diff --git a/Store Sales Prediction Using Deep Learning/Dataset/Readme.md b/Store Sales Prediction Using Deep Learning/Dataset/Readme.md new file mode 100644 index 000000000..75356c758 --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Dataset/Readme.md @@ -0,0 +1,2 @@ +https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview +Dataset \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Images/Readme.md b/Store Sales Prediction Using Deep Learning/Images/Readme.md new file mode 100644 index 000000000..fb6daa2d7 --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Images/Readme.md @@ -0,0 +1 @@ +Line Charts and box plots have been used for data visualisation by months \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Images/Screenshot (297).png b/Store Sales Prediction Using Deep Learning/Images/Screenshot (297).png new file mode 100644 index 000000000..838490ffb Binary files /dev/null and b/Store Sales Prediction Using Deep Learning/Images/Screenshot (297).png differ diff --git a/Store Sales Prediction Using Deep Learning/Images/Screenshot (298).png b/Store Sales Prediction Using Deep Learning/Images/Screenshot (298).png new file mode 100644 index 000000000..830ca705b Binary files /dev/null and b/Store Sales Prediction Using Deep Learning/Images/Screenshot (298).png differ diff --git a/Store Sales Prediction Using Deep Learning/Images/Screenshot (299).png b/Store Sales Prediction Using Deep Learning/Images/Screenshot (299).png new file mode 100644 index 000000000..dc03ac2f6 Binary files /dev/null and b/Store Sales Prediction Using Deep Learning/Images/Screenshot (299).png differ diff --git a/Store Sales Prediction Using Deep Learning/Images/Screenshot (303).png b/Store Sales Prediction Using Deep Learning/Images/Screenshot (303).png new file mode 100644 index 000000000..5ec1e9eb1 Binary files /dev/null and b/Store Sales Prediction Using Deep Learning/Images/Screenshot (303).png differ diff --git a/Store Sales Prediction Using Deep Learning/Models/eda-for-sales.ipynb b/Store Sales Prediction Using Deep Learning/Models/eda-for-sales.ipynb new file mode 100644 index 000000000..77bce3efe --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Models/eda-for-sales.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":29781,"databundleVersionId":2887556,"sourceType":"competition"}],"dockerImageVersionId":30162,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-17T08:08:34.411385Z","iopub.execute_input":"2023-12-17T08:08:34.411794Z","iopub.status.idle":"2023-12-17T08:08:34.450695Z","shell.execute_reply.started":"2023-12-17T08:08:34.411678Z","shell.execute_reply":"2023-12-17T08:08:34.449532Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/store-sales-time-series-forecasting/oil.csv\n/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv\n/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\n/kaggle/input/store-sales-time-series-forecasting/stores.csv\n/kaggle/input/store-sales-time-series-forecasting/train.csv\n/kaggle/input/store-sales-time-series-forecasting/test.csv\n/kaggle/input/store-sales-time-series-forecasting/transactions.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"transactions = pd.read_csv(\"/kaggle/input/store-sales-time-series-forecasting/transactions.csv\")\ntrain = pd.read_csv(\"/kaggle/input/store-sales-time-series-forecasting/train.csv\")\nstores = pd.read_csv(\"/kaggle/input/store-sales-time-series-forecasting/stores.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:10:40.586962Z","iopub.execute_input":"2023-12-17T08:10:40.587366Z","iopub.status.idle":"2023-12-17T08:10:44.610057Z","shell.execute_reply.started":"2023-12-17T08:10:40.587326Z","shell.execute_reply":"2023-12-17T08:10:44.608396Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"type(transactions[\"date\"][1])","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:11:11.813175Z","iopub.execute_input":"2023-12-17T08:11:11.813528Z","iopub.status.idle":"2023-12-17T08:11:11.831429Z","shell.execute_reply.started":"2023-12-17T08:11:11.813491Z","shell.execute_reply":"2023-12-17T08:11:11.830000Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"str"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:13:31.964720Z","iopub.execute_input":"2023-12-17T08:13:31.965129Z","iopub.status.idle":"2023-12-17T08:13:31.972374Z","shell.execute_reply.started":"2023-12-17T08:13:31.965089Z","shell.execute_reply":"2023-12-17T08:13:31.970530Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"transactions[\"date\"] = pd.to_datetime(transactions.date)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:13:35.087455Z","iopub.execute_input":"2023-12-17T08:13:35.088404Z","iopub.status.idle":"2023-12-17T08:13:35.121529Z","shell.execute_reply.started":"2023-12-17T08:13:35.088353Z","shell.execute_reply":"2023-12-17T08:13:35.120380Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"type(transactions[\"date\"])","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:13:38.165888Z","iopub.execute_input":"2023-12-17T08:13:38.166240Z","iopub.status.idle":"2023-12-17T08:13:38.174532Z","shell.execute_reply.started":"2023-12-17T08:13:38.166187Z","shell.execute_reply":"2023-12-17T08:13:38.173188Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"pandas.core.series.Series"},"metadata":{}}]},{"cell_type":"code","source":"a = transactions.copy()\na[\"year\"] = a.date.dt.year\na[\"month\"] = a.date.dt.month\npx.box(a, x=\"year\", y=\"transactions\" , color = \"month\", title = \"Transactions\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:13:41.383087Z","iopub.execute_input":"2023-12-17T08:13:41.383485Z","iopub.status.idle":"2023-12-17T08:13:43.606833Z","shell.execute_reply.started":"2023-12-17T08:13:41.383441Z","shell.execute_reply":"2023-12-17T08:13:43.605809Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/html":" \n "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"store_group = transactions.groupby(\"store_nbr\")[\"transactions\"].sum().reset_index()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:19:56.018957Z","iopub.execute_input":"2023-12-17T08:19:56.020255Z","iopub.status.idle":"2023-12-17T08:19:56.032962Z","shell.execute_reply.started":"2023-12-17T08:19:56.020195Z","shell.execute_reply":"2023-12-17T08:19:56.032069Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"px.line(store_group, x=\"store_nbr\", y=\"transactions\", title = \"Store wise transactions\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:19:59.169088Z","iopub.execute_input":"2023-12-17T08:19:59.170464Z","iopub.status.idle":"2023-12-17T08:19:59.294861Z","shell.execute_reply.started":"2023-12-17T08:19:59.170371Z","shell.execute_reply":"2023-12-17T08:19:59.293485Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"a = transactions.copy()\na[\"year\"] = a.date.dt.year\na[\"month\"] = a.date.dt.month\nb = a.groupby([\"month\",\"year\"])[\"transactions\"].sum().reset_index()\npx.line(b, x=\"month\", y=\"transactions\" ,color=\"year\", title = \"Transactions over months across years\")\n","metadata":{"execution":{"iopub.status.busy":"2023-12-17T08:20:05.164392Z","iopub.execute_input":"2023-12-17T08:20:05.164769Z","iopub.status.idle":"2023-12-17T08:20:05.328660Z","shell.execute_reply.started":"2023-12-17T08:20:05.164724Z","shell.execute_reply":"2023-12-17T08:20:05.327036Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Models/ml-store-sales.ipynb b/Store Sales Prediction Using Deep Learning/Models/ml-store-sales.ipynb new file mode 100644 index 000000000..347b0b67c --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Models/ml-store-sales.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":29781,"databundleVersionId":2887556,"sourceType":"competition"}],"dockerImageVersionId":30513,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport datetime\nimport math\nfrom collections import defaultdict\nimport itertools\nfrom scipy.stats import shapiro\nimport scipy.stats as stats\nimport matplotlib.pyplot as plt \nimport seaborn as sns\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\nfrom sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNetCV, ElasticNet\n# from xgboost import XGBRegressor\nimport catboost as cb\nimport lightgbm as lgb\n\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV, TimeSeriesSplit, KFold, RandomizedSearchCV\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler,OrdinalEncoder\nfrom sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error,mean_absolute_percentage_error,mean_squared_log_error,make_scorer\nfrom sklearn.compose import ColumnTransformer\nfrom category_encoders import TargetEncoder\nfrom category_encoders.one_hot import OneHotEncoder\nfrom sklearn.compose import make_column_selector as selector\n\n#Configurations\nfrom warnings import simplefilter\nsimplefilter(\"ignore\") # ignore warnings to clean up output cells\npd.set_option('display.max_columns', None)\npd.options.display.float_format = '{:.2f}'.format","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:48.516268Z","iopub.execute_input":"2023-12-17T09:28:48.517792Z","iopub.status.idle":"2023-12-17T09:28:48.535292Z","shell.execute_reply.started":"2023-12-17T09:28:48.517740Z","shell.execute_reply":"2023-12-17T09:28:48.533686Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"/kaggle/input/store-sales-time-series-forecasting/oil.csv\n/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv\n/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\n/kaggle/input/store-sales-time-series-forecasting/stores.csv\n/kaggle/input/store-sales-time-series-forecasting/train.csv\n/kaggle/input/store-sales-time-series-forecasting/test.csv\n/kaggle/input/store-sales-time-series-forecasting/transactions.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"> ","metadata":{}},{"cell_type":"code","source":"train_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')\ntest_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/test.csv')\n\nholidays_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv')\noil_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/oil.csv')\nsubmission_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv')\nsubmission_data.rename(columns = {'id':'id', 'sales':'submission_d'}, inplace = True )\nstores_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/stores.csv')\ntransactions_data = pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/transactions.csv')\n\nholidays_data['date'] = pd.to_datetime(holidays_data['date'], format = \"%Y-%m-%d\")\noil_data['date'] = pd.to_datetime(oil_data['date'], format = \"%Y-%m-%d\")\ntransactions_data['date'] = pd.to_datetime(transactions_data['date'], format = \"%Y-%m-%d\")\ntrain_data['date'] = pd.to_datetime(train_data['date'], format = \"%Y-%m-%d\")\ntest_data['date'] = pd.to_datetime(test_data['date'], format = \"%Y-%m-%d\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:48.537877Z","iopub.execute_input":"2023-12-17T09:28:48.538325Z","iopub.status.idle":"2023-12-17T09:28:51.886959Z","shell.execute_reply.started":"2023-12-17T09:28:48.538288Z","shell.execute_reply":"2023-12-17T09:28:51.885823Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"train_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:51.888311Z","iopub.execute_input":"2023-12-17T09:28:51.888688Z","iopub.status.idle":"2023-12-17T09:28:51.901861Z","shell.execute_reply.started":"2023-12-17T09:28:51.888657Z","shell.execute_reply":"2023-12-17T09:28:51.900520Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":" id date store_nbr family sales onpromotion\n0 0 2013-01-01 1 AUTOMOTIVE 0.00 0\n1 1 2013-01-01 1 BABY CARE 0.00 0\n2 2 2013-01-01 1 BEAUTY 0.00 0\n3 3 2013-01-01 1 BEVERAGES 0.00 0\n4 4 2013-01-01 1 BOOKS 0.00 0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
iddatestore_nbrfamilysalesonpromotion
002013-01-011AUTOMOTIVE0.000
112013-01-011BABY CARE0.000
222013-01-011BEAUTY0.000
332013-01-011BEVERAGES0.000
442013-01-011BOOKS0.000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"train_data_start = train_data.date.min().date()\ntrain_data_end = train_data.date.max().date()\n\nmissing_dates = pd.date_range(train_data_start, train_data_end\n ).difference(train_data.date.unique())\nmissing_dates = missing_dates.strftime(\"%Y-%m-%d\").tolist()\n\nmissing_dates","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:51.905237Z","iopub.execute_input":"2023-12-17T09:28:51.906568Z","iopub.status.idle":"2023-12-17T09:28:51.960521Z","shell.execute_reply.started":"2023-12-17T09:28:51.906524Z","shell.execute_reply":"2023-12-17T09:28:51.959304Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"['2013-12-25', '2014-12-25', '2015-12-25', '2016-12-25']"},"metadata":{}}]},{"cell_type":"code","source":"# reindex training data\nmulti_idx = pd.MultiIndex.from_product([pd.date_range(train_data_start, train_data_end), \n train_data.store_nbr.unique(), \n train_data.family.unique()],\n names=[\"date\", \"store_nbr\", \"family\"],)\ntrain_data = train_data.set_index([\"date\", \"store_nbr\", \"family\"]\n ).reindex(multi_idx).reset_index()\n# fill missing values with 0s\ntrain_data[[\"sales\", \"onpromotion\"]] = train_data[[\"sales\", \"onpromotion\"]].fillna(0.)\n# interpolate for the 'id'\ntrain_data.id = train_data.id.interpolate(method=\"linear\")\n\ntrain_data","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:51.962424Z","iopub.execute_input":"2023-12-17T09:28:51.962880Z","iopub.status.idle":"2023-12-17T09:28:54.357756Z","shell.execute_reply.started":"2023-12-17T09:28:51.962840Z","shell.execute_reply":"2023-12-17T09:28:54.356518Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" date store_nbr family id sales \\\n0 2013-01-01 1 AUTOMOTIVE 0.00 0.00 \n1 2013-01-01 1 BABY CARE 1.00 0.00 \n2 2013-01-01 1 BEAUTY 2.00 0.00 \n3 2013-01-01 1 BEVERAGES 3.00 0.00 \n4 2013-01-01 1 BOOKS 4.00 0.00 \n... ... ... ... ... ... \n3008011 2017-08-15 9 POULTRY 3000883.00 438.13 \n3008012 2017-08-15 9 PREPARED FOODS 3000884.00 154.55 \n3008013 2017-08-15 9 PRODUCE 3000885.00 2419.73 \n3008014 2017-08-15 9 SCHOOL AND OFFICE SUPPLIES 3000886.00 121.00 \n3008015 2017-08-15 9 SEAFOOD 3000887.00 16.00 \n\n onpromotion \n0 0.00 \n1 0.00 \n2 0.00 \n3 0.00 \n4 0.00 \n... ... \n3008011 0.00 \n3008012 1.00 \n3008013 148.00 \n3008014 8.00 \n3008015 0.00 \n\n[3008016 rows x 6 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datestore_nbrfamilyidsalesonpromotion
02013-01-011AUTOMOTIVE0.000.000.00
12013-01-011BABY CARE1.000.000.00
22013-01-011BEAUTY2.000.000.00
32013-01-011BEVERAGES3.000.000.00
42013-01-011BOOKS4.000.000.00
.....................
