a/examples/building_datasets.ipynb +++ b/examples/building_datasets.ipynb @@ -40,10 +40,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:43.082144Z", - "iopub.status.busy": "2024-06-17T14:23:43.082023Z", - "iopub.status.idle": "2024-06-17T14:23:58.299261Z", - "shell.execute_reply": "2024-06-17T14:23:58.297989Z" + "iopub.execute_input": "2024-06-17T19:18:59.785842Z", + "iopub.status.busy": "2024-06-17T19:18:59.785348Z", + "iopub.status.idle": "2024-06-17T19:19:18.875653Z", + "shell.execute_reply": "2024-06-17T19:19:18.872769Z" } }, "outputs": [ @@ -59,7 +59,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142353\"\n" + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191913\"\n" ] }, { @@ -85,7 +85,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142353\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191913\"\n" ] }, { @@ -99,8 +99,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 8.10 GB / 16.00 GB (50.6%)\n", - "Disk Space Avail: 363.56 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 6.12 GB / 16.00 GB (38.3%)\n", + "Disk Space Avail: 360.80 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -193,7 +193,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 8318.38 MB\n" + "\tAvailable Memory: 6291.05 MB\n" ] }, { @@ -354,7 +354,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.1s = Fit runtime\n" + "\t0.2s = Fit runtime\n" ] }, { @@ -375,7 +375,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Data preprocessing and feature engineering runtime = 0.18s ...\n" + "Data preprocessing and feature engineering runtime = 0.31s ...\n" ] }, { @@ -427,7 +427,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsUnif ... Training model for up to 2.82s of the 2.82s of remaining time.\n" + "Fitting model: KNeighborsUnif ... Training model for up to 2.69s of the 2.69s of remaining time.\n" ] }, { @@ -441,119 +441,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t1.47s\t = Training runtime\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\t0.1s\t = Validation runtime\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 1.24s of the 1.24s of remaining time.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\t0.766\t = Validation score (accuracy)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\t0.03s\t = Training runtime\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\t0.04s\t = Validation runtime\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fitting model: LightGBMXT ... Training model for up to 1.14s of the 1.14s of remaining time.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: \n", - "Dask dataframe query planning is disabled because dask-expr is not installed.\n", - "\n", - "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n", - "This will raise in a future version.\n", - "\n", - " warnings.warn(msg, FutureWarning)\n" + "\t4.12s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tRan out of time, early stopping on iteration 70. Best iteration is:\n", - "\t[58]\tvalid_set's binary_error: 0.1328\n" + "\t0.06s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.8672\t = Validation score (accuracy)\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.69s of the -1.67s of remaining time.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t2.64s\t = Training runtime\n" + "\tEnsemble Weights: {'KNeighborsUnif': 1.0}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.01s\t = Validation runtime\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.82s of the -1.63s of remaining time.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\tEnsemble Weights: {'LightGBMXT': 1.0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\t0.8672\t = Validation score (accuracy)\n" + "\t0.7752\t = Validation score (accuracy)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Training runtime\n" + "\t0.01s\t = Training runtime\n" ] }, { @@ -567,14 +490,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 4.74s ... Best model: \"WeightedEnsemble_L2\"\n" + "AutoGluon training complete, total runtime = 4.81s ... Best model: \"WeightedEnsemble_L2\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142353\")\n" + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191913\")\n" ] } ], @@ -594,10 +517,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:58.303672Z", - "iopub.status.busy": "2024-06-17T14:23:58.302733Z", - "iopub.status.idle": "2024-06-17T14:23:58.687650Z", - "shell.execute_reply": "2024-06-17T14:23:58.687313Z" + "iopub.execute_input": "2024-06-17T19:19:18.883486Z", + "iopub.status.busy": "2024-06-17T19:19:18.882443Z", + "iopub.status.idle": "2024-06-17T19:19:19.486093Z", + "shell.execute_reply": "2024-06-17T19:19:19.484472Z" } }, "outputs": [ @@ -655,116 +578,116 @@ " \n", " original\n", " Overall\n", - " 0.863036\n", - " 0.771720\n", - " 0.674453\n", - " 0.597347\n", - " 0.773438\n", - " 0.597929\n", - " 0.918163\n", + " 0.773365\n", + " 0.621548\n", + " 0.410543\n", + " 0.291887\n", + " 0.536161\n", + " 0.332614\n", + " 0.670449\n", " 2318.0\n", " 7451.0\n", " 0.237281\n", - " 0.183437\n", + " 0.147200\n", " \n", " \n", " Female\n", - " 0.934631\n", - " 0.760266\n", - " 0.638655\n", - " 0.617120\n", - " 0.785124\n", - " 0.538244\n", - " 0.938701\n", + " 0.860444\n", + " 0.624007\n", + " 0.331878\n", + " 0.254150\n", + " 0.341317\n", + " 0.322946\n", + " 0.651070\n", " 353.0\n", " 2936.0\n", " 0.107327\n", - " 0.073579\n", + " 0.101551\n", " \n", " \n", " Male\n", - " 0.826698\n", - " 0.765123\n", - " 0.680512\n", - " 0.571345\n", - " 0.771613\n", - " 0.608651\n", - " 0.896625\n", + " 0.729167\n", + " 0.617674\n", + " 0.428152\n", + " 0.287744\n", + " 0.595109\n", + " 0.334351\n", + " 0.667435\n", " 1965.0\n", " 4515.0\n", " 0.303241\n", - " 0.239198\n", + " 0.170370\n", " \n", " \n", " Maximum difference\n", - " 0.107933\n", - " 0.004857\n", - " 0.041857\n", - " 0.045775\n", - " 0.013511\n", - " 0.070408\n", - " 0.042075\n", + " 0.131277\n", + " 0.006333\n", + " 0.096275\n", + " 0.033594\n", + " 0.253791\n", + " 0.011405\n", + " 0.016364\n", " 1612.0\n", " 1579.0\n", " 0.195913\n", - " 0.165619\n", + " 0.068820\n", " \n", " \n", " updated\n", " Overall\n", - " 0.843177\n", - " 0.736263\n", - " 0.617191\n", - " 0.532205\n", - " 0.733373\n", - " 0.532787\n", - " 0.819932\n", + " 0.782270\n", + " 0.594546\n", + " 0.340874\n", + " 0.276910\n", + " 0.605061\n", + " 0.237274\n", + " 0.595486\n", " 2318.0\n", " 7451.0\n", " 0.237281\n", - " 0.172382\n", + " 0.093049\n", " \n", " \n", " Female\n", - " 0.894193\n", - " 0.859737\n", - " 0.623377\n", - " 0.587947\n", - " 0.504378\n", - " 0.815864\n", - " 0.938701\n", + " 0.860444\n", + " 0.624007\n", + " 0.331878\n", + " 0.254150\n", + " 0.341317\n", + " 0.322946\n", + " 0.651070\n", " 353.0\n", " 2936.0\n", " 0.107327\n", - " 0.173609\n", + " 0.101551\n", " \n", " \n", " Male\n", - " 0.817284\n", - " 0.722584\n", - " 0.615335\n", - " 0.542526\n", - " 0.850854\n", - " 0.481934\n", - " 0.896625\n", + " 0.742593\n", + " 0.595548\n", + " 0.343307\n", + " 0.308901\n", + " 0.758261\n", + " 0.221883\n", + " 0.667435\n", " 1965.0\n", " 4515.0\n", " 0.303241\n", - " 0.171759\n", + " 0.088735\n", " \n", " \n", " Maximum difference\n", - " 0.076909\n", - " 0.137153\n", - " 0.008042\n", - " 0.045421\n", - " 0.346475\n", - " 0.333930\n", - " 0.042075\n", + " 0.117851\n", + " 0.028459\n", + " 0.011429\n", + " 0.054751\n", + " 0.416944\n", + " 0.101063\n", + " 0.016364\n", " 1612.0\n", " 1579.0\n", " 0.195913\n", - " 0.001850\n", + " 0.012816\n", " \n", " \n", "\n", @@ -773,25 +696,25 @@ "text/plain": [ " Accuracy Balanced Accuracy F1 score MCC \\\n", " Groups \n", - "original Overall 0.863036 0.771720 0.674453 0.597347 \n", - " Female 0.934631 0.760266 0.638655 0.617120 \n", - " Male 0.826698 0.765123 0.680512 0.571345 \n", - " Maximum difference 0.107933 0.004857 0.041857 0.045775 \n", - "updated Overall 0.843177 0.736263 0.617191 0.532205 \n", - " Female 0.894193 0.859737 0.623377 0.587947 \n", - " Male 0.817284 0.722584 0.615335 0.542526 \n", - " Maximum difference 0.076909 0.137153 0.008042 0.045421 \n", + "original Overall 0.773365 0.621548 0.410543 0.291887 \n", + " Female 0.860444 0.624007 0.331878 0.254150 \n", + " Male 0.729167 0.617674 0.428152 0.287744 \n", + " Maximum difference 0.131277 0.006333 0.096275 0.033594 \n", + "updated Overall 0.782270 0.594546 0.340874 0.276910 \n", + " Female 0.860444 0.624007 0.331878 0.254150 \n", + " Male 0.742593 0.595548 0.343307 0.308901 \n", + " Maximum difference 0.117851 0.028459 0.011429 0.054751 \n", "\n", " Precision Recall ROC AUC Positive Count \\\n", " Groups \n", - "original Overall 0.773438 0.597929 0.918163 2318.0 \n", - " Female 0.785124 0.538244 0.938701 353.0 \n", - " Male 0.771613 0.608651 0.896625 1965.0 \n", - " Maximum difference 0.013511 0.070408 0.042075 1612.0 \n", - "updated Overall 0.733373 0.532787 0.819932 2318.0 \n", - " Female 0.504378 0.815864 0.938701 353.0 \n", - " Male 0.850854 0.481934 0.896625 1965.0 \n", - " Maximum difference 0.346475 0.333930 0.042075 1612.0 \n", + "original Overall 0.536161 0.332614 0.670449 2318.0 \n", + " Female 0.341317 0.322946 0.651070 353.0 \n", + " Male 0.595109 0.334351 0.667435 1965.0 \n", + " Maximum difference 0.253791 0.011405 0.016364 1612.0 \n", + "updated Overall 0.605061 0.237274 0.595486 2318.0 \n", + " Female 0.341317 0.322946 0.651070 353.0 \n", + " Male 0.758261 0.221883 0.667435 1965.0 \n", + " Maximum difference 0.416944 0.101063 0.016364 1612.0 \n", "\n", " Negative Count Positive Label Rate \\\n", " Groups \n", @@ -806,14 +729,14 @@ "\n", " Positive Prediction Rate \n", " Groups \n", - "original Overall 0.183437 \n", - " Female 0.073579 \n", - " Male 0.239198 \n", - " Maximum difference 0.165619 \n", - "updated Overall 0.172382 \n", - " Female 0.173609 \n", - " Male 0.171759 \n", - " Maximum difference 0.001850 " + "original Overall 0.147200 \n", + " Female 0.101551 \n", + " Male 0.170370 \n", + " Maximum difference 0.068820 \n", + "updated Overall 0.093049 \n", + " Female 0.101551 \n", + " Male 0.088735 \n", + " Maximum difference 0.012816 " ] }, "execution_count": 2, @@ -842,10 +765,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:58.695612Z", - "iopub.status.busy": "2024-06-17T14:23:58.694770Z", - "iopub.status.idle": "2024-06-17T14:24:05.935164Z", - "shell.execute_reply": "2024-06-17T14:24:05.934287Z" + "iopub.execute_input": "2024-06-17T19:19:19.499622Z", + "iopub.status.busy": "2024-06-17T19:19:19.498937Z", + "iopub.status.idle": "2024-06-17T19:19:28.645223Z", + "shell.execute_reply": "2024-06-17T19:19:28.644408Z" } }, "outputs": [ @@ -853,7 +776,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142358\"\n" + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191919\"\n" ] }, { @@ -879,7 +802,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142358\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191919\"\n" ] }, { @@ -893,8 +816,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 6.16 GB / 16.00 GB (38.5%)\n", - "Disk Space Avail: 363.53 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 3.22 GB / 16.00 GB (20.1%)\n", + "Disk Space Avail: 360.79 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -987,14 +910,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 6323.39 MB\n" + "\tAvailable Memory: 3320.73 MB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tTrain Data (Original) Memory Usage: 19.53 MB (0.3% of available memory)\n" + "\tTrain Data (Original) Memory Usage: 19.53 MB (0.6% of available memory)\n" ] }, { @@ -1148,7 +1071,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tTrain Data (Processed) Memory Usage: 2.05 MB (0.0% of available memory)\n" + "\tTrain Data (Processed) Memory Usage: 2.05 MB (0.1% of available memory)\n" ] }, { @@ -1235,7 +1158,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 2.71s of the 2.7s of remaining time.\n" + "Fitting model: KNeighborsDist ... Training model for up to 2.67s of the 2.67s of remaining time.\n" ] }, { @@ -1249,7 +1172,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.03s\t = Training runtime\n" + "\t0.05s\t = Training runtime\n" ] }, { @@ -1263,14 +1186,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: LightGBMXT ... Training model for up to 2.64s of the 2.64s of remaining time.\n" + "Fitting model: LightGBMXT ... Training model for up to 2.57s of the 2.57s of remaining time.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: \n", + "Dask dataframe query planning is disabled because dask-expr is not installed.\n", + "\n", + "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n", + "This will raise in a future version.\n", + "\n", + " warnings.warn(msg, FutureWarning)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tRan out of time, early stopping on iteration 124. Best iteration is:\n", + "\tRan out of time, early stopping on iteration 104. Best iteration is:\n", "\t[68]\tvalid_set's binary_error: 0.1288\n" ] }, @@ -1285,21 +1221,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t2.67s\t = Training runtime\n" + "\t4.15s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.01s\t = Validation runtime\n" + "\t0.03s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.77s of the -0.06s of remaining time.\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.77s of the -1.79s of remaining time.\n" ] }, { @@ -1320,7 +1256,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Training runtime\n" + "\t0.03s\t = Training runtime\n" ] }, { @@ -1334,21 +1270,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 3.16s ... Best model: \"WeightedEnsemble_L2\"\n" + "AutoGluon training complete, total runtime = 4.91s ... Best model: \"WeightedEnsemble_L2\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142358\")\n" + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191919\")\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142401\"\n" + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191924\"\n" ] }, { @@ -1374,7 +1310,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142401\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191924\"\n" ] }, { @@ -1388,8 +1324,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 5.80 GB / 16.00 GB (36.2%)\n", - "Disk Space Avail: 363.52 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 2.80 GB / 16.00 GB (17.5%)\n", + "Disk Space Avail: 360.78 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -1482,14 +1418,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 5956.42 MB\n" + "\tAvailable Memory: 2869.17 MB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tTrain Data (Original) Memory Usage: 19.53 MB (0.3% of available memory)\n" + "\tTrain Data (Original) Memory Usage: 19.53 MB (0.7% of available memory)\n" ] }, { @@ -1643,14 +1579,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tTrain Data (Processed) Memory Usage: 2.05 MB (0.0% of available memory)\n" + "\tTrain Data (Processed) Memory Usage: 2.05 MB (0.1% of available memory)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Data preprocessing and feature engineering runtime = 0.23s ...\n" + "Data preprocessing and feature engineering runtime = 0.28s ...\n" ] }, { @@ -1702,7 +1638,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsUnif ... Training model for up to 2.77s of the 2.77s of remaining time.\n" + "Fitting model: KNeighborsUnif ... Training model for up to 2.72s of the 2.71s of remaining time.\n" ] }, { @@ -1716,21 +1652,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.05s\t = Training runtime\n" + "\t0.04s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Validation runtime\n" + "\t0.04s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 2.67s of the 2.67s of remaining time.\n" + "Fitting model: KNeighborsDist ... Training model for up to 2.61s of the 2.61s of remaining time.\n" ] }, { @@ -1744,28 +1680,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.03s\t = Training runtime\n" + "\t0.05s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.03s\t = Validation runtime\n" + "\t0.02s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: LightGBMXT ... Training model for up to 2.61s of the 2.61s of remaining time.\n" + "Fitting model: LightGBMXT ... Training model for up to 2.53s of the 2.52s of remaining time.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tRan out of time, early stopping on iteration 108. Best iteration is:\n", + "\tRan out of time, early stopping on iteration 114. Best iteration is:\n", "\t[35]\tvalid_set's binary_error: 0.1476\n" ] }, @@ -1780,21 +1716,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t2.63s\t = Training runtime\n" + "\t2.57s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.01s\t = Validation runtime\n" + "\t0.03s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.77s of the -0.12s of remaining time.\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 2.72s of the -0.14s of remaining time.\n" ] }, { @@ -1815,7 +1751,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.03s\t = Training runtime\n" + "\t0.04s\t = Training runtime\n" ] }, { @@ -1829,14 +1765,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 3.23s ... Best model: \"WeightedEnsemble_L2\"\n" + "AutoGluon training complete, total runtime = 3.25s ... Best model: \"WeightedEnsemble_L2\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142401\")\n" + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191924\")\n" ] }, { @@ -2081,10 +2017,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:05.948220Z", - "iopub.status.busy": "2024-06-17T14:24:05.947769Z", - "iopub.status.idle": "2024-06-17T14:24:07.716296Z", - "shell.execute_reply": "2024-06-17T14:24:07.713465Z" + "iopub.execute_input": "2024-06-17T19:19:28.649192Z", + "iopub.status.busy": "2024-06-17T19:19:28.648259Z", + "iopub.status.idle": "2024-06-17T19:19:29.660928Z", + "shell.execute_reply": "2024-06-17T19:19:29.659894Z" } }, "outputs": [ @@ -2567,10 +2503,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:07.744222Z", - "iopub.status.busy": "2024-06-17T14:24:07.742915Z", - "iopub.status.idle": "2024-06-17T14:24:08.203473Z", - "shell.execute_reply": "2024-06-17T14:24:08.202967Z" + "iopub.execute_input": "2024-06-17T19:19:29.663790Z", + "iopub.status.busy": "2024-06-17T19:19:29.663594Z", + "iopub.status.idle": "2024-06-17T19:19:30.022265Z", + "shell.execute_reply": "2024-06-17T19:19:30.020948Z" } }, "outputs": [ @@ -2808,10 +2744,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:08.208364Z", - "iopub.status.busy": "2024-06-17T14:24:08.208212Z", - "iopub.status.idle": "2024-06-17T14:24:08.304035Z", - "shell.execute_reply": "2024-06-17T14:24:08.302735Z" + "iopub.execute_input": "2024-06-17T19:19:30.026203Z", + "iopub.status.busy": "2024-06-17T19:19:30.025926Z", + "iopub.status.idle": "2024-06-17T19:19:30.089869Z", + "shell.execute_reply": "2024-06-17T19:19:30.089068Z" } }, "outputs": [ @@ -2843,10 +2779,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:08.310162Z", - "iopub.status.busy": "2024-06-17T14:24:08.309168Z", - "iopub.status.idle": "2024-06-17T14:24:10.100622Z", - "shell.execute_reply": "2024-06-17T14:24:10.098153Z" + "iopub.execute_input": "2024-06-17T19:19:30.095041Z", + "iopub.status.busy": "2024-06-17T19:19:30.094581Z", + "iopub.status.idle": "2024-06-17T19:19:31.582712Z", + "shell.execute_reply": "2024-06-17T19:19:31.581172Z" } }, "outputs": [ @@ -3320,10 +3256,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:10.108349Z", - "iopub.status.busy": "2024-06-17T14:24:10.108087Z", - "iopub.status.idle": "2024-06-17T14:24:10.773570Z", - "shell.execute_reply": "2024-06-17T14:24:10.772634Z" + "iopub.execute_input": "2024-06-17T19:19:31.589248Z", + "iopub.status.busy": "2024-06-17T19:19:31.588579Z", + "iopub.status.idle": "2024-06-17T19:19:32.229382Z", + "shell.execute_reply": "2024-06-17T19:19:32.228674Z" } }, "outputs": [ @@ -3568,10 +3504,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:10.778568Z", - "iopub.status.busy": "2024-06-17T14:24:10.778204Z", - "iopub.status.idle": "2024-06-17T14:24:10.903411Z", - "shell.execute_reply": "2024-06-17T14:24:10.902785Z" + "iopub.execute_input": "2024-06-17T19:19:32.232925Z", + "iopub.status.busy": "2024-06-17T19:19:32.232695Z", + "iopub.status.idle": "2024-06-17T19:19:32.352089Z", + "shell.execute_reply": "2024-06-17T19:19:32.350466Z" } }, "outputs": [ diff --git a/examples/compas_autogluon.ipynb b/examples/compas_autogluon.ipynb index 87fa8e9..23b3050 100644 --- a/examples/compas_autogluon.ipynb +++ b/examples/compas_autogluon.ipynb @@ -20,10 +20,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:43.112034Z", - "iopub.status.busy": "2024-06-17T14:23:43.111650Z", - "iopub.status.idle": "2024-06-17T14:23:48.307918Z", - "shell.execute_reply": "2024-06-17T14:23:48.307590Z" + "iopub.execute_input": "2024-06-17T19:18:59.770600Z", + "iopub.status.busy": "2024-06-17T19:18:59.770368Z", + "iopub.status.idle": "2024-06-17T19:19:04.537414Z", + "shell.execute_reply": "2024-06-17T19:19:04.537064Z" } }, "outputs": [ @@ -63,10 +63,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.309556Z", - "iopub.status.busy": "2024-06-17T14:23:48.309463Z", - "iopub.status.idle": "2024-06-17T14:23:58.905691Z", - "shell.execute_reply": "2024-06-17T14:23:58.904633Z" + "iopub.execute_input": "2024-06-17T19:19:04.539129Z", + "iopub.status.busy": "2024-06-17T19:19:04.539027Z", + "iopub.status.idle": "2024-06-17T19:19:15.151112Z", + "shell.execute_reply": "2024-06-17T19:19:15.150017Z" } }, "outputs": [ @@ -74,7 +74,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142348\"\n" + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191904\"\n" ] }, { @@ -100,7 +100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142348\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191904\"\n" ] }, { @@ -114,8 +114,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 8.22 GB / 16.00 GB (51.4%)\n", - "Disk Space Avail: 363.57 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 6.44 GB / 16.00 GB (40.2%)\n", + "Disk Space Avail: 360.82 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -200,7 +200,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 8421.78 MB\n" + "\tAvailable Memory: 6611.62 MB\n" ] }, { @@ -448,7 +448,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t1.26s\t = Training runtime\n" + "\t1.43s\t = Training runtime\n" ] }, { @@ -462,7 +462,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 3.67s of the 3.67s of remaining time.\n" + "Fitting model: KNeighborsDist ... Training model for up to 3.5s of the 3.5s of remaining time.\n" ] }, { @@ -490,7 +490,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: LightGBMXT ... Training model for up to 3.65s of the 3.65s of remaining time.\n" + "Fitting model: LightGBMXT ... Training model for up to 3.48s of the 3.48s of remaining time.\n" ] }, { @@ -517,7 +517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t3.9s\t = Training runtime\n" + "\t2.71s\t = Training runtime\n" ] }, { @@ -531,28 +531,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.97s of the -0.3s of remaining time.\n" + "Fitting model: LightGBM ... Training model for up to 0.76s of the 0.76s of remaining time.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tEnsemble Weights: {'LightGBMXT': 1.0}\n" + "\tRan out of time, early stopping on iteration 69. Best iteration is:\n", + "\t[27]\tvalid_set's binary_error: 0.323326\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.6882\t = Validation score (accuracy)\n" + "\t0.6767\t = Validation score (accuracy)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.01s\t = Training runtime\n" + "\t0.76s\t = Training runtime\n" ] }, { @@ -566,21 +567,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 5.34s ... Best model: \"WeightedEnsemble_L2\"\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.97s of the -0.03s of remaining time.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\tEnsemble Weights: {'LightGBM': 0.667, 'LightGBMXT': 0.333}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142348\")\n" + "\t0.6928\t = Validation score (accuracy)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142353-001\"\n" + "\t0.02s\t = Training runtime\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\t0.0s\t = Validation runtime\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "AutoGluon training complete, total runtime = 5.07s ... Best model: \"WeightedEnsemble_L2\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191904\")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191909\"\n" ] }, { @@ -606,7 +642,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142353-001\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191909\"\n" ] }, { @@ -620,8 +656,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 8.10 GB / 16.00 GB (50.6%)\n", - "Disk Space Avail: 363.56 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 6.47 GB / 16.00 GB (40.4%)\n", + "Disk Space Avail: 360.82 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -714,7 +750,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 8296.98 MB\n" + "\tAvailable Memory: 6626.29 MB\n" ] }, { @@ -896,7 +932,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Data preprocessing and feature engineering runtime = 0.05s ...\n" + "Data preprocessing and feature engineering runtime = 0.04s ...\n" ] }, { @@ -948,7 +984,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsUnif ... Training model for up to 4.95s of the 4.95s of remaining time.\n" + "Fitting model: KNeighborsUnif ... Training model for up to 4.96s of the 4.96s of remaining time.\n" ] }, { @@ -976,7 +1012,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 4.93s of the 4.92s of remaining time.\n" + "Fitting model: KNeighborsDist ... Training model for up to 4.94s of the 4.94s of remaining time.\n" ] }, { @@ -997,21 +1033,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Validation runtime\n" + "\t0.01s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: LightGBMXT ... Training model for up to 4.9s of the 4.9s of remaining time.\n" + "Fitting model: LightGBMXT ... Training model for up to 4.92s of the 4.92s of remaining time.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\tRan out of time, early stopping on iteration 583. Best iteration is:\n", + "\tRan out of time, early stopping on iteration 551. Best iteration is:\n", "\t[360]\tvalid_set's binary_error: 0.344111\n" ] }, @@ -1026,7 +1062,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t4.92s\t = Training runtime\n" + "\t4.95s\t = Training runtime\n" ] }, { @@ -1040,7 +1076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.95s of the -0.13s of remaining time.\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.96s of the -0.24s of remaining time.\n" ] }, { @@ -1075,14 +1111,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 5.2s ... Best model: \"WeightedEnsemble_L2\"\n" + "AutoGluon training complete, total runtime = 5.41s ... Best model: \"WeightedEnsemble_L2\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142353-001\")\n" + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191909\")\n" ] } ], @@ -1102,10 +1138,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:58.909261Z", - "iopub.status.busy": "2024-06-17T14:23:58.908505Z", - "iopub.status.idle": "2024-06-17T14:23:59.090507Z", - "shell.execute_reply": "2024-06-17T14:23:59.090043Z" + "iopub.execute_input": "2024-06-17T19:19:15.155733Z", + "iopub.status.busy": "2024-06-17T19:19:15.155103Z", + "iopub.status.idle": "2024-06-17T19:19:15.367919Z", + "shell.execute_reply": "2024-06-17T19:19:15.366933Z" } }, "outputs": [], @@ -1129,10 +1165,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:59.093932Z", - "iopub.status.busy": "2024-06-17T14:23:59.093349Z", - "iopub.status.idle": "2024-06-17T14:23:59.101119Z", - "shell.execute_reply": "2024-06-17T14:23:59.099785Z" + "iopub.execute_input": "2024-06-17T19:19:15.372820Z", + "iopub.status.busy": "2024-06-17T19:19:15.372419Z", + "iopub.status.idle": "2024-06-17T19:19:15.400090Z", + "shell.execute_reply": "2024-06-17T19:19:15.398771Z" } }, "outputs": [], @@ -1163,10 +1199,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:59.108987Z", - "iopub.status.busy": "2024-06-17T14:23:59.108644Z", - "iopub.status.idle": "2024-06-17T14:24:04.894628Z", - "shell.execute_reply": "2024-06-17T14:24:04.893230Z" + "iopub.execute_input": "2024-06-17T19:19:15.406078Z", + "iopub.status.busy": "2024-06-17T19:19:15.405749Z", + "iopub.status.idle": "2024-06-17T19:19:21.212793Z", + "shell.execute_reply": "2024-06-17T19:19:21.211712Z" } }, "outputs": [ @@ -1174,7 +1210,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142359\"\n" + "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191915\"\n" ] }, { @@ -1200,7 +1236,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon will save models to \"AutogluonModels/ag-20240617_142359\"\n" + "AutoGluon will save models to \"AutogluonModels/ag-20240617_191915\"\n" ] }, { @@ -1214,8 +1250,8 @@ "Platform Machine: arm64\n", "Platform Version: Darwin Kernel Version 23.5.0: Wed May 1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n", "CPU Count: 10\n", - "Memory Avail: 5.94 GB / 16.00 GB (37.1%)\n", - "Disk Space Avail: 363.52 GB / 460.43 GB (79.0%)\n", + "Memory Avail: 5.34 GB / 16.00 GB (33.4%)\n", + "Disk Space Avail: 360.79 GB / 460.43 GB (78.4%)\n", "===================================================\n" ] }, @@ -1300,7 +1336,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\tAvailable Memory: 6083.89 MB\n" + "\tAvailable Memory: 5469.42 MB\n" ] }, { @@ -1482,7 +1518,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Data preprocessing and feature engineering runtime = 0.07s ...\n" + "Data preprocessing and feature engineering runtime = 0.15s ...\n" ] }, { @@ -1534,7 +1570,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsUnif ... Training model for up to 4.93s of the 4.93s of remaining time.\n" + "Fitting model: KNeighborsUnif ... Training model for up to 4.85s of the 4.85s of remaining time.\n" ] }, { @@ -1548,21 +1584,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.0s\t = Training runtime\n" + "\t0.01s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Validation runtime\n" + "\t0.03s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: KNeighborsDist ... Training model for up to 4.87s of the 4.87s of remaining time.\n" + "Fitting model: KNeighborsDist ... Training model for up to 4.8s of the 4.79s of remaining time.\n" ] }, { @@ -1576,7 +1612,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.01s\t = Training runtime\n" + "\t0.0s\t = Training runtime\n" ] }, { @@ -1590,7 +1626,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: LightGBMXT ... Training model for up to 4.81s of the 4.81s of remaining time.\n" + "Fitting model: LightGBMXT ... Training model for up to 4.73s of the 4.73s of remaining time.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\tRan out of time, early stopping on iteration 343. Best iteration is:\n", + "\t[81]\tvalid_set's binary_error: 0.30254\n" ] }, { @@ -1604,21 +1648,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t4.78s\t = Training runtime\n" + "\t4.75s\t = Training runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\t0.0s\t = Validation runtime\n" + "\t0.01s\t = Validation runtime\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.93s of the -0.03s of remaining time.\n" + "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.85s of the -0.08s of remaining time.\n" ] }, { @@ -1639,7 +1683,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\t0.02s\t = Training runtime\n" + "\t0.01s\t = Training runtime\n" ] }, { @@ -1653,14 +1697,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "AutoGluon training complete, total runtime = 5.12s ... Best model: \"WeightedEnsemble_L2\"\n" + "AutoGluon training complete, total runtime = 5.15s ... Best model: \"WeightedEnsemble_L2\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142359\")\n" + "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191915\")\n" ] }, { @@ -1701,10 +1745,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:04.899531Z", - "iopub.status.busy": "2024-06-17T14:24:04.898883Z", - "iopub.status.idle": "2024-06-17T14:24:07.009494Z", - "shell.execute_reply": "2024-06-17T14:24:07.005452Z" + "iopub.execute_input": "2024-06-17T19:19:21.224375Z", + "iopub.status.busy": "2024-06-17T19:19:21.221254Z", + "iopub.status.idle": "2024-06-17T19:19:23.079442Z", + "shell.execute_reply": "2024-06-17T19:19:23.077623Z" } }, "outputs": [ @@ -1714,16 +1758,16 @@ "text": [ "| | Measure (original) | Measure (updated) | Accuracy (original) | Accuracy (updated) |\n", "|:--------------------------------------------------------|---------------------:|--------------------:|----------------------:|---------------------:|\n", - "| Demographic Parity | 0.229454 | 0.0231172 | 0.667327 | 0.570297 |\n", - "| Disparate Impact | 0.572729 | 0.98373 | 0.667327 | 0.56396 |\n", - "| Average Group Difference in Conditional Acceptance Rate | 0.275048 | 0.000524707 | 0.667327 | 0.610693 |\n", - "| Average Group Difference in Conditional Rejectance Rate | 0.161647 | 0.00165289 | 0.667327 | 0.653069 |\n", - "| Average Group Difference in Accuracy | 0.0398999 | 0.0251424 | 0.667327 | 0.676436 |\n", - "| Average Group Difference in Recall | 0.188465 | 0.00202066 | 0.667327 | 0.589703 |\n", - "| Average Group Difference in Acceptance Rate | 0.042318 | 0.0965697 | 0.667327 | 0.607921 |\n", - "| Average Group Difference in Specificity | 0.191782 | 0.0693429 | 0.667327 | 0.638812 |\n", - "| Average Group Difference in Rejection Rate | 0.0740662 | 0.0754771 | 0.667327 | 0.669703 |\n", - "| Treatment Equality | 0.539507 | 0.0648079 | 0.667327 | 0.649505 |\n" + "| Demographic Parity | 0.234362 | 0.0352353 | 0.671287 | 0.554851 |\n", + "| Disparate Impact | 0.561681 | 0.98879 | 0.671287 | 0.54297 |\n", + "| Average Group Difference in Conditional Acceptance Rate | 0.30035 | 0.00895243 | 0.671287 | 0.622574 |\n", + "| Average Group Difference in Conditional Rejectance Rate | 0.165646 | 0.0179003 | 0.671287 | 0.653861 |\n", + "| Average Group Difference in Accuracy | 0.0431977 | 0.0422382 | 0.671287 | 0.666139 |\n", + "| Average Group Difference in Recall | 0.190611 | 0.0180442 | 0.671287 | 0.557624 |\n", + "| Average Group Difference in Acceptance Rate | 0.0332893 | 0.207826 | 0.671287 | 0.588515 |\n", + "| Average Group Difference in Specificity | 0.197344 | 0.076504 | 0.671287 | 0.651485 |\n", + "| Average Group Difference in Rejection Rate | 0.0740064 | 0.0772521 | 0.671287 | 0.666535 |\n", + "| Treatment Equality | 0.561487 | 0.0692925 | 0.671287 | 0.65901 |\n" ] } ], @@ -1745,10 +1789,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:07.023319Z", - "iopub.status.busy": "2024-06-17T14:24:07.023057Z", - "iopub.status.idle": "2024-06-17T14:24:07.771340Z", - "shell.execute_reply": "2024-06-17T14:24:07.769602Z" + "iopub.execute_input": "2024-06-17T19:19:23.104726Z", + "iopub.status.busy": "2024-06-17T19:19:23.104155Z", + "iopub.status.idle": "2024-06-17T19:19:23.481769Z", + "shell.execute_reply": "2024-06-17T19:19:23.480274Z" } }, "outputs": [ @@ -1785,10 +1829,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:24:07.783978Z", - "iopub.status.busy": "2024-06-17T14:24:07.782668Z", - "iopub.status.idle": "2024-06-17T14:24:09.427747Z", - "shell.execute_reply": "2024-06-17T14:24:09.427303Z" + "iopub.execute_input": "2024-06-17T19:19:23.485490Z", + "iopub.status.busy": "2024-06-17T19:19:23.485061Z", + "iopub.status.idle": "2024-06-17T19:19:25.030825Z", + "shell.execute_reply": "2024-06-17T19:19:25.028459Z" } }, "outputs": [ @@ -1805,14 +1849,14 @@ "text": [ "| | Measure (original) | Measure (updated) | Accuracy (original) | Accuracy (updated) |\n", "|:------------------------------------------------|---------------------:|--------------------:|----------------------:|---------------------:|\n", - "| Statistical Parity | 0.229454 | 0.0231172 | 0.667327 | 0.570297 |\n", - "| Predictive Parity | 0.042318 | 0.0965697 | 0.667327 | 0.607921 |\n", - "| Equal Opportunity | 0.188465 | 0.00202066 | 0.667327 | 0.589703 |\n", - "| Average Group Difference in False Negative Rate | 0.188465 | 0.00202066 | 0.667327 | 0.589703 |\n", - "| Equalized Odds | 0.190124 | 0.0223392 | 0.667327 | 0.593267 |\n", - "| Conditional Use Accuracy | 0.0581921 | 0.0769207 | 0.667327 | 0.594059 |\n", - "| Average Group Difference in Accuracy | 0.0398999 | 0.0251424 | 0.667327 | 0.676436 |\n", - "| Treatment Equality | 0.539507 | 0.0648079 | 0.667327 | 0.649505 |\n" + "| Statistical Parity | 0.234362 | 0.0352353 | 0.671287 | 0.554851 |\n", + "| Predictive Parity | 0.0332893 | 0.207826 | 0.671287 | 0.588515 |\n", + "| Equal Opportunity | 0.190611 | 0.0180442 | 0.671287 | 0.557624 |\n", + "| Average Group Difference in False Negative Rate | 0.190611 | 0.0180442 | 0.671287 | 0.557624 |\n", + "| Equalized Odds | 0.193978 | 0.0333313 | 0.671287 | 0.56396 |\n", + "| Conditional Use Accuracy | 0.0536478 | 0.0568714 | 0.671287 | 0.666931 |\n", + "| Average Group Difference in Accuracy | 0.0431977 | 0.0422382 | 0.671287 | 0.666139 |\n", + "| Treatment Equality | 0.561487 | 0.0692925 | 0.671287 | 0.65901 |\n" ] } ], diff --git a/examples/conditional_metrics.ipynb b/examples/conditional_metrics.ipynb index 06df6fd..1b26669 100644 --- a/examples/conditional_metrics.ipynb +++ b/examples/conditional_metrics.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:43.102618Z", - "iopub.status.busy": "2024-06-17T14:23:43.102402Z", - "iopub.status.idle": "2024-06-17T14:23:47.113740Z", - "shell.execute_reply": "2024-06-17T14:23:47.113354Z" + "iopub.execute_input": "2024-06-17T19:18:59.783922Z", + "iopub.status.busy": "2024-06-17T19:18:59.783664Z", + "iopub.status.idle": "2024-06-17T19:19:03.427708Z", + "shell.execute_reply": "2024-06-17T19:19:03.427274Z" } }, "outputs": [ @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:47.116174Z", - "iopub.status.busy": "2024-06-17T14:23:47.115839Z", - "iopub.status.idle": "2024-06-17T14:23:48.268272Z", - "shell.execute_reply": "2024-06-17T14:23:48.267875Z" + "iopub.execute_input": "2024-06-17T19:19:03.429759Z", + "iopub.status.busy": "2024-06-17T19:19:03.429455Z", + "iopub.status.idle": "2024-06-17T19:19:04.407771Z", + "shell.execute_reply": "2024-06-17T19:19:04.407279Z" } }, "outputs": [], @@ -53,10 +53,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.270196Z", - "iopub.status.busy": "2024-06-17T14:23:48.270058Z", - "iopub.status.idle": "2024-06-17T14:23:48.353869Z", - "shell.execute_reply": "2024-06-17T14:23:48.353575Z" + "iopub.execute_input": "2024-06-17T19:19:04.409570Z", + "iopub.status.busy": "2024-06-17T19:19:04.409433Z", + "iopub.status.idle": "2024-06-17T19:19:04.498858Z", + "shell.execute_reply": "2024-06-17T19:19:04.498533Z" } }, "outputs": [ @@ -93,10 +93,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.355559Z", - "iopub.status.busy": "2024-06-17T14:23:48.355416Z", - "iopub.status.idle": "2024-06-17T14:23:48.365108Z", - "shell.execute_reply": "2024-06-17T14:23:48.364814Z" + "iopub.execute_input": "2024-06-17T19:19:04.500439Z", + "iopub.status.busy": "2024-06-17T19:19:04.500309Z", + "iopub.status.idle": "2024-06-17T19:19:04.512549Z", + "shell.execute_reply": "2024-06-17T19:19:04.512134Z" } }, "outputs": [], @@ -110,10 +110,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.366685Z", - "iopub.status.busy": "2024-06-17T14:23:48.366583Z", - "iopub.status.idle": "2024-06-17T14:23:48.371397Z", - "shell.execute_reply": "2024-06-17T14:23:48.371147Z" + "iopub.execute_input": "2024-06-17T19:19:04.514063Z", + "iopub.status.busy": "2024-06-17T19:19:04.513967Z", + "iopub.status.idle": "2024-06-17T19:19:04.518946Z", + "shell.execute_reply": "2024-06-17T19:19:04.518666Z" } }, "outputs": [], @@ -127,10 +127,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.372814Z", - "iopub.status.busy": "2024-06-17T14:23:48.372725Z", - "iopub.status.idle": "2024-06-17T14:23:48.455150Z", - "shell.execute_reply": "2024-06-17T14:23:48.454842Z" + "iopub.execute_input": "2024-06-17T19:19:04.520325Z", + "iopub.status.busy": "2024-06-17T19:19:04.520238Z", + "iopub.status.idle": "2024-06-17T19:19:04.603630Z", + "shell.execute_reply": "2024-06-17T19:19:04.603304Z" } }, "outputs": [ @@ -188,144 +188,144 @@ " \n", " original\n", " Overall\n", - " 0.671904\n", - " 0.661324\n", - " 0.603529\n", - " 0.331271\n", - " 0.662580\n", - " 0.554143\n", - " 0.719851\n", + " 0.668577\n", + " 0.658923\n", + " 0.604107\n", + " 0.324770\n", + " 0.654232\n", + " 0.561116\n", + " 0.720615\n", " 2438.0\n", " 2972.0\n", " 0.450647\n", - " 0.376895\n", + " 0.386506\n", " \n", " \n", " African-American\n", - " 0.677850\n", - " 0.677279\n", - " 0.690038\n", - " 0.354851\n", - " 0.683162\n", - " 0.697055\n", - " 0.728011\n", + " 0.668831\n", + " 0.668284\n", + " 0.681028\n", + " 0.336810\n", + " 0.674931\n", + " 0.687237\n", + " 0.724012\n", " 1426.0\n", " 1346.0\n", " 0.514430\n", - " 0.524892\n", + " 0.523810\n", " \n", " \n", " Caucasian\n", - " 0.659783\n", - " 0.606250\n", - " 0.450877\n", - " 0.248174\n", - " 0.617788\n", - " 0.354972\n", - " 0.683747\n", + " 0.658152\n", + " 0.610242\n", + " 0.470093\n", + " 0.248212\n", + " 0.602592\n", + " 0.385359\n", + " 0.691688\n", " 724.0\n", " 1116.0\n", " 0.393478\n", - " 0.226087\n", + " 0.251630\n", " \n", " \n", " Other\n", - " 0.679198\n", - " 0.606944\n", - " 0.438596\n", - " 0.251968\n", - " 0.595238\n", - " 0.347222\n", - " 0.706383\n", + " 0.691729\n", + " 0.623550\n", + " 0.469828\n", + " 0.286221\n", + " 0.619318\n", + " 0.378472\n", + " 0.709576\n", " 288.0\n", " 510.0\n", " 0.360902\n", - " 0.210526\n", + " 0.220551\n", " \n", " \n", " Maximum difference\n", - " 0.019415\n", - " 0.071030\n", - " 0.251442\n", - " 0.106676\n", - " 0.087923\n", - " 0.349832\n", - " 0.044264\n", + " 0.033577\n", + " 0.058042\n", + " 0.211201\n", + " 0.088599\n", + " 0.072339\n", + " 0.308765\n", + " 0.032324\n", " 1138.0\n", " 836.0\n", " 0.153528\n", - " 0.314365\n", + " 0.303258\n", " \n", " \n", " updated\n", " Overall\n", - " 0.671904\n", - " 0.661324\n", - " 0.603529\n", - " 0.331271\n", - " 0.662580\n", - " 0.554143\n", - " 0.719851\n", + " 0.668577\n", + " 0.658923\n", + " 0.604107\n", + " 0.324770\n", + " 0.654232\n", + " 0.561116\n", + " 0.720615\n", " 2438.0\n", " 2972.0\n", " 0.450647\n", - " 0.376895\n", + " 0.386506\n", " \n", " \n", " African-American\n", - " 0.677850\n", - " 0.677279\n", - " 0.690038\n", - " 0.354851\n", - " 0.683162\n", - " 0.697055\n", - " 0.728011\n", + " 0.668831\n", + " 0.668284\n", + " 0.681028\n", + " 0.336810\n", + " 0.674931\n", + " 0.687237\n", + " 0.724012\n", " 1426.0\n", " 1346.0\n", " 0.514430\n", - " 0.524892\n", + " 0.523810\n", " \n", " \n", " Caucasian\n", - " 0.659783\n", - " 0.606250\n", - " 0.450877\n", - " 0.248174\n", - " 0.617788\n", - " 0.354972\n", - " 0.683747\n", + " 0.658152\n", + " 0.610242\n", + " 0.470093\n", + " 0.248212\n", + " 0.602592\n", + " 0.385359\n", + " 0.691688\n", " 724.0\n", " 1116.0\n", " 0.393478\n", - " 0.226087\n", + " 0.251630\n", " \n", " \n", " Other\n", - " 0.679198\n", - " 0.606944\n", - " 0.438596\n", - " 0.251968\n", - " 0.595238\n", - " 0.347222\n", - " 0.706383\n", + " 0.691729\n", + " 0.623550\n", + " 0.469828\n", + " 0.286221\n", + " 0.619318\n", + " 0.378472\n", + " 0.709576\n", " 288.0\n", " 510.0\n", " 0.360902\n", - " 0.210526\n", + " 0.220551\n", " \n", " \n", " Maximum difference\n", - " 0.019415\n", - " 0.071030\n", - " 0.251442\n", - " 0.106676\n", - " 0.087923\n", - " 0.349832\n", - " 0.044264\n", + " 0.033577\n", + " 0.058042\n", + " 0.211201\n", + " 0.088599\n", + " 0.072339\n", + " 0.308765\n", + " 0.032324\n", " 1138.0\n", " 836.0\n", " 0.153528\n", - " 0.314365\n", + " 0.303258\n", " \n", " \n", "\n", @@ -334,29 +334,29 @@ "text/plain": [ " Accuracy Balanced Accuracy F1 score MCC \\\n", " Groups \n", - "original Overall 0.671904 0.661324 0.603529 0.331271 \n", - " African-American 0.677850 0.677279 0.690038 0.354851 \n", - " Caucasian 0.659783 0.606250 0.450877 0.248174 \n", - " Other 0.679198 0.606944 0.438596 0.251968 \n", - " Maximum difference 0.019415 0.071030 0.251442 0.106676 \n", - "updated Overall 0.671904 0.661324 0.603529 0.331271 \n", - " African-American 0.677850 0.677279 0.690038 0.354851 \n", - " Caucasian 0.659783 0.606250 0.450877 0.248174 \n", - " Other 0.679198 0.606944 0.438596 0.251968 \n", - " Maximum difference 0.019415 0.071030 0.251442 0.106676 \n", + "original Overall 0.668577 0.658923 0.604107 0.324770 \n", + " African-American 0.668831 0.668284 0.681028 0.336810 \n", + " Caucasian 0.658152 0.610242 0.470093 0.248212 \n", + " Other 0.691729 0.623550 0.469828 0.286221 \n", + " Maximum difference 0.033577 0.058042 0.211201 0.088599 \n", + "updated Overall 0.668577 0.658923 0.604107 0.324770 \n", + " African-American 0.668831 0.668284 0.681028 0.336810 \n", + " Caucasian 0.658152 0.610242 0.470093 0.248212 \n", + " Other 0.691729 0.623550 0.469828 0.286221 \n", + " Maximum difference 0.033577 0.058042 0.211201 0.088599 \n", "\n", " Precision Recall ROC AUC Positive Count \\\n", " Groups \n", - "original Overall 0.662580 0.554143 0.719851 2438.0 \n", - " African-American 0.683162 0.697055 0.728011 1426.0 \n", - " Caucasian 0.617788 0.354972 0.683747 724.0 \n", - " Other 0.595238 0.347222 0.706383 288.0 \n", - " Maximum difference 0.087923 0.349832 0.044264 1138.0 \n", - "updated Overall 0.662580 0.554143 0.719851 2438.0 \n", - " African-American 0.683162 0.697055 0.728011 1426.0 \n", - " Caucasian 0.617788 0.354972 0.683747 724.0 \n", - " Other 0.595238 0.347222 0.706383 288.0 \n", - " Maximum difference 0.087923 0.349832 0.044264 1138.0 \n", + "original Overall 0.654232 0.561116 0.720615 2438.0 \n", + " African-American 0.674931 0.687237 0.724012 1426.0 \n", + " Caucasian 0.602592 0.385359 0.691688 724.0 \n", + " Other 0.619318 0.378472 0.709576 288.0 \n", + " Maximum difference 0.072339 0.308765 0.032324 1138.0 \n", + "updated Overall 0.654232 0.561116 0.720615 2438.0 \n", + " African-American 0.674931 0.687237 0.724012 1426.0 \n", + " Caucasian 0.602592 0.385359 0.691688 724.0 \n", + " Other 0.619318 0.378472 0.709576 288.0 \n", + " Maximum difference 0.072339 0.308765 0.032324 1138.0 \n", "\n", " Negative Count Positive Label Rate \\\n", " Groups \n", @@ -373,16 +373,16 @@ "\n", " Positive Prediction Rate \n", " Groups \n", - "original Overall 0.376895 \n", - " African-American 0.524892 \n", - " Caucasian 0.226087 \n", - " Other 0.210526 \n", - " Maximum difference 0.314365 \n", - "updated Overall 0.376895 \n", - " African-American 0.524892 \n", - " Caucasian 0.226087 \n", - " Other 0.210526 \n", - " Maximum difference 0.314365 " + "original Overall 0.386506 \n", + " African-American 0.523810 \n", + " Caucasian 0.251630 \n", + " Other 0.220551 \n", + " Maximum difference 0.303258 \n", + "updated Overall 0.386506 \n", + " African-American 0.523810 \n", + " Caucasian 0.251630 \n", + " Other 0.220551 \n", + " Maximum difference 0.303258 " ] }, "execution_count": 6, @@ -399,10 +399,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.456807Z", - "iopub.status.busy": "2024-06-17T14:23:48.456686Z", - "iopub.status.idle": "2024-06-17T14:23:48.585511Z", - "shell.execute_reply": "2024-06-17T14:23:48.585181Z" + "iopub.execute_input": "2024-06-17T19:19:04.605345Z", + "iopub.status.busy": "2024-06-17T19:19:04.605228Z", + "iopub.status.idle": "2024-06-17T19:19:04.733572Z", + "shell.execute_reply": "2024-06-17T19:19:04.733188Z" } }, "outputs": [ @@ -456,124 +456,124 @@ " \n", " original\n", " Overall\n", - " 0.669729\n", - " 0.545366\n", - " 0.770097\n", - " 0.229903\n", - " 0.439982\n", - " 0.376262\n", - " 0.623738\n", - " 0.448939\n", - " 0.551061\n", + " 0.664509\n", + " 0.551291\n", + " 0.759826\n", + " 0.240174\n", + " 0.431196\n", + " 0.388326\n", + " 0.611674\n", + " 0.450394\n", + " 0.549606\n", " \n", " \n", " African-American\n", - " 0.681977\n", - " 0.711035\n", - " 0.637478\n", - " 0.362522\n", - " 0.290024\n", - " 0.495254\n", - " 0.504746\n", - " 0.499050\n", - " 0.500950\n", + " 0.670420\n", + " 0.704626\n", + " 0.629113\n", + " 0.370887\n", + " 0.293899\n", + " 0.492888\n", + " 0.507112\n", + " 0.501030\n", + " 0.498970\n", " \n", " \n", " Caucasian\n", - " 0.646749\n", - " 0.302431\n", - " 0.883828\n", - " 0.116172\n", - " 0.676992\n", - " 0.269827\n", - " 0.730173\n", - " 0.412195\n", - " 0.587805\n", + " 0.643322\n", + " 0.324591\n", + " 0.867996\n", + " 0.132004\n", + " 0.651578\n", + " 0.303963\n", + " 0.696037\n", + " 0.413242\n", + " 0.586758\n", " \n", " \n", " Other\n", - " 0.680172\n", - " 0.365690\n", - " 0.868875\n", - " 0.131125\n", - " 0.656982\n", - " 0.208338\n", - " 0.791662\n", - " 0.359591\n", - " 0.640409\n", + " 0.692827\n", + " 0.394708\n", + " 0.866224\n", + " 0.133776\n", + " 0.620425\n", + " 0.219629\n", + " 0.780371\n", + " 0.360166\n", + " 0.639834\n", " \n", " \n", " Maximum difference\n", - " 0.035228\n", - " 0.408605\n", - " 0.246350\n", - " 0.246350\n", - " 0.386969\n", - " 0.286915\n", - " 0.286915\n", - " 0.139459\n", - " 0.139459\n", + " 0.049505\n", + " 0.380035\n", + " 0.238883\n", + " 0.238883\n", + " 0.357679\n", + " 0.273259\n", + " 0.273259\n", + " 0.140863\n", + " 0.140863\n", " \n", " \n", " updated\n", " Overall\n", - " 0.669729\n", - " 0.545366\n", - " 0.770097\n", - " 0.229903\n", - " 0.439982\n", - " 0.376262\n", - " 0.623738\n", - " 0.448939\n", - " 0.551061\n", + " 0.664509\n", + " 0.551291\n", + " 0.759826\n", + " 0.240174\n", + " 0.431196\n", + " 0.388326\n", + " 0.611674\n", + " 0.450394\n", + " 0.549606\n", " \n", " \n", " African-American\n", - " 0.681977\n", - " 0.711035\n", - " 0.637478\n", - " 0.362522\n", - " 0.290024\n", - " 0.495254\n", - " 0.504746\n", - " 0.499050\n", - " 0.500950\n", + " 0.670420\n", + " 0.704626\n", + " 0.629113\n", + " 0.370887\n", + " 0.293899\n", + " 0.492888\n", + " 0.507112\n", + " 0.501030\n", + " 0.498970\n", " \n", " \n", " Caucasian\n", - " 0.646749\n", - " 0.302431\n", - " 0.883828\n", - " 0.116172\n", - " 0.676992\n", - " 0.269827\n", - " 0.730173\n", - " 0.412195\n", - " 0.587805\n", + " 0.643322\n", + " 0.324591\n", + " 0.867996\n", + " 0.132004\n", + " 0.651578\n", + " 0.303963\n", + " 0.696037\n", + " 0.413242\n", + " 0.586758\n", " \n", " \n", " Other\n", - " 0.680172\n", - " 0.365690\n", - " 0.868875\n", - " 0.131125\n", - " 0.656982\n", - " 0.208338\n", - " 0.791662\n", - " 0.359591\n", - " 0.640409\n", + " 0.692827\n", + " 0.394708\n", + " 0.866224\n", + " 0.133776\n", + " 0.620425\n", + " 0.219629\n", + " 0.780371\n", + " 0.360166\n", + " 0.639834\n", " \n", " \n", " Maximum difference\n", - " 0.035228\n", - " 0.408605\n", - " 0.246350\n", - " 0.246350\n", - " 0.386969\n", - " 0.286915\n", - " 0.286915\n", - " 0.139459\n", - " 0.139459\n", + " 0.049505\n", + " 0.380035\n", + " 0.238883\n", + " 0.238883\n", + " 0.357679\n", + " 0.273259\n", + " 0.273259\n", + " 0.140863\n", + " 0.140863\n", " \n", " \n", "\n", @@ -582,120 +582,120 @@ "text/plain": [ " Conditional Accuracy \\\n", " Groups \n", - "original Overall 0.669729 \n", - " African-American 0.681977 \n", - " Caucasian 0.646749 \n", - " Other 0.680172 \n", - " Maximum difference 0.035228 \n", - "updated Overall 0.669729 \n", - " African-American 0.681977 \n", - " Caucasian 0.646749 \n", - " Other 0.680172 \n", - " Maximum difference 0.035228 \n", + "original Overall 0.664509 \n", + " African-American 0.670420 \n", + " Caucasian 0.643322 \n", + " Other 0.692827 \n", + " Maximum difference 0.049505 \n", + "updated Overall 0.664509 \n", + " African-American 0.670420 \n", + " Caucasian 0.643322 \n", + " Other 0.692827 \n", + " Maximum difference 0.049505 \n", "\n", " Conditional True Positive Rate \\\n", " Groups \n", - "original Overall 0.545366 \n", - " African-American 0.711035 \n", - " Caucasian 0.302431 \n", - " Other 0.365690 \n", - " Maximum difference 0.408605 \n", - "updated Overall 0.545366 \n", - " African-American 0.711035 \n", - " Caucasian 0.302431 \n", - " Other 0.365690 \n", - " Maximum difference 0.408605 \n", + "original Overall 0.551291 \n", + " African-American 0.704626 \n", + " Caucasian 0.324591 \n", + " Other 0.394708 \n", + " Maximum difference 0.380035 \n", + "updated Overall 0.551291 \n", + " African-American 0.704626 \n", + " Caucasian 0.324591 \n", + " Other 0.394708 \n", + " Maximum difference 0.380035 \n", "\n", " Conditional True Negative Rate \\\n", " Groups \n", - "original Overall 0.770097 \n", - " African-American 0.637478 \n", - " Caucasian 0.883828 \n", - " Other 0.868875 \n", - " Maximum difference 0.246350 \n", - "updated Overall 0.770097 \n", - " African-American 0.637478 \n", - " Caucasian 0.883828 \n", - " Other 0.868875 \n", - " Maximum difference 0.246350 \n", + "original Overall 0.759826 \n", + " African-American 0.629113 \n", + " Caucasian 0.867996 \n", + " Other 0.866224 \n", + " Maximum difference 0.238883 \n", + "updated Overall 0.759826 \n", + " African-American 0.629113 \n", + " Caucasian 0.867996 \n", + " Other 0.866224 \n", + " Maximum difference 0.238883 \n", "\n", " Conditional False Positive Rate \\\n", " Groups \n", - "original Overall 0.229903 \n", - " African-American 0.362522 \n", - " Caucasian 0.116172 \n", - " Other 0.131125 \n", - " Maximum difference 0.246350 \n", - "updated Overall 0.229903 \n", - " African-American 0.362522 \n", - " Caucasian 0.116172 \n", - " Other 0.131125 \n", - " Maximum difference 0.246350 \n", + "original