30080112017-08-159POULTRY3000883.00438.130.00
30080122017-08-159PREPARED FOODS3000884.00154.551.00
30080132017-08-159PRODUCE3000885.002419.73148.00
30080142017-08-159SCHOOL AND OFFICE SUPPLIES3000886.00121.008.00
30080152017-08-159SEAFOOD3000887.0016.000.00
\n

3008016 rows × 6 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Here we can see test_data:","metadata":{}},{"cell_type":"code","source":"test_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.359047Z","iopub.execute_input":"2023-12-17T09:28:54.359373Z","iopub.status.idle":"2023-12-17T09:28:54.372782Z","shell.execute_reply.started":"2023-12-17T09:28:54.359344Z","shell.execute_reply":"2023-12-17T09:28:54.371483Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" id date store_nbr family onpromotion\n0 3000888 2017-08-16 1 AUTOMOTIVE 0\n1 3000889 2017-08-16 1 BABY CARE 0\n2 3000890 2017-08-16 1 BEAUTY 2\n3 3000891 2017-08-16 1 BEVERAGES 20\n4 3000892 2017-08-16 1 BOOKS 0","text/html":"
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iddatestore_nbrfamilyonpromotion
030008882017-08-161AUTOMOTIVE0
130008892017-08-161BABY CARE0
230008902017-08-161BEAUTY2
330008912017-08-161BEVERAGES20
430008922017-08-161BOOKS0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"test_data_start = test_data.date.min().date()\ntest_data_end = test_data.date.max().date()\n\nmissing_dates_test = pd.date_range(test_data_start, test_data_end\n ).difference(test_data.date.unique())\nmissing_dates_test = missing_dates_test.strftime(\"%Y-%m-%d\").tolist()\n\nmissing_dates_test","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.374394Z","iopub.execute_input":"2023-12-17T09:28:54.374728Z","iopub.status.idle":"2023-12-17T09:28:54.388659Z","shell.execute_reply.started":"2023-12-17T09:28:54.374702Z","shell.execute_reply":"2023-12-17T09:28:54.387388Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}}]},{"cell_type":"markdown","source":"How we can notice there are no gaps in the test data.\n","metadata":{}},{"cell_type":"code","source":"#let's add an additional column that will help us separate test and training data in the future\ntest_data['test'] = 1\ntrain_data['test'] = 0","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.390073Z","iopub.execute_input":"2023-12-17T09:28:54.390564Z","iopub.status.idle":"2023-12-17T09:28:54.401378Z","shell.execute_reply.started":"2023-12-17T09:28:54.390525Z","shell.execute_reply":"2023-12-17T09:28:54.400242Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"oil_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.402992Z","iopub.execute_input":"2023-12-17T09:28:54.403480Z","iopub.status.idle":"2023-12-17T09:28:54.415853Z","shell.execute_reply.started":"2023-12-17T09:28:54.403383Z","shell.execute_reply":"2023-12-17T09:28:54.414974Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":" date dcoilwtico\n0 2013-01-01 NaN\n1 2013-01-02 93.14\n2 2013-01-03 92.97\n3 2013-01-04 93.12\n4 2013-01-07 93.20","text/html":"
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datedcoilwtico
02013-01-01NaN
12013-01-0293.14
22013-01-0392.97
32013-01-0493.12
42013-01-0793.20
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# reindex oil data\noil_data = oil_data.merge(pd.DataFrame({\"date\": pd.date_range(train_data_start, \n test_data_end)}),\n on=\"date\",how=\"outer\",).sort_values(\"date\", ignore_index=True)\n\n# fill missing values using linear interpolation\noil_data.dcoilwtico = oil_data.dcoilwtico.interpolate(method=\"linear\", limit_direction=\"both\")\n","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.671636Z","iopub.execute_input":"2023-12-17T09:28:54.671968Z","iopub.status.idle":"2023-12-17T09:28:54.689243Z","shell.execute_reply.started":"2023-12-17T09:28:54.671940Z","shell.execute_reply":"2023-12-17T09:28:54.688123Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"transactions_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.690847Z","iopub.execute_input":"2023-12-17T09:28:54.691186Z","iopub.status.idle":"2023-12-17T09:28:54.702274Z","shell.execute_reply.started":"2023-12-17T09:28:54.691158Z","shell.execute_reply":"2023-12-17T09:28:54.701101Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":" date store_nbr transactions\n0 2013-01-01 25 770\n1 2013-01-02 1 2111\n2 2013-01-02 2 2358\n3 2013-01-02 3 3487\n4 2013-01-02 4 1922","text/html":"
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datestore_nbrtransactions
02013-01-0125770
12013-01-0212111
22013-01-0222358
32013-01-0233487
42013-01-0241922
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"Fill in the missing values ​​for transactions using interpolation, except for days with zero sales","metadata":{}},{"cell_type":"code","source":"num_store = train_data.store_nbr.nunique()\ntrain_len = (train_data_end - train_data_start).days + 1\n\nnum_zero_sales = (train_data.groupby([\"date\", \"store_nbr\"]).sales.sum().eq(0)).sum()\ntotal_rec = num_store * train_len\ncurr_rec = len(transactions_data.index)\nmissing_rec = total_rec - curr_rec - num_zero_sales\n\n#total sales for each store\nstore_sales = train_data.groupby([\"date\", \"store_nbr\"]).sales.sum().reset_index()\n\n# reindex transaction data\ntransactions_data = transactions_data.merge(\n store_sales, on=[\"date\", \"store_nbr\"],how=\"outer\").sort_values(\n [\"date\", \"store_nbr\"],ignore_index=True)\n\n# fill missing values with 0s for days with zero sales\ntransactions_data.loc[transactions_data.sales.eq(0), \"transactions\"] = 0\ntransactions_data = transactions_data.drop(columns=[\"sales\"])\n\n# fill remaining missing values using linear interpolation\ntransactions_data.transactions = transactions_data.groupby(\n \"store_nbr\", group_keys=False).transactions.apply(\n lambda x: x.interpolate(method=\"linear\", limit_direction=\"both\"))","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:54.703827Z","iopub.execute_input":"2023-12-17T09:28:54.704224Z","iopub.status.idle":"2023-12-17T09:28:55.189467Z","shell.execute_reply.started":"2023-12-17T09:28:54.704175Z","shell.execute_reply":"2023-12-17T09:28:55.188057Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"#Here we can see holidays_data:\nholidays_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:55.191558Z","iopub.execute_input":"2023-12-17T09:28:55.192316Z","iopub.status.idle":"2023-12-17T09:28:55.206009Z","shell.execute_reply.started":"2023-12-17T09:28:55.192279Z","shell.execute_reply":"2023-12-17T09:28:55.204796Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" date type locale locale_name description \\\n0 2012-03-02 Holiday Local Manta Fundacion de Manta \n1 2012-04-01 Holiday Regional Cotopaxi Provincializacion de Cotopaxi \n2 2012-04-12 Holiday Local Cuenca Fundacion de Cuenca \n3 2012-04-14 Holiday Local Libertad Cantonizacion de Libertad \n4 2012-04-21 Holiday Local Riobamba Cantonizacion de Riobamba \n\n transferred \n0 False \n1 False \n2 False \n3 False \n4 False ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datetypelocalelocale_namedescriptiontransferred
02012-03-02HolidayLocalMantaFundacion de MantaFalse
12012-04-01HolidayRegionalCotopaxiProvincializacion de CotopaxiFalse
22012-04-12HolidayLocalCuencaFundacion de CuencaFalse
32012-04-14HolidayLocalLibertadCantonizacion de LibertadFalse
42012-04-21HolidayLocalRiobambaCantonizacion de RiobambaFalse
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see the most popular types are Holiday,National,Ecuador","metadata":{}},{"cell_type":"code","source":"print('Holidays types:', holidays_data['type'].unique())\nprint('Holidays region types:', holidays_data['locale'].unique()) \nprint('Holidays locale names:', holidays_data['locale_name'].unique()) \n\nnational_locale_name = sorted(holidays_data[holidays_data['locale']==\"National\"\n ]['locale_name'].unique().tolist())\nregional_locale_name = sorted(holidays_data[holidays_data['locale']==\"Regional\"\n ]['locale_name'].unique().tolist())\nlocal_locale_name = sorted(holidays_data[holidays_data['locale']==\"Local\"\n ]['locale_name'].unique().tolist())\n\nprint(f'Locale names for national holidays:{national_locale_name}')\nprint(f'Locale names for regional holidays:{regional_locale_name}')\nprint(f'Locale names for local holidays:{local_locale_name}')","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.371231Z","iopub.execute_input":"2023-12-17T09:28:56.371822Z","iopub.status.idle":"2023-12-17T09:28:56.390843Z","shell.execute_reply.started":"2023-12-17T09:28:56.371790Z","shell.execute_reply":"2023-12-17T09:28:56.389564Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Holidays types: ['Holiday' 'Transfer' 'Additional' 'Bridge' 'Work Day' 'Event']\nHolidays region types: ['Local' 'Regional' 'National']\nHolidays locale names: ['Manta' 'Cotopaxi' 'Cuenca' 'Libertad' 'Riobamba' 'Puyo' 'Guaranda'\n 'Imbabura' 'Latacunga' 'Machala' 'Santo Domingo' 'El Carmen' 'Cayambe'\n 'Esmeraldas' 'Ecuador' 'Ambato' 'Ibarra' 'Quevedo'\n 'Santo Domingo de los Tsachilas' 'Santa Elena' 'Quito' 'Loja' 'Salinas'\n 'Guayaquil']\nLocale names for national holidays:['Ecuador']\nLocale names for regional holidays:['Cotopaxi', 'Imbabura', 'Santa Elena', 'Santo Domingo de los Tsachilas']\nLocale names for local holidays:['Ambato', 'Cayambe', 'Cuenca', 'El Carmen', 'Esmeraldas', 'Guaranda', 'Guayaquil', 'Ibarra', 'Latacunga', 'Libertad', 'Loja', 'Machala', 'Manta', 'Puyo', 'Quevedo', 'Quito', 'Riobamba', 'Salinas', 'Santo Domingo']\n","output_type":"stream"}]},{"cell_type":"code","source":"def process_holiday(s):\n if \"futbol\" in s:\n return \"futbol\"\n to_remove = list(set(stores_data['city'].str.lower()) | set(stores_data['state'].str.lower()))\n for w in to_remove:\n s = s.replace(w, \"\")\n return s\n\nholidays_data['description'] = holidays_data.apply(\n lambda x: x['description'].lower().replace(x['locale_name'].lower(), \"\"), \n axis=1,).apply(process_holiday).replace(\n r\"[+-]\\d+|\\b(de|del|traslado|recupero|puente|-)\\b\", \"\", regex=True,).replace(\n r\"\\s+|-\", \" \", regex=True,).str.strip()\n\n# remove transferred holidays\nholidays_data = holidays_data[holidays_data['transferred'].eq(False)]\n\n#Saturdays designated as work days \nwork_days = holidays_data[holidays_data['type'].eq(\"Work Day\")]\nwork_days = work_days[[\"date\", \"type\"]].rename(columns={\"type\": \"work_day\"}\n ).reset_index(drop=True)\nwork_days['work_day'] = work_days['work_day'].notna().astype(int)\n\n# remove work days after extracting above\nholidays_data = holidays_data[holidays_data['type']!=\"Work Day\"].reset_index(drop=True)\nholidays_data","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.392303Z","iopub.execute_input":"2023-12-17T09:28:56.392673Z","iopub.status.idle":"2023-12-17T09:28:56.564866Z","shell.execute_reply.started":"2023-12-17T09:28:56.392642Z","shell.execute_reply":"2023-12-17T09:28:56.563990Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":" date type locale locale_name description \\\n0 2012-03-02 Holiday Local Manta fundacion \n1 2012-04-01 Holiday Regional Cotopaxi provincializacion \n2 2012-04-12 Holiday Local Cuenca fundacion \n3 2012-04-14 Holiday Local Libertad cantonizacion \n4 2012-04-21 Holiday Local Riobamba cantonizacion \n.. ... ... ... ... ... \n328 2017-12-22 Additional National Ecuador navidad \n329 2017-12-23 Additional National Ecuador navidad \n330 2017-12-24 Additional National Ecuador navidad \n331 2017-12-25 Holiday National Ecuador navidad \n332 2017-12-26 Additional National Ecuador navidad \n\n transferred \n0 False \n1 False \n2 False \n3 False \n4 False \n.. ... \n328 False \n329 False \n330 False \n331 False \n332 False \n\n[333 rows x 6 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datetypelocalelocale_namedescriptiontransferred
02012-03-02HolidayLocalMantafundacionFalse
12012-04-01HolidayRegionalCotopaxiprovincializacionFalse
22012-04-12HolidayLocalCuencafundacionFalse
32012-04-14HolidayLocalLibertadcantonizacionFalse
42012-04-21HolidayLocalRiobambacantonizacionFalse
.....................
3282017-12-22AdditionalNationalEcuadornavidadFalse
3292017-12-23AdditionalNationalEcuadornavidadFalse
3302017-12-24AdditionalNationalEcuadornavidadFalse
3312017-12-25HolidayNationalEcuadornavidadFalse
3322017-12-26AdditionalNationalEcuadornavidadFalse
\n

333 rows × 6 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"#local holidays (city level) \nlocal_holidays = holidays_data[holidays_data['locale'].eq(\"Local\")]\nlocal_holidays = local_holidays[[\"date\", \"locale_name\", \"description\"]].rename(\n columns={\"locale_name\": \"city\"}).reset_index(drop=True)\n\nlocal_holidays = local_holidays[~local_holidays.duplicated()]\nlocal_holidays = pd.get_dummies(local_holidays, columns=[\"description\"], prefix=\"loc\")\n\nlocal_holidays.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.566241Z","iopub.execute_input":"2023-12-17T09:28:56.566814Z","iopub.status.idle":"2023-12-17T09:28:56.587672Z","shell.execute_reply.started":"2023-12-17T09:28:56.566784Z","shell.execute_reply":"2023-12-17T09:28:56.586481Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":" date city loc_cantonizacion loc_fundacion loc_independencia\n0 2012-03-02 Manta 0 1 0\n1 2012-04-12 Cuenca 0 1 0\n2 2012-04-14 Libertad 1 0 0\n3 2012-04-21 Riobamba 1 0 0\n4 2012-05-12 Puyo 1 0 0","text/html":"
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datecityloc_cantonizacionloc_fundacionloc_independencia
02012-03-02Manta010
12012-04-12Cuenca010
22012-04-14Libertad100
32012-04-21Riobamba100
42012-05-12Puyo100
\n
"},"metadata":{}}]},{"cell_type":"code","source":"#regional holidays \nregional_holidays = holidays_data[holidays_data['locale'].eq(\"Regional\")]\nregional_holidays = regional_holidays[[\"date\", \"locale_name\", \"description\"]].rename(\n columns={\"locale_name\": \"state\", \"description\": \"provincializacion\"}).reset_index(drop=True)\nregional_holidays['provincializacion'] = regional_holidays['provincializacion'].eq(\n \"provincializacion\").astype(int)\n\nregional_holidays","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.589422Z","iopub.execute_input":"2023-12-17T09:28:56.591787Z","iopub.status.idle":"2023-12-17T09:28:56.609929Z","shell.execute_reply.started":"2023-12-17T09:28:56.591751Z","shell.execute_reply":"2023-12-17T09:28:56.608599Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":" date state provincializacion\n0 2012-04-01 Cotopaxi 1\n1 2012-06-25 Imbabura 1\n2 2012-11-06 Santo Domingo de los Tsachilas 1\n3 2012-11-07 Santa Elena 1\n4 2013-04-01 Cotopaxi 1\n5 2013-06-25 Imbabura 1\n6 2013-11-06 Santo Domingo de los Tsachilas 1\n7 2013-11-07 Santa Elena 1\n8 2014-04-01 Cotopaxi 1\n9 2014-06-25 Imbabura 1\n10 2014-11-06 Santo Domingo de los Tsachilas 1\n11 2014-11-07 Santa Elena 1\n12 2015-04-01 Cotopaxi 1\n13 2015-06-25 Imbabura 1\n14 2015-11-06 Santo Domingo de los Tsachilas 1\n15 2015-11-07 Santa Elena 1\n16 2016-04-01 Cotopaxi 1\n17 2016-06-25 Imbabura 1\n18 2016-11-06 Santo Domingo de los Tsachilas 1\n19 2016-11-07 Santa Elena 1\n20 2017-04-01 Cotopaxi 1\n21 2017-06-25 Imbabura 1\n22 2017-11-06 Santo Domingo de los Tsachilas 1\n23 2017-11-07 Santa Elena 1","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datestateprovincializacion
02012-04-01Cotopaxi1
12012-06-25Imbabura1
22012-11-06Santo Domingo de los Tsachilas1
32012-11-07Santa Elena1
42013-04-01Cotopaxi1
52013-06-25Imbabura1
62013-11-06Santo Domingo de los Tsachilas1
72013-11-07Santa Elena1
82014-04-01Cotopaxi1
92014-06-25Imbabura1
102014-11-06Santo Domingo de los Tsachilas1
112014-11-07Santa Elena1
122015-04-01Cotopaxi1
132015-06-25Imbabura1
142015-11-06Santo Domingo de los Tsachilas1
152015-11-07Santa Elena1
162016-04-01Cotopaxi1
172016-06-25Imbabura1
182016-11-06Santo Domingo de los Tsachilas1
192016-11-07Santa Elena1
202017-04-01Cotopaxi1
212017-06-25Imbabura1
222017-11-06Santo Domingo de los Tsachilas1
232017-11-07Santa Elena1
\n
"},"metadata":{}}]},{"cell_type":"code","source":"#national holidays \nnational_holidays = holidays_data[holidays_data['locale'].eq(\"National\")]\nnational_holidays = national_holidays[[\"date\", \"description\"]].reset_index(drop=True)\nnational_holidays = national_holidays[~national_holidays.duplicated()]\nnational_holidays = pd.get_dummies(national_holidays, columns=[\"description\"], prefix=\"nat\")\n\n# different national holidays may fall on the same day\nnational_holidays = national_holidays.groupby(\"date\").sum().reset_index()\n# shorten name for visualization purposes later\nnational_holidays = national_holidays.rename(columns={\n \"nat_primer grito independencia\": \"nat_primer grito\"})\n\nnational_holidays.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.611549Z","iopub.execute_input":"2023-12-17T09:28:56.611876Z","iopub.status.idle":"2023-12-17T09:28:56.645134Z","shell.execute_reply.started":"2023-12-17T09:28:56.611848Z","shell.execute_reply":"2023-12-17T09:28:56.643879Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":" date nat_batalla nat_black friday nat_carnaval nat_cyber monday \\\n0 2012-08-10 0 0 0 0 \n1 2012-10-12 0 0 0 0 \n2 2012-11-02 0 0 0 0 \n3 2012-11-03 0 0 0 0 \n4 2012-12-21 0 0 0 0 \n\n nat_dia difuntos nat_dia la madre nat_dia trabajo nat_futbol \\\n0 0 0 0 0 \n1 0 0 0 0 \n2 1 0 0 0 \n3 0 0 0 0 \n4 0 0 0 0 \n\n nat_independencia nat_navidad nat_primer dia ano nat_primer grito \\\n0 0 0 0 1 \n1 1 0 0 0 \n2 0 0 0 0 \n3 1 0 0 0 \n4 0 1 0 0 \n\n nat_terremoto nat_viernes santo \n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datenat_batallanat_black fridaynat_carnavalnat_cyber mondaynat_dia difuntosnat_dia la madrenat_dia trabajonat_futbolnat_independencianat_navidadnat_primer dia anonat_primer gritonat_terremotonat_viernes santo
02012-08-1000000000000100
12012-10-1200000000100000
22012-11-0200001000000000
32012-11-0300000000100000
42012-12-2100000000010000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"sales_ts = pd.pivot_table(train_data, values=\"sales\", index=\"date\", \n columns=[\"store_nbr\", \"family\"])\ntr_ts = pd.pivot_table(transactions_data, values=\"transactions\", index=\"date\", \n columns=\"store_nbr\")\npromo_ts = pd.pivot_table(train_data, values=\"onpromotion\", index=\"date\", \n columns=[\"store_nbr\", \"family\"])","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:28:56.646330Z","iopub.execute_input":"2023-12-17T09:28:56.646677Z","iopub.status.idle":"2023-12-17T09:29:00.746542Z","shell.execute_reply.started":"2023-12-17T09:28:56.646650Z","shell.execute_reply":"2023-12-17T09:29:00.745429Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"Here we can see stores_data:","metadata":{}},{"cell_type":"code","source":"stores_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:00.747748Z","iopub.execute_input":"2023-12-17T09:29:00.748108Z","iopub.status.idle":"2023-12-17T09:29:00.760260Z","shell.execute_reply.started":"2023-12-17T09:29:00.748080Z","shell.execute_reply":"2023-12-17T09:29:00.759320Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":" store_nbr city state type cluster\n0 1 Quito Pichincha D 13\n1 2 Quito Pichincha D 13\n2 3 Quito Pichincha D 8\n3 4 Quito Pichincha D 9\n4 5 Santo Domingo Santo Domingo de los Tsachilas D 4","text/html":"
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store_nbrcitystatetypecluster
01QuitoPichinchaD13
12QuitoPichinchaD13
23QuitoPichinchaD8
34QuitoPichinchaD9
45Santo DomingoSanto Domingo de los TsachilasD4
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"},"metadata":{}}]},{"cell_type":"code","source":"# scale target series\nscaler = MinMaxScaler()\nsales_ts_scaled = sales_ts.copy()\nsales_ts_scaled[sales_ts_scaled.columns] = scaler.fit_transform(sales_ts_scaled)\n\n# convert back to long form and add the holiday columns\nholiday_sales_merged = sales_ts_scaled.melt(\n value_name=\"sales\", ignore_index=False,).reset_index().merge(\n stores_data, on=\"store_nbr\", how=\"left\").merge(\n work_days, on=\"date\", how=\"left\").merge(\n local_holidays, on=[\"date\", \"city\"], how=\"left\").merge(\n regional_holidays, on=[\"date\", \"state\"], how=\"left\").merge(\n national_holidays, on=\"date\", how=\"left\").fillna(0)\n\n# include dummy variable for dates without any holidays\nholiday_list = [col for col in holiday_sales_merged if col.startswith((\n \"loc_\", \"nat_\", \"provincializacion\"))]\nholiday_sales_merged[\"no_holiday\"] = holiday_sales_merged[holiday_list].sum(\n axis=1).eq(0).astype(int)\n\nholiday_sales_merged.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:00.761621Z","iopub.execute_input":"2023-12-17T09:29:00.761946Z","iopub.status.idle":"2023-12-17T09:29:16.943295Z","shell.execute_reply.started":"2023-12-17T09:29:00.761916Z","shell.execute_reply":"2023-12-17T09:29:16.941973Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":" date store_nbr family sales city state type cluster \\\n0 2013-01-01 1 AUTOMOTIVE 0.00 Quito Pichincha D 13 \n1 2013-01-02 1 AUTOMOTIVE 0.11 Quito Pichincha D 13 \n2 2013-01-03 1 AUTOMOTIVE 0.16 Quito Pichincha D 13 \n3 2013-01-04 1 AUTOMOTIVE 0.16 Quito Pichincha D 13 \n4 2013-01-05 1 AUTOMOTIVE 0.26 Quito Pichincha D 13 \n\n work_day loc_cantonizacion loc_fundacion loc_independencia \\\n0 0.00 0.00 0.00 0.00 \n1 0.00 0.00 0.00 0.00 \n2 0.00 0.00 0.00 0.00 \n3 0.00 0.00 0.00 0.00 \n4 1.00 0.00 0.00 0.00 \n\n provincializacion nat_batalla nat_black friday nat_carnaval \\\n0 0.00 0.00 0.00 0.00 \n1 0.00 0.00 0.00 0.00 \n2 0.00 0.00 0.00 0.00 \n3 0.00 0.00 0.00 0.00 \n4 0.00 0.00 0.00 0.00 \n\n nat_cyber monday nat_dia difuntos nat_dia la madre nat_dia trabajo \\\n0 0.00 0.00 0.00 0.00 \n1 0.00 0.00 0.00 0.00 \n2 0.00 0.00 0.00 0.00 \n3 0.00 0.00 0.00 0.00 \n4 0.00 0.00 0.00 0.00 \n\n nat_futbol nat_independencia nat_navidad nat_primer dia ano \\\n0 0.00 0.00 0.00 1.00 \n1 0.00 0.00 0.00 0.00 \n2 0.00 0.00 0.00 0.00 \n3 0.00 0.00 0.00 0.00 \n4 0.00 0.00 0.00 0.00 \n\n nat_primer grito nat_terremoto nat_viernes santo no_holiday \n0 0.00 0.00 0.00 0 \n1 0.00 0.00 0.00 1 \n2 0.00 0.00 0.00 1 \n3 0.00 0.00 0.00 1 \n4 0.00 0.00 0.00 1 ","text/html":"
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datestore_nbrfamilysalescitystatetypeclusterwork_dayloc_cantonizacionloc_fundacionloc_independenciaprovincializacionnat_batallanat_black fridaynat_carnavalnat_cyber mondaynat_dia difuntosnat_dia la madrenat_dia trabajonat_futbolnat_independencianat_navidadnat_primer dia anonat_primer gritonat_terremotonat_viernes santono_holiday
02013-01-011AUTOMOTIVE0.00QuitoPichinchaD130.000.000.000.000.000.000.000.000.000.000.000.000.000.000.001.000.000.000.000
12013-01-021AUTOMOTIVE0.11QuitoPichinchaD130.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.001
22013-01-031AUTOMOTIVE0.16QuitoPichinchaD130.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.001
32013-01-041AUTOMOTIVE0.16QuitoPichinchaD130.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.001
42013-01-051AUTOMOTIVE0.26QuitoPichinchaD131.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.001
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"},"metadata":{}}]},{"cell_type":"code","source":"# keep selected national holidays with larger impacts on sales\nselected_holidays = [\"nat_terremoto\", \"nat_navidad\", \"nat_dia la madre\", \"nat_dia trabajo\",\n \"nat_primer dia ano\", \"nat_futbol\", \"nat_dia difuntos\"]\nkeep_national_holidays = national_holidays[[\"date\", *selected_holidays]]\n\ndata = pd.concat(\n [train_data, test_data], axis=0, ignore_index=True,\n).merge(\n stores_data, on=[\"store_nbr\"]\n).merge(\n oil_data, on=[\"date\"], how=\"left\"\n).merge(\n transactions_data, on=[\"date\", 'store_nbr'], how=\"left\"\n).merge(\n work_days, on=\"date\", how=\"left\", \n).merge(\n keep_national_holidays, on=[\"date\"],how=\"left\").sort_values([\"date\", \"store_nbr\", \"family\"], ignore_index=True)\n\ndata[[\"work_day\", *selected_holidays]] = data[[\"work_day\", *selected_holidays]].fillna(0)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:31.242171Z","iopub.execute_input":"2023-12-17T09:29:31.243181Z","iopub.status.idle":"2023-12-17T09:29:38.339948Z","shell.execute_reply.started":"2023-12-17T09:29:31.243140Z","shell.execute_reply":"2023-12-17T09:29:38.339084Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"## Select the date, days of the week, hours, month !not used in calculations\ndata['day_of_week'] = data.date.dt.dayofweek\ndata['day_of_year'] = data.date.dt.dayofyear\ndata['day_of_month'] = data.date.dt.day\ndata['year'] = data.date.dt.year\ndata['month'] = data.date.dt.month\n#seasons 0-winter;1-spring;2-summer;3-fall\ndata[\"season\"] = np.where(data.date.dt.month.isin([12,1,2]), 0, 1)\ndata[\"season\"] = np.where(data.date.dt.month.isin([3,4,5]), 1, data[\"season\"])\ndata[\"season\"] = np.where(data.date.dt.month.isin([6,7,8]), 2, data[\"season\"])\ndata[\"season\"] = np.where(data.date.dt.month.isin([9, 10, 11]), 3, data[\"season\"])\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:38.341094Z","iopub.execute_input":"2023-12-17T09:29:38.341860Z","iopub.status.idle":"2023-12-17T09:29:40.993734Z","shell.execute_reply.started":"2023-12-17T09:29:38.341815Z","shell.execute_reply":"2023-12-17T09:29:40.992860Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":" date store_nbr family id sales onpromotion test city \\\n0 2013-01-01 1 AUTOMOTIVE 0.00 0.00 0.00 0 Quito \n1 2013-01-01 1 BABY CARE 1.00 0.00 0.00 0 Quito \n2 2013-01-01 1 BEAUTY 2.00 0.00 0.00 0 Quito \n3 2013-01-01 1 BEVERAGES 3.00 0.00 0.00 0 Quito \n4 2013-01-01 1 BOOKS 4.00 0.00 0.00 0 Quito \n\n state type cluster dcoilwtico transactions work_day nat_terremoto \\\n0 Pichincha D 13 93.14 0.00 0.00 0.00 \n1 Pichincha D 13 93.14 0.00 0.00 0.00 \n2 Pichincha D 13 93.14 0.00 0.00 0.00 \n3 Pichincha D 13 93.14 0.00 0.00 0.00 \n4 Pichincha D 13 93.14 0.00 0.00 0.00 \n\n nat_navidad nat_dia la madre nat_dia trabajo nat_primer dia ano \\\n0 0.00 0.00 0.00 1.00 \n1 0.00 0.00 0.00 1.00 \n2 0.00 0.00 0.00 1.00 \n3 0.00 0.00 0.00 1.00 \n4 0.00 0.00 0.00 1.00 \n\n nat_futbol nat_dia difuntos day_of_week day_of_year day_of_month year \\\n0 0.00 0.00 1 1 1 2013 \n1 0.00 0.00 1 1 1 2013 \n2 0.00 0.00 1 1 1 2013 \n3 0.00 0.00 1 1 1 2013 \n4 0.00 0.00 1 1 1 2013 \n\n month season \n0 1 0 \n1 1 0 \n2 1 0 \n3 1 0 \n4 1 0 ","text/html":"
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datestore_nbrfamilyidsalesonpromotiontestcitystatetypeclusterdcoilwticotransactionswork_daynat_terremotonat_navidadnat_dia la madrenat_dia trabajonat_primer dia anonat_futbolnat_dia difuntosday_of_weekday_of_yearday_of_monthyearmonthseason
02013-01-011AUTOMOTIVE0.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310
12013-01-011BABY CARE1.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310
22013-01-011BEAUTY2.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310
32013-01-011BEVERAGES3.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310
42013-01-011BOOKS4.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310
\n
"},"metadata":{}}]},{"cell_type":"code","source":"#Let's keep data for 1-20 stores, due to the lack of memory\ndata = data.loc[data['store_nbr'].isin(list(range(1, 19)))]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:41.638633Z","iopub.execute_input":"2023-12-17T09:29:41.638996Z","iopub.status.idle":"2023-12-17T09:29:42.280888Z","shell.execute_reply.started":"2023-12-17T09:29:41.638958Z","shell.execute_reply":"2023-12-17T09:29:42.279794Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"train = data.loc[data['test'] == 0]\ntest = data.loc[data['test'] == 1]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:42.282429Z","iopub.execute_input":"2023-12-17T09:29:42.283037Z","iopub.status.idle":"2023-12-17T09:29:42.439409Z","shell.execute_reply.started":"2023-12-17T09:29:42.283005Z","shell.execute_reply":"2023-12-17T09:29:42.438050Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"sale_store = train.groupby([\"date\", \"store_nbr\"]).sales.sum().reset_index()\nsale_store = sale_store.pivot(index='date', columns='store_nbr', values='sales')\nsale_store.plot(figsize=(12, 6))\nplt.title(\"Sales per store\", fontsize=12)\nplt.xticks(rotation=30)\nplt.legend(bbox_to_anchor=(1, -.2), ncol=12)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:44.163276Z","iopub.execute_input":"2023-12-17T09:29:44.163736Z","iopub.status.idle":"2023-12-17T09:29:45.032010Z","shell.execute_reply.started":"2023-12-17T09:29:44.163693Z","shell.execute_reply":"2023-12-17T09:29:45.030880Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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the chart we can observe a point with abnormal sales in 2016 for the certain stores","metadata":{}},{"cell_type":"code","source":"tr_store = train.groupby(['date','store_nbr'], as_index=False)['transactions'].sum()\ntr_store = tr_store.pivot(index='date', columns='store_nbr', values='transactions')\ntr_store.plot(figsize=(12, 6))\nplt.title(\"Transactions per store\", fontsize=12)\nplt.xticks(rotation=30)\nplt.legend(bbox_to_anchor=(1, -.2), ncol=12) \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:45.033619Z","iopub.execute_input":"2023-12-17T09:29:45.033972Z","iopub.status.idle":"2023-12-17T09:29:46.240215Z","shell.execute_reply.started":"2023-12-17T09:29:45.033942Z","shell.execute_reply":"2023-12-17T09:29:46.239140Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"no anomalies are observed on the chart with transactions","metadata":{}},{"cell_type":"code","source":"list_days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\nlist_months = ['January','February','March','April','May','June','July','August','September','October','November','December'] \nlist_years = ['2013','2014','2015','2016','2017']\n\nframe1 = train.groupby(['day_of_week', 'year'], as_index=False)['sales'].sum()\nframe1 = frame1.pivot(index='year', columns='day_of_week', values='sales')\nframe1.plot(figsize=(8, 4))\nplt.xticks(range(2013,2018,1),rotation=30)\nplt.legend(list_days)\n\nframe4 = train.groupby(['month', 'year'], as_index=False)['sales'].sum()\nframe4 = frame4.pivot(index='year', columns='month', values='sales')\nframe4.plot(figsize=(8, 4))\nplt.xticks(range(2013,2018,1),rotation=30)\nplt.legend(list_months)\n\nframe2 = train.groupby(['day_of_week', 'month'], as_index=False)['sales'].sum()\nframe2 = frame2.pivot(index='month', columns='day_of_week', values='sales')\nframe2.plot(figsize=(8, 4))\nplt.xticks(range(1,13), labels=list_months,rotation=30)\nplt.legend(list_days)\n\nframe3 = train.groupby(['month', 'year'], as_index=False)['sales'].sum()\nframe3 = frame3.pivot(index='month', columns='year', values='sales')\nframe3.plot(figsize=(8, 4))\nplt.xticks(range(1,13), labels=list_months, rotation=30)\nplt.legend(list_years)\n\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:29:47.541910Z","iopub.execute_input":"2023-12-17T09:29:47.542359Z","iopub.status.idle":"2023-12-17T09:29:49.589160Z","shell.execute_reply.started":"2023-12-17T09:29:47.542320Z","shell.execute_reply":"2023-12-17T09:29:49.587928Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see, for many categories of goods, demand increases depending on whether it participates in promotions","metadata":{}},{"cell_type":"markdown","source":"we can notice that the more expensive oil is, the fewer sales","metadata":{}},{"cell_type":"markdown","source":"you can see that the number of sales increases with the number of transactions, which is quite expected","metadata":{}},{"cell_type":"markdown","source":"we can notice that the dependence of different types of goods on oil varies greatly, which means this is an important feature","metadata":{}},{"cell_type":"code","source":"data_seasons = train.copy()\ndata_winter = data_seasons[data_seasons['season']==0]\ndata_spring = data_seasons[data_seasons['season']==1]\ndata_summer = data_seasons[data_seasons['season']==2]\ndata_fall = data_seasons[data_seasons['season']==3]\n\nsales_family_winter = data_winter.groupby(['family'])['sales'].sum().reset_index()\nsales_family_spring = data_spring.groupby(['family'])['sales'].sum().reset_index()\nsales_family_summer = data_summer.groupby(['family'])['sales'].sum().reset_index()\nsales_family_fall = data_fall.groupby(['family'])['sales'].sum().reset_index()\n\nsales_family_winter = sales_family_winter.rename(columns = {\"sales\": \"sales_winter\"})\nsales_family_spring = sales_family_spring.rename(columns = {\"sales\": \"sales_spring\"})\nsales_family_summer = sales_family_summer.rename(columns = {\"sales\": \"sales_summer\"})\nsales_family_fall = sales_family_fall.rename(columns = {\"sales\": \"sales_fall\"})\n\ndf_all_seasons = sales_family_winter.merge(\n sales_family_spring, on=[\"family\"]).merge(\n sales_family_summer, on=[\"family\"]).merge(\n sales_family_fall, on=[\"family\"])\n\ndf_all_seasons.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:01.679476Z","iopub.execute_input":"2023-12-17T09:30:01.679865Z","iopub.status.idle":"2023-12-17T09:30:02.026916Z","shell.execute_reply.started":"2023-12-17T09:30:01.679831Z","shell.execute_reply":"2023-12-17T09:30:02.025855Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":" family sales_winter sales_spring sales_summer sales_fall\n0 AUTOMOTIVE 41574.00 45796.00 43230.00 34774.00\n1 BABY CARE 1001.00 1114.00 1489.00 1060.00\n2 BEAUTY 26919.00 27728.00 29905.00 23689.00\n3 BEVERAGES 17366197.00 18216018.00 18207011.00 16561615.00\n4 BOOKS 771.00 291.00 41.00 987.00","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
familysales_wintersales_springsales_summersales_fall
0AUTOMOTIVE41574.0045796.0043230.0034774.00
1BABY CARE1001.001114.001489.001060.00
2BEAUTY26919.0027728.0029905.0023689.00
3BEVERAGES17366197.0018216018.0018207011.0016561615.00
4BOOKS771.00291.0041.00987.00
\n
"},"metadata":{}}]},{"cell_type":"code","source":"del data_sales_prom,df_all_seasons,sales_family_winter,sales_family_spring,sales_family_summer,sales_family_fall,data_winter,data_spring,data_summer,data_fall","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:12.138536Z","iopub.execute_input":"2023-12-17T09:30:12.138992Z","iopub.status.idle":"2023-12-17T09:30:12.161167Z","shell.execute_reply.started":"2023-12-17T09:30:12.138950Z","shell.execute_reply":"2023-12-17T09:30:12.160274Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure()\nfig.set_size_inches((5, 5))\nplt.boxplot(train['sales'].values)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:21.391790Z","iopub.execute_input":"2023-12-17T09:30:21.392090Z","iopub.status.idle":"2023-12-17T09:30:21.792110Z","shell.execute_reply.started":"2023-12-17T09:30:21.392065Z","shell.execute_reply":"2023-12-17T09:30:21.790975Z"},"trusted":true},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"we may notice that there are outliers in the data, but we deliberately exclude them","metadata":{}},{"cell_type":"markdown","source":"**heatmap with correlation matrix**","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(15,7))\nsns.heatmap(train.corr(),annot=True)\nplt.tight_layout()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:21.793749Z","iopub.execute_input":"2023-12-17T09:30:21.794603Z","iopub.status.idle":"2023-12-17T09:30:25.949558Z","shell.execute_reply.started":"2023-12-17T09:30:21.794560Z","shell.execute_reply":"2023-12-17T09:30:25.948651Z"},"trusted":true},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"We can notice that the signs are weakly correlated","metadata":{}},{"cell_type":"code","source":"data_analyses = data.copy()\n\ntarget = 'sales'\ntrain = data_analyses.loc[data_analyses['test'] == 0]\ntest = data_analyses.loc[data_analyses['test'] == 1]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:25.950702Z","iopub.execute_input":"2023-12-17T09:30:25.951342Z","iopub.status.idle":"2023-12-17T09:30:26.251357Z","shell.execute_reply.started":"2023-12-17T09:30:25.951311Z","shell.execute_reply":"2023-12-17T09:30:26.250436Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"def CompareTwoGroups(dataframe, group, target):\n # Create Combinations\n item_comb = list(itertools.combinations(dataframe[group].unique(), 2))\n AB = pd.DataFrame()\n for i in range(0, len(item_comb)):\n # Define Groups\n groupA = dataframe[dataframe[group] == item_comb[i][0]][target]\n groupB = dataframe[dataframe[group] == item_comb[i][1]][target]\n # Assumption: Normality\n ntA = shapiro(groupA)[1] < 0.05\n ntB = shapiro(groupB)[1] < 0.05\n # H0: Distribution is Normal! - False\n # H1: Distribution is not Normal! - True\n if (ntA == False) & (ntB == False): # \"H0: Normal Distribution\"\n # Parametric Test\n # Assumption: Homogeneity of variances\n leveneTest = stats.levene(groupA, groupB)[1] < 0.05\n # H0: Homogeneity: False\n # H1: Heterogeneous: True\n if leveneTest == False:\n # Homogeneity\n ttest = stats.ttest_ind(groupA, groupB, equal_var=True)[1]\n # H0: M1 = M2 - False\n # H1: M1 != M2 - True\n else:\n # Heterogeneous\n ttest = stats.ttest_ind(groupA, groupB, equal_var=False)[1]\n # H0: M1 = M2 - False\n # H1: M1 != M2 - True\n else:\n # Non-Parametric Test\n ttest = stats.mannwhitneyu(groupA, groupB)[1] \n # H0: M1 = M2 - False\n # H1: M1 != M2 - True\n \n temp = pd.DataFrame({\"Compare Two Groups\":[ttest < 0.05], \n \"p-value\":[ttest],\n \"GroupA_Mean\":[groupA.mean()], \n \"GroupB_Mean\":[groupB.mean()],\n \"GroupA_Median\":[groupA.median()], \n \"GroupB_Median\":[groupB.median()],\n \"GroupA_Count\":[groupA.count()], \n \"GroupB_Count\":[groupB.count()]}, index = [item_comb[i]])\n temp[\"Compare Two Groups\"] = np.where(temp[\"Compare Two Groups\"] == True, \n \"Different Groups\", \"Similar Groups\")\n temp[\"TestType\"] = np.where((ntA == False) & (ntB == False), \"Parametric\", \"Non-Parametric\")\n \n AB = pd.concat([AB, temp[[\"TestType\", \"Compare Two Groups\", \n \"p-value\",\"GroupA_Median\", \"GroupB_Median\",\n \"GroupA_Mean\", \"GroupB_Mean\",\n \"GroupA_Count\", \"GroupB_Count\"]]]) \n return AB\n\nCompareTwoGroups(train, group = \"store_nbr\", target = \"sales\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:26.252975Z","iopub.execute_input":"2023-12-17T09:30:26.253555Z","iopub.status.idle":"2023-12-17T09:30:58.276749Z","shell.execute_reply.started":"2023-12-17T09:30:26.253523Z","shell.execute_reply":"2023-12-17T09:30:58.275280Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":" TestType Compare Two Groups p-value GroupA_Median \\\n(1, 2) Non-Parametric Different Groups 0.00 18.55 \n(1, 3) Non-Parametric Different Groups 0.00 18.55 \n(1, 4) Non-Parametric Different Groups 0.00 18.55 \n(1, 5) Non-Parametric Different Groups 0.00 18.55 \n(1, 6) Non-Parametric Different Groups 0.00 18.55 \n... ... ... ... ... \n(15, 17) Non-Parametric Different Groups 0.00 8.00 \n(15, 18) Non-Parametric Similar Groups 0.39 8.00 \n(16, 17) Non-Parametric Different Groups 0.00 6.00 \n(16, 18) Non-Parametric Different Groups 0.00 6.00 \n(17, 18) Non-Parametric Different Groups 0.00 10.00 \n\n GroupB_Median GroupA_Mean GroupB_Mean GroupA_Count GroupB_Count \n(1, 2) 23.00 253.93 387.00 55704 55704 \n(1, 3) 63.00 253.93 906.25 55704 55704 \n(1, 4) 19.00 253.93 339.47 55704 55704 \n(1, 5) 19.00 253.93 279.92 55704 55704 \n(1, 6) 28.00 253.93 452.10 55704 55704 \n... ... ... ... ... ... \n(15, 17) 10.00 205.57 323.69 55704 55704 \n(15, 18) 8.00 205.57 238.03 55704 55704 \n(16, 17) 10.00 197.23 323.69 55704 55704 \n(16, 18) 8.00 197.23 238.03 55704 55704 \n(17, 18) 8.00 323.69 238.03 55704 55704 \n\n[153 rows x 9 columns]","text/html":"
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TestTypeCompare Two Groupsp-valueGroupA_MedianGroupB_MedianGroupA_MeanGroupB_MeanGroupA_CountGroupB_Count
(1, 2)Non-ParametricDifferent Groups0.0018.5523.00253.93387.005570455704
(1, 3)Non-ParametricDifferent Groups0.0018.5563.00253.93906.255570455704
(1, 4)Non-ParametricDifferent Groups0.0018.5519.00253.93339.475570455704
(1, 5)Non-ParametricDifferent Groups0.0018.5519.00253.93279.925570455704
(1, 6)Non-ParametricDifferent Groups0.0018.5528.00253.93452.105570455704
..............................