Overall 0.240174 \n", + " African-American 0.370887 \n", + " Caucasian 0.132004 \n", + " Other 0.133776 \n", + " Maximum difference 0.238883 \n", + "updated Overall 0.240174 \n", + " African-American 0.370887 \n", + " Caucasian 0.132004 \n", + " Other 0.133776 \n", + " Maximum difference 0.238883 \n", "\n", " Conditional False Negative Rate \\\n", " Groups \n", - "original Overall 0.439982 \n", - " African-American 0.290024 \n", - " Caucasian 0.676992 \n", - " Other 0.656982 \n", - " Maximum difference 0.386969 \n", - "updated Overall 0.439982 \n", - " African-American 0.290024 \n", - " Caucasian 0.676992 \n", - " Other 0.656982 \n", - " Maximum difference 0.386969 \n", + "original Overall 0.431196 \n", + " African-American 0.293899 \n", + " Caucasian 0.651578 \n", + " Other 0.620425 \n", + " Maximum difference 0.357679 \n", + "updated Overall 0.431196 \n", + " African-American 0.293899 \n", + " Caucasian 0.651578 \n", + " Other 0.620425 \n", + " Maximum difference 0.357679 \n", "\n", " Conditional Positive Prediction Rate \\\n", " Groups \n", - "original Overall 0.376262 \n", - " African-American 0.495254 \n", - " Caucasian 0.269827 \n", - " Other 0.208338 \n", - " Maximum difference 0.286915 \n", - "updated Overall 0.376262 \n", - " African-American 0.495254 \n", - " Caucasian 0.269827 \n", - " Other 0.208338 \n", - " Maximum difference 0.286915 \n", + "original Overall 0.388326 \n", + " African-American 0.492888 \n", + " Caucasian 0.303963 \n", + " Other 0.219629 \n", + " Maximum difference 0.273259 \n", + "updated Overall 0.388326 \n", + " African-American 0.492888 \n", + " Caucasian 0.303963 \n", + " Other 0.219629 \n", + " Maximum difference 0.273259 \n", "\n", " Conditional Negative Prediction Rate \\\n", " Groups \n", - "original Overall 0.623738 \n", - " African-American 0.504746 \n", - " Caucasian 0.730173 \n", - " Other 0.791662 \n", - " Maximum difference 0.286915 \n", - "updated Overall 0.623738 \n", - " African-American 0.504746 \n", - " Caucasian 0.730173 \n", - " Other 0.791662 \n", - " Maximum difference 0.286915 \n", + "original Overall 0.611674 \n", + " African-American 0.507112 \n", + " Caucasian 0.696037 \n", + " Other 0.780371 \n", + " Maximum difference 0.273259 \n", + "updated Overall 0.611674 \n", + " African-American 0.507112 \n", + " Caucasian 0.696037 \n", + " Other 0.780371 \n", + " Maximum difference 0.273259 \n", "\n", " Conditional Positive Label Rate \\\n", " Groups \n", - "original Overall 0.448939 \n", - " African-American 0.499050 \n", - " Caucasian 0.412195 \n", - " Other 0.359591 \n", - " Maximum difference 0.139459 \n", - "updated Overall 0.448939 \n", - " African-American 0.499050 \n", - " Caucasian 0.412195 \n", - " Other 0.359591 \n", - " Maximum difference 0.139459 \n", + "original Overall 0.450394 \n", + " African-American 0.501030 \n", + " Caucasian 0.413242 \n", + " Other 0.360166 \n", + " Maximum difference 0.140863 \n", + "updated Overall 0.450394 \n", + " African-American 0.501030 \n", + " Caucasian 0.413242 \n", + " Other 0.360166 \n", + " Maximum difference 0.140863 \n", "\n", " Conditional Negative Label Rate \n", " Groups \n", - "original Overall 0.551061 \n", - " African-American 0.500950 \n", - " Caucasian 0.587805 \n", - " Other 0.640409 \n", - " Maximum difference 0.139459 \n", - "updated Overall 0.551061 \n", - " African-American 0.500950 \n", - " Caucasian 0.587805 \n", - " Other 0.640409 \n", - " Maximum difference 0.139459 " + "original Overall 0.549606 \n", + " African-American 0.498970 \n", + " Caucasian 0.586758 \n", + " Other 0.639834 \n", + " Maximum difference 0.140863 \n", + "updated Overall 0.549606 \n", + " African-American 0.498970 \n", + " Caucasian 0.586758 \n", + " Other 0.639834 \n", + " Maximum difference 0.140863 " ] }, "execution_count": 7, @@ -712,18 +712,18 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.587144Z", - "iopub.status.busy": "2024-06-17T14:23:48.587024Z", - "iopub.status.idle": "2024-06-17T14:23:48.639197Z", - "shell.execute_reply": "2024-06-17T14:23:48.638898Z" + "iopub.execute_input": "2024-06-17T19:19:04.735208Z", + "iopub.status.busy": "2024-06-17T19:19:04.735064Z", + "iopub.status.idle": "2024-06-17T19:19:04.785577Z", + "shell.execute_reply": "2024-06-17T19:19:04.785246Z" } }, "outputs": [ { "data": { "text/plain": [ - "(array([ 630., 0., 2453., 0., 236., 0., 974., 0., 213.,\n", - " 904.]),\n", + "(array([ 596., 0., 2497., 0., 217., 0., 945., 0., 218.,\n", + " 937.]),\n", " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n", " )" ] @@ -734,7 +734,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -752,22 +752,22 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.640785Z", - "iopub.status.busy": "2024-06-17T14:23:48.640616Z", - "iopub.status.idle": "2024-06-17T14:23:48.737908Z", - "shell.execute_reply": "2024-06-17T14:23:48.737599Z" + "iopub.execute_input": "2024-06-17T19:19:04.787168Z", + "iopub.status.busy": "2024-06-17T19:19:04.787048Z", + "iopub.status.idle": "2024-06-17T19:19:04.879704Z", + "shell.execute_reply": "2024-06-17T19:19:04.879313Z" } }, "outputs": [ { "data": { "text/plain": [ - "(array([[0.22510823, 0. , 0.96392496, 0. , 0.04761905,\n", - " 0. , 0.27200577, 0. , 0.0988456 , 0.39249639],\n", - " [0.2576087 , 0. , 0.81086957, 0. , 0.14565217,\n", - " 0. , 0.4826087 , 0. , 0.05869565, 0.24456522],\n", - " [0.20300752, 0. , 0.92982456, 0. , 0.09022556,\n", - " 0. , 0.38345865, 0. , 0.05513784, 0.33834586]]),\n", + "(array([[0.21067821, 0. , 0.97835498, 0. , 0.04401154,\n", + " 0. , 0.26118326, 0. , 0.09090909, 0.41486291],\n", + " [0.24347826, 0. , 0.82826087, 0. , 0.13913043,\n", + " 0. , 0.47391304, 0. , 0.07282609, 0.2423913 ],\n", + " [0.20050125, 0. , 0.94987469, 0. , 0.07017544,\n", + " 0. , 0.36842105, 0. , 0.06265664, 0.34837093]]),\n", " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n", " )" ] @@ -778,7 +778,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -798,10 +798,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:48.739482Z", - "iopub.status.busy": "2024-06-17T14:23:48.739364Z", - "iopub.status.idle": "2024-06-17T14:23:49.706594Z", - "shell.execute_reply": "2024-06-17T14:23:49.706157Z" + "iopub.execute_input": "2024-06-17T19:19:04.881484Z", + "iopub.status.busy": "2024-06-17T19:19:04.881352Z", + "iopub.status.idle": "2024-06-17T19:19:06.280142Z", + "shell.execute_reply": "2024-06-17T19:19:06.279726Z" } }, "outputs": [], @@ -814,10 +814,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.708845Z", - "iopub.status.busy": "2024-06-17T14:23:49.708688Z", - "iopub.status.idle": "2024-06-17T14:23:49.714314Z", - "shell.execute_reply": "2024-06-17T14:23:49.713968Z" + "iopub.execute_input": "2024-06-17T19:19:06.282253Z", + "iopub.status.busy": "2024-06-17T19:19:06.282066Z", + "iopub.status.idle": "2024-06-17T19:19:06.286992Z", + "shell.execute_reply": "2024-06-17T19:19:06.286643Z" } }, "outputs": [ @@ -1060,10 +1060,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.716275Z", - "iopub.status.busy": "2024-06-17T14:23:49.716162Z", - "iopub.status.idle": "2024-06-17T14:23:51.396194Z", - "shell.execute_reply": "2024-06-17T14:23:51.395669Z" + "iopub.execute_input": "2024-06-17T19:19:06.288647Z", + "iopub.status.busy": "2024-06-17T19:19:06.288488Z", + "iopub.status.idle": "2024-06-17T19:19:07.196515Z", + "shell.execute_reply": "2024-06-17T19:19:07.195975Z" } }, "outputs": [], @@ -1077,10 +1077,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:51.398839Z", - "iopub.status.busy": "2024-06-17T14:23:51.398667Z", - "iopub.status.idle": "2024-06-17T14:23:51.402106Z", - "shell.execute_reply": "2024-06-17T14:23:51.401435Z" + "iopub.execute_input": "2024-06-17T19:19:07.199064Z", + "iopub.status.busy": "2024-06-17T19:19:07.198927Z", + "iopub.status.idle": "2024-06-17T19:19:07.202392Z", + "shell.execute_reply": "2024-06-17T19:19:07.201943Z" } }, "outputs": [ @@ -1104,10 +1104,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:51.404500Z", - "iopub.status.busy": "2024-06-17T14:23:51.404341Z", - "iopub.status.idle": "2024-06-17T14:23:51.408538Z", - "shell.execute_reply": "2024-06-17T14:23:51.408103Z" + "iopub.execute_input": "2024-06-17T19:19:07.204514Z", + "iopub.status.busy": "2024-06-17T19:19:07.204387Z", + "iopub.status.idle": "2024-06-17T19:19:07.208197Z", + "shell.execute_reply": "2024-06-17T19:19:07.207765Z" } }, "outputs": [ diff --git a/examples/high-dim_fairlearn_comparision.ipynb b/examples/high-dim_fairlearn_comparision.ipynb index a2d4909..db7e07b 100644 --- a/examples/high-dim_fairlearn_comparision.ipynb +++ b/examples/high-dim_fairlearn_comparision.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:43.083831Z", - "iopub.status.busy": "2024-06-17T14:23:43.083606Z", - "iopub.status.idle": "2024-06-17T14:23:46.805192Z", - "shell.execute_reply": "2024-06-17T14:23:46.804410Z" + "iopub.execute_input": "2024-06-17T19:18:59.771207Z", + "iopub.status.busy": "2024-06-17T19:18:59.770841Z", + "iopub.status.idle": "2024-06-17T19:19:03.168703Z", + "shell.execute_reply": "2024-06-17T19:19:03.167948Z" } }, "outputs": [ @@ -47,10 +47,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:46.808300Z", - "iopub.status.busy": "2024-06-17T14:23:46.808028Z", - "iopub.status.idle": "2024-06-17T14:23:49.489914Z", - "shell.execute_reply": "2024-06-17T14:23:49.489497Z" + "iopub.execute_input": "2024-06-17T19:19:03.172959Z", + "iopub.status.busy": "2024-06-17T19:19:03.172051Z", + "iopub.status.idle": "2024-06-17T19:19:07.882594Z", + "shell.execute_reply": "2024-06-17T19:19:07.882019Z" } }, "outputs": [ @@ -89,10 +89,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.491785Z", - "iopub.status.busy": "2024-06-17T14:23:49.491664Z", - "iopub.status.idle": "2024-06-17T14:23:49.693781Z", - "shell.execute_reply": "2024-06-17T14:23:49.693395Z" + "iopub.execute_input": "2024-06-17T19:19:07.884959Z", + "iopub.status.busy": "2024-06-17T19:19:07.884801Z", + "iopub.status.idle": "2024-06-17T19:19:08.184546Z", + "shell.execute_reply": "2024-06-17T19:19:08.184122Z" } }, "outputs": [], @@ -113,10 +113,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.695634Z", - "iopub.status.busy": "2024-06-17T14:23:49.695476Z", - "iopub.status.idle": "2024-06-17T14:23:49.699419Z", - "shell.execute_reply": "2024-06-17T14:23:49.698862Z" + "iopub.execute_input": "2024-06-17T19:19:08.186753Z", + "iopub.status.busy": "2024-06-17T19:19:08.186618Z", + "iopub.status.idle": "2024-06-17T19:19:08.191184Z", + "shell.execute_reply": "2024-06-17T19:19:08.190779Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.701001Z", - "iopub.status.busy": "2024-06-17T14:23:49.700906Z", - "iopub.status.idle": "2024-06-17T14:23:49.719227Z", - "shell.execute_reply": "2024-06-17T14:23:49.718898Z" + "iopub.execute_input": "2024-06-17T19:19:08.193364Z", + "iopub.status.busy": "2024-06-17T19:19:08.193088Z", + "iopub.status.idle": "2024-06-17T19:19:08.212526Z", + "shell.execute_reply": "2024-06-17T19:19:08.212062Z" } }, "outputs": [ @@ -171,43 +171,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.004344\n", - " 0.008067\n", + " 0.016250\n", + " 0.014167\n", " \n", " \n", " Predictive Parity\n", - " 0.063636\n", - " 0.003268\n", + " 0.005435\n", + " 0.084615\n", " \n", " \n", " Equal Opportunity\n", - " 0.058009\n", - " 0.000866\n", + " 0.064935\n", + " 0.011255\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.058009\n", - " 0.000866\n", + " 0.064935\n", + " 0.011255\n", " \n", " \n", " Equalized Odds\n", - " 0.035523\n", - " 0.001063\n", + " 0.033288\n", + " 0.016807\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.044095\n", - " 0.006802\n", + " 0.009843\n", + " 0.044293\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.030186\n", - " 0.008910\n", + " 0.012500\n", + " 0.019583\n", " \n", " \n", " Treatment Equality\n", - " 0.047794\n", - " 0.003268\n", + " 0.050000\n", + " 0.245455\n", " \n", " \n", "\n", @@ -215,14 +215,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.004344 0.008067\n", - "Predictive Parity 0.063636 0.003268\n", - "Equal Opportunity 0.058009 0.000866\n", - "Average Group Difference in False Negative Rate 0.058009 0.000866\n", - "Equalized Odds 0.035523 0.001063\n", - "Conditional Use Accuracy 0.044095 0.006802\n", - "Average Group Difference in Accuracy 0.030186 0.008910\n", - "Treatment Equality 0.047794 0.003268" + "Statistical Parity 0.016250 0.014167\n", + "Predictive Parity 0.005435 0.084615\n", + "Equal Opportunity 0.064935 0.011255\n", + "Average Group Difference in False Negative Rate 0.064935 0.011255\n", + "Equalized Odds 0.033288 0.016807\n", + "Conditional Use Accuracy 0.009843 0.044293\n", + "Average Group Difference in Accuracy 0.012500 0.019583\n", + "Treatment Equality 0.050000 0.245455" ] }, "execution_count": 5, @@ -246,10 +246,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.720770Z", - "iopub.status.busy": "2024-06-17T14:23:49.720651Z", - "iopub.status.idle": "2024-06-17T14:23:49.735307Z", - "shell.execute_reply": "2024-06-17T14:23:49.735021Z" + "iopub.execute_input": "2024-06-17T19:19:08.214774Z", + "iopub.status.busy": "2024-06-17T19:19:08.214612Z", + "iopub.status.idle": "2024-06-17T19:19:08.231032Z", + "shell.execute_reply": "2024-06-17T19:19:08.230546Z" } }, "outputs": [ @@ -281,43 +281,43 @@ " \n", " \n", " Statistical Parity\n", - " 0.036249\n", - " 0.053908\n", + " 0.059044\n", + " 0.064624\n", " \n", " \n", " Predictive Parity\n", - " 0.043478\n", - " 0.000000\n", + " 0.163399\n", + " 0.108065\n", " \n", " \n", " Equal Opportunity\n", - " 0.117647\n", + " 0.147059\n", " 0.176471\n", " \n", " \n", " Average Group Difference in False Negative Rate\n", - " 0.117647\n", + " 0.147059\n", " 0.176471\n", " \n", " \n", " Equalized Odds\n", - " 0.062856\n", - " 0.088235\n", + " 0.093486\n", + " 0.107781\n", " \n", " \n", " Conditional Use Accuracy\n", - " 0.031996\n", - " 0.015126\n", + " 0.098748\n", + " 0.075119\n", " \n", " \n", " Average Group Difference in Accuracy\n", - " 0.026334\n", - " 0.031336\n", + " 0.004747\n", + " 0.011326\n", " \n", " \n", " Treatment Equality\n", - " 0.083333\n", - " 0.000000\n", + " 0.405983\n", + " 0.550802\n", " \n", " \n", "\n", @@ -325,14 +325,14 @@ ], "text/plain": [ " original updated\n", - "Statistical Parity 0.036249 0.053908\n", - "Predictive Parity 0.043478 0.000000\n", - "Equal Opportunity 0.117647 0.176471\n", - "Average Group Difference in False Negative Rate 0.117647 0.176471\n", - "Equalized Odds 0.062856 0.088235\n", - "Conditional Use Accuracy 0.031996 0.015126\n", - "Average Group Difference in Accuracy 0.026334 0.031336\n", - "Treatment Equality 0.083333 0.000000" + "Statistical Parity 0.059044 0.064624\n", + "Predictive Parity 0.163399 0.108065\n", + "Equal Opportunity 0.147059 0.176471\n", + "Average Group Difference in False Negative Rate 0.147059 0.176471\n", + "Equalized Odds 0.093486 0.107781\n", + "Conditional Use Accuracy 0.098748 0.075119\n", + "Average Group Difference in Accuracy 0.004747 0.011326\n", + "Treatment Equality 0.405983 0.550802" ] }, "execution_count": 6, @@ -356,10 +356,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.736730Z", - "iopub.status.busy": "2024-06-17T14:23:49.736638Z", - "iopub.status.idle": "2024-06-17T14:23:49.752457Z", - "shell.execute_reply": "2024-06-17T14:23:49.752129Z" + "iopub.execute_input": "2024-06-17T19:19:08.233234Z", + "iopub.status.busy": "2024-06-17T19:19:08.232903Z", + "iopub.status.idle": "2024-06-17T19:19:08.250511Z", + "shell.execute_reply": "2024-06-17T19:19:08.250104Z" } }, "outputs": [ @@ -391,38 +391,38 @@ " \n", " \n", " Accuracy\n", - " 0.867110\n", - " 0.877076\n", + " 0.893548\n", + " 0.903226\n", " \n", " \n", " Balanced Accuracy\n", - " 0.742331\n", - " 0.738355\n", + " 0.789074\n", + " 0.826993\n", " \n", " \n", " F1 score\n", - " 0.636364\n", - " 0.640777\n", + " 0.713043\n", + " 0.758065\n", " \n", " \n", " MCC\n", - " 0.584922\n", - " 0.621888\n", + " 0.667074\n", + " 0.703429\n", " \n", " \n", " Precision\n", - " 0.833333\n", - " 0.942857\n", + " 0.872340\n", + " 0.839286\n", " \n", " \n", " Recall\n", - " 0.514706\n", - " 0.485294\n", + " 0.602941\n", + " 0.691176\n", " \n", " \n", " ROC AUC\n", - " 0.882100\n", - " 0.877177\n", + " 0.897302\n", + " 0.881745\n", " \n", " \n", "\n", @@ -430,13 +430,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.867110 0.877076\n", - "Balanced Accuracy 0.742331 0.738355\n", - "F1 score 0.636364 0.640777\n", - "MCC 0.584922 0.621888\n", - "Precision 0.833333 0.942857\n", - "Recall 0.514706 0.485294\n", - "ROC AUC 0.882100 0.877177" + "Accuracy 0.893548 0.903226\n", + "Balanced Accuracy 0.789074 0.826993\n", + "F1 score 0.713043 0.758065\n", + "MCC 0.667074 0.703429\n", + "Precision 0.872340 0.839286\n", + "Recall 0.602941 0.691176\n", + "ROC AUC 0.897302 0.881745" ] }, "execution_count": 7, @@ -460,10 +460,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.753953Z", - "iopub.status.busy": "2024-06-17T14:23:49.753864Z", - "iopub.status.idle": "2024-06-17T14:23:49.768276Z", - "shell.execute_reply": "2024-06-17T14:23:49.767970Z" + "iopub.execute_input": "2024-06-17T19:19:08.252425Z", + "iopub.status.busy": "2024-06-17T19:19:08.252266Z", + "iopub.status.idle": "2024-06-17T19:19:08.269359Z", + "shell.execute_reply": "2024-06-17T19:19:08.269028Z" } }, "outputs": [ @@ -495,38 +495,38 @@ " \n", " \n", " Accuracy\n", - " 0.930233\n", - " 0.913621\n", + " 0.877419\n", + " 0.874194\n", " \n", " \n", " Balanced Accuracy\n", - " 0.850795\n", - " 0.808824\n", + " 0.757596\n", + " 0.771390\n", " \n", " \n", " F1 score\n", - " 0.820513\n", - " 0.763636\n", + " 0.660714\n", + " 0.672269\n", " \n", " \n", " MCC\n", - " 0.794727\n", - " 0.745415\n", + " 0.610892\n", + " 0.605831\n", " \n", " \n", " Precision\n", - " 0.979592\n", - " 1.000000\n", + " 0.840909\n", + " 0.784314\n", " \n", " \n", " Recall\n", - " 0.705882\n", - " 0.617647\n", + " 0.544118\n", + " 0.588235\n", " \n", " \n", " ROC AUC\n", - " 0.934549\n", - " 0.932687\n", + " 0.898396\n", + " 0.853610\n", " \n", " \n", "\n", @@ -534,13 +534,13 @@ ], "text/plain": [ " original updated\n", - "Accuracy 0.930233 0.913621\n", - "Balanced Accuracy 0.850795 0.808824\n", - "F1 score 0.820513 0.763636\n", - "MCC 0.794727 0.745415\n", - "Precision 0.979592 1.000000\n", - "Recall 0.705882 0.617647\n", - "ROC AUC 0.934549 0.932687" + "Accuracy 0.877419 0.874194\n", + "Balanced Accuracy 0.757596 0.771390\n", + "F1 score 0.660714 0.672269\n", + "MCC 0.610892 0.605831\n", + "Precision 0.840909 0.784314\n", + "Recall 0.544118 0.588235\n", + "ROC AUC 0.898396 0.853610" ] }, "execution_count": 8, @@ -564,10 +564,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-17T14:23:49.769834Z", - "iopub.status.busy": "2024-06-17T14:23:49.769728Z", - "iopub.status.idle": "2024-06-17T14:25:00.144662Z", - "shell.execute_reply": "2024-06-17T14:25:00.138627Z" + "iopub.execute_input": "2024-06-17T19:19:08.271274Z", + "iopub.status.busy": "2024-06-17T19:19:08.271012Z", + "iopub.status.idle": "2024-06-17T19:20:20.769000Z", + "shell.execute_reply": "2024-06-17T19:20:20.759023Z" } }, "outputs": [ @@ -1006,7 +1006,7 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x30af46320>,\n",
+       "
ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x3094d83a0>,\n",
        "                      estimator=XGBClassifier(base_score=None, booster=None,\n",
        "                                              callbacks=None,\n",
        "                                              colsample_bylevel=None,\n",
@@ -1032,7 +1032,7 @@
        "                                              n_estimators=None, n_jobs=None,\n",
        "                                              num_parallel_tree=None,\n",
        "                                              random_state=None, ...),\n",
-       "                      nu=0.0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
       ],
       "text/plain": [
-       "ExponentiatedGradient(constraints=,\n",
+       "ExponentiatedGradient(constraints=,\n",
        "                      estimator=XGBClassifier(base_score=None, booster=None,\n",
        "                                              callbacks=None,\n",
        "                                              colsample_bylevel=None,\n",
@@ -1133,10 +1133,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:00.213118Z",
-     "iopub.status.busy": "2024-06-17T14:25:00.212935Z",
-     "iopub.status.idle": "2024-06-17T14:25:00.280874Z",
-     "shell.execute_reply": "2024-06-17T14:25:00.280489Z"
+     "iopub.execute_input": "2024-06-17T19:20:20.927263Z",
+     "iopub.status.busy": "2024-06-17T19:20:20.926575Z",
+     "iopub.status.idle": "2024-06-17T19:20:21.014963Z",
+     "shell.execute_reply": "2024-06-17T19:20:21.014580Z"
     }
    },
    "outputs": [
@@ -1169,77 +1169,77 @@
        "    \n",
        "      Accuracy\n",
        "      1.000000\n",
-       "      0.930233\n",
+       "      0.877419\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
        "      1.000000\n",
-       "      0.850795\n",
+       "      0.757596\n",
        "    \n",
        "    \n",
        "      F1 score\n",
        "      1.000000\n",
-       "      0.820513\n",
+       "      0.660714\n",
        "    \n",
        "    \n",
        "      MCC\n",
        "      1.000000\n",
-       "      0.794727\n",
+       "      0.610892\n",
        "    \n",
        "    \n",
        "      Precision\n",
        "      1.000000\n",
-       "      0.979592\n",
+       "      0.840909\n",
        "    \n",
        "    \n",
        "      Recall\n",
        "      1.000000\n",
-       "      0.705882\n",
+       "      0.544118\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
        "      1.000000\n",
-       "      0.850795\n",
+       "      0.757596\n",
        "    \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.020577\n",
-       "      0.036249\n",
+       "      0.006611\n",
+       "      0.059044\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
        "      0.000000\n",
-       "      0.043478\n",
+       "      0.163399\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
        "      0.000000\n",
-       "      0.117647\n",
+       "      0.147059\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
        "      0.000000\n",
-       "      0.117647\n",
+       "      0.147059\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
        "      0.000000\n",
-       "      0.062856\n",
+       "      0.093486\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
        "      0.000000\n",
-       "      0.031996\n",
+       "      0.098748\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
        "      0.000000\n",
-       "      0.026334\n",
+       "      0.004747\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
        "      0.000000\n",
-       "      0.083333\n",
+       "      0.405983\n",
        "    \n",
        "  \n",
        "\n",
@@ -1247,21 +1247,21 @@
       ],
       "text/plain": [
        "                                                    train      test\n",
-       "Accuracy                                         1.000000  0.930233\n",
-       "Balanced Accuracy                                1.000000  0.850795\n",
-       "F1 score                                         1.000000  0.820513\n",
-       "MCC                                              1.000000  0.794727\n",
-       "Precision                                        1.000000  0.979592\n",
-       "Recall                                           1.000000  0.705882\n",
-       "ROC AUC                                          1.000000  0.850795\n",
-       "Statistical Parity                               0.020577  0.036249\n",
-       "Predictive Parity                                0.000000  0.043478\n",
-       "Equal Opportunity                                0.000000  0.117647\n",
-       "Average Group Difference in False Negative Rate  0.000000  0.117647\n",
-       "Equalized Odds                                   0.000000  0.062856\n",
-       "Conditional Use Accuracy                         0.000000  0.031996\n",
-       "Average Group Difference in Accuracy             0.000000  0.026334\n",
-       "Treatment Equality                               0.000000  0.083333"
+       "Accuracy                                         1.000000  0.877419\n",
+       "Balanced Accuracy                                1.000000  0.757596\n",
+       "F1 score                                         1.000000  0.660714\n",
+       "MCC                                              1.000000  0.610892\n",
+       "Precision                                        1.000000  0.840909\n",
+       "Recall                                           1.000000  0.544118\n",
+       "ROC AUC                                          1.000000  0.757596\n",
+       "Statistical Parity                               0.006611  0.059044\n",
+       "Predictive Parity                                0.000000  0.163399\n",
+       "Equal Opportunity                                0.000000  0.147059\n",
+       "Average Group Difference in False Negative Rate  0.000000  0.147059\n",
+       "Equalized Odds                                   0.000000  0.093486\n",
+       "Conditional Use Accuracy                         0.000000  0.098748\n",
+       "Average Group Difference in Accuracy             0.000000  0.004747\n",
+       "Treatment Equality                               0.000000  0.405983"
       ]
      },
      "execution_count": 10,
@@ -1291,10 +1291,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:00.283980Z",
-     "iopub.status.busy": "2024-06-17T14:25:00.283863Z",
-     "iopub.status.idle": "2024-06-17T14:25:00.310725Z",
-     "shell.execute_reply": "2024-06-17T14:25:00.310384Z"
+     "iopub.execute_input": "2024-06-17T19:20:21.019772Z",
+     "iopub.status.busy": "2024-06-17T19:20:21.019621Z",
+     "iopub.status.idle": "2024-06-17T19:20:21.049112Z",
+     "shell.execute_reply": "2024-06-17T19:20:21.048579Z"
     }
    },
    "outputs": [
@@ -1327,77 +1327,77 @@
        "    \n",
        "      Accuracy\n",
        "      1.000000\n",
-       "      0.930233\n",
+       "      0.877419\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
        "      1.000000\n",
-       "      0.850795\n",
+       "      0.757596\n",
        "    \n",
        "    \n",
        "      F1 score\n",
        "      1.000000\n",
-       "      0.820513\n",
+       "      0.660714\n",
        "    \n",
        "    \n",
        "      MCC\n",
        "      1.000000\n",
-       "      0.794727\n",
+       "      0.610892\n",
        "    \n",
        "    \n",
        "      Precision\n",
        "      1.000000\n",
-       "      0.979592\n",
+       "      0.840909\n",
        "    \n",
        "    \n",
        "      Recall\n",
        "      1.000000\n",
-       "      0.705882\n",
+       "      0.544118\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
        "      1.000000\n",
-       "      0.850795\n",
+       "      0.757596\n",
        "    \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.020577\n",
-       "      0.036249\n",
+       "      0.006611\n",
+       "      0.059044\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
        "      0.000000\n",
-       "      0.043478\n",
+       "      0.163399\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
        "      0.000000\n",
-       "      0.117647\n",
+       "      0.147059\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
        "      0.000000\n",
-       "      0.117647\n",
+       "      0.147059\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
        "      0.000000\n",
-       "      0.062856\n",
+       "      0.093486\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
        "      0.000000\n",
-       "      0.031996\n",
+       "      0.098748\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
        "      0.000000\n",
-       "      0.026334\n",
+       "      0.004747\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
        "      0.000000\n",
-       "      0.083333\n",
+       "      0.405983\n",
        "    \n",
        "  \n",
        "\n",
@@ -1405,21 +1405,21 @@
       ],
       "text/plain": [
        "                                                    train      test\n",
-       "Accuracy                                         1.000000  0.930233\n",
-       "Balanced Accuracy                                1.000000  0.850795\n",
-       "F1 score                                         1.000000  0.820513\n",
-       "MCC                                              1.000000  0.794727\n",
-       "Precision                                        1.000000  0.979592\n",
-       "Recall                                           1.000000  0.705882\n",
-       "ROC AUC                                          1.000000  0.850795\n",
-       "Statistical Parity                               0.020577  0.036249\n",
-       "Predictive Parity                                0.000000  0.043478\n",
-       "Equal Opportunity                                0.000000  0.117647\n",
-       "Average Group Difference in False Negative Rate  0.000000  0.117647\n",
-       "Equalized Odds                                   0.000000  0.062856\n",
-       "Conditional Use Accuracy                         0.000000  0.031996\n",
-       "Average Group Difference in Accuracy             0.000000  0.026334\n",
-       "Treatment Equality                               0.000000  0.083333"
+       "Accuracy                                         1.000000  0.877419\n",
+       "Balanced Accuracy                                1.000000  0.757596\n",
+       "F1 score                                         1.000000  0.660714\n",
+       "MCC                                              1.000000  0.610892\n",
+       "Precision                                        1.000000  0.840909\n",
+       "Recall                                           1.000000  0.544118\n",
+       "ROC AUC                                          1.000000  0.757596\n",
+       "Statistical Parity                               0.006611  0.059044\n",
+       "Predictive Parity                                0.000000  0.163399\n",
+       "Equal Opportunity                                0.000000  0.147059\n",
+       "Average Group Difference in False Negative Rate  0.000000  0.147059\n",
+       "Equalized Odds                                   0.000000  0.093486\n",
+       "Conditional Use Accuracy                         0.000000  0.098748\n",
+       "Average Group Difference in Accuracy             0.000000  0.004747\n",
+       "Treatment Equality                               0.000000  0.405983"
       ]
      },
      "execution_count": 11,
diff --git a/examples/levelling_up.ipynb b/examples/levelling_up.ipynb
index df1abbb..19033b6 100644
--- a/examples/levelling_up.ipynb
+++ b/examples/levelling_up.ipynb
@@ -20,10 +20,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:43.113351Z",
-     "iopub.status.busy": "2024-06-17T14:23:43.113163Z",
-     "iopub.status.idle": "2024-06-17T14:24:01.412473Z",
-     "shell.execute_reply": "2024-06-17T14:24:01.412016Z"
+     "iopub.execute_input": "2024-06-17T19:18:59.776572Z",
+     "iopub.status.busy": "2024-06-17T19:18:59.776323Z",
+     "iopub.status.idle": "2024-06-17T19:19:16.057744Z",
+     "shell.execute_reply": "2024-06-17T19:19:16.057271Z"
     }
    },
    "outputs": [
@@ -39,7 +39,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142356\"\n"
+      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191910\"\n"
      ]
     },
     {
@@ -65,7 +65,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon will save models to \"AutogluonModels/ag-20240617_142356\"\n"
+      "AutoGluon will save models to \"AutogluonModels/ag-20240617_191910\"\n"
      ]
     },
     {
@@ -79,8 +79,8 @@
       "Platform Machine:   arm64\n",
       "Platform Version:   Darwin Kernel Version 23.5.0: Wed May  1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n",
       "CPU Count:          10\n",
-      "Memory Avail:       7.78 GB / 16.00 GB (48.7%)\n",
-      "Disk Space Avail:   363.54 GB / 460.43 GB (79.0%)\n",
+      "Memory Avail:       6.45 GB / 16.00 GB (40.3%)\n",
+      "Disk Space Avail:   360.81 GB / 460.43 GB (78.4%)\n",
       "===================================================\n"
      ]
     },
@@ -173,7 +173,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tAvailable Memory:                    7988.39 MB\n"
+      "\tAvailable Memory:                    6626.69 MB\n"
      ]
     },
     {
@@ -334,7 +334,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.2s = Fit runtime\n"
+      "\t0.1s = Fit runtime\n"
      ]
     },
     {
@@ -355,7 +355,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Data preprocessing and feature engineering runtime = 0.21s ...\n"
+      "Data preprocessing and feature engineering runtime = 0.18s ...\n"
      ]
     },
     {
@@ -407,7 +407,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsUnif ... Training model for up to 4.79s of the 4.79s of remaining time.\n"
+      "Fitting model: KNeighborsUnif ... Training model for up to 4.82s of the 4.82s of remaining time.\n"
      ]
     },
     {
@@ -421,77 +421,119 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t4.54s\t = Training   runtime\n"
+      "\t1.64s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.06s\t = Validation runtime\n"
+      "\t0.04s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsDist ... Training model for up to 0.18s of the 0.18s of remaining time.\n"
+      "Fitting model: KNeighborsDist ... Training model for up to 3.14s of the 3.14s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tNot enough time to train KNN model on all training rows. Fit 20000/36573 rows. (Training KNN model on 36573 rows is expected to take 0.19s)\n"
+      "\t0.766\t = Validation score   (accuracy)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.7636\t = Validation score   (accuracy)\n"
+      "\t0.04s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.05s\t = Training   runtime\n"
+      "\t0.03s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.04s\t = Validation runtime\n"
+      "Fitting model: LightGBMXT ... Training model for up to 3.06s of the 3.06s of remaining time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: \n",
+      "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
+      "\n",
+      "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
+      "This will raise in a future version.\n",
+      "\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\tRan out of time, early stopping on iteration 112. Best iteration is:\n",
+      "\t[112]\tvalid_set's binary_error: 0.13\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.87\t = Validation score   (accuracy)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t3.62s\t = Training   runtime\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.02s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.79s of the -0.18s of remaining time.\n"
+      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.82s of the -0.64s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tEnsemble Weights: {'KNeighborsUnif': 0.6, 'KNeighborsDist': 0.4}\n"
+      "\tEnsemble Weights: {'LightGBMXT': 0.8, 'KNeighborsDist': 0.133, 'KNeighborsUnif': 0.067}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.7816\t = Validation score   (accuracy)\n"
+      "\t0.8724\t = Validation score   (accuracy)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.02s\t = Training   runtime\n"
+      "\t0.03s\t = Training   runtime\n"
      ]
     },
     {
@@ -505,14 +547,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon training complete, total runtime = 5.36s ... Best model: \"WeightedEnsemble_L2\"\n"
+      "AutoGluon training complete, total runtime = 5.79s ... Best model: \"WeightedEnsemble_L2\"\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142356\")\n"
+      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191910\")\n"
      ]
     }
    ],
@@ -532,10 +574,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:01.414757Z",
-     "iopub.status.busy": "2024-06-17T14:24:01.414556Z",
-     "iopub.status.idle": "2024-06-17T14:24:01.416763Z",
-     "shell.execute_reply": "2024-06-17T14:24:01.416409Z"
+     "iopub.execute_input": "2024-06-17T19:19:16.060842Z",
+     "iopub.status.busy": "2024-06-17T19:19:16.060621Z",
+     "iopub.status.idle": "2024-06-17T19:19:16.082547Z",
+     "shell.execute_reply": "2024-06-17T19:19:16.080944Z"
     }
    },
    "outputs": [],
@@ -550,10 +592,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:01.418823Z",
-     "iopub.status.busy": "2024-06-17T14:24:01.418638Z",
-     "iopub.status.idle": "2024-06-17T14:24:01.712755Z",
-     "shell.execute_reply": "2024-06-17T14:24:01.709835Z"
+     "iopub.execute_input": "2024-06-17T19:19:16.088278Z",
+     "iopub.status.busy": "2024-06-17T19:19:16.086694Z",
+     "iopub.status.idle": "2024-06-17T19:19:16.523009Z",
+     "shell.execute_reply": "2024-06-17T19:19:16.520478Z"
     }
    },
    "outputs": [],
@@ -570,10 +612,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:01.719717Z",
-     "iopub.status.busy": "2024-06-17T14:24:01.718562Z",
-     "iopub.status.idle": "2024-06-17T14:24:03.596732Z",
-     "shell.execute_reply": "2024-06-17T14:24:03.588319Z"
+     "iopub.execute_input": "2024-06-17T19:19:16.527139Z",
+     "iopub.status.busy": "2024-06-17T19:19:16.526750Z",
+     "iopub.status.idle": "2024-06-17T19:19:18.826152Z",
+     "shell.execute_reply": "2024-06-17T19:19:18.824825Z"
     }
    },
    "outputs": [
@@ -589,7 +631,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -609,17 +651,17 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:03.609552Z",
-     "iopub.status.busy": "2024-06-17T14:24:03.608285Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.544550Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.543749Z"
+     "iopub.execute_input": "2024-06-17T19:19:18.830727Z",
+     "iopub.status.busy": "2024-06-17T19:19:18.830392Z",
+     "iopub.status.idle": "2024-06-17T19:19:23.045746Z",
+     "shell.execute_reply": "2024-06-17T19:19:23.043214Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 5,
@@ -628,7 +670,7 @@
     },
     {
      "data": {
-      "image/png": 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C97q9evWi1m0sdXV17kkmk4lPxXCcPC01mtZwA/90dunSBW8TIcRz0aBLly54PkJo6NCh1dXVt2/fjo6O1tDQsLKyGjp0KL62GRMTw/NdFRB8k1VVVZWUlOBo3717hxBavHgx99HGv9fcR1tNTY16zWAw+Od8+fIFvy4pKdHR0eG+daqlpSUrK4urX1JSIisry71uvQec5+hNmzZt7969c+fOvXbt2r179+7fv6+pqclzHHR0dHgmqQOORD6MHz584HA4jWoqMmnSpPv37z948OD58+clJSX4bnRdXd3w4cMjIyOXLl0aExNz7949/Ecez06bcGXJ19c3KysL/20UFBRkZ2fXt2/fhgrPnj07MTHx2bNnCKGQkBAmk0n9bbF8+fLff/89KSnJzc1NXV19yJAhDT1HVFdXFxoa2qVLF2tr648fP378+HHo0KGKiorUxczi4uIuXbrIyNTzi1dcXEyj0XjemkbhOUT+/v6rVq0aN27chQsXkpOT79+/b2FhQR3V4uJiAe/dzz//XFFRgS+f7N27V19f393dXaLx4O8Cd3nuSfzVmzhxIvdXb8uWLSRJlpaW5uTkcM/HfyYK/nK1og7dJrNz5840Gs3T05P/zzpDQ0P8YtasWdu2bQsPD588efL58+f9/PxoNBpepKGh0adPH/5baPgHWozU1dXxZ45SWFjYhO3gC7P4Fhr+YeV5Junt27fUGWT//v2VlJRu3LiRk5MzZMgQgiCGDBmyffv2+/fv5+bm1vuXqXhdunSJw+HgaHFUy5cvp26tUXr27NmEjaurq+NrhtR3sqioqLa2Fu9IXV29tra2tLSUSnj1HnDu73NZWdnFixfXrFmzbNkyPKe6urq0tJRnFZ7tFBYWGhsbNzZ4NTU1Go325s0b0VfR1NSkmrxSnjx5kp6eHhoa6uXlhee8fPmSf90mtOgbPHhw79699+7dq6Sk9PDhw4buPmJTp0719/cPDQ3dsGHDsWPHxo0b17lzZ7xIVlbW39/f39//48ePN27c+PXXX11dXfPy8vhbwN64cQP/McfzF0NSUlJGRoaZmZmmpubt27fr6ur4E56mpiaHwyksLKw3rzOZTJ7mP/y/2jyH6Pjx499///3GjRupOe/fv+/UqRO1OwHvnbGxsZubW1BQkJub2/nz59etW0f94IiuUfGoq6vzfyyp1/gbsWfPHv4mwfhPQKotG/r2ZRT85WpFHfrcTkFBwcXFJTU1tU+fPjb/RX1nTE1N+/fvHxIScvLkyerqanzzHBs9ejRuSsuzrtiznZOT082bN6mTmLq6ujNnzjR2I+np6Rs3bmSz2fgCnZ2dnby8PPfP0Js3b27evDlkyBA8SafTBw0aFB0dffPmTXwd39HRUVZWduXKlTjziaFiDcvNzV28eLGqqipu3dezZ8/u3bunp6fb8KGuoDbKkCFDKisruZ9Vwvd1cL3wLTTuxqvh4eGCN0gQBEmS3I92HDx4kMPh8BTDf7Njd+/e/fvvv5vQfkdeXt7JyenMmTNNu4pAwT9G3DFzt4AVUUMnoD4+PpcuXVq+fLm2tvZ3330nYAudO3ceN27c0aNHL168WFhYWO/Fxk6dOk2cOHHBggX4fIK/wKFDh2RkZM6dOxfL5dixYwgh3LzZzc3ty5cv9T4aiG9A7Nu3r97w2Gw2vrmI3bx5s7KyUkB1EEIEQXAf1UuXLuXn53Pv7sWLFzdv3mxodV9f30ePHnl5edFoNMGt3kS8iCI4HhcXl6dPn3I/iXvy5EnqtYODQ6dOnTIyMvi/egwGg8Fg8H8ZBX+5WlEHOre7efMmz/dk5MiRu3btGjhwoKOj4//+9z82m11RUfHy5csLFy5wfxZnz579008/vX371t7envtMYv369dHR0fb29j4+Pj179vzy5UtOTs7ly5f3798v3geSVqxYceHChSFDhqxYsUJeXn7//v34inm9l2UoKSkpqqqqNTU1b9++jYmJOXbsmJaW1oULF/Alvk6dOq1aterXX3/9/vvvp06dWlJSsm7dOjk5OdwGARsyZMiiRYsQQvhMTl5e3t7e/vr163369NHS0hI9fllZWScnJ8FX7Z88eYIv/RcVFSUkJISEhNBotKioKOrW1B9//OHm5ubq6jpz5kw9Pb3S0tLMzMyHDx82IfEjhL7//vugoCAvL6+cnBxzc/Pbt29v3Lhx5MiRuKYjRoxwcHBYtGhReXm5tbV1YmIi/roKOOAqKiqDBg3atm2bhoYGm82Oj48/dOgQ9ecz5cGDB3Pnzv3uu+/y8vJWrFihp6eHr8c21o4dOwYOHNi/f/9ly5YZGxu/e/fu/Pnzf/zxR6Nyv4mJiZGR0bJly0iSVFNTu3DhAr722Cjm5ubh4eGnTp3q1q2bnJycubk5nj9jxozly5ffunVr5cqV+CMnwOzZs0+dOuXt7a2vr8992WDMmDH4QUz8yERgYKCBgQH3DWOspKTkr7/+cnV15b/ot3PnzqNHj27atGnq1KkhISHz5s17/vy5i4tLXV1dcnKyqanplClTHB0dPT09AwIC3r17N3r0aCaTmZqaqqCg8PPPPyOEPD09V61atXr1aicnp4yMjL1796qqqgquzujRo0NDQ01MTPr06ZOSkrJt2zbuHwQ/P79Tp065u7svW7bM1tb28+fP8fHxo0ePdnFxwQWGDRtmZmYWGxs7Y8YMwV80c3PzuLi4Cxcu6OrqKisrN3SdQ2g8hw8fHjVqVEBAgLa29okTJ/BVZUxJSWnPnj1eXl6lpaUTJ07U0tIqLi5OT08vLi5u6O8DwV+u1iTpZjBtAXUPnAdu05idnT179mw9PT06na6pqWlvb081k8PKysrk5eVRfQ26iouLfXx8DA0N6XS6mpqatbX1ihUrKisr8VLUmDaZPE2SnJycuJu6JSQk9O/fn8lk6ujoLFmyBLdx+vjxY7315c5YTCZTV1d3+PDhu3btotqnUQ4ePNinTx8Gg6Gqquru7k61JsXwn3vdu3en5uDLtv7+/tzFEEILFizgnsPdaA0X4Gm2x4373WEwGFpaWk5OThs3buRvcJienj5p0iQtLS06na6jozN48OD9+/dzb4S7YSd/w1QvLy9FRUVqsqSkZN68ebq6urKysgYGBsuXL//y5Qu1tLS0dNasWZ06dVJQUBg2bBi+oUU1sKy31eubN28mTJjQuXNnZWXlESNGPHnyhL/x3vXr1z09PTt16oQbxGZlZVGr8zdU8/LyMjAwwK952mSSJJmRkfHdd9+pq6szGIyuXbvOnDmTO35u/G8Q90aGDRumrKzcuXPn7777DrfI52l6ylNNno9uTk7O8OHDcZalosVmzpwpKyv75s2benfNjcPhsFgsxNcwcvv27fb29hoaGriOc+bMycnJ4V89MDAQIXTu3Dn+Rbh9YEREBEmSnz9/Xr16dffu3RkMhrq6+uDBg+/evUsFsHPnzt69e+Pvgp2d3YULF/Ci6urqpUuXslgsfEqdlpbG/7bytCj+8OHDnDlztLS0FBQUBg4cmJCQwPN1/vDhg6+vb9euXel0upaW1qhRo/DzdhT8hF9SUpLg45aWlubg4ICv6+LtNy0e/DHAz93OmTPnr7/+4v6xIkkyPj4e9+xFp9P19PRGjRp15swZAYEJ/nK1VptMguRqpwfai+HDh+fk5Lx48aK1A+koTp48OX369Dt37tjb2zdtC6GhobNmzbp//z7//TOp9PXrVzabPXDgQO5OAICIbGxsuLt3AGLRga5ktmv+/v5WVlYsFqu0tPTEiRPR0dHcT84CsQsLC8vPzzc3N5eRkUlKStq2bdugQYOanOo6lOLi4ufPn4eEhLx7945qswNEUV5e/uTJk4sXL6akpERFRbV2ONIGsl37wOFwVq9eXVhYSBCEmZnZsWPHZsyY0dpBSTNlZeXw8PCAgICqqipdXd2ZM2cGBAS0dlDtw6VLl2bNmqWrqxscHCzgwQPA7+HDhy4uLurq6mvWrJF0p5EdEFzJBAAAIP069BMIAAAAOgjIdgAAAKQfZDsAAADST9paqdTV1b19+1ZZWbmtDWAIAACgUUiSrKioaKiD08aStmz39u1b/JgqAAAAKZCXlyeW3qmkLdvhPh3y8vJUVFRaOxYAAABNV15ezmKxmtYXLj9py3b4AqaKigpkOwAAkALiui0FrVQAAABIP8h2AAAApB9kOwAAANIPsh0AAADpB9kOAACA9INsBwAAQPpBtgMAACD9INsBAACQfpDtAAAASD9p60sFAIA4HJSQgAoKkK4ucnRENJqQRfwzG9qCROcLDQOhRhSwt0d37wqplIhhtLBGHQf+ajZhI9yTWloIIVRUJPzDIOKOWv4ANoSULmVlZQihsrKy1g4EgFYSEUHq65MI/fNPX5+MiBC0iH/mkiX1b6GhLYtlvtAw1NVJdfVGFKDRhFSq3moKOHqt9fY1qpoNHWHBG+GZFOXDIGK0zTuA4v09h2wHgBSJiCAJ4j8/WARBEsQ/v+P8i/h/4Pj/4S0sWVL/lsUyX5QwJP2v3jCoo9dab19jq1DvERbvUaIOiCjRNu8Aivf3nCBJsnVPLsWrvLxcVVW1rKwMeoUGHQ6Hg9hs9OYN73yCQHp6CKF6FokOX6GS3Pw2iyCQvj7Kzpb4FbmG3r7GkvQRxgfk5UtkZCRStM04gOL9PYdWKgBIi4SE+n99SBK9edPcn9GGfkDFNb/NIkmUl4cSEiS+o4bevsaS9BHGByQ4WNRoW+wACgPZDgBpUVDQ2hFIrxY4tu3r7Xv1qnHl20DtINsBIC10dVs7AunVAse2fb19RkaNK98GagfZDgBp4eiI9PUR/9CX+MZJvYtER6PVv7q45rdZBIFYrH/a6EtUQ29fY0n6COMDMn++qNG22AEUBrIdANKCRkO7diGE/vMbhF/v2tXgIp6Z/AgCEQTy969ndXHNFyUMSas3DPw6MLAlHhqr9+1rlIaOsBhRB4TBECnaljyAQomlZWfbAU8ggI6O5xEoFqvB5+3wIv6ZPI9MUVtoaMtimS80DJ5nwoQW4H4Qrd5K1VtNAUevtd6+RlWzoSPctOftBHwYRIy2eQcQnkAQBJ5AAAD6UoG+VKSjLxXx/p5DtgMAANAWwfN2AAAAQONAtgMAACD9INsBAACQfpDtAAAASD/IdgAAAKQfZDsAAADSD7IdAAAA6QfZDgAAgPSDbAcAAED6QbYDAAAg/SDbAQAAkH6Q7QAAAEg/yHYAAACkH2Q7AAAA0g+yHQAAAOkn29oBAADErNWHIwWgDYJsB4BUiYxEvr7ozZt/JvX10a5dyMOjVWMCoA2AK5kASI/ISDRx4r+pDiGUn48mTkSRka0XEwBtA2Q7AKQEh4N8fRFJ/mcmnvTzQxxOqwQFQFsB2Q4AKZGQ8J+zOgpJorw8lJDQ4gEB0JbAfTsApERBgfCl0IAFdFiQ7QCQErq6QpZCAxbQkcGVTACkhKMj0tdHBME7nyAQi4Xev4cGLKBDg2wHgJSg0dCuXQih/yQ8/Hr7drRwITRgAR0aZDsApIeHBzp7Funp/TtHXx+dPYs0NaEBC+jo4L4dAFLFwwO5u/M2RQkLE7RKfj6Ki4OmK0DKQbYDQNrQaMjZ+T9zBDdgWbgQFRf/8xqargBpBVcyAZB+DTVgwahUh6DpCpBekO0AkH71NmCpFzRdAdIKsh0AHQJ/AxZNzfpLQtMVIJXgvh0AHQVPA5b8fDRjRoOFY2Kg3QqQKpDtAOhAuBuwxMUJKhkQ8M8LaLcCpANcyQSggxLcdIUC7VaAdIBsB0AHJWLTFWi3AqQDZDsAOi7+piv1gnYrQApAtgOgQ/PwQDk5KDYWnTyJVq4UVFLwiEIAtHHQSgWAjo5quhIX92/jFH66ujA8HmjHINsBAP6B263k5/OOlkAQSF8fvX+P2GwYHg+0V3AlEwDwDwFjBk2ZgiZNguHxQDsG2Q4A8K96xww6dQqFhcHweKB9g2wHAPgP7nYrsbEoOxuGxwPSQOLZLjg42NDQUE5OztraOqG+r0VkZOSwYcM0NTVVVFTs7OyuXbvGvTQiIsLMzIzJZJqZmUVFRUk6WgAA+tZuZepU5OyMaDQhrTELChCHg+LiUFgYiouDUz3QRkk22506dcrPz2/FihWpqamOjo5ubm65ubk8ZW7dujVs2LDLly+npKS4uLiMGTMmNTUVL0pMTJw8ebKnp2d6erqnp+ekSZOSk5MlGjAAgJ/g4fGyshCbjVxc0LRpyMUFsdlwMw+0RQTJczFerPr379+3b999+/bhSVNT03Hjxm3atEnAKr169Zo8efLq1asRQpMnTy4vL79y5QpeNGLEiM6dO4cJHIa5vLxcVVW1rKxMRUVFTJUAoKPjcBCbXX9bTTU1VFr6n/m4VcvZs9BcEzSXeH/PRT23q62tvXHjxh9//FFRUYEQevv2bWVlpeBVvn79mpKSMnz4cGrO8OHD7969K2CVurq6iooKNTU1PJmYmMi9uqura72rV1dXl3MRsUYAABE11FYTJzlovQLaBZGy3d9//21ubu7u7r5gwYLi4mKE0NatWxcvXix4rffv33M4HG1tbWqOtrZ2YWGhgFW2b99eVVU1adIkPFlYWCjK6ps2bVL9hsViiVIjAECj1NtWc906VFJST2FovQLaIJGyna+vr42NzYcPH+Tl5fGc8ePHx8TEiLIuwfXXIEmSRMMd0IaFha1du/bUqVNaWlqNWn358uVl3+Tl5YkSFQCgsfjbanbvLqg8tF4BbYpIfancvn37zp07DAaDmmNgYJCfny94LQ0NDRqNxn02VlRUxH2uxu3UqVNz5sw5c+bM0KFDqZk6OjqirM5kMplMpigVAQA0B/fweEi01ivQ9wpoI0Q6t6urq+P89w+zN2/eKCsrC16LwWBYW1tHR0dTc6Kjo+3t7flLhoWFzZw58+TJk6NGjeKeb2dnx7369evX610dANAqGhohjyCQujpauxb6XgFtCSmCSZMm/fDDDyRJKikpvX79uqKiYvDgwTNnzhS6Ynh4OJ1OP3ToUEZGhp+fn6KiYk5ODkmSy5Yt8/T0xGVOnjwpKysbFBRU8M3Hjx/xojt37tBotM2bN2dmZm7evFlWVjYpKUnwHsvKyhBCZWVlotQLANBMEREkQZAEQSL0zz/8Wl393znci1gssra2tYMG7YR4f89Fynb5+fk9evQwNTWVlZUdMGCAurp6z5493717J8q6QUFBBgYGDAajb9++8fHxeKaXl5eTkxN+7eTkxJOAvby8qNXPnDnTs2dPOp1uYmISEREhdHeQ7QBoYRERpL7+vymNxSLXrasn1VH/YmNbO2LQToj391zU5+0+f/4cHh6ekpJSV1fXt2/f6dOnUy1W2hR43g6AlsczEtDp02jatAYLnzyJJk2CkYOAcOL9PReplcqtW7fs7e1nzZo1a9YsPKe2tvbWrVuDBg1qfgQAgPYOWq+Atk+kczsajVZQUMD9YEBJSYmWlhan7bUphnM7AFod9L0CxKIV+lIh+R50KykpUVRUbP7uAQDSB/peAW2QkCuZHh4eCCGCIGbOnEk908bhcB49egQPAwAAGoL7XvH1/c8Vy7lz0Zo19RSm+l7hvhwKgHgJyXaqqqoIIZIklZWVqWYpDAZjwIABP/zwg8SjAwC0Wx4eyN2dt/WKAPn5KC4Omq4ASRGS7UJCQhBCbDZ78eLFcOkSANAojWq9snAhKi7+5zU0XQFiJ9kRf1oetFIBoM1qqPUKP2i6AlCrPIGAEDp79uzp06dzc3O/fv1KzXz48GHzIwAAdBC49crEif+2WGkISSKCQH5+yN0dLmkC8RCpTebu3btnzZqlpaWVmppqa2urrq7++vVrNzc3SQcHAJAy/CMHaWrWXxKGDQLiJdKVTBMTkzVr1kydOlVZWTk9Pb1bt26rV68uLS3du3dvC4TYKHAlE4C2j7vvlfx8NGNGgyVXrkRmZtBupYMS7++5SNlOQUEhMzPTwMBAS0srOjrawsIiKytrwIABJfWO5NiqINsB0L7ExSEXF+HFoN1KB9QKT5fr6OjgxGZgYJCUlIQQys7OlrLmLQCAVtHQsEE8YMAg0EwiZbvBgwdfuHABITRnzpyFCxcOGzZs8uTJ48ePl3BsAADpV2/HK/ygyxXQTCJdyayrq6urq5OVlUUInT59+vbt28bGxvPmzeMezbyNgCuZALRHkZH/6XhFgNhY6HKlo2iF+3b1ys/P1+NuWdU2QLYDoJ2imq5kZKCAgAaLnTyJpk5twbBA62mF+3Y8CgsLf/75Z2Nj4+bvHgAAMNzxytSpaMgQQcUyMlBcHFzPBI0mJNt9/Phx+vTpmpqaXbp02b17d11d3erVq7t165aUlHT48OGWCREA0KEIbrcSEIBcXBCbDS1WQOMIyXa//vrrrVu3vLy81NTUFi5cOHr06Nu3b1+5cuX+/ftT4WoCAEACRGm3Ak00QWMJyXaXLl0KCQn5/fffz58/T5Jkjx49bt686eTk1DLBAQA6Jv4uV3hAE03QWEKy3du3b83MzBBC3bp1k5OTmzt3botEBQDo6Dw8UE4Oio1FK1fWXwC6FgONIqRX6Lq6Ojqdjl/TaDQY9AcA0GJwu5WCAkFlYmJgSDwgEiHZjiRJatTyL1++zJs3jzvhRcJVcwCAhAkeFY96VgG6FgOCCcl2Xl5e1OsZArpuBUA6cHdXDCcLbQNuoil0VDzcbgWGxAMNgdFcAfiGpz8POFloMyIj0cSJCCEhCY8gkL4+ys6Gv1KkROs/XQ6AFMI/qNxdV0Ej9zZDaBNNDNqtAAHg3A5IA85XzuPghE+vChSMdM3nO9Jo6D8XJBFCCQkoPx8VFyNNTaSn9+9MXMbeHhkZ1dNLI5wstCUidi3m7Y0mTIDr0NKgrfST2TZBtuuAkpZGdt3h24XzT64qlVFXVEDMym+DL6qrI4QQz1iMPDM1NND79w3uAPohbmNEGRIPrkNLAbiSCcC/kpZG2m6bqMP597Ssc10Jo5Irt5WU8KY6/pkCUh1CQprAgxYnypB4cB0a8IBsB9oxzldO1x2+CJHcn2MCIWEjgzaS4CbwoMWJ0rUYdLYCeAh5AoHy4sWLuLi4oqKiuro6aubq1aslExUAInkcnGDJEWFItObQ1PznJh9oS3C7FcFD4lGNVuA6NEAiZrsDBw7873//09DQ0NHRIb79NUUQBGQ70Lo+vZL8Ncbp06G1Q9vk4YHc3VFCAoqIQHv3NlgsJgZarACERMx2AQEBGzZs+OWXXyQdDQCNomAk+WuM7u4S3wVoKty1GEKCsl1AAAoNhRYrQLT7dh8+fPjuu+8kHQoAjWU+3/EtTb9O3Pfp/sViwWXMtk9ooxVosQKQiNnuu+++u379uqRDAaCxaAxarv8uhFCd0KJNExgIl8DaPqGNVqDFCkAiXsk0NjZetWpVUlKSubk5NSQCQsjHx0digQEgkgFbPZLQWaPff9IkBT5F0ATr1sHFr/ZCaKMVaLECRHq63NDQsJ41CeL169cSCKlZ4OnyjinsyNchM/U1ULHYHqnR10c5OXBi175wOGjtWkHdrJw8iaZObcGAQPOI9/dcpHO77Ozs5u8JAMnRNWDMQ/vPool1CMmgf/6Aw/9RF7dIROB5BEEI6l0YXw7btQtSXbtDo6EhQwRlu3fvUFgYjG/RQTXuT2GSJKWspzEgHRwd0X19j4nobD76t+fg90i9BKlTk3lIfwKKmKkUUabM1bswi4WWLEH6+v/O0deHYWPaLwEtVmg0tHAhmjYNubggNhsarXQ4ovaTefTo0W3btmVlZSGEevTosWTJEk9PTwnH1hRwJbPDwmMYECTHESXoooICpJuAHBFC3JN1iIYQkkGc0SoJK2YX2Lp/+yMfhrWTIqIMD4TTIfxV08a1Qq/QO3bsWLVqlbe3t4ODA0mSd+7cCQoKCggIWLhwYfMjEC/Idh0Zz/h0ghEE/NhJLZ5PAv57hgeMb9H2tUK2MzQ0XLdu3ffff0/NOXLkyNq1a9vg/TzIdh0cPkmLjkYbNwovzGLBj53Uok7X371DAv4sh/Et2rJWaKVSUFBgb2/PPcfe3r4AOoYHbQ/uXEPEz2ZeHlq9Gg0bBhcvpRDVzUpYmKBiMTFwAbujEKmVirGx8enTp7nnnDp1qnv37pIJCYDmEn3Qgo0boc2ClBP8YQgIgHYrHYVIVzIjIiImT548dOhQBwcHgiBu374dExNz+vTp8ePHt0CIjQJXMgFCiMNBbLaoN/AwuI0nrfCHIT9fUKMVBO1W2qRWGM11woQJycnJGhoa586di4yM1NDQuHfvXhtMdQBguCspwaN98oOepaSSKIPhIehdrAMQ9QmE9gLO7QClUU00MWizIK1E/zDAZ6DtaLk2meXl5Xgf5eXl9RZogxkFsh3ghhvm5eejmBh0+jSqqhJSfto0xGYjGRnk7IycnaHZglShWmlmZAjqb+X4caSnB01X2oSWy3Y0Gq2goEBLS0tGRob471UAkiQJguC0vXN+yHagITExaOjQRpRXV0d//gl3caRQXBxycWlwqaYmKi7+57W+PgyM15paLtvFx8c7ODjIysrGx8fXW8DJyan5EYgXZDvQkCY0XUEIRUTAj520EbHdCoKmK62tFZ4uz83NZbFY3Kd3JEnm5eV17dq1+RGIF2Q7IADuU6pRt6phLASpJErvYhh0udKKWqFNpqGhYTF1bo8QQqi0tLTeYYAAaMvwKGjcXUAL9eYNSkiQWECgleBPgh5X9+CamvWXpAbGA+2dSH2p4Lt03HMqKyvl5OQkExIAEuThgdzd/2m6cu0aOnZM+CrQa5BUoj4JuEFKfj6aMaPBwvAZkAJCsp2/vz9CiCCIVatWKSgo4JkcDic5OdnS0lLSwQEgCVSfUnp6ImU70XtmAe0L9UlACMXFCSoJnwEpICTbpaamIoRIknz8+DGDwcAzGQyGhYXF4sWLJR4dAJKEx0IT3G5FXx85OrZUQKD14A8Df9MVfN8OPgNSQEi2i42NRQjNmjVr165d0O4DSBncy4bgdiswhnkHQX0YuEe2xzdwAgPhMyANRGqlEhgYWFtbyz2ntLS0oUfOAWhHBLRbUVeHxw86Fv6mKzCOvTQR6QkENze3MWPGzJ8/n5qzf//+8+fPX758WZKxNQU8gQCagOpy5d07VFICfal0aDCOfdvRCs/bqamp3blzx9TUlJrz7NkzBweHkpKS5kcgXpDtAABAOrTC83bV1dU8VzJramo+f/7c/N0DAAAALUCkbNevX78///yTe87+/futra0lExIAAAAgZiJluw0bNhw8eHDQoEHr1q1bt27doEGDDh8+vHHjRlHWDQ4ONjQ0lJOTs7a2TqivQ4KCgoJp06b17NlTRkbGz8+Pe1FoaCjxX1++fBFlpwAAAAA3kbKdg4NDYmIii8U6ffr0hQsXjI2NHz165CjCEyinTp3y8/NbsWJFamqqo6Ojm5tbbm4uT5nq6mpNTc0VK1ZYWFjwb0FFRaWAC3TgAgAAoAkkO5pr//79+/btu2/fPjxpamo6bty4TZs21VvY2dnZ0tIyMDCQmhMaGurn5/fx40fR9witVAAAQDq0XCsV6om68gYI3vTXr19TUlKGDx9OzRk+fPjdu3cbFV9lZaWBgYG+vv7o0aNxxy78qqurRY8KAABAByQo23Xu3LmoqAgh1KlTp87/hecI3vT79+85HI62tjY1R1tbu7CwUPTgTExMQkNDz58/HxYWJicn5+DgkJWVxV9s06ZNqt+wWCzRtw8AAKCDENRz2M2bN9XU1NC3/sOahmdUPJ6xFAQbMGDAgAED8GsHB4e+ffvu2bNn9+7dPMWWL1+Oe69GCJWXl0PCAwAAwENQtqOGJm/aGOUaGho0Go37ZK6oqIj7VK9RZGRk+vXrV++5HZPJZDKZTdssAACAjkBQtnv06JHglfv06SNgKYPBsLa2jo6OHj9+PJ4THR3t7u7e2BAxkiTT0tLMzc2btjoAAICOTFC2s7S0JAhCwOVHDocjeOv+/v6enp42NjZ2dnZ//vlnbm7uvHnzEELLly/Pz88/evQoLpaWloYQqqysLC4uTktLYzAYZmZmCKF169YNGDCge/fu5eXlu3fvTktLCwoKanwdAQAAdHSCsl12djZ+kZqaunjx4iVLltjZ2SGEEhMTt2/fvnXrVqFbnzx5cklJyfr16wsKCnr37n358mUDAwOEUEFBAfeDd1ZWVvhFSkrKyZMnDQwMcnJyEEIfP3788ccfCwsLVVVVraysbt26ZWtr28SKAgAA6MBEet7O1tZ27dq1I0eOpOZcvnx51apVKSkpkoytKeB5OwAAkA6t0Cv048ePDQ0NuecYGhpmZGQ0f/cAAABACxAp25mamgYEBFB9VFZXVwcEBHAPAAQAAAC0ZYLu21H2798/ZswYFouFu7JMT08nCOLixYsSjg0AAAAQD1H7yfz06dPx48efPXtGkqSZmdm0adMUFRUlHVwTwH07AEC7BoOnU8T7ey7SuR1CSEFB4ccff2z+/gAAADQkMhL5+qI3b/6Z1NdHu3YhD49WjUlaiHTfDiF07NixgQMHdunS5e+//0YI7dy586+//pJkYAAA0LFERqKJE/9NdQih/Hw0cSKKjGy9mKSISNlu3759/v7+bm5uHz58wE+Ud+7cmXtoHgAAAM3B4SBfX8RzZwlP+vkhYT15AOFEynZ79uw5cODAihUrZGX/ufJpY2Pz+PFjSQYGAAAdSELCf87qKCSJ8vJQQkKLByR1RLpvl52dTXV3gjGZzKqqKsmEBAAAHU5BgaClMTHQbqW5RMp2hoaGaWlpuNMv7MqVK7grSwAAAM2nqytoaUDAPy+g3UqTiZTtlixZsmDBgi9fvpAkee/evbCwsE2bNh08eFDSwQEAQAfh6Ij09VF+Pu+tOx643crZs5DwGk3U5+0OHDgQEBCQl5eHENLT01u7du2cOXMkHFtTwPN2AIB2CrfJREhIwiMIpK+PsrOl/5JmS/eTWVtbe+TIkTFjxvz9999FRUWFhYV5eXltM9UBAED75eGBzp5FenpCikG7laYRnu1kZWX/97//VVdXI4Q0NDS0tLQkHxUAAHREHh4oJwfFxqKTJ9HKlYJKRkSguDh4MqERRHoCoX///qmpqZIOBQAAAI2GnJ3R1KloyBBBxfbuRS4uiM2GZ89FJVIrlfnz5y9atOjNmzfW1tbc3WP26dNHYoEBAECHJkq7FWi0IjqRWqnIyPznFJAgCJIkCYLgtL2zaGilAgCQGqK0W5HiRiut0Ct0dnZ28/cEAACgUXC7Fe5+ovlRjVacnVsusPZIeLarqKh48eJFTU2Nra2thoZGC8QEAAAA8/BA7u4oIQFFRKC9exssFhMD3awIIaSVyqNHj0xMTEaMGDF69GhjY+MbN260TFgAAAAw3G5lwgRBZQICoMWKEEKy3bJly7p27ZqQkPDgwQMnJydvb++WCQsAAAA33GiFIBosAMMDCSaklYqWltbly5dtbGwQQiUlJVpaWmVlZUpKSi0VXqNBKxUAgLQS2mhFylqstGhfKu/fv+/atSt+ra6urqCgUFxc3Py9AgAAaCyhna1ANysCCMl2BEFUVFSUl5eXl5eXlZVxT5aXl7dMiAAAADDc2YrgblYKChCHg+LiUFgY9LfyLyFtMkmS7NGjB/ckHuiuzT5vBwAA0o1GQ0OG/DsGEL+sLMRm//vQAgwShAm5bxcfHy9gqZOTk7jjaS64bwcAkHocDmKz6+lmhSCQmhoqLf3PfNywpT32tyLe33NRR/xpLyDbAQA6Av4WKwSBSBKpq6OSEt7C7bT1SkuP+AMAAKCt4W+xoq+P1q2rJ9UhaL2CEIJsBwAA7RT38ECxsSg7G3XvLqh8QUFLRdYmidRPJgAAgDYId7NC0dUVVPjdOxQWhnR1O2gfY5DtAABASggYJIhGQwsX/vO6Y7bSbMSVzJcvX167du3z588IISlr2yJRnK+ctMC4uz+HpQXGcb7CMxsAAEmh0dCuXQihejoY435erGP2MSZStispKRk6dGiPHj1GjhxZUFCAEJo7d+6iRYskHJs0SFoa+U6BbbnQxX7vNMuFLu8U2ElLO9hHDADQgvhbr/BftMRnK35+HevBc5Gy3cKFC2VlZXNzcxUUFPCcyZMnX716VZKBSYOkpZG22ybqcP4dmUqHk2+7bSIkPACA5HC3Xtm5s/6U1gFbaYqU7a5fv75lyxZ9fX1qTvfu3f/++2+JRSUNOF85XXf4IkRyH2IZRCKEWDv84JImAEBycOuVqVORtragYhERHahrMZGyXVVVFXVWh71//57JZEomJCnxODihC+cN//GVQaQeJ+9xcEf6mwoA0EoEt9Lcuxe5uHSUgfFEynaDBg06evQofk0QRF1d3bZt21xcXCQZWLv36ZWgZ1sELwUAALEQOioe6jCNVkR6AmHbtm3Ozs4PHjz4+vXr0qVLnz59WlpaeufOHUkH164pGAn6m0rwUgAAEAvcSnPixH/6FasXSSKCQH5+yN1dmp/DE+nczszM7NGjR7a2tsOGDauqqvLw8EhNTTUyMpJ0cO2a+XzHtzT9OsT7N1UdIvJpLPP5jq0SFQCgoxE6Kh7qGI1WRH26XEdHZ926dRINRcrQGLRc/1062ybWIQI3TkEI4eSX5x+ox5Dev6AAAG2Mhwdyd0cJCSgiAu3d22CxiAiEkNT2tCLSuZ2hoeGqVaueP38u6WikzICtHveWnC2k/fs3VQFN/96SswO2drA+DAAArQ230pwwQVAZ6W60ItKIPzt27AgLC0tJSbGysvL09Jw8ebKu4IY+racNjvjD+cp5HJzw6VWBgpGu+XxHGpzVAQBaSUMD43FrO+Phtdr4di9evDhx4kR4ePjr169dXFxmzJjx/fffNz8C8WqD2Q4AANoO/oHx+LWR8fBafzTXpKSk//3vf48ePeK0vYcSIdsBAIBgkZHI1xe9eSOkWGzsfwZYaHmtOZrrvXv3/Pz8xo8f//z584n4zwMAAADtCtW1mLe3oGL5+S0VUIsQKdu9ePFizZo13bt3d3BwyMjI2Lx587t3706dOiXp4AAAAEiCKI1WFi6UquYqIl3JlJGRsbGxmTZt2pQpU3R0dFogrCaDK5kAACAiwY1WWr25inh/z0V63u7Zs2c9evRo/s4AAAC0HVRPK/WSsj5WRLqSCakOAACkEu5pRUOj/qXS1MeKoHM7NTW1Fy9eaGhodO7cmaivV9HS0lKJBQYAAKAleHigz5/RjBkNFoiJQQUFSFe3fXezIijb7dy5U1lZGb+oN9sBAACQAoJ70QwI+OeFvj7atav1nzpvmqY8b9eWQSsVAABoLFH6WEEt3m6lFZ63o9FoRUVF3HNKSkpo7feEFgAAABfcXAUhISPh4Vzo59cuhzsXKdvxn/9VV1czGAwJxAMAAKAViDIwEGrP7VaEPIGwe/duhBBBEAcPHlRSUsIzORzOrVu3TExMJB4dAACAlkINDFRQgDIy/r1dx6+goAXDEhMh2W7nzp0IIZIk9+/fT126ZDAYbDZ7//79Eo8OAABAC8J9rCCE4uIEZbu2OgqOIEKyXXZ2NkLIxcUlMjKyc+fOLRISAACAVuboiPT162m3godHcHRspbCaQaT7drGxsZDqAACg46i33Qp+HRjYLp+6E3Ru5+/v/9tvvykqKvr7+9dbYMeOHZKJCgAAQCvD7Va4xwbS10eBgf95/IDD+ec+X9t/9lxQtktNTa2pqcEv+JfC8+YAACDduNut8OcznnHy2vqz56SEBQUFsdlsJpPZt2/fW7du8Rd4+/bt1KlTe/ToQRCEr68vz9KzZ8+ampoyGAxTU9PIyEihuysrK0MIlZWViSV4AAAA9YqIIAmCROjffwRBEgQZESG2XYj397xxo7kihMrLy8+dO/fs2TNRCp86dcrPz2/FihWpqamOjo5ubm65ubk8ZaqrqzU1NVesWGFhYcGzKDExcfLkyZ6enunp6Z6enpMmTUpOTm5swAAAAMSLw0G+vrwNWNr4s+ci9Rw2adKkQYMGeXt7f/782cLCIicnhyTJ8PDwCYKHAkSof//+ffv23bdvH540NTUdN27cpk2b6i3s7OxsaWkZGBhIzZk8eXJ5efmVK1fw5IgRIzp37hwWFiZgj9BzGAAASFpcHHJxaXBpbOw/jzE0Uyv0HHbr1i1HR0eEUFRUFEmSHz9+3L17d4CAZzEQQgh9/fo1JSVl+PDh1Jzhw4ffvXtX9OASExO5V3d1da139erq6nIuom8fAABAEwh+ujwmBoWFobi4tnWSJ1K2KysrU1NTQwhdvXp1woQJCgoKo0aNysrKErzW+/fvORyOtrY2NUdbW7uwsFD04AoLC0VZfdOmTarfsFgs0bcPAACgCQQ/XR4QgKZNQy4uiM1GkZEtFZMwImU7FouVmJhYVVV19epVfLL14cMHOTk5UdblbrpJkmRjW3KKsvry5cvLvsnLy2vU9gEAADQWfvZc6M95fj6aOLGtJDyRsp2fn9/06dP19fW7dOni7OyMELp165a5ubngtTQ0NGg0GvfZWFFREfe5mlA6OjqirM5kMlW4iL59AAAATdAex0wQKdvNnz8/MTHx8OHDt2/flpGRQQh169ZN6H07BoNhbW0dHR1NzYmOjra3txc9ODs7O+7Vr1+/3qjVAQAASEi7GzNBSD+ZFBsbGxsbG/zUAkEQo0aNEmUtf39/T09PGxsbOzu7P//8Mzc3d968eQih5cuX5+fnHz16FBdLS0tDCFVWVhYXF6elpTEYDDMzM4SQr6/voEGDtmzZ4u7u/tdff924ceP27dtNqCQAAACxa2djJoj4XN6RI0d69+7NZDKZTKa5ufnRo0dFXDEoKMjAwIDBYPTt2zc+Ph7P9PLycnJyosrwhGRgYEAtOnPmTM+ePel0uomJSYQITy3C0+UAANDyYmP/86Q5z7/Y2KZsU7y/5yI9b7djx45Vq1Z5e3s7ODiQJHnnzp2goKCAgICFCxdKIgE3BzxvBwAALY/DQWx2g2MmZGc3pQtN8f6ei5TtDA0N161b9/3331Nzjhw5snbtWjweUJsC2Q4AAFpFZCSaOBEh9G/Cw21Yzp5tYueZrfB0eUFBAU/zEHt7+4I2cSEWAABAm8DfbkVfv+mpTuxEynbGxsanT5/mnnPq1Knu3btLJiQAAADtkocHyslBsbHo5EkUG4uys9tKqkMitslct27d5MmTb9265eDgQBDE7du3Y2JiePIfAAAAQKOJp5NMsRPp3G7ChAn37t3T0NA4d+5cZGSkhobGvXv3xo8fL+ngAAAAALEQfm5XUVGRlJRUU1MTGBiooaHRAjEBAAAA4iUk2z169MjNza2wsJAkSRUVlbNnzw4dOrRlIgMAAADERciVzGXLlnXt2jUhIeHBgwdOTk7e3t4tExYAAAAgRkLO7R48eHD58mUbGxuE0OHDh7W0tCorK5WUlFokNgAAAEA8hJzbvX//vmvXrvi1urq6goJCcXGx5KMCAAAAxEnIuR1BEBUVFXgoO5Ik8SQ1Pjj0VwIAAKBdEJLtSJLs0aMH96SVlRX6lvk4bWHMIgAAAEAYIdkuNja2ZeIAAAAAJEdItnNycmqZOAAAAADJEakvFQAAAKBdg2wHAABA+kG2AwAAIP0g2wEAAJB+jct2eXl5b968kVAoAAAAgISIlO1qa2tXrVqlqqrKZrMNDAxUVVVXrlxZU1Mj6eAAAAAAsRBpNFdvb++oqKitW7fa2dkhhBITE9euXfv+/fv9+/dLODwAAABADAiSJIUWUlVVDQ8Pd3Nzo+ZcuXJlypQpZWVlkoytKcrLy1VVVcvKyqBXMwAAaNfE+3su0pVMOTk5NpvNPYfNZjMYjObvHgAAAGgBImW7BQsW/Pbbb9XV1Xiyurp6w4YNMNYdAACA9kKk+3apqakxMTH6+voWFhYIofT09K9fvw4ZMsTDwwMXiIyMlGCMAAAAQPOIlO06deo0YcIEapLFYkksHgAAAED8RMp2ISEhko4DAAAAkBzoSwUAAID0E+ncztDQkCAI/vmvX78WdzwAAACA+ImU7fz8/KjXNTU1qampV69eXbJkiaSCAgAAAMRKpGzn6+vLMycoKOjBgwcSiAcAAAAQvybet3Nzc4uIiBBvKAAAAICENDHbnT17Vk1NTbyhAAAAABIi0pVMKysrqpUKSZKFhYXFxcXBwcGSDAwAAAAQG5Gy3bhx46jXMjIympqazs7OJiYmkgoKAAAAECuRxkBoR2AMBAAAkA7i/T0X6dwOIcThcM6dO5eZmUkQhJmZ2dixY2k0WvN3DwAAALQAkbLdy5cvR44cmZ+f37NnT5IkX7x4wWKxLl26ZGRkJOn4AAAAgOYTqU2mj4+PkZFRXl7ew4cPU1NTc3NzDQ0NfXx8JB0cAAAAIBYindvFx8cnJSVRjxyoq6tv3rzZwcFBkoEBAAAAYiPSuR2TyayoqOCeU1lZCWOXAwAAaC9EynajR4/+8ccfk5OTSZIkSTIpKWnevHljx46VdHAAAACAWIiU7Xbv3m1kZGRnZycnJycnJ+fg4GBsbLxr1y5JBwcAAACIhfD7diRJlpWVhYWFvX37NjMzkyRJMzMzY2PjFggOAAAAEAuRsl337t2fPn3avXt3SHIAAADaI+FXMmVkZLp3715SUtIC0QAAAACSINJ9u61bty5ZsuTJkyeSjgYAAACQBJH6yezcufOnT59qa2sZDIa8vDw1v7S0VJKxNQX0kwkAANKhFfrJDAwMbP6eAAAAgNYiUrbz8vKSdBwAAACA5Ig6BgJC6OnTpxwOB7+m0Wi9evWSTEgAAACAmAlppZKQkNCvXz/8esCAAVZWVpaWlpaWln369Llx44bkwwMAAADEQEi2Cw4O9vT0pCZjY2Ozs7Nfv37t6+u7b98+CccGAAAAiIeQbHf//n1bW1tqUl9f38DAgM1me3p6JiYmSjg2AAAAQDyEZLv8/HxdXV38+siRIzo6Ovi1mpoaPG8OAACgvRCS7ZSVlbOzs/FrDw8PBQUF/Do7OxseaAMAANBeCMl2/fv3P3r0KP/80NDQ/v37SyYkAAAAQMyEPIHg7+8/dOhQdXX1JUuWaGlpIYSKioq2bNly/Pjx69evt0iEAAAAQHMJ7zksODh44cKFtbW1KioqBEGUlZXJyspu377d29u7ZUJsFOg5DAAApIN4f89F6iczLy/v7NmzWVlZCKHu3btPnDiRxWI1f9+SANkOAACkg3h/z0UaA4HFYi1cuDA4OBif5zUq1QUHBxsaGsrJyVlbWyckJNRbJj4+3traWk5Orlu3bvv376fmh4aGEv/15csX0XcNAAAAYCJluyY7deqUn5/fihUrUlNTHR0d3dzccnNzecpkZ2ePHDnS0dExNTX1119/9fHxiYiIoJaqqKgUcJGTk5NowAAAAKSSSFcym6x///59+/alel0xNTUdN27cpk2buMv88ssv58+fz8zMxJPz5s1LT0/Hj66Hhob6+fl9/PhR9D3ClUwAAJAOrXAls2m+fv2akpIyfPhwas7w4cPv3r3LUywxMZG7jKur64MHD2pqavBkZWWlgYGBvr7+6NGjU1NT691RdXV1ORdx1wMAAEC7JzzbcTic+Pj4Dx8+NHbT79+/53A42tra1Bxtbe3CwkKeYoWFhTxlamtr379/jxAyMTEJDQ09f/58WFiYnJycg4MDbinDY9OmTarftNnmMwAAAFqR8GxHo9FcXV0bdTmRG0EQ1GuSJLknGypDzRkwYMCMGTMsLCwcHR1Pnz7do0ePPXv28K++fPnysm/y8vKaFicAAAApJtL4dubm5q9fvzY0NGzUpjU0NGg0GvfJXFFREfdpHKajo8NTRlZWVl1dnaeYjIxMv3796j23YzKZTCazUbEBAADoUES6b7dhw4bFixdfvHixoKBA9DtkDAbD2to6OjqamhMdHW1vb89TzM7OjrvM9evXbWxs6HQ6TzGSJNPS0qguqgEAAIBGIEVAPe4m8w1+LXTF8PBwOp1+6NChjIwMPz8/RUXFnJwckiSXLVvm6emJy7x+/VpBQWHhwoUZGRmHDh2i0+lnz57Fi9auXXv16tVXr16lpqbOmjVLVlY2OTlZ8B7LysoQQmVlZaLUCwAAQJsl3t9zka5kxsbGNi2VTp48uaSkZP369QUFBb179758+bKBgQFCqKCggHrwztDQ8PLlywsXLgwKCurSpcvu3bsnTJiAF338+PHHH38sLCxUVVW1srK6desW92B7AAAAgIgk+7xdy4Pn7QAAQDq0zvN2CQkJM2bMsLe3z8/PRwgdO3bs9u3bzd89AAAA0AJEynYRERGurq7y8vIPHz6srq5GCFVUVGzcuFHCsQEAAADiIVK2CwgI2L9//4EDB6imkvb29g8fPpRkYAAAAIDYiJTtnj9/PmjQIO45KioqTX7eHAAAAGhhImU7XV3dly9fcs+5fft2t27dJBMSAAAAIGYiZbuffvrJ19c3OTmZIIi3b9+eOHFi8eLF8+fPl3RwAAAAgFiI9Lzd0qVLy8rKXFxcvnz5MmjQICaTuXjxYm9vb0kHBwAAAIhFI563+/TpU0ZGRl1dnZmZmZKSkkTDajJ43g4AAKRDiz5v9+nTpwULFujp6Wlpac2dO5fNZtva2rbZVAcAAADUS0i2W7NmTWho6KhRo6ZMmRIdHf2///2vZcICAAAAxEjIfbvIyMhDhw5NmTIFITRjxgwHBwcOh0Oj0VokNgAAAEA8hJzb5eXlOTo64te2traysrJv376VfFQAAACAOAnJdhwOh8FgUJOysrK1tbUSDgkAAAAQMyFXMkmSnDlzJjUy+JcvX+bNm6eoqIgnIyMjJRsdAAAAIA5Csp2Xlxf35IwZMyQZDAAAACARQrJdSEhIy8QBAAAASI6o49sBAAAA7RdkOwAAANIPsh0AAADpB9kOAACA9INsBwAAQPpBtgMAACD9INsBAACQfpDtAAAASD/IdgAAAKQfZDsAAADSD7IdAAAA6Sekn0wpw+FwampqWjsKAFoTnU6HAZlBB9RRsh1JkoWFhR8/fmztQABofZ06ddLR0SEIorUDAaDldJRsh1OdlpaWgoICfMlBh0WS5KdPn4qKihBCurq6rR0OAC2nQ2Q7DoeDU526unprxwJAK5OXl0cIFRUVaWlpwSVN0HF0iFYq+F6dgoJCawcCQJuAvwtwDxt0KB0i22FwARMADL4LoAPqQNmuo1m7dq2lpWUL7IggiHPnzrXAjtq7r1+/Ghsb37lzp5nbiYuLIwhCaJOrxYsX+/j4NHNfAEgNyHYtbebMmcR/vXz5Uixb5sk6ixcvjomJaeY22Ww2QRDh4eHcM3v16kUQRGhoKJ4sKChwc3Nr5o5aBfUWKCkpWVhYUDUSulbTsvuff/5pYGDg4OCAEMrJySEIIi0trQnbsbe3LygoUFVVFVxs6dKlISEh2dnZTdgFANIHsl0rGDFiRAEXQ0ND7qVfv34Vy16UlJQa2yqn3hs5LBYrJCSEmkxKSiosLFRUVKTm6OjoMJnMJsfZ2HjEKyQkpKCgID09ffLkybNmzbp27Zrk9rVnz565c+eKUlLwZ4DBYIjy/ICWltbw4cP379/fiBABkF6Q7QThcFBcHAoLQ3FxiMMR22aZTKYOFxqN5uzs7O3t7e/vr6GhMWzYMIRQfHy8ra0tk8nU1dVdtmxZbW0tXtfZ2dnHx2fp0qVqamo6Ojpr167F89lsNkJo/PjxBEHg1zxXMkNCQkxNTeXk5ExMTIKDg/FMfIZx+vRpZ2dnOTm548eP80c7ffr0+Pj4vLw8PHn48OHp06fLyv7bmpc618Fbi4yMdHFxUVBQsLCwSExMrPcIPHv2bODAgXJycmZmZjdu3ODZAnc8dXV169ev19fXZzKZlpaWV69exVvguZqXlpZGEEROTg5CKDQ0tFOnTufOnevRo4ecnNywYcOo4PnhJ8+MjIx+/fVXNTW169ev4/n3798fNmyYhoaGqqqqk5PTw4cPGzrOCKELFy5YW1vLycl169Zt3bp11JvF7eHDhy9fvhw1ahSexH/iWFlZEQTh7OyMEJo5c+a4ceM2bdrUpUuXHj16IISOHz9uY2OjrKyso6Mzbdo0/NgAT91xZa9du2ZqaqqkpIT/kKJ2Onbs2LCwsIbqDkCHAtmuQZGRiM1GLi5o2jTk4oLYbBQZKcHdHTlyRFZW9s6dO3/88Ud+fv7IkSP79euXnp6+b9++Q4cOBQQEcJdUVFRMTk7eunXr+vXro6OjEUL3799H385U8GtuBw4cWLFixYYNGzIzMzdu3Lhq1aojR45QS3/55RcfH5/MzExXV1f+wLS1tV1dXXH5T58+nTp1avbs2QIqsmLFisWLF6elpfXo0WPq1Kn8P/11dXXjxo1TUFBITk7+888/V6xYwVOAO55du3Zt3779999/f/Tokaur69ixY7OysoQezE+fPm3YsOHIkSN37twpLy+fMmWK4PIcDuf06dOlpaV0Oh3Pqaio8PLySkhISEpK6t69+8iRIysqKlB9x/natWszZszw8fHJyMj4448/QkNDN2zYwL+LW7du9ejRQ0VFBU/eu3cPIXTjxo2CgoLIbx+smJiYzMzM6OjoixcvIoS+fv3622+/paennzt3Ljs7e+bMmQ1V9vfffz927NitW7dyc3MXL15MLbK1tc3Ly/v777+FHjEApB8pXcrKyhBCZWVl3DM/f/6ckZHx+fNn0bcTEUESBInQv/8IgiQIMiKiuRF6eXnRaDTFbyZOnEiSpJOTk6WlJVXm119/7dmzZ11dHZ4MCgpSUlLicDi45MCBA6mS/fr1++WXX/BrhFBUVBS1aM2aNRYWFvg1i8U6efIktei3336zs7MjSRLf1AkMDGwoWgMDg507d547d87IyKiuru7IkSNWVlYkSaqqqoaEhPDsF2/t4MGDeP7Tp08RQpmZmTzbvHLliqysbEFBAZ7E2Zp7C9zxdOnSZcOGDdz1nT9/PkmSsbGxCKEPHz7g+ampqQih7OxskiTxddekpCS8KDMzEyGUnJzMXzuEkJycnKKiIn7sTE1NLSsri79YbW2tsrLyhQsXeOqLOTo6bty4kZo8duyYrq4u/0Z8fX0HDx5MTeKapqamUnO8vLy0tbWrq6v51yVJEmfHiooKnrrjyr58+RIXCwoK0tbWptbCX4e4uDierTXhGwFAy6v397zJ4NyuHhwO8vVFJPmfmXjSz08MlzRdXFzSvtm9ezeeaWNjQxXIzMy0s7Ojbsw4ODhUVla+efMGT/bp04cqqaurS13gakhxcXFeXt6cOXOUvgkICHj16hVVgHvX9Ro1alRlZeWtW7cOHz4s+MSOOzzcVQd/eM+fP2exWDo6OnjS1taWpwAVT3l5+du3b3GzDszBwQFnL8FkZWWpjZiYmHTq1KmhtXbu3JmWlhYdHW1pablz505jY2M8v6ioaN68eT169FBVVVVVVa2srMzNza13CykpKevXr6eO7Q8//FBQUPDp0yeeYp8/f5aTkxMctrm5OYPBoCZTU1Pd3d0NDAyUlZXx1c56Y1BQUDAyMsKveT4P+EFy/mAA6IA6RF8qjZWQgL5llv8gSZSXhxISkLNzs7avqKhI/apyz+TaEcndBoEkScT1jBR1tQ3PrKurE7w7XODAgQP9+/enZnJ3osG963rJysp6enquWbMmOTk5KipKcGEqPBwwf3g8tePHEw/PocCTMjIy6NuRQfW1Z+HZRUN71NHRMTY2NjY2PnPmjJWVlY2NjZmZGUJo5syZxcXFgYGBBgYGTCbTzs6uoZYjdXV169at8/Dw4J7Jn9g0NDQeP35cf4W/4a54VVXV8OHDhw8ffvz4cU1NzdzcXFdX13pj4Pk8kFx/ppWWliKENDU1Be8XgI4Azu3qwXWbv9FLxcLMzOzu3bvUz9bdu3eVlZX19PQEr0Wn0zn1nXhqa2vr6em9fv3amAtPQ1ChZs+eHR8f7+7u3rlz50atyM/ExCQ3N/fdu3d4kv8uI0VFRaVLly63b9+m5ty9e9fU1BR9+wWnWmTwNOWvra198OABfv38+fOPHz+amJgIjsrY2HjChAnLly/HkwkJCT4+PiNHjuzVqxeTyXz//j1Vkuc49+3b9/nz58b/hZMxNysrq2fPnlHvKT6Hq/f9wp49e/b+/fvNmzc7OjqamJgIPYOv15MnT+h0eq9evZqwLgBSBs7t6iG4s9wW6Ep3/vz5gYGBP//8s7e39/Pnz9esWePv78//A8qDzWbHxMQ4ODgwmUyenLR27VofHx8VFRU3N7fq6uoHDx58+PDB399f9JBMTU3fv38vlt7Xhg0bZmRk5OXltXXr1oqKCtxKpaFzryVLlqxZs8bIyMjS0jIkJCQtLe3EiRMIIWNjYxaLtXbt2oCAgKysrO3bt3OvRafTf/755927d9PpdG9v7wEDBvBfL+W3aNEiCwuLBw8e2NjYGBsbHzt2zMbGpry8fMmSJfiSIMZznFevXj169GgWi/Xdd9/JyMg8evTo8ePH3K2KMBcXl6qqqqdPn/bu3RshpKWlJS8vf/XqVX19fTk5Of6H57p27cpgMPbs2TNv3rwnT5789ttvwo8sn4SEBEdHR+7gAeiw4NyuHo6OSF8f8f/8EgRisZCjo8QD0NPTu3z58r179ywsLObNmzdnzpyVK1cKXWv79u3R0dEsFsvKyopn0dy5cw8ePBgaGmpubu7k5BQaGtrYczuEkLq6ulh+N2k02rlz5yorK/v16zd37lxctYbuafn4+CxatGjRokXm5uZXr149f/589+7dEUJ0Oj0sLOzZs2cWFhZbtmzhyS4KCgq//PLLtGnT7Ozs5OXleZ6Ob4i5ufnQoUNXr16NEDp8+PCHDx+srKw8PT19fHy0tLSoYjzH2dXV9eLFi9HR0f369RswYMCOHTsMDAz4N66uru7h4YFTNUJIVlZ29+7df/zxR5cuXdzd3fnLa2pqhoaGnjlzxszMbPPmzb///rsoVeARFhb2ww8/NGFFAKSQWNq6tB3ibZPJ3SxTXG0yAQ98oZJqVdh8ISEhqqqq4tqaGD169EhLS6u8vLxldnfx4kVTU9Oamhr+RdAmE7QL0CazJXh4oLNnEfedMn19dPYs+m9bBNBEUVFR0dHROTk5N27c+PHHHx0cHKhWhVLM3Nx869at+BH4FlBVVRUSEsLdDwAAHRl8Exrk4YHc3VFCAiooQLq6yNERwVhg4lJRUbF06dK8vDwNDY2hQ4fy3HWTYl5eXi22r0mTJrXYvgBo+/7TXlkKlJeXq6qqlpWVUZ1WIIS+fPmSnZ1taGgo9IEnADoC+EaAdqHe3/MmgyuZAAAApB9kOwAAANIPsh0AAADpB9kOAACA9INsBwAAQPpBtgMAACD9INtJLZ6xy0GrKykp0dLSav7T5Xi8cqHFJk6cuGPHjmbuCwCpAdmupc2cOZP4r5cvX4plywRBnDt3jppcvHhxTExMM7fJZrMJguDpZ7JXr14EQYSGhjZz422Ks7MzfjsYDIaRkdHy5curq6tFWcvPz0/EXWzatGnMmDFsNhshFBcXRxDEx48fmxDq5MmTX7x4IbTY6tWrN2zYUF5e3oRdACB9INu1ghEjRhRw4emguaFx1BpLSUlJXV29UavwjxKHEGKxWHiAbCwpKamwsFDokHhiJ67DIgAeiPXly5dbt24NCgpau3atGDf++fPnQ4cOzZ07V5TCgisrLy/P3Ut1Q/r06cNms6l+qAHo4CDbCcThoLg4FBaG4uLEMGb5N0wmU4cLjUZzdnb29vb29/fX0NAYNmwYQig+Pt7W1pbJZOrq6i5btqy2thav6+zs7OPjs3TpUjU1NR0dHeoXGZ8xjB8/niAI/JrnSmZISIipqamcnJyJiUlwcDCemZOTQxDE6dOnnZ2d5eTkjh8/zh/t9OnT4+Pj8/Ly8OThw4enT5/O3fvijh07zM3NFRUVWSzW/PnzKysr8fzZs2f36dMHnyHV1NRYW1tPnz6df/sVFRXTp09XVFTU1dXduXMn99kSm80OCAiYOXOmqqoq7ss/IiICDzjHZrO5+xvjOa/t1KkTPvXEFQwPD7e3t5eTk+vVq1dcXFxD74uCgoKOjk7Xrl0nTJgwbNiw69ev4/klJSVTp07V19dXUFAwNzcPCwvD82fOnBkfH79r1y58UogvUWZkZIwcOVJJSUlbW9vT05MaGO/KlSuysrJ2dnY4KhcXF4RQ586dCYKYOXMmQoj/M9DQgeW+konf5WPHjrHZbFVV1SlTplRUVFA1Gjt2LBUtAB0cZLuGRUYiNhu5uKBp05CLC2KzUWSk5PZ25MgRWVnZO3fu/PHHH/n5+SNHjuzXr196evq+ffsOHTrEPaLNkSNHFBUVk5OTt27dun79+ujoaPRtTNSQkJCCggL+8VEPHDiwYsWKDRs2ZGZmbty4cdWqVUeOHKGW/vLLLz4+PpmZma6urvyBaWtru7q64vKfPn06derU7NmzuQvIyMjs3r37yZMnR44cuXnz5tKlS/H83bt3V1VVLVu2DCG0atWq9+/fU1mWm7+//507d86fPx8dHZ2QkPDw4UPupdu2bevdu3dKSsqqVatSUlImTZo0ZcqUx48fr127dtWqVSJeTV2yZMmiRYtSU1Pt7e3Hjh1bUlIiuHx6evqdO3eoMcG/fPlibW198eLFJ0+e/Pjjj56ensnJyQihXbt22dnZ4TPCgoICFotVUFDg5ORkaWn54MGDq1evvnv3juqs8tatWzY2Nvg1i8WKiIhACD1//rygoGDXrl14PvdnQMCB5fHq1atz585dvHjx4sWL8fHxmzdvphbZ2treu3dPlEuyAEg/sYyk0HaIa8Sff4b8ocb7Ed+QP15eXjQaTfGbiRMnkiSJfyKpMr/++mvPnj3r6urwZFBQkJKSEofDwSUHDhxIlezXr98vv/yCXyOEoqKiqEVr1qyxsLDAr1ks1smTJ6lFv/32m52dHUmS2dnZCKHAwMCGojUwMNi5c+e5c+eMjIzq6uqOHDliZWVFkqSqqmpISAh/+dOnT6urq1OTd+/epdPpq1atkpWVjY+P5y9fXl5Op9PPnDmDJz9+/KigoODr60vtfdy4cVThadOmDRs2jJpcsmSJmZlZvXWnwsMV3Lx5M55fU1Ojr6+/ZcsW/kicnJzodLqioiIeVVxGRubs2bP1HpORI0cuWrSIWouKliTJVatWDR8+nJrEJ8TPnz8nSdLd3X327NnUotjYWITQhw8fuAPg/gzw4D6w3EMarVmzRkFBgRpFaMmSJf3796fWSk9PRwjl5OTwbA1G/AHtQjsb8Sc4OBh3PmttbZ2QkFBvmfj4eGtrazk5uW7duu3fv597UUREhJmZGZPJNDMzi4qKknS0/+BwkK8v4ukvG0/6+TX/kqaLi0vaN7t378YzqT/8EUKZmZl2dnbUcN4ODg6VlZVv3rzBk3369KFK6urqFhUVCd5dcXFxXl7enDlzlL4JCAh49eoVVYB71/UaNWpUZWXlrVu3Dh8+zHNihxCKjY0dNmyYnp6esrLy999/X1JSUlVVhRfZ2dktXrz4t99+W7Ro0aBBg/i3/Pr165qaGmpgcVVV1Z49e3IX4DksDg4O1KSDg0NWVhZHhLcDXz9ECMnKytrY2GRmZtZbbPr06WlpaYmJiZMmTZo9e/aECRPwfA6Hs2HDhj59+qirqyspKV2/fj03N7feLaSkpMTGxlLH2cTEBCGED/Xnz5+FdsHM80YIOLDc2Gy2srIyfs3zecCj73769EnwfgHoCCSb7U6dOuXn57dixYrU1FRHR0c3Nzf+n4ns7OyRI0c6Ojqmpqb++uuvPj4++CIPQigxMXHy5Mmenp7p6emenp6TJk3CV5AkLiEBfUst/0GSKC8PNZCzRaeoqGj8ja6uLjWTaz8kwTV0OkmSCCFqDnWFDc+sq6sTvDtc4MCBA1SKffLkSVJSEnc8grcgKyvr6em5Zs2a5ORknntvf//998iRI3v37h0REZGSkhIUFIS4WrvU1dXduXOHRqNlZWXVu2WeqlFz6o2t3sOCEcR/RvOot7kNd+F656uqqhobG/ft2/f48ePx8fGHDh3C87dv375z586lS5fevHkzLS3N1dW1oVYkdXV1Y8aMSeOSlZWF07yGhsaHDx8ERMVTWcEHlpuAz0NpaSlCSFNTU/B+AegIJJvtduzYMWfOnLlz55qamgYGBrJYrH379vGU2b9/f9euXQMDA01NTefOnTt79uzff/8dLwoMDBw2bNjy5ctNTEyWL18+ZMiQwMBAiQb8j4KCpi8VBzMzs7t371I/33fv3lVWVtbjHlu2PnQ6vd4THW1tbT09vdevXxtz4WkIKtTs2bPj4+Pd3d07d+7MPf/Bgwe1tbXbt28fMGBAjx493r59y71027ZtmZmZ8fHx165d427YSTEyMqLT6ffu3cOT5eXlDeVFhJCZmRke6By7e/dujx49aDQaQkhTU7Pg2/uSlZXFczZDpfba2tqUlBR8yiUAnU7/9ddfV65cibeTkJDg7u4+Y8YMCwuLbt26cUfIYDC4j3nfvn2fPn3KZrO5DzXOYVZWVhkZGdwrIoQEnJgKPrAievLkib6+voaGRhPWBUDKSDDbff36NSUlZfjw4dSc4cOH3717l6dYYmIidxlXV9cHDx7gv2H5F/GvjhCqrq4u5yKG0L+dbzVlqTjMnz8/Ly/v559/fvbs2V9//bVmzRp/f38ZGSFvFpvNjomJKSws5D+HWLt27aZNm3bt2vXixYvHjx+HhIQ09rljU1PT9+/f82csIyOj2traPXv2vH79+tixY9wXotPS0lavXn3o0CEHB4ddu3b5+vq+fv2aZ3VlZWUvL68lS5bExsY+ffp09uzZMjIyDZ17LVq0KCYm5rfffnvx4sWRI0f27t27ePFivGjw4MF79+59+PDhgwcP5s2bx326gxAKCgqKiop69uzZggULPnz4wH8xlt+0adMIgsDNaoyNjaOjo+/evZuZmfnTTz8VFhZSxdhsdnJyck5Ozvv37+vq6hYsWFBaWjp16tR79+69fv36+vXrs2fPxinN1dX16dOn1FtjYGBAEMTFixeLi4upxpYiHljRJSQkcH+DAOjIJJjt3r9/z+FwtLW1qTna2trcvxRYYWEhT5na2lrcbpt/Ef/qCKFNmzapfsNiscQQuqMj0tdH/L+5BIFYLOToKIZdCKSnp3f58uV79+5ZWFjMmzdvzpw5K1euFLrW9u3bo6OjWSyWlZUVz6K5c+cePHgwNDTU3NzcyckpNDS0sed2CCF1dXV8H4ibpaXljh07tmzZ0rt37xMnTmzatAnP//Lly/Tp02fOnDlmzBiE0Jw5c4YOHerp6cl/NrNjxw47O7vRo0cPHTrUwcEBPyZRbwB9+/Y9ffp0eHh47969V69evX79etx2H9edxWINGjRo2rRpixcvVlBQ4F5x8+bNW7ZssbCwSEhI+Ouvv0Q512EwGN7e3lu3bq2srFy1alXfvn1dXV2dnZ11dHTGjRtHFVu8eDGNRjMzM9PU1MzNze3SpcudO3c4HI6rq2vv3r19fX1VVVXxnynm5uY2NjanT5/GK+rp6a1bt27ZsmXa2tre3t78ATR0YEX35cuXqKgo/OQGAECCbTLz8/MRQviKHBYQENCzZ0+eYt27d9+4cSM1iS9VFRQUkCRJp9O5WxIeP36cyWTy7+jLly9l3+BWcGJrk8ndLFNMbTKBAJWVlaqqqgcPHhTXBnGbzNTUVHFtsDkuXbpkamqK29a2gL1793I3YeUGbTJBuyDeNpmyAlNhs2hoaNBoNO6zsaKiIu5zNUxHR4enjKysLO4EhH8R/+oIISaTyWQyxRy9hwc6exb5+v7bXEVfHwUGIg8PMe+ow0tNTX327JmtrW1ZWdn69esRQu7u7q0dlESMHDkyKysrPz9fPFcghKHT6Xv27GmBHQHQLkjwSiaDwbC2tsbPPmPR0dH29vY8xezs7LjLXL9+3cbGBt934V/Ev7oEeXignBwUG4tOnkSxsSg7G1KdhPz+++8WFhZDhw6tqqpKSEiQ4lYVvr6+LZPqEEI//vgjz+McAHRoYjlDbEh4eDidTj906FBGRoafn5+ioiJ+0HXZsmWenp64zOvXrxUUFBYuXJiRkXHo0CE6nU491Ysbr2/evDkzM3Pz5s2ysrJJSUmC9yi2p8sBkF7wjQDtQru5kokQmjx5cklJyfr16wsKCnr37n358mUDAwOEUEFBAfXgnaGh4eXLlxcuXBgUFNSlS5fdu3dTT/Xa29uHh4evXLly1apVRkZGp06d6t+/v0QDBgAAIJX+80yuFCgvL1dVVS0rK1NRUaFmfvnyJTs7G3fp0oqxAdBGwDcCtAv1/p43WQfqFVrK8joATQbfBdABdYhsh9u8QG+BAGD4u8DzDD4A0k2y9+3aCBqN1qlTJ9xbroKCQkNddQAg9UiS/PTpU1FRUadOnXCnawB0EB0i2yGEdHR0EEJChwsAoCPo1KkT/kYA0HF0lGxHEISurq6Wlpbg3vEBkHp0Oh3O6kAH1FGyHUaj0eB7DgAAHVCHaKUCAACgg4NsBwAAQPpBtgMAACD9pO2+HX5sVjxjugIAAGg9+JdcXJ0hSFu2KykpQQi1WDfzAAAAJKqkpERVVbX525G2bKempoYQys3NFcvRaSPKy8tZLFZeXp5YOotrI6BS7QVUql2QvhohhMrKyrp27Yp/1ZtP2rKdjIwMQkhVVVWa3nJMRUUFKtUuQKXaC+mrlPTVCH37VRfDdsSyFQAAAKAtg2wHAABA+klbtmMymWvWrGEyma0diDhBpdoLqFR7IX2Vkr4aIXFXStpGcwUAAAD4Sdu5HQAAAMAPsh0AAADpB9kOAACA9INsBwAAQPq1s2wXHBxsaGgoJydnbW2dkJBQb5kTJ05YWFgoKCjo6urOmjUL9yXGLTw8nCCIcePGSTxc0TSnUqGhocR/ffnypQVjr18z36aPHz8uWLBAV1dXTk7O1NT08uXLLRW4IM2plLOzM8/bNGrUqBaMvUHNfKcCAwN79uwpLy/PYrEWLlzYFj57qHmVqqmpWb9+vZGRkZycnIWFxdWrV1swcEFEqVRQUJCpqam8vHzPnj2PHj3KvSgiIsLMzIzJZJqZmUVFRbVIyMI1p1JPnz6dMGECm80mCCIwMFCk/ZHtR3h4OJ1OP3DgQEZGhq+vr6Ki4t9//81TJiEhQUZGZteuXa9fv05ISOjVq9e4ceO4C+Tk5Ojp6Tk6Orq7u7dc6A1rZqVCQkJUVFQKuLR4DXg1s0bV1dU2NjYjR468fft2Tk5OQkJCWlpai1eCVzMrVVJSQr1BT548odFoISEhLV0HPs2s1PHjx5lM5okTJ7Kzs69du6arq+vn59fileDVzEotXbq0S5culy5devXqVXBwsJyc3MOHD1u8ErxEqVRwcLCysnJ4ePirV6/CwsKUlJTOnz+PF929e5dGo23cuDEzM3Pjxo2ysrJJSUktXglezazUvXv3Fi9eHBYWpqOjs3PnTlH22J6yna2t7bx586hJExOTZcuW8ZTZtm1bt27dqMndu3fr6+tTk7W1tQ4ODgcPHvTy8moj2a6ZlQoJCVFVVZV8mI3QzBrt27evW7duX79+bYFQRdf8zx5l586dysrKlZWVEgpVdM2s1IIFCwYPHkwt8vf3HzhwoCTjFUkzK6Wrq7t3715qkbu7+/Tp0yUZr0hEqZSdnd3ixYupSV9fXwcHB/x60qRJI0aMoBa5urpOmTJFkvGKpJmVohgYGIiY7drNlcyvX7+mpKQMHz6cmjN8+PC7d+/yFLO3t3/z5s3ly5dJknz37t3Zs2e5LxmtX79eU1Nzzpw5LRS0MGKpVGVlpYGBgb6+/ujRo1NTU1so9AY0v0bnz5+3s7NbsGCBtrZ27969N27cyOFwWq4C9RHL20Q5dOjQlClTFBUVJRu0MM2v1MCBA1NSUu7du4cQev369eXLl1v98mzzK1VdXS0nJ0eVlJeXv337dgtELoCIleKP/N69ezU1NQihxMRE7tVdXV35V29hza9UUzQ6I7eS/Px8hNCdO3eoORs2bOjRowd/yTNnzigpKcnKyiKExo4dS50l3L59W09Pr7i4mCTJNnJu1/xKJSYmHjt2LC0t7datWxMmTJCXl3/x4kULRV+f5teoZ8+eTCZz9uzZDx48CAsLU1NTW7duXQtF34DmV4qSnJyMEEpOTpZsxCIQS6V2795Np9Pxov/9738tEbdAza/U1KlTzczMXrx4weFwrl+/Li8vz2AwWij6BohYqeXLl+vo6Dx48KCuru7+/ftaWloIobdv35IkSafTT5w4QZU8ceKEFFSKIoXndhhBENRrkiS5J7GMjAwfH5/Vq1enpKRcvXo1Ozt73rx5CKGKiooZM2YcOHBAQ0OjRSMWQZMrhRAaMGDAjBkzLCwsHB0dT58+3aNHjz179rRc6A1oTo3q6uq0tLT+/PNPa2vrKVOmrFixYt++fS0XesOaUynKoUOHevfubWtrK/FwRdOcSsXFxW3YsCE4OPjhw4eRkZEXL1787bffWi70hjWnUrt27erevbuJiQmDwfD29p41axaNRmu50BsmtFKrVq1yc3MbMGAAnU53d3efOXMmQogKXujqraKZlWq0pubmllZdXU2j0SIjI6k5Pj4+gwYN4ik2Y8aMiRMnUpO4nc/bt2/xJT7aN7hdHI1Ge/nyZQtVoD7NrBT/BufOnct9gb7lNb9GgwYNGjJkCLUIN8isrq6WcOCCiOttqqqqUlFRCQwMlHTAomh+pQYOHMh9T+XYsWPy8vIcDkfCgQsirnfq8+fPb968qaurW7p0qZmZmaTDFkzESmFfv37Ny8urra3F7Tvw28FisXbs2EGV2bFjR9euXSUdtmDNrxRFCs/tGAyGtbV1dHQ0NSc6Otre3p6n2KdPn7gHQ8J/BZAkaWJi8vjx47Rvxo4d6+LikpaW1rqjnDezUjzFSJJMS0vT1dWVWLzCNb9GDg4OL1++rKurw4tevHihq6vLYDAkHnrDxPU2nT59urq6esaMGRKOVyTNrxT/IvybItm4BRLXOyUnJ6enp1dbWxsREeHu7i7hqIUQsVIYnU7X19en0Wjh4eGjR4/G1bSzs+Ne/fr16w2t3mKaX6mmaGxObkW4xeqhQ4cyMjL8/PwUFRVzcnJIkly2bJmnpycuExISIisrGxwc/OrVq9u3b9vY2Nja2vJvqo3ctyObXam1a9devXr11atXqamps2bNkpWVbfV7Qs2sUW5urpKSkre39/Pnzy9evKilpRUQENBqlflGLJ+9gQMHTp48uRWib0AzK7VmzRplZeWwsLDXr19fv37dyMho0qRJrVaZb5pZqaSkpIiIiFevXt26dWvw4MGGhoYfPnxorbpQRKnU8+fPjx079uLFi+Tk5MmTJ6upqWVnZ+NFd+7codFomzdvzszM3Lx5c5t6AqHJlaqurk5NTU1NTdXV1V28eHFqampWVpbgPbanbEeSZFBQkIGBAYPB6Nu3b3x8PJ7p5eXl5OREldm9e7eZmZm8vLyuru706dPfvHnDv522k+3I5lXKz8+va9euDAZDU1MTN2pq+fj5NfNtunv3bv/+/ZlMZrdu3TZs2FBbW9vC8dermZV6/vw5Quj69estHLZgzalUTU3N2rVr8YPYLBZr/vz5bSExkM2rVFxcnKmpKZPJVFdX9/T0zM/Pb/n46yW0UhkZGZaWlvLy8ioqKu7u7s+ePeNe/cyZMz179qTT6SYmJhERES0cfEOaU6ns7GyeMzfu97deMOIPAAAA6ddu7tsBAAAATQbZDgAAgPSDbAcAAED6QbYDAAAg/SDbAQAAkH6Q7QAAAEg/yHYAAACkH2Q7AAAA0g+yHQAShwePHjFiRGsHAkDHBX2pACBxc+fOVVJSOnjwYEZGRteuXSW0l5qaGjqdLqGNA9DewbkdAJJVVVV1+vTp//3vf6NHjw4NDaXmnz9/3sbGRk5OTkNDw8PDA8+srq5eunQpi8ViMpndu3c/dOgQQig0NLRTp07UiufOnaNGAlu7dq2lpeXhw4e7devGZDJJkrx69erAgQM7deqkrq4+evToV69eUSu+efNmypQpampqioqKNjY2ycnJOTk5MjIyDx48oMrs2bPHwMAA/ggG0geyHQCSderUqZ49e/bs2XPGjBkhISE4kVy6dMnDw2PUqFGpqakxMTE2Nja48Pfffx8eHr579+7MzMz9+/crKSkJ3f7Lly9Pnz4dERGRlpaGEKqqqvL3979//35MTIyMjMz48ePx8EmVlZVOTk5v3749f/58enr60qVL6+rq2Gz20KFDQ0JCqK2FhITMnDmzjYz2CYA4ibdPawAAD3t7ezyCa01NjYaGRnR0NEmSdnZ206dP5ymJx0nABbiFhISoqqpSk1FRUdQ3d82aNXQ6vaioqN5dFxUVIYQeP35MkuQff/yhrKxcUlLCU+bUqVOdO3f+8uULSZJpaWkEQVCDqgAgTeDcDgAJev78+b1796ZMmYIQkpWVnTx58uHDhxFCaWlpQ4YM4SmclpZGo9GcnJwatQsDAwNNTU1q8tWrV9OmTevWrZuKioqhoSFCKDc3F2/cyspKTU2NZ/Vx48bJysriDHr48GEXFxc2m93YagLQ9sm2dgAASLNDhw7V1tbq6enhSZIk6XT6hw8f5OXl+QvXOxMhJCMjQ3LdSKupqeFeqqioyD05ZswYFot14MCBLl261NXV9e7d++vXrwI2zmAwPD09Q0JCPDw8Tp48GRgY2IjqAdB+wLkdAJJSW1t79OjR7du3p32Tnp5uYGBw4sSJPn36xMTE8JQ3Nzevq6uLj4/nma+pqVlRUVFVVYUn8f25epWUlGRmZq5cuXLIkCGmpqYfPnygFvXp0yctLa20tJR/rblz5964cSM4OLimpoZqLwOAtGntS6kASK2oqCgGg/Hx40fumb/++qulpWVsbKyMjMzq1aszMjIePXq0ZcsWvHTmzJksFisqKur169exsbGnTp0iSbKkpERRUdHHxycrK+vEiRNdunRBXPftLCwsqI1zOBx1dfUZM2ZkZWXFxMT069cPIRQVFUWSZHV1dY8ePRwdHW/fvv3q1auzZ89yj3Rvb2/PYDDmzZsn2SMCQOuBczsAJOXQoUNDhw5VVVXlnjlhwoS0tDQVFZUzZ86cP3/e0tJy8ODBycnJeOm+ffsmTpw4f/58ExOTH374AZ/PqampHT9+/PLly+bm5mFhYWvXrm1ojzIyMuHh4SkpKb179164cOG2bduoRQwG4/r161paWiNHjjQ3N9+8eTONRqOWzpkz5+vXr7NnzxbvEQCg7YCnywEAaMOGDeHh4Y8fP27tQACQFDi3A6BDq6ysvH///p49e3x8fFo7FgAkCLIdAB2at7f3wIEDnZyc4DImkG5wJRMAAID0g3M7AAAA0g+yHQAAAOkH2Q4AAID0g2wHAABA+kG2AwAAIP0g2wEAAJB+kO0AAABIP8h2AAAApB9kOwAAANLv/930XEdHnvcAAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -656,10 +698,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.557582Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.556207Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.634264Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.611391Z"
+     "iopub.execute_input": "2024-06-17T19:19:23.053597Z",
+     "iopub.status.busy": "2024-06-17T19:19:23.053271Z",
+     "iopub.status.idle": "2024-06-17T19:19:23.095252Z",
+     "shell.execute_reply": "2024-06-17T19:19:23.094144Z"
     }
    },
    "outputs": [],
@@ -673,10 +715,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.648072Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.647843Z",
-     "iopub.status.idle": "2024-06-17T14:24:11.027022Z",
-     "shell.execute_reply": "2024-06-17T14:24:11.026120Z"
+     "iopub.execute_input": "2024-06-17T19:19:23.101047Z",
+     "iopub.status.busy": "2024-06-17T19:19:23.100375Z",
+     "iopub.status.idle": "2024-06-17T19:19:26.424565Z",
+     "shell.execute_reply": "2024-06-17T19:19:26.423246Z"
     }
    },
    "outputs": [
@@ -692,7 +734,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -712,17 +754,17 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:11.042719Z",
-     "iopub.status.busy": "2024-06-17T14:24:11.042514Z",
-     "iopub.status.idle": "2024-06-17T14:24:16.384600Z",
-     "shell.execute_reply": "2024-06-17T14:24:16.383969Z"
+     "iopub.execute_input": "2024-06-17T19:19:26.442391Z",
+     "iopub.status.busy": "2024-06-17T19:19:26.441817Z",
+     "iopub.status.idle": "2024-06-17T19:19:32.882858Z",
+     "shell.execute_reply": "2024-06-17T19:19:32.873919Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 8,
@@ -731,7 +773,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -754,10 +796,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:16.419389Z",
-     "iopub.status.busy": "2024-06-17T14:24:16.419189Z",
-     "iopub.status.idle": "2024-06-17T14:24:18.935566Z",
-     "shell.execute_reply": "2024-06-17T14:24:18.934999Z"
+     "iopub.execute_input": "2024-06-17T19:19:32.886775Z",
+     "iopub.status.busy": "2024-06-17T19:19:32.886453Z",
+     "iopub.status.idle": "2024-06-17T19:19:35.952957Z",
+     "shell.execute_reply": "2024-06-17T19:19:35.950255Z"
     }
    },
    "outputs": [
@@ -773,7 +815,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -793,17 +835,17 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:18.950077Z",
-     "iopub.status.busy": "2024-06-17T14:24:18.949900Z",
-     "iopub.status.idle": "2024-06-17T14:24:24.008521Z",
-     "shell.execute_reply": "2024-06-17T14:24:24.007949Z"
+     "iopub.execute_input": "2024-06-17T19:19:35.958715Z",
+     "iopub.status.busy": "2024-06-17T19:19:35.958549Z",
+     "iopub.status.idle": "2024-06-17T19:19:41.718785Z",
+     "shell.execute_reply": "2024-06-17T19:19:41.715538Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 10,
@@ -812,7 +854,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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7m0nx8PDQ0NB4/fo1vUgImT17NrOWPqSbN2+ubof0m/zIkSNMyuPHj7lc7vjx46XmFz+3nD17lhCyadMmZi3di+unn36iKOr48eOEkPT09Ko7iYyMFD9tUh8P+K5du6QW6uLi4uLiUl016DZWPc4DBw4U32T8+PGGhoZS9y+u9tcASktLz58/P3LkSA0NjYqPhgwZUlpaeuXKFUKIh4eHkZEREwP+9ttvz549mzJlCr34yy+/uLm5tWnThtmW/jJITEysRWW6d+/etm1b+rVAIOjYsSMTxCUmJn722Wf0zztCiIqKyujRo2vdanH0xQyao6OjmZkZ/akjhLi4uFRUVKxYseKTO5k8efI///xz5syZQ4cO8Xi8L7/88pObcDic4cOHM4vdunWT3al12LBhzGtra2tCCH13k0mRp0+seOn0jypCiKqqavv27Y2NjZnb8Lq6ugYGBrJ3uGvXLjU1NR6PZ21tnZycvHr16lmzZt27d+/Zs2c+Pj4qKv//ntTS0ho1atSVK1foK2m9e/cODw9fu3btlStXmN8ctTN58uSnT58yF4HDwsKMjIzot19ycvLLly99fX2Zt2VlZeXnn39+/fp1GX1rFy5cqKamJhAI7OzssrOzf/zxR+YQEUJGjRrFvP7tt98qKiomTpzI7F8gELi4uNAXh+mDMG7cOOZavZmZmaOjY3Xlnjlzxs3Njf6b1hT9XhV/vKV3797W1tbiVzuMjIx69+7NLH7ynXb69Gk1NTU6ZNm7d++OHTvE32kFBQUzZ84UCoWqqqp0HkIIHYhIJftYVTV+/Hg6hqYXIyMjy8rKmHtqNXr/7N+/X11dfezYsYQQLS2tL7/8Mikp6cGDB/RaGYf9zJkzHTt2HDhwoOz9yyD+biEyD9q7d+8SExNHjx4tfsGJ4erqamtry9yW3bNnD4fD+frrrxuuPm/fvr1+/bqXl5dAIKAzt2jRQvxM9csvv3A4nAkTJjB/UCMjI1tbW/oPSlFUhRh6kz/++GPAgAFCoZDZyaRJk969e1frDq3Pnj0jhBgYGDAp8fHxIpFo9uzZn9yWvhon/pGhf17SH5nu3bvzeLyvv/46IiJC4tbGL7/80rJly+HDhzOt6969u5GRUV2eTqp6nC9evCh+ycfAwKCgoOCTN9ZrHwEUFhZWVFTs2LFDTQx94qMvJKqqqvr4+MTGxtLXxsPDw42Njd3d3enN//nnn1OnTolv27lzZ2bbmtLT0xNf5PP5zAWWwsJCQ0ND8bUSixJUVVXFjyOjoqJCTU1NPMXIyEhiUcZ1xeqYmZkNGDAgNDQ0NDR07NixGhoan9xEQ0OD+dsTQvh8fmlpqYz8dBhO4/F4VVNkby67dB6PJ743eXY4evTo69ev37hx4969e4WFhXRnEfrQGRsbi+ds06ZNZWUlfX01KirK19d33759Dg4Ourq6EydOzM/Pl7/a4jw8PIyNjenY9NWrV3FxcRMnTuRyuYQQ+oKkt7e3+Dtz48aNFEW9fPmyuh36+/tfv349NTX10aNHeXl5EudZ8UbR++/Vq5f4/qOioui3PX0Qqr6vqiv3+fPnte4VWN0BF38Py/hYSdWvX7/r169fuXLl4MGD5ubmc+bMYW7iVFZWDh48OCYm5rvvvjt//vy1a9fo3wkydij7WFWlq6v7xRdfHDhwgP78hoeH9+7dmz6rkJq8fx4+fHjx4sWhQ4dSFPX69evXr197e3uTj48GEJmHvS5/EUKIhoaGeH9V2Qft1atXIpFIRnF+fn7nz5+/d+9eeXn53r17vb29ZbyX6qU+lZWVMt69//zzD0VRhoaG4n/QK1eu0H/QiIgI8XR6k8LCwqpvUfLx3VsLdFXFz2D0NVd5/mqFhYWqqqri8RaHw2FO+5aWlr///ruBgcHs2bMtLS0tLS1DQkKYhr9+/ZrH44k3MD8/v3ZfdrSqx/nDhw/iz5YLBAKKoj55bletdQ1atWrF5XJ9fHyqRk8WFhb0i8mTJ2/evPnIkSNjxoyJi4sLCAigz7OEEH19/W7dulW9JU//geuRnp6exH0m2d8choaGpaWlL1++FP9iKywsLCsrkwgdJPaTn5/fvn37WtRwypQpEyZMqKys3L17dy02r0f0B0O8X2Fd3qPVad26tb29vUQi/WUjccf02bNnKioqdIdefX394ODg4ODg7OzsuLi4RYsWFRQU0Nflaop+327fvv3169eHDx8W/6VIXyvasWNH1T7zMgJHU1PTqi1iiHe+o/d//PhxqU8i0Qeh6vuquj23bt1avO9ejTAHXPzc9+zZM+ZqWS3o6OjQx6FPnz59+vSxtbWdNWtWenq6iorKX3/9devWrfDwcPraMvnYYUIG2cdKqsmTJx87diw+Pr5t27bXr18X/0DJ//6hr3sfP36cvq7LiIiIWLt2LZfLlXHYZayS58Ml8ei87IOmq6vL5XJlvAHGjRu3cOHCnTt39u3bNz8/X56fuXWpT6tWrTgcjox3r76+PofDoZ/FF89DLw4fPpy+Ni5OT0+v6jmBfHxv1AK9oXg0T3+jP336VPxKg1R6enoVFRXPnz9nggCKovLz8+kbf4QQZ2dnZ2dnkUh048aNHTt2BAQEGBoajh07Vl9fX09Pr+qbTeLBb4ZAICgqKhJPqfpWqXqceTye+Eg29F0wqWPbiKv9NQANDQ36uedu3brZ/xfz08Ha2rpPnz5hYWES51lCyLBhw/766y9LS0uJbes9AnBxcfnjjz+YI1hZWVm1N6w4+gqexGP6R48eZVYxDh06xLxOTk5+8uSJRAdOOY0cOXLkyJFTpkxp9Ecc6e6mt2/fZlLozhws6NSpk4mJyeHDh6mPz8u9ffs2OjqafjRAPGfbtm3nzJkzaNCgmzdvSt3VJ3+qEkImT55cWloaGRkZHh7u4OBgZWVFpzs5ObVs2fLOnTv2VdCXT+rI3d1dVVX10aNHVfdPCOnUqZOxsXFkZCRzEJ48eZKcnFzd3jw8PBISEphnScTRZ1UZx+Gzzz4jhIj37Lt+/XpGRgbd5bvuOnTo8N133/3555/0R4n+LhE/9Us8KFi1wrKPlVSDBw82MTEJCwsLCwsTCARfffVV1Tyy3z8ikSgiIsLS0jLhv+bNm5eXl3fmzBki87B7eHjcv39faufNWny4ZB80dXV1FxeXY8eOVRemCwQC+qL01q1bu3fv7uTkVF1Bn3y3yFMfTU3N3r17x8TEML87S0pKTp06xWQYNmwYRVG5ubkSf82uXbsSQvT09Kr+iQcMGPDHH3/Q3/q0AwcOaGho1PpUSd+7efToEZMyePBgLpcrz68v+qMh/pGJjo5++/atxEeGy+X26dOHvv9Cv8eGDRtWWFgoEokkGi7xPALD3Nz8/v37TLBYWFhY9SRQ9Tg7OzszP7AJIY8fP6Z7vMkm7zWAP/74Q+LxuSFDhoSEhPTr18/Z2fmbb74xNzcvKSl5+PDhqVOnxD8AU6ZMmTFjxrNnzxwdHcUbvHr16vj4eEdHRz8/v06dOpWWlmZlZZ0+fXrPnj31+zj70qVLT506NWDAgKVLl6qrq+/Zs4e+ocvcb5bg5ub2xRdf+Pv7Z2Vl0d0xLl68uG3bti+++ELiC/7GjRvTpk378ssvc3Jyli5damJiMmvWLHpVYmLigAEDVqxYIU9XAIFAIPFro7EYGRkNHDhw/fr1rVq1MjMzO3/+PN23lgUqKiqbNm0aP378sGHDZsyYUVZWtnnz5tevX9NPGxYVFbm5uY0bN87KyqpFixbXr18/e/asl5eX1F117dr1woULp06dMjY2btGihdSPmZWVlYODw/r163Nycn766ScmXUtLa8eOHb6+vi9fvvT29jYwMHj+/PmtW7eeP39eL1dozM3NV69evXTp0sePH3/++eetWrX6559/rl27pqmpuWrVKhUVlTVr1kybNm3kyJHTp09//fp1UFCQjCu3dA/w/v37L1mypGvXrq9fvz579mxgYKCVlZWlpaW6uvqhQ4esra21tLTatGkjEVt36tTp66+/3rFjB92ROCsra/ny5UKhcO7cuXVvJm3+/Pl79uxZtWrV6NGj6SotWrSIoihdXd1Tp07Fx8eLZ6a/BkJCQnx9fdXU1Dp16iT7WEktkcvlTpw4cevWrdra2l5eXjo6OnS6/O+fM2fOPHv2bOPGjRIf9i5duvzwww/79+8fNmyYjMMeEBAQFRXl6em5aNGi3r17v3//PjExcdiwYW5ubrX4cH3yoG3durVfv359+vRZtGhR+/bt//nnn7i4uB9//JH5cTlr1qxNmzalpqbu27dPRkGffLfIWZ81a9Z8/vnngwYNmjdvnkgk2rhxo6amJvODmx4wY/LkyTdu3Ojfv7+mpmZeXt6lS5e6du36zTffSK3YypUr6R5jK1as0NXVPXTo0K+//rpp0ybmL1tTpqam7dq1u3Llip+fH51ibm6+ZMmSNWvWvH///quvvtLR0blz586LFy+qvscGDRrk7u6+cOHC4uJiJyen27dvr1y5skePHj4+PoSQPXv2/PHHH0OHDm3btm1paSl9z4j+0Th27NhDhw4NGTLE39+/d+/eampqT58+TUhI8PT0rPpoBiHEx8fnxx9/nDBhwvTp0wsLCzdt2lR1LBMulzto0KDAwMDKysqNGzcWFxeLV7iysvLatWtTp0799BH5ZF9Bicc5GHSv3czMzClTppiYmKipqbVu3drR0ZHpD08rKipSV1cnhOzdu1diz8+fP/fz87OwsFBTU9PV1bWzs1u6dOmbN2/otaQmzwLQN+0YEn0pk5KS+vTpw+fzjYyMFixYQD87x/QmrerDhw/ff/99586d+Xw+n8/v3Lnz999//+HDB4ljcu7cOR8fn5YtW9KPnz148IDJQFeYqX9V1T1xQB8WGW2vuqHE0SBVngUQf46Dzsw8GVF1h3l5ed7e3rq6ujo6OhMmTKBHgxbvrixRuouLS+fOncVTqv45xJH/9t2VcOLEiT59+ggEAk1NzQEDBly+fJlOLy0tnTlzZrdu3bS1tdXV1Tt16rRy5cq3b98ytRLvLZ+enu7k5ERfOaDfBlW7YVMURX/xq6uriz/MQktMTBw6dKiurq6ampqJicnQoUOPHTsmtcKy+x5X9xzNiRMn3NzctLW1+Xy+mZmZt7f377//zqzdt29fhw4deDxex44dQ0NDJVon8b7KycmZMmWKkZGRmppamzZtRo8eTd9tpSgqMjLSysqKvqVKbyLxVqHP0R07dlRTU9PX158wYUJOTg6ztupfVqImEqT+3elfQhERERRF3blzZ9CgQS1atGjVqtWXX36ZnZ0t0ZbFixe3adOGDs2ZP5bsY1XV/fv36ROUeId82e8fcSNGjODxeFJ7148dO1ZVVTU/P5+SedhfvXrl7+/ftm1bNTU1AwODoUOH3r17l15V0w+XPAftzp07X375pZ6eHo/Ha9u27aRJk0pLS8X34Orqqqur++7dOxkHjZL2bqldfeLi4rp160ZXZsOGDRJvOYqiQkND+/Tpo6mpqa6ubmlpOXHixBs3bsio2J9//jl8+HAdHR0ej2drayvxwAKp4bMAFEUtX768VatWEkfpwIEDvXr1EggEWlpa9GOldLrEe/79+/cLFy40MzNTU1MzNjb+5ptvXr16Ra9KSUkZOXKkmZkZn8/X09NzcXGJi4tjNiwvL//f//5na2tLF2FlZTVjxgzxrwwJERER1tbWAoHAxsaG7sIi8SzAxo0bV61aRY+m06NHj99++018c7pzYmpqqozjQONQdR6kTOEMHjw4KyuLOVPUAv3Q9vXr12VckAQAaFwFBQVmZmbffvutnGPEKoNnz55ZWFgcOHBAYvRVRZGVlWVhYbF58+b58+dXl8fHx+fx48f0M+qy1b4noAIJDAzs0aOHUCh8+fLloUOH4uPjG2WYTwAAdjx9+vTx48ebN29WUVHx9/dv7Oo0IW3atAkICFi3bt2XX35Z3b1ghfbo0aOoqCgZI4mJU4oIQCQSrVixIj8/n8Ph2NjYHDx4cMKECY1dKQCAhrJv377Vq1ebm5sfOnTIxMSksavTtCxbtkxDQyM3N/eT/f8VUXZ29g8//NCvXz95MivjXQAAAABQmGsgu3btsrCwoIddS0pKqprh0qVLTk5O9KwMVlZW27ZtY1bRc0iIq9EYOAAAAM2PYtwFiIqKCggI2LVrl5OT048//ujh4XHnzh1mGGCapqbmnDlzunXrpqmpeenSpRkzZmhqajKjs2lra4s/vys+JhQAAIASUoy7AH369OnZsyfzQLa1tfWIESMk5r6U4OXlpampefDgQUJIeHh4QECA+DSOAAAASk4BrgF8+PAhNTV10aJFTMrgwYNlDJRGCElLS0tOTl67di2T8ubNGzMzM5FI1L179zVr1jAz2YgrKytjhmGqrKx8+fKlnp6exLiYAACgWCiKKikpYUabgH99csSARpebm0sIYQaHoShq3bp1HTt2lJrZxMSEx+OpqKisXr2aSUxJSTl48GB6evrFixdHjRqlrq5+//79qtvSg1cAAEDzIz7gFdAU4BoATfy3OEVR1f00T0pKevPmzZUrV+hhMumBwfv27cuMI+3k5NSzZ88dO3bQs4mLW7x4cWBgIP26qKiobdu2OTk5VYdjhKri4oiPj5T0gwfJF1808b0DQDNXXFwsFAqrm4lHmSlABKCvr8/lcsWnQiooKKhuojZ6WsKuXbv+888/QUFBVacGUVFR6dWrFzPVtzh6DGDxFG1tbUQA8pgwgWhoEH9/wkxUJhSS4GBSzcj98hKJSFISyeNO6LpKo/Nef47svf9/7jxibEycnYnYJBkAALilW5UCRAA8Hs/Ozi4+Pp6ZRCE+Pt7T01P2VhRFiU/EKZ6enp5OT0MC9cjLi3h61udXcEyMeEjhJWzjuWZSUgetPA1L466znLk8rozcxNSUhITUNQABAGjeGvkuhHyOHDmipqa2f//+O3fuBAQEaGpqZmVlURS1aNEiHx8fOs8PP/wQFxd3//79+/fvh4aGamtrL126lF4VFBR09uzZR48epaWlTZ48WVVV9erVq7JLpKdnrjpnDLAjOpricChCpP8zNaWio/+bW2q+/2QCACWF83l1FOAaACFkzJgxhYWFq1evzsvL69Kly+nTp83MzAgheXl59ORUhJDKysrFixdnZmaqqqpaWlpu2LBhxowZ9KrXr19//fXX+fn5Ojo6PXr0uHjxYu/evRutMfApIhHx9ycyHlPNzSXe3uT4ceLlRYhIRD6O+iDp66+JpyduBwAASKUY4wGwr7i4WEdHp6ioCP0A2HfhAnFz+0QeDoeYmpLMTMK9cJ4MHFhtvt9/JwMG1GvtoFoikai8vLyxawHKS01NjSst4sf5vDqKcQ0AlEpe3qfzUBTJySFJScT1wgVZ+S5cQATAAoqi8vPzMegWNLqWLVsaGRmh05+cEAFAk2NsLG9OeWIFYAH99W9gYKChoYGTLzQKiqLevXtXUFBACDGW/ySi3BABQJPj7ExMTUlurqyuADRjY0IMXInY4I+SXF3rtWoghUgkor/+9fT0GrsuoNTU1dUJIQUFBQYGBlJvB4AEDJEITQ6XS0JCCCFExo9JDocIhcTZmRBXV1LdF4+eHiIAFtD3/jU0NBq7IgD//z5EfxQ5IQKApsjLixw/TkxMpK+lI4PgYMLlEsLlkp9+kp7vp5/wIABrcPEfmgK8D2sEEQA0UV5eJCuLJCSQw4fJqlXE1PTfVaamHx8FZLJGR/8nXjA1JdHRGBEIAEAG9AOApovL/fcq/tKlMgccrPchCQGqFxQUdOLEifT09IYuiMPhxMbGjhgxoqELAuWEawCgGOho4KuviKtrNV/un84BzdykSZM4//Xw4cN62TOHwzlx4gSzOH/+/PPnz9dxn+bm5hwO58iRI+KJnTt35nA44eHh9GJeXp6Hh0cdC2oUzJ9AS0vL1taWadEntxI/ztDQEAEAQCMQiciFCyQykly4QESietvt559/nieGniqM8eHDh3opRUtLq6YPPkjtmyYUCsPCwpjFK1eu5Ofna2pqMilGRkYS05XVFxb6yoWFheXl5d26dWvMmDGTJ0/+7bffGrpEqClEAADAtpgYYm5O3NzIuHHEzY2Ym5OYmPrZM5/PNxLD5XJdXV3nzJkTGBior68/aNAgQkhiYmLv3r35fL6xsfGiRYsqKirobV1dXf38/L777jtdXV0jI6OgoCA63dzcnBAycuRIDodDvw4KCurevTtTaFhYmLW1tUAgsLKy2rVrF52YlZXF4XCOHj3q6uoqEAh+/vnnqrUdP358YmJiTk4OvRgaGjp+/HhV1X9vzjK/iem9xcTEuLm5aWho2NrapqSkSD0Cd+/e7devn0AgsLGx+f333yX2IF6fysrK1atXm5qa8vn87t27nz17lt7DhQsXOBwOM75Teno6h8PJysoihISHh7ds2fLEiRMdO3YUCASDBg1iKl8VPTiPpaXlkiVLdHV1z507R6dfv3590KBB+vr6Ojo6Li4uN2/erO44E0JOnTplZ2cnEAjatWu3atUq5o8F9QIRADQTDfSbsoF3rYxiYoi397/zOJKPEz3UVxBQVUREhKqq6uXLl3/88cfc3NwhQ4b06tXr1q1bu3fv3r9//1qx8SQiIiI0NTWvXr26adOm1atXx8fHE0KuX79OPv6ipV+L27t379KlS9etW5eRkfH9998vX748IiKCWbtw4UI/P7+MjAx3d/eqFTM0NHR3d6fzv3v3LioqasqUKTIasnTp0vnz56enp3fs2PGrr76q+nVYWVk5YsQIDQ2Nq1ev/vTTT0uXLpXIIF6fkJCQLVu2/O9//7t9+7a7u/sXX3whddp0Ce/evVu3bl1ERMTly5eLi4vHjh0rO79IJDp69OjLly/V1NTolJKSEl9f36SkpCtXrnTo0GHIkCElJSVE2nH+7bffJkyY4Ofnd+fOnR9//DE8PHzdunWfrCHUQCPPTNRUYS4pxRIdTZmaVj95YBPddTPx/v37O3fuvH//Xp7MFRX/OZzMPw6HEgqpioo61cTX15fL5Wp+5O3tTVGUi4tL9+7dmTxLlizp1KlTZWUlvbhz504tLS2RSETn7NevH5OzV69eCxcupF8TQmJjY5lVK1eutLW1pV8LhcLDhw8zq9asWePg4EBRVGZmJiEkODi4utqamZlt27btxIkTlpaWlZWVERERPXr0oChKR0cnLCxMolx6b/v27aPT//77b0JIRkaGxD7PnDmjqqqal5dHL9IRjPgexOvTpk2bdevWibd31qxZFEUlJCQQQl69ekWnp6WlEUIyMzMpiqLvWVy5coVelZGRQQiROtUqIUQgEGhqatIj8+jq6j548KBqtoqKihYtWpw6dUqivTRnZ+fvv/+eWTx48KCxsbH0o/mR1HcjzufVwTUAUHgN+JuS/Z+rzV1S0n8OJ4OZ6KGO3Nzc0j/avn07nWhvb89kyMjIcHBwYJ4ad3JyevPmzdOPderWrRuT09jYmB5iVobnz5/n5ORMnTpV66O1a9c+evSIySBetFRDhw598+bNxYsXQ0NDZV8AEK8ePept1erdu3dPKBQaGRnRi1UnQWXqU1xc/OzZMycnJ2aVk5MT/Y0um6qqKrMTKyurli1bVrfVtm3b0tPT4+Pju3fvvm3btvbt29PpBQUFM2fO7Nixo46Ojo6Ozps3b5gpXiWkpqauXr2aObbTp0/Py8t79+7dJysJcsLTgKDYpE4lTFGEwyEBAXWbHLgBd628ZE/lUPeJHjQ1NZlvGvFE5jVFUeKDxlAURcSGkWGuVNOJlZWVsoujM+zdu7dPnz5Movh4tOJFS6Wqqurj47Ny5cqrV6/GxsbKzsxUj65w1epJtK4qifpIHAp6UUVFhXw8MkRan0GJIqor0cjIqH379u3btz927FiPHj3s7e1tbGwIIZMmTXr+/HlwcLCZmRmfz3dwcKiuh2ZlZeWqVau8/juwh0AgkNFAqBFcAwDF1oC/KRv656pSkj1jCwvzudjY2CQnJzNfb8nJyS1atDCpbvjJj9TU1ETSuoAYGhqamJg8fvy4vRiJBxA+acqUKYmJiZ6enq1atarRhlVZWVllZ2f/888/9GLVXgsMbW3tNm3aXLp0iUlJTk62trYmhLRu3ZoQkvcxHJMY9qCiouLGjRv063v37r1+/drKykp2rdq3bz9q1KjFixfTi0lJSX5+fkOGDOncuTOfz3/x4gWTU+I49+zZ8969e+3/iw5QoF7gGgAotgb8TSl74/PnMe5QLVQ37ROHQ0xNibNzg1dg1qxZwcHB33777Zw5c+7du7dy5crAwMBPfqmYm5ufP3/eycmJz+dLfE8HBQX5+flpa2t7eHiUlZXduHHj1atXgYGB8lfJ2tr6xYsX9TKxwqBBgywtLX19fTdt2lRSUkL3BKzuN/qCBQtWrlxpaWnZvXv3sLCw9PT0Q4cOEULat28vFAqDgoLWrl374MGDLVu2iG+lpqb27bffbt++XU1Nbc6cOX379q16r6GqefPm2dra3rhxw97evn379gcPHrS3ty8uLl6wYAE9nQ9N4jivWLFi2LBhQqHwyy+/VFFRuX379p9//rlWxkxgUEMIpkCxNeBvStkbr11bnw+xKQ2p0z79Z6KHBmZiYnL69Olr167Z2trOnDlz6tSpy5Yt++RWW7ZsiY+PFwqFPXr0kFg1bdq0ffv2hYeHd+3a1cXFJTw8vKbXAAghenp64l+Etcblck+cOPHmzZtevXpNmzaNblp1l839/PzmzZs3b968rl27nj17Ni4urkOHDoQQNTW1yMjIu3fv2trabty4UeIbV0NDY+HChePGjXNwcFBXV5cY0ag6Xbt2HThw4IoVKwghoaGhr1696tGjh4+Pj5+fn4GBAZNN4ji7u7v/8ssv8fHxvXr16tu379atW83MzGp7bEAKDvXJGViVUnFxsY6OTlFRkba2dmPXBWQRiYihISkslLJKT4/880/d+gGYm8uapZj+4vrPFAXKqLS0NDMz08LCQv4btDExxN//33ssQiEJDlbyo9ggLl++3K9fv4cPH1paWtbLDsPDwwMCApihApogqe9GnM+rg7sAANWgf656exMOR3oQgF6BtYVpHBpObGyslpZWhw4dHj586O/v7+TkVF9f/9D84C4AKLakJOkXAAghhYV17q4ne5Zigl6BtYdpHBpISUnJrFmzrKysJk2a1KtXr5MnTzZ2jaDpQgQAiq2hny77/1mKZd8qrodiAOrHxIkTHzx4UFpa+vTp0/Dw8JrOXyDbpEmTmvItAKgpRACg2Nh4uozLJQMGNHwxAACsQgQAio1+uqzq404cDhEK6+/pMpaKAQBgDyIAUGwsPV3W6A+xAQDUN0QAoPCqdtczNW2AZ/RYKgYAgCV4GhCagwZ9ukwkYvbs5fzIk5t0gVy4QAghrq7E1bXeigEAYBciAGgm6KfL6p3E2DXT9E6GEH+NwqeEELJ2LTE1JSEhuAwAAIoIdwEAqiUxOfBIEvNjobegUGy6oKdPyahRZO5ccuECkTZzDDRLQUFB3bt3b+xaANQVIgAA6SQmB1YhohDiTwgl5TMTHEzc3DBNQKObNGkS578ePnxYL3vmcDgnTpxgFufPn3/+/Pk67tPc3JzD4UiMq9+5c2cOhxMeHl7HnTcprq6u9J+Dx+NZWlouXry4rKxMnq0CAgIavnZKDREAgHQSkwM7kyQheSrrA5ObS7y9EQTISyQiFy6QyMj6vXzy+eef54mRmKSnunnoa0pLS6umI+2Ul5dXTRQKhWFhYczilStX8vPzNTU161q/GqqvwyLD9OnT8/LyHj58uGnTpp07dwYFBTV0iSAPRAAA0kkM9GdMPjXwH325ICAAtwM+LSaGmJsTNzcyblz9Xj7h8/lGYrhcrqur65w5cwIDA/X19QcNGkQISUxM7N27N5/PNzY2XrRoUUVFBb2tq6urn5/fd999p6ura2RkxHxLmZubE0JGjhzJ4XDo1xJ3AcLCwqytrQUCgZWV1a5du+jErKwsDodz9OhRV1dXgUDw888/V63t+PHjExMTc3Jy6MXQ0NDx48erqv7bPWvr1q1du3bV1NQUCoWzZs168+YNnT5lypRu3brRv6TLy8vt7OzGjx9fdf8lJSXjx4/X1NQ0Njbetm2b+K9qc3PztWvXTpo0SUdHZ/r06YSQ6Ojozp078/l8c3Nz8RmBJa5/tGzZkr5EQTfwyJEjjo6OAoGgc+fOF+gestJoaGgYGRm1bdt21KhRgwYNOnfuHJ1eWFj41VdfmZqaamhodO3aNTIykk6fNGlSYmJiSEgIffEgKyuLEHLnzp0hQ4ZoaWkZGhr6+Pi8ePGiuuJATogAAKSTGOgvj8gx8B+mCZCHRPcK0uCXTyIiIlRVVS9fvvzjjz/m5uYOGTKkV69et27d2r179/79+8Vnv42IiNDU1Lx69eqmTZtWr14dHx9PCLl+/TohJCwsLC8vj34tbu/evUuXLl23bl1GRsb333+/fPnyiIgIZu3ChQv9/PwyMjLc3d2rVszQ0NDd3Z3O/+7du6ioqClTpohnUFFR2b59+19//RUREfHHH3989913dPr27dvfvn27aNEiQsjy5ctfvHjBRB7iAgMDL1++HBcXFx8fn5SUdPPmTfG1mzdv7tKlS2pq6vLly1NTU0ePHj127Ng///wzKCho+fLlct6JWLBgwbx589LS0hwdHb/44ovC6mbp+OjWrVuXL19WU1OjF0tLS+3s7H755Ze//vrr66+/9vHxuXr1KiEkJCTEwcGBvnKQl5cnFArz8vJcXFy6d+9+48aNs2fP/vPPP6NHj5anhiALBdIUFRURQoqKihq7ItBoKiooU1OKw6EIoQihVEhFNjEVkY/LMv4dPtzYdWfV+/fv79y58/79e7ly04e16kHjcCihkKqoqEtNfH19uVyu5kfe3t4URdFfG0yeJUuWdOrUqbKykl7cuXOnlpaWSCSic/br14/J2atXr4ULF9KvCSGxsbHMqpUrV9ra2tKvhULhYbG/+Jo1axwcHCiKyszMJIQEBwdXV1szM7Nt27adOHHC0tKysrIyIiKiR48eFEXp6OiEhYVVzX/06FE9PT1mMTk5WU1Nbfny5aqqqomJiVXzFxcXq6mpHTt2jF58/fq1hoaGv78/U/qIESOYzOPGjRs0aBCzuGDBAhsbG6ltZ6pHN3DDhg10enl5uamp6caNG6vWxMXFRU1NTVNTk8fjEUJUVFSOHz8u9ZgMGTJk3rx5zFZMbSmKWr58+eDBg5lF+sLJvXv3JPYg9d2I83l1cA0AQDqJYQArCdefhBBCKkmVsYElYJoAGSS6VzDq6fKJm5tb+kfbt2+nE+3t7ZkMGRkZDg4OnI9jOzo5Ob158+bpxyp169aNyWlsbFxQUCC7uOfPn+fk5EydOlXro7Vr1z569IjJIF60VEOHDn3z5s3FixdDQ0MlLgAQQhISEgYNGmRiYtKiRYuJEycWFha+ffuWXuXg4DB//vw1a9bMmzevf//+Vff8+PHj8vLy3r1704s6OjqdOnUSzyBxWJycnJhFJyenBw8eiOS4n+Xg4EC/UFVVtbe3z8jIkJpt/Pjx6enpKSkpo0ePnjJlyqhRo+h0kUi0bt26bt266enpaWlpnTt3Ljs7W+oeUlNTExISmONsZWVFCBE/1FALGA8AoFr0MIDMeACxxMubHA8h/kIi7TuMEMLhEFPT2k8TIDb2UD2PatR0NPBkjpqamu3bt6+ayLymKIojNrQzRVGEECaFuTpNJ1ZWVsoujs6wd+/ePn36MIlcsT/cJ7v1qaqq+vj4rFy58urVq7GxseKrnjx5MmTIkJkzZ65Zs0ZXV/fSpUtTp05lehRWVlZevnyZy+U+ePBA6p4lmsakSK2b1MNC43A44otSuzSKZ5aarqOjQ/9dfv75586dO+/fv3/q1KmEkC1btmzbti04OJju7hAQEFBdt8TKysrhw4dv3LhRPNEY0Xbd4BoAgCz05MAJCYTuQRVLvMxJlitJ2EoCKInrAXWcJqDBOsc1LWxM5iiLjY1NcnIy85WWnJzcokULE/HBnqVRU1OT+oPY0NDQxMTk8ePH7cVIPIDwSVOmTElMTPT09GzVqpV4+o0bNyoqKrZs2dK3b9+OHTs+e/ZMfO3mzZszMjISExN/++038QcKGJaWlmpqateuXaMXi4uLq4sVCCE2NjaXLl1iFpOTkzt27EiHMq1bt877GJk9ePDg3bt34hteuXKFflFRUZGamkr/NJdBTU1tyZIly5Yto/eTlJTk6ek5YcIEW1vbdu3aideQx+OJH/OePXv+/fff5ubm4oea/ecmmhlEAACfQI82uG0bWbCAEEIqCTeRuM4j20aR6FxST9MEsN45rtE09iyLs2bNysnJ+fbbb+/evXvy5MmVK1cGBgaqqHziTGhubn7+/Pn8/PxXr15JrAoKClq/fn1ISMj9+/f//PPPsLCwrVu31qhK1tbWL168qPotbmlpWVFRsWPHjsePHx88eHDPnj3MqvT09BUrVuzfv9/JySkkJMTf3//x48cSm7do0cLX13fBggUJCQl///33lClTVFRUqvuNPm/evPPnz69Zs+b+/fsRERE//PDD/Pnz6VWfffbZDz/8cPPmzRs3bsycOVP8MgkhZOfOnbGxsXfv3p09e/arV6+q3sioaty4cRwOh+662L59+/j4+OTk5IyMjBkzZuTn5zPZzM3Nr169mpWV9eLFi8rKytmzZ798+fKrr766du3a48ePz507N2XKFHnuU4AMiAAA5CISkY9PKv0/+nqAG0mYo3tY9HsCycys5de/xNhDtOb6bGFjz7JoYmJy+vTpa9eu2drazpw5c+rUqcuWLfvkVlu2bImPjxcKhT169JBYNW3atH379oWHh3ft2tXFxSU8PLym1wAIIXp6eurq6hKJ3bt337p168aNG7t06XLo0KH169fT6aWlpePHj580adLw4cMJIVOnTh04cKCPj0/Vr8OtW7c6ODgMGzZs4MCBTk5O9COLUivQs2fPo0ePHjlypEuXLitWrFi9evWkSZOYtguFwv79+48bN27+/PkaGhriG27YsGHjxo22trZJSUknT57U19f/ZGN5PN6cOXM2bdr05s2b5cuX9+zZ093d3dXV1cjIaMSIEUy2+fPnc7lcGxub1q1bZ2dnt2nT5vLlyyKRyN3dvUuXLv7+/jo6Op8M3UA2jsSdIaAVFxfr6OgUFRVpa2s3dl2gSbhwgbi5Vbs2IaEOsxI04K7ZUFpampmZaWFhUd23ixQS0y0IhSQ4GNMrNKi3b9+amJhs2bKFvgFfd1lZWRYWFmlpaU1qgGSp70acz6uDnoAAcmnAHmwN3DmuKWrQyRzho7S0tLt37/bu3buoqGj16tWEEE9Pz8auFDQhiAAA5NKAPdgau3Nc42igyRzhv/73v//du3ePx+PZ2dklJSXJc5UelAciAAC50D3YcnMl79fX8QHABt41KLUePXqkpqY20M7Nzc1xE1nRKUw3il27dtG3duhItmqGS5cuOTk50R1qrKystm3bJr42OjraxsaGz+fb2NhIPHQLII8G7MHW2J3jAEA5KUYEEBUVFRAQsHTp0rS0NGdnZw8Pj6rjRmlqas6ZM+fixYsZGRnLli1btmzZTz/9RK9KSUkZM2aMj4/PrVu3fHx8Ro8eTQ89DVAj9ABBJvX0ACBbu2YJfg5CU4D3YY0oxrMAffr06dmz5+7du+lFa2vrESNGMM/GSOXl5aWpqXnw4EFCyJgxY4qLi8+cOUOv+vzzz1u1ahUp8WjXf6HvKFSnAQfuU8wxAUUi0f379w0MDGo6YS5AvSssLCwoKGCGM6LhfF4dBegH8OHDh9TUVHoWLNrgwYOTk5NlbJKWlpacnMxM+ZWSkjJ37lxmrbu7e3BwcNWtysrK6Nk2CSHFxcV1rzk0Sw3Yg00xO8dxudyWLVvSQ+hraGhUN+YMQIOiKOrdu3cFBQUtW7bkKkj03OgUIAJ48eKFSCQyNDRkUgwNDcWHjhJnamr6/PnzioqKoKCgadOm0Yn5+fnybL5+/fpVq1bVa90BlIKRkREh5JPz6AA0tJYtW9LvRpCHAkQANIlZK6r7nZGUlPTmzZsrV64sWrSoffv2X331lfybL168ODAwkH5dXFwsFArrrfYAzRqHwzE2NjYwMJA9bQxAg1JTU8Ov/xpRgAhAX1+fy+WK/2ovKCgQ/00vjh6Ps2vXrv/8809QUBAdARgZGcmzOZ/P5/P59Vx7AKXB5XJx/gVQIAoQAdBjWcTHx48cOZJOiY+P/+TIVhRFMTf1HRwc4uPjma4A586dc3R0bLgKg/JgqeseO8U0m1JqV4Q8W4nncXQkyckK120T4D8oRXDkyBE1NbX9+/ffuXMnICBAU1MzKyuLoqhFixb5+PjQeX744Ye4uLj79+/fv38/NDRUW1t76dKl9Cp6Fu0NGzZkZGRs2LBBVVX1ypUrskssKioihBQVFTVou0ChRUdTpqYUIf//z9SUio5W2GKaTSm1K0KerSTycLkN/7eH+oHzeXUUIwKgKGrnzp1mZmY8Hq9nz56JiYl0oq+vr4uLC/16+/btnTt31tDQ0NbW7tGjx65du0QiEbP5sWPHOnXqpKamZmVlFS3HZxXvGJAtOpricP79CiCE4nAoDqe+vwjYKabZlFK7IuTZqmqeBv/bQ73B+bw6ijEeAPvw/CjIIBIRc/N/Z7Zj0MP4ZmbW0yVhdoppNqXUrgh5tqouj/ylQKPC+bw6ijEmIECTkpQk/euAokhODpE2aHUTLqbZlFK7IuTZqro88pcC0CQpQE9AgKamgabzFYlI0gWR6EKSMcnr5GrMzc9tkGJqtB8FKqV2RcizlfzVa5ZTOUPzhQgAoMYaYjrfmBhy5uuYFYX+QvKUEELWklJtfUG9F1PT/ShQKbUrQp6t5K9ec53KGZop9AOQDveNQAb6vnB10/nW4l5wTAw5NCrmGPEmhGLuzFUSwiFEythVDXGHvh4b01il1K4IebaqLk8DNQTqG87n1UE/AIAaq9/pfEUiMtdPFEz8xb/+CSEqH7uaN+yswezMTcxCKbUrQp6tpOYRh6mcQUE19sMITRSeHoFPkng+XCis5eNgCQmUC0mo9kkzQqjWreuhGNnqqzGNXkrtipBnKxnjATTQ4YJ6gvN5dXAXQDpcNQJ51MsAd5GRJG5cZCQZV22On38mJiZ1LeaTda11Y2q0YcNllrFVjcb7MzAghJCCAimZMSagYsL5vFqNHYI0UYgZgTWfvAZQ8XtCQgJ1+DCVkEBVVNSqjIYbjK/p77lG+2FpoEdgFc7n1WE1AigvL4+Pj9+zZ09xcTFFUbm5uSUlJWxWQH54xwBrKiqotiYV2cRURCRHnRMRzssWwrYmFXX6Smq4wfia/p5rtB+WBnoEtuF8Xh327gI8efLk888/z87OLisru3//frt27QICAkpLS/fs2cNOBWoEV42ATceOkcjRMceJNyFEhfz/R7KScAgh3uR4LPFictJ9zo4fJ15eUvYjRcMNxtf091yj/bA00CM0ApzPq8PeswD+/v729vavXr1SV1enU0aOHHn+/HnWKgDQZLVuTWKJlzc5nktMmMSnxFTi65+Q/38eLSCAiETy7brhBuNr+nuu0X5YGugRoAlhb0SgS5cuXb58mcfjMSlmZma5uTJHPQNQDvRQcrHE6yTxdCZJxiQvjxgnEedKIuV3J/OV5Ooq965rt1bR91yj/bAzNiJAU8JeBFBZWSn678+Wp0+ftmjRgrUKADRZzFBylYSbSFzl2UTer6SGG4yv6e+5RvthZ2xEgKaEvbsAgwYNCg4Opl9zOJw3b96sXLlyyJAhrFUAoMlydiamptWONyOVvF9J1e2awyFCIXF2rkGRCrfnGu2n4ZoD0FSxFwFs27YtMTHRxsamtLR03Lhx5ubmubm5GzduZK0CAE3WJwedE1ezr6SGG4yv6e+5RvthZ2xEgCaFzQcP3r17FxoaOnv27G+++Wbv3r3v3r1js/QawdMjwL6qA9MtWPD/z6PV9fG0hhuMr+nvuUb7YWdsRGAXzufVYe9pwIsXLzo6Oqqq/tvzoKKiIjk5uX///uxUoEbw9Ag0iqqD18XEEH//f3upC4UkOFjuRwFl77rhKt3U9szCQITQhOF8Xh32IgAul5uXl2dAj7hJCCGksLDQwMBAJO9TTazCOwaaDnwlAdQFzufVYe9ZAIqiOP+9yVlYWKipqclaBQAUFJcr34N/AAA1wUYE4OXlRQjhcDiTJk3i8/l0okgkun37tqOjIwsVAAAAAAlsRAA6OjqEEIqiWrRowQwIyOPx+vbtO336dBYqAAAAABLYiADCwsIIIebm5vPnz8dlfwAAgKaAvZ6AigU9RwAAmgecz6vDXk9AQsjx48ePHj2anZ394cMHJvHmzZts1gEAAAAIm2MCbt++ffLkyQYGBmlpab1799bT03v8+LGHhwdrFQAAAAAGexHArl27fvrppx9++IHH43333Xfx8fF+fn70UE0AAADAMvYigOzsbPrZP3V19ZKSEkKIj49PZGQkaxUAAAAABnsRgJGRUWFhISHEzMzsypUrhJDMzEz0QwQAAGgU7EUAn3322alTpwghU6dOnTt37qBBg8aMGTNy5EjWKgAAAAAM9p4GrKysrKyspGcGOnr06KVLl9q3bz9z5kwej8dOBWoET48AADQPOJ9Xp5HHA8jNzTUxMWnEClQH7xgAgOYB5/PqsHcXQEJ+fv63337bvn37xqoAgFIRiciFCyQykly4QJrkfJwAwDY2IoDXr1+PHz++devWbdq02b59e2Vl5YoVK9q1a3flypXQ0FAWKgCg5GJiiLk5cXMj48YRNzdibk5iYhq7TgDQ2NgYE3DJkiUXL1709fU9e/bs3Llzz549W1paeubMGRcXFxZKB1ByMTHE25uI3+7LzSXe3uT4ceLl1XjVAoDGxsY1gF9//TUsLOx///tfXFwcRVEdO3b8448/8PUPwAKRiPj7E4nePvRiQABuBwAoNTYigGfPntnY2BBC2rVrJxAIpk2bxkKhAEAISUoiT59KSacokpNDkpJYrxAANBlsRACVlZVqamr0ay6XiwmCAViTl1f7tQDQvLHRD4CiqEmTJvH5fEJIaWnpzJkzxYOAGHRJAmgwxsa1XwsAzRsbEYCvry/zesKECSyUCAA0Z2diakpycyW7AnA4xNSUODs3UrUAoAlgIwIICwtjoRQAqIrLJSEhxNubcDj/BgEcDiGEBAcTLrcRqwYAjazRRgQCAHZ4eZHjx4n42JumplIeBcSQQQDKRmEigF27dllYWAgEAjs7uyRpPZhjYmIGDRrUunVrbW1tBweH3377jVkVHh7O+a/S0lIW6w7QyLy8SFYWSUgghw+ThASSmSn59Y8hgwCUkGJEAFFRUQEBAUuXLk1LS3N2dvbw8MjOzpbIc/HixUGDBp0+fTo1NdXNzW348OFpaWnMWm1t7TwxAoGA3RYANDIul7i6kq++Iq6ukhf/6SGDxB8apIcMQhAA0Lw18sxAcurTp0/Pnj13795NL1pbW48YMWL9+vUyNuncufOYMWNWrFhBCAkPDw8ICHj9+rX8JWImCVASIhExN5cyZgDdVTAzE30FQOHhfF4dBbgG8OHDh9TU1MGDBzMpgwcPTk5OlrFJZWVlSUmJrq4uk/LmzRszMzNTU9Nhw4aJXxsAUHIYMghAabHxLADj/v37Fy5cKCgoqKysZBLpn+kyvHjxQiQSGRoaMimGhob5+fkyNtmyZcvbt29Hjx5NL1pZWYWHh3ft2rW4uDgkJMTJyenWrVsdOnSQ2KqsrKysrIx+XVxcLH+7ABQXhgwCUFrsRQB79+795ptv9PX1jYyMOPTTSIRwOJxPRgBMTuY1RVHiixIiIyODgoJOnjxpYGBAp/Tt27dv3770aycnp549e+7YsWP79u0SG65fv37VqlXytwigGcCQQQBKi70IYO3atevWrVu4cGFNN9TX1+dyueI/+gsKCsQvCYiLioqaOnXqsWPHBg4cKDWDiopKr169Hjx4UHXV4sWLAwMD6dfFxcVCobCmVQVQOBgyCEBpsdcP4NWrV19++WUtNuTxeHZ2dvHx8UxKfHy8o6Nj1ZyRkZGTJk06fPjw0KFDq9sbRVHp6enG0n7a8Pl8bTG1qCqAwqGHDCIfhwmiYcggAGXAXgTw5Zdfnjt3rnbbBgYG7tu3LzQ0NCMjY+7cudnZ2TNnziSELF68eOLEiXSeyMjIiRMnbtmypW/fvvn5+fn5+UVFRfSqVatW/fbbb48fP05PT586dWp6ejq9OQAQuYcMAoBmhr27AO3bt1++fPmVK1e6du3KTBVICPHz8/vktmPGjCksLFy9enVeXl6XLl1Onz5tZmZGCMnLy2MGBvjxxx8rKipmz549e/ZsOsXX1zc8PJwQ8vr166+//jo/P19HR6dHjx4XL17s3bt3vTcQQHF5eRFPT5KURPLyiLExcXbGr3+A5o+98QAsLCykFM/hPH78mJ0K1AieHwUAaB5wPq8Oe9cAMjMzWSsLAAAAZGuEEYEoilKIgQgBAACaMVYjgAMHDnTt2lVdXV1dXb1bt24HDx5ks3QAAABgsHcXYOvWrcuXL58zZ46TkxNFUZcvX545c+aLFy/mzp3LWh0AAACAxmpPwFWrVjEP7xFCIiIigoKCmmb/APQcAQBoHnA+rw57dwHy8vIkhvFxdHTMw7DjAAAAjYG9CKB9+/ZHjx4VT4mKiqo6PQ8AAACwgL1+AKtWrRozZszFixednJw4HM6lS5fOnz8vERMAAAAAO9iLAEaNGnX16tVt27adOHGCoigbG5tr16716NGDtQoAQEMQiTCYIIBCYq8noGJBzxEAecTEEH9/8vTp/y+ampKQEEwoAE0LzufVafBrAMXFxfRBLy4ulpoBfxIABRUTQ7y9/zOtcG4u8fbGrEIAiqHBrwFwudy8vDwDAwMVFRWO+PyjhFAUxeFwRCJRg1agdhAzAsgmEhFz839//TM4HGJqSjIzcTsAmgqcz6vT4NcA/vjjD11dXUJIQkJCQ5cFAKxJSpLy9U8IoSiSk0OSkoirK9tVAoAaafAIwMXFhX5hYWEhFArFLwNQFJWTk9PQFQCAhiB7LA+M9AHQ9LE3HoCFhcXz58/FU16+fCl1ymAAaPqMjWu/FgCaAvYiAPquv3jKmzdvBAIBaxUAgHrk7ExMTcl/P9OEEMLhEKGQODs3Rp0AoCbYGA8gMDCQEMLhcJYvX66hoUEnikSiq1evdu/enYUKAEC943JJSAjx9iYczr+PA9ABQXAwugECKAA2IoC0tDRCCEVRf/75J4/HoxN5PJ6tre38+fNZqAAANAQvL3L8uOR4AMHBeBQQQDGwNyLQ5MmTQ0JCFOVhDDw9AiAnjAkITRzO59VhLwIoKioSiUT0k4G0ly9fqqqqNs0/Cd4xAADNA87n1WGvJ+DYsWOPHDkinnL06NGxY8eyVgEAAABgsBcBXL161c3NTTzF1dX16tWrrFUAAAAAGOzNDVhWVlZRUSGeUl5e/v79e9YqAACNDp0GAJoO9q4B9OrV66effhJP2bNnj52dHWsVAIDGFRNDzM2JmxsZN464uRFzcxIT09h1AlBi7F0DWLdu3cCBA2/dujVgwABCyPnz569fv37u3DnWKgAAjQgTCQI0NexdA3ByckpJSREKhUePHj116lT79u1v377tjJHDAJSASET8/YnEg0f0YkAAaZLzgwI0f+w9DahY8PQIQD26cIH8tx/wfyQkYCJBaEA4n1enwe8CFBcX0we9uLhYagb8SQCaPUwkCNAENXgE0KpVq7y8PAMDg5YtW0rMDETPFSTCFUCA5g4TCQI0QQ0eAfzxxx/0OIAJCQkNXRYANE30RIK5uZJdATgcYmqKiQQBGkeDRwAuLi4SLwBA2WAiQYAmqMEjgNu3b8vO0K1bt4auAwA0OkwkCNDUNPizACoqKhwOh77lLzVD0+wHgL6jAA0BYwIC+3A+r06DXwPIzMykX6Slpc2fP3/BggUODg6EkJSUlC1btmzatKmhKwAATQeXiwf/AJqKBo8AzMzM6Bdffvnl9u3bhwwZQi9269ZNKBQuX758xIgRDV0HAAAAkMDemIB//vmnhYWFeIqFhcWdO3dYqwAAAAAw2IsArK2t165dW1paSi+WlZWtXbvW2tqatQoAAAAAg72Zgfbs2TN8+HChUGhra0sIuXXrFofD+eWXX1irAAAoFnQbBGhQrM4L8O7du59//vnu3bsURdnY2IwbN05TU5O10msEfUcBGldMjOSjgyEheHQQagPn8+pgZiDp8I4BaERVpxKmnybGVMJQCzifV4e9fgCEkIMHD/br169NmzZPnjwhhGzbtu3kyZNsVgAAmj5MJQzADvYigN27dwcGBnp4eLx69YoeBahVq1bBwcGsVQAAFEJS0r8X/8VRFMnJIUlJrFcIoJliLwLYsWPH3r17ly5dqqr6/90P7e3t//zzTzk337Vrl4WFhUAgsLOzS5J2DoiJiRk0aFDr1q21tbUdHBx+++038bXR0dE2NjZ8Pt/GxiY2NraObQGAhoOphAHYwV4EkJmZ2aNHD/EUPp//9u1bebaNiooKCAhYunRpWlqas7Ozh4dHdna2RJ6LFy8OGjTo9OnTqampbm5uw4cPT0tLo1elpKSMGTPGx8fn1q1bPj4+o0ePvnr1ar00CgDqHaYSBmAHez0BbWxs1q9f7+np2aJFi1u3brVr12779u0RERGpqamf3LZPnz49e/bcvXs3vWhtbT1ixIj169fL2KRz585jxoxZsWIFIWTMmDHFxcVnzpyhV33++eetWrWKjIyUsTl6jgA0FpGImJtXO5VwZiYeC4Sawfm8OuxdA1iwYMHs2bOjoqIoirp27dq6deuWLFmyYMGCT2744cOH1NTUwYMHMymDBw9OTk6WsUllZWVJSYmuri69mJKSIr65u7u77M0BoBHRUwmTj/3/aZhKGKDesTci0OTJkysqKr777rt3796NGzfOxMQkJCRk7Nixn9zwxYsXIpHI0NCQSTE0NMzPz5exyZYtW96+fTt69Gh6MT8/X57Ny8rKysrK6NfFxcXyNAoAGgKmEgZgAUsRQEVFxaFDh4YPHz59+vQXL15UVlYaGBjUaA/ikwvLmGuYEBIZGRkUFHTy5EnxIuTZfP369atWrapRrQCggXh5EU9PjAkI0IBYigBUVVW/+eabjIwMQoi+vn6NttXX1+dyueK/2gsKCsR/04uLioqaOnXqsWPHBg4cyCQaGRnJs/nixYsDAwPp18XFxUKhsEb1BID6hamEARoUe/0A+vTpw3TOrxEej2dnZxcfH8+kxMfHOzo6Vs0ZGRk5adKkw4cPDx06VDzdwcFBfPNz585J3ZzP52uLqUVVAQAAFAV7/QBmzZo1b968p0+f2tnZiU8H0K1bt09uGxgY6OPjY29v7+Dg8NNPP2VnZ8+cOZMQsnjx4tzc3AMHDhBCIiMjJ06cGBIS0rdvX/oXv7q6uo6ODiHE39+/f//+Gzdu9PT0PHny5O+//37p0qWGaicAAIBCoNjC+S8VFRX6fzk337lzp5mZGY/H69mzZ2JiIp3o6+vr4uJCv3ZxcZFomq+vL7P5sWPHOnXqpKamZmVlFR0d/cniioqKCCFFRUU1aiMAADQ1OJ9Xh73xAOi5AKoyMzNjpwI1gudHAQCaB5zPq8PSXYCSkpL79++Xl5f37t27pj0BAQAAoN6xEQHcvn3bw8MjPz+foihtbe3jx4+Ld9QHAAAA9rHxLMCiRYvatm2blJR048YNFxeXOXPmsFAoACgnkYhcuEAiI8mFC5hKGEAWNq4B3Lhx4/Tp0/b29oSQ0NBQAwODN2/eaGlpsVA0ACiVmBjJkQRDQjCSIIB0bFwDePHiRdu2benXenp6Ghoaz58/Z6FcAFAqMTHE2/vfr39CSG4u8fYmMTGNVyeAJoyNCIDD4ZSUlBQXFxcXFxcVFYkvYvh9AKgXIhHx95ecTpBeDAjA7QAAKdi4C0BRVMeOHcUXe/ToQT6Ozy/CRxMA6iwp6T+//hkURXJySFISBhgGkMRGBJCQkMBCKQCgzPLyar8WQDmxEQFUHa0PAKB+GRvXfi2AcmJvZiAAgIbj7ExMTUnVeb85HCIUEmfnxqgTQNOGCAAAmgMul4SEEEL+EwTQr4ODCZfbOLUCaMoQAQBAM+HlRY4fJyYm/6aYmpLjxzEeAIB07M0ODADQ0Ly8iKcnSUoieXnE2Jg4O+PXP0C12I4AHj58+OjRo/79+6urq9NPA7JcAQBo3rhcyQf/RCLEBABSsHcXoLCwcODAgR07dhwyZEheXh4hZNq0afPmzWOtAgCghGJiiLk5cXMj48YRNzdibo4hAgH+H3sRwNy5c1VVVbOzszU0NOiUMWPGnD17lrUKAICywTjBADKwFwGcO3du48aNpqamTEqHDh2ePHnCWgUAQKlgnGAA2diLAN6+fcv8+qe9ePGCz+ezVgEAUCqfHCcYQMmxFwH079//wIED9GsOh1NZWbl582Y3NzfWKgAASgXjBAPIxt6zAJs3b3Z1db1x48aHDx++++67v//+++XLl5cvX2atAgCgVDBOMIBs7F0DsLGxuX37du/evQcNGvT27VsvL6+0tDRLS0vWKgAASgXjBAPIxqEk+skAIYSQ4uJiHR2doqIibW3txq4LANQS/SwAIf/2B6QDAgwUqFRwPq8Oe9cALCwsli9ffu/ePdZKBAAlh3GCAWRgLwL49ttvz549a21tbWdnFxwcnId+OADQ8Ly8SFYWSUgghw+ThASSmYmvf4D/x/ZdgPv37x86dOjIkSOPHz92c3ObMGHCxIkT2ayAnHDVCACgecD5vDqN1g/gypUr33zzze3bt0VNcmAOvGMAAJoHnM+r0whzA167du3w4cNRUVFFRUXedC8dAAAAYBd7EQB9/f/w4cNZWVlubm4bNmzw8vJq0aIFaxUAAAAABnsRgJWVlb29/ezZs8eOHWtkZMRauQAAAFAVexHA3bt3O3bsyFpxAAAAIAN7TwPi6x8AAKDpaPBrALq6uvfv39fX12/VqhWn6vichLx8+bKh6wAAAAASGjwC2LZtG93db9u2bVIjAAAAAGAf5gWQDs+PAgA0DzifV4e9fgBcLregoEA8pbCwkMvlslYBAAAAYLD3LEDViw1lZWU8Ho+1CgAAVEckIklJJC+PGBsTZ2eC3yagDNiIALZv304I4XA4+/bt09LSohNFItHFixetrKxYqAAAgAwxMcTfnzx9+v+LpqYkJAQTCEHzx0Y/AAsLC0LIkydPTE1Nmcv+PB7P3Nx89erVffr0aegK1ALuGwEoiZgY4u1NxE+EdJdlTCLcbOB8Xh32egK6ubnFxMS0atWKneLqCO8YAGUgEhFz839//TM4HGJqSjIzcTugOcD5vDrs9QRMSEhQlK9/AFASSUlSvv4JIRRFcnJIUhLrFQJgUYP3AwgMDFyzZo2mpmZgYKDUDFu3bm3oOgAASJWXV/u1AIquwSOAtLS08vJy+kXVtRgjCAAakbFx7dcCKDqFGRFo165dmzdvzsvL69y5c3BwsLOzs0SGvLy8efPmpaamPnjwwM/PLzg4mFkVHh4+efJk8czv378XCAQyisN9IwBlQPcDyM0lVU+ErVuTp08JHlhuBnA+rw57/QDEFRcXnzhx4u7du3Lmj4qKCggIWLp0aVpamrOzs4eHR3Z2tkSesrKy1q1bL1261NbWtuoetLW188TI/voHACXB5ZKQEEI+9v8X9/w5sbQkMTHsVwqAJexFAKNHj/7hhx8IIe/fv7e3tx89enTXrl2jo6Pl2Xbr1q1Tp06dNm2atbV1cHCwUCjcvXu3RB5zc/OQkJCJEyfq6OhU3QOHwzESU/fmAEDz4OVFjh8nJiZSVuXmEm9vBAHQbLEXAVy8eJG+dB8bG0tR1OvXr7dv37527dpPbvjhw4fU1NTBgwczKYMHD05OTq5R6W/evDEzMzM1NR02bJjUHgmEkLKysmIxNdo/ACguLy/y6BFp3Voynb41EBBARCL2KwXQ4NiLAIqKinR1dQkhZ8+eHTVqlIaGxtChQx88ePDJDV+8eCESiQwNDZkUQ0PD/Px8+Yu2srIKDw+Pi4uLjIwUCAROTk5Sy12/fr3OR0KhUP79A4CiS04mz59LScdjgdCMsRcBCIXClJSUt2/fnj17lv5B/+rVK/nvx4s/NUBRVI0eIujbt++ECRNsbW2dnZ2PHj3asWPHHTt2VM22ePHioo9ycnLk3z8AKDo8FghKiL2ZgQICAsaPH6+lpWVmZubq6koIuXjxYteuXT+5ob6+PpfLFf/RX1BQIH5JoEZUVFR69eol9RoAn8/n8/m12y0AKDQ8FghKiL1rALNmzUpJSQkNDb106ZKKigohpF27dvL0A+DxeHZ2dvHx8UxKfHy8o6Nj7apBUVR6eroxPtAAIMbZmZiaSnkigMMhQiGp8vQxQHPA3jUAQoi9vb29vT1FUfRl/KFDh8q5YWBgoI+Pj729vYODw08//ZSdnT1z5kxCyOLFi3Nzcw8cOEBnS09PJ4S8efPm+fPn6enpPB7PxsaGELJq1aq+fft26NChuLh4+/bt6enpO3fubJAWAoBioh8L9PYmHM6/YwPQAUFwMGYHgOaJ1fEADhw40LVrV3V1dXV19W7duh08eFDODceMGRMcHLx69eru3btfvHjx9OnTZmZmhJC8vDzxgQF69OjRo0eP1NTUw4cP9+jRY8iQIXT669evv/76a2tr68GDB+fm5l68eLF379713joAUGhVHws0NcUMgdCcsTcm4NatW5cvXz5nzhwnJyeKoi5fvrxz5861a9fOnTuXnQrUCMaQAlBOIhFJSiJ5ecTYmDg749d/c4DzeXXYiwAsLCxWrVo1ceJEJiUiIiIoKCgzM5OdCtQI3jEAAM0DzufVYe8uQF5enkT3PUdHxzw8ZAMAANAY2IsA2rdvf/ToUfGUqKioDh06sFYBAAAAYLD3LMCqVavGjBlz8eJFJycnDodz6dKl8+fPS8QEAAAAwA72rgGMGjXq2rVr+vr6J06ciImJ0dfXv3bt2siRI1mrAAAAADBYugZQUlJy5cqV8vLy4OBgfX19dgoFAACA6rARAdy+fdvDwyM/P5+iKG1t7ePHjw8cOJCFcgEAAKA6bNwFWLRoUdu2bZOSkm7cuOHi4jJnzhwWCgUAAAAZ2LgGcOPGjdOnT9vb2xNCQkNDDQwM3rx5o6WlxULRAAAAIBUb1wBevHjRtm1b+rWenp6GhsZzqRNxAwAAAFvYuAbA4XBKSkoEAgEhhJ4TqKSkpLi4mF6LQZoAAADYx0YEQFFUx44dxRd79OhBPkYDIpGIhToAAACAODYigISEBBZKAQAAAPmxEQG4uLiwUAoAAADIj70xAQEAAKDpYG9eAAAAhSYSkaQkkpdHjI2JszPhchu7QgB1gwgAAODTYmKIvz95+vT/F01NSUgI8fJq1DoB1A3uAgAAfEJMDPH2/vfrnxCSm0u8vUlMTOPVCaDOGiECyMnJeSr+SQIAaMJEIuLvTyjqP4n0YkAAwePMoLjYiwAqKiqWL1+uo6Njbm5uZmamo6OzbNmy8vJy1ioAAFALSUlE6m8WiiI5OSQpifUKAdQT9voBzJkzJzY2dtOmTQ4ODoSQlJSUoKCgFy9e7Nmzh7U6AADUVF5e7dcCNGXsRQCRkZFHjhzx8PCgF7t169a2bduxY8ciAgCApszYuPZrAZoy9u4CCAQCc3Nz8RRzc3Mej8daBQAAasHZmZiaEg5HMp3DIUIhcXZujDoB1Af2IoDZs2evWbOmrKyMXiwrK1u3bt2cOXNYqwAAQC1wuSQkhBDynyCAfh0cjFEBQIFxKIkerg1m5MiR58+f5/P5tra2hJBbt259+PBhwIABTIaYpvRgTXFxsY6OTlFREaYuBABSZTwAoZAEB2M8AMWA83l12OsH0LJly1GjRjGLQqGQtaIBAOrIy4t4emJMQGhW2LsGoFgQMwIANA84n1cHYwICAAAoI/buAlhYWHCq9qYl5PHjx6zVAQAAAGjsRQABAQHM6/Ly8rS0tLNnzy5YsIC1CgAAAACDvQjA399fImXnzp03btxgrQIAAADAaMx+AB4eHtHR0Y1YAQAAAKXVmBHA8ePHdXV1G7ECAAAASou9uwA9evRgegJSFJWfn//8+fNdu3axVgEAAABgsBcBjBgxgnmtoqLSunVrV1dXKysr1ioAAAAADIwIJB1GkAAAaB5wPq8Oe9cACCEikejEiRMZGRkcDsfGxuaLL77gYlxNAACAxsBeBPDw4cMhQ4bk5uZ26tSJoqj79+8LhcJff/3V0tKStToAAAAAjb1nAfz8/CwtLXNycm7evJmWlpadnW1hYeHn58daBQAAAIDB3jWAxMTEK1euMI//6enpbdiwwcnJibUKAAAAAIO9awB8Pr+kpEQ85c2bNzwej7UKAAAAAIO9CGDYsGFff/311atXKYqiKOrKlSszZ8784osvWKsAAAAAMNiLALZv325paeng4CAQCAQCgZOTU/v27UNCQuTcfNeuXRYWFgKBwM7OLikpqWqGvLy8cePGderUSUVFRXwWIlp0dLSNjQ2fz7exsYmNja1jWwAAABQdSxEARVFFRUWRkZH3798/fvz4sWPH7t27Fxsbq6OjI8/mUVFRAQEBS5cuTUtLc3Z29vDwyM7OlshTVlbWunXrpUuX2traSqxKSUkZM2aMj4/PrVu3fHx8Ro8effXq1fppGAAAgGJiaUSgyspKgUDw999/d+jQoRab9+nTp2fPnrt376YXra2tR4wYsX79eqmZXV1du3fvHhwczKSMGTOmuLj4zJkz9OLnn3/eqlWryMhIGSViBAkAgOYB5/PqsHQNQEVFpUOHDoWFhbXY9sOHD6mpqYMHD2ZSBg8enJycLP8eUlJSxDd3d3eXunlZWVmxmFpUFQAAQFGw1w9g06ZNCxYs+Ouvv2q64YsXL0QikaGhIZNiaGiYn58v/x7y8/Pl2Xz9+vU6HwmFwprWEwAAQIGwNx7AhAkT3r17Z2try+Px1NXVmfSXL1/KszkzryAhhKIo8cX62nzx4sWBgYH06+LiYgQBAADQjLEXAYjfmK8RfX19Lpcr/qu9oKBA/Df9JxkZGcmzOZ/P5/P5taskACgVkYgkJZG8PGJsTJydCWY4AUXEXgTg6+tbuw15PJ6dnV18fPzIkSPplPj4eE9PT/n34ODgEB8fP3fuXHrx3Llzjo6OtasMAEBMDPH3J0+f/v+iqSkJCSFeXo1aJ4CaY3VuQELI33//LRKJ6NdcLrdz587ybBUYGOjj42Nvb+/g4PDTTz9lZ2fPnDmTELJ48eLc3NwDBw7Q2dLT0wkhb968ef78eXp6Oo/Hs7GxIYT4+/v3799/48aNnp6eJ0+e/P333y9dutQQrQOAZi8mhnh7E/GHqHJzibc3OX4cQQAoGqrhXbx40d7enn6tpaWloqLC4XA4HI6Kikp8fLycO9m5c6eZmRmPx+vZs2diYiKd6Ovr6+LiwuSRaJqZmRmz6tixY506dVJTU7OysoqOjv5kcUVFRYSQoqIiOasHAMqgooIyNaUIkfzH4VBCIVVR0dj1A2lwPq8OG+MBfPXVVw4ODvQ0gC1atPj111/p7+bt27c/efIkOjq6oStQC3h+FACqunCBuLlVuzYhgbi6slcZkBPO59Vh42nA69ev9+7dm1k0NTU1MzMzNzf38fFJSUlhoQIAAPUiL6/2awGaGjYigNzcXGNjY/p1RESEkZER/VpXV7d2YwQBADSKj2ey2qwFaGrYiABatGiRmZlJv/by8tLQ0KBfZ2Zm4poMACgQZ2diakqqjifC4RChkDg7N0adAGqLjQigT58+THd9ceHh4X369GGhAgAA9YLLJfSEpuJBAP06OBijAoCCYSMCCAwMjIiIWLBgQUFBAZ1SUFAwb968n3/+mRmDDwBAIXh5kePHiYnJvymmpngUEBQSS3MD7tq1a+7cuRUVFdra2hwOp6ioSFVVdcuWLXPmzGGh9FpA31EAkAFjAioQnM+rw1IEQAjJyck5fvz4gwcPCCEdOnTw9vZuygPv4x0DANA84HxeHfYiAMWCdwwAQPOA83l12JsdGAAAAJoORAAAAADKCBEAAACAMmIpAhCJRImJia9evWKnOAAAAJCNpQiAy+W6u7u/fv2aneIAAABANvbuAnTt2vXx48esFQcAAAAysBcBrFu3bv78+b/88kteXl6xGNYqAAAAAAz2xgNQUfn/aIPzcUBtiqI4HI5IJGKnAjWC50cBAJoHnM+ro8paSQkJCayVBQAAALKxFwG4uLiwVhYAAADIxup4AElJSRMmTHB0dMzNzSWEHDx48NKlS2xWAAAAAGjsRQDR0dHu7u7q6uo3b94sKysjhJSUlHz//fesVQAAAAAY7EUAa9eu3bNnz969e9XU1OgUR0fHmzdvslYBAAAAYLAXAdy7d69///7iKdra2hgjCAAAoFGwFwEYGxs/fPhQPOXSpUvt2rVjrQIAAADAYC8CmDFjhr+//9WrVzkczrNnzw4dOjR//vxZs2axVgEAAABgsPc04HfffVdUVOTm5lZaWtq/f38+nz9//vw5c+awVgEAAABgsDcmIO3du3d37typrKy0sbHR0tJis+gawRhSAADNA87n1WHjLsC7d+9mz55tYmJiYGAwbdo0c3Pz3r17N+WvfwAAgGaPjQhg5cqV4eHhQ4cOHTt2bHx8/DfffMNCoQAAACADG/0AYmJi9u/fP3bsWELIhAkTnJycRCIRl8tloWgAAACQio1rADk5Oc7OzvTr3r17q6qqPnv2jIVyAQAAoDpsRAAikYjH4zGLqqqqFRUVLJQLAAAA1WHjLgBFUZMmTeLz+fRiaWnpzJkzNTU16cWYmBgW6gAAAADi2IgAfH19xRcnTJjAQqEAAAAgAxsRQFhYGAulAAAAgPzYGxUYAAAAmg5EAAAAAMoIEQAAAIAyQgQAAACgjBABAAAAKCNEAAAAAMoIEQAAAIAyQgQAAACgjBQmAti1a5eFhYVAILCzs0tKSpKaJzEx0c7OTiAQtGvXbs+ePUx6eHg4579KS0vZqjgAAEBTpBgRQFRUVEBAwNKlS9PS0pydnT08PLKzsyXyZGZmDhkyxNnZOS0tbcmSJX5+ftHR0cxabW3tPDECgYDdFgAAADQtHIqiGrsOn9anT5+ePXvu3r2bXrS2th4xYsT69evF8yxcuDAuLi4jI4NenDlz5q1bt1JSUggh4eHhAQEBr1+/lr/E4uJiHR2doqIibW3t+mkDAAA0BpzPq6MA1wA+fPiQmpo6ePBgJmXw4MHJyckS2VJSUsTzuLu737hxo7y8nF588+aNmZmZqanpsGHD0tLSpBZUVlZWLKa+2wEAANCEKEAE8OLFC5FIZGhoyKQYGhrm5+dLZMvPz5fIU1FR8eLFC0KIlZVVeHh4XFxcZGSkQCBwcnJ68OBB1YLWr1+v85FQKGyY1gAAADQJChAB0DgcDvOaoijxxeryMCl9+/adMGGCra2ts7Pz0aNHO3bsuGPHjqqbL168uOijnJyc+m8DAABAk8HG7MB1pK+vz+VyxX/0FxQUiP/cpxkZGUnkUVVV1dPTk8imoqLSq1cvqdcA+Hw+n8+vv4oDAAA0XQpwDYDH49nZ2cXHxzMp8fHxjo6OEtkcHBzE85w7d87e3l5NTU0iG0VR6enpxsbGDVdhAACApk8BIgBCSGBg4L59+0JDQzMyMubOnZudnT1z5kxCyOLFiydOnEjnmTlz5pMnTwIDAzMyMkJDQ/fv3z9//nx61apVq3777bfHjx+np6dPnTo1PT2d3hwAAEBpKcBdAELImDFjCgsLV69enZeX16VLl9OnT5uZmRFC8vLymIEBLCwsTp8+PXfu3J07d7Zp02b79u2jRo2iV71+/frrr7/Oz8/X0dHp0aPHxYsXe/fu3WiNAQAAaAIUYzwA9uH5UQCA5gHn8+ooxl0AAAAAqF+IAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEaIAAAAAJQRIgAAAABlhAgAAABAGSECAAAAUEYKEwHs2rXLwsJCIBDY2dklJSVJzZOYmGhnZycQCNq1a7dnzx7xVdHR0TY2Nnw+38bGJjY2lpUqAwAANF2KEQFERUUFBAQsXbo0LS3N2dnZw8MjOztbIk9mZuaQIUOcnZ3T0tKWLFni5+cXHR1Nr0pJSRkzZoyPj8+tW7d8fHxGjx599epV1hsBAADQhHAoimrsOnxanz59evbsuXv3bnrR2tp6xIgR69evF8+zcOHCuLi4jIwMenHmzJm3bt1KSUkhhIwZM6a4uPjMmTP0qs8//7xVq1aRkZEySiwuLtbR0SkqKtLW1q7/9gAAAFtwPq+OAlwD+PDhQ2pq6uDBg5mUwYMHJycnS2RLSUkRz+Pu7n7jxo3y8nKpq6puDgAAoFRUG7sCn/bixQuRSGRoaMikGBoa5ufnS2TLz8+XyFNRUfHixQtjY+Oqq6puTggpKysrKyujXxcVFRFCiouL67EhAADAPvpMrhAXvFmmABEAjcPhMK8pihJfrC6PeIo8m69fv37VqlXiKUKhsG61BgCAJqGkpERHR6exa9G0KEAEoK+vz+VyxX+1FxQUiP+mpxkZGUnkUVVV1dPTk7qq6uaEkMWLFwcGBtKvX79+bWZmlp2drdDvmOLiYqFQmJOTo9B3v9CKpgOtaFKaR0NYaAVFUSUlJW3atGmg/SsuBYgAeDyenZ1dfHz8yJEj6ZT4+HhPT0+JbA4ODqdOnWIWz507Z29vr6amRq+Kj4+fO3cus8rR0bFqQXw+n8/ni6fo6Ogo9EeLpq2tjVY0EWhF09E8WkGaS0MauhUK/Vuu4ShABEAICQwM9PHxsbe3d3Bw+Omnn7Kzs2fOnEkIWbx4cW5u7oEDBwghM2fO/OGHHwIDA6dPn56SkrJ//36mt7+/v3///v03btzo6el58uTJ33///dKlS43ZHgAAgMamGBHAmDFjCgsLV69enZeX16VLl9OnT5uZmRFC8vLymIEBLCwsTp8+PXfu3J07d7Zp02b79u2jRo2iVzk6Oh45cmTZsmXLly+3tLSMiorq06dPozUGAACgCVCMCIAQMmvWrFmzZkkkhoeHiy+6uLjcvHlT6ube3t7e3t7yF8fn81euXClxU0DhoBVNB1rRdDSPVpDm0pDm0QoFpRgjAgEAAED9UoARgQAAAKDeIQIAAABQRogAAAAAlBEiAAAAAGWkjBHArl27LCwsBAKBnZ1dUlKS1DyHDh2ytbXV0NAwNjaePHlyYWEhsyo6OtrGxobP59vY2MTGxrJVa0l1acXevXudnZ1btWrVqlWrgQMHXrt2jcWK/0cd/xa0I0eOcDicESNGNHh1q1fHhrx+/Xr27NnGxsYCgcDa2vr06dNsVfw/6tiK4ODgTp06qaurC4XCuXPnlpaWslXx/5CnFTt37rS2tlZXV+/UqRM9oAhDgT7g1bVCsT7gMv4WtKbwAW+2KCVz5MgRNTW1vXv33rlzx9/fX1NT88mTJxJ5kpKSVFRUQkJCHj9+nJSU1Llz5xEjRtCrkpOTuVzu999/n5GR8f3336uqql65coX1RtS1FePGjdu5c2daWlpGRsbkyZN1dHSePn3KeiPq2gpaVlaWiYmJs7Ozp6cne1X/rzo2pKyszN7efsiQIZcuXcrKykpKSkpPT2e9EXVtxc8//8zn8w8dOpSZmfnbb78ZGxsHBASw3gi5WrFr164WLVocOXLk0aNHkZGRWlpacXFx9CoF+oDLaIUCfcBltILWFD7gzZjSRQC9e/eeOXMms2hlZbVo0SKJPJs3b27Xrh2zuH37dlNTU/r16NGjP//8c2aVu7v72LFjG7K+0tWxFeIqKipatGgRERHRQFWVoe6tqKiocHJy2rdvn6+vbyOeIOrYkN27d7dr1+7Dhw8sVFWGOrZi9uzZn332GbMqMDCwX79+DVlf6eRphYODw/z585lFf39/Jycn+rUCfcBltEJcE/+Ay25FE/mAN2PKdRfgw4cPqampgwcPZlIGDx6cnJwskc3R0fHp06enT5+mKOqff/45fvz40KFD6VUpKSnim7u7u1fdvKHVvRXi3r17V15erqur27CVrqJeWrF69erWrVtPnTqVpUpLU/eGxMXFOTg4zJ4929DQsEuXLt9//71IJGKvAYSQ+mhFv379UlNT6QvOjx8/Pn36tNT3W4OSsxVlZWUCgYBZVFdXv3btWnl5OVGoD7iMVohr4h9w2a1oCh/w5k25IoAXL16IRCLxiQENDQ3Fpw2kOTo6Hjp0aMyYMTwez8jIqGXLljt27KBX5efnf3Lzhlb3VohbtGiRiYnJwIEDG7bSVdS9FZcvX96/f//evXvZq7Q0dW/I48ePjx8/LhKJTp8+vWzZsi1btqxbt469BhBC6qMVY8eOXbNmTb9+/dTU1CwtLd3c3BYtWsReAwghcrfC3d193759qampFEXduHEjNDS0vLz8xYsXRKE+4DJaIa6Jf8BltKKJfMCbN+WKAGgcDod5TVGU+CLtzp07fn5+K1asSE1NPXv2bGZmJj0RkZybs6OOraBt2rQpMjIyJiZGPAxnU61bUVJSMmHChL179+rr67Na42rU5c9RWVlpYGDw008/2dnZjR07dunSpbt372av6mLq0ooLFy6sW7du165dN2/ejImJ+eWXX9asWcNe1cV8shXLly/38PDo27evmpqap6fnpEmTCCFcLlfOzdlRx1bQmv4HvLpWNLUPeLPF9m2HRlVWVsblcmNiYpgUPz+//v37S2SbMGGCt7c3s0h3YX327BlFUUKhcOvWrcyqrVu3tm3btoFrLanuraBt3rxZR0fn+vXrDV1hqerYirS0NEII9yMOh8PhcLhc7sOHD1lqwEd1/3P0799/wIABzCr6QYCysrIGrvh/1L0V/fr1E7+he/DgQXV1dZFI1MAV/w85W0H78OFDTk5ORUUF3RmNrqoCfcBpUltBU4gPOK1qK5rOB7x5U65rADwez87OLj4+nkmJj493dHSUyPbu3TsVlX+PDB1WUxRFCHFwcBDf/Ny5c1U3b2h1bwUhZPPmzWvWrDl79qy9vX3DV1mKOrbCysrqzz//TP/oiy++cHNzS09PFwqF7NSfUfc/h5OT08OHDysrK+lV9+/fNzY25vF4DV51MXVvRdVV9CmmYev9X3K2gqampmZqasrlco8cOTJs2DC68gr0AadJbQVRnA84rWorms4HvJlrtNijkdAPqOzfv//OnTsBAQGamppZWVkURS1atMjHx4fOExYWpqqqumvXrkePHl26dMne3r537970qsuXL3O53A0bNmRkZGzYsKFxHxaqdSs2btzI4/GOHz+e91FJSYnCtUJc43YVrmNDsrOztbS05syZc+/evV9++cXAwGDt2rUK14qVK1e2aNEiMjLy8ePH586ds7S0HD16dNNsxb179w4ePHj//v2rV6+OGTNGV1c3MzOTXqVAH3AZrVCgD7iMVojDswANROkiAIqidu7caWZmxuPxevbsmZiYSCf6+vq6uLgwebZv325jY6Ourm5sbDx+/Hjxp2mPHTvWqVMnNTU1Kyur6OholivPqEsrzMzMJALBlStXst4Ciqrz34LR6CeIOjYkOTm5T58+fD6/Xbt269atq6ioYLn+tLq0ory8PCgoyNLSUiAQCIXCWbNmvXr1ivUWUJQcrbhz50737t3V1dW1tbU9PT3v3r0rvrmifMBltEKBPuCy/xaMRv+AN1eYHRgAAEAZKVc/AAAAAKAhAgAAAFBGiAAAAACUESIAAAAAZYQIAAAAQBkhAgAAAFBGiAAAAACUESIAAAAAZYQIAEAxJCcnc7nczz//vLErAgDNBMYEBFAM06ZN09LS2rdv3507d9q2bdtApZSXl6upqTXQzgGgScE1AAAF8Pbt26NHj37zzTfDhg0LDw9n0uPi4uzt7QUCgb6+vpeXF51YVlb23XffCYVCPp/foUOH/fv3E0LCw8NbtmzJbHjixAlmsvagoKDu3buHhoa2a9eOz+dTFHX27Nl+/fq1bNlST09v2LBhjx49YjZ8+vTp2LFjdXV1NTU17e3tr169mpWVpaKicuPGDSbPjh07zMzM8OsCoIlDBACgAKKiojp16tSpU6cJEyaEhYXRX66//vqrl5fX0KFD09LSzp8/z8wDO3HixCNHjmzfvj0jI2PPnj1aWlqf3P/Dhw+PHj0aHR2dnp5OCHn79m1gYOD169fPnz+voqIycuRIevLiN2/euLi4PHv2LC4u7tatW999911lZaW5ufnAgQPDwsKYvYWFhU2aNImJMACgiWrEWYkAQE6Ojo7BwcEURZWXl+vr68fHx1MU5eDgMH78eImc9+7dI4TQGcSFhYXp6Ogwi7GxsczHf+XKlWpqagUFBVKLLigoIIT8+eefFEX9+OOPLVq0KCwslMgTFRXVqlWr0tJSiqLS09M5HI7UOV4BoEnBNQCApu7evXvXrl0bO3YsIURVVXXMmDGhoaGEkPT09AEDBkhkTk9P53K5Li4uNSrCzMysdevWzOKjR4/GjRvXrl07bW1tCwsLQkh2dja98x49eujq6kpsPmLECFVVVTqqCA0NdXNzMzc3r2kzAYBlqo1dAQD4hP3791dUVJiYmNCLFEWpqam9evVKXV29amapiYQQFRUVSuzGfHl5ufhaTU1N8cXhw4cLhcK9e/e2adOmsrKyS5cuHz58kLFzHo/n4+MTFhbm5eV1+PDh4ODgGjQPABoJrgEANGkVFRUHDhzYsmVL+ke3bt0yMzM7dOhQt27dzp8/L5G/a9eulZWViYmJEumtW7cuKSl5+/YtvUjf75eqsLAwIyNj2bJlAwYMsLa2fvXqFbOqW7du6enpL1++rLrVtGnTfv/99127dpWXlzN9EgGgSWvs2xAAIEtsbCyPx3v9+rV44pIlS7p3756QkKCiorJixYo7d+7cvn1748aN9NpJkyYJhcLY2NjHjx8nJCRERUVRFFVYWKipqenn5/fgwYNDhw61adOGiPUDsLW1ZXYuEon09PQmTJjw4MGD8+fP9+rVixASGxtLUVRZWVnHjh2dnZ0vXbr06NGj48ePJycnMxs6OjryeLyZM2c27BEBgHqCawAATdr+/fsHDhyoo6Mjnjhq1Kj09HRtbe1jx47FxcV17979s88+u3r1Kr129+7d3t7es2bNsrKymj59Ov27X1dX9+effz59+nTXrl0jIyODgoKqK1FFReXIkSOpqaldunSZO3fu5s2bmVU8Hu/cuXMGBgZDhgzp2rXrhg0buFwus3bq1KkfPnyYMmVK/R4BAGggGBEIAOrHunXrjhw58ueffzZ2RQBALrgGAAB19ebNm+vXr+/YscPPz6+x6wIA8kIEAAB1NWfOnH79+rm4uOAWAIACwV0AAAAAZYRrAAAAAMoIEQAAAIAyQgQAAACgjBABAAAAKCNEAAAAAMoIEQAAAIAyQgQAAACgjBABAAAAKCNEAAAAAMro/wDroivHfQDb9wAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -837,10 +879,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:24.012493Z",
-     "iopub.status.busy": "2024-06-17T14:24:24.012223Z",
-     "iopub.status.idle": "2024-06-17T14:24:24.820482Z",
-     "shell.execute_reply": "2024-06-17T14:24:24.819699Z"
+     "iopub.execute_input": "2024-06-17T19:19:41.725677Z",
+     "iopub.status.busy": "2024-06-17T19:19:41.725476Z",
+     "iopub.status.idle": "2024-06-17T19:19:42.353296Z",
+     "shell.execute_reply": "2024-06-17T19:19:42.352414Z"
     }
    },
    "outputs": [],
@@ -856,10 +898,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:24.827693Z",
-     "iopub.status.busy": "2024-06-17T14:24:24.827407Z",
-     "iopub.status.idle": "2024-06-17T14:24:25.302867Z",
-     "shell.execute_reply": "2024-06-17T14:24:25.301516Z"
+     "iopub.execute_input": "2024-06-17T19:19:42.363041Z",
+     "iopub.status.busy": "2024-06-17T19:19:42.362716Z",
+     "iopub.status.idle": "2024-06-17T19:19:43.142920Z",
+     "shell.execute_reply": "2024-06-17T19:19:43.142472Z"
     }
    },
    "outputs": [
@@ -875,7 +917,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -896,17 +938,17 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:25.308683Z",
-     "iopub.status.busy": "2024-06-17T14:24:25.308327Z",
-     "iopub.status.idle": "2024-06-17T14:24:26.167451Z",
-     "shell.execute_reply": "2024-06-17T14:24:26.166536Z"
+     "iopub.execute_input": "2024-06-17T19:19:43.150824Z",
+     "iopub.status.busy": "2024-06-17T19:19:43.150610Z",
+     "iopub.status.idle": "2024-06-17T19:19:44.600413Z",
+     "shell.execute_reply": "2024-06-17T19:19:44.599848Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 13,
@@ -915,7 +957,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -940,10 +982,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:26.174017Z",
-     "iopub.status.busy": "2024-06-17T14:24:26.173792Z",
-     "iopub.status.idle": "2024-06-17T14:24:26.198781Z",
-     "shell.execute_reply": "2024-06-17T14:24:26.197575Z"
+     "iopub.execute_input": "2024-06-17T19:19:44.607762Z",
+     "iopub.status.busy": "2024-06-17T19:19:44.607571Z",
+     "iopub.status.idle": "2024-06-17T19:19:44.628117Z",
+     "shell.execute_reply": "2024-06-17T19:19:44.627734Z"
     }
    },
    "outputs": [],
@@ -957,10 +999,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:26.203730Z",
-     "iopub.status.busy": "2024-06-17T14:24:26.203106Z",
-     "iopub.status.idle": "2024-06-17T14:24:28.807033Z",
-     "shell.execute_reply": "2024-06-17T14:24:28.806643Z"
+     "iopub.execute_input": "2024-06-17T19:19:44.631829Z",
+     "iopub.status.busy": "2024-06-17T19:19:44.631677Z",
+     "iopub.status.idle": "2024-06-17T19:19:45.926445Z",
+     "shell.execute_reply": "2024-06-17T19:19:45.926032Z"
     }
    },
    "outputs": [
@@ -976,7 +1018,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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0j8VXr14dP3580qRJ9NWfrnEdN26cdGxr166lKKqoqIjZw549e65du3bu3Lnp06dnZmZ+/vnn9Qmvtv9J6R3GxsYSQk6fPp2Xl8c8tO7Xr9/Ro0crKysnTZrUrl07e3v7/fv3f+i49EM7Ly+v169fv379WldXt0+fPr///jt9JaErRapt8CJnlYJkvoTyP7SXL19yOByZ6wBj9OjRlpaW9IO0Xbt2vX379kM5sqHiKSwsVFNTk74YSsdW4xXb2tqaWb5ixQry73deJoa2bdvW4UJXVUtt26mvr8/hcCZOnFj1z2llZUW/mDJlyvr162NjY729vY8fPz5//nz6vzEhxNDQsGvXrlUfubVt27Zh42zTpo3Ms5/8/Hw55U1MTMrKyoqKiqQv6IWFheXl5TJXPZn95Ofnf/TRR3WI0M/Pz8fHRyKRRERE1GHzBkSnNOn2MnX7IVI3Mr/T+Xy+dCQywRgaGrJYrEuXLsm0dlOk8RuLxVq7du3gwYO3b98uvVz+PukLStXvEnPbFxMTY2VldeDAAeZEZOKvEf0fatOmTa9fv/7111/Ly8uZzEFXWmzevLlqW1Dpr6WtrS3dsGXAgAFisXjHjh2HDh0aN25c3cKr7X9SOzs7Jyen6Ojo6dOnR0dHt23bdsiQIcza0aNHjx49ury8/OrVq6GhoV988YWlpaWLi4vMToqLi3///Xfy722itF9//XXWrFl0BfuTJ0/Mzc1lCjCrqg2Pz+fT96OMql9vmS+h/A/NyMhILBbn5+dX+6ONzWbPnj37+++/DwsLCw8PHzhwYOfOnasNTI5axdOmTZvKysrCwkIm+Ulfo2q8Yp84cYLZG/0npveTl5cn/WPi2bNnTBVafbTUez4NDY0BAwZcv369a9eujv+L+dxtbW2dnZ2jo6Nl/hsTQkaOHJmenm5tbS2zbYNnPnd393PnzjFfcYlEcvDgQTnlBw0aRAiR6WZH14zRqxj79u1jXickJDx69Eim5ZiCvLy8vLy8/Pz8mrxDAn0Rv3nzJrOEfljbVMG8ePGCyTQVFRWnT59m1o4cOZKiqKdPn8p8f7p06aLIzgcNGjR48OAVK1ZIjyQgf5/Ozs7q6urSX4yrV69K1+uyWCwej8dckvLz82Xa3UlXRXzIlClTysrK9u/fv2vXLhcXFxsbG3q5m5ubnp5eRkaGYxX0fXxV69at09fXX7ZsGV1zVYfw6vCfdMqUKf/888/ly5dPnDjBVFfKHMXd3Z1uk3X9+vWqe/j111/fv3+/cuXK8//L0NCQrvAcMmQIh8Op9meiq6urrq7u1q1bKanmMAxLS8vs7Gzm4l5YWJiQkPChE6HJ/9Doul85P1inTZtGt+nLysqS39Bfke9GjfEMGDCA/O91SbqJTY1X7C5dusj8iT/55BNCSExMDLOTa9euZWZmDhw4sMZQa9Qy7vnOnTsn0w1g+PDhP//8c58+ffr27Ttz5kxLS8vS0tJ79+6dOHFCun2Xn5/f9OnTnz175urqKv2TZ8WKFfHx8a6urnPnzu3cuXNZWdnDhw/j4uK2bt3asB13lixZcuLEiYEDBy5ZskQgEGzdupV+LsJmV/+bY8CAAaNGjZo3b97Dhw/p5+F///33Tz/9NGrUKJnElpycPG3atM8+++zx48dLliwxMzObNWsWverixYsDBw5ctmyZIo/6+Hx+MxmlwtTUdNCgQaGhofr6+hYWFmfPnj18+HBTBePt7b1s2bIJEyZ89913ZWVlmzZtkq6FdnNz++qrr6ZMmZKcnNyvXz9NTc28vLzLly936dJl5syZiux/7dq1Dg4OL168YBqay9+ngYFBQEAA/eF4eXk9efIkJCREKBQyX6SRI0cePnx41qxZ48aNe/z48cqVK4VCofQoKl26dLlw4cKJEyeEQqG2tna1dwA2NjYuLi6hoaGPHz+WviXV0tLavHnz5MmTi4qKxo0bZ2xs/PLlyxs3brx8+fJDV159ff3AwMCFCxf++uuvPj4+dQivDv9JP//884CAgM8//5zul8IsX7Zs2ZMnTwYOHNiuXbvXr1///PPPXC7X3d296h527typr6//7bffSleqE0ImTZq0YcOGGzdudOvW7fvvv1+5cuX79+8///xzXV3djIyMgoKCkJAQLS2tsLCwadOmDRo0yN/f38TE5N69ezdu3Pjll18IIRMnTty2bZuPj4+/v39hYeG6det0dHSqPQuG/A+tb9++EydOXLVq1fPnz0eOHKmurn79+nUNDY2vv/6aLqCnpzdp0qSIiAgLCwvpZ/NVdenS5fDhwxEREQ4ODmw2m75xr208Q4YM6dev38KFC9++fevo6HjlyhWZvuSKXLGlde7c+auvvtq8eTObzR42bNjDhw+DgoLMzc0XLFgg/3NTiCLNYJoQ00RFBt02Micnx8/Pz8zMjMvlGhkZubq6Mu0bacXFxQKBgBASGRkps+eXL1/OnTvXysqKy+UaGBg4ODgsWbLkzZs39FpSm7adI0aMkN6zTCOuS5cu0T/YTU1Nv/vuO/r3ppxmZhUVFWvWrPn444/V1dXV1dU//vjjNWvWSLewoj+TM2fOTJw4UU9Pj24OfvfuXaYAHTATf1VyWkDV2LZTZkOZT4NUadsp3S63xrZYeXl548aNMzAw0NXV9fHxoUetk27bKXN06baRtKp/jqqqbdtZtf1wXFwc3VOqQ4cOv/zyi8yZUhQVFRXl7OysqakpEAisra0nTZqUnJxc7RGrPQT9/F8mfjn7lEgkq1atoju3de3a9eTJk926dZNu4vvDDz9YWlrSPaUiIyNlAk5LS3Nzc6Mf5dLfz6qNaSmKohOeQCCQboNHu3jx4ogRIwwMDLhcrpmZ2YgRI+R/hu/fv2/fvn3Hjh3pRpK1DY+q6T9ptehP1c3NTXrhyZMnhw0bZmZmxuPxjI2Nhw8ffunSparb3rhxgxAyf/78qqvu3LlDpBoq79mzp1evXnw+X0tLq0ePHsxXlKKouLg4d3d3TU1NDQ0NOzs7+mkobffu3ba2tnw+387O7sCBA1Xbdq5fv17muPI/NLFY/NNPP9nb2/N4PF1dXRcXF7otLoNuHvXDDz/I+cQoiqJ/0Ojp6dH3c3WO5/Xr135+fnp6ehoaGoMHD6Y/NOkLUY1XbBlisXjt2rWdOnXicrmGhoY+Pj6PHz9m1tanbSeLqu7GHJRnyJAhDx8+rNrHU3G7du2aMmXKtWvXPvTTDFRBTk6OjY1NcHDw999/39SxQDP1zTffREREPH78WKYVHrSM2s4WLSAgoEePHubm5kVFRfv27YuPj1eFkQChwd24cWP//v2urq46OjpZWVl0dRkz+ByAtKtXr2ZnZ4eHh0+fPh1prypkPqUTi8XLli3Lz89nsVh2dnZ79+718fFp6qCg5dHU1ExOTt65cyfd1L5///6rV6+W30kGVJaLi4uGhsbIkSNXrVrV1LE0R6jtBAAA1dJSezUAAADUDTIfAACoFmQ+AABQLa2thYtEInn27Jm2trYyZhYFAIBGQ1FUaWlp27ZtPzT0R521tsz37NmzquPpAQBAC/X48eOGHVqLtL7Mp62tTQh5/PhxjSMDAQBAc1ZSUmJubk5f1RtWa8t8dCWnjo4OMh8AQCugjEdXaOECAACqBZkPAABUCzIfAACoFmQ+AABQLch8AACgWpD5AABAtSDzAQCAakHmAwAA1YLMBwAAqqW1jeFSf2IJlZRT9KK0zFib72RlwGE3wcjXiAEAQHmUm/n+/vvv9evXp6Sk5OXlHTlyZMyYMdUWu3jxYkBAwO3bt9u2bbtw4cIZM2Ywq37//fegoKD79+9bW1uvXr3ay8tLqQGfSs8LOZGRV1xGvxXq8oM97TzshUo9aGuNQSZ3EkLkp1LkWgBoHMrNfG/fvu3WrduUKVM+/fTTD5XJyckZPny4v79/TEzMlStXZs2aZWRkRJdPTEz09vZeuXKll5fXkSNHxo8ff/nyZWdnZyVFeyo9b2ZMKiW1JL+4bGZMaoRPz0ZLPK0mBpncqafBJYS8fiei31ZNpVVzbdAIW31N9fokwtqm3truELkZoIViURRVc6n6H4bF+tA936JFi44fP56ZmUm/nTFjxo0bNxITEwkh3t7eJSUlf/75J73Kw8NDX19///79cg5UUlKiq6tbXFxc2xGrxRKqz9pzzJX3v8gJMdXlX170SSNc5lpNDFVzZ9W9EUKYVFpj+TrcdNY29dZ2hw1yH4zcCSBHna/nNWr6Fi6JiYlDhgxh3g4dOjQ5OVkkElW7KiEhoeoeysvLS6TULYyknKKql3tCCEVIXnFZUk5R3XargjGIJVTIiQz5v6fotSEnMsQSSpHy9E3nqfS8Go9Oo1Op9Im8fidi0l6D7LC2e6B30mftuc8jr86LTfs88mqftedqtTlNLKES7xceS3uaeL9QLGmMn60ArU/Tt3DJz883MTFh3pqYmFRWVhYUFAiFwqqr8vPzq+4hNDQ0JCSknmG8KK3mcq/g2obSOmL4UO6UIZ1KayxPEcIiJORExmA70xrvkxRMvfXcYa32QJRTh1yH+07pm04HC/2UR69wAwoqqOkzH/nf6Zfo2ldmicyqaidqCgwMDAgIoF/TMxnWIQZjbX6d1zaU1hFDrTK04oWZTOli3UZ+ydqm3jrvUPE9NJPcKZM42SzC3DTWM4kicULL0vSZz9TUVPpO7sWLF2pqam3atKl2lfQtIENdXV1dXb2eYThZGQh1+fnFZTKXJ/r5Ft0+QtlaRwy1ytC1TeeKZMoGT73Kuw9uzNxZNXFK15XWM4k2SQtkgDpr+ud8Li4u8fHxzNszZ844OjpyudxqV7m6uiopDA6bFexpR/5tfEGjXwd72jXO79nWEQOdO2ssxyJEqMt3sjJQsDxNkUzZ4KlX2ffBDZI75W9eYw2w9JPXGoNpkKeeeGAJTUi5me/NmzdpaWlpaWmEkJycnLS0tNzcXEJIYGDgpEmT6DIzZsx49OhRQEBAZmZmVFTUzp07v/32W3rVvHnzzpw5s3bt2jt37qxdu/avv/6aP3++8qL1sBdG+PQ01f3vQmaqy2/M7gStI4Zqc6cM6VSqSHkilSlrDKC2qbfOO1R8D02eOxWpAa5PEq1V4iQN1NgHoM6UW9uZnJw8YMAA+jX9KG7y5Mm7du3Ky8ujUyAhxMrKKi4ubsGCBVu2bGnbtu2mTZuYzn+urq6xsbFLly4NCgqytrY+cOCA8jrz0TzshYPtTJv26UUriIHOnXI6FZj+b+VY1fIyanXTSafSmTGprH+vyMrYYR3ug5VXh1xj7lS8BrjOSVTxmtv6P7BkHjEaaqoTFil4U45njVArjdSfr9Eor/8H1FZ9xnB59bZi5R/1eozU3Prz0Zd7UiV3Kni5p/tZfih31tjPMvF+4eeRVxWJc79/b/mp61ja03mxaR9a+/OE7qO7m8nZvP4dRmX+EAw8a2x9lHc9R+aDZqr+TQeb2xguTZg7P5Q4pTVIEq0xcdZzczmDHtTqlwS0CMq7njd9206AanHYrBrrzWq7hwbfYa00eB2yqcK5s8YaYMUrb+tZc1ufB5by2+nUtpcIqDJkPoDG04S5s2rilO7PV88kqnjirM8Dyxrb6Sj+rBFUHDIfQEtSn9wpkzjrPIZLfe4+63PLqGA7nfzi94oUA1WGzAegQmQSZ0MlUcUTZ31uGRXsqbnyj0wBj4OnfSAHWrgAQGOrW2MfRdrpkFo2/MEAbM0Z2nYqCpkPoEWoW9ap2sC1Woq0U8UAbM1fa56lCABUEF3vOrq7mYt1m9o+YpQeYKhaNY5H0yADsEHLhed8ANCSSD9izM4v3XLh/odKfqhFjJwB2JYfv41OEaoA93wA0MIw94t9OhrJKfahFjFyekfkl5T/cu5eA4QIzRsyHwC0VHUbTFx+74if/spGnWerh8wHAC1V3SbVqrF3RI2TTmCKpZYOz/kAoAWrQ7d6+k5RznAw8geCQaPQVgCZDwBattp2q6fvFGfEpMop86Ea0fpPsQTNAWo7AaDFq20fCQ974YJBHeUUqLZG9EONQilCvj9yq6JSUrugoekg8wGAKprzSUdTnWrSm5zWMXIahRa9FfUOPYumMS0FMh8AqCIOm7V8lB2rNq1j5DcKLXpbgb7wLQUyHwCoqKojwpjq8uU8sVNkyOwa24VCc4AWLgCgumrVOuZDUywxMEFgS4HMBwAqTfEpD5kpluQXU3AeQWhCqO0EAFAUXUFqoMmVU0bBeQShCSHzAQDUgoe98GrgIANNXtVV8kdNg+YDmQ8AoHZ4auw1XvaKtwvFaGfNDZ7zAQDUmuKjpmG0s2YIc7IDANRRjTPLVx3tjLYVo50pQHnXc9zzAQDUkfx2odWOdkZbfPgWpsBtQnjOBwCgFHJGO3v9ToQpcJsQMh8AgFLI79gXnZCD1i5NBZkPAEAp5Hfse/1OlJRT1GjBgDRkPgAApXCyMtATyOvzjtFemgoyHwCAUnDYrClulnIKYLSXpoLMBwCgLHM+6ainUf1tH0Z7aULIfAAAysJhs34Y26XavgujugnRq6GpIPMBACiRh73wq35WVZdv/zsH09g2FaVnvvDwcCsrKz6f7+DgcOnSpWrLbNmyxdbWViAQdO7cec+ePcxykUi0YsUKa2trPp/frVu3U6dOKTtaAICGJZZQx29Un+EwjW1TUW7mO3DgwPz585csWXL9+vW+ffsOGzYsNzdXpkxERERgYODy5ctv374dEhIye/bsEydO0KuWLl26bdu2zZs3Z2RkzJgxw8vL6/r160oNGACgYX2oPzszjW3jhwTKHbfT2dm5Z8+eERER9FtbW9sxY8aEhoZKl3F1dXVzc1u/fj39dv78+cnJyZcvXyaEtG3bdsmSJbNnz6ZXjRkzRktLKyYmRs4RMW4nADQrx9KezotN+9Danyd0H93drBHDaUla5LidFRUVKSkpixcvZpYMGTIkISFBplh5eTmf/1/TXoFAkJSUJBKJuFxu1VV0Rqy6h/Lycvp1SUlJQ54DAED9yO+6wKytcfBraEBKzHwFBQVisdjExIRZYmJikp+fL1Ns6NChO3bsGDNmTM+ePVNSUqKiokQiUUFBgVAoHDp06IYNG/r162dtbX327Nljx46JxeKqBwoNDQ0JCVHeiQAA1JmTlYFQl59fXCZTvcYixPTfjg0yMxnpCbhT3CznfNIR+U9JlN7ChcX67y9HUZT0W1pQUNCwYcN69+7N5XJHjx7t6+tLCOFwOISQn3/+uWPHjjY2Njweb86cOVOmTKGXywgMDCz+1+PHj5V4MgAAtcRhs4I97ciHp7GlZzKSfhb4+r3op7/uOqyKR+NPJVFi5jM0NORwONI3eS9evJC+BaQJBIKoqKh37949fPgwNzfX0tJSW1vb0NCQEGJkZHT06NG3b98+evTozp07WlpaVlbVNA5WV1fXkaK8MwIAqAN6GltT3f+qPU11+RE+PT3shXJmMnr9TjQzJhXJTxmUWNvJ4/EcHBzi4+O9vLzoJfHx8aNHj662MJfLbdeuHSEkNjZ25MiRbPZ/KZnP55uZmYlEot9//338+PHKCxgAQEk87IWD7UyrPsmTM5MRIYQiJOREBmbya3DKnZk2ICBg4sSJjo6OLi4u27dvz83NnTFjBiEkMDDw6dOndNe97OzspKQkZ2fnV69ebdiwIT09fffu3fTm//zzz9OnT7t37/706dPly5dLJJKFCxcqNWAAACWpdhrbGgetpns+yJn/FupA0cy3d+/erVu35uTkJCYmWlhYbNy40crK6kM3cAxvb+/CwsIVK1bk5eXZ29vHxcVZWFgQQvLy8piOfWKxOCwsLCsri8vlDhgwICEhwdLSkl5VVla2dOnSBw8eaGlpDR8+fO/evXp6enU7TwCAZkiRQasxpUODU6g/X0RExLJly+bPn7969er09PQOHTrs2rVr9+7d58+fb4QQawX9+QCgBRFLqD5rz8mp8CSE7PfvrZr3fMq7nivUwmXz5s2RkZFLlixhmlY6OjreunWrYUMBAFA1TMvParEwpYNyKJT5cnJyevToIb1EXV397du3ygkJAECFeNgLt/r0rHYyI4qQoBG2aN7S4BTKfFZWVmlpadJL/vzzTzu7D/5OAQAAxXnYC1OWDl4wqFPVOdxX/pGJjg0NTqEWLt99993s2bPLysooikpKStq/f39oaOiOHTuUHRwAgIrgsFnzBnXsaKw169dU6eX5xWUzY1Lpzn9NFVvro1DmmzJlSmVl5cKFC9+9e/fFF1+YmZn9/PPPEyZMUHZwAACqQyyhVv6RIbOQIoSFXn0NTdFeDf7+/v7+/gUFBRKJxNjYWKkxAQCooBrnM1LNFp7KoNBzvk8++eT169eEEENDQzrtlZSUfPLJJ0qNDABApcjvt4defQ1Iocx34cKFiooK6SVlZWUfmmAdAADqQMH5jKD+aqjtvHnzJv0iIyODGXtaLBafOnXKzAyzKQIANBhF5jPCNH4NoobM1717dxaLxWKxZOo2BQLB5s2blRkYAIBqoXu1z4xJZRHCJD+Z+Yykp/Ez0OSuGm0/vGvbJom2Rath9LJHjx5RFNWhQ4ekpCQjIyN6IY/HMzY2rnaqvCaH0csAoEWTSW9CXX6wp52HvZCexq/q9Xp6P6vA4a2zd7XyrucKjdvZgiDzAUBLV7VKU/7wnuFf9BzetRX29lPe9bwWsxRlZGTk5uZKN3UZNWpUw0YDAABV5zOSP41f0LH0ofbo7VcLCmW+Bw8eeHl53bp1i8X6/3tEFotFCBGLxcqNDgAAaurSUPi2Ar39akWhXg3z5s2zsrJ6/vy5hobG7du3//77b0dHxwsXLig5NgAAIESBLg3o7VcrCmW+xMTEFStWGBkZsdlsNpvdp0+f0NDQuXPnKjs4AAAghDhZGRhoVjOZAwO9/WpFocwnFou1tLQIIYaGhs+ePSOEWFhYZGVlKTc0AAAghBDCYbNWjbb/0FrM4VdbCmU+e3t7uku7s7PzunXrrly5smLFig4dOig5NgAA+H/Du7ad3s+q6nLWv739Gj+klkuhzLd06VKJREIIWbVq1aNHj/r27RsXF/fzzz8rOTYAAPhP4HC78C96GmjymCVCXT4mMKqDuvTnKyoq0tfXp5t3NjfozwcArZvqDGCmvOu5Qvd8MgwMDPLz8+fMmdOwoQAAQI3o3n6ju5u5WLeRk/bEEirxfuGxtKeJ9wvFklY1Ykn91dyfLyMj4/z581wud/z48Xp6egUFBatXr966dauVVTU1zgAA0OQ+NARa00bVfNRwz3fy5MkePXp8/fXXM2bMcHR0PH/+vK2tbVpa2sGDBzMyZOcOBgCAJkeP8Ck95kt+cdnMmNRT6XlNGFWzUkPmW7169YwZM0pKSn788ccHDx7MmDHj999/P3/+/MiRIxsnPgAAUJxYQoWcyJCp3KTfhpzIQLUnrYbMl5mZOXv2bC0trblz57LZ7I0bN/br169xIgMAgNr60AifFCF5xWVJOUWNH1IzVEPmKykp0dPTI4SoqakJBIJOnTo1RlAAAFAn8ocxwyBnNIVauNCzsVMUlZWV9fbtW2ZV165dlRgaAADUkvxhzDDIGa3mzDdw4ECmzx/9eI+esYHFYmGuBgCAZsXJykCoy88vLpN5oMcixBSDnP2rhsyXk5PTOHEAAED9cdisYE+7mTGprH8bthBC6E5/GOSMUUPms7CwaJw4AACgQXjYCyN8ekr359PX5K4abY/+fIy6jOECAADNmYe9MGiEHTPCZ9Fb0co/MtGfj4HMBwDQ2pxKz5v9a2rR2wpmCTqzS0PmAwBoVdCZvUbIfAAArQo6s9dI0cxXWVn5119/bdu2rbS0lBDy7NmzN2/eKLJheHi4lZUVn893cHC4dOlStWW2bNlia2srEAg6d+68Z88e6VUbN27s3LmzQCAwNzdfsGBBWRm6YQIAyIPO7DWquT8fIeTRo0ceHh65ubnl5eWDBw/W1tZet25dWVnZ1q1b5W944MCB+fPnh4eHu7m5bdu2bdiwYRkZGe3bt5cuExERERgYGBkZ2atXr6SkJH9/f319fU9PT0LIvn37Fi9eHBUV5erqmp2d7evrSwj56aef6niuAAAqAJ3Za6TQPd+8efMcHR1fvXolEAjoJV5eXmfPnq1xww0bNkydOnXatGm2trYbN240NzePiIiQKbN3797p06d7e3t36NBhwoQJU6dOXbt2Lb0qMTHRzc3tiy++sLS0HDJkyOeff56cnFybswMAUDl0Z/aqHfdYhAjRmZ0QomDmu3z58tKlS3k8HrPEwsLi6dOn8reqqKhISUkZMmQIs2TIkCEJCQkyxcrLy/n8/36DCASCpKQkkUhECOnTp09KSkpSUhIh5MGDB3FxcSNGjFAkYAAAlUV3Zif/dmCnoTO7NIUyn0QikRmo7MmTJ9ra2vK3KigoEIvFJiYmzBITExN6CFBpQ4cO3bFjR0pKCkVRycnJUVFRIpGooKCAEDJhwoSVK1f26dOHy+VaW1sPGDBg8eLFVQ9UXl5eIkWRMwIAaMXozuwmOurMEhMd9QifnujMTlMo8w0ePHjjxo30axaL9ebNm+Dg4OHDhyuyLYv13+8LerRPmQJBQUHDhg3r3bs3l8sdPXo0/TCPw+EQQi5cuLB69erw8PDU1NTDhw+fPHly5cqVVQ8RGhqq+y9zc3NFogIAUAFV7/qAEEJYzGjUcjx79mzAgAEcDufu3buOjo537941NDT8+++/jY2N5WxVUVGhoaFx8OBBLy8vesm8efPS0tIuXrxYtbBIJHr+/LlQKNy+ffuiRYtev37NZrP79u3bu3fv9evX02ViYmK++uqrN2/esNn/k7DLy8vLy8vp1yUlJebm5sXFxTo6OoqcPwBA60NPyy59cafzXsu67SspKdHV1VXG9Vyhtp1t27ZNS0vbv39/amqqRCKZOnXql19+ybR2+RAej+fg4BAfH89kvvj4+NGjR1dbmMvltmvXjhASGxs7cuRIOre9e/dOOslxOByKoqqmanV1dXV1dQIAAB/uyc4iJORExmA7UzzqUyjzvXv3TkNDw8/Pz8/Pr1Z7DwgImDhxoqOjo4uLy/bt23Nzc2fMmEEICQwMfPr0Kd11Lzs7OykpydnZ+dWrVxs2bEhPT9+9eze9uaen54YNG3r06OHs7Hzv3r2goKBRo0bRFaEAAFAt+T3Zf4rPdvvI0MnKQJXzn0KZz9jYeMyYMRMnThw8eLBMTaN83t7ehYWFK1asyMvLs7e3j4uLoyd/yMvLy83NpcuIxeKwsLCsrCwulztgwICEhARLS0t61dKlS1ks1tKlS58+fWpkZOTp6bl69eranR8AgIqR31f9l/P3fjl/T6jLD/a0a0E1nw1Loed8hw8f3r9//x9//KGjo+Pt7e3j49OrV69GCK4OlFcvDADQIiTeL/w88qr8Mi3isZ/yrucK3cCNHTv24MGDz58/Dw0NzczMdHV17dSp04oVKxo2FAAAqL8P9WSXpuIDWNei6lJbW3vKlClnzpy5ceOGpqZmSEiI8sICAIC6qbYne1WqPIB1LTJfWVnZb7/9NmbMmJ49exYWFn777bfKCwsAAOqM7sluqlvzEJ2qOYC1Qi1czpw5s2/fvqNHj3I4nHHjxp0+fdrd3V3ZkQEAQJ152AsH25km5RRduffyl/P3P1RMNQewVijzjRkzZsSIEbt37x4xYgSXy1V2TAAAUH8cNsvFuo2TlcHvqU/zi8tkHuixCDFV1QGsFcp8+fn5aCoJANAS0Y/9Zsaksv5t2EJUfgBrec/5pEd/LqmO8sMDAID6qvrYz1SX38y7NCiVvP58HA4nLy/P2NiYzWbLjDRNjz0tM4FDc4D+fAAA1RJLqKScohelZcba/BYxhkvTjNt57tw5AwMDQsj58+cb9qgAANDI6Md+TR1FsyAv8zENOK2srMzNzWXmG3r8+LFyQwMAAFAChfrzWVlZvXz5UnpJUVGRlZWVckICAABQIoUyX9UZZd+8ecPnq2IvEAAAaOlq6NUQEBBACGGxWEFBQRoaGvRCsVj8zz//dO/eXdnBAQAANLgaMt/169cJIRRF3bp1i8fj0Qt5PF63bt0wehkAALRENWQ+ulXnlClTfv75Z/QTAACAVkChMVyio6OVHQcAADSJFtfPr/4UynyEkGvXrh08eDA3N7eiooJZePjwYeVEBQAAjeFUel7IiYy84v+fsUFF5mpXqG1nbGysm5tbRkbGkSNHRCJRRkbGuXPndHV1lR0cAAAoz6n0vJkxqUzaI4TkF5fNjEk9lZ7XhFE1AoUy35o1a3766aeTJ0/yeLyff/45MzNz/Pjx7du3V3ZwAACgJGIJFXIiQ2b4ShWZq12hzHf//v0RI0YQQtTV1d++fctisRYsWLB9+3YlxwYAAMqSlFMkfbfHUIW52hXKfAYGBqWlpYQQMzOz9PR0Qsjr16/fvXun3NAAAEBp5M/G3rrnaleohUvfvn3j4+O7dOkyfvz4efPmnTt3Lj4+fuDAgcoODgAAlET+bOyte652hTLfL7/8UlZWRggJDAzkcrmXL18eO3ZsUFCQkmMDAABlcbIyEOryVXOudnnz87VEmJ8PAEBBdNtOUmWu9mYyaa3yrucKPeerOht7aWmpdMc+AABocVR2rnaFajv19PRk5mqgtWvXztfXNzg4mM1WKIMCAECz4mEvHGxnijFcqrFr164lS5b4+vo6OTlRFHXt2rXdu3cvXbr05cuXP/74o7q6+vfff6/sQAEAQBlUcK52hTLf7t27w8LCxo8fT78dNWpUly5dtm3bdvbs2fbt269evRqZDwAAWgqFaikTExN79OghvaRHjx6JiYmEkD59+uTm5iolNAAAACVQKPO1a9du586d0kt27txpbm5OCCksLNTX11dKaAAAAEqgUG3njz/++Nlnn/3555+9evVisVjXrl27c+fOoUOHCCHXrl3z9vZWcpAAAAANRtH+fA8fPty6dWt2djZFUTY2NtOnT7e0tFRybHWB/nwAAK2D8q7n6MkOAADNURP3ZCeEXLp0ycfHx9XV9enTp4SQvXv3Xr58uWFDAQAAaAQKZb7ff/996NChAoEgNTW1vLycEFJaWrpmzRolxwYAANDwFMp8q1at2rp1a2RkJJfLpZe4urqmpqYqsm14eLiVlRWfz3dwcLh06VK1ZbZs2WJraysQCDp37rxnzx5mef/+/Vn/i54mEAAAoM4UatuZlZXVr18/6SU6OjqvX7+uccMDBw7Mnz8/PDzczc1t27Ztw4YNy8jIkJnMPSIiIjAwMDIyslevXklJSf7+/vr6+p6enoSQw4cPM6ODFhYWduvW7bPPPlPwxAAAoA7EEqrVD2amUOYTCoX37t2Tbsx5+fLlDh061Ljhhg0bpk6dOm3aNELIxo0bT58+HRERERoaKl1m796906dPp7tGdOjQ4erVq2vXrqUzn4HBf9NkxMbGamhoIPMBACjPqfS8kBMZzFztQl1+sKdd6xvAWqHazunTp8+bN++ff/5hsVjPnj3bt2/ft99+O2vWLPlbVVRUpKSkDBkyhFkyZMiQhIQEmWLl5eV8/n8jhQsEgqSkJJFIJFNs586dEyZM0NTUrHqg8vJy6XkkFDkjAACQQU9axKQ9Qkh+cdnMmNRT6XlNGJUyKJT5Fi5cOGbMmAEDBrx586Zfv37Tpk2bPn36nDlz5G9VUFAgFotNTEyYJSYmJvn5+TLFhg4dumPHjpSUFIqikpOTo6KiRCJRQUGBdJmkpKT09HT63rGq0NBQ3X/RI8sAAECtiCVUyIkMmV5u9NuQExliSavq/6Zor4bVq1cXFBQkJSVdvXr15cuXK1euVHBD6emNKIqqOttRUFDQsGHDevfuzeVyR48e7evrSwjhcDjSZXbu3Glvb+/k5FTtIQIDA4v/9fjxYwUDAwAARlJOkfTdHoMiJK+4LCmnqPFDUp5azKunoaHh6Ojo5OSkpaWlSHlDQ0MOhyN9k/fixQvpW0CaQCCIiop69+7dw4cPc3NzLS0ttbW1DQ0NmQLv3r2LjY390A0fIURdXV1HiuJnBAAAtBel1aQ9Bde2ODW0cPHz8/vQKhaLJTOMtQwej+fg4BAfH+/l5UUviY+PHz16dLWFuVxuu3btCCGxsbEjR46Unur2t99+Ky8v9/HxkR8qAADUmbE2v85rW5waMt+rV6+qLhSLxX/99Vd5ebn8zEcICQgImDhxoqOjo4uLy/bt23Nzc2fMmEEICQwMfPr0Kd11Lzs7OykpydnZ+dWrVxs2bEhPT9+9e7f0Tnbu3DlmzJg2bVRr4kQAgMbkZGUg1OXnF5fJPNBjEWKqy3eyMqh+s5aphsx35MgRmSXHjh37/vvv1dXVly1bVuPevb29CwsLV6xYkZeXZ29vHxcXZ2FhQQjJy8tjZvUTi8VhYWFZWVlcLnfAgAEJCQnS3Seys7MvX7585syZWp0VAADUCofNCva0mxmTyvq3YQshhG6XEexp18p69dVixOorV64sWrTo+vXrc+bMWbx4cfOclg8jVgMA1Fmz6s+nvOu5Qj3Zb9++vXjx4lOnTk2aNCk2NpZ+IAcAAK2Mh71wsJ2pqo/h8vjx42XLlsXExIwcOfLmzZu2traNExYAADQJDpvlYt3K21XUkPk6d+7MYrG++eYbV1fXu3fv3r17V3rtqFGjlBkbAABAw6vhOZ907wLZLVkssVishJDqBc/5AABahyZ7zieRSBr2eAAAAE2rFmO4AAAAtALIfAAAoFqQ+QAAQLUg8wEAgGpB5gMAANWiaOZ7/fr1jh07AgMDi4qKCCGpqalPnz5VZmAAAABKodDoZTdv3hw0aJCuru7Dhw/9/f0NDAyOHDny6NEjerIFAACAFkShe76AgABfX9+7d+/y+f8/RdOwYcP+/vtvZQYGAACgFArd8127dm3btm3SS8zMzKQnWwcAgNZHLKFa5ejVCmU+Pp9fUlIivSQrK8vIyEg5IQEAQNNrVjMWNSyFajtHjx69YsUKkUhECGGxWLm5uYsXL/7000+VHBsAADSNU+l5M2NSmbRHCMkvLpsZk3oqPa8Jo2ooCmW+H3/88eXLl8bGxu/fv3d3d//oo4+0tbVXr16t7OAAAKDxiSVUyIkMmdkM6LchJzLEEkXnM2+2FKrt1NHRuXz58rlz51JTUyUSSc+ePQcNGqTsyAAAoEkk5RRJ3+0xKELyisuScopa+gR+CmU+2ieffPLJJ58oLxQAAGgOXpRWk/YUXNsiKJT5VqxYUe3yZcuWNWgwAADQ9Iy1+XVe2yIolPmOHDnCvBaJRDk5OWpqatbW1sh8AACtj5OVgVCXn19cJvNAj0WIqS7fycqgacJqOAplvuvXr0u/LSkp8fX19fLyUk5IAADQlDhsVrCn3cyYVNa/DVsIIXRXvmBPu1bQq68uI1br6OisWLEiKCiowaMBAIDmwMNeGOHT01T3v4pNU11+hE/P1tGfrxYtXKS9fv26uLi4YUMBAIDmw8NeONjOVHXHcNm0aRPzmqKovLy8vXv3enh4KC0qAABoehw2q6V3YKiWQpnvp59+Yl6z2WwjI6PJkycHBgYqLSoAAABlUSjz5eTkKDsOAACAxlFzC5fKyko1NbX09PRGiAYAAEDZas58ampqFhYWYrG4EaIBAABQNoV6NSxdujQwMLCoqEjZ0QAAAChbDc/5/v77bxcXl02bNt27d69t27YWFhaamprM2tTUVCWHBwAA0MBqyHwDBgzIy8sbM2ZMowQDAACgdDVkPoqiCCHBwcGNEgwAAIDS1fycj8VqJZ32AQAAiCL9+YKCgjQ0NKpdtWHDhho3Dw8PX79+fV5e3scff7xx48a+fftWLbNly5Zffvnl4cOH7du3X7JkyaRJk5hVr1+/XrJkyeHDh1+9emVlZRUWFjZ8+PAaDwoAAPAhNWe+W7du8Xi8qssVuRc8cODA/Pnzw8PD3dzctm3bNmzYsIyMjPbt20uXiYiICAwMjIyM7NWrV1JSkr+/v76+vqenJyGkoqJi8ODBxsbGhw4dateu3ePHj7W1tRU+NQAAaEhiCdU6hvFk0U/yPoTNZufn5xsbG9dt787Ozj179oyIiKDf2trajhkzJjQ0VLqMq6urm5vb+vXr6bfz589PTk6+fPkyIWTr1q3r16+/c+cOl8tV8IglJSW6urrFxcU6Ojp1ixkAAKo6lZ4XciIjr/j/J2QX6vKDPe2UOnWD8q7nNTznq89DvoqKipSUlCFDhjBLhgwZkpCQIFOsvLycz/9vIgyBQJCUlCQSiQghx48fd3FxmT17tomJib29/Zo1a6rtUF9eXl4ipc4BAwBAtU6l582MSWXSHiEkv7hsZkzqqfS8JoyqzmrIfPLvCOUrKCgQi8UmJibMEhMTk/z8fJliQ4cO3bFjR0pKCkVRycnJUVFRIpGooKCAEPLgwYNDhw6JxeK4uLilS5eGhYWtXr266oFCQ0N1/2Vubl7ngAEAoCqxhAo5kSGTDOi3IScyxJK6p4mmUkPmi46O1tXVrc8BpO8aKYqqehMZFBQ0bNiw3r17c7nc0aNH+/r6EkI4HA4hRCKRGBsbb9++3cHBYcKECUuWLGEqTqUFBgYW/+vx48f1iRYAAGQk5RRJ3+0xKELyisuSclre8F41ZL7Jkyerq6vXbdeGhoYcDkf6Ju/FixfSt4A0gUAQFRX17t27hw8f5ubmWlpaamtrGxoaEkKEQmGnTp3oLEgIsbW1zc/Pr6iokNmDurq6jpS6RQsAANV6UVpN2lNwbfOk0LiddcPj8RwcHOLj45kl8fHxrq6u1Rbmcrnt2rXjcDixsbEjR45ks9mEEDc3t3v37kkkErpMdna2UCistqEpAAAoibE2v85rmyclZj5CSEBAwI4dO6KiojIzMxcsWJCbmztjxgxCSGBgINNpLzs7OyYm5u7du0lJSRMmTEhPT1+zZg29aubMmYWFhfPmzcvOzv7jjz/WrFkze/ZspQYMAAAynKwMhLr8qs0dWYQIdflOVgZNEFP9KDQzbZ15e3sXFhauWLEiLy/P3t4+Li7OwsKCEJKXl5ebm0uXEYvFYWFhWVlZXC53wIABCQkJlpaW9Cpzc/MzZ84sWLCga9euZmZm8+bNW7RokVIDBgAAGRw2K9jTbmZMKuvfhi2EEDoRBnvatcRefTX055OWnJycmZnJYrFsbGwcHR2VGladoT8fAIAytKb+fArd8z158uTzzz+/cuWKnp4eIeT169eurq779+9HFwIAABXhYS8cbGfaOsZwUeg5n5+fn0gkyszMLCoqKioqyszMpChq6tSpyg4OAACaDw6b5WLdZnR3MxfrNi037REFazsFAkFCQkKPHj2YJampqW5ubu/fv1dmbHWB2k4AgNahyUYvo7Vv354eToxRWVlpZmbWsKEAAAA0AoUy37p1677++uvk5GT6BjE5OXnevHk//vijkmMDAABoeArVdurr6797966yslJNTY0QQr/Q1NRkChQVNZfRa1DbCQDQOjRx286NGzc27FEBAACaikKZb/LkycqOAwAAoHEolPmY8VZkyMyuDgAA0PwplPksLS2rnaK22nliAQAAmjOFMt/169eZ1yKR6Pr16xs2bKh2klgAAIBmTqHM161bN+m3jo6Obdu2Xb9+/dixY5UTFQAAgLLUca6GTp06Xbt2rWFDAQCAlkIsoVruGJ4KZb6SkhLmNUVReXl5y5cv79ixo9KiAgCA5qvx521oWAplPj09PekWLhRFmZubx8bGKi0qAABopk6l582MSZUeAyW/uGxmTGqET8+WkvwUynznz59nXrPZbCMjo48++ogezwUAAFSHWEKFnMiQGfqLIoRFSMiJjMF2pi2i2lOh7OXu7q7sOAAAoPlLyiliKjmlUYTkFZcl5RS5WLdp/KhqS9H7tvv372/cuJGek93W1nbevHnW1tZKjQwAAJqbF6XVpD0F1zYfCs3VcPr0aTs7u6SkpK5du9rb2//zzz8ff/xxfHy8soMDAIBmxVibX+e1zYdC93yLFy9esGDBDz/8IL1k0aJFgwcPVlpgAADQ7DhZGQh1+fnFZTKP+liEmOrynawMmiasWlLoni8zM3Pq1KnSS/z8/DIyMpQTEgAANFMccdnuj/5WJxXS7Vjo18Gedi2ieQtRMPMZGRmlpaVJL0lLSzM2NlZKRAAA0GxlHO90e+OBfi9Ndf+r2DTV5begLg1EwdpOf3//r7766sGDB66uriwW6/Lly2vXrv3mm2+UHRwAADQvt48QQroVn7+8yL/ljuGi0JzsFEVt3LgxLCzs2bNnhJC2bdt+9913c+fOrXYCh6aFOdkBABrYmxckcQuRVBJCSNJ2Iq4gHB5x+ooQQthqxGUO0TJSxmGVdz2vOfNVVlbu27dv6NChpqampaWlhBBtbe2GDaIBIfMBADSwvJtkxyAiLicsNiEsQokJi0MIRSgJ4aiTaX8RYVdlHFZ51/Oan/OpqanNnDmzvLycEKKtrd2c0x4AADQ8YVcy/SIx7EQIIZT4v38NO5HpF5WU9pRKoRYuzs7O0lP0AQCAajG2JVPPyC6ceoYY2zZFNPWlUAuXWbNmffPNN0+ePHFwcNDU1GSWd+3a8lI9AADUxaMEQkn+e0tJyKNEYjO86QKqO4Uyn7e3NyFk7ty59FsWi0VRFIvFEovFSgwNAACaj8zjhBDS3oUMXknOLCWPr5LM46058+Xk5Cg7DgAAaNaEPYiRLXH9mrA5ZEocSdhM1FrGWGVVKdSroQVB204AgNZBedfzGu75JBLJ7du3u3TpQgjZunVrRUUFvZzD4cycOZPNVqiBDAAAQPNRQ+aLjY3dtm3bxYsXCSHfffednp4ePSFtQUEBn8+XGcwTAACg+avhpi06OnrGjBnM24sXL+bk5OTk5Kxfvz4mJkbJsQEAADS8GjJfZmamnZ1d1eXu7u43btxQ5ADh4eFWVlZ8Pt/BweHSpUvVltmyZYutra1AIOjcufOePXuY5bt27WL9r7KyljHtIQAANFs11HYWFBRoaWnRrx88eNCmzf9PM8/lct++fVvj3g8cODB//vzw8HA3N7dt27YNGzYsIyOjffv20mUiIiICAwMjIyN79eqVlJTk7++vr6/v6elJr9XR0cnKymIK8/kttSkRAAA0EzXc85mYmDCJx8jIiGnSkpmZaWpqWuPeN2zYMHXq1GnTptna2m7cuNHc3DwiIkKmzN69e6dPn+7t7d2hQ4cJEyZMnTp17dq1zFoWi2UqpRZnBgAAUJ0aMt/AgQNXr14ts5CiqNDQ0IEDB8rftqKiIiUlZciQIcySIUOGJCQkyBQrLy+XvpMTCARJSUkikYh+++bNGwsLi3bt2o0cORIjqAEAQP3VkPmWLFmSnp7u7Ox88ODBGzdu3Lx587fffnN2dr59+/b3338vf9uCggKxWGxiYsIsMTExyc/Plyk2dOjQHTt2pKSkUBSVnJwcFRUlEokKCgoIITY2Nrt27Tp+/Pj+/fv5fL6bm9vdu3erHqi8vLxEikLnDQAAqqqG53zW1tbx8fG+vr7e3t70bHwURdnY2Jw5c+ajjz5S5ADSc/jRY57JFAgKCsrPz+/duzdFUSYmJr6+vuvWreNwOISQ3r179+7dmy7m5ubWs2fPzZs3b9q0SWYPoaGhISEhigQDAAANRSyhWujktDWPXubk5JSRkZGWlpadnU0I6dixY48ePRTZtaGhIYfDkb7Je/HihfQtIE0gEERFRW3btu358+dCoXD79u3a2tqGhoYyxdhsdq9evaq95wsMDAwICKBfl5SUmJubKxIeAADU2an0vJATGXnF/9/eXqjLD/a087AXNm1UClJo3E5CSPfu3bt3716rXfN4PAcHh/j4eC8vL3pJfHz86NGjqy3M5XLbtWtHCImNjR05cmTV0WEoikpLS6NHk5Ghrq6urq5eq9gAAKDOTqXnzYxJlR76Mr+4bGZMaoRPzxaR/BTNfHUTEBAwceJER0dHFxeX7du35+bm0v3iAwMDnz59Snfdy87OTkpKcnZ2fvXq1YYNG9LT03fv3k1vHhIS0rt3744dO5aUlGzatCktLW3Lli1KDRgAAOQTS6iQExkyIz5ThLAICTmRMdjOtPlXeyo383l7excWFq5YsSIvL8/e3j4uLs7CwoIQkpeXl5ubS5cRi8VhYWFZWVlcLnfAgAEJCQmWlpb0qtevX3/11Vf5+fm6uro9evT4+++/nZyclBowAADIl5RTxFRySqMIySsuS8opcrFu0/hR1QrmagAAgFo4lvZ0Xmzah9b+PKH76O5mDXIg5V3PMdkCAADUgrG2vLG05K9tJuTVdt68eVP+xl27dm3QYAAAoLlzsjIQ6vLzi8tkKgxZhJjq8p2sDJomrNqQl/m6d+/OYlVTHUovZLFYYrFYmbEBAECzw2Gzgj3tZsaksghh0gPdpiXY0675N28h8jNfTk5Oo8UBAAAthYe9MMKnp3R/PtMW1Z8PLVwAAKAulD2Gi/Ku57Xo1ZCRkZGbm1tRUcEsGTVqVMNGAwAALQWHzWr+HRiqpVDme/DggZeX161bt5jHfvTwm3jOBwAALY5CvRrmzZtnZWX1/PlzDQ2N27dv//33346OjhcuXFBybAAAAA1PoXu+xMTEc+fO0TPTstnsPn36hIaGzp07FxPmAQBAi6PQPZ9YLNbS0iKEGBoaPnv2jBBiYWHBzNUOAADQgih0z2dvb3/z5s0OHTo4OzuvW7eOx+Nt3769Q4cOyg4OAACgwSmU+ZYuXfr27VtCyKpVq0aOHNm3b982bdocOHBAybEBAAA0vLr05ysqKtLX1686u3pzgP58AACtQ7Poz8cwMGgBw7IBAABUS6HMV1ZWtnnz5vPnz7948UIikTDLU1NTlRYYAACAUiiU+fz8/OLj48eNG+fk5NQ8KzkBAAAUpFDm++OPP+Li4tzc3JQdDQAAgLIp1J/PzMxMW1tb2aEAAAA0AoUyX1hY2KJFix49eqTsaAAAoCUSS6jE+4XH0p4m3i8US5r7FEAK1XY6OjqWlZV16NBBQ0ODy+Uyy4uKipQWGAAAtAyn0vOk5+oTNvu5+hTKfJ9//vnTp0/XrFljYmKCFi4AAMA4lZ43MyZV+i4vv7hsZkxqhE/PZpv8FMp8CQkJiYmJ3bp1U3Y0AADQgoglVMiJDJnKTYoQFiEhJzIG25k2+HS1DUKh53w2Njbv379XdigAANCyJOUUMZWc0ihC8orLknKa6RMxhTLfDz/88M0331y4cKGwsLBEirKDAwCA5uxFaTVpT8G1TUih2k4PDw9CyMCBA5klFEWxWCzMyQ4AoMqMtfl1XtuEFMp858+fV3YcAADQ4jhZGQh1+fnFZTKP+liEmOrynaya6SDPCmU+d3d3ZccBAAAtDofNCva0mxmTyiKESX50m5ZgT7vm2byFyM98N2/etLe3Z7PZN2/erLZA165dlRMVAAC0DB72wgifntL9+UybfX8+efPzsdns/Px8Y2NjNpvNYsmWbJ7P+TA/HwBA4xNLqKScohelZcbafCcrgwa522ua+flycnKMjIzoFw17VAAAaE04bJaLdZumjkJR8jKfhYWFzAsAAICWTtE52bOzsy9cuCAzM+2yZcuUExUAAICyKJT5IiMjZ86caWhoaGpqyozbyWKxkPkAAKDFUSjzrVq1avXq1YsWLVJ2NAAAAMqm0Ohlr169+uyzz5QdCgAAQCNQKPN99tlnZ86cqdsBwsPDrays+Hy+g4PDpUuXqi2zZcsWW1tbgUDQuXPnPXv2VC0QGxvLYrHGjBlTtxgAAAAYCtV2fvTRR0FBQVevXu3SpYv0zLRz586Vv+GBAwfmz58fHh7u5ua2bdu2YcOGZWRktG/fXrpMREREYGBgZGRkr169kpKS/P399fX1PT09mQKPHj369ttv+/btW5vzAgAAqJ68nuwMKyurarZksR48eCB/Q2dn5549e0ZERNBvbW1tx4wZExoaKl3G1dXVzc1t/fr19Nv58+cnJydfvnyZfisWi93d3adMmXLp0qXXr18fPXpU/hHRkx0AoHVomp7sjLr1ZK+oqEhJSVm8eDGzZMiQIQkJCTLFysvL+fz/xvMWCARJSUkikYi+uVyxYoWRkdHUqVM/VFNK76G8vJx+jbmTAABAPoWe89VNQUGBWCw2MTFhlpiYmOTn58sUGzp06I4dO1JSUiiKSk5OjoqKEolEBQUFhJArV67s3LkzMjJS/oFCQ0N1/2Vubt7gJwIAAK2JvHu+gICAlStXampqBgQEVFtgw4YNNR6A6f9H/p3VT6ZAUFBQfn5+7969KYoyMTHx9fVdt24dh8MpLS318fGJjIw0NDSUf4jAwEAmwpKSEiQ/AACQQ17mu379ukgkol9UXVs1h8kwNDTkcDjSN3kvXryQvgWkCQSCqKiobdu2PX/+XCgUbt++XVtb29DQ8ObNmw8fPmSautBjx6ipqWVlZVlbW0vvQV1dXV1dXX4wAADQOJQxenXDkpf5mAlp6zYzLY/Hc3BwiI+P9/LyopfEx8ePHj262sJcLrddu3aEkNjY2JEjR7LZbBsbm1u3bjEFli5dWlpa+vPPP+OWDgCg2TqVnic9Y5GwWc5YpOi4nXUTEBAwceJER0dHFxeX7du35+bmzpgxgxASGBj49OlTuutednZ2UlKSs7Pzq1evNmzYkJ6evnv3bkIIn8+3t7dndqWnp0cIkV4CAADNyqn0vJkxqdIdBvKLy2bGpEb49GxWya+GzOfn5ydnbVRUlPzNvb29CwsLV6xYkZeXZ29vHxcXR0/7kJeXl5ubS5cRi8VhYWFZWVlcLnfAgAEJCQmWlpa1OAMAAGgGxBIq5ESGTD85ihAWISEnMgbbmTafas8a+vOx2WwLC4sePXpUW+zIkSNKC6yO0J8PAKBJJN4v/Dzy6ofW7vfvXdsJ/JqsP9+MGTNiY2MfPHjg5+fn4+NjYGDQsIcHAIDW4UVpWZ3XNrIa+vOFh4fn5eUtWrToxIkT5ubm48ePP336tCLDvgAAgEox1ubXeW0jq7knu7q6+ueffx4fH5+RkfHxxx/PmjXLwsLizZs3jRAcAAC0FE5WBkJdftVHeSxChLp8J6tmVGVYizFcWCwWi8WiKEp6WnYAAABCCIfNCva0I4RIJz/6dbCnXfNp3kIUyXzl5eX79+8fPHhw586db9269csvv+Tm5mppaTVCcAAA0IJ42AsjfHqa6v5XsWmqy29uXRpIjS1cZs2aFRsb2759+ylTpsTGxrZpU7uWOQAAoFI87IWD7Uyb+RguNfdqaN++fY8ePaodq+zw4cNKC6yO0KsBAKB1aLJeDZMmTapxfE4AAIAWpIbMt2vXrkYJAwAAoJEocX4+AACAZgiZDwAAVAsyHwAAqBZkPgAAUC3IfAAAoFqQ+QAAQLUg8wEAgGpB5gMAANWCzAcAAKoFmQ8AAFQLMh8AAKiWGsbtbGXEYrFIJGrqKKCxcblcDofT1FEAQHOhKpmPoqj8/PzXr183dSDQNPT09ExNTTHxCAAQ1cl8dNozNjbW0NDA5U+lUBT17t27Fy9eEEKEwuY1MTQANAmVyHxisZhOe5hTXjUJBAJCyIsXL4yNjVHtCQAq0cKFfranoaHR1IFAk6H/+njKCwBERTIfDZWcqgx/fQBgqFDmUynLly/v3r17U0cBANAcIfM1Nl9fX9b/unfvXv13y2Kxjh49yrz99ttvz549W//dAgC0PirRwqW58fDwiI6OZt4aGRkxrysqKng8Xv0PoaWlpaWlVatNRCIRl8ut/6EBAJo53PPJI5ZQifcLj6U9TbxfKJZQDbVbdXV1UykDBw6cM2dOQECAoaHh4MGDCSEXL150cnJSV1cXCoWLFy+urKykN+zfv//cuXMXLlxoYGBgamq6fPlyermlpSUhxMvLi8Vi0a9lajujo6NtbW35fL6NjU14eDi98OHDhywW67fffuvfvz+fz4+JiWmoEwQAaM5wz/dBp9LzQk5k5BWX0W+FuvxgTzsPe6V0CNu9e/fMmTOvXLlCUdTTp0+HDx/u6+u7Z8+eO3fu+Pv78/l8Jsnt3r07ICDgn3/+SUxM9PX1dXNzGzx48LVr14yNjaOjoz08PKq22o+MjAwODv7ll1969Ohx/fp1f39/TU3NyZMn02sXLVoUFhYWHR2trq6ujFMDAGhukPmqdyo9b2ZMqvRdXn5x2cyY1AifnvVPfidPnmSqIocNG0YI+eijj9atW0cvWbJkibm5+S+//MJisWxsbJ49e7Zo0aJly5ax2WxCSNeuXYODgwkhHTt2/OWXX86ePTt48GC6vpQepqTq4VauXBkWFjZ27FhCiJWVVUZGxrZt25jMN3/+fHoVAICKQOarhlhChZzIkKncpAhhERJyImOwnSmHXa8m8gMGDIiIiKBfa2pqfv75546OjszazMxMFxcXphW+m5vbmzdvnjx50r59e0JI165dmZJCoZAemkSOly9fPn78eOrUqf7+/vSSyspKXV1dpoD0oQEAVAEyXzWScoqYSk5pFCF5xWVJOUUu1vUaC0ZTU/Ojjz6SWfLfUShKuvMZRVFEqjuadCMUFoslkUjkH4suEBkZ6ezszCyUrhGVPjQAgCpQeguX8PBwKysrPp/v4OBw6dKlasts2bLF1tZWIBB07tx5z549zPLDhw87Ojrq6elpamp279597969yo6W9qK0mrSn4Nr6s7OzS0hIoBMeISQhIUFbW9vMzEz+VlwuVywWV11uYmJiZmb24MGDj6RYWVk1fNwAAC2Ecu/5Dhw4MH/+/PDwcDc3t23btg0bNiwjI4OutWNEREQEBgZGRkb26tUrKSnJ399fX1/f09OTEGJgYLBkyRIbGxsej3fy5MkpU6YYGxsPHTpUqTETQoy1+XVeW3+zZs3auHHj119/PWfOnKysrODg4ICAAPohnxyWlpZnz551c3NTV1fX19eXXrV8+fK5c+fq6OgMGzasvLw8OTn51atXAQEByjwJAIDmS7n3fBs2bJg6deq0adNsbW03btxobm7OPN9i7N27d/r06d7e3h06dJgwYcLUqVPXrl1Lr+rfv7+Xl5etra21tfW8efO6du16+fJlpQZMc7IyEOryqz7KYxEi1OU7WRko9ehmZmZxcXFJSUndunWbMWPG1KlTly5dWuNWYWFh8fHx5ubmPXr0kFk1bdq0HTt27Nq1q0uXLu7u7rt27cI9HwCoMhZTq9bgKioqNDQ0Dh486OXlRS+ZN29eWlraxYsXpYs5ODgMHz585cqV9NvAwMCwsLC3b99KP9CiKOrcuXOjRo06evQo3eNNWnl5eXl5Of26pKTE3Ny8uLhYR0eHKVBWVpaTk0NXuioYPN22kxDCfDp0ImyQtp3Q+OrwHQCAplVSUqKrqytzPW8QSrznKygoEIvFJiYmzBITE5P8/HyZYkOHDt2xY0dKSgpFUcnJyVFRUSKRqKCggF5bXFyspaXF4/FGjBixefPmqmmPEBIaGqr7L3Nz8wYJ3sNeGOHT01T3v6ukqS4faQ8AoBVQettOmWaKVYfMDwoKys/P7927N0VRJiYmvr6+69atYxofamtrp6WlvXnz5uzZswEBAR06dOjfv7/MHgIDA5mnVvQ9X4NE7mEvHGxnmpRT9KK0zFib72RlUM/ODAAA0BwoMfMZGhpyOBzpm7wXL15I3wLSBAJBVFTUtm3bnj9/LhQKt2/frq2tbWhoSK9ls9l0B4Du3btnZmaGhoZWzXzq6upKGn+Ew2bVswMDAAA0N0qs7eTxeA4ODvHx8cyS+Ph4V1fXagtzudx27dpxOJzY2NiRI0dW25SRoijmeR4AAEDdKLe2MyAgYOLEiY6Oji4uLtu3b8/NzZ0xYwYhJDAw8OnTp3TXvezs7KSkJGdn51evXm3YsCE9PX337t305qGhoY6OjtbW1hUVFXFxcXv27KnaNBQAAKBWlJv5vL29CwsLV6xYkZeXZ29vHxcXZ2FhQQjJy8vLzc2ly4jF4rCwsKysLC6XO2DAgISEBHq2AULI27dvZ82a9eTJE4FAYGNjExMT4+3trdSAAQCg1VNir4YmUW0rWLRoB3wHAFqcFtmrAQAAoBlC5gMAANWCzNfc0TOnp6WlyS+2a9cuPT09ZR+lobBYrKNHjzb+cQEACDJfk6AnzGvbti2Px7OwsJg3b15hYeGHCpubm9Ptg+Tv09vbOzs7u6EjVTpFzs7X13fMmDGNFREAtH7IfI3twYMHjo6O2dnZ+/fvv3fv3tatW8+ePevi4lJUVFS1cEVFBYfDMTU1VVOroRWuQCAwNjZWTsgKEYlEddhKwbOrg4qKigbfJwC0Dsh8NRG9J3//SETvG2p/s2fP5vF4Z86ccXd3b9++/bBhw/7666+nT58uWbKELmBpablq1SpfX19dXV1/f3+Z+sDjx4937NhRIBAMGDBg9+7dLBbr9evX5H9rO5cvX05PZ2hpaamrqzthwoTS0lJ61alTp/r06aOnp9emTZuRI0fev3+/xoAtLS1Xrlz5xRdfaGlptW3bdvPmzcwqFou1devW0aNHa2pqrlq1ihBy4sQJBwcHPp/foUOHkJCQyspKuuTdu3f79evH5/Pt7OykBzeQObvbt2+PGDFCR0dHW1u7b9++9+/fX758+e7du48dO8ZisVgs1oULFwght27d+uSTTwQCQZs2bb766qs3b97Qm9N3h6GhoW3btu3UqVOt/zYAoBqQ+WqScZycW0kyTzTIzoqKik6fPj1r1iyBQMAsNDU1/fLLLw8cOMD0MFm/fr29vX1KSkpQUJD05g8fPhw3btyYMWPS0tKmT5/OJMuq7t+/f/To0ZMnT548efLixYs//PADvfzt27cBAQHXrl07e/Ysm8328vKqcVZ3Op6uXbumpqYGBgYuWLBAOnUFBwePHj361q1bfn5+p0+f9vHxmTt3bkZGxrZt23bt2rV69WpCiEQiGTt2LIfDuXr16tatWxctWlTtUZ4+fUpnx3PnzqWkpPj5+VVWVn777bfjx4/38PDIy8vLy8tzdXV99+6dh4eHvr7+tWvXDh48+Ndff82ZM4fZydmzZzMzM+Pj40+ePFnjeQGAiqJal+LiYkJIcXGx9ML3799nZGS8f/++Lnvc500F61C/TmiQ8K5evUoIOXLkiMzyDRs2EEKeP39OUZSFhcWYMWOYVTk5OYSQ69evUxS1aNEie3t7ZhWd+V69ekVRVHR0tK6uLr08ODhYQ0OjpKSEfvvdd985OztXDebFixeEkFu3bskcRYaFhYWHhwfz1tvbe9iwYfRrQsj8+fOZVX379l2zZg3zdu/evUKhkKKo06dPczicx48f08v//PNP5kOQPm5gYKCVlVVFRYVMAJMnTx49ejTzdvv27fr6+m/evKHf/vHHH2w2Oz8/ny5pYmJSXl5e9Szq9R0AgFqqFEsS7hUcvf4k4V5BpVhSt51Uez1vEEqfq6FFevOCJG4hkkpCCLl/lhBC7v1FTi8hhBC2GnGZQ7SMGvaAFEURqXktHB0dqy2WlZXVq1cv5q2Tk9OHdmhpaamtrU2/FgqFdJIjhNy/fz8oKOjq1asFBQX03V5ubm6NzWdcXFykX2/cuJF5Kx1qSkrKtWvX6Ps8QohYLC4rK3v37l1mZmb79u3btWtXdW/S0tLS+vbtKz0vY7UyMzO7deumqalJv3Vzc5NIJFlZWfRg6F26dOHxePL3AABKdSo9L+RERl5xGf1WqMsP9rRrVlO8IfNVpzSfXI0g4nLCYv//lLQSMbkaTigJ4agT+0/rnPk++ugjFouVkZEh01jxzp07+vr6zAwVzGVdBvW/0zxRHx5/Rzp/sFgspkrT09PT3Nw8MjKybdu2EonE3t6+Di1BpGOQDlUikYSEhIwdO1a6MJ/Pl4mz6kxVNOkaYDmo6ua6YpZ86KMDgMZBT+st/X8+v7hsZkxqs5rfFM/5qiPsSqZfJIadCCGEEv/3r2EnMv0iEXat847btGkzePDg8PDw9+//azKTn5+/b98+b2/vD6UEho2NzbVr15i3ycnJtTp6YWFhZmbm0qVLBw4caGtr++rVKwU3pCtpmdc2NjbVFuvZs2dWVtZH/4vNZtvZ2eXm5j579owulpiYWO3mXbt2vXTpUtU2ojweTywWM2/t7OzS0tLevn1Lv71y5QqbzUZ7FoDmQCyhQk5kyPwkp9+GnMgQS5rLYJnIfB9gbEumnpFdOPUMMbat545/+eWX8vLyoUOH/v33348fPz516tTgwYPNzMyYSkI5pk+ffufOnUWLFmVnZ//222+7du0iH76FqkpfX79Nmzbbt2+/d+/euXPnmOl8a3TlypV169ZlZ2dv2bLl4MGD8+bNq7bYsmXL9uzZs3z58tu3b2dmZh44cGDp0qWEkEGDBnXu3HnSpEk3bty4dOnShxrmzJkzp6SkZMKECcnJyXfv3t27d29WVhYhxNLS8ubNm1lZWQUFBSKR6Msvv+Tz+ZMnT05PTz9//vzXX389ceLEqvM+AkDjS8opYio5pVGE5BWXJeVU03erSSDzfdijBEJJtXukJORR9TcrtdKxY8fk5GRra2tvb29ra+uvvvpqwIABiYmJBgYGNW5rZWV16NChw4cPd+3aNSIigk4his/Ky2azY2NjU1JS7O3tFyxYsH79egU3/Oabb1JSUnr06LFy5cqwsLChQ4dWW2zo0KEnT56Mj4/v1atX7969N2zYQE/NwWazjxw5Ul5e7uTkNG3atA/l+DZt2pw7d+7Nmzfu7u4ODg6RkZF0na2/v3/nzp0dHR2NjIyuXLmioaFx+vTpoqKiXr16jRs3buDAgb/88ouCJwIASvWitJq0p+DaxoS5Gj7syHRyI5a0dyGDV5IzS8njq6Tb58Rra8MHXVerV6/eunXr48ePlXoUS0vL+fPnz58/X6lHUTbM1QDQCBLvF34eefVDa/f793axbqP43pQ3VwNauHyYsAcxsiWuXxM2h0yJIwmbiVrTXzTDw8N79erVpk2bK1eurF+/XrorGwBA03KyMhDq8vOLy2TuqFiEmOrynaxqrtlqHMh8H9Z7xn+v2RzSZ36TRSLl7t27q1atKioqat++/TfffBMYGNjUEQEA/D8OmxXsaTczJpX1b8MW8v/t40mwpx2HrWijBGVDbSeoBHwHABpNQ/XnQ20nAAC0DB72wsF2pkk5RS9Ky4y1+U5WBs3nbo+GzAcAAA2Mw2bVqjFLI1OhXg2trF4XagV/fQBgqETmo7uFvXv3rqkDgSZD//VrHBQUAFSBStR2cjgcPT09etRmDQ0NxQc9gVaAoqh37969ePFCT0+Pw+E0dTgA0PRUIvMRQkxNTQkhzJQFoGr09PTo7wAAgKpkPhaLJRQKjY2Nqw6IDK0el8vF3R4AMFQl89E4HA6ugAAAKk4lWrgAAAAwkPkAAEC1IPMBAIBqaW3P+egOyyUlJU0dCAAA1At9JVfGMBStLfOVlpYSQszNzZs6EAAAaAClpaW6uroNu8/WNleDRCJ59uyZtrZ2M++uXlJSYm5u/vjx4wYfg7y5UZEzVZHTJCpzpipymqR5nylFUaWlpW3btmWzG/jBXGu752Oz2e3atWvqKBSlo6PTDL9tyqAiZ6oip0lU5kxV5DRJMz7TBr/bo6GFCwAAqBZkPgAAUC3IfE1DXV09ODhYXV29qQNROhU5UxU5TaIyZ6oip0lU6UyltbYWLgAAAPLhng8AAFQLMh8AAKgWZD4AAFAtyHwAAKBakPkaXnh4uJWVFZ/Pd3BwuHTpUrVl9u3b161bNw0NDaFQOGXKlMLCQmbV77//bmdnp66ubmdnd+TIkcaKui7qc6aRkZF9+/bV19fX19cfNGhQUlJSIwZea/X8m9JiY2NZLNaYMWOUHm5d1fM0X79+PXv2bKFQyOfzbW1t4+LiGivwWqvnmW7cuLFz584CgcDc3HzBggVlZWWNFXjtKHKaW7ZssbW1FQgEnTt33rNnj/SqFnQtqjUKGlRsbCyXy42MjMzIyJg3b56mpuajR49kyly6dInNZv/8888PHjy4dOnSxx9/PGbMGHpVQkICh8NZs2ZNZmbmmjVr1NTUrl692ugnoZB6nukXX3yxZcuW69evZ2ZmTpkyRVdX98mTJ41+Egqp55nSHj58aGZm1rdv39GjRzde6LVRz9MsLy93dHQcPnz45cuXHz58eOnSpbS0tEY/CYXU80xjYmLU1dX37duXk5Nz+vRpoVA4f/78Rj+JmilymuHh4dra2rGxsffv39+/f7+Wltbx48fpVS3oWlQHyHwNzMnJacaMGcxbGxubxYsXy5RZv359hw4dmLebNm1q164d/Xr8+PEeHh7MqqFDh06YMEGZ8dZdPc9UWmVlpba29u7du5UUaj3V/0wrKyvd3Nx27NgxefLkZpv56nmaERERHTp0qKioaIRQ66meZzp79uxPPvmEWRUQENCnTx9lxltHipymi4vLt99+y7ydN2+em5sb/boFXYvqALWdDamioiIlJWXIkCHMkiFDhiQkJMgUc3V1ffLkSVxcHEVRz58/P3To0IgRI+hViYmJ0psPHTq06ubNQf3PVNq7d+9EIpGBgYFyg66TBjnTFStWGBkZTZ06tZGCrr36n+bx48ddXFxmz55tYmJib2+/Zs0asVjceCegsPqfaZ8+fVJSUuj6+QcPHsTFxVX7rW5aCp5meXk5n89n3goEgqSkJJFIRFrOtahukPkaUkFBgVgsNjExYZaYmJjk5+fLFHN1dd23b5+3tzePxzM1NdXT09u8eTO9Kj8/v8bNm4P6n6m0xYsXm5mZDRo0SLlB10n9z/TKlSs7d+6MjIxsvKBrr/6n+eDBg0OHDonF4ri4uKVLl4aFha1evbrxTkBh9T/TCRMmrFy5sk+fPlwu19raesCAAYsXL268E1CMgqc5dOjQHTt2pKSkUBSVnJwcFRUlEokKCgpIy7kW1Q0yX8OTniCJoqiq8yVlZGTMnTt32bJlKSkpp06dysnJmTFjhuKbNx/1PFPaunXr9u/ff/jwYenfns1Nnc+0tLTUx8cnMjLS0NCwUSOuk/r8QSUSibGx8fbt2x0cHCZMmLBkyZKIiIjGC72W6nOmFy5cWL16dXh4eGpq6uHDh0+ePLly5crGC702ajzNoKCgYcOG9e7dm8vljh492tfXlxDC4XAU3LwFa4oq1larvLycw+EcPnyYWTJ37tx+/frJFPPx8Rk3bhzzlm5z9ezZM4qizM3NN2zYwKzasGFD+/btlRx1XdT/TGnr16/X1dW9du2asgOus3qe6fXr1wkhnH+xWCwWi8XhcO7du9dIJ6CY+v9B+/XrN3DgQGYV3bCzvLxcyYHXWv3PtE+fPtLPxvbu3SsQCMRisZIDrx0FT5NWUVHx+PHjyspKusELfS4t5VpUN7jna0g8Hs/BwSE+Pp5ZEh8f7+rqKlPs3bt30hMt0r+wKIoihLi4uEhvfubMmaqbNwf1P1NCyPr161euXHnq1ClHR0flh1xH9TxTGxubW7dupf1r1KhRAwYMSEtLMzc3b5z4FVT/P6ibm9u9e/ckEgm9Kjs7WygU8ng8pYdeS/U/06qr6IupcuOuJQVPk8blctu1a8fhcGJjY0eOHEmfXUu5FtVRk+XcVopuSbxz586MjIz58+dramo+fPiQoqjFixdPnDiRLhMdHa2mphYeHn7//v3Lly87Ojo6OTnRq65cucLhcH744YfMzMwffvihObckrueZrl27lsfjHTp0KO9fpaWlTXYyctXzTKU157ad9TzN3NxcLS2tOXPmZGVlnTx50tjYeNWqVU12MnLV80yDg4O1tbX379//4MGDM2fOWFtbjx8/vslO5sMUOc2srKy9e/dmZ2f/888/3t7eBgYGOTk59KoWdC2qA2S+hrdlyxYLCwsej9ezZ8+LFy/SCydPnuzu7s6U2bRpk52dnUAgEAqFX375pXRXtoMHD3bu3JnL5drY2Pz++++NHHyt1OdMLSwsZH6EBQcHN/oZKKqef1NGc858VL1PMyEhwdnZWV1dvUOHDqtXr66srGzk+BVXnzMViUTLly+3trbm8/nm5uazZs169epVo5+BQmo8zYyMjO7duwsEAh0dndGjR9+5c0d68xZ0LaotzFIEAACqBc/5AABAtSDzAQCAakHmAwAA1YLMBwAAqgWZDwAAVAsyHwAAqBZkPgAAUC3IfAAAoFqQ+QCUjp7e2sPDo6kDAQBCCMEYLgBKN23aNC0trR07dmRkZLRv315JRxGJRFwuV0k7B2hNcM8HoFxv37797bffZs6cOXLkyF27djHLjx8/7ujoyOfzDQ0Nx44dSy8sLy9fuHChubm5urp6x44dd+7cSQjZtWuXnp4es+HRo0eZmdKWL1/evXv3qKioDh06qKurUxR16tSpPn366OnptWnTZuTIkffv32c2fPLkyYQJEwwMDDQ1NR0dHf/555+HDx+y2ezk5GSmzObNmy0sLPCDGFo3ZD4A5Tpw4EDnzp07d+7s4+MTHR1NJ5U//vhj7NixI0aMuH79+tmzZ5l5miZNmhQbG7tp06bMzMytW7dqaWnVuP979+799ttvv//+e1paGiHk7du3AQEB165dO3v2LJvN9vLyomcOevPmjbu7+7Nnz44fP37jxo2FCxdKJBJLS8tBgwZFR0cze4uOjvb19W1Vc5ACVNWEo2UDqAJXV9eNGzdSFCUSiQwNDePj4ymKcnFx+fLLL2VKZmVlEULoAtKio6N1dXWZt0eOHGH+5wYHB3O53BcvXlR76BcvXhBCbt26RVHUtm3btLW1CwsLZcocOHBAX1+/rKyMoqi0tDQWi8XMUwPQWuGeD0CJsrKykpKSJkyYQAhRU1Pz9vaOiooihKSlpQ0cOFCmcFpaGofDcXd3r9UhLCwsjIyMmLf379//4osvOnTooKOjY2VlRQjJzc2ld96jRw8DAwOZzceMGaOmpkZn06ioqAEDBlhaWtb2NAFaFrWmDgCgNdu5c2dlZaWZmRn9lqIoLpf76tUrgUBQtXC1CwkhbDabknrwJhKJpNdqampKv/X09DQ3N4+MjGzbtq1EIrG3t6+oqJCzcx6PN3HixOjo6LFjx/76668bN26sxekBtEy45wNQlsrKyj179oSFhaX968aNGxYWFvv27evatevZs2dlynfp0kUikVy8eFFmuZGRUWlp6du3b+m39PO8ahUWFmZmZi5dunTgwIG2travXr1iVnXt2jUtLa2oqKjqVtOmTfvrr7/Cw8NFIhHT1gagNWvq6laAVuvIkSM8Hu/169fSC7///vvu3bufP3+ezWYvW7YsIyPj5s2ba9eupdf6+vqam5sfOXLkwYMH58+fP3DgAEVRhYWFmpqac+fOvXv37r59+9q2bUuknvN169aN2blYLG7Tpo2Pj8/du3fPnj3bq1cvQsiRI0coiiovL+/UqVPfvn0vX758//79Q4cOJSQkMBu6urryeLwZM2Yo9xMBaB5wzwegLDt37hw0aJCurq70wk8//TQtLU1HR+fgwYPHjx/v3r37J5988s8//9BrIyIixo0bN2vWLBsbG39/f/o+z8DAICYmJi4urkuXLvv371++fPmHjshms2NjY1NSUuzt7RcsWLB+/XpmFY/HO3PmjLGx8fDhw7t06fLDDz9wOBxm7dSpUysqKvz8/Br2EwBontCTHQDI6tWrY2Njb9261dSBADQG3PMBqLQ3b95cu3Zt8+bNc+fObepYABoJMh+ASpszZ06fPn3c3d1R1QmqA7WdAACgWnDPBwAAqgWZDwAAVAsyHwAAqBZkPgAAUC3IfAAAoFqQ+QAAQLUg8wEAgGpB5gMAANWCzAcAAKrl/wAL2qDVlRLUoQAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -996,17 +1038,17 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:28.818105Z",
-     "iopub.status.busy": "2024-06-17T14:24:28.817742Z",
-     "iopub.status.idle": "2024-06-17T14:24:30.602588Z",
-     "shell.execute_reply": "2024-06-17T14:24:30.601757Z"
+     "iopub.execute_input": "2024-06-17T19:19:45.931923Z",
+     "iopub.status.busy": "2024-06-17T19:19:45.931653Z",
+     "iopub.status.idle": "2024-06-17T19:19:48.604594Z",
+     "shell.execute_reply": "2024-06-17T19:19:48.603725Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 16,
@@ -1015,7 +1057,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1038,10 +1080,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:30.609495Z",
-     "iopub.status.busy": "2024-06-17T14:24:30.609304Z",
-     "iopub.status.idle": "2024-06-17T14:24:31.473190Z",
-     "shell.execute_reply": "2024-06-17T14:24:31.472600Z"
+     "iopub.execute_input": "2024-06-17T19:19:48.608996Z",
+     "iopub.status.busy": "2024-06-17T19:19:48.608408Z",
+     "iopub.status.idle": "2024-06-17T19:19:50.075933Z",
+     "shell.execute_reply": "2024-06-17T19:19:50.075118Z"
     }
    },
    "outputs": [
@@ -1057,7 +1099,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
diff --git a/examples/multi_group_fairlearn_comparision.ipynb b/examples/multi_group_fairlearn_comparision.ipynb
index a1804c5..a303a08 100644
--- a/examples/multi_group_fairlearn_comparision.ipynb
+++ b/examples/multi_group_fairlearn_comparision.ipynb
@@ -16,10 +16,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:43.314349Z",
-     "iopub.status.busy": "2024-06-17T14:23:43.313992Z",
-     "iopub.status.idle": "2024-06-17T14:23:55.636213Z",
-     "shell.execute_reply": "2024-06-17T14:23:55.635682Z"
+     "iopub.execute_input": "2024-06-17T19:18:59.780110Z",
+     "iopub.status.busy": "2024-06-17T19:18:59.779876Z",
+     "iopub.status.idle": "2024-06-17T19:19:12.732591Z",
+     "shell.execute_reply": "2024-06-17T19:19:12.731927Z"
     }
    },
    "outputs": [
@@ -36,7 +36,7 @@
      "output_type": "stream",
      "text": [
       "Training time of xgboost without fairness\n",
-      "0.5277408750262111\n"
+      "0.635854082996957\n"
      ]
     }
    ],
@@ -67,10 +67,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:55.638434Z",
-     "iopub.status.busy": "2024-06-17T14:23:55.638238Z",
-     "iopub.status.idle": "2024-06-17T14:23:56.298681Z",
-     "shell.execute_reply": "2024-06-17T14:23:56.297655Z"
+     "iopub.execute_input": "2024-06-17T19:19:12.735037Z",
+     "iopub.status.busy": "2024-06-17T19:19:12.734923Z",
+     "iopub.status.idle": "2024-06-17T19:19:13.249576Z",
+     "shell.execute_reply": "2024-06-17T19:19:13.248706Z"
     }
    },
    "outputs": [
@@ -106,10 +106,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:56.303111Z",
-     "iopub.status.busy": "2024-06-17T14:23:56.302256Z",
-     "iopub.status.idle": "2024-06-17T14:23:56.305978Z",
-     "shell.execute_reply": "2024-06-17T14:23:56.305088Z"
+     "iopub.execute_input": "2024-06-17T19:19:13.253506Z",
+     "iopub.status.busy": "2024-06-17T19:19:13.252878Z",
+     "iopub.status.idle": "2024-06-17T19:19:13.256530Z",
+     "shell.execute_reply": "2024-06-17T19:19:13.255782Z"
     }
    },
    "outputs": [],
@@ -124,10 +124,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:56.308622Z",
-     "iopub.status.busy": "2024-06-17T14:23:56.308428Z",
-     "iopub.status.idle": "2024-06-17T14:26:02.287261Z",
-     "shell.execute_reply": "2024-06-17T14:26:02.286527Z"
+     "iopub.execute_input": "2024-06-17T19:19:13.259854Z",
+     "iopub.status.busy": "2024-06-17T19:19:13.259366Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.415464Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.414759Z"
     }
    },
    "outputs": [
@@ -407,10 +407,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:02.302572Z",
-     "iopub.status.busy": "2024-06-17T14:26:02.302159Z",
-     "iopub.status.idle": "2024-06-17T14:26:02.308085Z",
-     "shell.execute_reply": "2024-06-17T14:26:02.307756Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.426894Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.426731Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.431204Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.430899Z"
     }
    },
    "outputs": [],
@@ -424,10 +424,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:02.310624Z",
-     "iopub.status.busy": "2024-06-17T14:26:02.310506Z",
-     "iopub.status.idle": "2024-06-17T14:26:02.322929Z",
-     "shell.execute_reply": "2024-06-17T14:26:02.322577Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.432852Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.432741Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.445436Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.445158Z"
     }
    },
    "outputs": [
@@ -463,39 +463,39 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.866162\n",
-       "      0.016523\n",
-       "      93.872962\n",
-       "      0.866025\n",
-       "      0.026250\n",
-       "      10.156691\n",
+       "      0.868482\n",
+       "      0.045112\n",
+       "      95.299039\n",
+       "      0.868687\n",
+       "      0.046261\n",
+       "      16.292495\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.865547\n",
-       "      0.016412\n",
-       "      0.827107\n",
-       "      0.864728\n",
-       "      0.025873\n",
-       "      9.209420\n",
+       "      0.868073\n",
+       "      0.029650\n",
+       "      1.049697\n",
+       "      0.870461\n",
+       "      0.015604\n",
+       "      10.474953\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.867049\n",
-       "      0.002780\n",
-       "      0.052135\n",
-       "      0.866844\n",
-       "      0.011727\n",
-       "      5.850473\n",
+       "      0.868209\n",
+       "      0.009898\n",
+       "      0.043179\n",
+       "      0.871280\n",
+       "      0.009638\n",
+       "      3.923120\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.867185\n",
-       "      0.001365\n",
-       "      0.034907\n",
-       "      0.867595\n",
-       "      0.005899\n",
-       "      5.608394\n",
+       "      0.870461\n",
+       "      0.013591\n",
+       "      0.026398\n",
+       "      0.870598\n",
+       "      0.027901\n",
+       "      4.758493\n",
        "    \n",
        "  \n",
        "\n",
@@ -503,16 +503,16 @@
       ],
       "text/plain": [
        "   Accuracy  Demographic Parity       Time  Accuracy  Demographic Parity  \\\n",
-       "0  0.866162            0.016523  93.872962  0.866025            0.026250   \n",
-       "1  0.865547            0.016412   0.827107  0.864728            0.025873   \n",
-       "2  0.867049            0.002780   0.052135  0.866844            0.011727   \n",
-       "3  0.867185            0.001365   0.034907  0.867595            0.005899   \n",
+       "0  0.868482            0.045112  95.299039  0.868687            0.046261   \n",
+       "1  0.868073            0.029650   1.049697  0.870461            0.015604   \n",
+       "2  0.868209            0.009898   0.043179  0.871280            0.009638   \n",
+       "3  0.870461            0.013591   0.026398  0.870598            0.027901   \n",
        "\n",
        "        Time  \n",
-       "0  10.156691  \n",
-       "1   9.209420  \n",
-       "2   5.850473  \n",
-       "3   5.608394  "
+       "0  16.292495  \n",
+       "1  10.474953  \n",
+       "2   3.923120  \n",
+       "3   4.758493  "
       ]
      },
      "execution_count": 6,
diff --git a/examples/pytorch_minimal_demo.ipynb b/examples/pytorch_minimal_demo.ipynb
index 45931bd..8e174ed 100644
--- a/examples/pytorch_minimal_demo.ipynb
+++ b/examples/pytorch_minimal_demo.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "This file shows a minimal pytorch example running on adult data, and demonstrating head-merging.\n",
     "\n",
-    "In general, we strongly recomend that you *do not* use neural networks on tabular data. Boosting is typically higher performing and much faster to run.\n",
+    "In general, we strongly recommend that you *do not* use neural networks on tabular data. Boosting is typically higher performing and much faster to run.\n",
     "\n",
     "However, this is a toy example, and unlike computer vision examples, it should train and run in a matter of minutes. The techniques shown will directly apply to computer vision and NLP without modification."