(15, 17)Non-ParametricDifferent Groups0.008.0010.00205.57323.695570455704
(15, 18)Non-ParametricSimilar Groups0.398.008.00205.57238.035570455704
(16, 17)Non-ParametricDifferent Groups0.006.0010.00197.23323.695570455704
(16, 18)Non-ParametricDifferent Groups0.006.008.00197.23238.035570455704
(17, 18)Non-ParametricDifferent Groups0.0010.008.00323.69238.035570455704
\n

153 rows × 9 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Rolling Summary Stats Features\n#A rolling mean is simply the mean of a certain number of previous periods in a time series.\nfor i in [16,17,18,19,20,21,22,46,76,106,365, 730]:\n data_analyses[\"sales_roll_mean_\"+str(i)]=data_analyses.groupby(\n [\"store_nbr\", \"family\"])['sales'].rolling(i).mean().shift(1).values\n \n# 2. Hypothesis Testing: Similarity\n# Store Based\nstoresales = train.groupby([\"date\", \"store_nbr\"])['sales'].sum().reset_index()\nctg_ss = CompareTwoGroups(storesales, group=\"store_nbr\", target=\"sales\")\ndel storesales\n#Let's find similar groups\nctg_ss[ctg_ss[\"Compare Two Groups\"] == \"Similar Groups\"]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:30:58.278810Z","iopub.execute_input":"2023-12-17T09:30:58.279241Z","iopub.status.idle":"2023-12-17T09:31:08.879397Z","shell.execute_reply.started":"2023-12-17T09:30:58.279206Z","shell.execute_reply":"2023-12-17T09:31:08.878280Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":" TestType Compare Two Groups p-value GroupA_Median \\\n(12, 16) Non-Parametric Similar Groups 0.61 6386.87 \n(14, 15) Non-Parametric Similar Groups 0.16 6529.03 \n\n GroupB_Median GroupA_Mean GroupB_Mean GroupA_Count GroupB_Count \n(12, 16) 6146.28 6314.70 6508.54 1688 1688 \n(14, 15) 6826.27 6746.40 6783.93 1688 1688 ","text/html":"
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TestTypeCompare Two Groupsp-valueGroupA_MedianGroupB_MedianGroupA_MeanGroupB_MeanGroupA_CountGroupB_Count
(12, 16)Non-ParametricSimilar Groups0.616386.876146.286314.706508.5416881688
(14, 15)Non-ParametricSimilar Groups0.166529.036826.276746.406783.9316881688
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see, there were two pairs of similar stores (12,16) and (14,15) and we can create a special feature reflecting the similarity of the stores","metadata":{}},{"cell_type":"code","source":"data_analyses[\"StoreSalesSimilarity\"] = np.where(data_analyses[\"store_nbr\"].isin([12,16]), 1, 0)\ndata_analyses[\"StoreSalesSimilarity\"] = np.where(data_analyses[\"store_nbr\"].isin([14,15]), 2, data_analyses[\"StoreSalesSimilarity\"])\n\n#Now let's filter the results by similarity of groups when grouped by \"family\"\n# Item Based\nitemsales = train.groupby([\"date\", \"family\"])['sales'].sum().reset_index()\nctg_is = CompareTwoGroups(itemsales, group = \"family\", target = \"sales\")\ndel itemsales\nctg_is[ctg_is[\"Compare Two Groups\"] == \"Similar Groups\"]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:08.880730Z","iopub.execute_input":"2023-12-17T09:31:08.881057Z","iopub.status.idle":"2023-12-17T09:31:24.951731Z","shell.execute_reply.started":"2023-12-17T09:31:08.881029Z","shell.execute_reply":"2023-12-17T09:31:24.950547Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" TestType \\\n(CELEBRATION, LINGERIE) Non-Parametric \n(GROCERY II, HOME AND KITCHEN I) Non-Parametric \n(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES) Non-Parametric \n(LAWN AND GARDEN, PLAYERS AND ELECTRONICS) Non-Parametric \n(MAGAZINES, PET SUPPLIES) Non-Parametric \n\n Compare Two Groups p-value \\\n(CELEBRATION, LINGERIE) Similar Groups 0.15 \n(GROCERY II, HOME AND KITCHEN I) Similar Groups 0.87 \n(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES) Similar Groups 0.05 \n(LAWN AND GARDEN, PLAYERS AND ELECTRONICS) Similar Groups 0.26 \n(MAGAZINES, PET SUPPLIES) Similar Groups 0.69 \n\n GroupA_Median GroupB_Median \\\n(CELEBRATION, LINGERIE) 184.00 157.00 \n(GROCERY II, HOME AND KITCHEN I) 359.00 376.00 \n(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES) 8.00 7.00 \n(LAWN AND GARDEN, PLAYERS AND ELECTRONICS) 82.00 127.00 \n(MAGAZINES, PET SUPPLIES) 26.50 66.00 \n\n GroupA_Mean GroupB_Mean \\\n(CELEBRATION, LINGERIE) 156.99 161.87 \n(GROCERY II, HOME AND KITCHEN I) 379.52 339.36 \n(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES) 8.09 19.18 \n(LAWN AND GARDEN, PLAYERS AND ELECTRONICS) 104.91 103.80 \n(MAGAZINES, PET SUPPLIES) 54.12 55.95 \n\n GroupA_Count GroupB_Count \n(CELEBRATION, LINGERIE) 1688 1688 \n(GROCERY II, HOME AND KITCHEN I) 1688 1688 \n(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES) 1688 1688 \n(LAWN AND GARDEN, PLAYERS AND ELECTRONICS) 1688 1688 \n(MAGAZINES, PET SUPPLIES) 1688 1688 ","text/html":"
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TestTypeCompare Two Groupsp-valueGroupA_MedianGroupB_MedianGroupA_MeanGroupB_MeanGroupA_CountGroupB_Count
(CELEBRATION, LINGERIE)Non-ParametricSimilar Groups0.15184.00157.00156.99161.8716881688
(GROCERY II, HOME AND KITCHEN I)Non-ParametricSimilar Groups0.87359.00376.00379.52339.3616881688
(HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES)Non-ParametricSimilar Groups0.058.007.008.0919.1816881688
(LAWN AND GARDEN, PLAYERS AND ELECTRONICS)Non-ParametricSimilar Groups0.2682.00127.00104.91103.8016881688
(MAGAZINES, PET SUPPLIES)Non-ParametricSimilar Groups0.6926.5066.0054.1255.9516881688
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"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see, there were two pairs of similar family (CELEBRATION, LINGERIE),(GROCERY II, HOME AND KITCHEN I), (HOME APPLIANCES, SCHOOL AND OFFICE SUPPLIES), (LAWN AND GARDEN, PLAYERS AND ELECTRONICS), (MAGAZINES, PET SUPPLIES) and we can create a special feature reflecting the similarity of the family","metadata":{}},{"cell_type":"code","source":"data_analyses[\"ItemSalesSimilarity\"] = np.where(data_analyses['family'].isin(['CELEBRATION', 'LINGERIE']), 1, 0)\ndata_analyses[\"ItemSalesSimilarity\"] = np.where(data_analyses['family'].isin(['GROCERY II', 'HOME AND KITCHEN I']), 2, data_analyses[\"ItemSalesSimilarity\"])\ndata_analyses[\"ItemSalesSimilarity\"] = np.where(data_analyses['family'].isin(['HOME APPLIANCES', 'SCHOOL AND OFFICE SUPPLIES']), 3, data_analyses[\"ItemSalesSimilarity\"])\ndata_analyses[\"ItemSalesSimilarity\"] = np.where(data_analyses['family'].isin(['LAWN AND GARDEN', 'PLAYERS AND ELECTRONICS']), 4, data_analyses[\"ItemSalesSimilarity\"])\ndata_analyses[\"ItemSalesSimilarity\"] = np.where(data_analyses['family'].isin(['MAGAZINES', 'PET SUPPLIES']), 5, data_analyses[\"ItemSalesSimilarity\"])","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:24.953344Z","iopub.execute_input":"2023-12-17T09:31:24.953817Z","iopub.status.idle":"2023-12-17T09:31:25.222326Z","shell.execute_reply.started":"2023-12-17T09:31:24.953777Z","shell.execute_reply":"2023-12-17T09:31:25.220807Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"data_analyses.sort_values(by=['store_nbr', 'family', 'date'], axis=0, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:25.223680Z","iopub.execute_input":"2023-12-17T09:31:25.224028Z","iopub.status.idle":"2023-12-17T09:31:26.205130Z","shell.execute_reply.started":"2023-12-17T09:31:25.224000Z","shell.execute_reply":"2023-12-17T09:31:26.203858Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"def lag_features(dataframe, lags, groups = [\"store_nbr\", \"family\"], target = \"sales\", prefix = ''):\n dataframe = dataframe.copy()\n for lag in lags:\n dataframe[prefix + str(lag)] = dataframe.groupby(groups)[target].transform(\n lambda x: x.shift(lag))\n return dataframe\n\n#Let's create lags\ndata_analyses = lag_features(data_analyses, \n lags = [16,17,18,19,20,21,22,46,76,106,365, 730],\n groups = [\"store_nbr\", \"family\"], target = 'sales', \n prefix = 'sales_lag_')\ndata_analyses","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:26.206622Z","iopub.execute_input":"2023-12-17T09:31:26.207060Z","iopub.status.idle":"2023-12-17T09:31:30.486631Z","shell.execute_reply.started":"2023-12-17T09:31:26.207018Z","shell.execute_reply":"2023-12-17T09:31:30.485547Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":" date store_nbr family id sales onpromotion \\\n0 2013-01-01 1 AUTOMOTIVE 0.00 0.00 0.00 \n1782 2013-01-02 1 AUTOMOTIVE 1782.00 2.00 0.00 \n3564 2013-01-03 1 AUTOMOTIVE 3564.00 3.00 0.00 \n5346 2013-01-04 1 AUTOMOTIVE 5346.00 3.00 0.00 \n7128 2013-01-05 1 AUTOMOTIVE 7128.00 5.00 0.00 \n... ... ... ... ... ... ... \n3028211 2017-08-27 18 SEAFOOD 3020819.00 NaN 0.00 \n3029993 2017-08-28 18 SEAFOOD 3022601.00 NaN 0.00 \n3031775 2017-08-29 18 SEAFOOD 3024383.00 NaN 0.00 \n3033557 2017-08-30 18 SEAFOOD 3026165.00 NaN 0.00 \n3035339 2017-08-31 18 SEAFOOD 3027947.00 NaN 0.00 \n\n test city state type cluster dcoilwtico transactions \\\n0 0 Quito Pichincha D 13 93.14 0.00 \n1782 0 Quito Pichincha D 13 93.14 2111.00 \n3564 0 Quito Pichincha D 13 92.97 1833.00 \n5346 0 Quito Pichincha D 13 93.12 1863.00 \n7128 0 Quito Pichincha D 13 93.15 1509.00 \n... ... ... ... ... ... ... ... \n3028211 1 Quito Pichincha B 16 46.82 NaN \n3029993 1 Quito Pichincha B 16 46.40 NaN \n3031775 1 Quito Pichincha B 16 46.46 NaN \n3033557 1 Quito Pichincha B 16 45.96 NaN \n3035339 1 Quito Pichincha B 16 47.26 NaN \n\n work_day nat_terremoto nat_navidad nat_dia la madre \\\n0 0.00 0.00 0.00 0.00 \n1782 0.00 0.00 0.00 0.00 \n3564 0.00 0.00 0.00 0.00 \n5346 0.00 0.00 0.00 0.00 \n7128 1.00 0.00 0.00 0.00 \n... ... ... ... ... \n3028211 0.00 0.00 0.00 0.00 \n3029993 0.00 0.00 0.00 0.00 \n3031775 0.00 0.00 0.00 0.00 \n3033557 0.00 0.00 0.00 0.00 \n3035339 0.00 0.00 0.00 0.00 \n\n nat_dia trabajo nat_primer dia ano nat_futbol nat_dia difuntos \\\n0 0.00 1.00 0.00 0.00 \n1782 0.00 0.00 0.00 0.00 \n3564 0.00 0.00 0.00 0.00 \n5346 0.00 0.00 0.00 0.00 \n7128 0.00 0.00 0.00 0.00 \n... ... ... ... ... \n3028211 0.00 0.00 0.00 0.00 \n3029993 0.00 0.00 0.00 0.00 \n3031775 0.00 0.00 0.00 0.00 \n3033557 0.00 0.00 0.00 0.00 \n3035339 0.00 0.00 0.00 0.00 \n\n day_of_week day_of_year day_of_month year month season \\\n0 1 1 1 2013 1 0 \n1782 2 2 2 2013 1 0 \n3564 3 3 3 2013 1 0 \n5346 4 4 4 2013 1 0 \n7128 5 5 5 2013 1 0 \n... ... ... ... ... ... ... \n3028211 6 239 27 2017 8 2 \n3029993 0 240 28 2017 8 2 \n3031775 1 241 29 2017 8 2 \n3033557 2 242 30 2017 8 2 \n3035339 3 243 31 2017 8 2 \n\n sales_roll_mean_16 sales_roll_mean_17 sales_roll_mean_18 \\\n0 NaN NaN NaN \n1782 1.56 1.65 1.94 \n3564 5.06 4.94 4.89 \n5346 0.00 0.00 0.00 \n7128 0.00 0.00 0.00 \n... ... ... ... \n3028211 0.50 0.53 0.78 \n3029993 3.06 3.35 3.28 \n3031775 5.67 5.63 5.74 \n3033557 6.54 6.33 6.30 \n3035339 NaN NaN NaN \n\n sales_roll_mean_19 sales_roll_mean_20 sales_roll_mean_21 \\\n0 NaN NaN NaN \n1782 1.89 2.05 2.05 \n3564 5.11 5.25 5.14 \n5346 0.00 0.00 0.00 \n7128 0.00 0.00 0.00 \n... ... ... ... \n3028211 1.21 1.15 1.14 \n3029993 3.32 3.20 3.14 \n3031775 5.96 6.56 6.64 \n3033557 6.30 6.16 5.87 \n3035339 NaN NaN NaN \n\n sales_roll_mean_22 sales_roll_mean_46 sales_roll_mean_76 \\\n0 NaN NaN NaN \n1782 1.95 2.89 2.62 \n3564 5.36 5.37 4.88 \n5346 0.00 0.00 0.00 \n7128 0.00 0.00 0.00 \n... ... ... ... \n3028211 1.09 1.22 3.79 \n3029993 3.14 3.17 4.16 \n3031775 6.53 6.81 7.74 \n3033557 5.74 6.38 6.55 \n3035339 NaN NaN NaN \n\n sales_roll_mean_106 sales_roll_mean_365 sales_roll_mean_730 \\\n0 NaN NaN NaN \n1782 2.44 2.38 NaN \n3564 4.84 3.69 3.22 \n5346 NaN NaN NaN \n7128 0.00 0.00 NaN \n... ... ... ... \n3028211 2.80 0.91 1.72 \n3029993 4.59 2.19 1.86 \n3031775 9.39 9.91 NaN \n3033557 7.27 6.98 8.42 \n3035339 NaN NaN NaN \n\n StoreSalesSimilarity ItemSalesSimilarity sales_lag_16 \\\n0 0 0 NaN \n1782 0 0 NaN \n3564 0 0 NaN \n5346 0 0 NaN \n7128 0 0 NaN \n... ... ... ... \n3028211 0 0 8.00 \n3029993 0 0 13.32 \n3031775 0 0 15.93 \n3033557 0 0 8.99 \n3035339 0 0 21.36 \n\n sales_lag_17 sales_lag_18 sales_lag_19 sales_lag_20 sales_lag_21 \\\n0 NaN NaN NaN NaN NaN \n1782 NaN NaN NaN NaN NaN \n3564 NaN NaN NaN NaN NaN \n5346 NaN NaN NaN NaN NaN \n7128 NaN NaN NaN NaN NaN \n... ... ... ... ... ... \n3028211 11.00 9.89 19.12 12.24 19.98 \n3029993 8.00 11.00 9.89 19.12 12.24 \n3031775 13.32 8.00 11.00 9.89 19.12 \n3033557 15.93 13.32 8.00 11.00 9.89 \n3035339 8.99 15.93 13.32 8.00 11.00 \n\n sales_lag_22 sales_lag_46 sales_lag_76 sales_lag_106 \\\n0 NaN NaN NaN NaN \n1782 NaN NaN NaN NaN \n3564 NaN NaN NaN NaN \n5346 NaN NaN NaN NaN \n7128 NaN NaN NaN NaN \n... ... ... ... ... \n3028211 22.00 6.51 11.33 20.41 \n3029993 19.98 10.09 11.63 18.53 \n3031775 12.24 8.72 5.00 12.32 \n3033557 19.12 25.73 5.00 7.62 \n3035339 9.89 42.47 4.00 12.35 \n\n sales_lag_365 sales_lag_730 \n0 NaN NaN \n1782 NaN NaN \n3564 NaN NaN \n5346 NaN NaN \n7128 NaN NaN \n... ... ... \n3028211 0.00 7.22 \n3029993 0.00 14.92 \n3031775 0.00 14.35 \n3033557 0.00 7.28 \n3035339 0.00 8.62 \n\n[1012176 rows x 53 columns]","text/html":"
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datestore_nbrfamilyidsalesonpromotiontestcitystatetypeclusterdcoilwticotransactionswork_daynat_terremotonat_navidadnat_dia la madrenat_dia trabajonat_primer dia anonat_futbolnat_dia difuntosday_of_weekday_of_yearday_of_monthyearmonthseasonsales_roll_mean_16sales_roll_mean_17sales_roll_mean_18sales_roll_mean_19sales_roll_mean_20sales_roll_mean_21sales_roll_mean_22sales_roll_mean_46sales_roll_mean_76sales_roll_mean_106sales_roll_mean_365sales_roll_mean_730StoreSalesSimilarityItemSalesSimilaritysales_lag_16sales_lag_17sales_lag_18sales_lag_19sales_lag_20sales_lag_21sales_lag_22sales_lag_46sales_lag_76sales_lag_106sales_lag_365sales_lag_730
02013-01-011AUTOMOTIVE0.000.000.000QuitoPichinchaD1393.140.000.000.000.000.000.001.000.000.00111201310NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
17822013-01-021AUTOMOTIVE1782.002.000.000QuitoPichinchaD1393.142111.000.000.000.000.000.000.000.000.002222013101.561.651.941.892.052.051.952.892.622.442.38NaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
35642013-01-031AUTOMOTIVE3564.003.000.000QuitoPichinchaD1392.971833.000.000.000.000.000.000.000.000.003332013105.064.944.895.115.255.145.365.374.884.843.693.2200NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
53462013-01-041AUTOMOTIVE5346.003.000.000QuitoPichinchaD1393.121863.000.000.000.000.000.000.000.000.004442013100.000.000.000.000.000.000.000.000.00NaNNaNNaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71282013-01-051AUTOMOTIVE7128.005.000.000QuitoPichinchaD1393.151509.001.000.000.000.000.000.000.000.005552013100.000.000.000.000.000.000.000.000.000.000.00NaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................................................................................................................