    ]
@@ -18,10 +18,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:43.300631Z",
-     "iopub.status.busy": "2024-06-17T14:23:43.300473Z",
-     "iopub.status.idle": "2024-06-17T14:23:47.957431Z",
-     "shell.execute_reply": "2024-06-17T14:23:47.956993Z"
+     "iopub.execute_input": "2024-06-17T19:18:59.786451Z",
+     "iopub.status.busy": "2024-06-17T19:18:59.786070Z",
+     "iopub.status.idle": "2024-06-17T19:19:04.141132Z",
+     "shell.execute_reply": "2024-06-17T19:19:04.140766Z"
     }
    },
    "outputs": [
@@ -51,10 +51,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:47.959280Z",
-     "iopub.status.busy": "2024-06-17T14:23:47.959087Z",
-     "iopub.status.idle": "2024-06-17T14:23:55.593617Z",
-     "shell.execute_reply": "2024-06-17T14:23:55.592547Z"
+     "iopub.execute_input": "2024-06-17T19:19:04.142997Z",
+     "iopub.status.busy": "2024-06-17T19:19:04.142811Z",
+     "iopub.status.idle": "2024-06-17T19:19:12.275644Z",
+     "shell.execute_reply": "2024-06-17T19:19:12.274964Z"
     }
    },
    "outputs": [],
@@ -68,10 +68,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:55.607947Z",
-     "iopub.status.busy": "2024-06-17T14:23:55.602759Z",
-     "iopub.status.idle": "2024-06-17T14:23:55.616107Z",
-     "shell.execute_reply": "2024-06-17T14:23:55.614028Z"
+     "iopub.execute_input": "2024-06-17T19:19:12.278230Z",
+     "iopub.status.busy": "2024-06-17T19:19:12.277862Z",
+     "iopub.status.idle": "2024-06-17T19:19:12.282936Z",
+     "shell.execute_reply": "2024-06-17T19:19:12.282509Z"
     }
    },
    "outputs": [],
@@ -88,10 +88,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:55.623305Z",
-     "iopub.status.busy": "2024-06-17T14:23:55.622503Z",
-     "iopub.status.idle": "2024-06-17T14:23:55.631965Z",
-     "shell.execute_reply": "2024-06-17T14:23:55.630811Z"
+     "iopub.execute_input": "2024-06-17T19:19:12.285243Z",
+     "iopub.status.busy": "2024-06-17T19:19:12.284809Z",
+     "iopub.status.idle": "2024-06-17T19:19:12.289646Z",
+     "shell.execute_reply": "2024-06-17T19:19:12.289119Z"
     }
    },
    "outputs": [],
@@ -109,10 +109,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:55.638778Z",
-     "iopub.status.busy": "2024-06-17T14:23:55.637073Z",
-     "iopub.status.idle": "2024-06-17T14:23:55.643636Z",
-     "shell.execute_reply": "2024-06-17T14:23:55.641581Z"
+     "iopub.execute_input": "2024-06-17T19:19:12.291648Z",
+     "iopub.status.busy": "2024-06-17T19:19:12.291459Z",
+     "iopub.status.idle": "2024-06-17T19:19:12.294650Z",
+     "shell.execute_reply": "2024-06-17T19:19:12.294038Z"
     }
    },
    "outputs": [],
@@ -130,10 +130,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:55.647290Z",
-     "iopub.status.busy": "2024-06-17T14:23:55.646346Z",
-     "iopub.status.idle": "2024-06-17T14:23:56.994506Z",
-     "shell.execute_reply": "2024-06-17T14:23:56.993352Z"
+     "iopub.execute_input": "2024-06-17T19:19:12.297813Z",
+     "iopub.status.busy": "2024-06-17T19:19:12.296845Z",
+     "iopub.status.idle": "2024-06-17T19:19:13.143869Z",
+     "shell.execute_reply": "2024-06-17T19:19:13.083352Z"
     }
    },
    "outputs": [],
@@ -152,10 +152,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:57.003938Z",
-     "iopub.status.busy": "2024-06-17T14:23:57.002040Z",
-     "iopub.status.idle": "2024-06-17T14:24:11.896988Z",
-     "shell.execute_reply": "2024-06-17T14:24:11.872141Z"
+     "iopub.execute_input": "2024-06-17T19:19:13.146915Z",
+     "iopub.status.busy": "2024-06-17T19:19:13.146763Z",
+     "iopub.status.idle": "2024-06-17T19:19:27.847654Z",
+     "shell.execute_reply": "2024-06-17T19:19:27.845472Z"
     }
    },
    "outputs": [
@@ -220,10 +220,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:11.968095Z",
-     "iopub.status.busy": "2024-06-17T14:24:11.966647Z",
-     "iopub.status.idle": "2024-06-17T14:24:12.038349Z",
-     "shell.execute_reply": "2024-06-17T14:24:12.037641Z"
+     "iopub.execute_input": "2024-06-17T19:19:27.861148Z",
+     "iopub.status.busy": "2024-06-17T19:19:27.860200Z",
+     "iopub.status.idle": "2024-06-17T19:19:27.979998Z",
+     "shell.execute_reply": "2024-06-17T19:19:27.978176Z"
     }
    },
    "outputs": [],
@@ -236,13 +236,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To see if training works we visualise the outputs of the second head (corresponding to gender)\n",
+    "To see if training works we visualize the outputs of the second head (corresponding to gender)\n",
     "\n",
-    "Ideally, this should be a two peaked distribution centred on 0 and 1, with more values at 1 -- corresponding to the male label.\n",
+    "Ideally, this should be a two peaked distribution centered on 0 and 1, with more values at 1 -- corresponding to the male label.\n",
     "\n",
-    "Instead we find we have 1 peak centered on 1, a broader central peak corresponding to 'don't know' centered around 0.5 and a third smaller peak around 0.2 and 0.\n",
+    "Instead, we find we have 1 peak centered on 1, a broader central peak corresponding to 'don't know' centered around 0.5 and a third smaller peak around 0.2 and 0.\n",
     "\n",
-    "This  is fine. The broad central peak reflects the ambiguity of the task, and otherwise we do have something of a split into male and female."
+    "This is fine. The broad central peak reflects the ambiguity of the task, and otherwise we do have something of a split into male and female."
    ]
   },
   {
@@ -250,16 +250,16 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:12.043628Z",
-     "iopub.status.busy": "2024-06-17T14:24:12.043377Z",
-     "iopub.status.idle": "2024-06-17T14:24:13.740181Z",
-     "shell.execute_reply": "2024-06-17T14:24:13.739523Z"
+     "iopub.execute_input": "2024-06-17T19:19:27.986127Z",
+     "iopub.status.busy": "2024-06-17T19:19:27.985936Z",
+     "iopub.status.idle": "2024-06-17T19:19:29.234054Z",
+     "shell.execute_reply": "2024-06-17T19:19:29.231943Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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2NezcuXNdXV0mk0kIkZSUZLPZTpw4oXUdP37cZrMlJyd7UgYAYHBwa53U3t7+ySefaNuNjY11dXXh4eFms/nHP/7x6dOn/+u//qu7u1u7JhQeHh4cHPzpp58WFxc//vjjERER9fX1q1evnjx58rRp04QQEyZMSEtLy8nJ0e4OX7ZsWUZGBjfdAQCEm5l06tSpmTNnatvaZZ7FixcXFha+++67Qogf/OAHzpHvvffejBkzgoOD//rXv/7ud79rb2+Piop64oknXnrppYCAAG1McXHxqlWrUlNThRCZmZk9vvYEALhvuZVJM2bMUFX1zvZeG4UQUVFRFRUVfb1beHj4rl273KwPAHD/4PfuAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsnArk44ePTpnzhyz2azT6Q4cOOBsV1W1sLDQbDYPGzZsxowZ586dc3Y5HI6VK1dGRESEhIRkZmZeuXLF2dXS0mKxWBRFURTFYrG0trZ6bTYAAH/mVibdvHlz0qRJGzdu7NH+6quvrl+/fuPGjSdPnjQajY899lhbW5vWlZubW1JSsnfv3srKyvb29oyMjO7ubq0rOzu7rq6utLS0tLS0rq7OYrF4cT4AAP+lU1X1G4zW6UpKSubOnSuEUFXVbDbn5uY+//zzQgiHwxEZGfnrX//6qaeestlsDzzwwM6dOxcsWCCEuHr1alRU1KFDh2bPnn3+/PmHHnqopqYmISFBCFFTU5OUlPTRRx+NHz++rz/UbrcrimKz2cLCwjycLQB40Zg1B+9hr4uvPOH1SvxFv5/n9349qbGx0Wq1pqamai/1ev306dOrqqqEELW1tV1dXc4us9kcGxurdVVXVyuKogWSECIxMVFRFK3LlcPhsLu45yIBAH7k3jPJarUKISIjI50tkZGRWqPVag0ODh4xYkSvXQaDwfV9DAaD1uWqqKhIuS0qKuqeiwQA+BFP77vT6XTObVVVXV+6cu3qMabXvQoKCmy3Xb582cMiAQB+4d4zyWg0iturJU1zc7O2bDIajZ2dnS0tLb12Xbt2zfV9rl+/7rrY0uj1+jAX91wkAMCP3HsmjR071mg0lpWVaS87OzsrKiqSk5OFEPHx8UFBQc6upqams2fPal1JSUk2m+3EiRNa1/Hjx202m9YFALjPBbozqL29/ZNPPtG2Gxsb6+rqwsPDR48enZubu27dupiYmJiYmHXr1g0fPjw7O1sIoSjK0qVLV69ePXLkyPDw8Pz8/Li4uJSUFCHEhAkT0tLScnJytmzZIoRYtmxZRkbGXW66AwDcP9zKpFOnTs2cOVPbzsvLE0IsXrx4x44dzz33XEdHx/Lly1taWhISEg4fPhwaGqoN27BhQ2BgYFZWVkdHx6xZs3bs2BEQEKB1FRcXr1q1SrsrLzMz886vPQEA7k/f7PtJPsH3kwDIie8nfVPf4veTAADwLjIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIItAT3YeM2bMpUuXXFuWL1/+xhtvLFmy5K233nI2JiQk1NTUaNsOhyM/P3/Pnj0dHR2zZs3atGnTqFGjPKkBAPzLmDUH++q6+MoTA1mJhDxaJ508ebLptrKyMiHET37yE60rLS3N2XXo0CHnLrm5uSUlJXv37q2srGxvb8/IyOju7vakBgDAoOHROumBBx5wbr/yyivf+973pk+frr3U6/VGo7HHeJvNtm3btp07d6akpAghdu3aFRUVVV5ePnv2bE/KAAAMDt65ntTZ2blr166f//znOp1Oa3n//fcNBsO4ceNycnKam5u1xtra2q6urtTUVO2l2WyOjY2tqqrySg0AAH/n0TrJ6cCBA62trUuWLNFepqen/+QnP4mOjm5sbPzlL3/56KOP1tbW6vV6q9UaHBw8YsQI546RkZFWq/XON3Q4HA6HQ9u22+1eKRIAIDnvZNK2bdvS09PNZrP2csGCBdpGbGzs1KlTo6OjDx48OG/evDt3VFXVubRyVVRUtHbtWq/UBgDwF144d3fp0qXy8vJf/OIXvfaaTKbo6OiGhgYhhNFo7OzsbGlpcfY2NzdHRkbeuVdBQYHttsuXL3teJABAfl7IpO3btxsMhiee6P0Wxhs3bly+fNlkMgkh4uPjg4KCtDv0hBBNTU1nz55NTk6+cy+9Xh/mwvMiAQDy8/Tc3a1bt7Zv37548eLAwP97q/b29sLCwvnz55tMposXL77wwgsRERFPPvmkEEJRlKVLl65evXrkyJHh4eH5+flxcXHaPXgAAHiaSeXl5Z9//vnPf/5zZ0tAQMCZM2fefvvt1tZWk8k0c+bMffv2hYaGar0bNmwIDAzMysrSvjO7Y8eOgIAAD2sAAAwOOlVVfV1DP+x2u6IoNpuNk3gApHKXX2S4N4P+dxz6/Tzn9+4AALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACy8PSZfgB86y6P8Bn0D+PB4MM6CQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALnjMLDFo8ghZ+x6NMKiwsXLt2rfNlZGSk1WoVQqiqunbt2j/+8Y8tLS0JCQlvvPHGxIkTtTEOhyM/P3/Pnj0dHR2zZs3atGnTqFGjPKkBuB/cJV2AwcTTc3cTJ05suu3MmTNa46uvvrp+/fqNGzeePHnSaDQ+9thjbW1tWldubm5JScnevXsrKyvb29szMjK6u7s9rAEAMDh4eu4uMDDQaDS6tqiq+tvf/vbFF1+cN2+eEOKtt96KjIzcvXv3U089ZbPZtm3btnPnzpSUFCHErl27oqKiysvLZ8+e7WEZAIBBwNN1UkNDg9lsHjt27MKFCz/77DMhRGNjo9VqTU1N1Qbo9frp06dXVVUJIWpra7u6upxdZrM5NjZW6+rB4XDYXXhYJADAL3i0TkpISHj77bfHjRt37dq1l19+OTk5+dy5c9olpcjISOewyMjIS5cuCSGsVmtwcPCIESNcu7TxPRQVFbleqQLuB1w0AjxaJ6Wnp8+fPz8uLi4lJeXgwYNCiLfeekvr0ul0zmGqqrq+dNVXV0FBge22y5cve1IkAMBfeO37SSEhIXFxcQ0NDdrlJdfVT3Nzs7ZsMhqNnZ2dLS0td3b1oNfrw1x4q0gAgMy8lkkOh+P8+fMmk2ns2LFGo7GsrExr7+zsrKioSE5OFkLEx8cHBQU5u5qams6ePat1AQDg0fWk/Pz8OXPmjB49urm5+eWXX7bb7YsXL9bpdLm5uevWrYuJiYmJiVm3bt3w4cOzs7OFEIqiLF26dPXq1SNHjgwPD8/Pz9fO+3lpLgDgZVzkG2AeZdKVK1d++tOffvHFFw888EBiYmJNTU10dLQQ4rnnnuvo6Fi+fLn2ndnDhw+HhoZqu2zYsCEwMDArK0v7zuyOHTsCAgK8MA8AgP/Tqarq6xr6YbfbFUWx2WxcWMIgIMm/u/ltITcN8N/XoP976ffznN9gBQDIgkwCAMiCTAIAyIJMAgDIgkwCAMiCTAIAyIJMAgDIgmefA94nyZeQAL/DOgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgC74zC9wjvhgLeB2ZBNyP7hKog/5Rp5AZ5+4AALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsuD37oC74YdWgYHEOgkAIAsyCQAgCzIJACALjzKpqKjohz/8YWhoqMFgmDt37oULF5xdS5Ys0blITEx0djkcjpUrV0ZERISEhGRmZl65csWTGgAAg4ZHmVRRUfHMM8/U1NSUlZV9/fXXqampN2/edPampaU13Xbo0CFne25ubklJyd69eysrK9vb2zMyMrq7uz0pAwAwOHh0311paalze/v27QaDoba29p//+Z+1Fr1ebzQae+xis9m2bdu2c+fOlJQUIcSuXbuioqLKy8tnz57tSSUAvIVH0MKHvHY9yWazCSHCw8OdLe+//77BYBg3blxOTk5zc7PWWFtb29XVlZqaqr00m82xsbFVVVU93s3hcNhdeKtIAIDMvJNJqqrm5eX96Ec/io2N1VrS09OLi4uPHDny2muvnTx58tFHH3U4HEIIq9UaHBw8YsQI576RkZFWq7XHGxYVFSm3RUVFeaVIAIDkvPOd2RUrVnz44YeVlZXOlgULFmgbsbGxU6dOjY6OPnjw4Lx58+7cV1VVnU7Xo7GgoCAvL0/bttvtxBIA3A+8kEkrV6589913jx49OmrUqF4HmEym6OjohoYGIYTRaOzs7GxpaXEulZqbm5OTk3vsotfr9Xq957UB7uDHGgBJeHTuTlXVFStW7N+//8iRI2PHju1r2I0bNy5fvmwymYQQ8fHxQUFBZWVlWldTU9PZs2fvzCQAwH3Io3XSM888s3v37nfeeSc0NFS7JqQoyrBhw9rb2wsLC+fPn28ymS5evPjCCy9EREQ8+eST2oClS5euXr165MiR4eHh+fn5cXFx2j14AID7nEeZtHnzZiHEjBkznC3bt29fsmRJQEDAmTNn3n777dbWVpPJNHPmzH379oWGhmpjNmzYEBgYmJWV1dHRMWvWrB07dgQEBHhSBgBgcPAok1RV7bV92LBhf/nLX/raa+jQoa+//vrrr7/uyR8NABh8+L07AIAsyCQAgCzIJACALMgkAIAsePY57hd8MRaQH+skAIAsyCQAgCw4dwfvkOShO5ygA/wamYRvndfjiuDxFUn+5YFBjHN3AABZsE7CNzCQCxQWQ7gPsRIlkwDc7/gHkDzIJPTE8QnAV8gk/zBYV/TkHwBXZNJ9ijAAICEyCYAXDNalPAYYmSSRe1u78FkAYNDg+0kAAFmQSQAAWXDubjDjRgYA/oV1EgBAFmQSAEAWZBIAQBZkEgBAFtzjAODbxVfo4D7WSQAAWbBOGmjcnw0AfWGdBACQBeskAPAD98llOTLpW8EJOsAdA/k5y1HpFzh3BwCQBeskAH7mPjmLdX8ik+4dpwKAbw/H1/3JZ+fuNm3aNHbs2KFDh8bHxx87dsxXZQAA5OGbddK+fftyc3M3bdo0bdq0LVu2pKen19fXjx492ifFABg0WF35O52qqgP/pyYkJEyZMmXz5s3aywkTJsydO7eoqKjXwXa7XVEUm80WFhY2gDX+H/4XB+C/ZLvA1u/nuQ/WSZ2dnbW1tWvWrHG2pKamVlVVuY5xOBwOh0PbttlsQgi73T6QRTrdcnzpkz8XADznq0/Ovmj13GUt5INM+uKLL7q7uyMjI50tkZGRVqvVdUxRUdHatWtdW6KiogaoPgAYLJTf+rqC3rS1tSmK0muXz+670+l0zm1VVV1fCiEKCgry8vK07dbW1ujo6M8//7yvOcjPbrdHRUVdvnzZJ6cfvYIpyMDfp+Dv9Qum4DFVVdva2sxmc18DfJBJERERAQEBrguj5uZm12WTEEKv1+v1etcWRVH8938CTVhYGFPwOabgc/5ev2AKnrn76sIH94IHBwfHx8eXlZU5W8rKypKTkwe+EgCAVHxz7i4vL89isUydOjUpKemPf/zj559//vTTT/ukEgCAPHyTSQsWLLhx48Z//Md/NDU1xcbGHjp0KDo6uq/Ber3+pZde6nEqz78wBRkwBZ/z9/oFU/j2+eb7SQAA3InfBQcAyIJMAgDIgkwCAMiCTAIAyELeTGppabFYLIqiKIpisVhaW1vvHNPV1fX888/HxcWFhISYzeaf/exnV69eHfBK/4E7z+CoqKiIj48fOnTogw8++Ic//GGAK+xXv1PYv3//Y4899sADD4SFhSUlJf3lL38Z+CLvzv0nofztb38LDAz8wQ9+MFClucWd+h0Ox4svvhgdHa3X67/3ve+9+eabA1zk3bkzheLi4kmTJg0fPtxkMv3Lv/zLjRs3BrjIuzh69OicOXPMZrNOpztw4EBfw2Q+lt2ZgozHsiqrtLS02NjYqqqqqqqq2NjYjIyMO8e0trampKTs27fvo48+qq6uTkhIiI+PH/hSnfbu3RsUFLR169b6+vpnn302JCTk0qVLPcZ89tlnw4cPf/bZZ+vr67du3RoUFPTnP//ZJ9X2yp0pPPvss7/+9a9PnDjx8ccfFxQUBAUFnT592ifV9sqdKWhaW1sffPDB1NTUSZMmDWyNd+Nm/ZmZmQkJCWVlZY2NjcePH//b3/428KX2xZ0pHDt2bMiQIb/73e8+++yzY8eOTZw4ce7cuT6ptleHDh168cUX//M//1MIUVJS0usYyY9ld6Yg4bEsaSbV19cLIWpqarSX1dXVQoiPPvro7nudOHFCCNHXB9AAeOSRR55++mnny+9///tr1qzpMea55577/ve/73z51FNPJSYmDlB9bnBnCj089NBDa9eu/Zbr+gbcn8KCBQv+/d///aWXXpIqk9yp/7//+78VRblx48bAluYud6bwm9/85sEHH3S+/P3vfz9q1KgBqu+buMsHuuTHstNdptCDDMeypOfuqqurFUVJSEjQXiYmJiqK0uN5Fney2Ww6ne473/nOt15fb7RncKSmpjpb7nwGhxCiurradczs2bNPnTrV1dU1QFXelZtTcHXr1q22trbw8PBvvzq3uD+F7du3f/rppy+99NIAVtc/N+t/9913p06d+uqrr373u98dN25cfn5+R0fHwFbaJzenkJycfOXKlUOHDqmqeu3atT//+c9PPCHXk376JfOxfA8kOZZ99rvgd2e1Wg0Gg2uLwWDo8TyLHr766qs1a9ZkZ2f76ocF3XkGhxDCarX2GPP1119/8cUXJpNpgArtm5tTcPXaa6/dvHkzKyvr26/OLW5OoaGhYc2aNceOHQsMlOsQcLP+zz77rLKycujQoSUlJV988cXy5cv/93//V5JLSm5OITk5ubi4eMGCBV999dXXX3+dmZn5+uuvD2ylnpL5WL4HkhzLPlsnFRYW6vpw6tQp8Y8PsxC9Pc/CVVdX18KFC2/durVp06ZvvfS7uvszOHodI+6YrG+5MwXNnj17CgsL9+3b1+MfED539yl0d3dnZ2evXbt23LhxA16aW/r9K7h165ZOpysuLn7kkUcef/zx9evX79ixQ56lknBjCvX19atWrfrVr35VW1tbWlra2Njojz96Kfmx7D55jmWf/SNxxYoVCxcu7LVrzJgxH3744bVr11wbr1+/3uN5Fk5dXV1ZWVmNjY1Hjhzx4Q/Iu/MMDiGE0WjsMSYwMHDkyJEDVOVduTkFzb59+5YuXfqnP/0pJSVloArsnztTaGtrO3Xq1AcffLBixQohxK1bt1RVDQwMPHz48KOPPjrQFf8jN/8KTCbTd7/7Xedv/k+YMEFV1StXrsTExAxcrX1wcwpFRUXTpk37t3/7NyHEww8/HBIS8k//9E8vv/yyHy0yZD6WvxGpjmWfrZMiIiK+34ehQ4cmJSXZbDbtngUhxPHjx202W6/Ps9ACqaGhoby83Lf/N7j5DI6kpCTXMYcPH546dWpQUNAAVXlX7j9GZM+ePUuWLNm9e7ds1wDcmUJYWNiZM2fqbnv66afHjx9fV1fnvH7pQ27+FUybNu3q1avt7e3ay48//njIkCGjRo0auEL75uYUvvzyyyFD/v/nT0BAgLjrI7ElJPOx7D7pjmXf3FrhhrS0tIcffri6urq6ujouLs71XvDx48fv379fVdWurq7MzMxRo0bV1dU13eZwOHxVs3YL7LZt2+rr63Nzc0NCQi5evKiq6po1aywWizZGu3/0X//1X+vr67dt2ybb/aPuTGH37t2BgYFvvPGG8795a2urT6v+B+5MwZVs9925U39bW9uoUaN+/OMfnzt3rqKiIiYm5he/+IVPq/4H7kxh+/btgYGBmzZt+vTTTysrK6dOnfrII4/4tOp/0NbW9sEHH3zwwQdCiPXr13/wwQfaDb1+dCy7MwUJj2V5M+nGjRuLFi0KDQ0NDQ1dtGhRS0uLs0sIsX37dlVVGxsb70zZ9957z0clq6qqvvHGG9HR0cHBwVOmTKmoqNAaFy9ePH36dOeY999/f/LkycHBwWPGjNm8ebNvCu1bv1OYPn16j//mixcv9lW1vXLnb8FJtkxS3av//PnzKSkpw4YNGzVqVF5e3pdffumbWvvgzhR+//vfP/TQQ8OGDTOZTIsWLbpy5Ypvau3Ne++91+v/5H50LLszBQmPZZ5VAQCQhaTfTwIA3IfIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAs/h+D0dq/zG5dxQAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -279,10 +279,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:13.751242Z",
-     "iopub.status.busy": "2024-06-17T14:24:13.750799Z",
-     "iopub.status.idle": "2024-06-17T14:24:13.783396Z",
-     "shell.execute_reply": "2024-06-17T14:24:13.782831Z"
+     "iopub.execute_input": "2024-06-17T19:19:29.247695Z",
+     "iopub.status.busy": "2024-06-17T19:19:29.247389Z",
+     "iopub.status.idle": "2024-06-17T19:19:29.289863Z",
+     "shell.execute_reply": "2024-06-17T19:19:29.287757Z"
     }
    },
    "outputs": [],
@@ -303,10 +303,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:13.786922Z",
-     "iopub.status.busy": "2024-06-17T14:24:13.786786Z",
-     "iopub.status.idle": "2024-06-17T14:24:30.647213Z",
-     "shell.execute_reply": "2024-06-17T14:24:30.619294Z"
+     "iopub.execute_input": "2024-06-17T19:19:29.294341Z",
+     "iopub.status.busy": "2024-06-17T19:19:29.293700Z",
+     "iopub.status.idle": "2024-06-17T19:19:58.363387Z",
+     "shell.execute_reply": "2024-06-17T19:19:58.345518Z"
     }
    },
    "outputs": [],
@@ -320,10 +320,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:30.723416Z",
-     "iopub.status.busy": "2024-06-17T14:24:30.720575Z",
-     "iopub.status.idle": "2024-06-17T14:24:31.265653Z",
-     "shell.execute_reply": "2024-06-17T14:24:31.264403Z"
+     "iopub.execute_input": "2024-06-17T19:19:58.436634Z",
+     "iopub.status.busy": "2024-06-17T19:19:58.434899Z",
+     "iopub.status.idle": "2024-06-17T19:19:58.756768Z",
+     "shell.execute_reply": "2024-06-17T19:19:58.756355Z"
     }
    },
    "outputs": [
@@ -355,38 +355,38 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.844963\n",
-       "      0.830958\n",
+       "      0.846355\n",
+       "      0.832187\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
-       "      0.742577\n",
-       "      0.692558\n",
+       "      0.771054\n",
+       "      0.717877\n",
        "    \n",
        "    \n",
        "      F1 score\n",
-       "      0.627729\n",
-       "      0.547368\n",
+       "      0.661250\n",
+       "      0.587145\n",
        "    \n",
        "    \n",
        "      MCC\n",
-       "      0.542169\n",
-       "      0.482121\n",
+       "      0.563726\n",
+       "      0.498290\n",
        "    \n",
        "    \n",
        "      Precision\n",
-       "      0.737864\n",
-       "      0.761905\n",
+       "      0.699924\n",
+       "      0.713866\n",
        "    \n",
        "    \n",
        "      Recall\n",
-       "      0.546201\n",
-       "      0.427105\n",
+       "      0.626626\n",
+       "      0.498631\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
-       "      0.896564\n",
-       "      0.880271\n",
+       "      0.898968\n",
+       "      0.876756\n",
        "    \n",
        "  \n",
        "\n",
@@ -394,13 +394,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.844963  0.830958\n",
-       "Balanced Accuracy  0.742577  0.692558\n",
-       "F1 score           0.627729  0.547368\n",
-       "MCC                0.542169  0.482121\n",
-       "Precision          0.737864  0.761905\n",
-       "Recall             0.546201  0.427105\n",
-       "ROC AUC            0.896564  0.880271"
+       "Accuracy           0.846355  0.832187\n",
+       "Balanced Accuracy  0.771054  0.717877\n",
+       "F1 score           0.661250  0.587145\n",
+       "MCC                0.563726  0.498290\n",
+       "Precision          0.699924  0.713866\n",
+       "Recall             0.626626  0.498631\n",
+       "ROC AUC            0.898968  0.876756"
       ]
      },
      "execution_count": 12,
@@ -418,10 +418,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:31.322458Z",
-     "iopub.status.busy": "2024-06-17T14:24:31.322267Z",
-     "iopub.status.idle": "2024-06-17T14:24:31.407782Z",
-     "shell.execute_reply": "2024-06-17T14:24:31.407177Z"
+     "iopub.execute_input": "2024-06-17T19:19:58.803222Z",
+     "iopub.status.busy": "2024-06-17T19:19:58.802592Z",
+     "iopub.status.idle": "2024-06-17T19:19:58.886894Z",
+     "shell.execute_reply": "2024-06-17T19:19:58.886449Z"
     }
    },
    "outputs": [
@@ -453,43 +453,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.177424\n",
-       "      0.017017\n",
+       "      0.223317\n",
+       "      0.016872\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.033768\n",
-       "      0.244559\n",
+       "      0.008117\n",
+       "      0.301545\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.159556\n",
-       "      0.278824\n",
+       "      0.243709\n",
+       "      0.261034\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.159556\n",
-       "      0.278824\n",
+       "      0.243709\n",
+       "      0.261034\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.117196\n",
-       "      0.151115\n",
+       "      0.173466\n",
+       "      0.149859\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.068033\n",
-       "      0.209902\n",
+       "      0.041894\n",
+       "      0.231932\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.115149\n",
-       "      0.122796\n",
+       "      0.106414\n",
+       "      0.089916\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.270023\n",
-       "      1.239793\n",
+       "      0.549310\n",
+       "      2.313295\n",
        "    \n",
        "  \n",
        "\n",
@@ -497,14 +497,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.177424  0.017017\n",
-       "Predictive Parity                                0.033768  0.244559\n",
-       "Equal Opportunity                                0.159556  0.278824\n",
-       "Average Group Difference in False Negative Rate  0.159556  0.278824\n",
-       "Equalized Odds                                   0.117196  0.151115\n",
-       "Conditional Use Accuracy                         0.068033  0.209902\n",
-       "Average Group Difference in Accuracy             0.115149  0.122796\n",
-       "Treatment Equality                               0.270023  1.239793"
+       "Statistical Parity                               0.223317  0.016872\n",
+       "Predictive Parity                                0.008117  0.301545\n",
+       "Equal Opportunity                                0.243709  0.261034\n",
+       "Average Group Difference in False Negative Rate  0.243709  0.261034\n",
+       "Equalized Odds                                   0.173466  0.149859\n",
+       "Conditional Use Accuracy                         0.041894  0.231932\n",
+       "Average Group Difference in Accuracy             0.106414  0.089916\n",
+       "Treatment Equality                               0.549310  2.313295"
       ]
      },
      "execution_count": 13,
@@ -522,16 +522,16 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:31.413169Z",
-     "iopub.status.busy": "2024-06-17T14:24:31.413017Z",
-     "iopub.status.idle": "2024-06-17T14:24:31.920610Z",
-     "shell.execute_reply": "2024-06-17T14:24:31.920091Z"
+     "iopub.execute_input": "2024-06-17T19:19:58.891498Z",
+     "iopub.status.busy": "2024-06-17T19:19:58.891329Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.377465Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.376942Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -550,16 +550,16 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:31.926513Z",
-     "iopub.status.busy": "2024-06-17T14:24:31.926330Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.314353Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.313626Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.382198Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.381925Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.789703Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.789389Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -579,10 +579,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.320909Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.320465Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.323998Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.323477Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.792911Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.792623Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.795145Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.794795Z"
     }
    },
    "outputs": [],
@@ -596,10 +596,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.326338Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.326235Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.406365Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.405990Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.797298Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.797154Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.854729Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.854107Z"
     }
    },
    "outputs": [
@@ -631,38 +631,38 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.845222\n",
-       "      0.834166\n",
+       "      0.841291\n",
+       "      0.834575\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
-       "      0.738637\n",
-       "      0.691140\n",
+       "      0.766900\n",
+       "      0.695163\n",
        "    \n",
        "    \n",
        "      F1 score\n",
-       "      0.622905\n",
-       "      0.546066\n",
+       "      0.653061\n",
+       "      0.553097\n",
        "    \n",
        "    \n",
        "      MCC\n",
-       "      0.540639\n",
-       "      0.491434\n",
+       "      0.551446\n",
+       "      0.493792\n",
        "    \n",
        "    \n",
        "      Precision\n",
-       "      0.746890\n",
-       "      0.791423\n",
+       "      0.684685\n",
+       "      0.782228\n",
        "    \n",
        "    \n",
        "      Recall\n",
-       "      0.534223\n",
-       "      0.416838\n",
+       "      0.624230\n",
+       "      0.427789\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
-       "      0.899362\n",
-       "      0.888664\n",
+       "      0.895597\n",
+       "      0.884229\n",
        "    \n",
        "  \n",
        "\n",
@@ -670,13 +670,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.845222  0.834166\n",
-       "Balanced Accuracy  0.738637  0.691140\n",
-       "F1 score           0.622905  0.546066\n",
-       "MCC                0.540639  0.491434\n",
-       "Precision          0.746890  0.791423\n",
-       "Recall             0.534223  0.416838\n",
-       "ROC AUC            0.899362  0.888664"
+       "Accuracy           0.841291  0.834575\n",
+       "Balanced Accuracy  0.766900  0.695163\n",
+       "F1 score           0.653061  0.553097\n",
+       "MCC                0.551446  0.493792\n",
+       "Precision          0.684685  0.782228\n",
+       "Recall             0.624230  0.427789\n",
+       "ROC AUC            0.895597  0.884229"
       ]
      },
      "execution_count": 17,
@@ -694,10 +694,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.410252Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.410088Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.496344Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.495622Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.857148Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.856991Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.937419Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.937029Z"
     }
    },
    "outputs": [
@@ -729,43 +729,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.177424\n",
-       "      0.047841\n",
+       "      0.223317\n",
+       "      0.048679\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.033768\n",
-       "      0.178745\n",
+       "      0.008117\n",
+       "      0.200809\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.159556\n",
-       "      0.187793\n",
+       "      0.243709\n",
+       "      0.173597\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.159556\n",
-       "      0.187793\n",
+       "      0.243709\n",
+       "      0.173597\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.117196\n",
-       "      0.095403\n",
+       "      0.173466\n",
+       "      0.089706\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.068033\n",
-       "      0.170960\n",
+       "      0.041894\n",
+       "      0.180334\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.115149\n",
-       "      0.127269\n",
+       "      0.106414\n",
+       "      0.121237\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.270023\n",
-       "      0.627618\n",
+       "      0.549310\n",
+       "      0.680241\n",
        "    \n",
        "  \n",
        "\n",
@@ -773,14 +773,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.177424  0.047841\n",
-       "Predictive Parity                                0.033768  0.178745\n",
-       "Equal Opportunity                                0.159556  0.187793\n",
-       "Average Group Difference in False Negative Rate  0.159556  0.187793\n",
-       "Equalized Odds                                   0.117196  0.095403\n",
-       "Conditional Use Accuracy                         0.068033  0.170960\n",
-       "Average Group Difference in Accuracy             0.115149  0.127269\n",
-       "Treatment Equality                               0.270023  0.627618"
+       "Statistical Parity                               0.223317  0.048679\n",
+       "Predictive Parity                                0.008117  0.200809\n",
+       "Equal Opportunity                                0.243709  0.173597\n",
+       "Average Group Difference in False Negative Rate  0.243709  0.173597\n",
+       "Equalized Odds                                   0.173466  0.089706\n",
+       "Conditional Use Accuracy                         0.041894  0.180334\n",
+       "Average Group Difference in Accuracy             0.106414  0.121237\n",
+       "Treatment Equality                               0.549310  0.680241"
       ]
      },
      "execution_count": 18,
@@ -797,17 +797,17 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.500697Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.500451Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.507065Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.506716Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.940294Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.939973Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.942597Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.942335Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-1.77, 1.082)"
+       "(-1.778, 0.8525)"
       ]
      },
      "execution_count": 19,
@@ -846,10 +846,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.509806Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.509623Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.585602Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.584822Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.944167Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.944036Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.959314Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.958777Z"
     }
    },
    "outputs": [],
@@ -872,10 +872,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.593131Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.591423Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.645153Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.644381Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.962110Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.961919Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.983719Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.983212Z"
     }
    },
    "outputs": [],
@@ -889,10 +889,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.649306Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.648746Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.659400Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.659056Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.986126Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.985985Z",
+     "iopub.status.idle": "2024-06-17T19:19:59.994638Z",
+     "shell.execute_reply": "2024-06-17T19:19:59.994199Z"
     }
    },
    "outputs": [
@@ -923,31 +923,31 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.834166\n",
+       "      0.834575\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
-       "      0.691140\n",
+       "      0.695163\n",
        "    \n",
        "    \n",
        "      F1 score\n",
-       "      0.546066\n",
+       "      0.553097\n",
        "    \n",
        "    \n",
        "      MCC\n",
-       "      0.491434\n",
+       "      0.493792\n",
        "    \n",
        "    \n",
        "      Precision\n",
-       "      0.791423\n",
+       "      0.782228\n",
        "    \n",
        "    \n",
        "      Recall\n",
-       "      0.416838\n",
+       "      0.427789\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
-       "      0.888663\n",
+       "      0.884229\n",
        "    \n",
        "  \n",
        "\n",
@@ -955,13 +955,13 @@
       ],
       "text/plain": [
        "                          0\n",
-       "Accuracy           0.834166\n",
-       "Balanced Accuracy  0.691140\n",
-       "F1 score           0.546066\n",
-       "MCC                0.491434\n",
-       "Precision          0.791423\n",
-       "Recall             0.416838\n",
-       "ROC AUC            0.888663"
+       "Accuracy           0.834575\n",
+       "Balanced Accuracy  0.695163\n",
+       "F1 score           0.553097\n",
+       "MCC                0.493792\n",
+       "Precision          0.782228\n",
+       "Recall             0.427789\n",
+       "ROC AUC            0.884229"
       ]
      },
      "execution_count": 22,
@@ -980,10 +980,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.674543Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.674334Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.721202Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.720748Z"
+     "iopub.execute_input": "2024-06-17T19:19:59.996913Z",
+     "iopub.status.busy": "2024-06-17T19:19:59.996781Z",
+     "iopub.status.idle": "2024-06-17T19:20:00.038725Z",
+     "shell.execute_reply": "2024-06-17T19:20:00.037915Z"
     }
    },
    "outputs": [
@@ -1014,35 +1014,35 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.041087\n",
+       "      0.053119\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.185338\n",
+       "      0.214987\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.204611\n",
+       "      0.133063\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.204611\n",
+       "      0.133063\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.105339\n",
+       "      0.069230\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.176297\n",
+       "      0.185445\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.130187\n",
+       "      0.118119\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.642273\n",
+       "      0.587310\n",
        "    \n",
        "  \n",
        "\n",
@@ -1050,14 +1050,14 @@
       ],
       "text/plain": [
        "                                                        0\n",
-       "Statistical Parity                               0.041087\n",
-       "Predictive Parity                                0.185338\n",
-       "Equal Opportunity                                0.204611\n",
-       "Average Group Difference in False Negative Rate  0.204611\n",
-       "Equalized Odds                                   0.105339\n",
-       "Conditional Use Accuracy                         0.176297\n",
-       "Average Group Difference in Accuracy             0.130187\n",
-       "Treatment Equality                               0.642273"
+       "Statistical Parity                               0.053119\n",
+       "Predictive Parity                                0.214987\n",
+       "Equal Opportunity                                0.133063\n",
+       "Average Group Difference in False Negative Rate  0.133063\n",
+       "Equalized Odds                                   0.069230\n",
+       "Conditional Use Accuracy                         0.185445\n",
+       "Average Group Difference in Accuracy             0.118119\n",
+       "Treatment Equality                               0.587310"
       ]
      },
      "execution_count": 23,
@@ -1075,10 +1075,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.725268Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.725093Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.731231Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.729479Z"
+     "iopub.execute_input": "2024-06-17T19:20:00.041072Z",
+     "iopub.status.busy": "2024-06-17T19:20:00.040869Z",
+     "iopub.status.idle": "2024-06-17T19:20:00.044125Z",
+     "shell.execute_reply": "2024-06-17T19:20:00.043628Z"
     }
    },
    "outputs": [],
@@ -1101,10 +1101,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.735405Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.735079Z",
-     "iopub.status.idle": "2024-06-17T14:24:32.770751Z",
-     "shell.execute_reply": "2024-06-17T14:24:32.770112Z"
+     "iopub.execute_input": "2024-06-17T19:20:00.046278Z",
+     "iopub.status.busy": "2024-06-17T19:20:00.046145Z",
+     "iopub.status.idle": "2024-06-17T19:20:00.065088Z",
+     "shell.execute_reply": "2024-06-17T19:20:00.064691Z"
     }
    },
    "outputs": [
@@ -1127,16 +1127,16 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:32.774743Z",
-     "iopub.status.busy": "2024-06-17T14:24:32.774140Z",
-     "iopub.status.idle": "2024-06-17T14:24:33.888346Z",
-     "shell.execute_reply": "2024-06-17T14:24:33.887766Z"
+     "iopub.execute_input": "2024-06-17T19:20:00.067975Z",
+     "iopub.status.busy": "2024-06-17T19:20:00.067792Z",
+     "iopub.status.idle": "2024-06-17T19:20:01.051254Z",
+     "shell.execute_reply": "2024-06-17T19:20:01.050803Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
diff --git a/examples/quickstart_DeepFairPredictor_computer_vision.ipynb b/examples/quickstart_DeepFairPredictor_computer_vision.ipynb
index b27a1f6..8fbc1bf 100644
--- a/examples/quickstart_DeepFairPredictor_computer_vision.ipynb
+++ b/examples/quickstart_DeepFairPredictor_computer_vision.ipynb
@@ -32,10 +32,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:43.083436Z",
-     "iopub.status.busy": "2024-06-17T14:23:43.083331Z",
-     "iopub.status.idle": "2024-06-17T14:23:47.179076Z",
-     "shell.execute_reply": "2024-06-17T14:23:47.178754Z"
+     "iopub.execute_input": "2024-06-17T19:18:59.804035Z",
+     "iopub.status.busy": "2024-06-17T19:18:59.803913Z",
+     "iopub.status.idle": "2024-06-17T19:19:03.497916Z",
+     "shell.execute_reply": "2024-06-17T19:19:03.497540Z"
     }
    },
    "outputs": [
@@ -69,10 +69,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:47.181037Z",
-     "iopub.status.busy": "2024-06-17T14:23:47.180814Z",
-     "iopub.status.idle": "2024-06-17T14:23:47.183995Z",
-     "shell.execute_reply": "2024-06-17T14:23:47.183593Z"
+     "iopub.execute_input": "2024-06-17T19:19:03.500035Z",
+     "iopub.status.busy": "2024-06-17T19:19:03.499806Z",
+     "iopub.status.idle": "2024-06-17T19:19:03.503155Z",
+     "shell.execute_reply": "2024-06-17T19:19:03.502782Z"
     }
    },
    "outputs": [],
@@ -115,10 +115,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:47.185702Z",
-     "iopub.status.busy": "2024-06-17T14:23:47.185538Z",
-     "iopub.status.idle": "2024-06-17T14:23:47.187687Z",
-     "shell.execute_reply": "2024-06-17T14:23:47.187360Z"
+     "iopub.execute_input": "2024-06-17T19:19:03.504775Z",
+     "iopub.status.busy": "2024-06-17T19:19:03.504637Z",
+     "iopub.status.idle": "2024-06-17T19:19:03.506610Z",
+     "shell.execute_reply": "2024-06-17T19:19:03.506284Z"
     }
    },
    "outputs": [],
@@ -136,10 +136,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:47.189258Z",
-     "iopub.status.busy": "2024-06-17T14:23:47.189117Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.118378Z",
-     "shell.execute_reply": "2024-06-17T14:23:54.117893Z"
+     "iopub.execute_input": "2024-06-17T19:19:03.508199Z",
+     "iopub.status.busy": "2024-06-17T19:19:03.508057Z",
+     "iopub.status.idle": "2024-06-17T19:19:10.908294Z",
+     "shell.execute_reply": "2024-06-17T19:19:10.907715Z"
     }
    },
    "outputs": [],
@@ -152,10 +152,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:54.121033Z",
-     "iopub.status.busy": "2024-06-17T14:23:54.120806Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.123478Z",
-     "shell.execute_reply": "2024-06-17T14:23:54.122955Z"
+     "iopub.execute_input": "2024-06-17T19:19:10.911613Z",
+     "iopub.status.busy": "2024-06-17T19:19:10.911446Z",
+     "iopub.status.idle": "2024-06-17T19:19:10.914253Z",
+     "shell.execute_reply": "2024-06-17T19:19:10.913828Z"
     }
    },
    "outputs": [],
@@ -171,10 +171,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:54.125999Z",
-     "iopub.status.busy": "2024-06-17T14:23:54.125723Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.128804Z",
-     "shell.execute_reply": "2024-06-17T14:23:54.128261Z"
+     "iopub.execute_input": "2024-06-17T19:19:10.916692Z",
+     "iopub.status.busy": "2024-06-17T19:19:10.916514Z",
+     "iopub.status.idle": "2024-06-17T19:19:10.919353Z",
+     "shell.execute_reply": "2024-06-17T19:19:10.918921Z"
     }
    },
    "outputs": [],
@@ -194,10 +194,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:54.131008Z",
-     "iopub.status.busy": "2024-06-17T14:23:54.130862Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.135560Z",
-     "shell.execute_reply": "2024-06-17T14:23:54.135038Z"
+     "iopub.execute_input": "2024-06-17T19:19:10.921947Z",
+     "iopub.status.busy": "2024-06-17T19:19:10.921659Z",
+     "iopub.status.idle": "2024-06-17T19:19:10.926151Z",
+     "shell.execute_reply": "2024-06-17T19:19:10.925777Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:54.137648Z",
-     "iopub.status.busy": "2024-06-17T14:23:54.137514Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.309137Z",
-     "shell.execute_reply": "2024-06-17T14:23:54.308510Z"
+     "iopub.execute_input": "2024-06-17T19:19:10.928360Z",
+     "iopub.status.busy": "2024-06-17T19:19:10.928192Z",
+     "iopub.status.idle": "2024-06-17T19:19:11.107333Z",
+     "shell.execute_reply": "2024-06-17T19:19:11.106382Z"
     }
    },
    "outputs": [
@@ -307,10 +307,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:54.311889Z",
-     "iopub.status.busy": "2024-06-17T14:23:54.311674Z",
-     "iopub.status.idle": "2024-06-17T14:23:54.314472Z",
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@@ -324,10 +324,10 @@
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@@ -346,10 +346,10 @@
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@@ -370,10 +370,10 @@
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@@ -387,10 +387,10 @@
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@@ -404,10 +404,10 @@
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@@ -501,10 +501,10 @@
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@@ -737,10 +737,10 @@
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@@ -974,10 +974,10 @@
    "metadata": {
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-     "shell.execute_reply": "2024-06-17T14:24:04.661878Z"
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      "slide_type": ""
@@ -1005,10 +1005,10 @@
    "execution_count": 18,
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-     "shell.execute_reply": "2024-06-17T14:24:04.973353Z"
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    "outputs": [
@@ -1032,10 +1032,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:04.982690Z",
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-     "shell.execute_reply": "2024-06-17T14:24:05.016830Z"
+     "iopub.execute_input": "2024-06-17T19:19:20.832103Z",
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+     "shell.execute_reply": "2024-06-17T19:19:20.870212Z"
     }
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    "outputs": [
@@ -1135,10 +1135,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:05.020386Z",
-     "iopub.status.busy": "2024-06-17T14:24:05.020224Z",
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-     "shell.execute_reply": "2024-06-17T14:24:05.040884Z"
+     "iopub.execute_input": "2024-06-17T19:19:20.875642Z",
+     "iopub.status.busy": "2024-06-17T19:19:20.875310Z",
+     "iopub.status.idle": "2024-06-17T19:19:20.897766Z",
+     "shell.execute_reply": "2024-06-17T19:19:20.896132Z"
     }
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    "outputs": [
@@ -1202,10 +1202,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:05.047363Z",
-     "iopub.status.busy": "2024-06-17T14:24:05.047096Z",
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-     "shell.execute_reply": "2024-06-17T14:24:05.058827Z"
+     "iopub.execute_input": "2024-06-17T19:19:20.902838Z",
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+     "shell.execute_reply": "2024-06-17T19:19:20.919534Z"
     }
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    "outputs": [
@@ -1280,10 +1280,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:05.067305Z",
-     "iopub.status.busy": "2024-06-17T14:24:05.066581Z",
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-     "shell.execute_reply": "2024-06-17T14:24:05.073505Z"
+     "iopub.execute_input": "2024-06-17T19:19:20.926824Z",
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+     "shell.execute_reply": "2024-06-17T19:19:20.929536Z"
     }
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    "outputs": [],
@@ -1297,10 +1297,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:05.081303Z",
-     "iopub.status.busy": "2024-06-17T14:24:05.081108Z",
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-     "shell.execute_reply": "2024-06-17T14:24:05.092825Z"
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    "outputs": [],
@@ -1319,10 +1319,10 @@
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    "metadata": {
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-     "iopub.execute_input": "2024-06-17T14:24:05.099120Z",
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-     "shell.execute_reply": "2024-06-17T14:24:07.425351Z"
+     "iopub.execute_input": "2024-06-17T19:19:20.951915Z",
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+     "shell.execute_reply": "2024-06-17T19:19:24.065074Z"
     }
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    "outputs": [],
@@ -1335,10 +1335,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.435237Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.435039Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.450762Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.449910Z"
+     "iopub.execute_input": "2024-06-17T19:19:24.080412Z",
+     "iopub.status.busy": "2024-06-17T19:19:24.079920Z",
+     "iopub.status.idle": "2024-06-17T19:19:24.086332Z",
+     "shell.execute_reply": "2024-06-17T19:19:24.085618Z"
     }
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    "outputs": [
@@ -1368,10 +1368,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.453834Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.453700Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.456071Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.455545Z"
+     "iopub.execute_input": "2024-06-17T19:19:24.096017Z",
+     "iopub.status.busy": "2024-06-17T19:19:24.095438Z",
+     "iopub.status.idle": "2024-06-17T19:19:24.101172Z",
+     "shell.execute_reply": "2024-06-17T19:19:24.099813Z"
     }
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    "outputs": [],
@@ -1389,10 +1389,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.462466Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.462288Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.465816Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.465082Z"
+     "iopub.execute_input": "2024-06-17T19:19:24.104813Z",
+     "iopub.status.busy": "2024-06-17T19:19:24.104557Z",
+     "iopub.status.idle": "2024-06-17T19:19:24.107413Z",
+     "shell.execute_reply": "2024-06-17T19:19:24.106804Z"
     }
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    "outputs": [],
@@ -1406,10 +1406,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:07.469053Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.468699Z",
-     "iopub.status.idle": "2024-06-17T14:24:07.476038Z",
-     "shell.execute_reply": "2024-06-17T14:24:07.475306Z"
+     "iopub.execute_input": "2024-06-17T19:19:24.110887Z",
+     "iopub.status.busy": "2024-06-17T19:19:24.110627Z",
+     "iopub.status.idle": "2024-06-17T19:19:24.115868Z",
+     "shell.execute_reply": "2024-06-17T19:19:24.113591Z"
     }
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    "outputs": [],
@@ -1422,10 +1422,10 @@
    "execution_count": 29,
    "metadata": {
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-     "iopub.execute_input": "2024-06-17T14:24:07.481077Z",
-     "iopub.status.busy": "2024-06-17T14:24:07.480810Z",
-     "iopub.status.idle": "2024-06-17T14:24:10.209917Z",
-     "shell.execute_reply": "2024-06-17T14:24:10.203488Z"
+     "iopub.execute_input": "2024-06-17T19:19:24.120008Z",
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+     "iopub.status.idle": "2024-06-17T19:19:26.406933Z",
+     "shell.execute_reply": "2024-06-17T19:19:26.404946Z"
     }
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    "outputs": [],
@@ -1438,10 +1438,10 @@
    "execution_count": 30,
    "metadata": {
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-     "iopub.execute_input": "2024-06-17T14:24:10.224640Z",
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-     "iopub.status.idle": "2024-06-17T14:24:10.486867Z",
-     "shell.execute_reply": "2024-06-17T14:24:10.485320Z"
+     "iopub.execute_input": "2024-06-17T19:19:26.433101Z",
+     "iopub.status.busy": "2024-06-17T19:19:26.430763Z",
+     "iopub.status.idle": "2024-06-17T19:19:26.694610Z",
+     "shell.execute_reply": "2024-06-17T19:19:26.693457Z"
     }
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    "outputs": [
@@ -1465,10 +1465,10 @@
    "execution_count": 31,
    "metadata": {
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-     "iopub.execute_input": "2024-06-17T14:24:10.493460Z",
-     "iopub.status.busy": "2024-06-17T14:24:10.493175Z",
-     "iopub.status.idle": "2024-06-17T14:24:10.724437Z",
-     "shell.execute_reply": "2024-06-17T14:24:10.719473Z"
+     "iopub.execute_input": "2024-06-17T19:19:26.705713Z",
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+     "iopub.status.idle": "2024-06-17T19:19:26.904031Z",
+     "shell.execute_reply": "2024-06-17T19:19:26.902361Z"
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    "outputs": [
@@ -1492,10 +1492,10 @@
    "execution_count": 32,
    "metadata": {
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-     "iopub.execute_input": "2024-06-17T14:24:10.736118Z",
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-     "iopub.status.idle": "2024-06-17T14:24:10.777758Z",
-     "shell.execute_reply": "2024-06-17T14:24:10.776641Z"
+     "iopub.execute_input": "2024-06-17T19:19:26.909793Z",
+     "iopub.status.busy": "2024-06-17T19:19:26.909459Z",
+     "iopub.status.idle": "2024-06-17T19:19:26.957409Z",
+     "shell.execute_reply": "2024-06-17T19:19:26.956249Z"
     }
    },
    "outputs": [
diff --git a/examples/quickstart_autogluon.ipynb b/examples/quickstart_autogluon.ipynb
index 61b9443..8f884f9 100644
--- a/examples/quickstart_autogluon.ipynb
+++ b/examples/quickstart_autogluon.ipynb
@@ -5,9 +5,9 @@
    "metadata": {},
    "source": [
     "# FairPredictor Autogluon Examples\n",
-    "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups),  and enforcing a range of fairness definition on COMPAS.\n",
+    "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups), and enforcing a range of fairness definition on COMPAS.\n",
     "\n",
-    "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e. it does not require access to protected attributes at test time. \n",
+    "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e., it does not require access to protected attributes at test time. \n",
     "Under the hood, FairPredictor works by adjusting the decision boundary for each group individually. Where groups are not available, it makes use of inferred group membership to adjust decision boundaries.\n",
     "\n",
     "The key idea underlying this toolkit is that for a wide range of use cases, the most suitable classifier should do more than maximize some form of accuracy.\n",
@@ -37,10 +37,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:23:43.100557Z",
-     "iopub.status.busy": "2024-06-17T14:23:43.100407Z",
-     "iopub.status.idle": "2024-06-17T14:24:02.704567Z",
-     "shell.execute_reply": "2024-06-17T14:24:02.704105Z"
+     "iopub.execute_input": "2024-06-17T19:18:59.770976Z",
+     "iopub.status.busy": "2024-06-17T19:18:59.770525Z",
+     "iopub.status.idle": "2024-06-17T19:19:14.708127Z",
+     "shell.execute_reply": "2024-06-17T19:19:14.706485Z"
     }
    },
    "outputs": [
@@ -56,7 +56,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142354\"\n"
+      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_191908\"\n"
      ]
     },
     {
@@ -82,7 +82,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon will save models to \"AutogluonModels/ag-20240617_142354\"\n"
+      "AutoGluon will save models to \"AutogluonModels/ag-20240617_191908\"\n"
      ]
     },
     {
@@ -96,8 +96,8 @@
       "Platform Machine:   arm64\n",
       "Platform Version:   Darwin Kernel Version 23.5.0: Wed May  1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n",
       "CPU Count:          10\n",
-      "Memory Avail:       7.91 GB / 16.00 GB (49.5%)\n",
-      "Disk Space Avail:   363.55 GB / 460.43 GB (79.0%)\n",
+      "Memory Avail:       6.56 GB / 16.00 GB (41.0%)\n",
+      "Disk Space Avail:   360.82 GB / 460.43 GB (78.4%)\n",
       "===================================================\n"
      ]
     },
@@ -190,7 +190,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tAvailable Memory:                    8126.53 MB\n"
+      "\tAvailable Memory:                    6735.36 MB\n"
      ]
     },
     {
@@ -351,7 +351,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.2s = Fit runtime\n"
+      "\t0.1s = Fit runtime\n"
      ]
     },
     {
@@ -372,7 +372,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Data preprocessing and feature engineering runtime = 0.19s ...\n"
+      "Data preprocessing and feature engineering runtime = 0.17s ...\n"
      ]
     },
     {
@@ -424,7 +424,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsUnif ... Training model for up to 4.81s of the 4.81s of remaining time.\n"
+      "Fitting model: KNeighborsUnif ... Training model for up to 4.83s of the 4.82s of remaining time.\n"
      ]
     },
     {
@@ -438,21 +438,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t3.2s\t = Training   runtime\n"
+      "\t1.42s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.05s\t = Validation runtime\n"
+      "\t0.04s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsDist ... Training model for up to 1.53s of the 1.52s of remaining time.\n"
+      "Fitting model: KNeighborsDist ... Training model for up to 3.36s of the 3.36s of remaining time.\n"
      ]
     },
     {
@@ -480,7 +480,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: LightGBMXT ... Training model for up to 1.45s of the 1.45s of remaining time.\n"
+      "Fitting model: LightGBMXT ... Training model for up to 3.32s of the 3.32s of remaining time.\n"
      ]
     },
     {
@@ -500,22 +500,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tRan out of time, early stopping on iteration 71. Best iteration is:\n",
-      "\t[58]\tvalid_set's binary_error: 0.1328\n"
+      "\tRan out of time, early stopping on iteration 156. Best iteration is:\n",
+      "\t[145]\tvalid_set's binary_error: 0.1284\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.8672\t = Validation score   (accuracy)\n"
+      "\t0.8716\t = Validation score   (accuracy)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t4.03s\t = Training   runtime\n"
+      "\t4.17s\t = Training   runtime\n"
      ]
     },
     {
@@ -529,21 +529,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.81s of the -2.61s of remaining time.\n"
+      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.83s of the -1.03s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tEnsemble Weights: {'LightGBMXT': 1.0}\n"
+      "\tEnsemble Weights: {'LightGBMXT': 0.842, 'KNeighborsUnif': 0.105, 'KNeighborsDist': 0.053}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.8672\t = Validation score   (accuracy)\n"
+      "\t0.8744\t = Validation score   (accuracy)\n"
      ]
     },
     {
@@ -564,14 +564,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon training complete, total runtime = 7.73s ... Best model: \"WeightedEnsemble_L2\"\n"
+      "AutoGluon training complete, total runtime = 6.13s ... Best model: \"WeightedEnsemble_L2\"\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142354\")\n"
+      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_191908\")\n"
      ]
     }
    ],
@@ -591,10 +591,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:02.707319Z",
-     "iopub.status.busy": "2024-06-17T14:24:02.706710Z",
-     "iopub.status.idle": "2024-06-17T14:24:02.802109Z",
-     "shell.execute_reply": "2024-06-17T14:24:02.801074Z"
+     "iopub.execute_input": "2024-06-17T19:19:14.715124Z",
+     "iopub.status.busy": "2024-06-17T19:19:14.714539Z",
+     "iopub.status.idle": "2024-06-17T19:19:15.155238Z",
+     "shell.execute_reply": "2024-06-17T19:19:15.154020Z"
     }
    },
    "outputs": [],
@@ -610,10 +610,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:02.806891Z",
-     "iopub.status.busy": "2024-06-17T14:24:02.806616Z",
-     "iopub.status.idle": "2024-06-17T14:24:02.853890Z",
-     "shell.execute_reply": "2024-06-17T14:24:02.852531Z"
+     "iopub.execute_input": "2024-06-17T19:19:15.159470Z",
+     "iopub.status.busy": "2024-06-17T19:19:15.158888Z",
+     "iopub.status.idle": "2024-06-17T19:19:15.313776Z",
+     "shell.execute_reply": "2024-06-17T19:19:15.312793Z"
     }
    },
    "outputs": [
@@ -649,10 +649,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:02.858009Z",
-     "iopub.status.busy": "2024-06-17T14:24:02.857696Z",
-     "iopub.status.idle": "2024-06-17T14:24:02.981764Z",
-     "shell.execute_reply": "2024-06-17T14:24:02.977437Z"
+     "iopub.execute_input": "2024-06-17T19:19:15.317613Z",
+     "iopub.status.busy": "2024-06-17T19:19:15.317317Z",
+     "iopub.status.idle": "2024-06-17T19:19:15.632565Z",
+     "shell.execute_reply": "2024-06-17T19:19:15.632003Z"
     }
    },
    "outputs": [
@@ -684,38 +684,38 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.863036\n",
-       "      0.843177\n",
+       "      0.868973\n",
+       "      0.842563\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
-       "      0.771720\n",
-       "      0.736263\n",
+       "      0.776058\n",
+       "      0.723973\n",
        "    \n",
        "    \n",
        "      F1 score\n",
-       "      0.674453\n",
-       "      0.617191\n",
+       "      0.684574\n",
+       "      0.600312\n",
        "    \n",
        "    \n",
        "      MCC\n",
-       "      0.597347\n",
-       "      0.532205\n",
+       "      0.613887\n",
+       "      0.524332\n",
        "    \n",
        "    \n",
        "      Precision\n",
-       "      0.773438\n",
-       "      0.733373\n",
+       "      0.798276\n",
+       "      0.754902\n",
        "    \n",
        "    \n",
        "      Recall\n",
-       "      0.597929\n",
-       "      0.532787\n",
+       "      0.599223\n",
+       "      0.498274\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
-       "      0.918163\n",
-       "      0.819932\n",
+       "      0.919433\n",
+       "      0.817906\n",
        "    \n",
        "  \n",
        "\n",
@@ -723,13 +723,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.863036  0.843177\n",
-       "Balanced Accuracy  0.771720  0.736263\n",
-       "F1 score           0.674453  0.617191\n",
-       "MCC                0.597347  0.532205\n",
-       "Precision          0.773438  0.733373\n",
-       "Recall             0.597929  0.532787\n",
-       "ROC AUC            0.918163  0.819932"
+       "Accuracy           0.868973  0.842563\n",
+       "Balanced Accuracy  0.776058  0.723973\n",
+       "F1 score           0.684574  0.600312\n",
+       "MCC                0.613887  0.524332\n",
+       "Precision          0.798276  0.754902\n",
+       "Recall             0.599223  0.498274\n",
+       "ROC AUC            0.919433  0.817906"
       ]
      },
      "execution_count": 4,
@@ -747,10 +747,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:02.987291Z",
-     "iopub.status.busy": "2024-06-17T14:24:02.986939Z",
-     "iopub.status.idle": "2024-06-17T14:24:03.138350Z",
-     "shell.execute_reply": "2024-06-17T14:24:03.133318Z"
+     "iopub.execute_input": "2024-06-17T19:19:15.637027Z",
+     "iopub.status.busy": "2024-06-17T19:19:15.636462Z",
+     "iopub.status.idle": "2024-06-17T19:19:16.033572Z",
+     "shell.execute_reply": "2024-06-17T19:19:16.031149Z"
     }
    },
    "outputs": [
@@ -782,43 +782,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.165619\n",
-       "      0.001850\n",
+       "      0.155761\n",
+       "      0.013698\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.013511\n",
-       "      0.346475\n",
+       "      0.020717\n",
+       "      0.354613\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.070408\n",
-       "      0.333930\n",
+       "      0.065251\n",
+       "      0.371301\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.070408\n",
-       "      0.333930\n",
+       "      0.065251\n",
+       "      0.371301\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.065551\n",
-       "      0.196777\n",
+       "      0.056320\n",
+       "      0.216631\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.058000\n",
-       "      0.256119\n",
+       "      0.060920\n",
+       "      0.265098\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.107933\n",
-       "      0.076909\n",
+       "      0.098982\n",
+       "      0.088835\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.141320\n",
-       "      4.190781\n",
+       "      0.051315\n",
+       "      3.802436\n",
        "    \n",
        "  \n",
        "\n",
@@ -826,14 +826,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.165619  0.001850\n",
-       "Predictive Parity                                0.013511  0.346475\n",
-       "Equal Opportunity                                0.070408  0.333930\n",
-       "Average Group Difference in False Negative Rate  0.070408  0.333930\n",
-       "Equalized Odds                                   0.065551  0.196777\n",
-       "Conditional Use Accuracy                         0.058000  0.256119\n",
-       "Average Group Difference in Accuracy             0.107933  0.076909\n",
-       "Treatment Equality                               0.141320  4.190781"
+       "Statistical Parity                               0.155761  0.013698\n",
+       "Predictive Parity                                0.020717  0.354613\n",
+       "Equal Opportunity                                0.065251  0.371301\n",
+       "Average Group Difference in False Negative Rate  0.065251  0.371301\n",
+       "Equalized Odds                                   0.056320  0.216631\n",
+       "Conditional Use Accuracy                         0.060920  0.265098\n",
+       "Average Group Difference in Accuracy             0.098982  0.088835\n",
+       "Treatment Equality                               0.051315  3.802436"
       ]
      },
      "execution_count": 5,
@@ -851,10 +851,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:03.151207Z",
-     "iopub.status.busy": "2024-06-17T14:24:03.149829Z",
-     "iopub.status.idle": "2024-06-17T14:24:03.341316Z",
-     "shell.execute_reply": "2024-06-17T14:24:03.339336Z"
+     "iopub.execute_input": "2024-06-17T19:19:16.040489Z",
+     "iopub.status.busy": "2024-06-17T19:19:16.039575Z",
+     "iopub.status.idle": "2024-06-17T19:19:16.634654Z",
+     "shell.execute_reply": "2024-06-17T19:19:16.633981Z"
     }
    },
    "outputs": [
@@ -912,116 +912,116 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.863036\n",
-       "      0.771720\n",
-       "      0.674453\n",
-       "      0.597347\n",
-       "      0.773438\n",
-       "      0.597929\n",
-       "      0.918163\n",
+       "      0.868973\n",
+       "      0.776058\n",
+       "      0.684574\n",
+       "      0.613887\n",
+       "      0.798276\n",
+       "      0.599223\n",
+       "      0.919433\n",
        "      2318.0\n",
        "      7451.0\n",
        "      0.237281\n",
-       "      0.183437\n",
+       "      0.178114\n",
        "    \n",
        "    \n",
        "      Female\n",
        "      0.934631\n",
-       "      0.760266\n",
-       "      0.638655\n",
-       "      0.617120\n",
-       "      0.785124\n",
-       "      0.538244\n",
-       "      0.938701\n",
+       "      0.762758\n",
+       "      0.641068\n",
+       "      0.618349\n",
+       "      0.780488\n",
+       "      0.543909\n",
+       "      0.930589\n",
        "      353.0\n",
        "      2936.0\n",
        "      0.107327\n",
-       "      0.073579\n",
+       "      0.074795\n",
        "    \n",
        "    \n",
        "      Male\n",
-       "      0.826698\n",
-       "      0.765123\n",
-       "      0.680512\n",
-       "      0.571345\n",
-       "      0.771613\n",
-       "      0.608651\n",
-       "      0.896625\n",
+       "      0.835648\n",
+       "      0.771690\n",
+       "      0.692108\n",
+       "      0.593010\n",
+       "      0.801205\n",
+       "      0.609160\n",
+       "      0.900545\n",
        "      1965.0\n",
        "      4515.0\n",
        "      0.303241\n",
-       "      0.239198\n",
+       "      0.230556\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.107933\n",
-       "      0.004857\n",
-       "      0.041857\n",
-       "      0.045775\n",
-       "      0.013511\n",
-       "      0.070408\n",
-       "      0.042075\n",
+       "      0.098982\n",
+       "      0.008931\n",
+       "      0.051039\n",
+       "      0.025339\n",
+       "      0.020717\n",
+       "      0.065251\n",
+       "      0.030044\n",
        "      1612.0\n",
        "      1579.0\n",
        "      0.195913\n",
-       "      0.165619\n",
+       "      0.155761\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.843177\n",
-       "      0.736263\n",
-       "      0.617191\n",
-       "      0.532205\n",
-       "      0.733373\n",
-       "      0.532787\n",
-       "      0.819932\n",
+       "      0.842563\n",
+       "      0.723973\n",
+       "      0.600312\n",
+       "      0.524332\n",
+       "      0.754902\n",
+       "      0.498274\n",
+       "      0.817906\n",
        "      2318.0\n",
        "      7451.0\n",
        "      0.237281\n",
-       "      0.172382\n",
+       "      0.156618\n",
        "    \n",
        "    \n",
        "      Female\n",
-       "      0.894193\n",
-       "      0.859737\n",
-       "      0.623377\n",
-       "      0.587947\n",
-       "      0.504378\n",
-       "      0.815864\n",
-       "      0.938701\n",
+       "      0.901490\n",
+       "      0.862578\n",
+       "      0.639198\n",
+       "      0.603680\n",
+       "      0.526606\n",
+       "      0.813031\n",
+       "      0.930589\n",
        "      353.0\n",
        "      2936.0\n",
        "      0.107327\n",
-       "      0.173609\n",
+       "      0.165704\n",
        "    \n",
        "    \n",
        "      Male\n",
-       "      0.817284\n",
-       "      0.722584\n",
-       "      0.615335\n",
-       "      0.542526\n",
-       "      0.850854\n",
-       "      0.481934\n",
-       "      0.896625\n",
+       "      0.812654\n",
+       "      0.707908\n",
+       "      0.588475\n",
+       "      0.532366\n",
+       "      0.881218\n",
+       "      0.441730\n",
+       "      0.900545\n",
        "      1965.0\n",
        "      4515.0\n",
        "      0.303241\n",
-       "      0.171759\n",
+       "      0.152006\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.076909\n",
-       "      0.137153\n",
-       "      0.008042\n",
-       "      0.045421\n",
-       "      0.346475\n",
-       "      0.333930\n",
-       "      0.042075\n",
+       "      0.088835\n",
+       "      0.154670\n",
+       "      0.050724\n",
+       "      0.071314\n",
+       "      0.354613\n",
+       "      0.371301\n",
+       "      0.030044\n",
        "      1612.0\n",
        "      1579.0\n",
        "      0.195913\n",
-       "      0.001850\n",
+       "      0.013698\n",
        "    \n",
        "  \n",
        "\n",
@@ -1030,25 +1030,25 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.863036           0.771720  0.674453  0.597347   \n",
-       "          Female             0.934631           0.760266  0.638655  0.617120   \n",
-       "          Male               0.826698           0.765123  0.680512  0.571345   \n",
-       "         Maximum difference  0.107933           0.004857  0.041857  0.045775   \n",
-       "updated  Overall             0.843177           0.736263  0.617191  0.532205   \n",
-       "          Female             0.894193           0.859737  0.623377  0.587947   \n",
-       "          Male               0.817284           0.722584  0.615335  0.542526   \n",
-       "         Maximum difference  0.076909           0.137153  0.008042  0.045421   \n",
+       "original Overall             0.868973           0.776058  0.684574  0.613887   \n",
+       "          Female             0.934631           0.762758  0.641068  0.618349   \n",
+       "          Male               0.835648           0.771690  0.692108  0.593010   \n",
+       "         Maximum difference  0.098982           0.008931  0.051039  0.025339   \n",
+       "updated  Overall             0.842563           0.723973  0.600312  0.524332   \n",
+       "          Female             0.901490           0.862578  0.639198  0.603680   \n",
+       "          Male               0.812654           0.707908  0.588475  0.532366   \n",
+       "         Maximum difference  0.088835           0.154670  0.050724  0.071314   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.773438  0.597929  0.918163          2318.0   \n",
-       "          Female              0.785124  0.538244  0.938701           353.0   \n",
-       "          Male                0.771613  0.608651  0.896625          1965.0   \n",
-       "         Maximum difference   0.013511  0.070408  0.042075          1612.0   \n",
-       "updated  Overall              0.733373  0.532787  0.819932          2318.0   \n",
-       "          Female              0.504378  0.815864  0.938701           353.0   \n",
-       "          Male                0.850854  0.481934  0.896625          1965.0   \n",
-       "         Maximum difference   0.346475  0.333930  0.042075          1612.0   \n",
+       "original Overall              0.798276  0.599223  0.919433          2318.0   \n",
+       "          Female              0.780488  0.543909  0.930589           353.0   \n",
+       "          Male                0.801205  0.609160  0.900545          1965.0   \n",
+       "         Maximum difference   0.020717  0.065251  0.030044          1612.0   \n",
+       "updated  Overall              0.754902  0.498274  0.817906          2318.0   \n",
+       "          Female              0.526606  0.813031  0.930589           353.0   \n",
+       "          Male                0.881218  0.441730  0.900545          1965.0   \n",
+       "         Maximum difference   0.354613  0.371301  0.030044          1612.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
@@ -1063,14 +1063,14 @@
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.183437  \n",
-       "          Female                             0.073579  \n",
-       "          Male                               0.239198  \n",
-       "         Maximum difference                  0.165619  \n",
-       "updated  Overall                             0.172382  \n",
-       "          Female                             0.173609  \n",
-       "          Male                               0.171759  \n",
-       "         Maximum difference                  0.001850  "
+       "original Overall                             0.178114  \n",
+       "          Female                             0.074795  \n",
+       "          Male                               0.230556  \n",
+       "         Maximum difference                  0.155761  \n",
+       "updated  Overall                             0.156618  \n",
+       "          Female                             0.165704  \n",
+       "          Male                               0.152006  \n",
+       "         Maximum difference                  0.013698  "
       ]
      },
      "execution_count": 6,
@@ -1088,10 +1088,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:24:03.348837Z",
-     "iopub.status.busy": "2024-06-17T14:24:03.348169Z",
-     "iopub.status.idle": "2024-06-17T14:25:29.142078Z",
-     "shell.execute_reply": "2024-06-17T14:25:29.132028Z"
+     "iopub.execute_input": "2024-06-17T19:19:16.639839Z",
+     "iopub.status.busy": "2024-06-17T19:19:16.639439Z",
+     "iopub.status.idle": "2024-06-17T19:20:39.414319Z",
+     "shell.execute_reply": "2024-06-17T19:20:39.408483Z"
     }
    },
    "outputs": [],
@@ -1107,10 +1107,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:29.178862Z",
-     "iopub.status.busy": "2024-06-17T14:25:29.178057Z",
-     "iopub.status.idle": "2024-06-17T14:25:29.392267Z",
-     "shell.execute_reply": "2024-06-17T14:25:29.391944Z"
+     "iopub.execute_input": "2024-06-17T19:20:39.435818Z",
+     "iopub.status.busy": "2024-06-17T19:20:39.435388Z",
+     "iopub.status.idle": "2024-06-17T19:20:39.820251Z",
+     "shell.execute_reply": "2024-06-17T19:20:39.819926Z"
     }
    },
    "outputs": [
@@ -1142,43 +1142,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.089937\n",
-       "      0.032217\n",
+       "      0.089690\n",
+       "      0.043099\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.117495\n",
-       "      0.217334\n",
+       "      0.132235\n",
+       "      0.241701\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.093219\n",
-       "      0.146708\n",
+       "      0.153663\n",
+       "      0.160266\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.093219\n",
-       "      0.146708\n",
+       "      0.153663\n",
+       "      0.160266\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.065597\n",
-       "      0.091284\n",
+       "      0.092069\n",
+       "      0.104162\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.077975\n",
-       "      0.139855\n",
+       "      0.082708\n",
+       "      0.152206\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.050923\n",
-       "      0.024168\n",
+       "      0.043393\n",
+       "      0.022143\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.305085\n",
-       "      2.004378\n",
+       "      0.222273\n",
+       "      2.197229\n",
        "    \n",
        "  \n",
        "\n",
@@ -1186,14 +1186,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.089937  0.032217\n",
-       "Predictive Parity                                0.117495  0.217334\n",
-       "Equal Opportunity                                0.093219  0.146708\n",
-       "Average Group Difference in False Negative Rate  0.093219  0.146708\n",
-       "Equalized Odds                                   0.065597  0.091284\n",
-       "Conditional Use Accuracy                         0.077975  0.139855\n",
-       "Average Group Difference in Accuracy             0.050923  0.024168\n",
-       "Treatment Equality                               0.305085  2.004378"
+       "Statistical Parity                               0.089690  0.043099\n",
+       "Predictive Parity                                0.132235  0.241701\n",
+       "Equal Opportunity                                0.153663  0.160266\n",
+       "Average Group Difference in False Negative Rate  0.153663  0.160266\n",
+       "Equalized Odds                                   0.092069  0.104162\n",
+       "Conditional Use Accuracy                         0.082708  0.152206\n",
+       "Average Group Difference in Accuracy             0.043393  0.022143\n",
+       "Treatment Equality                               0.222273  2.197229"
       ]
      },
      "execution_count": 8,
@@ -1211,16 +1211,16 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:29.397792Z",
-     "iopub.status.busy": "2024-06-17T14:25:29.397658Z",
-     "iopub.status.idle": "2024-06-17T14:25:31.501435Z",
-     "shell.execute_reply": "2024-06-17T14:25:31.501004Z"
+     "iopub.execute_input": "2024-06-17T19:20:39.824487Z",
+     "iopub.status.busy": "2024-06-17T19:20:39.824220Z",
+     "iopub.status.idle": "2024-06-17T19:20:42.456476Z",
+     "shell.execute_reply": "2024-06-17T19:20:42.456153Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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vIxFCtkzsSirrlMj5nBx9wJvaG08AmqVrW0h5MenxLeHyiERMrmwiKgLSfXpDNgEhV4kGCLlKz2W1erFF4+IQYqTFf11Q3YgSRo0hRxWbkarfW0Eq64fpqbcIG9WJEBJy/AEz/t5Emx8y7DN6JfV544mcBwEAmr7mEXLh4eFr164ViUSfffbZTz/95O7uXrHO3r1716xZ8+TJEx0dnYEDB65bt87A4P8vfL1//37hwoWHDx9+9+6dlZXV+vXrBw8eXM3mlB1ylXbXBtiaVPWahqre9dCIpvWyqnR0ZaVqDDljfls576xoqPJ6dWg5sbsF/cYTgogCgGo1g5CLiYnx8fEJDw93c3PbunXrr7/+mpyc3Lp1a+k6f/31V+/evX/88UcvL6+XL19Onz69ffv2R44cIYSUlpa6ubkZGRn98MMPrVq1yszM1NLScnBwqGaLSg25qrprgf3b/3j2icI3p3DSN6gqPqldw3NylVnuuMfL1h53VgBAGZpByLm4uHTt2jUiIoKetLGxGTFiRFhYmHSddevWRUREpKWl0ZObNm1as2ZNZmYmIWTLli1r1659+PBhixYt5Nyi8kKutFzSPexcbmGpTDmHEB21Fu8/1uvOWTUPGBWXSeRfecVhb8Zaqu7tW6rzVRT1xhM9Db6dqTaXy9FooWGhbVGfvQYAqEZTD7nS0lJ1dfWDBw+OHDmSLgkICEhKSrp48aJ0tStXrvTp0+fIkSODBg168+bNmDFjbGxstmzZQggZPHiwvr6+urr6sWPHWrZsOX78+Pnz5/N4PJkNlZSUlJT8/62d/Px8c3NzhYdc3H3RD0fu1fktTdWoarS69C296i8tmmjzxzm3tjTUqHHYGwBAM9LUX9CcnZ0tFouNjY2ZEmNj46ysLJlqPXr02Lt3r7e3d3FxcXl5+bBhwzZt+v+bQE+fPk1ISJgwYUJsbOyTJ09mzJhRXl6+ZMkSmTWEhYUtXbpUeTsiz6dGdNVa5H0sk/9dDwyTf8YEyowVNJEanLllYleZMRpLhtrqafCrijEel9Pc37kOAKAQSuzJvXr1yszM7MqVK66urnTJihUrdu/e/fDhQ+lqycnJ/fv3nz17tqenp0gk+u6777p16xYVFUUI6dChQ3FxcXp6Ot1727BhAz2GRWZDSu3Jyfmpkdn9O/x09jGpoitW6ThDPzfL/rYmCn/ACACAHZp6T87Q0JDH40l33d68eSPdsaOFhYW5ubl99913hBB7e3sNDQ13d/fQ0FChUCgUClu0aMFcn7SxscnKyiotLVVVVZVeA5/P5/P5StqLGt9QRT8lNrNvu44mmlV1xSImdpXnDQ7V9MDQOQMAqAMlhpyqqqqjo2N8fDxzTy4+Pn748OEy1YqKilRU/m0GHWl0/9LNzW3fvn0SiYTL5RJCHj9+LBQKZRJO2eQZ/U+/VW+gnXCArUml/a1qZgEAgPIo9ysEc+bM8fHxcXJycnV13bZtW0ZGxvTp0wkhQUFBL1++3LVrFyHEy8trypQpERER9OXKwMBAZ2dnU1NTQsjXX3+9adOmgICAb7/99smTJytXrpw1a5ZSG1xR7ofqHprWFqisGW3P9MnQFQMAaFKUG3Le3t45OTnLli0TiUR2dnaxsbEWFhaEEJFIlJGRQdfx9fUtKCjYvHnz3LlzdXV1+/btu3r1anqWubn5mTNnZs+ebW9vb2ZmFhAQMH/+fKU2uCJ9jeo6jsFD5X1vLAAANDy81qsG1b9rWPqtxAAAoEAKOZ/je3I1oL89VuksISu+OAwAwGIIuRrQX3Gs9JOY7PjiMAAAiyHkakZ/6Fb432/ssumLwwAAbKXcgSesMcDWRIvfgnnNI/MefQAAaMoQcjWT+WTM77deVPVxGQAAaFJwubIG9IsrpV96kpVX/PWeW3H3Zd8uBgAATQ1CrjpiCbX0RLLMMxb05NITyWIJq56+AABgH4Rcdap6cSVFiCiv+Hp6bsM3CQAA5IeQq5JYQl1Oza6mwpuCml9rCQAAjQgDTyonM9ikUkZalT8kDgAATQRCrhI1fiWV/rwOXncCANDE4XKlrEoHm0ijn4/D604AAJo+9ORk1fiVVJMqPnkKAABNDUJOVvXDSWb2aTt7QEf04QAAmgVcrpRV/XASt3YtkXAAAM0FQk4W/W2dSj87gG/rAAA0Lwg5WfS3dcg/A0xoGGwCANAcIeQqQX9bx0Tq2zom+LYOAEAzhIEnlRtoJxxga3I9PfdNQbGRlsDZSh99OACAZgchVyUel+Pa1qCxWwEAAHWHy5UAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1kLIAQAAayHkAACAtRByAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBAABrIeQAAIC1EHIAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2lh1x4eLiVlZVAIHB0dLx06VKldfbu3evg4KCuri4UCr/88sucnByZCgcOHOBwOCNGjFB2awEAgE2UG3IxMTGBgYELFy68ffu2u7v7oEGDMjIyZOr89ddfkyZN8vPze/DgwcGDB2/cuOHv7y9d4fnz5/PmzXN3d1dqUwEAgH2UG3IbNmzw8/Pz9/e3sbH56aefzM3NIyIiZOpcu3bN0tJy1qxZVlZWPXv2nDZt2s2bN5m5YrF4woQJS5cubdOmjVKbCgAA7KPEkCstLU1MTPTw8GBKPDw8rly5IlOtR48eL168iI2NpSjq9evXhw4dGjJkCDN32bJlLVu29PPzq2ZDJSUl+VIUuxcAANB8KTHksrOzxWKxsbExU2JsbJyVlSVTrUePHnv37vX29lZVVTUxMdHV1d20aRM96/Lly1FRUZGRkdVvKCwsTOcf5ubmit0LAABovpQ+8ITD4TC/KYqSnqQlJyfPmjVryZIliYmJcXFx6enp06dPJ4QUFBRMnDgxMjLS0NCw+k0EBQXl/SMzM1PhuwAAAM2UivJWbWhoyOPxpLtub968ke7Y0cLCwtzc3L777jtCiL29vYaGhru7e2ho6OvXr589e+bl5UVXk0gkhBAVFZVHjx61bdtWeg18Pp/P5ytvRwAAoJlSYk9OVVXV0dExPj6eKYmPj+/Ro4dMtaKiIi7332bweDxCCEVR1tbW9+7dS/rHsGHD+vTpk5SUhAuSAAAgJyX25Aghc+bM8fHxcXJycnV13bZtW0ZGBn0pMigo6OXLl7t27SKEeHl5TZkyJSIiwtPTUyQSBQYGOjs7m5qaEkLs7OyYVenq6sqUAAAAVE+5Ieft7Z2Tk7Ns2TKRSGRnZxcbG2thYUEIEYlEzANzvr6+BQUFmzdvnjt3rq6ubt++fVevXq3UVgEAwCeCQ1FUY7dBkfLz83V0dPLy8rS1tRu7LQAAUHcKOZ/j3ZUAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1kLIAQAAayHkAACAtRByAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBAABrIeQAAIC1EHIAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1kLIAQAAayHkAACAtRByAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBAABrIeQAAIC1EHIAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyl9JALDw+3srISCASOjo6XLl2qtM7evXsdHBzU1dWFQuGXX36Zk5NDl0dGRrq7u+vp6enp6fXv3//69evKbi0AALCJXCFnaWm5bNmyjIyM2q49JiYmMDBw4cKFt2/fdnd3HzRoUMWV/PXXX5MmTfLz83vw4MHBgwdv3Ljh7+9Pz7pw4cK4cePOnz9/9erV1q1be3h4vHz5srZtAACATxclh59//rlr1648Hq9///779+8vLi6WZymKopydnadPn85MWltbL1iwQKbO2rVr27RpI72tVq1aVVxVeXm5lpbWzp07q99iXl4eISQvL0/OFgIAQNOkkPO5XD25b7/9NjExMTEx0dbWdtasWUKhcObMmbdu3ap+qdLS0sTERA8PD6bEw8PjypUrMtV69Ojx4sWL2NhYiqJev3596NChIUOGVFxbUVFRWVmZvr6+PA0GAAAgtbon5+DgsHHjxpcvXwYHB//666/dunVzcHDYvn07RVGV1s/OzhaLxcbGxkyJsbFxVlaWTLUePXrs3bvX29tbVVXVxMREV1d306ZNFde2YMECMzOz/v37V5xVUlKSL0X+PQIAAHarRciVlZX99ttvw4YNmzt3rpOT06+//jpmzJiFCxdOmDChmqU4HA7zm6Io6UlacnLyrFmzlixZkpiYGBcXl56ePn36dJk6a9as2b9//+HDhwUCQcVNhIWF6fzD3Nxc/j0CAACWk+eaZmJi4syZMw0MDIyMjObOnZuSksLMun79ukAgqHSpkpISHo93+PBhpmTWrFm9evWSqTZx4sTRo0czk/QIzFevXjEla9eu1dHRuXHjRlXNKy4uzvtHZmYmwT05AIDmTyH35FTkCcJu3boNGDAgIiJixIgRLVq0kJ5la2s7duzYSpdSVVV1dHSMj48fOXIkXRIfHz98+HCZakVFRSoq/zaDx+PR0UtPrl27NjQ09PTp005OTlU1j8/n8/l8eXYEAAA+KXKF3NOnTy0sLCqdpaGhsWPHjqoWnDNnjo+Pj5OTk6ur67Zt2zIyMuhLkUFBQS9fvty1axchxMvLa8qUKREREZ6eniKRKDAw0NnZ2dTUlBCyZs2axYsX79u3z9LSkr6Zp6mpqampWYf9BACAT5BcIffmzZusrCwXFxem5O+//+bxeNX0rmje3t45OTnLli0TiUR2dnaxsbF0WIpEIuaBOV9f34KCgs2bN8+dO1dXV7dv376rV6+mZ4WHh5eWlo4ePZpZYXBwcEhISG12EAAAPl0cqoqxkdKcnZ2///576bA5fPjw6tWr//77b2W2rS7y8/N1dHTy8vK0tbUbuy0AAFB3CjmfyzW6Mjk5uWvXrtIlXbp0SU5OrvNWAQAAGoBcIcfn81+/fi1dIhKJpEeLAAAANEFyhdyAAQOCgoLo0ZyEkPfv3//www8DBgxQZsMAAADqS67e2Pr163v16mVhYdGlSxdCSFJSkrGx8e7du5XcNgAAgHqRK+TMzMzu3r27d+/eO3fuqKmpffnll+PGjZN5YA4AAKCpkfe+moaGxtSpU5XaFAAAAMWqxeCR5OTkjIyM0tJSpmTYsGFKaBIAAIBiyPvGk5EjR967d4/D+f/n6uj3LIvFYuW2DgAAoB7kGl0ZEBBgZWX1+vVrdXX1Bw8e/Pnnn05OThcuXFBy2wAAAOpFrp7c1atXExISWrZsyeVyuVxuz549w8LCZs2adfv2bWW3DwAAoM7k6smJxWL6tciGhoavXr0ihFhYWDx69Ei5TQMAAKgfuXpydnZ2d+/ebdOmjYuLy5o1a1RVVbdt29amTRtlNw4AAKA+5Aq5RYsWFRYWEkJCQ0OHDh3q7u5uYGAQExOj5LYBAADUi1xfIZCRm5urp6dHD7BsavAVAgAAdmigrxCUl5erqKjcv3+fKdHX12+aCQcAACCt5pBTUVGxsLDAI3EAANDsyDW6ctGiRUFBQbm5ucpuDQAAgALJNfDk559/Tk1NNTU1tbCw0NDQYMpv3bqltIYBAADUl1whN2LECCU3AwAAQPHqMrqyKcPoSgAAdmig0ZUAAADNlFyXK7lcbqXPDGDIJQAANGVyhdyRI0eY32VlZbdv3965c+fSpUuV1ioAAAAFqOM9uX379sXExBw7dkzhDaon3JMDAGCHxrwn5+Licvbs2TpvFQAAoAHUJeQ+fvy4adOmVq1aKbw1AAAACiTXPTnp1zFTFFVQUKCurr5nzx5lNgwAAKC+5Aq5H3/8kQk5LpfbsmVLFxcXPT09ZTYMAACgvuQKOV9fXyU3AwAAQPHkuie3Y8eOgwcPSpccPHhw586dymkSAACAYsgVcqtWrTI0NJQuMTIyWrlypXKaBAAAoBhyhdzz58+trKykSywsLDIyMpTTJAAAAMWQK+SMjIzu3r0rXXLnzh0DAwPlNAkAAEAx5Aq5sWPHzpo16/z582KxWCwWJyQkBAQEjB07VtmNAwAAqA+5RleGhoY+f/68X79+KioqhBCJRDJp0iTckwMAgCauFu+ufPLkSVJSkpqaWqdOnSwsLJTarDrDuysBANhBIedzuXpytPbt27dv377OWwIAAGhgct2TGz169KpVq6RL1q5d+8UXXyinSQAAAIohV8hdvHhxyJAh0iUDBw78888/ldMkAAAAxZAr5D58+KCqqipd0qJFi/z8fOU0CQAAQDHkCjk7O7uYmBjpkgMHDtja2iqnSQAAAIoh18CTxYsX/+9//0tLS+vbty8h5Ny5c/v27Tt06JCS2wYAAFAvcoXcsGHDjh49unLlykOHDqmpqTk4OCQkJGCMPgAANHG1eE6O9v79+71790ZFRd25c0csFiupWXWG5+QAANhBIedzue7J0RISEiZOnGhqarp58+bBgwffvHmzzlsFAABoADVfrnzx4kV0dPT27dsLCwvHjBlTVlb2+++/Y9QJAAA0fTX05AYPHmxra5ucnLxp06ZXr15t2rSpYZoFAABQfzX05M6cOTNr1qyvv/4aL/QCAIBmp4ae3KVLlwoKCpycnFxcXDZv3vz27duGaRYAAED91RByrq6ukZGRIpFo2rRpBw4cMDMzk0gk8fHxBQUFDdM+AACAOqvdIwSPHj2KioravXv3+/fvBwwYcPz4ceW1rG7wCAEAADs09CMEhJCOHTuuWbPmxYsX+/fvr/MmAQAAGkatHwZv4tCTAwBgh0boyQEAADQjSg+58PBwKysrgUDg6Oh46dKlSuvs3bvXwcFBXV1dKBR++eWXOTk5zCz6wXM+n29ra3vkyBFltxYAANhEuSEXExMTGBi4cOHC27dvu7u7Dxo0KCMjQ6bOX3/9NWnSJD8/vwcPHhw8ePDGjRv+/v70rKtXr3p7e/v4+Ny5c8fHx2fMmDF///23UhsMAABsotx7ci4uLl27do2IiKAnbWxsRowYERYWJl1n3bp1ERERaWlp9OSmTZvWrFmTmZlJCPH29s7Pzz916hQ9a+DAgXp6etWPecE9OQAAdmjq9+RKS0sTExM9PDyYEg8PjytXrshU69Gjx4sXL2JjYymKev369aFDh4YMGULPunr1qvTinp6eFRcnhJSUlORLUcKuAABAs6TEkMvOzhaLxcbGxkyJsbFxVlaWTLUePXrs3bvX29tbVVXVxMREV1eXeUNmVlZWjYsTQsLCwnT+YW5uroRdAQCAZknpA084HA7zm6Io6UlacnLyrFmzlixZkpiYGBcXl56ePn36dPkXJ4QEBQXl/YO+zgkAAEDk/DJ43RgaGvJ4POm+15s3b6R7ZrSwsDA3N7fvvvuOEGJvb6+hoeHu7h4aGioUCk1MTGpcnBDC5/P5fL5ydgIAAJoxJfbkVFVVHR0d4+PjmZL4+PgePXrIVCsqKuJy/20Gj8cjhNDDYVxdXaUXP3PmTMXFAQAAqqLEnhwhZM6cOT4+Pk5OTq6urtu2bcvIyKAvRQYFBb18+XLXrl2EEC8vrylTpkRERHh6eopEosDAQGdnZ1NTU0JIQEBAr169Vq9ePXz48GPHjp09e/avv/5SaoMBAIBNlBty3t7eOTk5y5YtE4lEdnZ2sbGxFhYWhBCRSMQ8MOfr61tQULB58+a5c+fq6ur27dt39erV9KwePXocOHBg0aJFixcvbtu2bUxMjIuLi1IbDAAAbIJ3VwIAQFPU1J+TAwAAaFwIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1kLIAQAAayHkAACAtRByAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBAABrIeQAAIC1EHIAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1kLIAQAAayHkAACAtRByAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBAABrIeQAAIC1EHIAAMBaCDkAAGAthBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwAArIWQAwAA1lJ6yIWHh1tZWQkEAkdHx0uXLlWs4Ovry/mvzz77jJn7008/dezYUU1NzdzcfPbs2cXFxcpuMAAAsIZyQy4mJiYwMHDhwoW3b992d3cfNGhQRkaGTJ2NGzeK/pGZmamvr//FF1/Qs/bu3btgwYLg4OCUlJSoqKiYmJigoCClNhgAANiEQ1GU8tbu4uLStWvXiIgIetLGxmbEiBFhYWFV1T969OioUaPS09MtLCwIITNnzkxJSTl37hw9d+7cudevX6+0O8jIz8/X0dHJy8vT1tZW3H4AAEBDU8j5XIk9udLS0sTERA8PD6bEw8PjypUr1SwSFRXVv39/OuEIIT179kxMTLx+/Toh5OnTp7GxsUOGDKm4VElJSb4Uhe4EAAA0YyrKW3V2drZYLDY2NmZKjI2Ns7KyqqovEolOnTq1b98+pmTs2LFv377t2bMnRVHl5eVff/31ggULKi4YFha2dOlSxTYeAABYQOkDTzgcDvOboijpSRnR0dG6urojRoxgSi5cuLBixYrw8PBbt24dPnz45MmTy5cvr7hgUFBQ3j8yMzMV2nwAAGjGlNiTMzQ05PF40l23N2/eSHfspFEUtX37dh8fH1VVVaZw8eLFPj4+/v7+hJBOnToVFhZOnTp14cKFXO5/spnP5/P5fOXsBAAANGNK7Mmpqqo6OjrGx8czJfHx8T169Ki08sWLF1NTU/38/KQLi4qKpPOMx+NRFKXUkTIAAMAmSuzJEULmzJnj4+Pj5OTk6uq6bdu2jIyM6dOnE0KCgoJevny5a9cupmZUVJSLi4udnZ304l5eXhs2bOjSpYuLi0tqaurixYuHDRvG4/GU2mYAAGAN5Yact7d3Tk7OsmXLRCKRnZ1dbGwsPXJSJBJJPzCXl5f3+++/b9y4UWbxRYsWcTicRYsWvXz5smXLll5eXitWrFBqgwEAgE2U+5xcw8NzcgDNiFgsLisra+xWQONTVVWVGWxBFHQ+V25PDgCgUhRFZWVlvX//vrEbAk0Cl8u1srKSHnioKAg5AGgEdMIZGRmpq6tX82QRfAokEsmrV69EIlHr1q0V/pcBIQcADU0sFtMJZ2Bg0NhtgSahZcuWr169Ki8vb9GihWLXjE/tAEBDo+/DqaurN3ZDoKmgL1SKxWKFrxkhBwCNA1cpgaG8vwwIOQAAYC2EHACA0oWEhHTu3LmxW/EpQsgBwCfN19eX81+pqan1Xy2Hwzl69CgzOW/ePObTmNCQMLoSAJoNsYS6np77pqDYSEvgbKXP4yrmRs7AgQN37NjBTLZs2ZL5XVpaqpCHtzQ1NTU1NWu1SFlZmcKHGn6C0JMDgOYh7r6o5+qEcZHXAg4kjYu81nN1Qtx9kULWzOfzTaT069dv5syZc+bMMTQ0HDBgACHk4sWLzs7OfD5fKBQuWLCgvLycXvDzzz+fNWvW999/r6+vb2JiEhISQpdbWloSQkaOHMnhcOjfMpcrd+zYYWNjIxAIrK2tw8PD6cJnz55xOJzffvvt888/FwgEe/bsUcjefeIQcgDQDMTdF32955Yor5gpycor/nrPLUXlnIydO3eqqKhcvnx569atL1++HDx4cLdu3e7cuRMREREVFRUaGipdU0ND4++//16zZs2yZcvo767cuHGDELJjxw6RSET/lhYZGblw4cIVK1akpKSsXLly8eLFO3fuZObOnz9/1qxZKSkpnp6eyti1Tw0uVwJAUyeWUEtPJMu8ZpcihEPI0hPJA2xN6nnd8uTJk8y1xEGDBhFC2rVrt2bNGrpk4cKF5ubmmzdv5nA41tbWr169mj9//pIlS+h3Ldrb2wcHBxNC2rdvv3nz5nPnzg0YMIC+4Kmrq2tiYlJxc8uXL1+/fv2oUaMIIVZWVsnJyVu3bp08eTI9NzAwkJ4FCoGQA4Cm7np6rnQfjkERIsorvp6e69q2Xm9O6dOnT0REBP1bQ0Nj3LhxTk5OzNyUlBRXV1fmQS43N7cPHz68ePGidevWhBB7e3umplAofPPmTfXbevv2bWZmpp+f35QpU+iS8vJyHR0dpoL0pqH+EHIA0NS9Kagk4eScKw8NDY127drJlDC/KYqSflSZ/nILUyI9NoTD4Ugkkuq3RVeIjIx0cXFhCqU/kym9aag/hBwANHVGWoI6z60/W1vb33//nYm6K1euaGlpmZmZVb9UixYtKn1JlbGxsZmZ2dOnTydMmKCU5sJ/YeAJADR1zlb6Qh1BxdtuHEKEOgJnK32lbv2bb77JzMz89ttvHz58eOzYseDg4Dlz5lT8+JkMS0vLc+fOZWVlvXv3TmZWSEhIWFjYxo0bHz9+fO/evR07dmzYsEFpzf/UIeQAoKnjcTnBXraEEOmco38He9kq6mm5qpiZmcXGxl6/ft3BwWH69Ol+fn6LFi2qcan169fHx8ebm5t36dJFZpa/v/+vv/4aHR3dqVOn3r17R0dHW1lZKaftgC+DA0CDKy4uTk9Pt7KyEghqcaUx7r5o6YlkZgSKUEcQ7GU70E6onDZCg6r0rwS+DA4An5CBdsIBtibKeOMJsBhCDgCaDR6XU8+nBeBTg3tyAADAWgg5AABgLYQcAACwFkIOAABYCyEHAACshZADAADWQsgBANQC/WnTpKSk6qtFR0fr6uoqeyuKwuFwjh492vDbbQAIOQD41NHfvjE1NVVVVbWwsAgICMjJyamqsrm5uUgksrOzq36d3t7ejx8/VnRLlU6evfP19R0xYkRDtai+EHIA8El7+vSpk5PT48eP9+/fn5qaumXLlnPnzrm6uubm5lasXFpayuPxTExMVFRqeJOGmpqakZGRcposl7KysjosJefe1UFpaanC1ykPhBwANCtlH8mf60jZR0Wtb8aMGaqqqmfOnOndu3fr1q0HDRp09uzZly9fLly4kK5gaWkZGhrq6+uro6MzZcoUmQt6x48fb9++vZqaWp8+fXbu3MnhcN6/f0/+e7kyJCSkc+fOu3fvtrS01NHRGTt2bEFBAT0rLi6uZ8+eurq6BgYGQ4cOTUtLq7HBlpaWy5cvHz9+vKampqmp6aZNm5hZHA5ny5Ytw4cP19DQCA0NJYScOHHC0dFRIBC0adNm6dKl5eXldM0nT5706tVLIBDY2trGx8cza5DZuwcPHgwZMkRbW1tLS8vd3T0tLS0kJGTnzp3Hjh3jcDgcDufChQuEkHv37vXt21dNTc3AwGDq1KkfPnygF6f7fGFhYaamph06dKj1n40iIOQAoFlJPk4SlpOUEwpZWW5u7unTp7/55hs1NTWm0MTEZMKECTExMcz769euXWtnZ5eYmLh48WLpxZ89ezZ69OgRI0YkJSVNmzaNycWK0tLSjh49evLkyZMnT168eHHVqlV0eWFh4Zw5c27cuHHu3Dkulzty5MgaP7tKt8fe3v7WrVtBQUGzZ8+WTqng4ODhw4ffu3fvq6++On369MSJE2fNmpWcnLx169bo6OgVK1YQQiQSyahRo3g83rVr17Zs2TJ//vxKt/Ly5Us6CBMSEhITE7/66qvy8vJ58+aNGTNm4MCBIpFIJBL16NGjqKho4MCBenp6N27cOHjw4NmzZ2fOnMms5Ny5cykpKfHx8SdPnqxxv5SCYpe8vDxCSF5eXmM3BACq9PHjx+Tk5I8fP9Zl4b3eVLA2tW+sQlpy7do1QsiRI0dkyukPvL1+/ZqiKAsLixEjRjCz0tPTCSG3b9+mKGr+/Pl2dnbMLDrk3r17R1HUjh07dHR06PLg4GB1dfX8/Hx68rvvvnNxcanYmDdv3hBC7t27J7MVGRYWFgMHDmQmvb29Bw0aRP8mhAQGBjKz3N3dV65cyUzu3r1bKBRSFHX69Gkej5eZmUmXnzp1ijkI0tsNCgqysrIqLS2VacDkyZOHDx/OTG7btk1PT+/Dhw/05B9//MHlcrOysuiaxsbGJSUlFfdCRqV/JRRyPscLmgGgyfvwhlz9hUjKCSEk7RwhhKSeJacXEkIIV4W4ziSaLRW7QYqiCCH0p8AJIU5OTpVWe/ToUbdu3ZhJZ2fnqlZoaWmppaVF/xYKhXSeEULS0tIWL1587dq17Oxsug+XkZFR46gWV1dX6d8//fQTMynd1MTExBs3btC9N0KIWCwuLi4uKipKSUlp3bp1q1atKq5NWlJSkru7e4sWLapvTEpKioODg4aGBj3p5uYmkUgePXpkbGxMCOnUqZOqqmr1a1AqhBwANHkFWeRaBBGXEA73/7+WKhGTa+GEkhAen9j9r84h165dOw6Hk5ycLDNc8OHDh3p6eoaGhvQkcwaXQVEUE4Tkn2islHRUcDgc5pqkl5eXubl5ZGSkqampRCKxs7OrwwAN6TZIN1UikSxdunTUqFHSlQUCgUw7pReXJn0JtxoyB0FmnVUdugaDe3IA0OQJ7cm0i8SwAyGEUOJ//9+wA5l2kQjt67xiAwODAQMGhIeHf/z470iWrKysvXv3ent7V3X2Z1hbW9+4cYOZvHnzZq22npOTk5KSsmjRon79+tnY2Lx7907OBemrrMxva2vrSqt17dr10aNH7f6Ly+Xa2tpmZGS8evWKrnb16tVKF7e3t7906VLFUZqqqqpisZiZtLW1TUpKKiwspCcvX77M5XIba5hJRQg5AGgOjGyI3xnZQr8zxMimnivevHlzSUmJp6fnn3/+mZmZGRcXN2DAADMzM+YqXzWmTZv28OHD+fPnP378+LfffouOjiZVd4wq0tPTMzAw2LZtW2pqakJCwpw5c+Rc8PLly2vWrHn8+PEvv/xy8ODBgICASqstWbJk165dISEhDx48SElJiYmJWbRoESGkf//+HTt2nDRp0p07dy5dulTVeJmZM2fm5+ePHTv25s2bT5482b1796NHjwghlpaWd+/effToUXZ2dllZ2YQJEwQCweTJk+/fv3/+/Plvv/3Wx8eHvlbZFCDkAKCZeH6FUFIjDykJeV55F6RW2rdvf/PmzbZt23p7e7dt23bq1Kl9+vS5evWqvr5+jctaWVkdOnTo8OHD9vb2ERERdFrw+Xw5N83lcg8cOJCYmGhnZzd79uy1a9fKueDcuXMTExO7dOmyfPny9evXe3p6VlrN09Pz5MmT8fHx3bp16969+4YNGywsLOjtHjlypKSkxNnZ2d/fv6o4NzAwSEhI+PDhQ+/evR0dHSMjI+mLrlOmTOnYsaOTk1PLli0vX76srq5++vTp3Nzcbt26jR49ul+/fps3b5ZzRxoAp5qLyM1Rfn6+jo5OXl6etrZ2Y7cFACpXXFycnp5uZWUlEAhqsdiRaeTOAdLalQxYTs4sIpnXiMM4MnKL0ppZaytWrNiyZUtmZqZSt2JpaRkYGBgYGKjUrTSwSv9KKOR8joEnANBMCLuQljakx7eEyyNfxpIrm4hKbTJSOcLDw7t162ZgYHD58uW1a9dKPyIGTQFCDgCaie7T//3N5ZGegY3WEilPnjwJDQ3Nzc1t3br13Llzg4KCGrtF8B8IOQCAuvvxxx9//PHHhtzis2fPGnJzzR0GngAAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWAshBwCgAPRXsJW0cpkPdisbh8M5evRow29XGRByAPBJ+/zzz2VekXX06FH5X7JcK80uM8zNzUUiUfXft1NqutcfQg4AgP0qfjFHHjwez8TEREVF8a8NqcNn8+oGIQcAzcDz/OfJOckV//c8/7lStxsSEtK5c+etW7eam5urq6t/8cUX79+/p2eJxeI5c+bo6uoaGBh8//330i+7j4uL69mzJz1r6NChaWlpdLmVlRUhpEuXLhwO5/PPP6cLd+zYYWNjIxAIrK2tw8PDmZVcv369S5cuAoHAycnp9u3bVbXQ0tJy+fLl48eP19TUNDU13bRpEzOLw+Fs2bJl+PDhGhoaoaGhhJATJ044OjoKBII2bdosXbq0vLycrvnkyZNevXoJBAJbW9v4+HhmDTJdzwcPHgwZMkRbW1tLS8vd3T0tLS0kJGTnzp3Hjh3jcDgcDufChQuEkHv37vXt21dNTc3AwGDq1KkfPnygF6f7fGFhYaampg32wTm81gsAmrrn+c+HHhla1dyTI09aaFsob+upqam//fbbiRMn8vPz/fz8ZsyYsXfvXkLI+vXrt2/fHhUVZWtru379+iNHjvTt25depLCwcM6cOZ06dSosLFyyZMnIkSOTkpK4XO7169ednZ3Pnj372WefqaqqEkIiIyODg4M3b97cpUuX27dvT5kyRUNDY/LkyYWFhUOHDu3bt++ePXvS09Or+mIcbe3atT/88ENISMjp06dnz55tbW09YMAAelZwcHBYWNiPP/7I4/FOnz49ceLEn3/+mc6nqVOn0hUkEsmoUaMMDQ2vXbuWn59f1fcNXr582atXr88//zwhIUFbW/vy5cvl5eXz5s1LSUnJz8/fsWMHIURfX7+oqGjgwIHdu3e/cePGmzdv/P39Z86cSX9pjxBy7tw5bW3t+Pj4hvsADsUueXl5hJC8vLw6r6FcLLmSmn309osrqdnlYokC2wYAtI8fPyYnJ3/8+FHO+g+yH9hF21X1vwfZD+rTmN69ewcEBEiXHDlyhDk3BgcH83i8zMxMevLUqVNcLlckElEUJRQKV61aRZeXlZW1atVq+PDhFdf/5s0bQsi9e/coikpPTyeE3L59m5lrbm6+b98+ZnL58uWurq4URW3dulVfX7+wsJAuj4iIkFmQYWFhMXDgQGbS29t70KBB9G9CSGBgIDPL3d195cqVzOTu3buFQiFFUadPn5bZR0LIkSNHZBocFBRkZWVVWloq04DJkydL7/i2bdv09PQ+fPhAT/7xxx9cLjcrK4uuaWxsXFJSUnEvKv0rUf/zOUVRSr9cGR4eTn8iyNHR8dKlSxUr+Pr6cv7rs88+Y+a+f/9+xowZQqFQIBDY2NjExsYqtbVx90U9VyeMi7wWcCBpXOS1nqsT4u6LlLpFAGjiWrdu3apVK/q3q6urRCJ59OhRXl6eSCRydXWly1VUVJycnJhF0tLSxo8f36ZNG21tbfoSZUZGRsU1v337NjMz08/PT/MfoaGh9LXNlJQUBwcHdXV1ZrvVtFB6rqura0pKCjMp3arExMRly5Yx25oyZYpIJCoqKkpJSZHZx0q3kpSU5O7uTn83tRp0yzU0NOhJNzc3+ojRk506daK7sA1GuZcrY2JiAgMDw8PD3dzctm7dOmjQoOTk5NatW0vX2bhx46pVq+jf5eXlDg4OX3zxBT1ZWlo6YMAAIyOjQ4cOtWrVKjMzU0tLS3mtjbsv+nrPLekudFZe8dd7bkVM7DrQTqi87QJAI9LW1qZ7DIz3799X9ZVOetRljWMvvby8zM3NIyMjTU1NJRKJnZ1dpeMsJBIJISQyMtLFxYUp5PF4hBCqHlfzpJvHhA29uaVLl44aNUq6skAgkNlWVXunpqYmz9Ypiqq4BqZEuj0NQ7k9uQ0bNvj5+fn7+9vY2Pz000/m5uZ0p1uajo6OyT9u3rz57t27L7/8kp61ffv23Nzco0ePurm5WVhY9OzZ08HBQUlNFUuopSeSZf5a0ZNLTySLJaz6fjoAMKytrW/evCldcuPGjY4dOzKTGRkZr169on9fvXqVy+V26NBBR0dHKBReu3aNLi8vL09MTKR/5+TkpKSkLFq0qF+/fjY2Nu/evWNWRXdixGIxPWlsbGxmZvb06dN2Uuien62t7Z07dz5+/EjXZDZUKem5165ds7a2rrRa165dHz161O6/uFyura2tzD5Wuri9vf2lS5cqjtJUVVVl9ohueVJSUmFhIT15+fJl+ohV036lUmLIlZaWJiYmenh4MCUeHh5XrlypZpGoqKj+/ftbWPz/PeTjx4+7urrOmDHD2NjYzs5u5cqV0odSsa6n54ryiiuWU4SI8oqvp+cqabsA0Li++eabtLS0GTNm3Llz5/Hjx7/88ktUVNR3333HVBAIBJMnT75z586lS5dmzZo1ZswYExMTQkhAQMCqVauOHDny8OHDb775hhl1qaenZ2BgsG3bttTU1ISEhDlz5jCrMjIyUlNTi4uLe/36Nd19DAkJCQsL27hx4+PHj+/du7djx44NGzYQQsaPH8/lcv38/JKTk2NjY9etW1fNLly+fHnNmjV04w8ePFjVKJUlS5bs2rUrJCTkwYMHKSkpMTExixYtIoT079+/Y8eOkyZNovdx4cKFlS4+c+bM/Pz8sWPH3rx588mTJ7t376YvQlpaWt69e/fRo0fZ2dllZWUTJkygj9j9+/fPnz//7bff+vj4GBsb1+KPRKGUGHLZ2dlisVh634yNjbOysqqqLxKJTp065e/vz5Q8ffr00KFDYrE4NjZ20aJF69evX7FiRcUFS0pK8qXUrbVvCipJODnnAkDzZWlpeenSpbS0NA8Pj27dukVHR0dHRzM3TQgh7dq1GzVq1ODBgz08POzs7JhR/nPnzp00aZKvr6+rq6uWltbIkSPpci6Xe+DAgcTERDs7u9mzZ69du5ZZlYqKys8//7x161ZTU9Phw4cTQvz9/X/99dfo6OhOnTr17t07Ojqa7slpamqeOHEiOTm5S5cuCxcuXL16dTW7MHfu3MTExC5duixfvnz9+vWenp6VVvP09Dx58mR8fHy3bt26d+++YcMGukfB5XKPHDlSUlLi7Ozs7+9f6WmWEGJgYJCQkPDhw4fevXs7OjpGRkbS9+emTJnSsWNHJyenli1bXr58WV1d/fTp07m5ud26dRs9enS/fv02b94s95+G4nHqc+W3eq9evTIzM7ty5QpzG3PFihW7d+9++PBhpfXDwsLWr1//6tUr5rZkhw4diouL09PT6YvUGzZsWLt2rUgkOxIkJCRk6dKl0iV5eXlVXVKvytW0nHGRVV4Q2D+lu2tbg1qtEACqQv93TQ9Jk6d+ck6y90nvqubGDI2xNbBVXOv+IyQk5OjRo035HSWWlpaBgYFVjftvLir9K5Gfn6+jo1OH87k0JfbkDA0NeTyedNftzZs3VXVaKYravn27j4+P9MAboVDYoUMHOuEIITY2NllZWRXv3wYFBeX9IzMzs26tdbbSF+oIKt5v5RAi1BE4W+nXbbUAUH8aLaobrVD9XPjEKXF0paqqqqOjY3x8PNOLj4+Pp3voFV28eDE1NdXPz0+60M3Nbd++fRKJhMvlEkIeP34sFAorDj/l8/l8Pr+ereVxOcFetl/vucX5Z7wJIYTOvGAvWx5XKS+yAwB5WGhbnBx5srCssOIsjRYaSn0SHJo7JV6uJITExMT4+Phs2bLF1dV127ZtkZGRDx48sLCwCAoKevny5a5du5iaPj4+T548kRlBlJmZaWtr6+vr++233z558uSrr76aNWtWVTdFafXs3sbdFy09kcyMQBHqCIK9bPH8AIBi1fZyJbCe8i5XKvc5OW9v75ycnGXLltHvsY6NjaXvc4pEIulHI/Py8n7//feNGzfKLG5ubn7mzJnZs2fb29ubmZkFBATMnz9fqQ0eaCccYGtyPT33TUGxkZbA2UoffTgAgOZLuT25hqeQ5AcApUJPDmQ0y4EnAADVYNm/sKE+lPeXASEHAA2Nfr6qqKiosRsCTQU9bJ4ZS69A+NQOADQ0Ho+nq6tLv55fXV1dSZ/hhuZCIpG8fftWXV1dGV9nRcgBQCOg34xF5xwAl8tt3bq1Mv65g5ADgEbA4XCEQqGRkVHFF/7CJ0hVVZV+HlrhEHIA0Gh4PJ4ybsMAMDDwBAAAWAshBwAArIWQAwAA1mLbPTn6icI6f1UOAACaCPpMXs/nxNkWcgUFBYQQc3Pzxm4IAAAoQEFBgY6OTp0XZ9u7KyUSyatXr7S0tOrzvEV+fr65uXlmZiZegCkDR6YqODJVwZGpCo5MVZgjo6WlVVBQYGpqWp+nC9jWk+Nyua1atVLIqrS1tfGXr1I4MlXBkakKjkxVcGSqQh+Z+vThaBh4AgAArIWQAwAA1kLIVYLP5wcHB/P5/MZuSJODI1MVHJmq4MhUBUemKoo9MmwbeAIAAMBATw4AAFgLIQcAAKyFkAMAANZCyAEAAGt9ciEXHh5uZWUlEAgcHR0vXbpUaZ2LFy86OjoKBII2bdps2bJFetbvv/9ua2vL5/NtbW2PHDnSIE1uIPU5MpGRke7u7np6enp6ev37979+/XpDtboh1PPvDO3AgQMcDmfEiBHKbWvDqueRef/+/YwZM4RCoUAgsLGxiY2NbZBWN4R6HpmffvqpY8eOampq5ubms2fPLi4ubpBWN4Qaj4xIJBo/fnzHjh25XG5gYKDM3LqcgalPyYEDB1q0aBEZGZmcnBwQEKChofH8+XOZOk+fPlVXVw8ICEhOTo6MjGzRosWhQ4foWVeuXOHxeCtXrkxJSVm5cqWKisq1a9cafCeUop5HZvz48b/88svt27dTUlK+/PJLHR2dFy9eNPhOKEU9jwzt2bNnZmZm7u7uw4cPb7imK1k9j0xJSYmTk9PgwYP/+uuvZ8+eXbp0KSkpqcF3QinqeWT27NnD5/P37t2bnp5++vRpoVAYGBjY4DuhFPIcmfT09FmzZu3cubNz584BAQHSs+p2Bv60Qs7Z2Xn69OnMpLW19YIFC2TqfP/999bW1szktGnTunfvTv8eM2bMwIEDmVmenp5jx45VZnsbTj2PjLTy8nItLa2dO3cqqakNrP5Hpry83M3N7ddff508eTKbQq6eRyYiIqJNmzalpaUN0NQGVs8jM2PGjL59+zKz5syZ07NnT2W2t+HIc2QYvXv3lgm5up2BP6HLlaWlpYmJiR4eHkyJh4fHlStXZKpdvXpVuo6np+fNmzfLysoqnVVx8eao/kdGWlFRUVlZmb6+vvIa3GAUcmSWLVvWsmVLPz+/Bmhwg6n/kTl+/Lirq+uMGTOMjY3t7OxWrlwpFosbpvFKVf8j07Nnz8TERPqa/9OnT2NjY4cMGdIgbVcuOY9MNep2BmbbC5qrkZ2dLRaLjY2NmRJjY+OsrCyZallZWTJ1ysvLs7OzhUJhxVkVF2+O6n9kpKstWLDAzMysf//+Sm1zw6j/kbl8+XJUVFRSUlLDNLjB1P/IPH36NCEhYcKECbGxsU+ePJkxY0Z5efmSJUsaaAeUpv5HZuzYsW/fvqV7b+Xl5V9//fWCBQsaqPXKJOeRqUbdzsCfUMjRpD/BQ1FUpV/kkakjXSLP4s1UPY8Mbc2aNfv3779w4YJAIFBaSxtanY9MQUHBxIkTIyMjDQ0NG6CdDa8+f2ckEomRkdG2bdt4PJ6jo+OrV6/Wrl3LgpCj1efIXLhwYcWKFeHh4S4uLqmpqQEBAUKhcPHixcpvdUOo5ym0Dot/QiFnaGjI4/Gkk//NmzfS/y6gmZiYyNRRUVExMDCodFbFxZuj+h8Z2rp161auXHn27Fl7e3tlt7lh1PPIPHjw4NmzZ15eXnS5RCIhhKioqDx69Kht27bKb74S1f/vjFAobNGiBY/Ho2fZ2NhkZWWVlpaqqqoqv/lKVP8js3jxYh8fH39/f0JIp06dCgsLp06dunDhwvp8U60pkPPIVKNuZ+DmfdRqRVVV1dHRMT4+nimJj4/v0aOHTDVXV1fpOmfOnHFycmrRokWlsyou3hzV/8gQQtauXbt8+fK4uDgnJ6cGaHPDqOeRsba2vnfvXtI/hg0b1qdPn6SkJBZ8ub7+f2fc3NxSU1Pp4CeEPH78WCgUNveEI4o4MkVFRdJ5xuPx6AEUSm640sl5ZKpRxzNwzQNiWIQewBoVFZWcnBwYGKihofHs2TOKohYsWODj40PXoYf2zp49Ozk5OSoqSnpo7+XLl3k83qpVq1JSUlatWsW+RwjqfGRWr16tqqp66NAh0T8KCgoabWcUqp5HRhrLRlfW88hkZGRoamrOnDnz0aNHJ0+eNDIyCg0NbbSdUah6Hpng4GAtLa39+/c/ffr0zJkzbdu2HTNmTKPtjELJc2Qoirp9+/bt27cdHR3Hjx9/+/btBw8e0OV1OwN/WiFHUdQvv/xiYWGhqqratWvXixcv0oWTJ0/u3bs3U+fChQtdunRRVVW1tLSMiIiQXvzgwYMdO3ak/5H++++/N2TLla0+R8bCwkLmH0/BwcEN23wlquffGQbLQo6q95G5cuWKi4sLn89v06bNihUrysvLG7LxSlWfI1NWVhYSEtK2bVuBQGBubv7NN9+8e/euYZuvRPIcGZmTiYWFBTOrDmdgfGoHAABY6xO6JwcAAJ8ahBwAALAWQg4AAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgBsEB0draurW+ksX1/fBv4ieUhISOfOnRtyiwBVQcjBJ8TX15fD4XA4nBYtWhgbGw8YMGD79u3M6xPZauPGjdHR0fLXlz5Kbdq0mTdvXmFhYa22OG/evHPnzjFra+CIBZCGkINPy8CBA0Ui0bNnz06dOtWnT5+AgIChQ4eWl5c3drv+X2lpqcLXqaOjU1Unryr0UXr69GloaGh4ePi8efPkXJCiqPLyck1NTenPUwA0IoQcfFr4fL6JiYmZmVnXrl1/+OGHY8eOnTp1iuno5OXlTZ061cjISFtbu2/fvnfu3KHL6etv27dvb926taam5tdffy0Wi9esWWNiYmJkZLRixQpm/RkZGcOHD9fU1NTW1h4zZszr16+ZWaGhoUZGRlpaWv7+/gsWLGAu6NF9nbCwMFNT0w4dOhBC9uzZ4+TkpKWlZWJiMn78+Ddv3tA1L1y4wOFw/vjjDwcHB4FA4OLicu/ePem9O336tI2NjaamJp1S0uunf0skktWrV7dr147P57du3Vq65RWPkrm5+fjx4ydMmHD06NEaW3X69GknJyc+n3/p0iXmcmVISMjOnTuPHTtGdw0vXLjQt2/fmTNnMhvKycnh8/kJCQm1+TMEqAWEHHzS+vbt6+DgcPjwYUIIRVFDhgzJysqKjY1NTEzs2rVrv379cnNz6ZppaWmnTp2Ki4vbv3//9u3bhwwZ8uLFi4sXL65evXrRokXXrl2j1zBixIjc3NyLFy/Gx8enpaV5e3vTi+/du3fFihWrV69OTExs3bp1RESEdDPOnTuXkpISHx9/8uRJQkhpaeny5cvv3Llz9OjR9PR0X19f6crffffdunXrbty4YWRkNGzYsLKyMrq8qKho3bp1u3fv/vPPPzMyMirtfgUFBa1evXrx4sXJycn79u2T53Ncampq9Caqb9X3338fFhaWkpIi/TXBefPmjRkzhk5ckUjUo0cPf3//ffv2lZSUMIfF1NS0T58+NTYDoI4U9W5pgKav0u8AeHt729jYUBR17tw5bW3t4uJiZlbbtm23bt1KUVRwcLC6unp+fj5d7unpaWlpKRaL6cmOHTuGhYVRFHXmzBkej5eRkUGXP3jwgBBy/fp1iqJcXFxmzJjBrNnNzc3BwYFplbGxcUlJSaVtvn79OiGE/nTR+fPnCSEHDhygZ+Xk5KipqcXExFAUtWPHDkJIamoqPeuXX34xNjaW2ev8/Hw+nx8ZGSn/Ufr7778NDAwqfuqlYquOHj3KzA0ODpbeO+ljXlxcrK+vT7eZoqjOnTuHhIRU3x6A+kBPDj51FEVxOBxCSGJi4ocPHwwMDDT/kZ6enpaWRleztLTU0tKifxsbG9va2jJftjQ2Nqav3aWkpJibmzPfRLW1tdXV1U1JSSGEPHr0yNnZmdmo9G9CSKdOnaS/F3r79u3hw4dbWFhoaWl9/vnnhJCMjAxmrqurK/1DX1+/Y8eO9PoJIerq6swHx4VCIXM5kZGSklJSUtKvX78aj8nJkyc1NTUFAoGrq2uvXr02bdpUY6vk/Fgun8+fOHHi9u3bCSFJSUl37tyR6RECKJZKYzcAoJGlpKRYWVkRQiQSiVAovHDhgvRcZsgG8w10Qgg98lB6kh6iyeQlQ7pEehb1309caWhoML8LCws9PDw8PDz27NnTsmXLjIwMT0/PagakMKuVaRJV4StaampqVa1ERp8+fSIiIlq0aGFqakqvtsZWSe9C9fz9/Tt37vzixYvt27f369ev4scIARQIPTn4pCUkJNy7d+9///sfIaRr165ZWVkqKirtpBgaGsq/Nltb24yMjMzMTHoyOTk5Ly/PxsaGENKxY0f6Eh/t5s2bVa3k4cOH2dnZq1atcnd3t7a2rtgho+//EULevXv3+PFja2trOZvXvn17NTU1ZnB/NTQ0NNq1a2dhYcEEZ42tqoqqqqpYLJYu6dSpk5OTU2Rk5L59+7766is51wNQNwg5+LSUlJRkZWW9fPny1q1bK1euHD58+NChQydNmkQI6d+/v6ur64gRI06fPv3s2bMrV64sWrSomjSqqH///vb29hMmTLh169b169cnTZrUu3dv+jret99+GxUVtXPnzidPnoSGht69e1emz8do3bq1qqrqpk2bnj59evz48eXLl8tUWLZs2blz5+7fv+/r62toaCj/U2gCgWD+/Pnff//9rl270tLSrl27FhUVJeeyNbaqKpaWlnfv3n306FF2djYzRsbf33/VqlVisXjkyJFyrgegbhBy8GmJi4sTCoWWlpYDBw48f/78zz//fOzYMR6PRwjhcDixsbG9evX66quvOnToMHbs2GfPnskz/pDB4XCOHj2qp6fXq1ev/v37t2nTJiYmhp41YcKEoKCgefPmde3alR6aKBAIKl1Jy5Yto6OjDx48aGtru2rVqnXr1slUWLVqVUBAgKOjo0gkOn78uPTNvBotXrx47ty5S5YssbGx8fb2lr9DVmOrqjJlypSOHTs6OTm1bNny8uXLdOG4ceNUVFTGjx9f1UEAUJRKLtwDgLINGDDAxMRk9+7dtVrqwoULffr0effuXW0f7m5qMjMzLS0tb9y40bVr18ZuC7AcBp4ANISioqItW7Z4enryeLz9+/efPXs2Pj6+sRvVCMrKykQi0YIFC7p3746EgwaAkANoCPS10NDQ0JKSko4dO/7+++/9+/dv7EY1gsuXL/fp06dDhw6HDh1q7LbAJwGXKwEAgLUw8AQAAFgLIQcAAKyFkAMAANZCyAEAAGsh5AAAgLUQcgAAwFoIOQAAYC2EHAAAsBZCDgAAWOv/AOamBjj9hVVAAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "
"
       ]
@@ -1239,16 +1239,16 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:31.505048Z",
-     "iopub.status.busy": "2024-06-17T14:25:31.504775Z",
-     "iopub.status.idle": "2024-06-17T14:25:31.950017Z",
-     "shell.execute_reply": "2024-06-17T14:25:31.949552Z"
+     "iopub.execute_input": "2024-06-17T19:20:42.460837Z",
+     "iopub.status.busy": "2024-06-17T19:20:42.460694Z",
+     "iopub.status.idle": "2024-06-17T19:20:43.130390Z",
+     "shell.execute_reply": "2024-06-17T19:20:43.130014Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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azH4dZnt2po8M87VyPOVFNYu8bYi2lwDKhgCDuquqx7OPZeJlpx5cz8hT43FHdm/zde/2PC5HbsxU8x5CRbaop6lKCGGaoRtoqY3oZuZpa6p4sK2KE9Z2r/U1VSmpjaor3KKha1udu88Lqpoq84gMj8vZPM7BTE9YuQUgcxbF7Nq19NxqtuvWsXXl3W/k5s4AyoAAgzqq6q0cXdrq3JP6ml4dn7Y6Pk1LjVdU9vcdGibnquqIlkh1Vlv9FmWeoMorKotJehqT9FQm2Kp55PZpbi36Mu9sqh3m87lMOq67eO6xYotbGbWqJsDkZkZVz+fKXJypQ9vlRm7uDKAMaA4LdVFVj2cUIffkfUcz6UX+yblTKS+r6uk8KvHvzmpr3GJV3heXS2dbNX2JWhrWoi9zJ0t9lw6GPC6HvhA3vFsblw6GbfQ1FFz8/3q0ldu1XfU9CtItAJcNt/N3b1/V9dWq+hwiVXf1VIdFAJobBBjURT27uKUImXfkblV9FEkosvva04bdIqmiL9EfavNArtyZv3ZTqH25nqaqa0cj5WVGHTr9Y3U/gQAElxChburfp1lJeXXtDp7lFcs01qjnFqt69aKGGs/T1liRdhyetsYaarzK49VVFfoVuHpUFx6Xo9RHZOrQdpnV/QQCIMCgLpR9k7+4tKL3mgsN3u+t3BSMmtRzyi83q88wT1vjqEk95U7SUtWqZkFKrG6qox427HMmopSaGXVou8yibooAZKArKagLsYTqveZC5SYADYLDkW10TvfqpKepml9cXp8t7p/Sq6ov649l4lVxwqe5xZaGmvSlQulBuedejGcFz4rKi0rLJTFJGS/fl5jp8f1crB6/+VBUwrM16oDTGoC6qfH7HAEGdSS3x7MG+WPSVOMVl8l2KsEhRFdTNb+4nNRpK3TjuivzPZAlAGxR4/c5GnFAHcltAmDftl6/G7gcMtReUDm9CCH0o1fBAzpJb1FfU5VuMV+92jaUkHntpILFA0Ajwz0wqDu5t3Pk3k+Sfg5MLi9bE2crg4kulqcfiE7dk9PenWZppHllvkflnjjOCXOOprzIK/q76bzMc2C1aihR1dPZiiwLAI0JlxCh4cntieN6Rm7g3tuVu6mVubh3LT33y6jrVa25mptYDdLFlNyXqpA6vQkCAOoJ98CgGVHkRUFVNQ9phJtY9KYrP22G+2cATaLp74FFRERYWVnx+XwHB4fExES58+zdu7dr166ampoCgeCrr77Kzf23Y7f3798HBgYKBAI+n29jYxMXF6fsgkF5FHlytgl7iKjqWWnmGTLlbRoA6kC598AOHjwYHBwcERHh5ua2bdu2QYMGCYXCdu3aSc9z5cqVSZMmbdy40cfH58WLF9OnTw8ICDh69CghpKyszNPT09jY+PDhw23bts3OztbW1lZqwaBsijwF1ZgvxJNW/bPS9X92GwAalnID7Mcff/T39w8ICCCE/PTTT2fOnImMjAwPD5ee5/r165aWlrNmzSKEWFlZTZs2be3atfSkmJiYvLy8q1evqqqqEkIsLCyUWi00DkWenG2SHiLQQTsAuyjxEmJZWVlycrKXlxczxsvL6+rVqzKzubq6Pn/+PC4ujqKoV69eHT58eMiQIfSkEydOuLi4BAYGmpiY2NnZrVq1SiyuriUbtCTSHeY2zs0nuoP22na2CwBNRYkB9vbtW7FYbGJiwowxMTHJyZF9QZ+rq+vevXt9fX3V1NRMTU319PQ2b95MT8rIyDh8+LBYLI6Li1u0aNGGDRtWrlxZeUOlpaUFUpS3R9CyoYN2AHZReiMODufff/YURUkP0oRC4axZs5YsWZKcnBwfH5+ZmTl9+nR6kkQiMTY23r59u4ODw9ixYxcuXBgZGVl5E+Hh4br/MDdvgB7z4JM10E4wtY+V9B8ph0Om9rFCG3qAZkiJAWZkZMTj8aRPuV6/fi19QkYLDw93c3P77rvv7O3tvb29IyIiYmJiRCIRIUQgEHTq1InH+7sbOhsbm5ycnLKyMpk1hISE5P8jOztbeXsELV78A9H2PzKlO9+QUGT7H5ly3yUGAE1LiQGmpqbm4OCQkJDAjElISHB1dZWZrbi4mMv9tww6ruin09zc3J48eSKR/P3ejUePHgkEAjU1NZk1qKur60hRxr7Ap6Cad2bKfZcYADQt5V5CnDNnzo4dO2JiYlJTU2fPnp2VlUVfHgwJCZk0aRI9j4+Pz2+//RYZGZmRkZGUlDRr1iwnJyczMzNCyIwZM3Jzc4OCgh49evT777+vWrUqMDBQqQXDpwzPgQGwi3Kb0fv6+ubm5i5btkwkEtnZ2cXFxdFN4UUiUVZWFj2Pn59fYWHhli1b5s6dq6en5+HhsWbNGnqSubn52bNnZ8+ebW9v36ZNm6CgoPnz5yu1YPiU4TkwAHZBV1IAf6tzN4wAoAxN35UUAFvgOTAAdkGAAfwNz4EBsAsCDOBfinQ3DADNBF5oCfA/mqQbRgCoAwQYgCxFuhsGgCaHS4gAAMBKCDAAAGAlBBgAALASAgwAAFgJAQYAAKyEAAMAAFZCgAEAACshwAAAgJUQYAAAwEoIMAAAYCUEGAAAsBICDAAAWAkBBgAArIQAAwAAVkKAAQAAKyHAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWQoABAAArIcAAAICVEGAAAMBKCDAAAGAlBBgAALASAgwAAFgJAQYAAKyEAAMAAFZCgAEAACshwAAAgJUQYAAAwEoIMAAAYCUEGAAAsBICDAAAWAkBBgAArIQAAwAAVkKAAQAAKyHAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWQoABAAArKT3AIiIirKys+Hy+g4NDYmKi3Hn27t3btWtXTU1NgUDw1Vdf5ebmysxw4MABDoczYsQIZVcLAABsodwAO3jwYHBw8MKFC+/cuePu7j5o0KCsrCyZea5cuTJp0iR/f/+HDx8eOnTo5s2bAQEB0jM8e/Zs3rx57u7uSi0VAADYRbkB9uOPP/r7+wcEBNjY2Pz000/m5uaRkZEy81y/ft3S0nLWrFlWVla9e/eeNm3arVu3mKlisXj8+PFLly5t3769UksFAAB2UWKAlZWVJScne3l5MWO8vLyuXr0qM5urq+vz58/j4uIoinr16tXhw4eHDBnCTF22bFnr1q39/f2VVycAALCRivJW/fbtW7FYbGJiwowxMTHJycmRmc3V1XXv3r2+vr4lJSUVFRXDhg3bvHkzPSkpKSk6OjolJaX6DZWWlpaWltKfCwoKGmwHAACgGVN6Iw4Oh8N8pihKepAmFApnzZq1ZMmS5OTk+Pj4zMzM6dOnE0IKCwsnTJgQFRVlZGRU/SbCw8N1/2Fubt7guwAAAM0Qh6IoJa26rKxMU1Pz0KFDI0eOpMcEBQWlpKRcvnxZeraJEyeWlJQcOnSIHrxy5Yq7u/vLly9fvXrVvXt3Ho9Hj5dIJIQQLpeblpbWoUMH6TXInIGZm5vn5+fr6Ogoab8AAKARFBQU6OrqVvN9rsRLiGpqag4ODgkJCUyAJSQkDB8+XGa24uJiFZV/y6ATi6Ioa2vr+/fvM+MXLVpUWFi4adOmyudY6urq6urqStkHAABorpQYYISQOXPmTJw40dHR0cXFZfv27VlZWfTlwZCQkBcvXvzyyy+EEB8fnylTpkRGRnp7e4tEouDgYCcnJzMzM0KInZ0dsyo9PT2ZMQAA8ClTboD5+vrm5uYuW7ZMJBLZ2dnFxcVZWFgQQkQiEfNAmJ+fX2Fh4ZYtW+bOnaunp+fh4bFmzRqlVgUAAC2AEu+BNYkar5kCAAAr1Ph9jr4QAQCAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWQoABAAArIcAAAICVEGAAAMBKCDAAAGAlBBgAALCScnujh5ZBLKFuZOa9Liwx1uY7WRnwuLKv1QYAaHwIMKhB/APR0pNCUX4JPSjQ5Yf62A60EzRtVQAAuIQI1Yl/IJqx5zaTXoSQnPySGXtuxz8QNWFVAAAEAQbVEEuopSeFMu+LoweXnhSKJS3qTXIAwDoIMKjSjcw86XMvBkWIKL/kRmZe45cEAMBAgEGVXhfKSS8FpwIAKBsCDKpkrM2v81QAAGVDgEGVnKwMBLr8yk3mOYQIdPlOVgZNUBMAwD8QYFAlHpcT6mNLCJHOMPpzqI8tngYDgKaFAIPqDLQTRE7oYar779VCU11+5IQeeA4MAJocHmSGGgy0E3jamqInDgBobhBgUDMel+PSwbCpqwAA+B+4hAgAAKyEAAMAAFZCgAEAACshwAAAgJUQYAAAwEoIMAAAYCU0owfWwIuhAUAaAgzYAS+GBgAZuIQILIAXQwNAZQgwaO7wYmgAkAsBBs0dXgwNAHIhwKC5w4uhAUAuBBg0d3gxNADIhQCD5g4vhgYAuRBg0NzhxdAAIBcCDFgAL4YGgMoUepDZ0tLy66+/9vPza9eunbILApALL4YGABkKnYHNnTv3+PHj7du39/T0PHDgQGlpqbLLAqiMfjH08G5tXDoYIr0AQKEA+/bbb5OTk5OTk21tbWfNmiUQCGbOnHn79m1lFwcAAFAVDkXVriOD8vLyiIiI+fPnl5eX29nZBQUFffXVVxxOc/k5XFBQoKurm5+fr6Oj09S1AABA3dX4fV6LznzLy8uPHj26c+fOhISEXr16+fv7v3z5cuHChefOndu3b18DFQwAAKAQhQLs9u3bO3fu3L9/P4/Hmzhx4saNG62trelJXl5effr0UWaFAAAAcigUYD179vT09IyMjBwxYoSqqqr0JFtb27FjxyqnNgAAgCopFGAZGRkWFhZyJ2lpae3cubNBSwIAAKiZQq0QX79+/eeff0qP+fPPP2/duqWckgAAAGqmUIAFBgZmZ2dLj3nx4kVgYKBySgIAAKiZQgEmFAp79OghPaZ79+5CoVA5JQEAANRMoQBTV1d/9eqV9BiRSKSiUosm+AAAAA1LoQDz9PQMCQnJz8+nB9+/f//DDz94enoqsmxERISVlRWfz3dwcEhMTJQ7z969e7t27aqpqSkQCL766qvc3Fx6fFRUlLu7u76+vr6+/oABA27cuKHIFgEA4FOgUIBt2LAhOzvbwsKiX79+/fr1s7KyysnJ2bBhQ40LHjx4MDg4eOHChXfu3HF3dx80aFBWVpbMPFeuXJk0aZK/v//Dhw8PHTp08+bNgIAAetKlS5e+/PLLixcvXrt2rV27dl5eXi9evKjtHgIAQIukaFdSRUVFe/fuvXv3roaGhr29/ZdffinzQJhczs7OPXr0iIyMpAdtbGxGjBgRHh4uPc/69esjIyPT09Ppwc2bN69du1amzQghRCwW6+vrb9myZdKkSdVsEV1JAQC0DA3WlZSWltbUqVNrte2ysrLk5OQFCxYwY7y8vK5evSozm6ur68KFC+Pi4gYNGvT69evDhw8PGTKk8tqKi4vLy8sNDPD6XQAAIKRWfSEKhcKsrKyysjJmzLBhw6qZ/+3bt2Kx2MTEhBljYmKSk5MjM5urq+vevXt9fX1LSkoqKiqGDRu2efPmymtbsGBBmzZtBgwYUHlSaWkp84aXgoICxfcIAADYS9GeOEaOHHn//n0O5+9LjnT382KxuMZlpTuqpyiqcr/1QqFw1qxZS5Ys8fb2FolE33333fTp06Ojo6XnWbt27f79+y9dusTn80kl4eHhS5cuVWRHAACgxVCoEUdQUJCVldWrV680NTUfPnz4xx9/ODo6Xrp0qfqljIyMeDye9CnX69evpU/IaOHh4W5ubt999529vb23t3dERERMTIxIJGJmWL9+/apVq86ePWtvby93Q3QLSVrlm2cAANAiKRRg165dW7ZsWevWrblcLpfL7d27d3h4+KxZs6pfSk1NzcHBISEhgRmTkJDg6uoqM1txcTGX+28ZPB6PEMI0LVm3bt3y5cvj4+MdHR2r2pC6urqOFEX2CAAA2E6hABOLxa1atSKEGBkZvXz5khBiYWGRlpZW44Jz5szZsWNHTExMamrq7Nmzs7Kypk+fTggJCQlhGhP6+Pj89ttvkZGRGRkZSUlJs2bNcnJyMjMzI4SsXbt20aJFMTExlpaWOTk5OTk5Hz58qPOuAgBAS6LQPTA7O7t79+61b9/e2dl57dq1ampq27dvb9++fY0L+vr65ubmLlu2TCQS2dnZxcXF0b3ai0Qi5oEwPz+/wsLCLVu2zJ07V09Pz8PDY82aNfSkiIiIsrKy0aNHMysMDQ0NCwur7U4CAEDLo9BzYGfOnCkqKho1alRGRsbQoUP/+usvQ0PDgwcPenh4NEKJtYLnwAAAWoYav88VfZBZWl5enr6+fuX2hM0BAgwAoGWo8fu85ntgFRUVKioqDx48YMYYGBg0z/QCAIBPR80BpqKiYmFhocgjXwAAAI1GoVaIixYtCgkJycvLU3Y1AAAAClKoFeJ///vfJ0+emJmZWVhYaGlpMeNv376ttMIAAACqo1CAjRgxQsllAAAA1E5dWiE2Z2iFCADQMjRAK0QAAIBmSKFLiFwuV267eTRNBACApqJQgB09epT5XF5efufOnV27duENJgAA0ITqeA9s3759Bw8ePH78eIMXVE+4BwYA0DIo6x6Ys7PzuXPn6lEYAABAvdQlwD5+/Lh58+a2bds2eDUAAAAKUugemHTXvRRFFRYWampq7tmzR5mFAQAAVEehANu4cSMTYFwut3Xr1s7Ozvr6+sosDAAAoDoKBZifn5+SywAAAKgdhe6B7dy589ChQ9JjDh06tGvXLuWUBAAAUDOFAmz16tVGRkbSY4yNjVetWqWckgAAAGqmUIA9e/bMyspKeoyFhUVWVpZySgIAAKiZQgFmbGx879496TF37941NDRUTkkAAAA1UyjAxo4dO2vWrIsXL4rFYrFYfOHChaCgoLFjxyq7OAAAgKoo1ApxxYoVz54969+/v4qKCiFEIpFMmjQJ98AAAKAJ1aIvxMePH6ekpGhoaHTp0sXCwkKpZdUZ+kIEAGgZavw+V+gMjPbZZ5999tlnDVQYAABAvSh0D2z06NGrV6+WHrNu3br//Oc/yikJAACgZgoF2OXLl4cMGSI9ZuDAgX/88YdySgIAAKiZQgH24cMHNTU16TGqqqoFBQXKKQkAAKBmCgWYnZ3dwYMHpcccOHDA1tZWOSUBAADUTKFGHIsXL/6///u/9PR0Dw8PQsj58+f37dt3+PBhJdcGAABQJYUCbNiwYceOHVu1atXhw4c1NDS6du164cIFtFMHAIAmVIvnwGjv37/fu3dvdHT03bt3xWKxksqqMzwHBgDQMtT4fa7QPTDahQsXJkyYYGZmtmXLlsGDB9+6dauBigQAAKi1mi8hPn/+PDY2NiYmpqioaMyYMeXl5UeOHEELDgAAaFo1nIENHjzY1tZWKBRu3rz55cuXmzdvbpyyAAAAqlfDGdjZs2dnzZo1Y8YMdCIFAADNSg1nYImJiYWFhY6Ojs7Ozlu2bHnz5k3jlAUAAFC9GgLMxcUlKipKJBJNmzbtwIEDbdq0kUgkCQkJhYWFjVMfAACAXLVrRp+WlhYdHb179+737997enqeOHFCeZXVDZrRAwC0DA3ZjJ4Q0rlz57Vr1z5//nz//v0NUR4AAEAd1fpB5mYOZ2AAAC1DA5+BAQAANBMIMAAAYCUEGAAAsBICDAAAWAkBBgAArIQAAwAAVkKAAQAAKyHAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYSekBFhERYWVlxefzHRwcEhMT5c6zd+/erl27ampqCgSCr776Kjc3l5l05MgRW1tbdXV1W1vbo0ePKrtaAABgC+UG2MGDB4ODgxcuXHjnzh13d/dBgwZlZWXJzHPlypVJkyb5+/s/fPjw0KFDN2/eDAgIoCddu3bN19d34sSJd+/enThx4pgxY/7880+lFgwAAGyh3NepODs79+jRIzIykh60sbEZMWJEeHi49Dzr16+PjIxMT0+nBzdv3rx27drs7GxCiK+vb0FBwenTp+lJAwcO1NfXr/5VZHidCgBAy9CUr1MpKytLTk728vJixnh5eV29elVmNldX1+fPn8fFxVEU9erVq8OHDw8ZMoSedO3aNenFvb29Ky8OAACfJiUG2Nu3b8VisYmJCTPGxMQkJydHZjZXV9e9e/f6+vqqqamZmprq6elt3ryZnpSTk1Pj4oSQ0tLSAilK2BUAAGh2lN6Ig8PhMJ8pipIepAmFwlmzZi1ZsiQ5OTk+Pj4zM3P69OmKL04ICQ8P1/2Hubl5Q+8BAAA0R0oMMCMjIx6PJ33O9Pr1a+kzKlp4eLibm9t3331nb2/v7e0dERERExMjEokIIaampjUuTggJCQnJ/wd98wwAAFo8JQaYmpqag4NDQkICMyYhIcHV1VVmtuLiYi733zJ4PB4hhG5a4uLiIr342bNnKy9OCFFXV9eR0rB7AQAAzZOKUtc+Z86ciRMnOjo6uri4bN++PSsri748GBIS8uLFi19++YUQ4uPjM2XKlMjISG9vb5FIFBwc7OTkZGZmRggJCgrq06fPmjVrhg8ffvz48XPnzl25ckWpBQMAAFsoN8B8fX1zc3OXLVsmEons7Ozi4uIsLCwIISKRiHkgzM/Pr7CwcMuWLXPnztXT0/Pw8FizZg09ydXV9cCBA4sWLVq8eHGHDh0OHjzo7Oys1IIBAIAtlPscWOPDc2AAAC1DUz4HBgAAoDwIMAAAYCUEGAAAsBICDAAAWAkBBgAArIQAAwAAVkKAAQAAKyHAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWUu77wEBBYgl1IzPvdWGJsTbfycqAx+U0dUUAAM0dAqzpxT8QLT0pFOWX0IMCXX6oj+1AO0HTVgUA0MzhEmITi38gmrHnNpNehJCc/JIZe27HPxA1YVUAAM0fAqwpiSXU0pNCmVdi04NLTwrFkhb1smwAgIaFAGtKNzLzpM+9GBQhovySG5l5jV8SAABbIMCa0utCOeml4FQAgE8cAqwpGWvz6zwVAOAThwBrSk5WBgJdfuUm8xxCBLp8JyuDJqgJAIAlEGBNicflhPrYEkKkM4z+HOpji6fBAACqgQBrYgPtBJETepjq/nu10FSXHzmhB54DAwCoHh5kbnoD7QSetqboiQMAoFYQYM0Cj8tx6WDY1FUAALAJLiECAAArIcAAAICVEGAAAMBKCDAAAGAlBBgAALASAgwAAFgJAQYAAKyEAAMAAFZCgAEAACshwAAAgJUQYAAAwEoIMAAAYCUEGAAAsBICDAAAWAkBBgAArIQAAwAAVkKAAQAAKyHAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWQoABAAArIcAAAICVlB5gERERVlZWfD7fwcEhMTGx8gx+fn6c//X5558zU3/66afOnTtraGiYm5vPnj27pKRE2QUDAAArqCh17QcPHgwODo6IiHBzc9u2bdugQYOEQmG7du2k59m0adPq1avpzxUVFV27dv3Pf/5DD+7du3fBggUxMTGurq6PHj3y8/MjhGzcuFGpNQNAoxGLxeXl5U1dBTQ9NTU1LrfWJ1QciqKUUQ3N2dm5R48ekZGR9KCNjc2IESPCw8Ormv/YsWOjRo3KzMy0sLAghMycOTM1NfX8+fP01Llz5964cUPuaRyjoKBAV1c3Pz9fR0en4fYDABoYRVE5OTnv379v6kKgWeByuVZWVmpqatIja/w+V+IZWFlZWXJy8oIFC5gxXl5eV69erWaR6OjoAQMG0OlFCOndu/eePXtu3Ljh5OSUkZERFxc3efJk5RUMAI2GTi9jY2NNTU0Oh9PU5UBTkkgkL1++FIlE7dq1q9UfgxID7O3bt2Kx2MTEhBljYmKSk5NT1fwikej06dP79u1jxowdO/bNmze9e/emKKqiomLGjBnSccgoLS0tLS2lPxcUFDTcHgCAUojFYjq9DA0Nm7oWaBZat2798uXLiooKVVVVxZdSeiMO6TilKKqadI2NjdXT0xsxYgQz5tKlSytXroyIiLh9+/Zvv/126tSp5cuXV14wPDxc9x/m5uYNWj4ANDz6vpempmZTFwLNBX3xUCwW12opJZ6BGRkZ8Xg86VOu169fS5+QSaMoKiYmZuLEidLXQBcvXjxx4sSAgABCSJcuXYqKiqZOnbpw4UKZe30hISFz5syhPxcUFCDDAFgBVw6BUbc/BiWegampqTk4OCQkJDBjEhISXF1d5c58+fLlJ0+e+Pv7S48sLi6Wzioej0dRVOVWJ+rq6jpSGm4PAACg+VLuJcQ5c+bs2LEjJiYmNTV19uzZWVlZ06dPJ4SEhIRMmjRJes7o6GhnZ2c7OzvpkT4+PpGRkQcOHMjMzExISFi8ePGwYcN4PJ5SawYAaHBhYWHdunVr6ipaGuU+B+br65ubm7ts2TKRSGRnZxcXF0e3MBSJRFlZWcxs+fn5R44c2bRpk8ziixYt4nA4ixYtevHiRevWrX18fFauXKnUggHgU+Pn57dr1y7pMY8fP+7YsWM9V8vhcI4ePcrc1J83b963335bz3WCDOU+B9b48BwYQPNXUlKSmZlJ99FTqwXFEupGZt7rwhJjbb6TlQGP2wB30fz8/F69erVz505mTOvWrZkrPWVlZTIPJylIJsDqoLy8vFZN8lhN7p9Ejd/n6AsRANgh/oGo95oLX0ZdDzqQ8mXU9d5rLsQ/EDXImtXV1U2l9O/ff+bMmXPmzDEyMvL09CSEXL582cnJSV1dXSAQLFiwoKKigl7wiy++mDVr1vfff29gYGBqahoWFkaPt7S0JISMHDmSw+HQn2UuIe7cudPGxobP51tbW0dERNAjnz59yuFwfv311y+++ILP5+/Zs6dB9q4FQ4ABAAvEPxDN2HNblP9vb6g5+SUz9txuqAyTsWvXLhUVlaSkpG3btr148WLw4ME9e/a8e/duZGRkdHT0ihUrpOfU0tL6888/165du2zZMrrZ2s2bNwkhO3fuFIlE9GdpUVFRCxcuXLlyZWpq6qpVqxYvXix9DXP+/PmzZs1KTU319vZWxq61JMq9BwYAUH9iCbX0pFDmbgdFCIeQpSeFnram9byWeOrUqVatWtGfBw0aRAjp2LHj2rVr6TELFy40NzffsmULh8OxtrZ++fLl/PnzlyxZQreRtre3Dw0NJYR89tlnW7ZsOX/+vKenZ+vWrQkhenp6pqamlTe3fPnyDRs2jBo1ihBiZWUlFAq3bdvGdDMUHBxMT4IaIcAAoLm7kZknfe7FoAgR5ZfcyMxz6VCvHj369evHdNmqpaX15ZdfOjo6MlNTU1NdXFyYB5Xc3Nw+fPjw/Plzul9ye3t7Zk6BQPD69evqt/XmzZvs7Gx/f/8pU6bQYyoqKnR1dZkZpDcN1UOAAUBz97qwuvcoVT9VEVpaWjLNDrW0tJjPMl0I0Q3fmDHS7Sw4HI5EIql+W/QMUVFRzs7OzEjpp4OkNw3VQ4ABQHNnrF1dY8Xqp9afra3tkSNHmBi7evWqtrZ2mzZtql9KVVVVbsdIJiYmbdq0ycjIGD9+vFLK/ZSgEQcANHdOVgYCXX7l21wcQgS6fCcrA6Vu/ZtvvsnOzv7222//+uuv48ePh4aGzpkzp8aXV1laWp4/fz4nJ+fdu3cyk8LCwsLDwzdt2vTo0aP79+/v3Lnzxx9/VFr5LRkCDACaOx6XE+pjSwiRzjD6c6iPbYM8DVaNNm3axMXF3bhxo2vXrtOnT/f391+0aFGNS23YsCEhIcHc3Lx79+4ykwICAnbs2BEbG9ulS5e+ffvGxsZaWVkpp/YWDg8yA0Bjq9uDzPEPREtPCpnWHAJdfqiP7UA7gXJqhEZVtweZcQ8MANhhoJ3A09a0wXviAPZCgAEAa/C4nHq2mIeWBPfAAACAlRBgAADASggwAABgJQQYAACwEgIMAABYCQEGAACshAADAKgF+rWTKSkp1c8WGxurp6en7K00FA6Hc+zYscbfbj0hwADgU0e/38TMzExNTc3CwiIoKCg3N7eqmc3NzUUikZ2dXfXr9PX1ffToUUNXqnSK7J2fn9+IESMaq6LqIMAA4JOWkZHh6Oj46NGj/fv3P3nyZOvWrefPn3dxccnLy6s8c1lZGY/HMzU1VVGpoRcIDQ0NY2Nj5ZSskPLy8jospeDe1UFZWVmDrxMBBgCsUv6R/LGelH9sqPUFBgaqqamdPXu2b9++7dq1GzRo0Llz5168eLFw4UJ6BktLyxUrVvj5+enq6k6ZMkXmItuJEyc+++wzDQ2Nfv367dq1i8PhvH//nvzvJcSwsLBu3brt3r3b0tJSV1d37NixhYWF9KT4+PjevXvr6ekZGhoOHTo0PT29xoItLS2XL18+bty4Vq1amZmZbd68mZnE4XC2bt06fPhwLS2tFStWEEJOnjzp4ODA5/Pbt2+/dOnSiooKes7Hjx/36dOHz+fb2tomJCQwa5DZu4cPHw4ZMkRHR0dbW9vd3T09PT0sLGzXrl3Hjx/ncDgcDufSpUuEkPv373t4eGhoaBgaGk6dOvXDhw/04vS5Wnh4uJmZWadOnWr936YmCDAAYBXhCXJhOUk92SAry8vLO3PmzDfffKOhocGMNDU1HT9+/MGDB5m+ztetW2dnZ5ecnLx48WLpxZ8+fTp69OgRI0akpKRMmzaNybzK0tPTjx07durUqVOnTl2+fHn16tX0+KKiojlz5ty8efP8+fNcLnfkyJE1vhKTrsfe3v727dshISGzZ8+WTqDQ0NDhw4ffv3//66+/PnPmzIQJE2bNmiUUCrdt2xYbG7ty5UpCiEQiGTVqFI/Hu379+tatW+fPny93Ky9evKBD7sKFC8nJyV9//XVFRcW8efPGjBkzcOBAkUgkEolcXV2Li4sHDhyor69/8+bNQ4cOnTt3bubMmcxKzp8/n5qampCQcOrUqRr3q9aoliU/P58Qkp+f39SFAECVPn78KBQKP378WJeF9/pSoTrUvrENUsn169cJIUePHpUZT7+g69WrVxRFWVhYjBgxgpmUmZlJCLlz5w5FUfPnz7ezs2Mm0QH27t07iqJ27typq6tLjw8NDdXU1CwoKKAHv/vuO2dn58rFvH79mhBy//59ma3IsLCwGDhwIDPo6+s7aNAg+jMhJDg4mJnk7u6+atUqZnD37t0CgYCiqDNnzvB4vOzsbHr86dOnmYMgvd2QkBArK6uysjKZAiZPnjx8+HBmcPv27fr6+h8+fKAHf//9dy6Xm5OTQ89pYmJSWlpaeS9kyP2TqPH7HJ35AkCz9+E1ufYzkVQQQkj6eUIIeXKOnFlICCFcFeIyk7Rq3bAbpMOAfgUzIcTR0VHubGlpaT179mQGnZycqlqhpaWltrY2/VkgENBZRQhJT09fvHjx9evX3759S597ZWVl1dhCxMXFRfrzTz/9xAxKl5qcnHzz5k36rIsQIhaLS0pKiouLU1NT27Vr17Zt28prk5aSkuLu7q6qqlp9MampqV27dtXS0qIH3dzcJBJJWlqaiYkJIaRLly5qamrVr6HOEGAA0OwV5pDrkURcSjjcv99kKRGT6xGEkhCeOrH7vzoHWMeOHTkcjlAolGlW99dff+nr6xsZGdGDzLezDIqimJAj/8SeXNIxwOFwmOuEPj4+5ubmUVFRZmZmEonEzs6uDo0dpGuQLlUikSxdunTUqFHSM/P5fJk6pReXJn1ZtRoyB0FmnVUdugaBe2AA0OwJ7Mm0y8SoEyGEUOJ//9+oE5l2mQjs67xiQ0NDT0/PiIiIjx//bRWSk5Ozd+9eX1/fqr7ZGdbW1jdv3mQGb926Vaut5+bmpqamLlq0qH///jY2Nu/evVNwQfrKJ/PZ2tpa7mw9evRIS0vr+L+4XK6trW1WVtbLly/p2a5duyZ3cXt7+8TExMqtGdXU1MRiMTNoa2ubkpJSVFREDyYlJXG5XGU02agMAQYAbGBsQ/zPyo70P0uMbeq54i1btpSWlnp7e//xxx/Z2dnx8fGenp5t2rRhrrxVY9q0aX/99df8+fMfPXr066+/xsbGkqpPaCrT19c3NDTcvn37kydPLly4MGfOHAUXTEpKWrt27aNHj37++edDhw4FBQXJnW3JkiW//PJLWFjYw4cPU1NTDx48uGjRIkLIgAEDOnfuPGnSpLt37yYmJlbV9mTmzJkFBQVjx469devW48ePd+/enZaWRgixtLS8d+9eWlra27dvy8vLx48fz+fzJ0+e/ODBg4sXL3777bcTJ06krx8qGwIMAFji2VVCSbXQoyTkmfxTh1r57LPPbt261aFDB19f3w4dOkydOrVfv37Xrl0zMDCocVkrK6vDhw//9ttv9vb2kZGRdBKoq6sruGkul3vgwIHk5GQ7O7vZs2evW7dOwQXnzp2bnJzcvXv35cuXb9iwwdvbW+5s3t7ep06dSkhI6NmzZ69evX788UcLCwt6u0ePHi0tLXVycgoICKgqqg0NDS9cuPDhw4e+ffs6ODhERUXRF0KnTJnSuXNnR0fH1q1bJyUlaWpqnjlzJi8vr2fPnqNHj+7fv/+WLVsU3JF64lRz0ZaNCgoKdHV18/PzdXR0mroWAJCvpKQkMzPTysqKz+fXYrGj08jdA6SdC/FcTs4uItnXSdcvycitSiuz1lauXLl169bs7GylbsXS0jI4ODg4OFipW2lkcv8kavw+RyMOAGAJQXfS2oa4fku4PPJVHLm6majUJv+UIyIiomfPnoaGhklJSevWrZN+BAqUDQEGACzRa/q/n7k80ju4ySqR8vjx4xUrVuTl5bVr127u3LkhISFNXdEnBAEGAFB3Gzdu3LhxY2Nu8enTp425ueYMjTgAAICVEGAAAMBKCDAAAGAlBBgAALASAgwAAFgJAQYAAKyEAAMAaAD024eVtHKZFyUrG4fDOXbsWONvt7YQYADwSfviiy9kumU6duyY4h3y1kozz4PKzM3NRSJR9e8nU2pyVw8BBgDQ8lV+K4oieDyeqampikrDd3lRh9eeVYYAAwAWeFbwTJgrrPy/ZwXPlLrdsLCwbt26bdu2zdzcXFNT8z//+c/79+/pSWKxeM6cOXp6eoaGht9//710x+jx8fG9e/emJw0dOjQ9PZ0eb2VlRQjp3r07h8P54osv6JE7d+60sbHh8/nW1tYRERHMSm7cuNG9e3c+n+/o6Hjnzp2qKrS0tFy+fPm4ceNatWplZma2efNmZhKHw9m6devw4cO1tLRWrFhBCDl58qSDgwOfz2/fvv3SpUsrKiroOR8/ftynTx8+n29ra5uQkMCsQeaU8eHDh0OGDNHR0dHW1nZ3d09PTw8LC9u1a9fx48c5HA6Hw7l06RIh5P79+x4eHhoaGoaGhlOnTv3w4QO9OH2uFh4ebmZm1iAvDENXUgDQ3D0reDb06NCqpp4aecpCx0J5W3/y5Mmvv/568uTJgoICf3//wMDAvXv3EkI2bNgQExMTHR1ta2u7YcOGo0ePenh40IsUFRXNmTOnS5cuRUVFS5YsGTlyZEpKCpfLvXHjhpOT07lz5z7//HM1NTVCSFRUVGho6JYtW7p3737nzp0pU6ZoaWlNnjy5qKho6NChHh4ee/bsyczMrOqNX7R169b98MMPYWFhZ86cmT17trW1taenJz0pNDQ0PDx848aNPB7vzJkzEyZM+O9//0tnz9SpU+kZJBLJqFGjjIyMrl+/XlBQUFU/9y9evOjTp88XX3xx4cIFHR2dpKSkioqKefPmpaamFhQU7Ny5kxBiYGBQXFw8cODAXr163bx58/Xr1wEBATNnzqTflEYIOX/+vI6OTkJCQsO8CIVqWfLz8wkh+fn5dV5Dabl4xx/pi4/d3/FHemm5uAFrAwDax48fhULhx48fFZz/4duHdrF2Vf3v4duH9Smmb9++QUFB0mOOHj3KfDeGhobyeLzs7Gx68PTp01wuVyQSURQlEAhWr15Njy8vL2/btu3w4cMrr//169eEkPv371MUlZmZSQi5c+cOM9Xc3Hzfvn3M4PLly11cXCiK2rZtm4GBQVFRET0+MjJSZkGGhYXFwIEDmUFfX99BgwbRnwkhwcHBzCR3d/dVq1Yxg7t37xYIBBRFnTlzRmYfCSFHjx6VKTgkJMTKyqqsrEymgMmTJ0vv+Pbt2/X19T98+EAP/v7771wuNycnh57TxMSktLS08l7I/ZOo8fscZ2D/IzxOGJWYKfnnl8HKuNQp7lYhg22btCgAaErt2rVr27Yt/dnFxUUikaSlpWloaIhEIhcXF3q8ioqKo6Mj9c9ZRXp6+uLFi69fv/727VuJREIIycrKqtwU4s2bN9nZ2f7+/lOmTKHHVFRU6OrqEkJSU1O7du2qqanJbLeaCqWnuri4/PTTT8ygo6Mj8zk5OfnmzZvM6yvFYnFJSUlxcXFqaqrMPsrdSkpKiru7O/1Oy2rQlWtpadGDbm5u9BGj39HcpUsX+tSzQSDA/hUeJ9z2R6b0GAlF6DHIMICWSkdHh/6lz3j//n1Vb1CkWyfW2EbRx8fH3Nw8KirKzMxMIpHY2dnJbbNAZ1tUVJSzszMzksfjEUKoelxhky6PCRJ6c0uXLh01apT0zHw+X2ZbVe2dhoaGIlunKKryGpgx0vXUHxpx/K2sQhKVmCl3UlRiZlmFRO4kAGA7a2vrW7duSY+5efNm586dmcGsrKyXL1/Sn69du8blcjt16qSrqysQCK5fv06Pr6ioSE5Opj/n5uampqYuWrSof//+NjY27969Y1ZFn3yIxWJ60MTEpE2bNhkZGR2l0A09bG1t7969+/HjR3pOZkNySU+9fv26tbW13Nl69OiRlpbW8X9xuVxbW1uZfZS7uL29fWJiYuXWjGpqaswe0ZWnpKQUFRXRg0lJSfQRq6b+OkOA/W33taeSKn7xSCiy+9rTRq0GABrLN998k56eHhgYePfu3UePHv3888/R0dHfffcdMwOfz588efLdu3cTExNnzZo1ZswYU1NTQkhQUNDq1auPHj36119/ffPNN0zrRH19fUNDw+3btz958uTChQtz5sxhVmVsbKyhoREfH//q1Sv6tC8sLCw8PHzTpk2PHj26f//+zp07f/zxR0LIuHHjuFyuv7+/UCiMi4tbv359NbuQlJS0du1auvhDhw5V1eJjyZIlv/zyS1hY2MOHD1NTUw8ePLho0SJCyIABAzp37jxp0iR6HxcuXCh38ZkzZxYUFIwdO/bWrVuPHz/evXt3WloaIcTS0vLevXtpaWlv374tLy8fP348fcQePHhw8eLFb7/9duLEifT1wwaHAPvbs7ziOk8FAPaytLRMTExMT0/38vLq2bNnbGxsbGzsf/7zH2aGjh07jho1avDgwV5eXnZ2dkxL97lz506aNMnPz8/FxUVbW3vkyJH0eC6Xe+DAgeTkZDs7u9mzZ69bt45ZlYqKyn//+99t27aZmZkNHz6cEBIQELBjx47Y2NguXbr07ds3NjaWPgNr1arVyZMnhUJh9+7dFy5cuGbNmmp2Ye7cucnJyd27d1++fPmGDRu8vb3lzubt7X3q1KmEhISePXv26tXrxx9/tLCwoAs+evRoaWmpk5NTQEAAc5NMhqGh4YULFz58+NC3b18HB4eoqCj6ftiUKVM6d+7s6OjYunXrpKQkTU3NM2fO5OXl9ezZc/To0f3799+yZYvC/zVqh1OfK63NUEFBga6ubn5+flWXsKsSnZix/PfUqqYuHmLj796+3tUBACGElJSUZGZmWllZ8fl8ReYX5gp9T/lWNfXg0IO2hsq6Sx0WFnbs2LHm3HeGpaVlcHBwVW3f2ULun0SN3+c4A/vbRBdLbhX3ZbkcMtHFslGrAQApWqrV3fmvfiq0YGiF+Dc1Fe4UdyuZVoi0Ke5WaipIeoAmY6FjcWrkqaLyosqTtFS1lPoUMzRnuIT4P2SeA+NyCJ4DA2hwtb2ECC1eM72EGBERQdfk4OCQmJhYeQY/Pz/O//r888+Zqe/fvw8MDBQIBHw+38bGJi4uTqnVhgy2/Wv5oMVDbCa5WCweYvPX8kFILwCA5km5lxAPHjwYHBwcERHh5ua2bdu2QYMGCYXCdu3aSc+zadOm1atX058rKiq6du3KtP8pKyvz9PQ0NjY+fPhw27Zts7OztbW1lVowIURNhYv2GgAAzZ9yA+zHH3/09/cPCAgghPz0009nzpyJjIwMDw+XnkdXV5fuOoUQcuzYsXfv3n311Vf0YExMTF5e3tWrV+nGmnSLTwBoGVrY/Quoj7r9MSjxEmJZWVlycrKXlxczxsvL6+rVq9UsEh0dPWDAACaoTpw44eLiEhgYaGJiYmdnt2rVKunnvRmlpaUFUhp2LwCgwdE/SYuL8Xgl/I3uaovuRktxSjwDe/v2rVgsln4A28TEJCcnp6r5RSLR6dOn9+3bx4zJyMi4cOHC+PHj4+LiHj9+HBgYWFFRsWTJEpkFw8PDly5d2uD1A4CS8Hg8PT09upt2TU1NJb3+GNhCIpG8efNGU1Oztm/OVHozeuk/TbmdPDJiY2P19PSkX00tkUiMjY23b9/O4/EcHBxevny5bt26ygEWEhLCdNZSUFBgbm7ekDsAAEpA98ZEZxgAl8tt165dbX/KKDHAjIyMeDye9CnX69evq+oRi6KomJiYiRMnSve0LxAIVFVVmZNKGxubnJycsrIymd741dXV1dXVlbAHAKAsHA5HIBAYGxvX7VX30MKoqalxubW+paXEAFNTU3NwcEhISGC6CEtISKC7/6rs8uXLT5488ff3lx7p5ua2b98+iURC79ijR48EAkEDvksGAJoWj8er7W0PAIZynwObM2fOjh07YmJiUlNTZ8+enZWVNX36dEJISEjIpEmTpOeMjo52dnaWeeHbjBkzcnNzg4KCHj169Pvvv69atSowMFCpBQMAAFso9x6Yr69vbm7usmXLRCKRnZ1dXFwc3cJQJBJlZWUxs+Xn5x85cmTTpk0yi5ubm589e3b27Nn29vZt2rQJCgqaP3++UgsGAAC2QFdSAADQHNX4fd7SOvOl8xhPgwEAsB39TV7NWVZLC7DCwkJCCFrSAwC0DIWFhUxvTTJa2iVEiUTy8uVLbW3tuj0aST9Glp2djSuQOBQMHAoGDgUDh4KhvENBUVRhYaGZmVlVLexb2hkYl8tt27ZtPVeio6ODP0oaDgUDh4KBQ8HAoWAo6VBUde5Fw3saAQCAlRBgAADASgiw/6Gurh4aGoqOqQgOhRQcCgYOBQOHgtGEh6KlNeIAAIBPBM7AAACAlRBgAADASggwAABgJQQYAACw0icUYBEREVZWVnw+38HBITExUe48ly9fdnBw4PP57du337p1q/SkI0eO2Nraqqur29raHj16tFFKVpb6HIqoqCh3d3d9fX19ff0BAwbcuHGjsapWinr+VdAOHDjA4XCkXybORvU8FO/fvw8MDBQIBHw+38bGJi4urlGqVop6Hoqffvqpc+fOGhoa5ubms2fPLikpaZSqlaLGQyESicaNG9e5c2culxscHCwzVelfm9Sn4cCBA6qqqlFRUUKhMCgoSEtL69mzZzLzZGRkaGpqBgUFCYXCqKgoVVXVw4cP05OuXr3K4/FWrVqVmpq6atUqFRWV69evN/pONIx6Hopx48b9/PPPd+7cSU1N/eqrr3R1dZ8/f97oO9Ew6nkoaE+fPm3Tpo27u/vw4cMbr/SGVs9DUVpa6ujoOHjw4CtXrjx9+jQxMTElJaXRd6Jh1PNQ7NmzR11dfe/evZmZmWfOnBEIBMHBwY2+Ew1DkUORmZk5a9asXbt2devWLSgoSHpSI3xtfioB5uTkNH36dGbQ2tp6wYIFMvN8//331tbWzOC0adN69epFfx4zZszAgQOZSd7e3mPHjlVmvUpUz0MhraKiQltbe9euXUoqVdnqfygqKirc3Nx27NgxefJkVgdYPQ9FZGRk+/bty8rKGqFUZavnoQgMDPTw8GAmzZkzp3fv3sqsV4kUORSMvn37ygRYI3xtfhKXEMvKypKTk728vJgxXl5eV69elZnt2rVr0vN4e3vfunWrvLxc7qTKi7NC/Q+FtOLi4vLycgMDA+UVrDwNciiWLVvWunVrf3//RihYeep/KE6cOOHi4hIYGGhiYmJnZ7dq1SqxWNw4xTes+h+K3r17Jycn05fWMzIy4uLihgwZ0ii1NzAFD0U1GuFrs6V15ivX27dvxWKxiYkJM8bExCQnJ0dmtpycHJl5Kioq3r59KxAIKk+qvDgr1P9QSM+2YMGCNm3aDBgwQKk1K0n9D0VSUlJ0dHRKSkrjFKw89T8UGRkZFy5cGD9+fFxc3OPHjwMDAysqKpYsWdJIO9Bw6n8oxo4d++bNG/qsq6KiYsaMGQsWLGik6huUgoeiGo3wtflJBBhN+gUrFEXJfd+KzDzSYxRZnC3qeShoa9eu3b9//6VLl/h8vtIqVbo6H4rCwsIJEyZERUUZGRk1Qp2NoD5/FRKJxNjYePv27Twez8HB4eXLl+vWrWNjgNHqcyguXbq0cuXKiIgIZ2fnJ0+eBAUFCQSCxYsXK79qpajn956yvzY/iQAzMjLi8XjS4f/69WvpnwY0U1NTmXlUVFQMDQ3lTqq8OCvU/1DQ1q9fv2rVqnPnztnb2yu7ZiWp56F4+PDh06dPfXx86PESiYQQoqKikpaW1qFDB+WX35Dq/1chEAhUVVV5PB49ycbGJicnp6ysTE1NTfnlN6T6H4rFixdPnDgxICCAENKlS5eioqKpU6cuXLiwqjdaNVsKHopqNMLXJsuOad2oqak5ODgkJCQwYxISElxdXWVmc3FxkZ7n7Nmzjo6OqqqqcidVXpwV6n8oCCHr1q1bvnx5fHy8o6NjI9SsJPU8FNbW1vfv30/5x7Bhw/r165eSksLGt4HX/6/Czc3tyZMndIoTQh49eiQQCFiXXqQhDkVxcbF0VvF4PLq5gZILb3gKHopqNMbXZsO2CWm26Pag0dHRQqEwODhYS0vr6dOnFEUtWLBg4sSJ9Dx009jZs2cLhcLo6GjpprFJSUk8Hm/16tWpqamrV69uAc3o63wo1qxZo6amdvjwYdE/CgsLm2xn6qeeh0Ia21sh1vNQZGVltWrVaubMmWlpaadOnTI2Nl6xYkWT7Uz91PNQhIaGamtr79+/PyMj4+zZsx06dBgzZkyT7Uz9KHIoKIq6c+fOnTt3HBwcxo0bd+fOnYcPH9LjG+Fr81MJMIqifv75ZwsLCzU1tR49ely+fJkeOXny5L59+zLzXLp0qXv37mpqapaWlpGRkdKLHzp0qHPnzvRP7yNHjjRm5Q2uPofCwsJC5jdQaGho45bfkOr5V8Fge4BR9T4UV69edXZ2VldXb9++/cqVKysqKhqz+IZVn0NRXl4eFhbWoUMHPp9vbm7+zTffvHv3rnHLb0iKHAqZLwQLCwtmkrK/NvE6FQAAYKVP4h4YAAC0PAgwAABgJQQYAACwEgIMAABYCQEGAACshAADAABWQoABAAArIcAAWoLY2Fg9PT25k/z8/Br5bdFhYWHdunVrzC3CpwkBBp8QPz8/DofD4XBUVVVNTEw8PT1jYmKYHvxaqk2bNsXGxio+v/RRat++/bx584qKimq1xXnz5p0/f55ZWyPHJ3w6EGDwaRk4cKBIJHr69Onp06f79esXFBQ0dOjQioqKpq7rb2VlZQ2+Tl1d3apOzqpCH6WMjIwVK1ZERETMmzdPwQUpiqqoqGjVqpX0uwsAlAQBBp8WdXV1U1PTNm3a9OjR44cffjh+/Pjp06eZE5T8/PypU6caGxvr6Oh4eHjcvXuXHk9fE4uJiWnXrl2rVq1mzJghFovXrl1rampqbGy8cuVKZv1ZWVnDhw9v1aqVjo7OmDFjXr16xUxasWKFsbGxtrZ2QEDAggULmIts9DlKeHi4mZlZp06dCCF79uxxdHTU1tY2NTUdN27c69ev6TkvXbrE4XB+//33rl278vl8Z2fn+/fvS+/dmTNnbGxsWrVqRSeQ9PrpzxKJZM2aNR07dlRXV2/Xrp105ZWPkrm5+bhx48aPH3/s2LEaqzpz5oyjo6O6unpiYiJzCTEsLGzXrl3Hjx+nT+kuXbrk4eExc+ZMZkO5ubnq6uoXLlyozX9DgL8hwOCT5uHh0bVr199++40QQlHUkCFDcnJy4uLikpOTe/To0b9//7y8PHrO9PT006dPx8fH79+/PyYmZsiQIc+fP798+fKaNWsWLVp0/fp1eg0jRozIy8u7fPlyQkJCenq6r68vvfjevXtXrly5Zs2a5OTkdu3aRUZGSpdx/vz51NTUhISEU6dOEULKysqWL19+9+7dY8eOZWZm+vn5Sc/83XffrV+//ubNm8bGxsOGDaPfZE8IKS4uXr9+/e7du//444+srCy5p00hISFr1qxZvHixUCjct2+fIu9n0tDQoDdRfVXff/99eHh4amqq9Cvi5s2bN2bMGDpNRSKRq6trQEDAvn37SktLmcNiZmbWr1+/GssAkKPBuwcGaLbk9hnv6+trY2NDUdT58+d1dHRKSkqYSR06dNi2bRtFUaGhoZqamgUFBfR4b29vS0tLsVhMD3bu3Dk8PJyiqLNnz/J4vKysLHr8w4cPCSE3btygKMrZ2TkwMJBZs5ubW9euXZmqTExMSktL5dZ848YNQgj9zpqLFy8SQg4cOEBPys3N1dDQOHjwIEVRO3fuJIQ8efKEnvTzzz+bmJjI7HVBQYG6unpUVJTiR+nPP/80NDSs/EKQylUdO3aMmRoaGiq9d9LHvKSkxMDAgK6Zoqhu3bqFhYVVXw9AVXAGBp866p83nScnJ3/48MHQ0LDVPzIzM9PT0+nZLC0ttbW16c8mJia2trbMewtNTEzo62mpqanm5ubMOy1tbW319PRSU1MJIWlpaU5OTsxGpT8TQrp06SL9+sc7d+4MHz7cwsJCW1v7iy++IIRkZWUxU11cXOgPBgYGnTt3ptdPCNHU1GReBi0QCJhLfIzU1NTS0tL+/fvXeExOnTrVqlUrPp/v4uLSp0+fzZs311iVgm83VVdXnzBhQkxMDCEkJSXl7t27MmdyAIpTaeoCAJpYamqqlZUVIUQikQgEgkuXLklPZZo/MC+kJoTQLfSkB+mmjEwWMqTHSE+i/vc1RlpaWsznoqIiLy8vLy+vPXv2tG7dOisry9vbu5rGHcxqZUqiKr0pSUNDo6qVyOjXr19kZKSqqqqZmRm92hqrkt6F6gUEBHTr1u358+cxMTH9+/ev/IY5AAXhDAw+aRcuXLh///7//d//EUJ69OiRk5OjoqLSUYqRkZHia7O1tc3KysrOzqYHhUJhfn6+jY0NIaRz5870ZTfarVu3qlrJX3/99fbt29WrV7u7u1tbW1c+kaLvtxFC3r179+jRI2trawXL++yzzzQ0NJgG7tXQ0tLq2LGjhYUFE4o1VlUVNTU1sVgsPaZLly6Ojo5RUVH79u37+uuvFVwPQGUIMPi0lJaW5uTkvHjx4vbt26tWrRo+fPjQoUMnTZpECBkwYICLi8uIESPOnDnz9OnTq1evLlq0qJqkqWzAgAH29vbjx4+/ffv2jRs3Jk2a1LdvX/ra2rfffhsdHb1r167Hjx+vWLHi3r17MudqjHbt2qmpqW3evDkjI+PEiRPLly+XmWHZsmXnz59/8OCBn5+fkZGR4k9Z8fn8+fPnf//997/88kt6evr169ejo6MVXLbGqqpiaWl57969tLS0t2/fMu1NAgICVq9eLRaLR44cqeB6ACpDgMGnJT4+XiAQWFpaDhw48OLFi//973+PHz/O4/EIIRwOJy4urk+fPl9//XWnTp3Gjh379OlTRdrpMTgczrFjx/T19fv06TNgwID27dsfPHiQnjR+/PiQkJB58+b16NGDbsLH5/PlrqR169axsbGHDh2ytbVdvXr1+vXrZWZYvXp1UFCQg4ODSCQ6ceKE9M2zGi1evHju3LlLliyxsbHx9fVV/ESqxqqqMmXKlM6dOzs6OrZu3TopKYke+eWXX6qoqIwbN66qgwCgCDkXygFA2Tw9PU1NTXfv3l2rpS5dutSvX793797V9sHk5iY7O9vS0vLmzZs9evRo6lqAxdCIA6AxFBcXb9261dvbm8fj7d+//9y5cwkJCU1dVBMoLy8XiUQLFizo1asX0gvqCQEG0Bjo65MrVqwoLS3t3LnzkSNHBgwY0NRFNYGkpKR+/fp16tTp8OHDTV0LsB4uIQIAACuhEQcAALASAgwAAFgJAQYAAKyEAAMAAFZCgAEAACshwAAAgJUQYAAAwEoIMAAAYCUEGAAAsNL/AzK1N6E9FxcGAAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -1267,10 +1267,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:31.953561Z",
-     "iopub.status.busy": "2024-06-17T14:25:31.953419Z",
-     "iopub.status.idle": "2024-06-17T14:25:32.159580Z",
-     "shell.execute_reply": "2024-06-17T14:25:32.159026Z"
+     "iopub.execute_input": "2024-06-17T19:20:43.133407Z",
+     "iopub.status.busy": "2024-06-17T19:20:43.133279Z",
+     "iopub.status.idle": "2024-06-17T19:20:43.491964Z",
+     "shell.execute_reply": "2024-06-17T19:20:43.491628Z"
     }
    },
    "outputs": [
@@ -1328,200 +1328,200 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.863036\n",
-       "      0.771720\n",
-       "      0.674453\n",
-       "      0.597347\n",
-       "      0.773438\n",
-       "      0.597929\n",
-       "      0.918163\n",
+       "      0.868973\n",
+       "      0.776058\n",
+       "      0.684574\n",
+       "      0.613887\n",
+       "      0.798276\n",
+       "      0.599223\n",
+       "      0.919433\n",
        "      2318.0\n",
        "      7451.0\n",
        "      0.237281\n",
-       "      0.183437\n",
+       "      0.178114\n",
        "    \n",
        "    \n",
        "      Amer-Indian-Eskimo\n",
-       "      0.943089\n",
-       "      0.722808\n",
-       "      0.588235\n",
-       "      0.590335\n",
-       "      0.833333\n",
-       "      0.454545\n",
-       "      0.917208\n",
+       "      0.934959\n",
+       "      0.636364\n",
+       "      0.428571\n",
+       "      0.504525\n",
+       "      1.000000\n",
+       "      0.272727\n",
+       "      0.928571\n",
        "      11.0\n",
        "      112.0\n",
        "      0.089431\n",
-       "      0.048780\n",
+       "      0.024390\n",
        "    \n",
        "    \n",
        "      Asian-Pac-Islander\n",
-       "      0.853659\n",
-       "      0.778341\n",
-       "      0.671233\n",
-       "      0.578916\n",
-       "      0.710145\n",
-       "      0.636364\n",
-       "      0.905107\n",
+       "      0.859756\n",
+       "      0.764319\n",
+       "      0.661765\n",
+       "      0.583363\n",
+       "      0.762712\n",
+       "      0.584416\n",
+       "      0.900864\n",
        "      77.0\n",
        "      251.0\n",
        "      0.234756\n",
-       "      0.210366\n",
+       "      0.179878\n",
        "    \n",
        "    \n",
        "      Black\n",
-       "      0.930415\n",
-       "      0.746597\n",
-       "      0.635294\n",
-       "      0.625434\n",
-       "      0.857143\n",
-       "      0.504673\n",
-       "      0.942358\n",
+       "      0.928171\n",
+       "      0.737251\n",
+       "      0.619048\n",
+       "      0.610779\n",
+       "      0.852459\n",
+       "      0.485981\n",
+       "      0.940647\n",
        "      107.0\n",
        "      784.0\n",
        "      0.120090\n",
-       "      0.070707\n",
+       "      0.068462\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.901235\n",
-       "      0.728873\n",
-       "      0.555556\n",
-       "      0.504717\n",
-       "      0.625000\n",
+       "      0.913580\n",
+       "      0.735915\n",
+       "      0.588235\n",
+       "      0.552396\n",
+       "      0.714286\n",
        "      0.500000\n",
-       "      0.938028\n",
+       "      0.863380\n",
        "      10.0\n",
        "      71.0\n",
        "      0.123457\n",
-       "      0.098765\n",
+       "      0.086420\n",
        "    \n",
        "    \n",
        "      White\n",
-       "      0.854661\n",
-       "      0.771309\n",
-       "      0.677308\n",
-       "      0.592980\n",
-       "      0.773390\n",
-       "      0.602461\n",
-       "      0.914314\n",
+       "      0.861610\n",
+       "      0.777682\n",
+       "      0.689766\n",
+       "      0.612016\n",
+       "      0.797516\n",
+       "      0.607667\n",
+       "      0.916398\n",
        "      2113.0\n",
        "      6233.0\n",
        "      0.253175\n",
-       "      0.197220\n",
+       "      0.192907\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.089431\n",
-       "      0.055533\n",
-       "      0.121752\n",
-       "      0.120717\n",
-       "      0.232143\n",
-       "      0.181818\n",
-       "      0.037251\n",
+       "      0.075203\n",
+       "      0.141319\n",
+       "      0.261195\n",
+       "      0.107491\n",
+       "      0.285714\n",
+       "      0.334940\n",
+       "      0.077267\n",
        "      2103.0\n",
        "      6162.0\n",
        "      0.163744\n",
-       "      0.161585\n",
+       "      0.168517\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.860375\n",
-       "      0.773690\n",
-       "      0.674152\n",
-       "      0.592139\n",
-       "      0.755353\n",
-       "      0.608714\n",
-       "      0.904434\n",
+       "      0.864981\n",
+       "      0.780276\n",
+       "      0.685128\n",
+       "      0.606017\n",
+       "      0.766970\n",
+       "      0.619068\n",
+       "      0.902293\n",
        "      2318.0\n",
        "      7451.0\n",
        "      0.237281\n",
-       "      0.191217\n",
+       "      0.191524\n",
        "    \n",
        "    \n",
        "      Amer-Indian-Eskimo\n",
-       "      0.886179\n",
-       "      0.814529\n",
-       "      0.533333\n",
-       "      0.496710\n",
-       "      0.421053\n",
-       "      0.727273\n",
-       "      0.917208\n",
+       "      0.878049\n",
+       "      0.769075\n",
+       "      0.482759\n",
+       "      0.434487\n",
+       "      0.388889\n",
+       "      0.636364\n",
+       "      0.928571\n",
        "      11.0\n",
        "      112.0\n",
        "      0.089431\n",
-       "      0.154472\n",
+       "      0.146341\n",
        "    \n",
        "    \n",
        "      Asian-Pac-Islander\n",
-       "      0.859756\n",
-       "      0.732809\n",
-       "      0.622951\n",
-       "      0.573605\n",
-       "      0.844444\n",
-       "      0.493506\n",
-       "      0.905107\n",
+       "      0.850610\n",
+       "      0.713329\n",
+       "      0.588235\n",
+       "      0.541195\n",
+       "      0.833333\n",
+       "      0.454545\n",
+       "      0.900864\n",
        "      77.0\n",
        "      251.0\n",
        "      0.234756\n",
-       "      0.137195\n",
+       "      0.128049\n",
        "    \n",
        "    \n",
        "      Black\n",
-       "      0.901235\n",
-       "      0.814753\n",
-       "      0.630252\n",
-       "      0.577840\n",
-       "      0.572519\n",
-       "      0.700935\n",
-       "      0.942358\n",
+       "      0.897868\n",
+       "      0.824945\n",
+       "      0.631579\n",
+       "      0.580505\n",
+       "      0.557143\n",
+       "      0.728972\n",
+       "      0.940647\n",
        "      107.0\n",
        "      784.0\n",
        "      0.120090\n",
-       "      0.147026\n",
+       "      0.157127\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.888889\n",
-       "      0.850704\n",
-       "      0.640000\n",
-       "      0.593995\n",
-       "      0.533333\n",
+       "      0.864198\n",
+       "      0.836620\n",
+       "      0.592593\n",
+       "      0.543856\n",
+       "      0.470588\n",
        "      0.800000\n",
-       "      0.938028\n",
+       "      0.863380\n",
        "      10.0\n",
        "      71.0\n",
        "      0.123457\n",
-       "      0.185185\n",
+       "      0.209877\n",
        "    \n",
        "    \n",
        "      White\n",
-       "      0.855380\n",
-       "      0.773198\n",
-       "      0.679926\n",
-       "      0.595478\n",
-       "      0.773221\n",
-       "      0.606720\n",
-       "      0.914314\n",
+       "      0.861850\n",
+       "      0.781440\n",
+       "      0.693921\n",
+       "      0.614000\n",
+       "      0.790206\n",
+       "      0.618552\n",
+       "      0.916398\n",
        "      2113.0\n",
        "      6233.0\n",
        "      0.253175\n",
-       "      0.198658\n",
+       "      0.198179\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.045855\n",
-       "      0.117895\n",
-       "      0.146592\n",
-       "      0.098768\n",
-       "      0.423392\n",
-       "      0.306494\n",
-       "      0.037251\n",
+       "      0.047258\n",
+       "      0.123291\n",
+       "      0.211162\n",
+       "      0.179513\n",
+       "      0.444444\n",
+       "      0.345455\n",
+       "      0.077267\n",
        "      2103.0\n",
        "      6162.0\n",
        "      0.163744\n",
-       "      0.061463\n",
+       "      0.081828\n",
        "    \n",
        "  \n",
        "\n",
@@ -1530,37 +1530,37 @@
       "text/plain": [
        "                              Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                 \n",
-       "original Overall              0.863036           0.771720  0.674453  0.597347   \n",
-       "          Amer-Indian-Eskimo  0.943089           0.722808  0.588235  0.590335   \n",
-       "          Asian-Pac-Islander  0.853659           0.778341  0.671233  0.578916   \n",
-       "          Black               0.930415           0.746597  0.635294  0.625434   \n",
-       "          Other               0.901235           0.728873  0.555556  0.504717   \n",
-       "          White               0.854661           0.771309  0.677308  0.592980   \n",
-       "         Maximum difference   0.089431           0.055533  0.121752  0.120717   \n",
-       "updated  Overall              0.860375           0.773690  0.674152  0.592139   \n",
-       "          Amer-Indian-Eskimo  0.886179           0.814529  0.533333  0.496710   \n",
-       "          Asian-Pac-Islander  0.859756           0.732809  0.622951  0.573605   \n",
-       "          Black               0.901235           0.814753  0.630252  0.577840   \n",
-       "          Other               0.888889           0.850704  0.640000  0.593995   \n",
-       "          White               0.855380           0.773198  0.679926  0.595478   \n",
-       "         Maximum difference   0.045855           0.117895  0.146592  0.098768   \n",
+       "original Overall              0.868973           0.776058  0.684574  0.613887   \n",
+       "          Amer-Indian-Eskimo  0.934959           0.636364  0.428571  0.504525   \n",
+       "          Asian-Pac-Islander  0.859756           0.764319  0.661765  0.583363   \n",
+       "          Black               0.928171           0.737251  0.619048  0.610779   \n",
+       "          Other               0.913580           0.735915  0.588235  0.552396   \n",
+       "          White               0.861610           0.777682  0.689766  0.612016   \n",
+       "         Maximum difference   0.075203           0.141319  0.261195  0.107491   \n",
+       "updated  Overall              0.864981           0.780276  0.685128  0.606017   \n",
+       "          Amer-Indian-Eskimo  0.878049           0.769075  0.482759  0.434487   \n",
+       "          Asian-Pac-Islander  0.850610           0.713329  0.588235  0.541195   \n",
+       "          Black               0.897868           0.824945  0.631579  0.580505   \n",
+       "          Other               0.864198           0.836620  0.592593  0.543856   \n",
+       "          White               0.861850           0.781440  0.693921  0.614000   \n",
+       "         Maximum difference   0.047258           0.123291  0.211162  0.179513   \n",
        "\n",
        "                              Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                               \n",
-       "original Overall               0.773438  0.597929  0.918163          2318.0   \n",
-       "          Amer-Indian-Eskimo   0.833333  0.454545  0.917208            11.0   \n",
-       "          Asian-Pac-Islander   0.710145  0.636364  0.905107            77.0   \n",
-       "          Black                0.857143  0.504673  0.942358           107.0   \n",
-       "          Other                0.625000  0.500000  0.938028            10.0   \n",
-       "          White                0.773390  0.602461  0.914314          2113.0   \n",
-       "         Maximum difference    0.232143  0.181818  0.037251          2103.0   \n",
-       "updated  Overall               0.755353  0.608714  0.904434          2318.0   \n",
-       "          Amer-Indian-Eskimo   0.421053  0.727273  0.917208            11.0   \n",
-       "          Asian-Pac-Islander   0.844444  0.493506  0.905107            77.0   \n",
-       "          Black                0.572519  0.700935  0.942358           107.0   \n",
-       "          Other                0.533333  0.800000  0.938028            10.0   \n",
-       "          White                0.773221  0.606720  0.914314          2113.0   \n",
-       "         Maximum difference    0.423392  0.306494  0.037251          2103.0   \n",
+       "original Overall               0.798276  0.599223  0.919433          2318.0   \n",
+       "          Amer-Indian-Eskimo   1.000000  0.272727  0.928571            11.0   \n",
+       "          Asian-Pac-Islander   0.762712  0.584416  0.900864            77.0   \n",
+       "          Black                0.852459  0.485981  0.940647           107.0   \n",
+       "          Other                0.714286  0.500000  0.863380            10.0   \n",
+       "          White                0.797516  0.607667  0.916398          2113.0   \n",
+       "         Maximum difference    0.285714  0.334940  0.077267          2103.0   \n",
+       "updated  Overall               0.766970  0.619068  0.902293          2318.0   \n",
+       "          Amer-Indian-Eskimo   0.388889  0.636364  0.928571            11.0   \n",
+       "          Asian-Pac-Islander   0.833333  0.454545  0.900864            77.0   \n",
+       "          Black                0.557143  0.728972  0.940647           107.0   \n",
+       "          Other                0.470588  0.800000  0.863380            10.0   \n",
+       "          White                0.790206  0.618552  0.916398          2113.0   \n",
+       "         Maximum difference    0.444444  0.345455  0.077267          2103.0   \n",
        "\n",
        "                              Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                     \n",
@@ -1581,20 +1581,20 @@
        "\n",
        "                              Positive Prediction Rate  \n",
        "         Groups                                         \n",
-       "original Overall                              0.183437  \n",
-       "          Amer-Indian-Eskimo                  0.048780  \n",
-       "          Asian-Pac-Islander                  0.210366  \n",
-       "          Black                               0.070707  \n",
-       "          Other                               0.098765  \n",
-       "          White                               0.197220  \n",
-       "         Maximum difference                   0.161585  \n",
-       "updated  Overall                              0.191217  \n",
-       "          Amer-Indian-Eskimo                  0.154472  \n",
-       "          Asian-Pac-Islander                  0.137195  \n",
-       "          Black                               0.147026  \n",
-       "          Other                               0.185185  \n",
-       "          White                               0.198658  \n",
-       "         Maximum difference                   0.061463  "
+       "original Overall                              0.178114  \n",
+       "          Amer-Indian-Eskimo                  0.024390  \n",
+       "          Asian-Pac-Islander                  0.179878  \n",
+       "          Black                               0.068462  \n",
+       "          Other                               0.086420  \n",
+       "          White                               0.192907  \n",
+       "         Maximum difference                   0.168517  \n",
+       "updated  Overall                              0.191524  \n",
+       "          Amer-Indian-Eskimo                  0.146341  \n",
+       "          Asian-Pac-Islander                  0.128049  \n",
+       "          Black                               0.157127  \n",
+       "          Other                               0.209877  \n",
+       "          White                               0.198179  \n",
+       "         Maximum difference                   0.081828  "
       ]
      },
      "execution_count": 11,
@@ -1613,10 +1613,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:32.163369Z",
-     "iopub.status.busy": "2024-06-17T14:25:32.163223Z",
-     "iopub.status.idle": "2024-06-17T14:25:32.841154Z",
-     "shell.execute_reply": "2024-06-17T14:25:32.840734Z"
+     "iopub.execute_input": "2024-06-17T19:20:43.494315Z",
+     "iopub.status.busy": "2024-06-17T19:20:43.494162Z",
+     "iopub.status.idle": "2024-06-17T19:20:44.705811Z",
+     "shell.execute_reply": "2024-06-17T19:20:44.705529Z"
     }
    },
    "outputs": [
@@ -1674,27 +1674,27 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.863282\n",
-       "      0.773841\n",
-       "      0.678619\n",
-       "      0.601415\n",
-       "      0.777609\n",
-       "      0.601985\n",
-       "      0.918321\n",
+       "      0.888951\n",
+       "      0.812828\n",
+       "      0.742171\n",
+       "      0.679654\n",
+       "      0.837131\n",
+       "      0.666560\n",
+       "      0.951884\n",
        "      9369.0\n",
        "      29704.0\n",
        "      0.239782\n",
-       "      0.185627\n",
+       "      0.190925\n",
        "    \n",
        "    \n",
        "      Amer-Indian-Eskimo\n",
-       "      0.919308\n",
-       "      0.759526\n",
-       "      0.631579\n",
-       "      0.596937\n",
-       "      0.750000\n",
-       "      0.545455\n",
-       "      0.916067\n",
+       "      0.936599\n",
+       "      0.798567\n",
+       "      0.710526\n",
+       "      0.686736\n",
+       "      0.843750\n",
+       "      0.613636\n",
+       "      0.957846\n",
        "      44.0\n",
        "      303.0\n",
        "      0.126801\n",
@@ -1702,172 +1702,172 @@
        "    \n",
        "    \n",
        "      Asian-Pac-Islander\n",
-       "      0.835432\n",
-       "      0.781507\n",
-       "      0.690852\n",
-       "      0.580271\n",
-       "      0.725166\n",
-       "      0.659639\n",
-       "      0.898432\n",
+       "      0.874895\n",
+       "      0.827344\n",
+       "      0.762360\n",
+       "      0.680040\n",
+       "      0.810169\n",
+       "      0.719880\n",
+       "      0.947550\n",
        "      332.0\n",
        "      859.0\n",
        "      0.278757\n",
-       "      0.253568\n",
+       "      0.247691\n",
        "    \n",
        "    \n",
        "      Black\n",
-       "      0.919873\n",
-       "      0.747755\n",
-       "      0.611253\n",
-       "      0.578999\n",
-       "      0.739938\n",
-       "      0.520697\n",
-       "      0.947958\n",
+       "      0.935424\n",
+       "      0.781025\n",
+       "      0.683871\n",
+       "      0.663316\n",
+       "      0.838608\n",
+       "      0.577342\n",
+       "      0.973068\n",
        "      459.0\n",
        "      3335.0\n",
        "      0.120980\n",
-       "      0.085134\n",
+       "      0.083289\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.913846\n",
-       "      0.725219\n",
-       "      0.575758\n",
-       "      0.545464\n",
-       "      0.730769\n",
-       "      0.475000\n",
-       "      0.952895\n",
+       "      0.926154\n",
+       "      0.742982\n",
+       "      0.625000\n",
+       "      0.610476\n",
+       "      0.833333\n",
+       "      0.500000\n",
+       "      0.980877\n",
        "      40.0\n",
        "      285.0\n",
        "      0.123077\n",
-       "      0.080000\n",
+       "      0.073846\n",
        "    \n",
        "    \n",
        "      White\n",
-       "      0.856775\n",
-       "      0.773798\n",
-       "      0.682289\n",
-       "      0.599909\n",
-       "      0.782192\n",
-       "      0.605015\n",
-       "      0.914439\n",
+       "      0.883319\n",
+       "      0.813129\n",
+       "      0.744947\n",
+       "      0.677550\n",
+       "      0.838216\n",
+       "      0.670356\n",
+       "      0.948752\n",
        "      8494.0\n",
        "      24922.0\n",
        "      0.254190\n",
-       "      0.196612\n",
+       "      0.203286\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.084441\n",
-       "      0.056288\n",
-       "      0.115094\n",
-       "      0.054446\n",
-       "      0.057026\n",
-       "      0.184639\n",
-       "      0.054463\n",
+       "      0.061704\n",
+       "      0.084361\n",
+       "      0.137360\n",
+       "      0.076260\n",
+       "      0.033581\n",
+       "      0.219880\n",
+       "      0.033327\n",
        "      8454.0\n",
        "      24637.0\n",
        "      0.157777\n",
-       "      0.173568\n",
+       "      0.173845\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.862386\n",
-       "      0.777490\n",
-       "      0.681627\n",
-       "      0.601039\n",
-       "      0.765426\n",
-       "      0.614367\n",
-       "      0.903170\n",
+       "      0.888260\n",
+       "      0.820083\n",
+       "      0.747309\n",
+       "      0.680235\n",
+       "      0.816285\n",
+       "      0.689081\n",
+       "      0.938464\n",
        "      9369.0\n",
        "      29704.0\n",
        "      0.239782\n",
-       "      0.192460\n",
+       "      0.202416\n",
        "    \n",
        "    \n",
        "      Amer-Indian-Eskimo\n",
-       "      0.884726\n",
-       "      0.788291\n",
-       "      0.591837\n",
-       "      0.529272\n",
-       "      0.537037\n",
-       "      0.659091\n",
-       "      0.916067\n",
+       "      0.907781\n",
+       "      0.879200\n",
+       "      0.698113\n",
+       "      0.658761\n",
+       "      0.596774\n",
+       "      0.840909\n",
+       "      0.957846\n",
        "      44.0\n",
        "      303.0\n",
        "      0.126801\n",
-       "      0.155620\n",
+       "      0.178674\n",
        "    \n",
        "    \n",
        "      Asian-Pac-Islander\n",
-       "      0.832914\n",
-       "      0.729868\n",
-       "      0.623819\n",
-       "      0.554814\n",
-       "      0.837563\n",
-       "      0.496988\n",
-       "      0.898432\n",
+       "      0.865659\n",
+       "      0.777515\n",
+       "      0.705882\n",
+       "      0.650614\n",
+       "      0.905660\n",
+       "      0.578313\n",
+       "      0.947550\n",
        "      332.0\n",
        "      859.0\n",
        "      0.278757\n",
-       "      0.165407\n",
+       "      0.178002\n",
        "    \n",
        "    \n",
        "      Black\n",
-       "      0.909858\n",
-       "      0.851028\n",
-       "      0.674905\n",
-       "      0.630458\n",
-       "      0.598651\n",
-       "      0.773420\n",
-       "      0.947958\n",
+       "      0.927517\n",
+       "      0.898649\n",
+       "      0.741784\n",
+       "      0.709708\n",
+       "      0.651815\n",
+       "      0.860566\n",
+       "      0.973068\n",
        "      459.0\n",
        "      3335.0\n",
        "      0.120980\n",
-       "      0.156299\n",
+       "      0.159726\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.920000\n",
-       "      0.868421\n",
-       "      0.711111\n",
-       "      0.670927\n",
-       "      0.640000\n",
-       "      0.800000\n",
-       "      0.952895\n",
+       "      0.938462\n",
+       "      0.954167\n",
+       "      0.795918\n",
+       "      0.779342\n",
+       "      0.672414\n",
+       "      0.975000\n",
+       "      0.980877\n",
        "      40.0\n",
        "      285.0\n",
        "      0.123077\n",
-       "      0.153846\n",
+       "      0.178462\n",
        "    \n",
        "    \n",
        "      White\n",
-       "      0.857254\n",
-       "      0.775516\n",
-       "      0.684524\n",
-       "      0.601745\n",
-       "      0.781014\n",
-       "      0.609254\n",
-       "      0.914439\n",
+       "      0.883918\n",
+       "      0.817372\n",
+       "      0.749176\n",
+       "      0.680184\n",
+       "      0.831014\n",
+       "      0.682011\n",
+       "      0.948752\n",
        "      8494.0\n",
        "      24922.0\n",
        "      0.254190\n",
-       "      0.198288\n",
+       "      0.208613\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.087086\n",
-       "      0.138553\n",
-       "      0.119274\n",
-       "      0.141656\n",
-       "      0.300526\n",
-       "      0.303012\n",
-       "      0.054463\n",
+       "      0.072802\n",
+       "      0.176651\n",
+       "      0.097805\n",
+       "      0.128728\n",
+       "      0.308886\n",
+       "      0.396687\n",
+       "      0.033327\n",
        "      8454.0\n",
        "      24637.0\n",
        "      0.157777\n",
-       "      0.044442\n",
+       "      0.048887\n",
        "    \n",
        "  \n",
        "\n",
@@ -1876,37 +1876,37 @@
       "text/plain": [
        "                              Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                 \n",
-       "original Overall              0.863282           0.773841  0.678619  0.601415   \n",
-       "          Amer-Indian-Eskimo  0.919308           0.759526  0.631579  0.596937   \n",
-       "          Asian-Pac-Islander  0.835432           0.781507  0.690852  0.580271   \n",
-       "          Black               0.919873           0.747755  0.611253  0.578999   \n",
-       "          Other               0.913846           0.725219  0.575758  0.545464   \n",
-       "          White               0.856775           0.773798  0.682289  0.599909   \n",