30282112017-08-2718SEAFOOD3020819.00NaN0.001QuitoPichinchaB1646.82NaN0.000.000.000.000.000.000.000.006239272017820.500.530.781.211.151.141.091.223.792.800.911.72008.0011.009.8919.1212.2419.9822.006.5111.3320.410.007.22
30299932017-08-2818SEAFOOD3022601.00NaN0.001QuitoPichinchaB1646.40NaN0.000.000.000.000.000.000.000.000240282017823.063.353.283.323.203.143.143.174.164.592.191.860013.328.0011.009.8919.1212.2419.9810.0911.6318.530.0014.92
30317752017-08-2918SEAFOOD3024383.00NaN0.001QuitoPichinchaB1646.46NaN0.000.000.000.000.000.000.000.001241292017825.675.635.745.966.566.646.536.817.749.399.91NaN0015.9313.328.0011.009.8919.1212.248.725.0012.320.0014.35
30335572017-08-3018SEAFOOD3026165.00NaN0.001QuitoPichinchaB1645.96NaN0.000.000.000.000.000.000.000.002242302017826.546.336.306.306.165.875.746.386.557.276.988.42008.9915.9313.328.0011.009.8919.1225.735.007.620.007.28
30353392017-08-3118SEAFOOD3027947.00NaN0.001QuitoPichinchaB1647.26NaN0.000.000.000.000.000.000.000.00324331201782NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0021.368.9915.9313.328.0011.009.8942.474.0012.350.008.62
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1012176 rows × 53 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"#remove the most correlated features\ndef drop_cor(dataframe, name, index):\n ind = dataframe[dataframe.columns[dataframe.columns.str.contains(name)].tolist()+[\n \"sales\"]].corr().sales.sort_values(ascending = False).index[1:index]\n ind = dataframe.drop(ind, axis = 1).columns[dataframe.drop(ind, axis = 1).columns.str.contains(name)]\n dataframe.drop(ind, axis = 1, inplace = True)\n\ndrop_cor(data_analyses, \"sales_lag\", 6)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:30.488144Z","iopub.execute_input":"2023-12-17T09:31:30.489287Z","iopub.status.idle":"2023-12-17T09:31:31.692719Z","shell.execute_reply.started":"2023-12-17T09:31:30.489252Z","shell.execute_reply":"2023-12-17T09:31:31.691700Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"# 4. New Features -->Last i. Months \ndata_analyses[\"monthyear\"] = data_analyses.date.dt.to_period('M')\n\n# Store-Item Based\nfor i in [3, 6, 9, 12, 15, 18, 21, 24]:\n last_months = data_analyses.groupby([\"store_nbr\", \"family\", \"monthyear\"\n ]).sales.agg([\"sum\",\"mean\",\"std\",\"min\",\"max\"]).shift(i).reset_index()\n last_months.columns = ['store_nbr', 'family', 'monthyear', 'last_'+str(i)+'months_sales_sum',\n 'last_'+str(i)+'months_sales_mean', 'last_'+str(i)+'months_sales_std',\n 'last_'+str(i)+'months_sales_min', 'last_'+str(i)+'months_sales_max']\n data_analyses = pd.merge(data_analyses, last_months, how = \"left\", on = [\"store_nbr\", \"family\", \"monthyear\"])\ndel last_months, i\n\ndrop_cor(data_analyses, \"last_\", 6)\n\n# Store Based\nfor i in [3, 6, 9, 12]:\n last_months = data_analyses.groupby([\"store_nbr\", \"monthyear\"\n ]).sales.agg([\"sum\", \"mean\", \"std\", \"min\", \"max\"]).shift(i).reset_index()\n last_months.columns = ['store_nbr', 'monthyear', 'store_last_'+str(i)+'months_sales_sum',\n 'store_last_'+str(i)+'months_sales_mean', 'store_last_'+str(i)+'months_sales_std',\n 'store_last_'+str(i)+'months_sales_min', 'store_last_'+str(i)+'months_sales_max']\n data_analyses = pd.merge(data_analyses, last_months, how = \"left\", on = [\"store_nbr\", \"monthyear\"])\ndel last_months, i\n\n# Item Based\nfor i in [3, 6, 9, 12]:\n last_months = data_analyses.groupby([\"family\", \"monthyear\"\n ]).sales.agg([\"sum\", \"mean\", \"std\", \"min\", \"max\"]).shift(i).reset_index()\n last_months.columns = ['family', 'monthyear', 'item_last_'+str(i)+'months_sales_sum',\n 'item_last_'+str(i)+'months_sales_mean', 'item_last_'+str(i)+'months_sales_std',\n 'item_last_'+str(i)+'months_sales_min', 'item_last_'+str(i)+'months_sales_max']\n data_analyses = pd.merge(data_analyses, last_months, how = \"left\", on = [\"family\", \"monthyear\"])\ndel last_months, i\n\n# Similarity Based\nfor i in [3, 6, 9, 12]:\n last_months = data_analyses.groupby([\"StoreSalesSimilarity\", \"monthyear\"\n ]).sales.agg([\"sum\", \"mean\", \"std\", \"min\", \"max\"]).shift(i).reset_index()\n last_months.columns = ['StoreSalesSimilarity', 'monthyear', 'storesim_last_'+str(i)+'months_sales_sum',\n 'storesim_last_'+str(i)+'months_sales_mean', 'storesim_last_'+str(i)+'months_sales_std',\n 'storesim_last_'+str(i)+'months_sales_min', 'storesim_last_'+str(i)+'months_sales_max']\n data_analyses = pd.merge(data_analyses, last_months, how = \"left\", on = [\"StoreSalesSimilarity\", \"monthyear\"])\ndel last_months, i\n\n\nfor i in [3, 6, 9, 12]:\n last_months = data_analyses.groupby([\"ItemSalesSimilarity\", \"monthyear\"\n ]).sales.agg([\"sum\", \"mean\", \"std\", \"min\", \"max\"]).shift(i).reset_index()\n last_months.columns = ['ItemSalesSimilarity', 'monthyear', 'itemsim_last_'+str(i)+'months_sales_sum',\n 'itemsim_last_'+str(i)+'months_sales_mean', 'itemsim_last_'+str(i)+'months_sales_std',\n 'itemsim_last_'+str(i)+'months_sales_min', 'itemsim_last_'+str(i)+'months_sales_max']\n data_analyses = pd.merge(data_analyses, last_months, how = \"left\", on = [\"ItemSalesSimilarity\", \"monthyear\"])\ndel last_months, i\n\ndata_analyses.drop(\"monthyear\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:31.694230Z","iopub.execute_input":"2023-12-17T09:31:31.694562Z","iopub.status.idle":"2023-12-17T09:31:59.620328Z","shell.execute_reply.started":"2023-12-17T09:31:31.694535Z","shell.execute_reply":"2023-12-17T09:31:59.619262Z"},"trusted":true},"execution_count":60,"outputs":[]},{"cell_type":"code","source":"# 5. New Features -->Last i. day of week\ndata_analyses.sort_values([\"store_nbr\", \"family\", \"day_of_week\", \"date\"], inplace = True)\n\ndata_analyses = lag_features(data_analyses, \n lags = np.arange(12,41, 1).tolist()+[16,46,76,106],\n groups = [\"store_nbr\", \"family\", \"day_of_week\"], \n target = 'sales', \n prefix = 'dayofweek_sales_lag_')\n\ndata_analyses[data_analyses.columns[data_analyses.columns.str.contains(\"dayofweek_sales_lag_\")].tolist()+[\"sales\"]].corr().sales.sort_values(ascending = False)\n\ndrop_cor(data_analyses, \"dayofweek_sales_lag_\", 6)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:31:59.622130Z","iopub.execute_input":"2023-12-17T09:31:59.622601Z","iopub.status.idle":"2023-12-17T09:32:52.301988Z","shell.execute_reply.started":"2023-12-17T09:31:59.622560Z","shell.execute_reply":"2023-12-17T09:32:52.301109Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"data_analyses.sort_values([\"store_nbr\", \"family\", \"date\"], inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:32:52.303512Z","iopub.execute_input":"2023-12-17T09:32:52.304416Z","iopub.status.idle":"2023-12-17T09:32:53.185778Z","shell.execute_reply.started":"2023-12-17T09:32:52.304382Z","shell.execute_reply":"2023-12-17T09:32:53.184304Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"# Exponentially Weighted Mean Features\ndef ewm_features(dataframe, alphas, lags):\n dataframe = dataframe.copy()\n for alpha in alphas:\n for lag in lags:\n dataframe['sales_ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)\n ] = dataframe.groupby([\"store_nbr\", \"family\"]\n )['sales'].transform(lambda x: x.shift(lag).ewm(alpha=alpha).mean())\n return dataframe\n\nalphas = [0.95, 0.9, 0.8, 0.7, 0.5]\nlags = [16,17,18,19,20,21,22,46,76,106,365, 730]\n\ndata_analyses = ewm_features(data_analyses, alphas, lags)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:32:53.188166Z","iopub.execute_input":"2023-12-17T09:32:53.188627Z","iopub.status.idle":"2023-12-17T09:33:21.609675Z","shell.execute_reply.started":"2023-12-17T09:32:53.188586Z","shell.execute_reply":"2023-12-17T09:33:21.608472Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"# Day of year\ndata_analyses.sort_values([\"day_of_year\", \"store_nbr\", \"family\"], inplace = True)\ndata_analyses = lag_features(data_analyses, \n lags = [1,2,3,4],\n groups = [\"day_of_year\", \"store_nbr\", \"family\"], \n target = 'sales', \n prefix = 'dayofyear_sales_lag_')\n\n# pd.cut\nclus = data_analyses.groupby([\"store_nbr\"])['sales'].mean().reset_index()\nclus[\"store_cluster\"] = pd.cut(clus['sales'], bins = 4, labels = range(1,5))\nclus.drop(\"sales\", axis = 1, inplace = True)\ndata_analyses = pd.merge(data_analyses, clus, how = \"left\")\nclus = data_analyses.groupby([\"family\"])['sales'].mean().reset_index()\nclus[\"family_cluster\"] = pd.cut(clus['sales'], bins = 5, labels = range(1,6))\nclus.drop(\"sales\", axis = 1, inplace = True)\ndata_analyses = pd.merge(data_analyses, clus, how = \"left\")\ndel clus\n\ndata_analyses.shape","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:33:21.611107Z","iopub.execute_input":"2023-12-17T09:33:21.611437Z","iopub.status.idle":"2023-12-17T09:37:07.692640Z","shell.execute_reply.started":"2023-12-17T09:33:21.611411Z","shell.execute_reply":"2023-12-17T09:37:07.691530Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"(1012176, 202)"},"metadata":{}}]},{"cell_type":"code","source":"# Dataframe must be sorted by date because of Time Series Split \ndata_analyses = data_analyses.sort_values(\"date\").reset_index(drop = True)\n\n#Let's bring all the columns into a single form to avoid further errors\ncolumns_old = data_analyses.columns\ncolumns_new = [column.replace(\" \", \"_\") for column in columns_old]\ndata_analyses.columns = columns_new\ndel columns_old, columns_new \n\n#Let's define the columns that will be further used in the analysis\nfeatures = [col for col in data_analyses.columns if col not in ['date', 'id', \"sales\", 'transactions',\n 'day_of_week','day_of_year','day_of_month',\n 'year', 'month', 'season','test']]\n#Let's fill NA\ndata_analyses = data_analyses.fillna(0)\n\n#Let's make the data readable\ndata_analyses['store_nbr']=data_analyses['store_nbr'].apply(lambda x: (f\"store_nbr_{x}\"))\ndata_analyses['cluster']=data_analyses['cluster'].apply(lambda x: (f\"cluster_{x}\"))\ndata_analyses['type'] = data_analyses['type'].apply(lambda x: (f\"type_{x}\"))\ndata_analyses['city'] = data_analyses['city'].apply(lambda x: (f\"city_{x.lower()}\"))\ndata_analyses['state'] = data_analyses['state'].apply(lambda x: (f\"state_{x.lower()}\"))\ndata_analyses.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:37:07.694401Z","iopub.execute_input":"2023-12-17T09:37:07.694878Z","iopub.status.idle":"2023-12-17T09:37:18.855607Z","shell.execute_reply.started":"2023-12-17T09:37:07.694837Z","shell.execute_reply":"2023-12-17T09:37:18.854092Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":" date store_nbr family id sales \\\n0 2013-01-01 store_nbr_1 AUTOMOTIVE 0.00 0.00 \n1 2013-01-01 store_nbr_8 PERSONAL CARE 1741.00 0.00 \n2 2013-01-01 store_nbr_8 PET SUPPLIES 1742.00 0.00 \n3 2013-01-01 store_nbr_8 PLAYERS AND ELECTRONICS 1743.00 0.00 \n4 2013-01-01 store_nbr_8 POULTRY 1744.00 0.00 \n\n onpromotion test city state type cluster \\\n0 0.00 0 city_quito state_pichincha type_D cluster_13 \n1 0.00 0 city_quito state_pichincha type_D cluster_8 \n2 0.00 0 city_quito state_pichincha type_D cluster_8 \n3 0.00 0 city_quito state_pichincha type_D cluster_8 \n4 0.00 0 city_quito state_pichincha type_D cluster_8 \n\n dcoilwtico transactions work_day nat_terremoto nat_navidad \\\n0 93.14 0.00 0.00 0.00 0.00 \n1 93.14 0.00 0.00 0.00 0.00 \n2 93.14 0.00 0.00 0.00 0.00 \n3 93.14 0.00 0.00 0.00 0.00 \n4 93.14 0.00 0.00 0.00 0.00 \n\n nat_dia_la_madre nat_dia_trabajo nat_primer_dia_ano nat_futbol \\\n0 0.00 0.00 1.00 0.00 \n1 0.00 0.00 1.00 0.00 \n2 0.00 0.00 1.00 0.00 \n3 0.00 0.00 1.00 0.00 \n4 0.00 0.00 1.00 0.00 \n\n nat_dia_difuntos day_of_week day_of_year day_of_month year month \\\n0 0.00 1 1 1 2013 1 \n1 0.00 1 1 1 2013 1 \n2 0.00 1 1 1 2013 1 \n3 0.00 1 1 1 2013 1 \n4 0.00 1 1 1 2013 1 \n\n season sales_roll_mean_16 sales_roll_mean_17 sales_roll_mean_18 \\\n0 0 0.00 0.00 0.00 \n1 0 2.56 2.65 2.67 \n2 0 2.75 2.65 2.72 \n3 0 2.75 2.59 2.50 \n4 0 2.38 2.65 2.50 \n\n sales_roll_mean_19 sales_roll_mean_20 sales_roll_mean_21 \\\n0 0.00 0.00 0.00 \n1 2.63 2.50 2.48 \n2 2.74 2.70 2.57 \n3 2.58 2.60 2.57 \n4 2.42 2.50 2.52 \n\n sales_roll_mean_22 sales_roll_mean_46 sales_roll_mean_76 \\\n0 0.00 0.00 0.00 \n1 2.45 2.89 2.64 \n2 2.55 2.98 2.70 \n3 2.45 2.93 2.70 \n4 2.50 2.91 2.71 \n\n sales_roll_mean_106 sales_roll_mean_365 sales_roll_mean_730 \\\n0 0.00 0.00 0.00 \n1 2.54 0.00 0.00 \n2 2.56 0.00 0.00 \n3 2.53 0.00 0.00 \n4 2.54 0.00 0.00 \n\n StoreSalesSimilarity ItemSalesSimilarity sales_lag_16 sales_lag_17 \\\n0 0 0 0.00 0.00 \n1 0 0 0.00 0.00 \n2 0 5 0.00 0.00 \n3 0 4 0.00 0.00 \n4 0 0 0.00 0.00 \n\n sales_lag_20 sales_lag_21 sales_lag_22 last_3months_sales_sum \\\n0 0.00 0.00 0.00 0.00 \n1 0.00 0.00 0.00 15495.74 \n2 0.00 0.00 0.00 11766.00 \n3 0.00 0.00 0.00 656.00 \n4 0.00 0.00 0.00 684.00 \n\n last_3months_sales_mean last_3months_sales_max last_6months_sales_sum \\\n0 0.00 0.00 0.00 \n1 516.52 919.32 16311.32 \n2 392.20 719.00 11646.00 \n3 21.87 51.00 583.00 \n4 22.80 34.00 660.00 \n\n last_6months_sales_mean store_last_3months_sales_sum \\\n0 0.00 0.00 \n1 526.17 648858.26 \n2 375.68 648858.26 \n3 18.81 648858.26 \n4 21.29 648858.26 \n\n store_last_3months_sales_mean store_last_3months_sales_std \\\n0 0.00 0.00 \n1 655.41 1440.96 \n2 655.41 1440.96 \n3 655.41 1440.96 \n4 655.41 1440.96 \n\n store_last_3months_sales_min store_last_3months_sales_max \\\n0 0.00 0.00 \n1 0.00 8787.46 \n2 0.00 8787.46 \n3 0.00 8787.46 \n4 0.00 8787.46 \n\n store_last_6months_sales_sum store_last_6months_sales_mean \\\n0 0.00 0.00 \n1 696476.47 680.82 \n2 696476.47 680.82 \n3 696476.47 680.82 \n4 696476.47 680.82 \n\n store_last_6months_sales_std store_last_6months_sales_min \\\n0 0.00 0.00 \n1 1508.93 0.00 \n2 1508.93 0.00 \n3 1508.93 0.00 \n4 1508.93 0.00 \n\n store_last_6months_sales_max store_last_9months_sales_sum \\\n0 0.00 0.00 \n1 10292.73 658439.18 \n2 10292.73 658439.18 \n3 10292.73 658439.18 \n4 10292.73 658439.18 \n\n store_last_9months_sales_mean store_last_9months_sales_std \\\n0 0.00 0.00 \n1 643.64 1433.50 \n2 643.64 1433.50 \n3 643.64 1433.50 \n4 643.64 1433.50 \n\n store_last_9months_sales_min store_last_9months_sales_max \\\n0 0.00 0.00 \n1 0.00 8233.36 \n2 0.00 8233.36 \n3 0.00 8233.36 \n4 0.00 8233.36 \n\n store_last_12months_sales_sum store_last_12months_sales_mean \\\n0 0.00 0.00 \n1 621351.66 627.63 \n2 621351.66 627.63 \n3 621351.66 627.63 \n4 621351.66 627.63 \n\n store_last_12months_sales_std store_last_12months_sales_min \\\n0 0.00 0.00 \n1 1380.04 0.00 \n2 1380.04 0.00 \n3 1380.04 0.00 \n4 1380.04 0.00 \n\n store_last_12months_sales_max item_last_3months_sales_sum \\\n0 0.00 0.00 \n1 7804.91 194873.19 \n2 7804.91 151160.00 \n3 7804.91 3614.00 \n4 7804.91 5488.00 \n\n item_last_3months_sales_mean item_last_3months_sales_std \\\n0 0.00 0.00 \n1 360.88 271.58 \n2 279.93 155.56 \n3 6.69 7.35 \n4 10.16 7.84 \n\n item_last_3months_sales_min item_last_3months_sales_max \\\n0 0.00 0.00 \n1 28.47 1839.42 \n2 72.00 1118.00 \n3 0.00 51.00 \n4 0.00 48.00 \n\n item_last_6months_sales_sum item_last_6months_sales_mean \\\n0 0.00 0.00 \n1 197148.87 353.31 \n2 160587.00 287.79 \n3 3573.00 6.40 \n4 5772.00 10.34 \n\n item_last_6months_sales_std item_last_6months_sales_min \\\n0 0.00 0.00 \n1 262.75 35.08 \n2 152.15 70.00 \n3 6.93 0.00 \n4 7.86 0.00 \n\n item_last_6months_sales_max item_last_9months_sales_sum \\\n0 0.00 0.00 \n1 1675.09 188888.88 \n2 990.00 171197.00 \n3 51.00 3492.00 \n4 47.00 8395.00 \n\n item_last_9months_sales_mean item_last_9months_sales_std \\\n0 0.00 0.00 \n1 338.51 272.06 \n2 306.80 181.92 \n3 6.26 7.47 \n4 15.04 13.13 \n\n item_last_9months_sales_min item_last_9months_sales_max \\\n0 0.00 0.00 \n1 0.00 1751.67 \n2 0.00 1091.00 \n3 0.00 41.00 \n4 0.00 103.00 \n\n item_last_12months_sales_sum item_last_12months_sales_mean \\\n0 0.00 0.00 \n1 182115.08 337.25 \n2 145140.00 268.78 \n3 2766.00 5.12 \n4 4839.00 8.96 \n\n item_last_12months_sales_std item_last_12months_sales_min \\\n0 0.00 0.00 \n1 265.08 0.00 \n2 165.25 0.00 \n3 6.45 0.00 \n4 8.60 0.00 \n\n item_last_12months_sales_max