-       "         Maximum difference   0.084441           0.056288  0.115094  0.054446   \n",
-       "updated  Overall              0.862386           0.777490  0.681627  0.601039   \n",
-       "          Amer-Indian-Eskimo  0.884726           0.788291  0.591837  0.529272   \n",
-       "          Asian-Pac-Islander  0.832914           0.729868  0.623819  0.554814   \n",
-       "          Black               0.909858           0.851028  0.674905  0.630458   \n",
-       "          Other               0.920000           0.868421  0.711111  0.670927   \n",
-       "          White               0.857254           0.775516  0.684524  0.601745   \n",
-       "         Maximum difference   0.087086           0.138553  0.119274  0.141656   \n",
+       "original Overall              0.888951           0.812828  0.742171  0.679654   \n",
+       "          Amer-Indian-Eskimo  0.936599           0.798567  0.710526  0.686736   \n",
+       "          Asian-Pac-Islander  0.874895           0.827344  0.762360  0.680040   \n",
+       "          Black               0.935424           0.781025  0.683871  0.663316   \n",
+       "          Other               0.926154           0.742982  0.625000  0.610476   \n",
+       "          White               0.883319           0.813129  0.744947  0.677550   \n",
+       "         Maximum difference   0.061704           0.084361  0.137360  0.076260   \n",
+       "updated  Overall              0.888260           0.820083  0.747309  0.680235   \n",
+       "          Amer-Indian-Eskimo  0.907781           0.879200  0.698113  0.658761   \n",
+       "          Asian-Pac-Islander  0.865659           0.777515  0.705882  0.650614   \n",
+       "          Black               0.927517           0.898649  0.741784  0.709708   \n",
+       "          Other               0.938462           0.954167  0.795918  0.779342   \n",
+       "          White               0.883918           0.817372  0.749176  0.680184   \n",
+       "         Maximum difference   0.072802           0.176651  0.097805  0.128728   \n",
        "\n",
        "                              Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                               \n",
-       "original Overall               0.777609  0.601985  0.918321          9369.0   \n",
-       "          Amer-Indian-Eskimo   0.750000  0.545455  0.916067            44.0   \n",
-       "          Asian-Pac-Islander   0.725166  0.659639  0.898432           332.0   \n",
-       "          Black                0.739938  0.520697  0.947958           459.0   \n",
-       "          Other                0.730769  0.475000  0.952895            40.0   \n",
-       "          White                0.782192  0.605015  0.914439          8494.0   \n",
-       "         Maximum difference    0.057026  0.184639  0.054463          8454.0   \n",
-       "updated  Overall               0.765426  0.614367  0.903170          9369.0   \n",
-       "          Amer-Indian-Eskimo   0.537037  0.659091  0.916067            44.0   \n",
-       "          Asian-Pac-Islander   0.837563  0.496988  0.898432           332.0   \n",
-       "          Black                0.598651  0.773420  0.947958           459.0   \n",
-       "          Other                0.640000  0.800000  0.952895            40.0   \n",
-       "          White                0.781014  0.609254  0.914439          8494.0   \n",
-       "         Maximum difference    0.300526  0.303012  0.054463          8454.0   \n",
+       "original Overall               0.837131  0.666560  0.951884          9369.0   \n",
+       "          Amer-Indian-Eskimo   0.843750  0.613636  0.957846            44.0   \n",
+       "          Asian-Pac-Islander   0.810169  0.719880  0.947550           332.0   \n",
+       "          Black                0.838608  0.577342  0.973068           459.0   \n",
+       "          Other                0.833333  0.500000  0.980877            40.0   \n",
+       "          White                0.838216  0.670356  0.948752          8494.0   \n",
+       "         Maximum difference    0.033581  0.219880  0.033327          8454.0   \n",
+       "updated  Overall               0.816285  0.689081  0.938464          9369.0   \n",
+       "          Amer-Indian-Eskimo   0.596774  0.840909  0.957846            44.0   \n",
+       "          Asian-Pac-Islander   0.905660  0.578313  0.947550           332.0   \n",
+       "          Black                0.651815  0.860566  0.973068           459.0   \n",
+       "          Other                0.672414  0.975000  0.980877            40.0   \n",
+       "          White                0.831014  0.682011  0.948752          8494.0   \n",
+       "         Maximum difference    0.308886  0.396687  0.033327          8454.0   \n",
        "\n",
        "                              Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                     \n",
@@ -1927,20 +1927,20 @@
        "\n",
        "                              Positive Prediction Rate  \n",
        "         Groups                                         \n",
-       "original Overall                              0.185627  \n",
+       "original Overall                              0.190925  \n",
        "          Amer-Indian-Eskimo                  0.092219  \n",
-       "          Asian-Pac-Islander                  0.253568  \n",
-       "          Black                               0.085134  \n",
-       "          Other                               0.080000  \n",
-       "          White                               0.196612  \n",
-       "         Maximum difference                   0.173568  \n",
-       "updated  Overall                              0.192460  \n",
-       "          Amer-Indian-Eskimo                  0.155620  \n",
-       "          Asian-Pac-Islander                  0.165407  \n",
-       "          Black                               0.156299  \n",
-       "          Other                               0.153846  \n",
-       "          White                               0.198288  \n",
-       "         Maximum difference                   0.044442  "
+       "          Asian-Pac-Islander                  0.247691  \n",
+       "          Black                               0.083289  \n",
+       "          Other                               0.073846  \n",
+       "          White                               0.203286  \n",
+       "         Maximum difference                   0.173845  \n",
+       "updated  Overall                              0.202416  \n",
+       "          Amer-Indian-Eskimo                  0.178674  \n",
+       "          Asian-Pac-Islander                  0.178002  \n",
+       "          Black                               0.159726  \n",
+       "          Other                               0.178462  \n",
+       "          White                               0.208613  \n",
+       "         Maximum difference                   0.048887  "
       ]
      },
      "execution_count": 12,
@@ -1975,10 +1975,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:32.843216Z",
-     "iopub.status.busy": "2024-06-17T14:25:32.843033Z",
-     "iopub.status.idle": "2024-06-17T14:25:33.437559Z",
-     "shell.execute_reply": "2024-06-17T14:25:33.437220Z"
+     "iopub.execute_input": "2024-06-17T19:20:44.708055Z",
+     "iopub.status.busy": "2024-06-17T19:20:44.707943Z",
+     "iopub.status.idle": "2024-06-17T19:20:45.349115Z",
+     "shell.execute_reply": "2024-06-17T19:20:45.348796Z"
     }
    },
    "outputs": [],
@@ -2000,10 +2000,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:33.440723Z",
-     "iopub.status.busy": "2024-06-17T14:25:33.440458Z",
-     "iopub.status.idle": "2024-06-17T14:25:43.692589Z",
-     "shell.execute_reply": "2024-06-17T14:25:43.692078Z"
+     "iopub.execute_input": "2024-06-17T19:20:45.351727Z",
+     "iopub.status.busy": "2024-06-17T19:20:45.351607Z",
+     "iopub.status.idle": "2024-06-17T19:20:55.582861Z",
+     "shell.execute_reply": "2024-06-17T19:20:55.582379Z"
     }
    },
    "outputs": [
@@ -2011,7 +2011,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142533\"\n"
+      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_192045\"\n"
      ]
     },
     {
@@ -2037,7 +2037,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon will save models to \"AutogluonModels/ag-20240617_142533\"\n"
+      "AutoGluon will save models to \"AutogluonModels/ag-20240617_192045\"\n"
      ]
     },
     {
@@ -2051,8 +2051,8 @@
       "Platform Machine:   arm64\n",
       "Platform Version:   Darwin Kernel Version 23.5.0: Wed May  1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n",
       "CPU Count:          10\n",
-      "Memory Avail:       10.59 GB / 16.00 GB (66.2%)\n",
-      "Disk Space Avail:   362.50 GB / 460.43 GB (78.7%)\n",
+      "Memory Avail:       9.80 GB / 16.00 GB (61.3%)\n",
+      "Disk Space Avail:   357.78 GB / 460.43 GB (77.7%)\n",
       "===================================================\n"
      ]
     },
@@ -2137,7 +2137,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tAvailable Memory:                    10845.11 MB\n"
+      "\tAvailable Memory:                    10034.31 MB\n"
      ]
     },
     {
@@ -2319,7 +2319,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Data preprocessing and feature engineering runtime = 0.04s ...\n"
+      "Data preprocessing and feature engineering runtime = 0.03s ...\n"
      ]
     },
     {
@@ -2371,7 +2371,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsUnif ... Training model for up to 4.96s of the 4.96s of remaining time.\n"
+      "Fitting model: KNeighborsUnif ... Training model for up to 4.97s of the 4.97s of remaining time.\n"
      ]
     },
     {
@@ -2385,21 +2385,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.01s\t = Training   runtime\n"
+      "\t0.0s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.02s\t = Validation runtime\n"
+      "\t0.01s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsDist ... Training model for up to 4.93s of the 4.93s of remaining time.\n"
+      "Fitting model: KNeighborsDist ... Training model for up to 4.94s of the 4.94s of remaining time.\n"
      ]
     },
     {
@@ -2420,14 +2420,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.02s\t = Validation runtime\n"
+      "\t0.01s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: LightGBMXT ... Training model for up to 4.89s of the 4.89s of remaining time.\n"
+      "Fitting model: LightGBMXT ... Training model for up to 4.92s of the 4.92s of remaining time.\n"
      ]
     },
     {
@@ -2441,7 +2441,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t3.27s\t = Training   runtime\n"
+      "\t1.56s\t = Training   runtime\n"
      ]
     },
     {
@@ -2455,43 +2455,105 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: LightGBM ... Training model for up to 1.61s of the 1.6s of remaining time.\n"
+      "Fitting model: LightGBM ... Training model for up to 3.35s of the 3.35s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tRan out of time, early stopping on iteration 174. Best iteration is:\n",
-      "\t[27]\tvalid_set's binary_error: 0.323326\n"
+      "\t0.6767\t = Validation score   (accuracy)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.6767\t = Validation score   (accuracy)\n"
+      "\t2.09s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t1.62s\t = Training   runtime\n"
+      "\t0.0s\t = Validation runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.0s\t = Validation runtime\n"
+      "Fitting model: RandomForestGini ... Training model for up to 1.26s of the 1.26s of remaining time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.6074\t = Validation score   (accuracy)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.46s\t = Training   runtime\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.96s of the -0.04s of remaining time.\n"
+      "\t0.04s\t = Validation runtime\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: RandomForestEntr ... Training model for up to 0.74s of the 0.74s of remaining time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.6097\t = Validation score   (accuracy)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.35s\t = Training   runtime\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\t0.04s\t = Validation runtime\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: CatBoost ... Training model for up to 0.33s of the 0.33s of remaining time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\tTime limit exceeded... Skipping CatBoost.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.97s of the -0.06s of remaining time.\n"
      ]
     },
     {
@@ -2526,21 +2588,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon training complete, total runtime = 5.12s ... Best model: \"WeightedEnsemble_L2\"\n"
+      "AutoGluon training complete, total runtime = 5.11s ... Best model: \"WeightedEnsemble_L2\"\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142533\")\n"
+      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_192045\")\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142538\"\n"
+      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_192050\"\n"
      ]
     },
     {
@@ -2566,7 +2628,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon will save models to \"AutogluonModels/ag-20240617_142538\"\n"
+      "AutoGluon will save models to \"AutogluonModels/ag-20240617_192050\"\n"
      ]
     },
     {
@@ -2580,8 +2642,8 @@
       "Platform Machine:   arm64\n",
       "Platform Version:   Darwin Kernel Version 23.5.0: Wed May  1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n",
       "CPU Count:          10\n",
-      "Memory Avail:       10.37 GB / 16.00 GB (64.8%)\n",
-      "Disk Space Avail:   362.50 GB / 460.43 GB (78.7%)\n",
+      "Memory Avail:       10.55 GB / 16.00 GB (65.9%)\n",
+      "Disk Space Avail:   358.74 GB / 460.43 GB (77.9%)\n",
       "===================================================\n"
      ]
     },
@@ -2674,7 +2736,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tAvailable Memory:                    10620.94 MB\n"
+      "\tAvailable Memory:                    10803.08 MB\n"
      ]
     },
     {
@@ -2957,7 +3019,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t0.02s\t = Validation runtime\n"
+      "\t0.01s\t = Validation runtime\n"
      ]
     },
     {
@@ -2978,7 +3040,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t2.88s\t = Training   runtime\n"
+      "\t3.28s\t = Training   runtime\n"
      ]
     },
     {
@@ -2992,14 +3054,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: LightGBM ... Training model for up to 2.02s of the 2.02s of remaining time.\n"
+      "Fitting model: LightGBM ... Training model for up to 1.63s of the 1.63s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tRan out of time, early stopping on iteration 216. Best iteration is:\n",
+      "\tRan out of time, early stopping on iteration 134. Best iteration is:\n",
       "\t[4]\tvalid_set's binary_error: 0.364896\n"
      ]
     },
@@ -3014,7 +3076,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t2.03s\t = Training   runtime\n"
+      "\t1.63s\t = Training   runtime\n"
      ]
     },
     {
@@ -3063,14 +3125,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon training complete, total runtime = 5.09s ... Best model: \"WeightedEnsemble_L2\"\n"
+      "AutoGluon training complete, total runtime = 5.08s ... Best model: \"WeightedEnsemble_L2\"\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142538\")\n"
+      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_192050\")\n"
      ]
     }
    ],
@@ -3085,10 +3147,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:43.695558Z",
-     "iopub.status.busy": "2024-06-17T14:25:43.695420Z",
-     "iopub.status.idle": "2024-06-17T14:25:43.828281Z",
-     "shell.execute_reply": "2024-06-17T14:25:43.827763Z"
+     "iopub.execute_input": "2024-06-17T19:20:55.586135Z",
+     "iopub.status.busy": "2024-06-17T19:20:55.585848Z",
+     "iopub.status.idle": "2024-06-17T19:20:55.712300Z",
+     "shell.execute_reply": "2024-06-17T19:20:55.711572Z"
     }
    },
    "outputs": [],
@@ -3104,10 +3166,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:43.831243Z",
-     "iopub.status.busy": "2024-06-17T14:25:43.831077Z",
-     "iopub.status.idle": "2024-06-17T14:25:43.834798Z",
-     "shell.execute_reply": "2024-06-17T14:25:43.834288Z"
+     "iopub.execute_input": "2024-06-17T19:20:55.716187Z",
+     "iopub.status.busy": "2024-06-17T19:20:55.715980Z",
+     "iopub.status.idle": "2024-06-17T19:20:55.719871Z",
+     "shell.execute_reply": "2024-06-17T19:20:55.719294Z"
     }
    },
    "outputs": [],
@@ -3136,10 +3198,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:43.837065Z",
-     "iopub.status.busy": "2024-06-17T14:25:43.836964Z",
-     "iopub.status.idle": "2024-06-17T14:25:48.946647Z",
-     "shell.execute_reply": "2024-06-17T14:25:48.946179Z"
+     "iopub.execute_input": "2024-06-17T19:20:55.722720Z",
+     "iopub.status.busy": "2024-06-17T19:20:55.722572Z",
+     "iopub.status.idle": "2024-06-17T19:21:00.856229Z",
+     "shell.execute_reply": "2024-06-17T19:21:00.855874Z"
     }
    },
    "outputs": [
@@ -3147,7 +3209,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_142543\"\n"
+      "No path specified. Models will be saved in: \"AutogluonModels/ag-20240617_192055\"\n"
      ]
     },
     {
@@ -3173,7 +3235,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon will save models to \"AutogluonModels/ag-20240617_142543\"\n"
+      "AutoGluon will save models to \"AutogluonModels/ag-20240617_192055\"\n"
      ]
     },
     {
@@ -3187,8 +3249,8 @@
       "Platform Machine:   arm64\n",
       "Platform Version:   Darwin Kernel Version 23.5.0: Wed May  1 20:14:38 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6020\n",
       "CPU Count:          10\n",
-      "Memory Avail:       10.26 GB / 16.00 GB (64.1%)\n",
-      "Disk Space Avail:   362.50 GB / 460.43 GB (78.7%)\n",
+      "Memory Avail:       7.88 GB / 16.00 GB (49.2%)\n",
+      "Disk Space Avail:   358.74 GB / 460.43 GB (77.9%)\n",
       "===================================================\n"
      ]
     },
@@ -3273,7 +3335,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tAvailable Memory:                    10509.47 MB\n"
+      "\tAvailable Memory:                    8069.29 MB\n"
      ]
     },
     {
@@ -3535,7 +3597,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: KNeighborsDist ... Training model for up to 4.93s of the 4.93s of remaining time.\n"
+      "Fitting model: KNeighborsDist ... Training model for up to 4.94s of the 4.94s of remaining time.\n"
      ]
     },
     {
@@ -3577,7 +3639,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t3.06s\t = Training   runtime\n"
+      "\t2.65s\t = Training   runtime\n"
      ]
     },
     {
@@ -3591,14 +3653,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: LightGBM ... Training model for up to 1.84s of the 1.84s of remaining time.\n"
+      "Fitting model: LightGBM ... Training model for up to 2.24s of the 2.24s of remaining time.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\tRan out of time, early stopping on iteration 209. Best iteration is:\n",
+      "\tRan out of time, early stopping on iteration 249. Best iteration is:\n",
       "\t[42]\tvalid_set's binary_error: 0.325635\n"
      ]
     },
@@ -3613,7 +3675,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\t1.85s\t = Training   runtime\n"
+      "\t2.25s\t = Training   runtime\n"
      ]
     },
     {
@@ -3627,7 +3689,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.96s of the -0.04s of remaining time.\n"
+      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.96s of the -0.08s of remaining time.\n"
      ]
     },
     {
@@ -3662,14 +3724,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AutoGluon training complete, total runtime = 5.09s ... Best model: \"WeightedEnsemble_L2\"\n"
+      "AutoGluon training complete, total runtime = 5.12s ... Best model: \"WeightedEnsemble_L2\"\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_142543\")\n"
+      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20240617_192055\")\n"
      ]
     }
    ],
@@ -3686,10 +3748,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:48.949466Z",
-     "iopub.status.busy": "2024-06-17T14:25:48.949284Z",
-     "iopub.status.idle": "2024-06-17T14:25:49.349542Z",
-     "shell.execute_reply": "2024-06-17T14:25:49.349009Z"
+     "iopub.execute_input": "2024-06-17T19:21:00.858380Z",
+     "iopub.status.busy": "2024-06-17T19:21:00.858264Z",
+     "iopub.status.idle": "2024-06-17T19:21:01.258334Z",
+     "shell.execute_reply": "2024-06-17T19:21:01.257694Z"
     }
    },
    "outputs": [
@@ -3863,10 +3925,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:49.352331Z",
-     "iopub.status.busy": "2024-06-17T14:25:49.352204Z",
-     "iopub.status.idle": "2024-06-17T14:25:50.790011Z",
-     "shell.execute_reply": "2024-06-17T14:25:50.789390Z"
+     "iopub.execute_input": "2024-06-17T19:21:01.260845Z",
+     "iopub.status.busy": "2024-06-17T19:21:01.260654Z",
+     "iopub.status.idle": "2024-06-17T19:21:02.672497Z",
+     "shell.execute_reply": "2024-06-17T19:21:02.671929Z"
     }
    },
    "outputs": [
diff --git a/examples/quickstart_xgboost.ipynb b/examples/quickstart_xgboost.ipynb
index c7d3c81..c33f8b9 100644
--- a/examples/quickstart_xgboost.ipynb
+++ b/examples/quickstart_xgboost.ipynb
@@ -5,11 +5,11 @@
    "metadata": {},
    "source": [
     "# FairPredictor XGBoost Examples\n",
-    "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups),  and enforcing a range of fairness definition on COMPAS.\n",
+    "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups), and enforcing a range of fairness definition on COMPAS.\n",
     "\n",
     "It is a modified version of [quickstart_autogluon.ipynb](quickstart_autogluon.ipynb)\n",
     "\n",
-    "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e. it does not require access to protected attributes at test time. \n",
+    "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e., it does not require access to protected attributes at test time. \n",
     "Under the hood, FairPredictor works by adjusting the decision boundary for each group individually. Where groups are not available, it makes use of inferred group membership to adjust decision boundaries.\n",
     "\n",
     "The key idea underlying this toolkit is that for a wide range of use cases, the most suitable classifier should do more than maximize some form of accuracy.\n",
@@ -39,10 +39,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:32.763662Z",
-     "iopub.status.busy": "2024-06-17T14:25:32.763527Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.786857Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.786491Z"
+     "iopub.execute_input": "2024-06-17T19:20:41.442722Z",
+     "iopub.status.busy": "2024-06-17T19:20:41.442599Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.841551Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.840928Z"
     }
    },
    "outputs": [
@@ -74,10 +74,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.788891Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.788670Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.792372Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.792078Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.844308Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.844131Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.848616Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.848139Z"
     }
    },
    "outputs": [
@@ -101,10 +101,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.793873Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.793769Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.818969Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.818485Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.850974Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.850786Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.875719Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.875176Z"
     }
    },
    "outputs": [
@@ -128,17 +128,17 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.821584Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.821377Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.830510Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.829969Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.878359Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.878167Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.886881Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.886133Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, ..., 0, 0, 0])"
+       "array([1, 0, 1, ..., 0, 0, 0])"
       ]
      },
      "execution_count": 4,
@@ -156,10 +156,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.832801Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.832641Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.862714Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.862209Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.889405Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.889209Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.919984Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.919264Z"
     }
    },
    "outputs": [
@@ -191,38 +191,38 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.872164\n",
-       "      0.852510\n",
+       "      0.868315\n",
+       "      0.848743\n",
        "    \n",
        "    \n",
        "      Balanced Accuracy\n",
-       "      0.799977\n",
-       "      0.751872\n",
+       "      0.797330\n",
+       "      0.768163\n",
        "    \n",
        "    \n",
        "      F1 score\n",
-       "      0.712364\n",
-       "      0.644563\n",
+       "      0.706140\n",
+       "      0.660040\n",
        "    \n",
        "    \n",
        "      MCC\n",
-       "      0.633898\n",
-       "      0.564798\n",
+       "      0.624184\n",
+       "      0.566166\n",
        "    \n",
        "    \n",
        "      Precision\n",
-       "      0.771657\n",
-       "      0.761305\n",
+       "      0.757647\n",
+       "      0.714058\n",
        "    \n",
        "    \n",
        "      Recall\n",
-       "      0.661533\n",
-       "      0.558864\n",
+       "      0.661191\n",
+       "      0.613621\n",
        "    \n",
        "    \n",
        "      ROC AUC\n",
-       "      0.928449\n",
-       "      0.822116\n",
+       "      0.925353\n",
+       "      0.821574\n",
        "    \n",
        "  \n",
        "\n",
@@ -230,13 +230,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.872164  0.852510\n",
-       "Balanced Accuracy  0.799977  0.751872\n",
-       "F1 score           0.712364  0.644563\n",
-       "MCC                0.633898  0.564798\n",
-       "Precision          0.771657  0.761305\n",
-       "Recall             0.661533  0.558864\n",
-       "ROC AUC            0.928449  0.822116"
+       "Accuracy           0.868315  0.848743\n",
+       "Balanced Accuracy  0.797330  0.768163\n",
+       "F1 score           0.706140  0.660040\n",
+       "MCC                0.624184  0.566166\n",
+       "Precision          0.757647  0.714058\n",
+       "Recall             0.661191  0.613621\n",
+       "ROC AUC            0.925353  0.821574"
       ]
      },
      "execution_count": 5,
@@ -254,10 +254,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.868747Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.868494Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.894637Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.894155Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.922055Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.921915Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.946433Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.945879Z"
     }
    },
    "outputs": [
@@ -289,43 +289,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.180490\n",
-       "      0.004832\n",
+       "      0.192654\n",
+       "      0.013454\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.023388\n",
-       "      0.340631\n",
+       "      0.023061\n",
+       "      0.363074\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.094358\n",
-       "      0.330496\n",
+       "      0.107284\n",
+       "      0.265979\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.094358\n",
-       "      0.330496\n",
+       "      0.107284\n",
+       "      0.265979\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.078240\n",
-       "      0.194312\n",
+       "      0.091696\n",
+       "      0.166141\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.053734\n",
-       "      0.250397\n",
+       "      0.053394\n",
+       "      0.252662\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.091453\n",
-       "      0.073553\n",
+       "      0.100167\n",
+       "      0.042973\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.146262\n",
-       "      4.453745\n",
+       "      0.303553\n",
+       "      5.708978\n",
        "    \n",
        "  \n",
        "\n",
@@ -333,14 +333,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.180490  0.004832\n",
-       "Predictive Parity                                0.023388  0.340631\n",
-       "Equal Opportunity                                0.094358  0.330496\n",
-       "Average Group Difference in False Negative Rate  0.094358  0.330496\n",
-       "Equalized Odds                                   0.078240  0.194312\n",
-       "Conditional Use Accuracy                         0.053734  0.250397\n",
-       "Average Group Difference in Accuracy             0.091453  0.073553\n",
-       "Treatment Equality                               0.146262  4.453745"
+       "Statistical Parity                               0.192654  0.013454\n",
+       "Predictive Parity                                0.023061  0.363074\n",
+       "Equal Opportunity                                0.107284  0.265979\n",
+       "Average Group Difference in False Negative Rate  0.107284  0.265979\n",
+       "Equalized Odds                                   0.091696  0.166141\n",
+       "Conditional Use Accuracy                         0.053394  0.252662\n",
+       "Average Group Difference in Accuracy             0.100167  0.042973\n",
+       "Treatment Equality                               0.303553  5.708978"
       ]
      },
      "execution_count": 6,
@@ -358,10 +358,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.897520Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.897386Z",
-     "iopub.status.idle": "2024-06-17T14:25:39.949665Z",
-     "shell.execute_reply": "2024-06-17T14:25:39.949280Z"
+     "iopub.execute_input": "2024-06-17T19:20:49.948771Z",
+     "iopub.status.busy": "2024-06-17T19:20:49.948616Z",
+     "iopub.status.idle": "2024-06-17T19:20:49.998948Z",
+     "shell.execute_reply": "2024-06-17T19:20:49.998544Z"
     }
    },
    "outputs": [
@@ -419,116 +419,116 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.872164\n",
-       "      0.799977\n",
-       "      0.712364\n",
-       "      0.633898\n",
-       "      0.771657\n",
-       "      0.661533\n",
-       "      0.928449\n",
+       "      0.868315\n",
+       "      0.797330\n",
+       "      0.706140\n",
+       "      0.624184\n",
+       "      0.757647\n",
+       "      0.661191\n",
+       "      0.925353\n",
        "      2922.0\n",
        "      9289.0\n",
        "      0.239292\n",
-       "      0.205143\n",
+       "      0.208828\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.933300\n",
-       "      0.778938\n",
-       "      0.655612\n",
-       "      0.625599\n",
-       "      0.751462\n",
-       "      0.581448\n",
-       "      0.946810\n",
+       "      0.935277\n",
+       "      0.775085\n",
+       "      0.657963\n",
+       "      0.632329\n",
+       "      0.777778\n",
+       "      0.570136\n",
+       "      0.941812\n",
        "      442.0\n",
        "      3606.0\n",
        "      0.109190\n",
-       "      0.084486\n",
+       "      0.080040\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.841847\n",
-       "      0.795056\n",
-       "      0.721947\n",
-       "      0.614958\n",
-       "      0.774850\n",
-       "      0.675806\n",
-       "      0.910547\n",
+       "      0.835110\n",
+       "      0.790672\n",
+       "      0.713982\n",
+       "      0.600346\n",
+       "      0.754717\n",
+       "      0.677419\n",
+       "      0.907388\n",
        "      2480.0\n",
        "      5683.0\n",
        "      0.303810\n",
-       "      0.264976\n",
+       "      0.272694\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.091453\n",
-       "      0.016118\n",
-       "      0.066335\n",
-       "      0.010641\n",
-       "      0.023388\n",
-       "      0.094358\n",
-       "      0.036263\n",
+       "      0.100167\n",
+       "      0.015587\n",
+       "      0.056019\n",
+       "      0.031983\n",
+       "      0.023061\n",
+       "      0.107284\n",
+       "      0.034423\n",
        "      2038.0\n",
        "      2077.0\n",
        "      0.194620\n",
-       "      0.180490\n",
+       "      0.192654\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.852510\n",
-       "      0.751872\n",
-       "      0.644563\n",
-       "      0.564798\n",
-       "      0.761305\n",
-       "      0.558864\n",
-       "      0.822116\n",
+       "      0.848743\n",
+       "      0.768163\n",
+       "      0.660040\n",
+       "      0.566166\n",
+       "      0.714058\n",
+       "      0.613621\n",
+       "      0.821574\n",
        "      2922.0\n",
        "      9289.0\n",
        "      0.239292\n",
-       "      0.175661\n",
+       "      0.205634\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.901680\n",
-       "      0.874342\n",
-       "      0.650877\n",
-       "      0.618120\n",
-       "      0.531519\n",
+       "      0.877470\n",
+       "      0.860754\n",
+       "      0.599354\n",
+       "      0.566152\n",
+       "      0.466080\n",
        "      0.839367\n",
-       "      0.946810\n",
+       "      0.941812\n",
        "      442.0\n",
        "      3606.0\n",
        "      0.109190\n",
-       "      0.172431\n",
+       "      0.196640\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.828127\n",
-       "      0.738159\n",
-       "      0.642730\n",
-       "      0.573617\n",
-       "      0.872149\n",
-       "      0.508871\n",
-       "      0.910547\n",
+       "      0.834497\n",
+       "      0.760915\n",
+       "      0.677950\n",
+       "      0.589114\n",
+       "      0.829155\n",
+       "      0.573387\n",
+       "      0.907388\n",
        "      2480.0\n",
        "      5683.0\n",
        "      0.303810\n",
-       "      0.177263\n",
+       "      0.210094\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.073553\n",
-       "      0.136183\n",
-       "      0.008147\n",
-       "      0.044503\n",
-       "      0.340631\n",
-       "      0.330496\n",
-       "      0.036263\n",
+       "      0.042973\n",
+       "      0.099839\n",
+       "      0.078596\n",
+       "      0.022962\n",
+       "      0.363074\n",
+       "      0.265979\n",
+       "      0.034423\n",
        "      2038.0\n",
        "      2077.0\n",
        "      0.194620\n",
-       "      0.004832\n",
+       "      0.013454\n",
        "    \n",
        "  \n",
        "\n",
@@ -537,25 +537,25 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.872164           0.799977  0.712364  0.633898   \n",
-       "         0                   0.933300           0.778938  0.655612  0.625599   \n",
-       "         1                   0.841847           0.795056  0.721947  0.614958   \n",
-       "         Maximum difference  0.091453           0.016118  0.066335  0.010641   \n",
-       "updated  Overall             0.852510           0.751872  0.644563  0.564798   \n",
-       "         0                   0.901680           0.874342  0.650877  0.618120   \n",
-       "         1                   0.828127           0.738159  0.642730  0.573617   \n",
-       "         Maximum difference  0.073553           0.136183  0.008147  0.044503   \n",
+       "original Overall             0.868315           0.797330  0.706140  0.624184   \n",
+       "         0                   0.935277           0.775085  0.657963  0.632329   \n",
+       "         1                   0.835110           0.790672  0.713982  0.600346   \n",
+       "         Maximum difference  0.100167           0.015587  0.056019  0.031983   \n",
+       "updated  Overall             0.848743           0.768163  0.660040  0.566166   \n",
+       "         0                   0.877470           0.860754  0.599354  0.566152   \n",
+       "         1                   0.834497           0.760915  0.677950  0.589114   \n",
+       "         Maximum difference  0.042973           0.099839  0.078596  0.022962   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.771657  0.661533  0.928449          2922.0   \n",
-       "         0                    0.751462  0.581448  0.946810           442.0   \n",
-       "         1                    0.774850  0.675806  0.910547          2480.0   \n",
-       "         Maximum difference   0.023388  0.094358  0.036263          2038.0   \n",
-       "updated  Overall              0.761305  0.558864  0.822116          2922.0   \n",
-       "         0                    0.531519  0.839367  0.946810           442.0   \n",
-       "         1                    0.872149  0.508871  0.910547          2480.0   \n",
-       "         Maximum difference   0.340631  0.330496  0.036263          2038.0   \n",
+       "original Overall              0.757647  0.661191  0.925353          2922.0   \n",
+       "         0                    0.777778  0.570136  0.941812           442.0   \n",
+       "         1                    0.754717  0.677419  0.907388          2480.0   \n",
+       "         Maximum difference   0.023061  0.107284  0.034423          2038.0   \n",
+       "updated  Overall              0.714058  0.613621  0.821574          2922.0   \n",
+       "         0                    0.466080  0.839367  0.941812           442.0   \n",
+       "         1                    0.829155  0.573387  0.907388          2480.0   \n",
+       "         Maximum difference   0.363074  0.265979  0.034423          2038.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
@@ -570,14 +570,14 @@
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.205143  \n",
-       "         0                                   0.084486  \n",
-       "         1                                   0.264976  \n",
-       "         Maximum difference                  0.180490  \n",
-       "updated  Overall                             0.175661  \n",
-       "         0                                   0.172431  \n",
-       "         1                                   0.177263  \n",
-       "         Maximum difference                  0.004832  "
+       "original Overall                             0.208828  \n",
+       "         0                                   0.080040  \n",
+       "         1                                   0.272694  \n",
+       "         Maximum difference                  0.192654  \n",
+       "updated  Overall                             0.205634  \n",
+       "         0                                   0.196640  \n",
+       "         1                                   0.210094  \n",
+       "         Maximum difference                  0.013454  "
       ]
      },
      "execution_count": 7,
@@ -595,10 +595,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:39.951536Z",
-     "iopub.status.busy": "2024-06-17T14:25:39.951380Z",
-     "iopub.status.idle": "2024-06-17T14:25:43.967533Z",
-     "shell.execute_reply": "2024-06-17T14:25:43.966892Z"
+     "iopub.execute_input": "2024-06-17T19:20:50.001279Z",
+     "iopub.status.busy": "2024-06-17T19:20:50.001111Z",
+     "iopub.status.idle": "2024-06-17T19:20:54.692206Z",
+     "shell.execute_reply": "2024-06-17T19:20:54.691715Z"
     }
    },
    "outputs": [],
@@ -612,10 +612,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:25:43.970351Z",
-     "iopub.status.busy": "2024-06-17T14:25:43.970019Z",
-     "iopub.status.idle": "2024-06-17T14:26:17.919545Z",
-     "shell.execute_reply": "2024-06-17T14:26:17.913963Z"
+     "iopub.execute_input": "2024-06-17T19:20:54.694914Z",
+     "iopub.status.busy": "2024-06-17T19:20:54.694743Z",
+     "iopub.status.idle": "2024-06-17T19:21:20.557631Z",
+     "shell.execute_reply": "2024-06-17T19:21:20.556715Z"
     }
    },
    "outputs": [
@@ -638,10 +638,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:17.927076Z",
-     "iopub.status.busy": "2024-06-17T14:26:17.926927Z",
-     "iopub.status.idle": "2024-06-17T14:26:17.990683Z",
-     "shell.execute_reply": "2024-06-17T14:26:17.989879Z"
+     "iopub.execute_input": "2024-06-17T19:21:20.561396Z",
+     "iopub.status.busy": "2024-06-17T19:21:20.561247Z",
+     "iopub.status.idle": "2024-06-17T19:21:20.593515Z",
+     "shell.execute_reply": "2024-06-17T19:21:20.593088Z"
     }
    },
    "outputs": [
@@ -673,43 +673,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.092993\n",
-       "      0.074443\n",
+       "      0.103547\n",
+       "      0.049493\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.064486\n",
-       "      0.173749\n",
+       "      0.074152\n",
+       "      0.184299\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.149332\n",
-       "      0.176780\n",
+       "      0.210605\n",
+       "      0.209524\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.149332\n",
-       "      0.176780\n",
+       "      0.210605\n",
+       "      0.209524\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.087968\n",
-       "      0.103295\n",
+       "      0.124660\n",
+       "      0.119844\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.061980\n",
-       "      0.125346\n",
+       "      0.060003\n",
+       "      0.128876\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.057213\n",
-       "      0.043561\n",
+       "      0.057328\n",
+       "      0.040328\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      1.008716\n",
-       "      5.134823\n",
+       "      0.284902\n",
+       "      2.099200\n",
        "    \n",
        "  \n",
        "\n",
@@ -717,14 +717,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.092993  0.074443\n",
-       "Predictive Parity                                0.064486  0.173749\n",
-       "Equal Opportunity                                0.149332  0.176780\n",
-       "Average Group Difference in False Negative Rate  0.149332  0.176780\n",
-       "Equalized Odds                                   0.087968  0.103295\n",
-       "Conditional Use Accuracy                         0.061980  0.125346\n",
-       "Average Group Difference in Accuracy             0.057213  0.043561\n",
-       "Treatment Equality                               1.008716  5.134823"
+       "Statistical Parity                               0.103547  0.049493\n",
+       "Predictive Parity                                0.074152  0.184299\n",
+       "Equal Opportunity                                0.210605  0.209524\n",
+       "Average Group Difference in False Negative Rate  0.210605  0.209524\n",
+       "Equalized Odds                                   0.124660  0.119844\n",
+       "Conditional Use Accuracy                         0.060003  0.128876\n",
+       "Average Group Difference in Accuracy             0.057328  0.040328\n",
+       "Treatment Equality                               0.284902  2.099200"
       ]
      },
      "execution_count": 10,
@@ -742,10 +742,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:17.992502Z",
-     "iopub.status.busy": "2024-06-17T14:26:17.992385Z",
-     "iopub.status.idle": "2024-06-17T14:26:18.014082Z",
-     "shell.execute_reply": "2024-06-17T14:26:18.013841Z"
+     "iopub.execute_input": "2024-06-17T19:21:20.595709Z",
+     "iopub.status.busy": "2024-06-17T19:21:20.595582Z",
+     "iopub.status.idle": "2024-06-17T19:21:20.615596Z",
+     "shell.execute_reply": "2024-06-17T19:21:20.615276Z"
     }
    },
    "outputs": [
@@ -777,43 +777,43 @@
        "  \n",
        "    \n",
        "      Statistical Parity\n",
-       "      0.086590\n",
-       "      0.019697\n",
+       "      0.093956\n",
+       "      0.019959\n",
        "    \n",
        "    \n",
        "      Predictive Parity\n",
-       "      0.233008\n",
-       "      0.292160\n",
+       "      0.191814\n",
+       "      0.133510\n",
        "    \n",
        "    \n",
        "      Equal Opportunity\n",
-       "      0.045345\n",
-       "      0.148495\n",
+       "      0.145525\n",
+       "      0.191602\n",
        "    \n",
        "    \n",
        "      Average Group Difference in False Negative Rate\n",
-       "      0.045345\n",
-       "      0.148495\n",
+       "      0.145525\n",
+       "      0.191602\n",
        "    \n",
        "    \n",
        "      Equalized Odds\n",
-       "      0.040168\n",
-       "      0.095018\n",
+       "      0.093755\n",
+       "      0.107181\n",
        "    \n",
        "    \n",
        "      Conditional Use Accuracy\n",
-       "      0.139236\n",
-       "      0.174533\n",
+       "      0.115694\n",
+       "      0.104046\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.050472\n",
-       "      0.037915\n",
+       "      0.054694\n",
+       "      0.047595\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.714552\n",
-       "      2.669082\n",
+       "      0.292782\n",
+       "      2.399059\n",
        "    \n",
        "  \n",
        "\n",
@@ -821,14 +821,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.086590  0.019697\n",
-       "Predictive Parity                                0.233008  0.292160\n",
-       "Equal Opportunity                                0.045345  0.148495\n",
-       "Average Group Difference in False Negative Rate  0.045345  0.148495\n",
-       "Equalized Odds                                   0.040168  0.095018\n",
-       "Conditional Use Accuracy                         0.139236  0.174533\n",
-       "Average Group Difference in Accuracy             0.050472  0.037915\n",
-       "Treatment Equality                               0.714552  2.669082"
+       "Statistical Parity                               0.093956  0.019959\n",
+       "Predictive Parity                                0.191814  0.133510\n",
+       "Equal Opportunity                                0.145525  0.191602\n",
+       "Average Group Difference in False Negative Rate  0.145525  0.191602\n",
+       "Equalized Odds                                   0.093755  0.107181\n",
+       "Conditional Use Accuracy                         0.115694  0.104046\n",
+       "Average Group Difference in Accuracy             0.054694  0.047595\n",
+       "Treatment Equality                               0.292782  2.399059"
       ]
      },
      "execution_count": 11,
@@ -845,16 +845,16 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:18.015657Z",
-     "iopub.status.busy": "2024-06-17T14:26:18.015566Z",
-     "iopub.status.idle": "2024-06-17T14:26:18.615479Z",
-     "shell.execute_reply": "2024-06-17T14:26:18.615187Z"
+     "iopub.execute_input": "2024-06-17T19:21:20.617231Z",
+     "iopub.status.busy": "2024-06-17T19:21:20.617123Z",
+     "iopub.status.idle": "2024-06-17T19:21:21.137764Z",
+     "shell.execute_reply": "2024-06-17T19:21:21.137302Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -873,16 +873,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:18.617816Z",
-     "iopub.status.busy": "2024-06-17T14:26:18.617645Z",
-     "iopub.status.idle": "2024-06-17T14:26:18.738003Z",
-     "shell.execute_reply": "2024-06-17T14:26:18.737658Z"
+     "iopub.execute_input": "2024-06-17T19:21:21.139908Z",
+     "iopub.status.busy": "2024-06-17T19:21:21.139680Z",
+     "iopub.status.idle": "2024-06-17T19:21:21.287872Z",
+     "shell.execute_reply": "2024-06-17T19:21:21.287394Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -901,10 +901,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:18.739839Z",
-     "iopub.status.busy": "2024-06-17T14:26:18.739704Z",
-     "iopub.status.idle": "2024-06-17T14:26:18.786243Z",
-     "shell.execute_reply": "2024-06-17T14:26:18.785921Z"
+     "iopub.execute_input": "2024-06-17T19:21:21.292402Z",
+     "iopub.status.busy": "2024-06-17T19:21:21.292266Z",
+     "iopub.status.idle": "2024-06-17T19:21:21.341972Z",
+     "shell.execute_reply": "2024-06-17T19:21:21.341555Z"
     }
    },
    "outputs": [
@@ -962,200 +962,200 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.865941\n",
-       "      0.791664\n",
-       "      0.698582\n",
-       "      0.615966\n",
-       "      0.756078\n",
-       "      0.649213\n",
-       "      0.922685\n",
+       "      0.867988\n",
+       "      0.795825\n",
+       "      0.704437\n",
+       "      0.622647\n",
+       "      0.758689\n",
+       "      0.657426\n",
+       "      0.923695\n",
        "      2922.0\n",
        "      9289.0\n",
        "      0.239292\n",
-       "      0.205470\n",
+       "      0.207354\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.872340\n",
-       "      0.738732\n",
-       "      0.600000\n",
-       "      0.532348\n",
-       "      0.692308\n",
-       "      0.529412\n",
-       "      0.952636\n",
-       "      17.0\n",
-       "      77.0\n",
-       "      0.180851\n",
-       "      0.138298\n",
+       "      0.892308\n",
+       "      0.589364\n",
+       "      0.300000\n",
+       "      0.340009\n",
+       "      0.750000\n",
+       "      0.187500\n",
+       "      0.877741\n",
+       "      16.0\n",
+       "      114.0\n",
+       "      0.123077\n",
+       "      0.030769\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.851541\n",
-       "      0.804440\n",
-       "      0.736318\n",
-       "      0.637235\n",
-       "      0.795699\n",
-       "      0.685185\n",
-       "      0.920051\n",
-       "      108.0\n",
-       "      249.0\n",
-       "      0.302521\n",
-       "      0.260504\n",
+       "      0.831978\n",
+       "      0.754766\n",
+       "      0.655556\n",
+       "      0.553843\n",
+       "      0.746835\n",
+       "      0.584158\n",
+       "      0.908711\n",
+       "      101.0\n",
+       "      268.0\n",
+       "      0.273713\n",
+       "      0.214092\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.927773\n",
-       "      0.782429\n",
-       "      0.652893\n",
-       "      0.616809\n",
-       "      0.724771\n",
-       "      0.593985\n",
-       "      0.944594\n",
-       "      133.0\n",
-       "      1030.0\n",
-       "      0.114359\n",
-       "      0.093723\n",
+       "      0.929614\n",
+       "      0.767418\n",
+       "      0.658333\n",
+       "      0.635246\n",
+       "      0.814433\n",
+       "      0.552448\n",
+       "      0.947019\n",
+       "      143.0\n",
+       "      1022.0\n",
+       "      0.122747\n",
+       "      0.083262\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.960000\n",
-       "      0.912442\n",
+       "      0.941176\n",
+       "      0.815747\n",
        "      0.750000\n",
-       "      0.735431\n",
-       "      0.666667\n",
-       "      0.857143\n",
-       "      0.938556\n",
-       "      7.0\n",
-       "      93.0\n",
-       "      0.070000\n",
-       "      0.090000\n",
+       "      0.730769\n",
+       "      0.900000\n",
+       "      0.642857\n",
+       "      0.956169\n",
+       "      14.0\n",
+       "      88.0\n",
+       "      0.137255\n",
+       "      0.098039\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.858626\n",
-       "      0.789908\n",
-       "      0.699717\n",
-       "      0.610908\n",
-       "      0.756674\n",
-       "      0.650734\n",
-       "      0.918673\n",
-       "      2657.0\n",
-       "      7840.0\n",
-       "      0.253120\n",
-       "      0.217681\n",
+       "      0.861369\n",
+       "      0.797787\n",
+       "      0.709820\n",
+       "      0.621215\n",
+       "      0.756191\n",
+       "      0.668807\n",
+       "      0.921111\n",
+       "      2648.0\n",
+       "      7797.0\n",
+       "      0.253518\n",
+       "      0.224222\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.108459\n",
-       "      0.173711\n",
-       "      0.150000\n",
-       "      0.203082\n",
-       "      0.129032\n",
-       "      0.327731\n",
-       "      0.033962\n",
-       "      2650.0\n",
-       "      7763.0\n",
-       "      0.232521\n",
-       "      0.170504\n",
+       "      0.109198\n",
+       "      0.226383\n",
+       "      0.450000\n",
+       "      0.390761\n",
+       "      0.153165\n",
+       "      0.481307\n",
+       "      0.078428\n",
+       "      2634.0\n",
+       "      7709.0\n",
+       "      0.150966\n",
+       "      0.193453\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.864303\n",
-       "      0.783316\n",
-       "      0.688943\n",
-       "      0.607886\n",
-       "      0.762994\n",
-       "      0.627995\n",
-       "      0.902943\n",
+       "      0.864057\n",
+       "      0.769549\n",
+       "      0.674382\n",
+       "      0.601041\n",
+       "      0.789982\n",
+       "      0.588296\n",
+       "      0.900496\n",
        "      2922.0\n",
        "      9289.0\n",
        "      0.239292\n",
-       "      0.196954\n",
+       "      0.178200\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.882979\n",
-       "      0.905653\n",
-       "      0.744186\n",
-       "      0.698093\n",
-       "      0.615385\n",
-       "      0.941176\n",
-       "      0.952636\n",
-       "      17.0\n",
-       "      77.0\n",
-       "      0.180851\n",
-       "      0.276596\n",
+       "      0.869231\n",
+       "      0.683662\n",
+       "      0.451613\n",
+       "      0.377718\n",
+       "      0.466667\n",
+       "      0.437500\n",
+       "      0.877741\n",
+       "      16.0\n",
+       "      114.0\n",
+       "      0.123077\n",
+       "      0.115385\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.834734\n",
-       "      0.774041\n",
-       "      0.694301\n",
-       "      0.591102\n",
-       "      0.788235\n",
-       "      0.620370\n",
-       "      0.920051\n",
-       "      108.0\n",
-       "      249.0\n",
-       "      0.302521\n",
-       "      0.238095\n",
+       "      0.826558\n",
+       "      0.726356\n",
+       "      0.614458\n",
+       "      0.529854\n",
+       "      0.784615\n",
+       "      0.504950\n",
+       "      0.908711\n",
+       "      101.0\n",
+       "      268.0\n",
+       "      0.273713\n",
+       "      0.176152\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.907137\n",
-       "      0.855902\n",
-       "      0.660377\n",
-       "      0.619369\n",
-       "      0.567568\n",
-       "      0.789474\n",
-       "      0.944594\n",
-       "      133.0\n",
-       "      1030.0\n",
-       "      0.114359\n",
-       "      0.159071\n",
+       "      0.915880\n",
+       "      0.828757\n",
+       "      0.675497\n",
+       "      0.628493\n",
+       "      0.641509\n",
+       "      0.713287\n",
+       "      0.947019\n",
+       "      143.0\n",
+       "      1022.0\n",
+       "      0.122747\n",
+       "      0.136481\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.920000\n",
-       "      0.890937\n",
-       "      0.600000\n",
-       "      0.593193\n",
-       "      0.461538\n",
+       "      0.882353\n",
+       "      0.871753\n",
+       "      0.666667\n",
+       "      0.622062\n",
+       "      0.545455\n",
        "      0.857143\n",
-       "      0.938556\n",
-       "      7.0\n",
-       "      93.0\n",
-       "      0.070000\n",
-       "      0.130000\n",
+       "      0.956169\n",
+       "      14.0\n",
+       "      88.0\n",
+       "      0.137255\n",
+       "      0.215686\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.859865\n",
-       "      0.779789\n",
-       "      0.690511\n",
-       "      0.608630\n",
-       "      0.782920\n",
-       "      0.617614\n",
-       "      0.918673\n",
-       "      2657.0\n",
-       "      7840.0\n",
-       "      0.253120\n",
-       "      0.199676\n",
+       "      0.859359\n",
+       "      0.768508\n",
+       "      0.678063\n",
+       "      0.603743\n",
+       "      0.807833\n",
+       "      0.584215\n",
+       "      0.921111\n",
+       "      2648.0\n",
+       "      7797.0\n",
+       "      0.253518\n",
+       "      0.183341\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.085266\n",
-       "      0.131613\n",
-       "      0.144186\n",
-       "      0.106991\n",
-       "      0.326697\n",
-       "      0.323563\n",
-       "      0.033962\n",
-       "      2650.0\n",
-       "      7763.0\n",
-       "      0.232521\n",
-       "      0.146596\n",
+       "      0.089322\n",
+       "      0.188091\n",
+       "      0.226450\n",
+       "      0.250775\n",
+       "      0.341166\n",
+       "      0.419643\n",
+       "      0.078428\n",
+       "      2634.0\n",
+       "      7709.0\n",
+       "      0.150966\n",
+       "      0.100302\n",
        "    \n",
        "  \n",
        "\n",
@@ -1164,71 +1164,71 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.865941           0.791664  0.698582  0.615966   \n",
-       "         0                   0.872340           0.738732  0.600000  0.532348   \n",
-       "         1                   0.851541           0.804440  0.736318  0.637235   \n",
-       "         2                   0.927773           0.782429  0.652893  0.616809   \n",
-       "         3                   0.960000           0.912442  0.750000  0.735431   \n",
-       "         4                   0.858626           0.789908  0.699717  0.610908   \n",
-       "         Maximum difference  0.108459           0.173711  0.150000  0.203082   \n",
-       "updated  Overall             0.864303           0.783316  0.688943  0.607886   \n",
-       "         0                   0.882979           0.905653  0.744186  0.698093   \n",
-       "         1                   0.834734           0.774041  0.694301  0.591102   \n",
-       "         2                   0.907137           0.855902  0.660377  0.619369   \n",
-       "         3                   0.920000           0.890937  0.600000  0.593193   \n",
-       "         4                   0.859865           0.779789  0.690511  0.608630   \n",
-       "         Maximum difference  0.085266           0.131613  0.144186  0.106991   \n",
+       "original Overall             0.867988           0.795825  0.704437  0.622647   \n",
+       "         0                   0.892308           0.589364  0.300000  0.340009   \n",
+       "         1                   0.831978           0.754766  0.655556  0.553843   \n",
+       "         2                   0.929614           0.767418  0.658333  0.635246   \n",
+       "         3                   0.941176           0.815747  0.750000  0.730769   \n",
+       "         4                   0.861369           0.797787  0.709820  0.621215   \n",
+       "         Maximum difference  0.109198           0.226383  0.450000  0.390761   \n",
+       "updated  Overall             0.864057           0.769549  0.674382  0.601041   \n",
+       "         0                   0.869231           0.683662  0.451613  0.377718   \n",
+       "         1                   0.826558           0.726356  0.614458  0.529854   \n",
+       "         2                   0.915880           0.828757  0.675497  0.628493   \n",
+       "         3                   0.882353           0.871753  0.666667  0.622062   \n",
+       "         4                   0.859359           0.768508  0.678063  0.603743   \n",
+       "         Maximum difference  0.089322           0.188091  0.226450  0.250775   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.756078  0.649213  0.922685          2922.0   \n",
-       "         0                    0.692308  0.529412  0.952636            17.0   \n",
-       "         1                    0.795699  0.685185  0.920051           108.0   \n",
-       "         2                    0.724771  0.593985  0.944594           133.0   \n",
-       "         3                    0.666667  0.857143  0.938556             7.0   \n",
-       "         4                    0.756674  0.650734  0.918673          2657.0   \n",
-       "         Maximum difference   0.129032  0.327731  0.033962          2650.0   \n",
-       "updated  Overall              0.762994  0.627995  0.902943          2922.0   \n",
-       "         0                    0.615385  0.941176  0.952636            17.0   \n",
-       "         1                    0.788235  0.620370  0.920051           108.0   \n",
-       "         2                    0.567568  0.789474  0.944594           133.0   \n",
-       "         3                    0.461538  0.857143  0.938556             7.0   \n",
-       "         4                    0.782920  0.617614  0.918673          2657.0   \n",
-       "         Maximum difference   0.326697  0.323563  0.033962          2650.0   \n",
+       "original Overall              0.758689  0.657426  0.923695          2922.0   \n",
+       "         0                    0.750000  0.187500  0.877741            16.0   \n",
+       "         1                    0.746835  0.584158  0.908711           101.0   \n",
+       "         2                    0.814433  0.552448  0.947019           143.0   \n",
+       "         3                    0.900000  0.642857  0.956169            14.0   \n",
+       "         4                    0.756191  0.668807  0.921111          2648.0   \n",
+       "         Maximum difference   0.153165  0.481307  0.078428          2634.0   \n",
+       "updated  Overall              0.789982  0.588296  0.900496          2922.0   \n",
+       "         0                    0.466667  0.437500  0.877741            16.0   \n",
+       "         1                    0.784615  0.504950  0.908711           101.0   \n",
+       "         2                    0.641509  0.713287  0.947019           143.0   \n",
+       "         3                    0.545455  0.857143  0.956169            14.0   \n",
+       "         4                    0.807833  0.584215  0.921111          2648.0   \n",
+       "         Maximum difference   0.341166  0.419643  0.078428          2634.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
        "original Overall                     9289.0             0.239292   \n",
-       "         0                             77.0             0.180851   \n",
-       "         1                            249.0             0.302521   \n",
-       "         2                           1030.0             0.114359   \n",
-       "         3                             93.0             0.070000   \n",
-       "         4                           7840.0             0.253120   \n",
-       "         Maximum difference          7763.0             0.232521   \n",
+       "         0                            114.0             0.123077   \n",
+       "         1                            268.0             0.273713   \n",
+       "         2                           1022.0             0.122747   \n",
+       "         3                             88.0             0.137255   \n",
+       "         4                           7797.0             0.253518   \n",
+       "         Maximum difference          7709.0             0.150966   \n",
        "updated  Overall                     9289.0             0.239292   \n",
-       "         0                             77.0             0.180851   \n",
-       "         1                            249.0             0.302521   \n",
-       "         2                           1030.0             0.114359   \n",
-       "         3                             93.0             0.070000   \n",
-       "         4                           7840.0             0.253120   \n",
-       "         Maximum difference          7763.0             0.232521   \n",
+       "         0                            114.0             0.123077   \n",
+       "         1                            268.0             0.273713   \n",
+       "         2                           1022.0             0.122747   \n",
+       "         3                             88.0             0.137255   \n",
+       "         4                           7797.0             0.253518   \n",
+       "         Maximum difference          7709.0             0.150966   \n",
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.205470  \n",
-       "         0                                   0.138298  \n",
-       "         1                                   0.260504  \n",
-       "         2                                   0.093723  \n",
-       "         3                                   0.090000  \n",
-       "         4                                   0.217681  \n",
-       "         Maximum difference                  0.170504  \n",
-       "updated  Overall                             0.196954  \n",
-       "         0                                   0.276596  \n",
-       "         1                                   0.238095  \n",
-       "         2                                   0.159071  \n",
-       "         3                                   0.130000  \n",
-       "         4                                   0.199676  \n",
-       "         Maximum difference                  0.146596  "
+       "original Overall                             0.207354  \n",
+       "         0                                   0.030769  \n",
+       "         1                                   0.214092  \n",
+       "         2                                   0.083262  \n",
+       "         3                                   0.098039  \n",
+       "         4                                   0.224222  \n",
+       "         Maximum difference                  0.193453  \n",
+       "updated  Overall                             0.178200  \n",
+       "         0                                   0.115385  \n",
+       "         1                                   0.176152  \n",
+       "         2                                   0.136481  \n",
+       "         3                                   0.215686  \n",
+       "         4                                   0.183341  \n",
+       "         Maximum difference                  0.100302  "
       ]
      },
      "execution_count": 14,
@@ -1247,10 +1247,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:18.787757Z",
-     "iopub.status.busy": "2024-06-17T14:26:18.787657Z",
-     "iopub.status.idle": "2024-06-17T14:26:18.831031Z",
-     "shell.execute_reply": "2024-06-17T14:26:18.830764Z"
+     "iopub.execute_input": "2024-06-17T19:21:21.346173Z",
+     "iopub.status.busy": "2024-06-17T19:21:21.345994Z",
+     "iopub.status.idle": "2024-06-17T19:21:21.396723Z",
+     "shell.execute_reply": "2024-06-17T19:21:21.396269Z"
     }
    },
    "outputs": [
@@ -1308,200 +1308,200 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.867649\n",
-       "      0.793729\n",
-       "      0.702175\n",
-       "      0.620785\n",
-       "      0.760783\n",
-       "      0.651951\n",
-       "      0.924542\n",
+       "      0.866749\n",
+       "      0.790909\n",
+       "      0.698648\n",
+       "      0.617308\n",
+       "      0.761405\n",
+       "      0.645448\n",
+       "      0.920445\n",
        "      2922.0\n",
        "      9288.0\n",
        "      0.239312\n",
-       "      0.205078\n",
+       "      0.202867\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.934426\n",
-       "      0.763975\n",
-       "      0.500000\n",
-       "      0.469711\n",
-       "      0.444444\n",
-       "      0.571429\n",
-       "      0.893168\n",
-       "      7.0\n",
-       "      115.0\n",
-       "      0.057377\n",
-       "      0.073770\n",
+       "      0.883929\n",
+       "      0.658163\n",
+       "      0.434783\n",
+       "      0.384833\n",
+       "      0.555556\n",
+       "      0.357143\n",
+       "      0.948251\n",
+       "      14.0\n",
+       "      98.0\n",
+       "      0.125000\n",
+       "      0.080357\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.876033\n",
-       "      0.794547\n",
-       "      0.705882\n",
-       "      0.633181\n",
-       "      0.782609\n",
-       "      0.642857\n",
-       "      0.919013\n",
-       "      84.0\n",
-       "      279.0\n",
-       "      0.231405\n",
-       "      0.190083\n",
+       "      0.836412\n",
+       "      0.756772\n",
+       "      0.651685\n",
+       "      0.551016\n",
+       "      0.725000\n",
+       "      0.591837\n",
+       "      0.893093\n",
+       "      98.0\n",
+       "      281.0\n",
+       "      0.258575\n",
+       "      0.211082\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.929371\n",
-       "      0.795517\n",
-       "      0.698529\n",
-       "      0.667782\n",
-       "      0.811966\n",
-       "      0.612903\n",
-       "      0.958167\n",
-       "      155.0\n",
-       "      1006.0\n",
-       "      0.133506\n",
-       "      0.100775\n",
+       "      0.931217\n",
+       "      0.751402\n",
+       "      0.628571\n",
+       "      0.608799\n",
+       "      0.795181\n",
+       "      0.519685\n",
+       "      0.953139\n",
+       "      127.0\n",
+       "      1007.0\n",
+       "      0.111993\n",
+       "      0.073192\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.955752\n",
-       "      0.791667\n",
-       "      0.736842\n",
-       "      0.745532\n",
+       "      0.937500\n",
+       "      0.730769\n",
+       "      0.631579\n",
+       "      0.656551\n",
        "      1.000000\n",
-       "      0.583333\n",
-       "      0.931518\n",
-       "      12.0\n",
-       "      101.0\n",
-       "      0.106195\n",
-       "      0.061947\n",
+       "      0.461538\n",
+       "      0.963481\n",
+       "      13.0\n",
+       "      99.0\n",
+       "      0.116071\n",
+       "      0.053571\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.858769\n",
-       "      0.791750\n",
-       "      0.702779\n",
-       "      0.613605\n",
-       "      0.758036\n",
-       "      0.655030\n",
-       "      0.919732\n",
-       "      2664.0\n",
-       "      7787.0\n",
-       "      0.254904\n",
-       "      0.220266\n",
+       "      0.859926\n",
+       "      0.792788\n",
+       "      0.704770\n",
+       "      0.616571\n",
+       "      0.761635\n",
+       "      0.655805\n",
+       "      0.916189\n",
+       "      2670.0\n",
+       "      7803.0\n",
+       "      0.254941\n",
+       "      0.219517\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.096983\n",
-       "      0.031542\n",
-       "      0.236842\n",
-       "      0.275821\n",
-       "      0.555556\n",
-       "      0.083601\n",
-       "      0.064999\n",
+       "      0.101088\n",
+       "      0.134625\n",
+       "      0.269987\n",
+       "      0.271718\n",
+       "      0.444444\n",
+       "      0.298662\n",
+       "      0.070388\n",
        "      2657.0\n",
-       "      7686.0\n",
-       "      0.197527\n",
-       "      0.158319\n",
+       "      7705.0\n",
+       "      0.146582\n",
+       "      0.165945\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.867076\n",
-       "      0.784791\n",
-       "      0.693021\n",
-       "      0.614942\n",
-       "      0.774630\n",
-       "      0.626968\n",
-       "      0.905492\n",
+       "      0.864537\n",
+       "      0.770221\n",
+       "      0.675559\n",
+       "      0.602537\n",
+       "      0.791360\n",
+       "      0.589322\n",
+       "      0.894418\n",
        "      2922.0\n",
        "      9288.0\n",
        "      0.239312\n",
-       "      0.193694\n",
+       "      0.178215\n",
        "    \n",
        "    \n",
        "      0\n",
-       "      0.852459\n",
-       "      0.720497\n",
-       "      0.307692\n",
-       "      0.282836\n",
-       "      0.210526\n",
-       "      0.571429\n",
-       "      0.893168\n",
-       "      7.0\n",
-       "      115.0\n",
-       "      0.057377\n",
-       "      0.155738\n",
+       "      0.910714\n",
+       "      0.887755\n",
+       "      0.705882\n",
+       "      0.669662\n",
+       "      0.600000\n",
+       "      0.857143\n",
+       "      0.948251\n",
+       "      14.0\n",
+       "      98.0\n",
+       "      0.125000\n",
+       "      0.178571\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.876033\n",
-       "      0.782066\n",
-       "      0.693878\n",
-       "      0.628191\n",
-       "      0.809524\n",
-       "      0.607143\n",
-       "      0.919013\n",
-       "      84.0\n",
-       "      279.0\n",
-       "      0.231405\n",
-       "      0.173554\n",
+       "      0.828496\n",
+       "      0.724853\n",
+       "      0.606061\n",
+       "      0.516155\n",
+       "      0.746269\n",
+       "      0.510204\n",
+       "      0.893093\n",
+       "      98.0\n",
+       "      281.0\n",
+       "      0.258575\n",
+       "      0.176781\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.925926\n",
-       "      0.883579\n",
-       "      0.748538\n",
-       "      0.709822\n",
-       "      0.684492\n",
-       "      0.825806\n",
-       "      0.958167\n",
-       "      155.0\n",
-       "      1006.0\n",
-       "      0.133506\n",
-       "      0.161068\n",
+       "      0.921517\n",
+       "      0.845714\n",
+       "      0.681004\n",
+       "      0.640009\n",
+       "      0.625000\n",
+       "      0.748031\n",
+       "      0.953139\n",
+       "      127.0\n",
+       "      1007.0\n",
+       "      0.111993\n",
+       "      0.134039\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.902655\n",
-       "      0.872112\n",
-       "      0.645161\n",
-       "      0.613076\n",
-       "      0.526316\n",
-       "      0.833333\n",
-       "      0.931518\n",
-       "      12.0\n",
-       "      101.0\n",
-       "      0.106195\n",
-       "      0.168142\n",
+       "      0.901786\n",
+       "      0.877622\n",
+       "      0.666667\n",
+       "      0.631638\n",
+       "      0.550000\n",
+       "      0.846154\n",
+       "      0.963481\n",
+       "      13.0\n",
+       "      99.0\n",
+       "      0.116071\n",
+       "      0.178571\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.860013\n",
-       "      0.779496\n",
-       "      0.691415\n",