storesim_last_3months_sales_sum \\\n0 0.00 0.00 \n1 1632.45 0.00 \n2 1124.00 0.00 \n3 38.00 0.00 \n4 58.00 0.00 \n\n storesim_last_3months_sales_mean storesim_last_3months_sales_std \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_3months_sales_min storesim_last_3months_sales_max \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_6months_sales_sum storesim_last_6months_sales_mean \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_6months_sales_std storesim_last_6months_sales_min \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_6months_sales_max storesim_last_9months_sales_sum \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_9months_sales_mean storesim_last_9months_sales_std \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_9months_sales_min storesim_last_9months_sales_max \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_12months_sales_sum storesim_last_12months_sales_mean \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_12months_sales_std storesim_last_12months_sales_min \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n storesim_last_12months_sales_max itemsim_last_3months_sales_sum \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 12938.00 \n3 0.00 764.00 \n4 0.00 0.00 \n\n itemsim_last_3months_sales_mean itemsim_last_3months_sales_std \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 11.98 15.59 \n3 0.71 1.49 \n4 0.00 0.00 \n\n itemsim_last_3months_sales_min itemsim_last_3months_sales_max \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 133.00 \n3 0.00 17.00 \n4 0.00 0.00 \n\n itemsim_last_6months_sales_sum itemsim_last_6months_sales_mean \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 13303.00 11.92 \n3 1202.00 1.08 \n4 0.00 0.00 \n\n itemsim_last_6months_sales_std itemsim_last_6months_sales_min \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 15.23 0.00 \n3 3.31 0.00 \n4 0.00 0.00 \n\n itemsim_last_6months_sales_max itemsim_last_9months_sales_sum \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 105.00 15572.00 \n3 73.00 513.00 \n4 0.00 0.00 \n\n itemsim_last_9months_sales_mean itemsim_last_9months_sales_std \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 13.95 16.44 \n3 0.46 1.24 \n4 0.00 0.00 \n\n itemsim_last_9months_sales_min itemsim_last_9months_sales_max \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 110.00 \n3 0.00 11.00 \n4 0.00 0.00 \n\n itemsim_last_12months_sales_sum itemsim_last_12months_sales_mean \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 7592.00 7.03 \n3 3458.00 3.20 \n4 0.00 0.00 \n\n itemsim_last_12months_sales_std itemsim_last_12months_sales_min \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 8.30 0.00 \n3 16.41 0.00 \n4 0.00 0.00 \n\n itemsim_last_12months_sales_max dayofweek_sales_lag_12 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 58.00 0.00 \n3 326.00 0.00 \n4 0.00 0.00 \n\n dayofweek_sales_lag_13 dayofweek_sales_lag_17 dayofweek_sales_lag_35 \\\n0 0.00 0.00 0.00 \n1 0.00 0.00 0.00 \n2 0.00 0.00 0.00 \n3 0.00 0.00 0.00 \n4 0.00 0.00 0.00 \n\n dayofweek_sales_lag_39 sales_ewm_alpha_095_lag_16 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_17 sales_ewm_alpha_095_lag_18 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_19 sales_ewm_alpha_095_lag_20 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_21 sales_ewm_alpha_095_lag_22 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_46 sales_ewm_alpha_095_lag_76 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_106 sales_ewm_alpha_095_lag_365 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_095_lag_730 sales_ewm_alpha_09_lag_16 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_17 sales_ewm_alpha_09_lag_18 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_19 sales_ewm_alpha_09_lag_20 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_21 sales_ewm_alpha_09_lag_22 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_46 sales_ewm_alpha_09_lag_76 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_106 sales_ewm_alpha_09_lag_365 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_09_lag_730 sales_ewm_alpha_08_lag_16 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_17 sales_ewm_alpha_08_lag_18 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_19 sales_ewm_alpha_08_lag_20 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_21 sales_ewm_alpha_08_lag_22 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_46 sales_ewm_alpha_08_lag_76 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_106 sales_ewm_alpha_08_lag_365 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_08_lag_730 sales_ewm_alpha_07_lag_16 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_17 sales_ewm_alpha_07_lag_18 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_19 sales_ewm_alpha_07_lag_20 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_21 sales_ewm_alpha_07_lag_22 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_46 sales_ewm_alpha_07_lag_76 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_106 sales_ewm_alpha_07_lag_365 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_07_lag_730 sales_ewm_alpha_05_lag_16 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_17 sales_ewm_alpha_05_lag_18 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_19 sales_ewm_alpha_05_lag_20 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_21 sales_ewm_alpha_05_lag_22 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_46 sales_ewm_alpha_05_lag_76 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_106 sales_ewm_alpha_05_lag_365 \\\n0 0.00 0.00 \n1 0.00 0.00 \n2 0.00 0.00 \n3 0.00 0.00 \n4 0.00 0.00 \n\n sales_ewm_alpha_05_lag_730 dayofyear_sales_lag_1 dayofyear_sales_lag_2 \\\n0 0.00 0.00 0.00 \n1 0.00 0.00 0.00 \n2 0.00 0.00 0.00 \n3 0.00 0.00 0.00 \n4 0.00 0.00 0.00 \n\n dayofyear_sales_lag_3 dayofyear_sales_lag_4 store_cluster family_cluster \n0 0.00 0.00 1 1 \n1 0.00 0.00 3 1 \n2 0.00 0.00 3 1 \n3 0.00 0.00 3 1 \n4 0.00 0.00 3 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datestore_nbrfamilyidsalesonpromotiontestcitystatetypeclusterdcoilwticotransactionswork_daynat_terremotonat_navidadnat_dia_la_madrenat_dia_trabajonat_primer_dia_anonat_futbolnat_dia_difuntosday_of_weekday_of_yearday_of_monthyearmonthseasonsales_roll_mean_16sales_roll_mean_17sales_roll_mean_18sales_roll_mean_19sales_roll_mean_20sales_roll_mean_21sales_roll_mean_22sales_roll_mean_46sales_roll_mean_76sales_roll_mean_106sales_roll_mean_365sales_roll_mean_730StoreSalesSimilarityItemSalesSimilaritysales_lag_16sales_lag_17sales_lag_20sales_lag_21sales_lag_22last_3months_sales_sumlast_3months_sales_meanlast_3months_sales_maxlast_6months_sales_sumlast_6months_sales_meanstore_last_3months_sales_sumstore_last_3months_sales_meanstore_last_3months_sales_stdstore_last_3months_sales_minstore_last_3months_sales_maxstore_last_6months_sales_sumstore_last_6months_sales_meanstore_last_6months_sales_stdstore_last_6months_sales_minstore_last_6months_sales_maxstore_last_9months_sales_sumstore_last_9months_sales_meanstore_last_9months_sales_stdstore_last_9months_sales_minstore_last_9months_sales_maxstore_last_12months_sales_sumstore_last_12months_sales_meanstore_last_12months_sales_stdstore_last_12months_sales_minstore_last_12months_sales_maxitem_last_3months_sales_sumitem_last_3months_sales_meanitem_last_3months_sales_stditem_last_3months_sales_minitem_last_3months_sales_maxitem_last_6months_sales_sumitem_last_6months_sales_meanitem_last_6months_sales_stditem_last_6months_sales_minitem_last_6months_sales_maxitem_last_9months_sales_sumitem_last_9months_sales_meanitem_last_9months_sales_stditem_last_9months_sales_minitem_last_9months_sales_maxitem_last_12months_sales_sumitem_last_12months_sales_meanitem_last_12months_sales_stditem_last_12months_sales_minitem_last_12months_sales_maxstoresim_last_3months_sales_sumstoresim_last_3months_sales_meanstoresim_last_3months_sales_stdstoresim_last_3months_sales_minstoresim_last_3months_sales_maxstoresim_last_6months_sales_sumstoresim_last_6months_sales_meanstoresim_last_6months_sales_stdstoresim_last_6months_sales_minstoresim_last_6months_sales_maxstoresim_last_9months_sales_sumstoresim_last_9months_sales_meanstoresim_last_9months_sales_stdstoresim_last_9months_sales_minstoresim_last_9months_sales_maxstoresim_last_12months_sales_sumstoresim_last_12months_sales_meanstoresim_last_12months_sales_stdstoresim_last_12months_sales_minstoresim_last_12months_sales_maxitemsim_last_3months_sales_sumitemsim_last_3months_sales_meanitemsim_last_3months_sales_stditemsim_last_3months_sales_minitemsim_last_3months_sales_maxitemsim_last_6months_sales_sumitemsim_last_6months_sales_meanitemsim_last_6months_sales_stditemsim_last_6months_sales_minitemsim_last_6months_sales_maxitemsim_last_9months_sales_sumitemsim_last_9months_sales_meanitemsim_last_9months_sales_stditemsim_last_9months_sales_minitemsim_last_9months_sales_maxitemsim_last_12months_sales_sumitemsim_last_12months_sales_meanitemsim_last_12months_sales_stditemsim_last_12months_sales_minitemsim_last_12months_sales_maxdayofweek_sales_lag_12dayofweek_sales_lag_13dayofweek_sales_lag_17dayofweek_sales_lag_35dayofweek_sales_lag_39sales_ewm_alpha_095_lag_16sales_ewm_alpha_095_lag_17sales_ewm_alpha_095_lag_18sales_ewm_alpha_095_lag_19sales_ewm_alpha_095_lag_20sales_ewm_alpha_095_lag_21sales_ewm_alpha_095_lag_22sales_ewm_alpha_095_lag_46sales_ewm_alpha_095_lag_76sales_ewm_alpha_095_lag_106sales_ewm_alpha_095_lag_365sales_ewm_alpha_095_lag_730sales_ewm_alpha_09_lag_16sales_ewm_alpha_09_lag_17sales_ewm_alpha_09_lag_18sales_ewm_alpha_09_lag_19sales_ewm_alpha_09_lag_20sales_ewm_alpha_09_lag_21sales_ewm_alpha_09_lag_22sales_ewm_alpha_09_lag_46sales_ewm_alpha_09_lag_76sales_ewm_alpha_09_lag_106sales_ewm_alpha_09_lag_365sales_ewm_alpha_09_lag_730sales_ewm_alpha_08_lag_16sales_ewm_alpha_08_lag_17sales_ewm_alpha_08_lag_18sales_ewm_alpha_08_lag_19sales_ewm_alpha_08_lag_20sales_ewm_alpha_08_lag_21sales_ewm_alpha_08_lag_22sales_ewm_alpha_08_lag_46sales_ewm_alpha_08_lag_76sales_ewm_alpha_08_lag_106sales_ewm_alpha_08_lag_365sales_ewm_alpha_08_lag_730sales_ewm_alpha_07_lag_16sales_ewm_alpha_07_lag_17sales_ewm_alpha_07_lag_18sales_ewm_alpha_07_lag_19sales_ewm_alpha_07_lag_20sales_ewm_alpha_07_lag_21sales_ewm_alpha_07_lag_22sales_ewm_alpha_07_lag_46sales_ewm_alpha_07_lag_76sales_ewm_alpha_07_lag_106sales_ewm_alpha_07_lag_365sales_ewm_alpha_07_lag_730sales_ewm_alpha_05_lag_16sales_ewm_alpha_05_lag_17sales_ewm_alpha_05_lag_18sales_ewm_alpha_05_lag_19sales_ewm_alpha_05_lag_20sales_ewm_alpha_05_lag_21sales_ewm_alpha_05_lag_22sales_ewm_alpha_05_lag_46sales_ewm_alpha_05_lag_76sales_ewm_alpha_05_lag_106sales_ewm_alpha_05_lag_365sales_ewm_alpha_05_lag_730dayofyear_sales_lag_1dayofyear_sales_lag_2dayofyear_sales_lag_3dayofyear_sales_lag_4store_clusterfamily_cluster
02013-01-01store_nbr_1AUTOMOTIVE0.000.000.000city_quitostate_pichinchatype_Dcluster_1393.140.000.000.000.000.000.001.000.000.001112013100.000.000.000.000.000.000.000.000.000.000.000.00000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0011
12013-01-01store_nbr_8PERSONAL CARE1741.000.000.000city_quitostate_pichinchatype_Dcluster_893.140.000.000.000.000.000.001.000.000.001112013102.562.652.672.632.502.482.452.892.642.540.000.00000.000.000.000.000.0015495.74516.52919.3216311.32526.17648858.26655.411440.960.008787.46696476.47680.821508.930.0010292.73658439.18643.641433.500.008233.36621351.66627.631380.040.007804.91194873.19360.88271.5828.471839.42197148.87353.31262.7535.081675.09188888.88338.51272.060.001751.67182115.08337.25265.080.001632.450.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0031
22013-01-01store_nbr_8PET SUPPLIES1742.000.000.000city_quitostate_pichinchatype_Dcluster_893.140.000.000.000.000.000.001.000.000.001112013102.752.652.722.742.702.572.552.982.702.560.000.00050.000.000.000.000.0011766.00392.20719.0011646.00375.68648858.26655.411440.960.008787.46696476.47680.821508.930.0010292.73658439.18643.641433.500.008233.36621351.66627.631380.040.007804.91151160.00279.93155.5672.001118.00160587.00287.79152.1570.00990.00171197.00306.80181.920.001091.00145140.00268.78165.250.001124.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0012938.0011.9815.590.00133.0013303.0011.9215.230.00105.0015572.0013.9516.440.00110.007592.007.038.300.0058.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0031
32013-01-01store_nbr_8PLAYERS AND ELECTRONICS1743.000.000.000city_quitostate_pichinchatype_Dcluster_893.140.000.000.000.000.000.001.000.000.001112013102.752.592.502.582.602.572.452.932.702.530.000.00040.000.000.000.000.00656.0021.8751.00583.0018.81648858.26655.411440.960.008787.46696476.47680.821508.930.0010292.73658439.18643.641433.500.008233.36621351.66627.631380.040.007804.913614.006.697.350.0051.003573.006.406.930.0051.003492.006.267.470.0041.002766.005.126.450.0038.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00764.000.711.490.0017.001202.001.083.310.0073.00513.000.461.240.0011.003458.003.2016.410.00326.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0031
42013-01-01store_nbr_8POULTRY1744.000.000.000city_quitostate_pichinchatype_Dcluster_893.140.000.000.000.000.000.001.000.000.001112013102.382.652.502.422.502.522.502.912.712.540.000.00000.000.000.000.000.00684.0022.8034.00660.0021.29648858.26655.411440.960.008787.46696476.47680.821508.930.0010292.73658439.18643.641433.500.008233.36621351.66627.631380.040.007804.915488.0010.167.840.0048.005772.0010.347.860.0047.008395.0015.0413.130.00103.004839.008.968.600.0058.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0031
\n
"},"metadata":{}}]},{"cell_type":"code","source":"#let's divide the data into training and validation and use them at the stage of identifying important features\ntrain_f = data_analyses.copy()\nval_f = data_analyses.copy()\ntrain_f = train_f.loc[(train_f[\"date\"] < \"2017-01-01\"), :]\nval_f = val_f.loc[(val_f[\"date\"] >= \"2017-01-01\") & (val_f[\"date\"] < \"2017-08-16\"), :]\nY_train = train_f['sales']\nX_train = train_f[features]\nY_val = val_f['sales']\nX_val = val_f[features]\n\n#let's define object columns\nobject_cols = X_train.loc[:,X_train.dtypes==np.object].columns\nobject_cols = list(object_cols)\ncols_for_le = object_cols \ncols_for_le = [list(X_train.columns).index(col) for col in cols_for_le]\n\n#let's transform categorical features \nt = [('MeanTargetEncoder', TargetEncoder(), cols_for_le)]\ncol_transform = ColumnTransformer(transformers=t)\ncol_transform.set_output(transform=\"pandas\")\nX_trans_tr =col_transform.fit_transform(X_train,Y_train)\nX_val_tr =col_transform.fit_transform(X_val,Y_val)\n\nY_train.shape, X_trans_tr.shape, Y_val.shape, X_val_tr.shape","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:37:18.858038Z","iopub.execute_input":"2023-12-17T09:37:18.859116Z","iopub.status.idle":"2023-12-17T09:37:40.781672Z","shell.execute_reply.started":"2023-12-17T09:37:18.859079Z","shell.execute_reply":"2023-12-17T09:37:40.780535Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"((867834,), (867834, 6), (134838,), (134838, 6))"},"metadata":{}}]},{"cell_type":"code","source":"for c in object_cols:\n X_train[c] = X_train[c].astype('category')\n X_val[c] = X_val[c].astype('category')","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:37:40.783347Z","iopub.execute_input":"2023-12-17T09:37:40.783729Z","iopub.status.idle":"2023-12-17T09:37:41.755381Z","shell.execute_reply.started":"2023-12-17T09:37:40.783698Z","shell.execute_reply":"2023-12-17T09:37:41.754066Z"},"trusted":true},"execution_count":67,"outputs":[]},{"cell_type":"code","source":"# SMAPE: Symmetric mean absolute percentage error\ndef smape(preds, target):\n smape_val=1/len(target) * np.sum(2 * np.abs(preds-target) / (np.abs(target) + np.abs(preds))*100)\n return smape_val","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:37:41.756885Z","iopub.execute_input":"2023-12-17T09:37:41.757737Z","iopub.status.idle":"2023-12-17T09:37:41.763511Z","shell.execute_reply.started":"2023-12-17T09:37:41.757702Z","shell.execute_reply":"2023-12-17T09:37:41.762368Z"},"trusted":true},"execution_count":68,"outputs":[]},{"cell_type":"code","source":"first_model = lgb.LGBMRegressor(random_state=384\n ).fit(X_train, Y_train, \n eval_metric= lambda y_true, y_pred: [mean_squared_error(y_true, y_pred)],\n categorical_feature = object_cols)\n\nprint(\"TRAIN SMAPE:\", smape(Y_train, first_model.predict(X_train)))\nprint(\"VALID SMAPE:\", smape(Y_val, first_model.predict(X_val)))","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:37:41.764895Z","iopub.execute_input":"2023-12-17T09:37:41.765218Z","iopub.status.idle":"2023-12-17T09:38:24.036375Z","shell.execute_reply.started":"2023-12-17T09:37:41.765192Z","shell.execute_reply":"2023-12-17T09:38:24.035397Z"},"trusted":true},"execution_count":69,"outputs":[{"name":"stdout","text":"TRAIN SMAPE: 86.99681223346806\nVALID SMAPE: 65.10337069148287\n","output_type":"stream"}]},{"cell_type":"code","source":"def plot_lgb_importances(model, plot=False, num=120):\n # SKLEARN API\n gain = model.booster_.feature_importance(importance_type='gain')\n feat_imp = pd.DataFrame({'feature': model.feature_name_,\n 'split': model.booster_.feature_importance(importance_type='split'),\n 'gain': 100 * gain / gain.sum()}).sort_values('gain', ascending=False)\n if plot:\n plt.figure(figsize=(10, 10))\n sns.set(font_scale=1)\n sns.barplot(x=\"gain\", y=\"feature\", data=feat_imp[0:25])\n plt.title('feature')\n plt.tight_layout()\n plt.show()\n else:\n print(feat_imp.head(num))\n return feat_imp\n\nfeature_imp_df = plot_lgb_importances(first_model, num=200)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:38:24.038141Z","iopub.execute_input":"2023-12-17T09:38:24.038796Z","iopub.status.idle":"2023-12-17T09:38:24.052440Z","shell.execute_reply.started":"2023-12-17T09:38:24.038762Z","shell.execute_reply":"2023-12-17T09:38:24.051651Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":" feature split gain\n166 sales_ewm_alpha_07_lag_21 25 33.79\n173 sales_ewm_alpha_05_lag_16 45 18.53\n154 sales_ewm_alpha_08_lag_21 28 13.03\n177 sales_ewm_alpha_05_lag_20 14 6.07\n33 sales_lag_21 60 4.86\n.. ... ... ...