-       "      0.610483\n",
-       "      0.789119\n",
-       "      0.615240\n",
-       "      0.919732\n",
-       "      2664.0\n",
-       "      7787.0\n",
-       "      0.254904\n",
-       "      0.198737\n",
+       "      0.858780\n",
+       "      0.767751\n",
+       "      0.677567\n",
+       "      0.603533\n",
+       "      0.810642\n",
+       "      0.582022\n",
+       "      0.916189\n",
+       "      2670.0\n",
+       "      7803.0\n",
+       "      0.254941\n",
+       "      0.183042\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.073467\n",
-       "      0.163082\n",
-       "      0.440846\n",
-       "      0.426986\n",
-       "      0.598997\n",
-       "      0.261905\n",
-       "      0.064999\n",
+       "      0.093021\n",
+       "      0.162902\n",
+       "      0.099822\n",
+       "      0.153507\n",
+       "      0.260642\n",
+       "      0.346939\n",
+       "      0.070388\n",
        "      2657.0\n",
-       "      7686.0\n",
-       "      0.197527\n",
-       "      0.042999\n",
+       "      7705.0\n",
+       "      0.146582\n",
+       "      0.049003\n",
        "    \n",
        "  \n",
        "\n",
@@ -1510,71 +1510,71 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.867649           0.793729  0.702175  0.620785   \n",
-       "         0                   0.934426           0.763975  0.500000  0.469711   \n",
-       "         1                   0.876033           0.794547  0.705882  0.633181   \n",
-       "         2                   0.929371           0.795517  0.698529  0.667782   \n",
-       "         3                   0.955752           0.791667  0.736842  0.745532   \n",
-       "         4                   0.858769           0.791750  0.702779  0.613605   \n",
-       "         Maximum difference  0.096983           0.031542  0.236842  0.275821   \n",
-       "updated  Overall             0.867076           0.784791  0.693021  0.614942   \n",
-       "         0                   0.852459           0.720497  0.307692  0.282836   \n",
-       "         1                   0.876033           0.782066  0.693878  0.628191   \n",
-       "         2                   0.925926           0.883579  0.748538  0.709822   \n",
-       "         3                   0.902655           0.872112  0.645161  0.613076   \n",
-       "         4                   0.860013           0.779496  0.691415  0.610483   \n",
-       "         Maximum difference  0.073467           0.163082  0.440846  0.426986   \n",
+       "original Overall             0.866749           0.790909  0.698648  0.617308   \n",
+       "         0                   0.883929           0.658163  0.434783  0.384833   \n",
+       "         1                   0.836412           0.756772  0.651685  0.551016   \n",
+       "         2                   0.931217           0.751402  0.628571  0.608799   \n",
+       "         3                   0.937500           0.730769  0.631579  0.656551   \n",
+       "         4                   0.859926           0.792788  0.704770  0.616571   \n",
+       "         Maximum difference  0.101088           0.134625  0.269987  0.271718   \n",
+       "updated  Overall             0.864537           0.770221  0.675559  0.602537   \n",
+       "         0                   0.910714           0.887755  0.705882  0.669662   \n",
+       "         1                   0.828496           0.724853  0.606061  0.516155   \n",
+       "         2                   0.921517           0.845714  0.681004  0.640009   \n",
+       "         3                   0.901786           0.877622  0.666667  0.631638   \n",
+       "         4                   0.858780           0.767751  0.677567  0.603533   \n",
+       "         Maximum difference  0.093021           0.162902  0.099822  0.153507   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.760783  0.651951  0.924542          2922.0   \n",
-       "         0                    0.444444  0.571429  0.893168             7.0   \n",
-       "         1                    0.782609  0.642857  0.919013            84.0   \n",
-       "         2                    0.811966  0.612903  0.958167           155.0   \n",
-       "         3                    1.000000  0.583333  0.931518            12.0   \n",
-       "         4                    0.758036  0.655030  0.919732          2664.0   \n",
-       "         Maximum difference   0.555556  0.083601  0.064999          2657.0   \n",
-       "updated  Overall              0.774630  0.626968  0.905492          2922.0   \n",
-       "         0                    0.210526  0.571429  0.893168             7.0   \n",
-       "         1                    0.809524  0.607143  0.919013            84.0   \n",
-       "         2                    0.684492  0.825806  0.958167           155.0   \n",
-       "         3                    0.526316  0.833333  0.931518            12.0   \n",
-       "         4                    0.789119  0.615240  0.919732          2664.0   \n",
-       "         Maximum difference   0.598997  0.261905  0.064999          2657.0   \n",
+       "original Overall              0.761405  0.645448  0.920445          2922.0   \n",
+       "         0                    0.555556  0.357143  0.948251            14.0   \n",
+       "         1                    0.725000  0.591837  0.893093            98.0   \n",
+       "         2                    0.795181  0.519685  0.953139           127.0   \n",
+       "         3                    1.000000  0.461538  0.963481            13.0   \n",
+       "         4                    0.761635  0.655805  0.916189          2670.0   \n",
+       "         Maximum difference   0.444444  0.298662  0.070388          2657.0   \n",
+       "updated  Overall              0.791360  0.589322  0.894418          2922.0   \n",
+       "         0                    0.600000  0.857143  0.948251            14.0   \n",
+       "         1                    0.746269  0.510204  0.893093            98.0   \n",
+       "         2                    0.625000  0.748031  0.953139           127.0   \n",
+       "         3                    0.550000  0.846154  0.963481            13.0   \n",
+       "         4                    0.810642  0.582022  0.916189          2670.0   \n",
+       "         Maximum difference   0.260642  0.346939  0.070388          2657.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
        "original Overall                     9288.0             0.239312   \n",
-       "         0                            115.0             0.057377   \n",
-       "         1                            279.0             0.231405   \n",
-       "         2                           1006.0             0.133506   \n",
-       "         3                            101.0             0.106195   \n",
-       "         4                           7787.0             0.254904   \n",
-       "         Maximum difference          7686.0             0.197527   \n",
+       "         0                             98.0             0.125000   \n",
+       "         1                            281.0             0.258575   \n",
+       "         2                           1007.0             0.111993   \n",
+       "         3                             99.0             0.116071   \n",
+       "         4                           7803.0             0.254941   \n",
+       "         Maximum difference          7705.0             0.146582   \n",
        "updated  Overall                     9288.0             0.239312   \n",
-       "         0                            115.0             0.057377   \n",
-       "         1                            279.0             0.231405   \n",
-       "         2                           1006.0             0.133506   \n",
-       "         3                            101.0             0.106195   \n",
-       "         4                           7787.0             0.254904   \n",
-       "         Maximum difference          7686.0             0.197527   \n",
+       "         0                             98.0             0.125000   \n",
+       "         1                            281.0             0.258575   \n",
+       "         2                           1007.0             0.111993   \n",
+       "         3                             99.0             0.116071   \n",
+       "         4                           7803.0             0.254941   \n",
+       "         Maximum difference          7705.0             0.146582   \n",
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.205078  \n",
-       "         0                                   0.073770  \n",
-       "         1                                   0.190083  \n",
-       "         2                                   0.100775  \n",
-       "         3                                   0.061947  \n",
-       "         4                                   0.220266  \n",
-       "         Maximum difference                  0.158319  \n",
-       "updated  Overall                             0.193694  \n",
-       "         0                                   0.155738  \n",
-       "         1                                   0.173554  \n",
-       "         2                                   0.161068  \n",
-       "         3                                   0.168142  \n",
-       "         4                                   0.198737  \n",
-       "         Maximum difference                  0.042999  "
+       "original Overall                             0.202867  \n",
+       "         0                                   0.080357  \n",
+       "         1                                   0.211082  \n",
+       "         2                                   0.073192  \n",
+       "         3                                   0.053571  \n",
+       "         4                                   0.219517  \n",
+       "         Maximum difference                  0.165945  \n",
+       "updated  Overall                             0.178215  \n",
+       "         0                                   0.178571  \n",
+       "         1                                   0.176781  \n",
+       "         2                                   0.134039  \n",
+       "         3                                   0.178571  \n",
+       "         4                                   0.183042  \n",
+       "         Maximum difference                  0.049003  "
       ]
      },
      "execution_count": 15,
@@ -1609,10 +1609,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:18.832756Z",
-     "iopub.status.busy": "2024-06-17T14:26:18.832632Z",
-     "iopub.status.idle": "2024-06-17T14:26:20.253241Z",
-     "shell.execute_reply": "2024-06-17T14:26:20.252778Z"
+     "iopub.execute_input": "2024-06-17T19:21:21.398854Z",
+     "iopub.status.busy": "2024-06-17T19:21:21.398702Z",
+     "iopub.status.idle": "2024-06-17T19:21:23.010811Z",
+     "shell.execute_reply": "2024-06-17T19:21:23.010340Z"
     }
    },
    "outputs": [],
@@ -1627,10 +1627,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:20.255114Z",
-     "iopub.status.busy": "2024-06-17T14:26:20.254977Z",
-     "iopub.status.idle": "2024-06-17T14:26:21.062142Z",
-     "shell.execute_reply": "2024-06-17T14:26:21.061840Z"
+     "iopub.execute_input": "2024-06-17T19:21:23.013427Z",
+     "iopub.status.busy": "2024-06-17T19:21:23.013300Z",
+     "iopub.status.idle": "2024-06-17T19:21:24.403737Z",
+     "shell.execute_reply": "2024-06-17T19:21:24.403246Z"
     }
    },
    "outputs": [],
@@ -1645,10 +1645,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:21.063848Z",
-     "iopub.status.busy": "2024-06-17T14:26:21.063752Z",
-     "iopub.status.idle": "2024-06-17T14:26:21.640360Z",
-     "shell.execute_reply": "2024-06-17T14:26:21.639989Z"
+     "iopub.execute_input": "2024-06-17T19:21:24.406237Z",
+     "iopub.status.busy": "2024-06-17T19:21:24.406077Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.115068Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.114579Z"
     }
    },
    "outputs": [],
@@ -1662,10 +1662,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:21.642296Z",
-     "iopub.status.busy": "2024-06-17T14:26:21.642101Z",
-     "iopub.status.idle": "2024-06-17T14:26:21.645661Z",
-     "shell.execute_reply": "2024-06-17T14:26:21.645343Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.117654Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.117485Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.121478Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.121011Z"
     }
    },
    "outputs": [],
@@ -1694,10 +1694,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:21.646961Z",
-     "iopub.status.busy": "2024-06-17T14:26:21.646871Z",
-     "iopub.status.idle": "2024-06-17T14:26:21.649291Z",
-     "shell.execute_reply": "2024-06-17T14:26:21.649001Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.123458Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.123357Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.125997Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.125602Z"
     }
    },
    "outputs": [
@@ -1723,10 +1723,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:21.651064Z",
-     "iopub.status.busy": "2024-06-17T14:26:21.650951Z",
-     "iopub.status.idle": "2024-06-17T14:26:21.911398Z",
-     "shell.execute_reply": "2024-06-17T14:26:21.911089Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.127993Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.127879Z",
+     "iopub.status.idle": "2024-06-17T19:21:25.413549Z",
+     "shell.execute_reply": "2024-06-17T19:21:25.413175Z"
     }
    },
    "outputs": [
@@ -1767,73 +1767,73 @@
        "  \n",
        "    \n",
        "      Demographic Parity\n",
-       "      0.159356\n",
-       "      0.027836\n",
-       "      0.667227\n",
-       "      0.649580\n",
+       "      0.134287\n",
+       "      0.016033\n",
+       "      0.657983\n",
+       "      0.650000\n",
        "    \n",
        "    \n",
        "      Disparate Impact\n",
-       "      0.655049\n",
-       "      0.975417\n",
-       "      0.667227\n",
-       "      0.648319\n",
+       "      0.706830\n",
+       "      0.981370\n",
+       "      0.657983\n",
+       "      0.647479\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Acceptance Rate\n",
-       "      0.261954\n",
-       "      0.036533\n",
-       "      0.667227\n",
-       "      0.660924\n",
+       "      0.197906\n",
+       "      0.008440\n",
+       "      0.657983\n",
+       "      0.655462\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Rejectance Rate\n",
-       "      0.066705\n",
-       "      0.006861\n",
-       "      0.667227\n",
-       "      0.657143\n",
+       "      0.046598\n",
+       "      0.016439\n",
+       "      0.657983\n",
+       "      0.655462\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.030784\n",
-       "      0.040582\n",
-       "      0.667227\n",
-       "      0.657563\n",
+       "      0.015688\n",
+       "      0.021157\n",
+       "      0.657983\n",
+       "      0.654622\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Recall\n",
-       "      0.136659\n",
-       "      0.054300\n",
-       "      0.667227\n",
-       "      0.664286\n",
+       "      0.141640\n",
+       "      0.058647\n",
+       "      0.657983\n",
+       "      0.652101\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Acceptance Rate\n",
-       "      0.057303\n",
-       "      0.048984\n",
-       "      0.667227\n",
-       "      0.660084\n",
+       "      0.087016\n",
+       "      0.076756\n",
+       "      0.657983\n",
+       "      0.660504\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Specificity\n",
-       "      0.120978\n",
-       "      0.048584\n",
-       "      0.667227\n",
-       "      0.656723\n",
+       "      0.091102\n",
+       "      0.053444\n",
+       "      0.657983\n",
+       "      0.658824\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Rejection Rate\n",
-       "      0.057967\n",
-       "      0.046000\n",
-       "      0.667227\n",
+       "      0.052209\n",
+       "      0.035084\n",
+       "      0.657983\n",
        "      0.660924\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.276107\n",
-       "      0.043130\n",
-       "      0.667227\n",
-       "      0.659664\n",
+       "      0.185564\n",
+       "      0.055206\n",
+       "      0.657983\n",
+       "      0.655462\n",
        "    \n",
        "  \n",
        "\n",
@@ -1841,52 +1841,52 @@
       ],
       "text/plain": [
        "                                                    Measure (original)  \\\n",
-       "Demographic Parity                                            0.159356   \n",
-       "Disparate Impact                                              0.655049   \n",
-       "Average Group Difference in Conditional Accepta...            0.261954   \n",
-       "Average Group Difference in Conditional Rejecta...            0.066705   \n",
-       "Average Group Difference in Accuracy                          0.030784   \n",
-       "Average Group Difference in Recall                            0.136659   \n",
-       "Average Group Difference in Acceptance Rate                   0.057303   \n",
-       "Average Group Difference in Specificity                       0.120978   \n",
-       "Average Group Difference in Rejection Rate                    0.057967   \n",
-       "Treatment Equality                                            0.276107   \n",
+       "Demographic Parity                                            0.134287   \n",
+       "Disparate Impact                                              0.706830   \n",
+       "Average Group Difference in Conditional Accepta...            0.197906   \n",
+       "Average Group Difference in Conditional Rejecta...            0.046598   \n",
+       "Average Group Difference in Accuracy                          0.015688   \n",
+       "Average Group Difference in Recall                            0.141640   \n",
+       "Average Group Difference in Acceptance Rate                   0.087016   \n",
+       "Average Group Difference in Specificity                       0.091102   \n",
+       "Average Group Difference in Rejection Rate                    0.052209   \n",
+       "Treatment Equality                                            0.185564   \n",
        "\n",
        "                                                    Measure (updated)  \\\n",
-       "Demographic Parity                                           0.027836   \n",
-       "Disparate Impact                                             0.975417   \n",
-       "Average Group Difference in Conditional Accepta...           0.036533   \n",
-       "Average Group Difference in Conditional Rejecta...           0.006861   \n",
-       "Average Group Difference in Accuracy                         0.040582   \n",
-       "Average Group Difference in Recall                           0.054300   \n",
-       "Average Group Difference in Acceptance Rate                  0.048984   \n",
-       "Average Group Difference in Specificity                      0.048584   \n",
-       "Average Group Difference in Rejection Rate                   0.046000   \n",
-       "Treatment Equality                                           0.043130   \n",
+       "Demographic Parity                                           0.016033   \n",
+       "Disparate Impact                                             0.981370   \n",
+       "Average Group Difference in Conditional Accepta...           0.008440   \n",
+       "Average Group Difference in Conditional Rejecta...           0.016439   \n",
+       "Average Group Difference in Accuracy                         0.021157   \n",
+       "Average Group Difference in Recall                           0.058647   \n",
+       "Average Group Difference in Acceptance Rate                  0.076756   \n",
+       "Average Group Difference in Specificity                      0.053444   \n",
+       "Average Group Difference in Rejection Rate                   0.035084   \n",
+       "Treatment Equality                                           0.055206   \n",
        "\n",
        "                                                    Accuracy (original)  \\\n",
-       "Demographic Parity                                             0.667227   \n",
-       "Disparate Impact                                               0.667227   \n",
-       "Average Group Difference in Conditional Accepta...             0.667227   \n",
-       "Average Group Difference in Conditional Rejecta...             0.667227   \n",
-       "Average Group Difference in Accuracy                           0.667227   \n",
-       "Average Group Difference in Recall                             0.667227   \n",
-       "Average Group Difference in Acceptance Rate                    0.667227   \n",
-       "Average Group Difference in Specificity                        0.667227   \n",
-       "Average Group Difference in Rejection Rate                     0.667227   \n",
-       "Treatment Equality                                             0.667227   \n",
+       "Demographic Parity                                             0.657983   \n",
+       "Disparate Impact                                               0.657983   \n",
+       "Average Group Difference in Conditional Accepta...             0.657983   \n",
+       "Average Group Difference in Conditional Rejecta...             0.657983   \n",
+       "Average Group Difference in Accuracy                           0.657983   \n",
+       "Average Group Difference in Recall                             0.657983   \n",
+       "Average Group Difference in Acceptance Rate                    0.657983   \n",
+       "Average Group Difference in Specificity                        0.657983   \n",
+       "Average Group Difference in Rejection Rate                     0.657983   \n",
+       "Treatment Equality                                             0.657983   \n",
        "\n",
        "                                                    Accuracy (updated)  \n",
-       "Demographic Parity                                            0.649580  \n",
-       "Disparate Impact                                              0.648319  \n",
-       "Average Group Difference in Conditional Accepta...            0.660924  \n",
-       "Average Group Difference in Conditional Rejecta...            0.657143  \n",
-       "Average Group Difference in Accuracy                          0.657563  \n",
-       "Average Group Difference in Recall                            0.664286  \n",
-       "Average Group Difference in Acceptance Rate                   0.660084  \n",
-       "Average Group Difference in Specificity                       0.656723  \n",
+       "Demographic Parity                                            0.650000  \n",
+       "Disparate Impact                                              0.647479  \n",
+       "Average Group Difference in Conditional Accepta...            0.655462  \n",
+       "Average Group Difference in Conditional Rejecta...            0.655462  \n",
+       "Average Group Difference in Accuracy                          0.654622  \n",
+       "Average Group Difference in Recall                            0.652101  \n",
+       "Average Group Difference in Acceptance Rate                   0.660504  \n",
+       "Average Group Difference in Specificity                       0.658824  \n",
        "Average Group Difference in Rejection Rate                    0.660924  \n",
-       "Treatment Equality                                            0.659664  "
+       "Treatment Equality                                            0.655462  "
       ]
      },
      "execution_count": 21,
@@ -1911,10 +1911,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:21.912932Z",
-     "iopub.status.busy": "2024-06-17T14:26:21.912794Z",
-     "iopub.status.idle": "2024-06-17T14:26:25.138444Z",
-     "shell.execute_reply": "2024-06-17T14:26:25.138104Z"
+     "iopub.execute_input": "2024-06-17T19:21:25.415329Z",
+     "iopub.status.busy": "2024-06-17T19:21:25.415128Z",
+     "iopub.status.idle": "2024-06-17T19:21:29.921083Z",
+     "shell.execute_reply": "2024-06-17T19:21:29.920745Z"
     }
    },
    "outputs": [
@@ -1948,73 +1948,73 @@
        "  \n",
        "    \n",
        "      Demographic Parity\n",
-       "      0.128301\n",
-       "      0.097556\n",
-       "      0.648319\n",
-       "      0.597479\n",
+       "      0.153068\n",
+       "      0.102293\n",
+       "      0.663866\n",
+       "      0.621429\n",
        "    \n",
        "    \n",
        "      Disparate Impact\n",
-       "      0.719860\n",
-       "      0.740289\n",
-       "      0.648319\n",
-       "      0.587815\n",
+       "      0.697450\n",
+       "      0.686713\n",
+       "      0.663866\n",
+       "      0.611765\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Acceptance Rate\n",
-       "      0.188722\n",
-       "      0.065641\n",
-       "      0.648319\n",
-       "      0.625210\n",
+       "      0.266809\n",
+       "      0.155159\n",
+       "      0.663866\n",
+       "      0.647059\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Rejectance Rate\n",
-       "      0.041879\n",
-       "      0.022910\n",
-       "      0.648319\n",
-       "      0.639496\n",
+       "      0.098374\n",
+       "      0.038179\n",
+       "      0.663866\n",
+       "      0.661765\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Accuracy\n",
-       "      0.041810\n",
-       "      0.044545\n",
-       "      0.648319\n",
-       "      0.647059\n",
+       "      0.005545\n",
+       "      0.019626\n",
+       "      0.663866\n",
+       "      0.661345\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Recall\n",
-       "      0.105007\n",
-       "      0.078278\n",
-       "      0.648319\n",
-       "      0.602521\n",
+       "      0.157070\n",
+       "      0.118976\n",
+       "      0.663866\n",
+       "      0.628571\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Acceptance Rate\n",
-       "      0.038439\n",
-       "      0.050376\n",
-       "      0.648319\n",
-       "      0.665126\n",
+       "      0.078017\n",
+       "      0.084274\n",
+       "      0.663866\n",
+       "      0.657983\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Specificity\n",
-       "      0.106902\n",
-       "      0.090412\n",
-       "      0.648319\n",
-       "      0.642017\n",
+       "      0.107762\n",
+       "      0.089286\n",
+       "      0.663866\n",
+       "      0.649160\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Rejection Rate\n",
-       "      0.080178\n",
-       "      0.086052\n",
-       "      0.648319\n",
-       "      0.646218\n",
+       "      0.043247\n",
+       "      0.041363\n",
+       "      0.663866\n",
+       "      0.660504\n",
        "    \n",
        "    \n",
        "      Treatment Equality\n",
-       "      0.174760\n",
-       "      0.063491\n",
-       "      0.648319\n",
-       "      0.631513\n",
+       "      0.318437\n",
+       "      0.143180\n",
+       "      0.663866\n",
+       "      0.646639\n",
        "    \n",
        "  \n",
        "\n",
@@ -2022,52 +2022,52 @@
       ],
       "text/plain": [
        "                                                    Measure (original)  \\\n",
-       "Demographic Parity                                            0.128301   \n",
-       "Disparate Impact                                              0.719860   \n",
-       "Average Group Difference in Conditional Accepta...            0.188722   \n",
-       "Average Group Difference in Conditional Rejecta...            0.041879   \n",
-       "Average Group Difference in Accuracy                          0.041810   \n",
-       "Average Group Difference in Recall                            0.105007   \n",
-       "Average Group Difference in Acceptance Rate                   0.038439   \n",
-       "Average Group Difference in Specificity                       0.106902   \n",
-       "Average Group Difference in Rejection Rate                    0.080178   \n",
-       "Treatment Equality                                            0.174760   \n",
+       "Demographic Parity                                            0.153068   \n",
+       "Disparate Impact                                              0.697450   \n",
+       "Average Group Difference in Conditional Accepta...            0.266809   \n",
+       "Average Group Difference in Conditional Rejecta...            0.098374   \n",
+       "Average Group Difference in Accuracy                          0.005545   \n",
+       "Average Group Difference in Recall                            0.157070   \n",
+       "Average Group Difference in Acceptance Rate                   0.078017   \n",
+       "Average Group Difference in Specificity                       0.107762   \n",
+       "Average Group Difference in Rejection Rate                    0.043247   \n",
+       "Treatment Equality                                            0.318437   \n",
        "\n",
        "                                                    Measure (updated)  \\\n",
-       "Demographic Parity                                           0.097556   \n",
-       "Disparate Impact                                             0.740289   \n",
-       "Average Group Difference in Conditional Accepta...           0.065641   \n",
-       "Average Group Difference in Conditional Rejecta...           0.022910   \n",
-       "Average Group Difference in Accuracy                         0.044545   \n",
-       "Average Group Difference in Recall                           0.078278   \n",
-       "Average Group Difference in Acceptance Rate                  0.050376   \n",
-       "Average Group Difference in Specificity                      0.090412   \n",
-       "Average Group Difference in Rejection Rate                   0.086052   \n",
-       "Treatment Equality                                           0.063491   \n",
+       "Demographic Parity                                           0.102293   \n",
+       "Disparate Impact                                             0.686713   \n",
+       "Average Group Difference in Conditional Accepta...           0.155159   \n",
+       "Average Group Difference in Conditional Rejecta...           0.038179   \n",
+       "Average Group Difference in Accuracy                         0.019626   \n",
+       "Average Group Difference in Recall                           0.118976   \n",
+       "Average Group Difference in Acceptance Rate                  0.084274   \n",
+       "Average Group Difference in Specificity                      0.089286   \n",
+       "Average Group Difference in Rejection Rate                   0.041363   \n",
+       "Treatment Equality                                           0.143180   \n",
        "\n",
        "                                                    Accuracy (original)  \\\n",
-       "Demographic Parity                                             0.648319   \n",
-       "Disparate Impact                                               0.648319   \n",
-       "Average Group Difference in Conditional Accepta...             0.648319   \n",
-       "Average Group Difference in Conditional Rejecta...             0.648319   \n",
-       "Average Group Difference in Accuracy                           0.648319   \n",
-       "Average Group Difference in Recall                             0.648319   \n",
-       "Average Group Difference in Acceptance Rate                    0.648319   \n",
-       "Average Group Difference in Specificity                        0.648319   \n",
-       "Average Group Difference in Rejection Rate                     0.648319   \n",
-       "Treatment Equality                                             0.648319   \n",
+       "Demographic Parity                                             0.663866   \n",
+       "Disparate Impact                                               0.663866   \n",
+       "Average Group Difference in Conditional Accepta...             0.663866   \n",
+       "Average Group Difference in Conditional Rejecta...             0.663866   \n",
+       "Average Group Difference in Accuracy                           0.663866   \n",
+       "Average Group Difference in Recall                             0.663866   \n",
+       "Average Group Difference in Acceptance Rate                    0.663866   \n",
+       "Average Group Difference in Specificity                        0.663866   \n",
+       "Average Group Difference in Rejection Rate                     0.663866   \n",
+       "Treatment Equality                                             0.663866   \n",
        "\n",
        "                                                    Accuracy (updated)  \n",
-       "Demographic Parity                                            0.597479  \n",
-       "Disparate Impact                                              0.587815  \n",
-       "Average Group Difference in Conditional Accepta...            0.625210  \n",
-       "Average Group Difference in Conditional Rejecta...            0.639496  \n",
-       "Average Group Difference in Accuracy                          0.647059  \n",
-       "Average Group Difference in Recall                            0.602521  \n",
-       "Average Group Difference in Acceptance Rate                   0.665126  \n",
-       "Average Group Difference in Specificity                       0.642017  \n",
-       "Average Group Difference in Rejection Rate                    0.646218  \n",
-       "Treatment Equality                                            0.631513  "
+       "Demographic Parity                                            0.621429  \n",
+       "Disparate Impact                                              0.611765  \n",
+       "Average Group Difference in Conditional Accepta...            0.647059  \n",
+       "Average Group Difference in Conditional Rejecta...            0.661765  \n",
+       "Average Group Difference in Accuracy                          0.661345  \n",
+       "Average Group Difference in Recall                            0.628571  \n",
+       "Average Group Difference in Acceptance Rate                   0.657983  \n",
+       "Average Group Difference in Specificity                       0.649160  \n",
+       "Average Group Difference in Rejection Rate                    0.660504  \n",
+       "Treatment Equality                                            0.646639  "
       ]
      },
      "execution_count": 22,
@@ -2089,10 +2089,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:25.141055Z",
-     "iopub.status.busy": "2024-06-17T14:26:25.140933Z",
-     "iopub.status.idle": "2024-06-17T14:26:25.184791Z",
-     "shell.execute_reply": "2024-06-17T14:26:25.184470Z"
+     "iopub.execute_input": "2024-06-17T19:21:29.922746Z",
+     "iopub.status.busy": "2024-06-17T19:21:29.922627Z",
+     "iopub.status.idle": "2024-06-17T19:21:29.967142Z",
+     "shell.execute_reply": "2024-06-17T19:21:29.966853Z"
     }
    },
    "outputs": [
@@ -2150,144 +2150,144 @@
        "    \n",
        "      original\n",
        "      Overall\n",
-       "      0.648319\n",
-       "      0.637420\n",
-       "      0.574479\n",
-       "      0.282383\n",
-       "      0.631991\n",
-       "      0.526561\n",
-       "      0.697051\n",
+       "      0.663866\n",
+       "      0.655245\n",
+       "      0.603568\n",
+       "      0.315750\n",
+       "      0.644444\n",
+       "      0.567568\n",
+       "      0.705639\n",
        "      1073.0\n",
        "      1307.0\n",
        "      0.450840\n",
-       "      0.375630\n",
+       "      0.397059\n",
        "    \n",
        "    \n",
        "      African-American\n",
-       "      0.626743\n",
-       "      0.627826\n",
-       "      0.619247\n",
-       "      0.256141\n",
-       "      0.651408\n",
-       "      0.590112\n",
-       "      0.683808\n",
+       "      0.661198\n",
+       "      0.661083\n",
+       "      0.668805\n",
+       "      0.322081\n",
+       "      0.672581\n",
+       "      0.665072\n",
+       "      0.704855\n",
        "      627.0\n",
        "      592.0\n",
        "      0.514356\n",
-       "      0.465956\n",
+       "      0.508614\n",
        "    \n",
        "    \n",
        "      Caucasian\n",
-       "      0.662963\n",
-       "      0.622615\n",
-       "      0.502732\n",
-       "      0.265724\n",
-       "      0.600000\n",
-       "      0.432602\n",
-       "      0.677189\n",
+       "      0.665432\n",
+       "      0.624102\n",
+       "      0.502752\n",
+       "      0.270386\n",
+       "      0.606195\n",
+       "      0.429467\n",
+       "      0.683915\n",
        "      319.0\n",
        "      491.0\n",
        "      0.393827\n",
-       "      0.283951\n",
+       "      0.279012\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.689459\n",
-       "      0.637356\n",
-       "      0.511211\n",
-       "      0.296140\n",
-       "      0.593750\n",
-       "      0.448819\n",
-       "      0.724040\n",
+       "      0.669516\n",
+       "      0.618321\n",
+       "      0.486726\n",
+       "      0.252697\n",
+       "      0.555556\n",
+       "      0.433071\n",
+       "      0.703881\n",
        "      127.0\n",
        "      224.0\n",
        "      0.361823\n",
-       "      0.273504\n",
+       "      0.282051\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.062715\n",
-       "      0.014741\n",
-       "      0.116515\n",
-       "      0.039999\n",
-       "      0.057658\n",
-       "      0.157510\n",
-       "      0.046851\n",
+       "      0.008318\n",
+       "      0.042762\n",
+       "      0.182079\n",
+       "      0.069384\n",
+       "      0.117025\n",
+       "      0.235605\n",
+       "      0.020940\n",
        "      500.0\n",
        "      368.0\n",
        "      0.152533\n",
-       "      0.192451\n",
+       "      0.229601\n",
        "    \n",
        "    \n",
        "      updated\n",
        "      Overall\n",
-       "      0.631513\n",
-       "      0.621534\n",
-       "      0.559960\n",
-       "      0.248367\n",
-       "      0.606522\n",
-       "      0.520037\n",
-       "      0.675403\n",
+       "      0.646639\n",
+       "      0.629716\n",
+       "      0.538673\n",
+       "      0.277867\n",
+       "      0.654667\n",
+       "      0.457596\n",
+       "      0.692327\n",
        "      1073.0\n",
        "      1307.0\n",
        "      0.450840\n",
-       "      0.386555\n",
+       "      0.315126\n",
        "    \n",
        "    \n",
        "      African-American\n",
-       "      0.620180\n",
-       "      0.621447\n",
-       "      0.609941\n",
-       "      0.243598\n",
-       "      0.646429\n",
-       "      0.577352\n",
-       "      0.672565\n",
+       "      0.639869\n",
+       "      0.642660\n",
+       "      0.609083\n",
+       "      0.290279\n",
+       "      0.689516\n",
+       "      0.545455\n",
+       "      0.690321\n",
        "      627.0\n",
        "      592.0\n",
        "      0.514356\n",
-       "      0.459393\n",
+       "      0.406891\n",
        "    \n",
        "    \n",
        "      Caucasian\n",
-       "      0.630864\n",
-       "      0.596138\n",
-       "      0.480000\n",
-       "      0.202063\n",
-       "      0.539062\n",
-       "      0.432602\n",
-       "      0.636571\n",
+       "      0.644444\n",
+       "      0.590868\n",
+       "      0.428571\n",
+       "      0.211519\n",
+       "      0.583784\n",
+       "      0.338558\n",
+       "      0.667708\n",
        "      319.0\n",
        "      491.0\n",
        "      0.393827\n",
-       "      0.316049\n",
+       "      0.228395\n",
        "    \n",
        "    \n",
        "      Other\n",
-       "      0.672365\n",
-       "      0.625668\n",
-       "      0.502165\n",
-       "      0.264494\n",
-       "      0.557692\n",
-       "      0.456693\n",
-       "      0.708257\n",
+       "      0.675214\n",
+       "      0.598917\n",
+       "      0.418367\n",
+       "      0.239210\n",
+       "      0.594203\n",
+       "      0.322835\n",
+       "      0.705884\n",
        "      127.0\n",
        "      224.0\n",
        "      0.361823\n",
-       "      0.296296\n",
+       "      0.196581\n",
        "    \n",
        "    \n",
        "      Maximum difference\n",
-       "      0.052184\n",
-       "      0.029530\n",
-       "      0.129941\n",
-       "      0.062431\n",
-       "      0.107366\n",
-       "      0.144751\n",
-       "      0.071686\n",
+       "      0.035345\n",
+       "      0.051792\n",
+       "      0.190715\n",
+       "      0.078760\n",
+       "      0.105732\n",
+       "      0.222620\n",
+       "      0.038176\n",
        "      500.0\n",
        "      368.0\n",
        "      0.152533\n",
-       "      0.163097\n",
+       "      0.210310\n",
        "    \n",
        "  \n",
        "\n",
@@ -2296,29 +2296,29 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.648319           0.637420  0.574479  0.282383   \n",
-       "         African-American    0.626743           0.627826  0.619247  0.256141   \n",
-       "         Caucasian           0.662963           0.622615  0.502732  0.265724   \n",
-       "         Other               0.689459           0.637356  0.511211  0.296140   \n",
-       "         Maximum difference  0.062715           0.014741  0.116515  0.039999   \n",
-       "updated  Overall             0.631513           0.621534  0.559960  0.248367   \n",
-       "         African-American    0.620180           0.621447  0.609941  0.243598   \n",
-       "         Caucasian           0.630864           0.596138  0.480000  0.202063   \n",
-       "         Other               0.672365           0.625668  0.502165  0.264494   \n",
-       "         Maximum difference  0.052184           0.029530  0.129941  0.062431   \n",
+       "original Overall             0.663866           0.655245  0.603568  0.315750   \n",
+       "         African-American    0.661198           0.661083  0.668805  0.322081   \n",
+       "         Caucasian           0.665432           0.624102  0.502752  0.270386   \n",
+       "         Other               0.669516           0.618321  0.486726  0.252697   \n",
+       "         Maximum difference  0.008318           0.042762  0.182079  0.069384   \n",
+       "updated  Overall             0.646639           0.629716  0.538673  0.277867   \n",
+       "         African-American    0.639869           0.642660  0.609083  0.290279   \n",
+       "         Caucasian           0.644444           0.590868  0.428571  0.211519   \n",
+       "         Other               0.675214           0.598917  0.418367  0.239210   \n",
+       "         Maximum difference  0.035345           0.051792  0.190715  0.078760   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.631991  0.526561  0.697051          1073.0   \n",
-       "         African-American     0.651408  0.590112  0.683808           627.0   \n",
-       "         Caucasian            0.600000  0.432602  0.677189           319.0   \n",
-       "         Other                0.593750  0.448819  0.724040           127.0   \n",
-       "         Maximum difference   0.057658  0.157510  0.046851           500.0   \n",
-       "updated  Overall              0.606522  0.520037  0.675403          1073.0   \n",
-       "         African-American     0.646429  0.577352  0.672565           627.0   \n",
-       "         Caucasian            0.539062  0.432602  0.636571           319.0   \n",
-       "         Other                0.557692  0.456693  0.708257           127.0   \n",
-       "         Maximum difference   0.107366  0.144751  0.071686           500.0   \n",
+       "original Overall              0.644444  0.567568  0.705639          1073.0   \n",
+       "         African-American     0.672581  0.665072  0.704855           627.0   \n",
+       "         Caucasian            0.606195  0.429467  0.683915           319.0   \n",
+       "         Other                0.555556  0.433071  0.703881           127.0   \n",
+       "         Maximum difference   0.117025  0.235605  0.020940           500.0   \n",
+       "updated  Overall              0.654667  0.457596  0.692327          1073.0   \n",
+       "         African-American     0.689516  0.545455  0.690321           627.0   \n",
+       "         Caucasian            0.583784  0.338558  0.667708           319.0   \n",
+       "         Other                0.594203  0.322835  0.705884           127.0   \n",
+       "         Maximum difference   0.105732  0.222620  0.038176           500.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
@@ -2335,16 +2335,16 @@
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.375630  \n",
-       "         African-American                    0.465956  \n",
-       "         Caucasian                           0.283951  \n",
-       "         Other                               0.273504  \n",
-       "         Maximum difference                  0.192451  \n",
-       "updated  Overall                             0.386555  \n",
-       "         African-American                    0.459393  \n",
-       "         Caucasian                           0.316049  \n",
-       "         Other                               0.296296  \n",
-       "         Maximum difference                  0.163097  "
+       "original Overall                             0.397059  \n",
+       "         African-American                    0.508614  \n",
+       "         Caucasian                           0.279012  \n",
+       "         Other                               0.282051  \n",
+       "         Maximum difference                  0.229601  \n",
+       "updated  Overall                             0.315126  \n",
+       "         African-American                    0.406891  \n",
+       "         Caucasian                           0.228395  \n",
+       "         Other                               0.196581  \n",
+       "         Maximum difference                  0.210310  "
       ]
      },
      "execution_count": 23,
@@ -2361,10 +2361,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:25.186419Z",
-     "iopub.status.busy": "2024-06-17T14:26:25.186292Z",
-     "iopub.status.idle": "2024-06-17T14:26:25.605996Z",
-     "shell.execute_reply": "2024-06-17T14:26:25.605599Z"
+     "iopub.execute_input": "2024-06-17T19:21:29.968836Z",
+     "iopub.status.busy": "2024-06-17T19:21:29.968711Z",
+     "iopub.status.idle": "2024-06-17T19:21:30.728647Z",
+     "shell.execute_reply": "2024-06-17T19:21:30.728237Z"
     }
    },
    "outputs": [],
@@ -2379,10 +2379,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:25.608111Z",
-     "iopub.status.busy": "2024-06-17T14:26:25.607998Z",
-     "iopub.status.idle": "2024-06-17T14:26:25.629325Z",
-     "shell.execute_reply": "2024-06-17T14:26:25.628987Z"
+     "iopub.execute_input": "2024-06-17T19:21:30.730457Z",
+     "iopub.status.busy": "2024-06-17T19:21:30.730328Z",
+     "iopub.status.idle": "2024-06-17T19:21:30.750226Z",
+     "shell.execute_reply": "2024-06-17T19:21:30.749941Z"
     }
    },
    "outputs": [
@@ -2414,13 +2414,13 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.747585\n",
-       "      0.699286\n",
+       "      0.736455\n",
+       "      0.681856\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Positive Prediction Rate\n",
-       "      0.129142\n",
-       "      0.019776\n",
+       "      0.136081\n",
+       "      0.019586\n",
        "    \n",
        "  \n",
        "\n",
@@ -2428,8 +2428,8 @@
       ],
       "text/plain": [
        "                                                    original   updated\n",
-       "Accuracy                                            0.747585  0.699286\n",
-       "Average Group Difference in Conditional Positiv...  0.129142  0.019776"
+       "Accuracy                                            0.736455  0.681856\n",
+       "Average Group Difference in Conditional Positiv...  0.136081  0.019586"
       ]
      },
      "execution_count": 25,
@@ -2446,10 +2446,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-17T14:26:25.630921Z",
-     "iopub.status.busy": "2024-06-17T14:26:25.630821Z",
-     "iopub.status.idle": "2024-06-17T14:26:25.643761Z",
-     "shell.execute_reply": "2024-06-17T14:26:25.643487Z"
+     "iopub.execute_input": "2024-06-17T19:21:30.751715Z",
+     "iopub.status.busy": "2024-06-17T19:21:30.751600Z",
+     "iopub.status.idle": "2024-06-17T19:21:30.764769Z",
+     "shell.execute_reply": "2024-06-17T19:21:30.764470Z"
     }
    },
    "outputs": [
@@ -2481,22 +2481,22 @@
        "  \n",
        "    \n",
        "      Accuracy\n",
-       "      0.648319\n",
-       "      0.60084\n",
+       "      0.663866\n",
+       "      0.619328\n",
        "    \n",
        "    \n",
        "      Average Group Difference in Conditional Positive Prediction Rate\n",
-       "      0.130677\n",
-       "      0.08557\n",
+       "      0.151770\n",
+       "      0.111426\n",
        "    \n",
        "  \n",
        "\n",
        "
" ], "text/plain": [ - " original updated\n", - "Accuracy 0.648319 0.60084\n", - "Average Group Difference in Conditional Positiv... 0.130677 0.08557" + " original updated\n", + "Accuracy 0.663866 0.619328\n", + "Average Group Difference in Conditional Positiv... 0.151770 0.111426" ] }, "execution_count": 26, From 03ba45dad3323650c48d78f25ae45b3fe90c0a5a Mon Sep 17 00:00:00 2001 From: Chris Russell Date: Wed, 19 Jun 2024 14:13:56 +0100 Subject: [PATCH 16/16] rename build_x to use CamelCase to indicate constructors --- examples/building_datasets.ipynb | 478 +----------------- examples/compas_autogluon.ipynb | 7 +- examples/conditional_metrics.ipynb | 9 +- examples/high-dim_fairlearn_comparision.ipynb | 11 +- examples/levelling_up.ipynb | 4 +- .../multi_group_fairlearn_comparision.ipynb | 4 +- examples/pytorch_minimal_demo.ipynb | 9 +- ...rt_DeepFairPredictor_computer_vision.ipynb | 10 +- examples/quickstart_autogluon.ipynb | 2 +- examples/quickstart_xgboost.ipynb | 4 +- sklearn.md | 4 +- src/oxonfair/__init__.py | 8 +- src/oxonfair/learners/__init__.py | 8 +- src/oxonfair/learners/fair.py | 10 +- src/oxonfair/utils/dataset_loader.py | 8 +- .../unittests/test_additional_constraints.py | 4 +- tests/unittests/test_scipy.py | 4 +- 17 files changed, 58 insertions(+), 526 deletions(-) diff --git a/examples/building_datasets.ipynb b/examples/building_datasets.ipynb index 060149f..6077c0c 100644 --- a/examples/building_datasets.ipynb +++ b/examples/building_datasets.ipynb @@ -6,12 +6,12 @@ "source": [ "# Building Datasets\n", "\n", - "In most of our examples, we use dataset_loader to avoid boilerplate code when training fair classifiers. \n", + "In most of our examples, we use `dataset_loader` to avoid boilerplate code when training fair classifiers. \n", "This notebook sets out how to create similar code for new datasets.\n", "\n", - "For evaluating and fitting fair classifiers we require access to the group each datapoint is assigned to and the target (i.e. ground-truth) label the classifier is trying to predict. \n", + "For evaluating and fitting fair classifiers we require access to the group each datapoint is assigned to and the target (i.e., ground-truth) label the classifier is trying to predict. \n", "\n", - "For sklearn, classifiers assume that they only recieve the data used to predict, and as the target labels should never be passed with the rest of the data, and the groups should only be passed if the classifier uses them directly (i.e. if we are not using infered attributes).\n", + "For sklearn, classifiers assume that they only receive the data used to predict; that target labels should never be passed with the rest of the data; and that the groups should only be passed if the classifier uses them directly (i.e., if we are not using inferred attributes).\n", "\n", "\n", "\n", @@ -19,13 +19,13 @@ "1. Fair Classifiers using autogluon.\n", " Create a dataframe or tabular dataset containing all data used for classification, target labels, and groups.\n", " Autogluon takes pandas dataframes or their own internal tabular dataset type and only uses the columns the model was trained on to classify the data.\n", - " When using infered attributes you should ensure that neither the classifier predicting groups nor the classifier predicting target labels has access to the groups or target labels at training time. This is taken care for you automatically by using `oxonfair.inferred_attribute_builder`.\n", + " When using inferred attributes you should ensure that neither the classifier predicting groups nor the classifier predicting target labels has access to the groups or target labels at training time. This is taken care for you automatically by using `oxonfair.inferred_attribute_builder`.\n", "2. Fair Classifiers using Sklearn with known attributes.\n", - " Create a dataset by calling `oxonfair.build_data_dict` with two arguments - the target labels `y` and the data `X` used by the classifier. \n", + " Create a dataset by calling `oxonfair.DataDict` with two arguments -- the target labels `y` and the data `X` used by the classifier. \n", "3. Fair Classifiers using Sklearn with inferred attributes. \n", - " Create a dataset by calling `oxonfair.build_data_dict` with three arguments - the target labels `y`, the data `X` used by the classifier, and the groups. \n", + " Create a dataset by calling `oxonfair.DataDict` with three arguments -- the target labels `y`, the data `X` used by the classifier, and the groups. \n", "4. Fair Classifiers using Deep networks.\n", - " Create a classifier by calling `oxonfair.DeepFairPredictor` with three arguments - the target labels, the predictions made by the classifier, and the groups. See [this notebook for examples](quickstart_DeepFairPredictor_computer_vision.ipynb)." + " Create a classifier by calling `oxonfair.DeepFairPredictor` with three arguments -- the target labels, the predictions made by the classifier, and the groups. See [this notebook for examples](quickstart_DeepFairPredictor_computer_vision.ipynb)." ] }, { @@ -757,7 +757,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Autogluon with inferered attributes " + "# Autogluon with inferred attributes " ] }, { @@ -2014,7 +2014,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-06-17T19:19:28.649192Z", @@ -2023,455 +2023,7 @@ "shell.execute_reply": "2024-06-17T19:19:29.659894Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", - " colsample_bylevel=None, colsample_bynode=None,\n", - " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", - " enable_categorical=False, eval_metric=None, feature_types=None,\n", - " gamma=None, grow_policy=None, importance_type=None,\n", - " interaction_constraints=None, learning_rate=None, max_bin=None,\n", - " max_cat_threshold=None, max_cat_to_onehot=None,\n", - " max_delta_step=None, max_depth=None, max_leaves=None,\n", - " min_child_weight=None, missing=nan, monotone_constraints=None,\n", - " multi_strategy=None, n_estimators=None, n_jobs=None,\n", - " num_parallel_tree=None, random_state=None, ...)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import xgboost\n", "import pandas as pd\n", @@ -2491,8 +2043,8 @@ "### We pass dictionaries that represent the entire dataset to get round this.\n", "### They contain 'target' 'data', 'groups' (optional), and 'factor' (optional)\n", " \n", - "training_set = oxonfair.build_data_dict(training_target,training_data)\n", - "testing_set = oxonfair.build_data_dict(testing_target, testing_data) \n", + "training_set = oxonfair.DataDict(training_target,training_data)\n", + "testing_set = oxonfair.DataDict(testing_target, testing_data) \n", "#train base classifier\n", "classifier = xgboost.XGBClassifier()\n", "classifier.fit(y=training_target, X=training_data)\n" @@ -2771,7 +2323,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## SKlearn with infered groups" + "## SKlearn with inferred groups" ] }, { @@ -3238,11 +2790,11 @@ "y_train = training_set['target']\n", "groups_train = training_set['data']['sex_ Female']\n", "X_train = training_set['data'].drop('sex_ Female', axis=1)\n", - "training_set = oxonfair.build_data_dict(y_train, X_train,groups_train)\n", + "training_set = oxonfair.DataDict(y_train, X_train,groups_train)\n", "y_test = testing_set['target']\n", "groups_test = testing_set['data']['sex_ Female']\n", "X_test = testing_set['data'].drop('sex_ Female', axis=1)\n", - "test_set = oxonfair.build_data_dict(y_test, X_test,groups_test)\n", + "test_set = oxonfair.DataDict(y_test, X_test,groups_test)\n", "\n", "#train base classifiers\n", "classifier = xgboost.XGBClassifier()\n", diff --git a/examples/compas_autogluon.ipynb b/examples/compas_autogluon.ipynb index 23b3050..0b72d4a 100644 --- a/examples/compas_autogluon.ipynb +++ b/examples/compas_autogluon.ipynb @@ -1156,7 +1156,8 @@ "source": [ "Now we will show how a family of fairness measures can be individually optimized. First, we consider the measures of Sagemaker Clarify. \n", "\n", - "The following code plots a table showing the change in accuracy and the fairness measure on a held-out test set as we decrease the fairness measure to less than 0.025 (on validation) for all measures except for disparate impact which we raise to above 0.975.\n", + "The following code plots a table showing the change in accuracy and the fairness measure on a held-out test set.\n", + "We decrease each fairness measure to less than 0.025 (on validation) for all measures except for disparate impact which we raise to above 0.975.\n", "We define a helper function for evaluation:" ] }, @@ -1737,7 +1738,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In contrast, even though the base classifiers have similar accuracy, when using inferred attributes (N.B. the base classifier is not directly trained to maximize accuracy, which is why it can have higher accuracy when it doesn't use race), we see a much greater drop in accuracy as fairness is enforced which is consistent with [Lipton et al.](https://arxiv.org/pdf/1711.07076.pdf)\n" + "In contrast, even though the base classifiers have similar accuracy, when using inferred attributes (N.B. the base classifier is not directly trained to maximize accuracy, which is why it can have higher accuracy when it doesn't use race), we see a much greater drop in accuracy as fairness is enforced. This is consistent with [Lipton et al.](https://arxiv.org/pdf/1711.07076.pdf)\n" ] }, { @@ -1881,7 +1882,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.1.-1" } }, "nbformat": 4, diff --git a/examples/conditional_metrics.ipynb b/examples/conditional_metrics.ipynb index 1b26669..2d6be35 100644 --- a/examples/conditional_metrics.ipynb +++ b/examples/conditional_metrics.ipynb @@ -1178,13 +1178,6 @@ "source": [ "all_data.iloc[0]" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1203,7 +1196,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/examples/high-dim_fairlearn_comparision.ipynb b/examples/high-dim_fairlearn_comparision.ipynb index db7e07b..23d48ae 100644 --- a/examples/high-dim_fairlearn_comparision.ipynb +++ b/examples/high-dim_fairlearn_comparision.ipynb @@ -8,7 +8,7 @@ "\n", "We use sex as the protected attribute.\n", "\n", - "The initial dataset is balanced, and to induce unfairness in the downstream classifier, we drop half the datapoints that satisfy sex=1 and target_label=0.\n", + "The initial dataset is balanced, and to induce unfairness in the downstream classifier, we drop half the datapoints that satisfy sex=1 and target_label=0.\n", "\n", "Because the dataset is relatively high-dimensional (dims ~= 100) with around 1,000 training points, xgboost overfits perfectly obtaining zero error on the train set." ] @@ -1125,7 +1125,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To evaluate fairlearn, we write a helper function to evaluate performance and fairness on train or test, and concat the outputs together. " + "To evaluate fairlearn, we write a helper function to evaluate performance and fairness on train or test, and concatenate the outputs together. " ] }, { @@ -1432,13 +1432,6 @@ "out.columns = ['train', 'test']\n", "out" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/levelling_up.ipynb b/examples/levelling_up.ipynb index 19033b6..4f028f7 100644 --- a/examples/levelling_up.ipynb +++ b/examples/levelling_up.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Levelling Up\n", - "This code demonstrates new forms of fairness that 'level-up'. That is they improve measures such as recall rate or selection rates for disadvantaged groups. This is a change from standard measures of fairness that equalize harms across groups (and consequentially 'level down' and decrease rates for some groups, and harms them more than they were harmed by the original classifier).\n", + "This code demonstrates new forms of fairness that 'level-up'. That is they improve measures such as recall rate or selection rates for disadvantaged groups. This is a change from standard measures of fairness that equalize harms across groups (and consequentially 'level down' and decrease rates for some groups, and harms them more than they were harmed by the original classifier).\n", "\n", "More details are in the [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4331652).\n", "\n", @@ -1131,7 +1131,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.1.-1" }, "vscode": { "interpreter": { diff --git a/examples/multi_group_fairlearn_comparision.ipynb b/examples/multi_group_fairlearn_comparision.ipynb index a303a08..ed5d4aa 100644 --- a/examples/multi_group_fairlearn_comparision.ipynb +++ b/examples/multi_group_fairlearn_comparision.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A runtime comparision with FairLearn reductions on multi-group adult data.\n", + "A runtime comparison with FairLearn reductions on multi-group adult data.\n", "\n", "There is relatively little to see here, as both FairLearn and OxonFair naturally support multiple groups. \n", "\n", @@ -541,7 +541,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/examples/pytorch_minimal_demo.ipynb b/examples/pytorch_minimal_demo.ipynb index 8e174ed..81d1838 100644 --- a/examples/pytorch_minimal_demo.ipynb +++ b/examples/pytorch_minimal_demo.ipynb @@ -588,7 +588,7 @@ "outputs": [], "source": [ "# to evaluate how it's working on the test set, we'll create a new dataset holder\n", - "test_network = oxonfair.build_deep_dict(test['target'], test_output, test['groups'])" + "test_network = oxonfair.DeepDataDict(test['target'], test_output, test['groups'])" ] }, { @@ -1151,13 +1151,6 @@ "fpred_thresh.plot_frontier(prefix='Single threshold ', new_plot=False, show_original=False)\n", "fpred.plot_frontier(prefix='Two heads ', new_plot=False)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/quickstart_DeepFairPredictor_computer_vision.ipynb b/examples/quickstart_DeepFairPredictor_computer_vision.ipynb index 8fbc1bf..49d03fe 100644 --- a/examples/quickstart_DeepFairPredictor_computer_vision.ipynb +++ b/examples/quickstart_DeepFairPredictor_computer_vision.ipynb @@ -10,9 +10,9 @@ "\n", "We demonstrate how different notions of fairness and performance can be measured and enforced with our toolkit. \n", "\n", - "We recommend you first consider the quickstart_xgboost.ipynb notebook for an introduction to the toolkit's functionality. \n", + "We recommend you first try [quickstart_xgboost.ipynb](quickstart_xgboost.ipynb) notebook for an introduction to the toolkit's functionality. \n", "\n", - "We first show an example on [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). The protected groups are given by the labels for the attribute `Male` . You can specify which target attribute (e.g., Wearing_Earrings) you want to enforce fairness for. \n", + "We first show an example on [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). The protected groups are given by the labels for the attribute `Male`. You can specify which target attribute (e.g., Wearing_Earrings) you want to enforce fairness for. \n", "You can also specify which fairness and performance metrics to measure.\n", "\n", "At the end of the notebook, we look at an example using medical data from [Fitzpatrick-17k](https://arxiv.org/pdf/2104.09957).\n", @@ -22,9 +22,9 @@ "1. We need the validation and test labels for the protected and target attributes.\n", "2. We also require the model outputs. These will typically be logits for the target attribute and probabilities for the inferred protected attribute. It is also possible to use the true group labels (non-inferred). This data used in this notebook demonstration is fetched from an anonymous repository.\n", "\n", - "We use DeepFairPredictor (reccomended), which is optimized for deep learning classifiers. \n", + "We use DeepFairPredictor (recommended), which is optimized for deep learning classifiers. \n", "\n", - "We also reccomend checking out this [paper](https://arxiv.org/pdf/2203.04913) for theoretically explanations into why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models and this [paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/698c05933e5f7fde98e567a669d2c752-Paper-Conference.pdf) for more details on the two/multi headed approach for a post-processing approach to enforce fairness with validation data when working with deep learning models. \n" + "For more details about the theory, check out this [paper](https://arxiv.org/pdf/2203.04913) for theoretically explanations into why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models and this [paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/698c05933e5f7fde98e567a669d2c752-Paper-Conference.pdf) for more details on the two/multi- headed approach for a post-processing approach to enforce fairness with validation data when working with deep learning models. \n" ] }, { @@ -1270,7 +1270,7 @@ "source": [ "### Fitzpatrick-17k Example\n", "\n", - "Next we demonstrate how our toolkit could be used with medical data. The implementation details will be similar but practitioners may want to think carefully about how they measure and enforce fairness in high stakes domains. We reccomend a harms first approach emphasising metrics such as per group recall or selection rate. \n", + "Next we demonstrate how our toolkit could be used with medical data. The implementation details will be similar, but practitioners may want to think carefully about how they measure and enforce fairness in high stakes domains. We recommend a harms-first approach emphasizing metrics such as per group recall or selection rate. \n", "\n", "Here the target label classifies if a skin condition is malignant or benign. The protected label indicates race. Data is preprocessed following the description and code of [Zong et al.](https://arxiv.org/pdf/2210.01725) " ] diff --git a/examples/quickstart_autogluon.ipynb b/examples/quickstart_autogluon.ipynb index 8f884f9..5248392 100644 --- a/examples/quickstart_autogluon.ipynb +++ b/examples/quickstart_autogluon.ipynb @@ -4112,7 +4112,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.1.-1" }, "vscode": { "interpreter": { diff --git a/examples/quickstart_xgboost.ipynb b/examples/quickstart_xgboost.ipynb index c33f8b9..726dc0d 100644 --- a/examples/quickstart_xgboost.ipynb +++ b/examples/quickstart_xgboost.ipynb @@ -7,7 +7,7 @@ "# FairPredictor XGBoost Examples\n", "This file contains demo code for an extended version of the example in Readme.md (additionally handling more fairness over multiple groups), and enforcing a range of fairness definition on COMPAS.\n", "\n", - "It is a modified version of [quickstart_autogluon.ipynb](quickstart_autogluon.ipynb)\n", + "It is a modified version of [quickstart_autogluon.ipynb](./quickstart_autogluon.ipynb)\n", "\n", "FairPredictor is a postprocessing approach for enforcing fairness, with support for a wide range of performance metrics and fairness criteria, and support for inferred attributes, i.e., it does not require access to protected attributes at test time. \n", "Under the hood, FairPredictor works by adjusting the decision boundary for each group individually. Where groups are not available, it makes use of inferred group membership to adjust decision boundaries.\n", @@ -2525,7 +2525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.1.-1" } }, "nbformat": 4, diff --git a/sklearn.md b/sklearn.md index 4f6214a..e050a35 100644 --- a/sklearn.md +++ b/sklearn.md @@ -40,8 +40,8 @@ Step 1 requires considerably more preamble when using sklearn. ### We pass dictionaries that represent the entire dataset to get round this. ### They contain 'target' 'data', 'groups' (optional), and 'factor' (optional) - val_dict = fair.build_data_dict(val_target,val_data) - test_dict = fair.build_data_dict(test_target, test_data) + val_dict = fair.DataDict(val_target,val_data) + test_dict = fair.DataDict(test_target, test_data) ## Train a classifier diff --git a/src/oxonfair/__init__.py b/src/oxonfair/__init__.py index 978f8b2..c789ea6 100644 --- a/src/oxonfair/__init__.py +++ b/src/oxonfair/__init__.py @@ -1,6 +1,6 @@ -from .learners import (FairPredictor, inferred_attribute_builder, single_threshold, build_data_dict, - DeepFairPredictor, build_deep_dict) +from .learners import (FairPredictor, inferred_attribute_builder, single_threshold, DataDict, + DeepFairPredictor, DeepDataDict) from .utils import performance, group_metrics, conditional_group_metrics, dataset_loader -__all__ = (FairPredictor, inferred_attribute_builder, single_threshold, build_data_dict, - performance, group_metrics, conditional_group_metrics, DeepFairPredictor, build_deep_dict, dataset_loader) +__all__ = (FairPredictor, inferred_attribute_builder, single_threshold, DataDict, + performance, group_metrics, conditional_group_metrics, DeepFairPredictor, DeepDataDict, dataset_loader) diff --git a/src/oxonfair/learners/__init__.py b/src/oxonfair/learners/__init__.py index ed49438..ac29e10 100644 --- a/src/oxonfair/learners/__init__.py +++ b/src/oxonfair/learners/__init__.py @@ -1,5 +1,5 @@ -from .fair import (FairPredictor, inferred_attribute_builder, single_threshold, build_data_dict, - DeepFairPredictor, build_deep_dict) +from .fair import (FairPredictor, inferred_attribute_builder, single_threshold, DataDict, + DeepFairPredictor, DeepDataDict) -__all__ = (FairPredictor, inferred_attribute_builder, single_threshold, build_data_dict, DeepFairPredictor, - build_deep_dict) +__all__ = (FairPredictor, inferred_attribute_builder, single_threshold, DataDict, DeepFairPredictor, + DeepDataDict) diff --git a/src/oxonfair/learners/fair.py b/src/oxonfair/learners/fair.py index b15c2c3..7fa4ab2 100644 --- a/src/oxonfair/learners/fair.py +++ b/src/oxonfair/learners/fair.py @@ -956,7 +956,7 @@ def single_threshold(x) -> np.ndarray: return np.ones((x.shape[0], 1)) -def build_data_dict(target, data, groups=None, conditioning_factor=None) -> dict: +def DataDict(target, data, groups=None, conditioning_factor=None) -> dict: "Helper function that builds dictionaries for use with sklearn classifiers" assert target.shape[0] == data.shape[0] assert data.ndim == 2 @@ -974,8 +974,8 @@ def build_data_dict(target, data, groups=None, conditioning_factor=None) -> dict return out -def build_deep_dict(target, score, groups, groups_inferred=None, *, - conditioning_factor=None) -> dict: +def DeepDataDict(target, score, groups, groups_inferred=None, *, + conditioning_factor=None) -> dict: """Wrapper around build_data_dict for deeplearning with inferred attributes. It transforms the input data into a dict, and creates helper functions so fairpredictor treats them appropriately. @@ -998,7 +998,7 @@ def build_deep_dict(target, score, groups, groups_inferred=None, *, else: # assert score.shape[1] > 1, 'When groups_inferred is None, score must also contain inferred group information' data = score - return build_data_dict(target, data, groups, conditioning_factor=conditioning_factor) + return DataDict(target, data, groups, conditioning_factor=conditioning_factor) def DeepFairPredictor(target, score, groups, groups_inferred=None, @@ -1020,7 +1020,7 @@ def DeepFairPredictor(target, score, groups, groups_inferred=None, with the output of the fast pathway). By default 'hybrid' unless use_actual_groups is true, in which case True """ - val_data = build_deep_dict(target, score, groups, groups_inferred, conditioning_factor=conditioning_factor) + val_data = DeepDataDict(target, score, groups, groups_inferred, conditioning_factor=conditioning_factor) def square_align(array): return np.stack((array[:, 1], 1-array[:, 1]), 1) diff --git a/src/oxonfair/utils/dataset_loader.py b/src/oxonfair/utils/dataset_loader.py index 6adb3e2..f89f91a 100755 --- a/src/oxonfair/utils/dataset_loader.py +++ b/src/oxonfair/utils/dataset_loader.py @@ -1,6 +1,6 @@ import numpy as np import pandas as pd -from oxonfair import build_data_dict +from oxonfair import DataDict from sklearn.preprocessing import LabelEncoder @@ -126,9 +126,9 @@ def __call__(self, groups=None, train_proportion=0.5, test_proportion=0.25, *, test_y = target[part == 1] test_groups = groups.iloc[part == 1] - train_dict = build_data_dict(train_y, train, train_groups) - val_dict = build_data_dict(val_y, val, val_groups) - test_dict = build_data_dict(test_y, test, test_groups) + train_dict = DataDict(train_y, train, train_groups) + val_dict = DataDict(val_y, val, val_groups) + test_dict = DataDict(test_y, test, test_groups) return train_dict, val_dict, test_dict diff --git a/tests/unittests/test_additional_constraints.py b/tests/unittests/test_additional_constraints.py index 877d2ad..731df69 100644 --- a/tests/unittests/test_additional_constraints.py +++ b/tests/unittests/test_additional_constraints.py @@ -31,8 +31,8 @@ val_dict = {"data": val, "target": val_y} test_dict = {"data": test, "target": test_y} -val_dict_g = fair.build_data_dict(val_y, val, val['sex_ Female']) -test_dict_g = fair.build_data_dict(test_y, test, test['sex_ Female']) +val_dict_g = fair.DataDict(val_y, val, val['sex_ Female']) +test_dict_g = fair.DataDict(test_y, test, test['sex_ Female']) def test_slack_constraints(use_fast=True): diff --git a/tests/unittests/test_scipy.py b/tests/unittests/test_scipy.py index 6a9576d..3c1f97f 100644 --- a/tests/unittests/test_scipy.py +++ b/tests/unittests/test_scipy.py @@ -39,8 +39,8 @@ val_dict = {"data": val, "target": val_y} test_dict = {"data": test, "target": test_y} -val_dict_g = fair.build_data_dict(val_y, val, val['sex_ Female']) -test_dict_g = fair.build_data_dict(test_y, test, test['sex_ Female']) +val_dict_g = fair.DataDict(val_y, val, val['sex_ Female']) +test_dict_g = fair.DataDict(test_y, test, test['sex_ Female']) def test_base_functionality(val_dict=val_dict, test_dict=test_dict):