\n108 itemsim_last_6months_sales_min 0 0.00\n118 itemsim_last_12months_sales_min 0 0.00\n117 itemsim_last_12months_sales_std 0 0.00\n113 itemsim_last_9months_sales_min 0 0.00\n43 store_last_3months_sales_min 0 0.00\n\n[191 rows x 3 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"feature_imp_df.shape, feature_imp_df[feature_imp_df.gain > 0].shape, feature_imp_df[feature_imp_df.gain > 0.57].shape","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:38:24.053893Z","iopub.execute_input":"2023-12-17T09:38:24.054503Z","iopub.status.idle":"2023-12-17T09:38:24.067523Z","shell.execute_reply.started":"2023-12-17T09:38:24.054472Z","shell.execute_reply":"2023-12-17T09:38:24.066717Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"((191, 3), (168, 3), (14, 3))"},"metadata":{}}]},{"cell_type":"code","source":"# feature importance\ncols = feature_imp_df[feature_imp_df.gain > 0.015].feature.tolist()\nprint(\"Independent Variables:\", len(cols))","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:38:24.068975Z","iopub.execute_input":"2023-12-17T09:38:24.069624Z","iopub.status.idle":"2023-12-17T09:38:24.079298Z","shell.execute_reply.started":"2023-12-17T09:38:24.069594Z","shell.execute_reply":"2023-12-17T09:38:24.078499Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"Independent Variables: 107\n","output_type":"stream"}]},{"cell_type":"code","source":"train = data_analyses.loc[data_analyses['test'] == 0]\ntest = data_analyses.loc[data_analyses['test'] == 1]\n\nX = train[cols]\ny = train[target]","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:38:24.080798Z","iopub.execute_input":"2023-12-17T09:38:24.081404Z","iopub.status.idle":"2023-12-17T09:38:25.423222Z","shell.execute_reply.started":"2023-12-17T09:38:24.081372Z","shell.execute_reply":"2023-12-17T09:38:25.422111Z"},"trusted":true},"execution_count":73,"outputs":[]},{"cell_type":"markdown","source":"how can we notice the most important features are new","metadata":{}},{"cell_type":"markdown","source":"# 5. Model comparison","metadata":{}},{"cell_type":"code","source":"\"\"\"\nWhen validating time-structured models, it is important that we train \nthe model on early data and test the prediction on later data.\n\"\"\"\nnum_folds = 5\ntscv = TimeSeriesSplit(n_splits=num_folds)\n\n#creating dictionaries to record results\nmse_scores = defaultdict(list)\nrmse_scores = defaultdict(list)\nr2_scores = defaultdict(list)\nmae_scores = defaultdict(list)\n# mape_scores = defaultdict(list)\n# smape_scores = defaultdict(list)\nmodels = defaultdict(list)\n\n#Metrics used to evaluate models\ndef metrics_regression(y_true, y_pred):\n #MSE\n mse = mean_squared_error(y_true, y_pred) #!\n #RMSE Root Mean Square Error\n rmse = math.sqrt(mse)\n #R^2\n r2 = r2_score(y_true, y_pred)\n #MAE(mean absolute error)\n mae = mean_absolute_error(y_true, y_pred) #!\n #MAPE(mean absolute percentage error)\n\n \n return mse,rmse,r2,mae","metadata":{"execution":{"iopub.status.busy":"2023-12-17T10:40:43.676052Z","iopub.execute_input":"2023-12-17T10:40:43.676561Z","iopub.status.idle":"2023-12-17T10:40:43.696233Z","shell.execute_reply.started":"2023-12-17T10:40:43.676515Z","shell.execute_reply":"2023-12-17T10:40:43.694751Z"},"trusted":true},"execution_count":96,"outputs":[]},{"cell_type":"code","source":"#X.dtypes [X.dtypes != 'float64']\nobject_cols_lr=['family','store_nbr','family_cluster','store_cluster']\n\nfor c in object_cols_lr:\n X[c] = X[c].astype('category')\n \ncategory_cols_lr=['family','store_nbr','family_cluster','store_cluster']\nnumeric_cols = list(X.select_dtypes(exclude='category').columns)\n\ncols_for_oh = [list(X.columns).index(col) for col in category_cols_lr]\nnumeric_cols_idx = [list(X.columns).index(col) for col in numeric_cols]\n\nt = [('OneHotEncoder', OneHotEncoder(), cols_for_oh),\n ('StandardScaler', StandardScaler(), numeric_cols_idx)\n ]\n\ncol_transform = ColumnTransformer(transformers=t)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T10:40:48.733234Z","iopub.execute_input":"2023-12-17T10:40:48.733660Z","iopub.status.idle":"2023-12-17T10:40:49.862722Z","shell.execute_reply.started":"2023-12-17T10:40:48.733626Z","shell.execute_reply":"2023-12-17T10:40:49.861687Z"},"trusted":true},"execution_count":97,"outputs":[]},{"cell_type":"code","source":"%%time\n\nfor fold_idx, (train_index, test_index) in enumerate(tscv.split(train)):\n print(f\"Fold {fold_idx + 1}\")\n\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n\n pipe_LR = Pipeline([(\"column_transformer\",col_transform),\n (\"linear_regression\", LinearRegression())])\n\n pipe_LR.fit(X_train, y_train)\n\n y_pred_LR = pipe_LR.predict(X_test)\n\n models['linear_regression'].append(pipe_LR)\n \n mse_lr,rmse_lr,r2_lr,mae_lr = metrics_regression(y_test, y_pred_LR)\n\n mse_scores['linear_regression'].append(mse_lr)\n rmse_scores['linear_regression'].append(rmse_lr)\n r2_scores['linear_regression'].append(r2_lr)\n mae_scores['linear_regression'].append(mae_lr)\n\n #rmsle_scores['linear_regression'].append(rmsle_lr)\n \n #print(f\"\\t Score for linear regression: {mse_scores,rmse_scores,r2_scores,mae_scores,mape_scores,smape_scores}\")\n print('*'*60)\n\nprint(f\"\\t\\t Mean MSE \\n\\t LR: {np.mean(mse_scores['linear_regression'])}\")\nprint(f\"\\t\\t Mean RMSE \\n\\t LR: {np.mean(rmse_scores['linear_regression'])}\")\nprint(f\"\\t\\t Mean R2 \\n\\t LR: {np.mean(r2_scores['linear_regression'])}\")\nprint(f\"\\t\\t Mean MAE \\n\\t LR: {np.mean(mae_scores['linear_regression'])}\")\n\nprint('*'*60)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T10:40:56.540934Z","iopub.execute_input":"2023-12-17T10:40:56.541347Z","iopub.status.idle":"2023-12-17T10:42:20.960385Z","shell.execute_reply.started":"2023-12-17T10:40:56.541315Z","shell.execute_reply":"2023-12-17T10:42:20.958441Z"},"trusted":true},"execution_count":98,"outputs":[{"name":"stdout","text":"Fold 1\n************************************************************\nFold 2\n************************************************************\nFold 3\n************************************************************\nFold 4\n************************************************************\nFold 5\n************************************************************\n\t\t Mean MSE \n\t LR: 186377138.88678402\n\t\t Mean RMSE \n\t LR: 6444.559993101588\n\t\t Mean R2 \n\t LR: -127.50112056598098\n\t\t Mean MAE \n\t LR: 1072.3656849671513\n************************************************************\nCPU times: user 1min 58s, sys: 26.2 s, total: 2min 25s\nWall time: 1min 24s\n","output_type":"stream"}]},{"cell_type":"code","source":"%%time\n\nfor fold_idx, (train_index, test_index) in enumerate(tscv.split(train)):\n print(f\"Fold {fold_idx + 1}\")\n\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n\n pipe_DTR = Pipeline([(\"column_transformer\",col_transform),\n (\"decision_tree\", \n DecisionTreeRegressor())])\n\n pipe_DTR.fit(X_train, y_train)\n\n y_pred_DTR = pipe_DTR.predict(X_test)\n\n models['decision_tree'].append(pipe_DTR)\n \n mse_dtr,rmse_dtr,r2_dtr,mae_dtr = metrics_regression(y_test, y_pred_DTR)\n\n mse_scores['decision_tree'].append(mse_dtr)\n rmse_scores['decision_tree'].append(rmse_dtr)\n r2_scores['decision_tree'].append(r2_dtr)\n mae_scores['decision_tree'].append(mae_dtr)\n# mape_scores['decision_tree'].append(mape_dtr)\n# smape_scores['decision_tree'].append(smape_dtr)\n \n #print(f\"\\t Score for decision_tree: {mse_scores,rmse_scores, r2_scores,mae_scores,mape_scores,smape_scores}\")\n print('*'*60)\n\nprint(f\"\\t\\t Mean MSE \\n\\t DTR: {np.mean(mse_scores['decision_tree'])}\")\nprint(f\"\\t\\t Mean RMSE \\n\\t DTR: {np.mean(rmse_scores['decision_tree'])}\")\nprint(f\"\\t\\t Mean R2 \\n\\t DTR: {np.mean(r2_scores['decision_tree'])}\")\nprint(f\"\\t\\t Mean MAE \\n\\t DTR: {np.mean(mae_scores['decision_tree'])}\")\n# print(f\"\\t\\t Mean MAPE \\n\\t DTR: {np.mean(mape_scores['decision_tree'])}\")\n# print(f\"\\t\\t Mean SMAPE \\n\\t DTR: {np.mean(smape_scores['decision_tree'])}\")\nprint('*'*60)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:39:53.766601Z","iopub.execute_input":"2023-12-17T09:39:53.767222Z","iopub.status.idle":"2023-12-17T09:51:35.765343Z","shell.execute_reply.started":"2023-12-17T09:39:53.767180Z","shell.execute_reply":"2023-12-17T09:51:35.763827Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"Fold 1\n************************************************************\nFold 2\n************************************************************\nFold 3\n************************************************************\nFold 4\n************************************************************\nFold 5\n************************************************************\n\t\t Mean MSE \n\t DTR: 288698.8741885273\n\t\t Mean RMSE \n\t DTR: 531.5158445378331\n\t\t Mean R2 \n\t DTR: 0.7366278143449343\n\t\t Mean MAE \n\t DTR: 120.78833533297104\n************************************************************\nCPU times: user 11min 26s, sys: 11.5 s, total: 11min 37s\nWall time: 11min 41s\n","output_type":"stream"}]},{"cell_type":"code","source":"%%time\n\nfor fold_idx, (train_index, test_index) in enumerate(tscv.split(train)):\n print(f\"Fold {fold_idx + 1}\")\n\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n\n pipe_lasso = Pipeline([(\"column_transformer\",col_transform),\n (\"Lasso\", Lasso())])\n\n pipe_lasso.fit(X_train, y_train)\n\n y_pred_lasso = pipe_lasso.predict(X_test)\n\n models['Lasso'].append(pipe_lasso)\n \n mse_lasso,rmse_lasso,r2_lasso,mae_lasso = metrics_regression(\n y_test, y_pred_lasso)\n\n mse_scores['Lasso'].append(mse_lasso)\n rmse_scores['Lasso'].append(rmse_lasso)\n r2_scores['Lasso'].append(r2_lasso)\n mae_scores['Lasso'].append(mae_lasso)\n# mape_scores['Lasso'].append(mape_lasso)\n# smape_scores['Lasso'].append(smape_lasso)\n \n #print(f\"\\t Score for Lasso: {mse_scores,rmse_scores, r2_scores,mae_scores,mape_scores,smape_scores}\")\n print('*'*60)\n\nprint(f\"\\t\\t Mean MSE \\n\\t Lasso: {np.mean(mse_scores['Lasso'])}\")\nprint(f\"\\t\\t Mean RMSE \\n\\t Lasso: {np.mean(rmse_scores['Lasso'])}\")\nprint(f\"\\t\\t Mean R2 \\n\\t Lasso: {np.mean(r2_scores['Lasso'])}\")\nprint(f\"\\t\\t Mean MAE \\n\\t Lasso: {np.mean(mae_scores['Lasso'])}\")\n# print(f\"\\t\\t Mean MAPE \\n\\t Lasso: {np.mean(mape_scores['Lasso'])}\")\n# print(f\"\\t\\t Mean SMAPE \\n\\t Lasso: {np.mean(smape_scores['Lasso'])}\")\nprint('*'*60)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:51:35.767288Z","iopub.execute_input":"2023-12-17T09:51:35.767670Z","iopub.status.idle":"2023-12-17T09:58:45.248493Z","shell.execute_reply.started":"2023-12-17T09:51:35.767633Z","shell.execute_reply":"2023-12-17T09:58:45.246317Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Fold 1\n************************************************************\nFold 2\n************************************************************\nFold 3\n************************************************************\nFold 4\n************************************************************\nFold 5\n************************************************************\n\t\t Mean MSE \n\t Lasso: 145727.6737291878\n\t\t Mean RMSE \n\t Lasso: 379.8940187191824\n\t\t Mean R2 \n\t Lasso: 0.8584271995586541\n\t\t Mean MAE \n\t Lasso: 116.92034669672475\n************************************************************\nCPU times: user 23min 58s, sys: 1min 43s, total: 25min 41s\nWall time: 7min 9s\n","output_type":"stream"}]},{"cell_type":"code","source":"# %%time\n\nfor fold_idx, (train_index, test_index) in enumerate(tscv.split(train)):\n print(f\"Fold {fold_idx + 1}\")\n\n X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n\n pipe_ridge = Pipeline([(\"column_transformer\",col_transform),\n (\"Ridge\", Ridge())])\n\n pipe_ridge.fit(X_train, y_train)\n\n y_pred_ridge = pipe_ridge.predict(X_test)\n\n models['Ridge'].append(pipe_ridge)\n \n mse_ridge, rmse_ridge, r2_ridge, mae_ridge = metrics_regression(\n y_test, y_pred_ridge)\n\n mse_scores['Ridge'].append(mse_ridge)\n rmse_scores['Ridge'].append(rmse_ridge)\n r2_scores['Ridge'].append(r2_ridge)\n mae_scores['Ridge'].append(mae_ridge)\n# mape_scores['Ridge'].append(mape_ridge)\n# smape_scores['Ridge'].append(smape_ridge)\n \n #print(f\"\\t Score for Ridge: {mse_scores,rmse_scores, r2_scores,mae_scores,mape_scores,smape_scores}\")\n print('*'*60)\n\nprint(f\"\\t\\t Mean MSE \\n\\t Ridge: {np.mean(mse_scores['Ridge'])}\")\nprint(f\"\\t\\t Mean RMSE \\n\\t Ridge: {np.mean(rmse_scores['Ridge'])}\")\nprint(f\"\\t\\t Mean R2 \\n\\t Ridge: {np.mean(r2_scores['Ridge'])}\")\nprint(f\"\\t\\t Mean MAE \\n\\t Ridge: {np.mean(mae_scores['Ridge'])}\")\n# print(f\"\\t\\t Mean MAPE \\n\\t Ridge: {np.mean(mape_scores['Ridge'])}\")\n# print(f\"\\t\\t Mean SMAPE \\n\\t Ridge: {np.mean(smape_scores['Ridge'])}\")\nprint('*'*60)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:58:45.264055Z","iopub.execute_input":"2023-12-17T09:58:45.265019Z","iopub.status.idle":"2023-12-17T09:59:30.548377Z","shell.execute_reply.started":"2023-12-17T09:58:45.264963Z","shell.execute_reply":"2023-12-17T09:59:30.546846Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"Fold 1\n************************************************************\nFold 2\n************************************************************\nFold 3\n************************************************************\nFold 4\n************************************************************\nFold 5\n************************************************************\n\t\t Mean MSE \n\t Ridge: 644372.4426859005\n\t\t Mean RMSE \n\t Ridge: 648.2378860194829\n\t\t Mean R2 \n\t Ridge: 0.504789268055221\n\t\t Mean MAE \n\t Ridge: 185.55318501614337\n************************************************************\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Let's choose the best model!","metadata":{}},{"cell_type":"code","source":"df_mse = pd.DataFrame.from_dict(data=mse_scores, orient='index', columns=['fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\ndf_mse['mean'] = df_mse[['fold_1','fold_2','fold_3','fold_4', 'fold_5']].mean(axis= 1)\ndf_mse","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:59:30.560341Z","iopub.execute_input":"2023-12-17T09:59:30.561892Z","iopub.status.idle":"2023-12-17T09:59:30.596517Z","shell.execute_reply.started":"2023-12-17T09:59:30.561849Z","shell.execute_reply":"2023-12-17T09:59:30.595026Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":" fold_1 fold_2 fold_3 fold_4 fold_5 \\\nlinear_regression 268400.57 125000.06 138350.63 215911.73 931138031.45 \ndecision_tree 192866.60 249128.80 243756.03 307759.66 449983.28 \nLasso 147455.25 129900.66 124571.47 203554.00 123156.98 \nRidge 211606.16 124017.03 138305.11 217533.72 2530400.20 \n\n mean \nlinear_regression 186377138.89 \ndecision_tree 288698.87 \nLasso 145727.67 \nRidge 644372.44 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
fold_1fold_2fold_3fold_4fold_5mean
linear_regression268400.57125000.06138350.63215911.73931138031.45186377138.89
decision_tree192866.60249128.80243756.03307759.66449983.28288698.87
Lasso147455.25129900.66124571.47203554.00123156.98145727.67
Ridge211606.16124017.03138305.11217533.722530400.20644372.44
\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_mse['mean'].plot.barh()","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:59:30.598264Z","iopub.execute_input":"2023-12-17T09:59:30.599245Z","iopub.status.idle":"2023-12-17T09:59:30.857637Z","shell.execute_reply.started":"2023-12-17T09:59:30.599177Z","shell.execute_reply":"2023-12-17T09:59:30.856645Z"},"trusted":true},"execution_count":83,"outputs":[{"execution_count":83,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"Such an anomaly for linear regression can be explained by an outlier in the data on fold 5.","metadata":{}},{"cell_type":"code","source":"df_rmse = pd.DataFrame.from_dict(data=rmse_scores, orient='index',columns=['fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\ndf_rmse['mean'] = df_rmse[['fold_1','fold_2','fold_3','fold_4', 'fold_5']].mean(axis= 1)\ndf_rmse","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:59:30.859147Z","iopub.execute_input":"2023-12-17T09:59:30.860100Z","iopub.status.idle":"2023-12-17T09:59:30.877280Z","shell.execute_reply.started":"2023-12-17T09:59:30.860053Z","shell.execute_reply":"2023-12-17T09:59:30.876265Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":" fold_1 fold_2 fold_3 fold_4 fold_5 mean\nlinear_regression 518.07 353.55 371.96 464.66 30514.55 6444.56\ndecision_tree 439.17 499.13 493.72 554.76 670.81 531.52\nLasso 384.00 360.42 352.95 451.17 350.94 379.89\nRidge 460.01 352.16 371.89 466.41 1590.72 648.24","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
fold_1fold_2fold_3fold_4fold_5mean
linear_regression518.07353.55371.96464.6630514.556444.56
decision_tree439.17499.13493.72554.76670.81531.52
Lasso384.00360.42352.95451.17350.94379.89
Ridge460.01352.16371.89466.411590.72648.24
\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_mae = pd.DataFrame.from_dict(data=mae_scores, orient='index',columns=['fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\ndf_mae['mean'] = df_mae[['fold_1','fold_2','fold_3','fold_4', 'fold_5']].mean(axis= 1)\ndf_mae","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:59:31.142872Z","iopub.execute_input":"2023-12-17T09:59:31.143299Z","iopub.status.idle":"2023-12-17T09:59:31.159245Z","shell.execute_reply.started":"2023-12-17T09:59:31.143265Z","shell.execute_reply":"2023-12-17T09:59:31.158170Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":" fold_1 fold_2 fold_3 fold_4 fold_5 mean\nlinear_regression 241.48 152.51 121.12 106.73 4739.99 1072.37\ndecision_tree 106.31 134.85 134.38 115.53 112.86 120.79\nLasso 106.70 168.25 107.27 95.98 106.41 116.92\nRidge 211.99 151.86 121.39 106.25 336.29 185.55","text/html":"
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fold_1fold_2fold_3fold_4fold_5mean
linear_regression241.48152.51121.12106.734739.991072.37
decision_tree106.31134.85134.38115.53112.86120.79
Lasso106.70168.25107.2795.98106.41116.92
Ridge211.99151.86121.39106.25336.29185.55
\n
"},"metadata":{}}]},{"cell_type":"code","source":"%%time\nresult = pipe_lasso.predict(test[cols])\nsample_submission = pd.DataFrame({'id':test.id,'sales':result}).set_index('id')\nsample_submission","metadata":{"execution":{"iopub.status.busy":"2023-12-17T09:59:31.806916Z","iopub.execute_input":"2023-12-17T09:59:31.807387Z","iopub.status.idle":"2023-12-17T09:59:31.904820Z","shell.execute_reply.started":"2023-12-17T09:59:31.807341Z","shell.execute_reply":"2023-12-17T09:59:31.903010Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"CPU times: user 76.8 ms, sys: 7.04 ms, total: 83.8 ms\nWall time: 72.2 ms\n","output_type":"stream"},{"execution_count":94,"output_type":"execute_result","data":{"text/plain":" sales\nid \n3000991.00 4.70\n3000917.00 62.54\n3001151.00 1.21\n3001145.00 10.72\n3002601.00 7993.33\n... ...\n3029302.00 1.73\n3029303.00 11.83\n3029304.00 3793.45\n3029306.00 617.97\n3029087.00 -8.92\n\n[9504 rows x 1 columns]","text/html":"
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sales
id
3000991.004.70
3000917.0062.54
3001151.001.21
3001145.0010.72
3002601.007993.33
......
3029302.001.73
3029303.0011.83
3029304.003793.45
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9504 rows × 1 columns

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"},"metadata":{}}]},{"cell_type":"markdown","source":"Let's save the results to csv","metadata":{}}]} \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Models/readme.md b/Store Sales Prediction Using Deep Learning/Models/readme.md new file mode 100644 index 000000000..baec8bc7a --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Models/readme.md @@ -0,0 +1,35 @@ +**PROJECT TITLE** +Store Sales Prediction using Deep Learning + +**GOAL** +Store Sales Prediction using Deep Learning +**DATASET** +https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview + +**DESCRIPTION** +The project uses RNN to make predictions for store sales.Dataset is updated daily and is dynamic. The project also aims to compare performance of Lasso, Ridge and Decision Tree regression models with respect to the use of Regression models +**WHAT I HAD DONE** +1. Used EDA and correlation matrix to figure out needed features +2. Tested using basic ML models like Ridge, Lasso, Linear and Decision Tree Regression +3. Tested using RNNs. Used multilayer networks for time-series data +4. RNNs have proven to be far more useful and versatile + +**MODELS USED** +Lasso Regression, Ridge Regression,Decision Tree Regression, RNN + +**LIBRARIES NEEDED** +Pandas, Numpy, Keras,TensorFlow, ScikitLearn, Seaborn, Matplotlib + +**VISUALIZATION** + We use correlation matrix to visualize required features. + Line Charts are used to visualize day/month/store wise sales + +**ACCURACIES** +MAE is lowest for RNNs at 55 to 70. +The highest MAE is provided by Linear Regression at 1000+ and considerably better by Lasso Regression and Decision Tree at a little over 100. + +**CONCLUSION** +Recurrent Neural Networks (RNNs) are employed for time series data due to their ability to capture temporal dependencies. RNNs maintain a memory of past information, enabling them to process sequential data with contextual awareness. This makes them well-suited for tasks such as stock price prediction or weather forecasting, where understanding patterns over time is crucial. The recurrent nature of RNNs facilitates the modeling of dynamic relationships within time series datasets, enhancing their effectiveness in capturing temporal dependencies. + +**YOUR NAME** +Aindree Chatterjee \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Models/store-sales-rnn.ipynb b/Store Sales Prediction Using Deep Learning/Models/store-sales-rnn.ipynb new file mode 100644 index 000000000..3ea063195 --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Models/store-sales-rnn.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":29781,"databundleVersionId":2887556,"sourceType":"competition"}],"dockerImageVersionId":30461,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-17T07:07:13.555636Z","iopub.execute_input":"2023-12-17T07:07:13.556434Z","iopub.status.idle":"2023-12-17T07:07:13.563407Z","shell.execute_reply.started":"2023-12-17T07:07:13.556389Z","shell.execute_reply":"2023-12-17T07:07:13.562348Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/store-sales-time-series-forecasting/oil.csv\n/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv\n/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\n/kaggle/input/store-sales-time-series-forecasting/stores.csv\n/kaggle/input/store-sales-time-series-forecasting/train.csv\n/kaggle/input/store-sales-time-series-forecasting/test.csv\n/kaggle/input/store-sales-time-series-forecasting/transactions.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom IPython.display import clear_output\n\ndef replace_nan_with_next_value(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n col_values = df[column_name].values\n\n for i in range(len(col_values) - 1):\n if pd.isna(col_values[i]):\n j = i + 1\n while j < len(col_values) and pd.isna(col_values[j]):\n j += 1\n if j < len(col_values):\n col_values[i] = col_values[j]\n\n df[column_name] = col_values\n return df\n \ndef delete_rows_with_true(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df = df[df[column_name] != True]\n return df\n\ndef delete_rows_with_work_day(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df[column_name] = df[column_name].astype(str)\n\n df = df[df[column_name] != 'Work Day']\n \n return df\n\ndef replace_categories_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n category_to_integer = {\n 'Holiday': 1,\n 'Event': 2,\n 'Additional': 3,\n 'Transfer': 4,\n 'Bridge': 5,\n 'Regional' : 1,\n 'Local' : 2,\n 'National' : 3\n }\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ndef replace_locale_names_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n 'Ecuador', 'Riobamba', 'Quito', 'Guaranda', 'Latacunga', 'Ambato',\n 'Guayaquil', 'Salinas', 'Loja', 'Santa Elena',\n 'Santo Domingo de los Tsachilas', 'Quevedo', 'Ibarra', 'Manta',\n 'Esmeraldas', 'Cotopaxi', 'El Carmen', 'Santo Domingo', 'Machala',\n 'Imbabura', 'Puyo', 'Libertad', 'Cuenca', 'Cayambe'\n ]\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ndef merge_categories_with_same_prefix(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df[column_name] = df[column_name].str.replace(r'[-+]\\d+', '').str.strip()\n return df\n\ndef replace_description_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n \"Navidad\",\n \"Terremoto Manabi\",\n \"Fundacion de Quito\",\n \"Dia de la Madre\",\n \"Carnaval\",\n \"Fundacion de Guayaquil\",\n \"Primer dia del ano\",\n \"Independencia de Latacunga\",\n \"Independencia de Cuenca\",\n \"Provincializacion de Santo Domingo\",\n \"Provincializacion Santa Elena\",\n \"Independencia de Guaranda\",\n \"Fundacion de Loja\",\n \"Independencia de Ambato\",\n \"Cantonizacion de Quevedo\",\n \"Provincializacion de Cotopaxi\",\n \"Cantonizacion de Salinas\",\n \"Dia de Difuntos\",\n \"Fundacion de Manta\",\n \"Cantonizacion de Libertad\",\n \"Fundacion de Machala\",\n \"Fundacion de Ibarra\",\n \"Cantonizacion de Riobamba\",\n \"Cantonizacion del Puyo\",\n \"Cantonizacion de Guaranda\",\n \"Fundacion de Cuenca\",\n \"Cantonizacion de Latacunga\",\n \"Provincializacion de Imbabura\",\n \"Fundacion de Santo Domingo\",\n \"Cantonizacion de El Carmen\",\n \"Cantonizacion de Cayambe\",\n \"Fundacion de Esmeraldas\",\n \"Fundacion de Riobamba\",\n \"Fundacion de Ambato\",\n \"Viernes Santo\",\n \"Dia del Trabajo\",\n \"Mundial de futbol Brasil: Octavos de Final\",\n \"Primer Grito de Independencia\",\n \"Independencia de Guayaquil\",\n \"Cyber Monday\",\n \"Black Friday\",\n \"Traslado Independencia de Guayaquil\",\n \"Batalla de Pichincha\",\n \"Puente Navidad\",\n \"Mundial de futbol Brasil: Cuartos de Final\",\n \"Mundial de futbol Brasil: Semifinales\",\n \"Traslado Primer Grito de Independencia\",\n \"Traslado Batalla de Pichincha\",\n \"Puente Primer dia del ano\",\n \"Traslado Primer dia del ano\",\n \"Puente Dia de Difuntos\",\n \"Traslado Fundacion de Guayaquil\",\n \"Mundial de futbol Brasil: Ecuador-Honduras\",\n \"Mundial de futbol Brasil: Ecuador-Francia\",\n \"Inauguracion Mundial de futbol Brasil\",\n \"Mundial de futbol Brasil: Final\",\n \"Mundial de futbol Brasil: Tercer y cuarto lugar\",\n \"Mundial de futbol Brasil: Ecuador-Suiza\",\n \"Traslado Fundacion de Quito\"\n ]\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ncounter = 0\n\n\ndef replace_family_names_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n \"AUTOMOTIVE\",\n \"HOME APPLIANCES\",\n \"SCHOOL AND OFFICE SUPPLIES\",\n \"PRODUCE\",\n \"PREPARED FOODS\",\n \"POULTRY\",\n \"PLAYERS AND ELECTRONICS\",\n \"PET SUPPLIES\",\n \"PERSONAL CARE\",\n \"MEATS\",\n \"MAGAZINES\",\n \"LIQUOR,WINE,BEER\",\n \"LINGERIE\",\n \"LAWN AND GARDEN\",\n \"LADIESWEAR\",\n \"HOME CARE\",\n \"HOME AND KITCHEN II\",\n \"BABY CARE\",\n \"HOME AND KITCHEN I\",\n \"HARDWARE\",\n \"GROCERY II\",\n \"GROCERY I\",\n \"FROZEN FOODS\",\n \"EGGS\",\n \"DELI\",\n \"DAIRY\",\n \"CLEANING\",\n \"CELEBRATION\",\n \"BREAD/BAKERY\",\n \"BOOKS\",\n \"BEVERAGES\",\n \"BEAUTY\",\n \"SEAFOOD\"\n ]\n\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ndef csv_process(filepath1, filepath2, filepath3, filepath4):\n\n oil = pd.read_csv(filepath1)\n \n replace_nan_with_next_value(oil, \"dcoilwtico\")\n \n #print(oil['dcoilwtico'].value_counts())\n \n #print(oil)\n \n holiday = pd.read_csv(filepath2)\n \n holiday = delete_rows_with_true(holiday, \"transferred\")\n \n holiday = delete_rows_with_work_day(holiday, \"type\")\n \n holiday = holiday.drop('transferred', axis=1)\n \n holiday = replace_categories_with_integers(holiday, \"type\")\n \n holiday = replace_categories_with_integers(holiday, \"locale\")\n \n holiday = replace_locale_names_with_integers(holiday, \"locale_name\")\n \n holiday = merge_categories_with_same_prefix(holiday, \"description\")\n \n holiday = replace_description_with_integers(holiday, \"description\")\n \n train = pd.read_csv(filepath3)\n \n transactions = pd.read_csv(filepath4)\n \n #train = train.head()\n #train = train.tail()\n \n #transactions = transactions.head(100000)\n #transactions = transactions.tail(20)\n \n #holiday = holiday.head(1000)\n #holiday = holiday.tail(20)\n \n #oil = oil.head(1000)\n #oil = oil.tail(20)\n \n #print(\"done\")\n \n #print(train.info())\n #print(transactions.info())\n \n def drop_before_jan_first_2013(df):\n # Convert the 'date' column to a datetime object\n df['date'] = pd.to_datetime(df['date'])\n\n # Select rows after January 1st, 2013\n df = df.loc[df['date'] >= '2013-01-01']\n\n # Reset the index of the resulting dataframe\n df = df.reset_index(drop=True)\n\n # Return the resulting dataframe\n return df\n \n train = drop_before_jan_first_2013(train)\n \n transactions = drop_before_jan_first_2013(transactions)\n \n holiday = drop_before_jan_first_2013(holiday)\n \n oil = drop_before_jan_first_2013(oil)\n \n def drop_after_august_fifteenth_2017(df):\n # Convert the 'date' column to a datetime object\n df['date'] = pd.to_datetime(df['date'])\n\n # Select rows after January 1st, 2013\n df = df.loc[df['date'] <= '2017-08-15']\n\n # Reset the index of the resulting dataframe\n df = df.reset_index(drop=True)\n\n # Return the resulting dataframe\n return df\n\n train = drop_after_august_fifteenth_2017(train)\n \n transactions = drop_after_august_fifteenth_2017(transactions)\n \n holiday = drop_after_august_fifteenth_2017(holiday)\n \n oil = drop_after_august_fifteenth_2017(oil)\n \n \n \n #train = train.merge(transactions, on=['store_nbr', 'date'], how='left')\n \n train = replace_family_names_with_integers(train, \"family\")\n \n \n #print(train)\n #print(holiday.info())\n #print(holiday['date'].value_counts())\n holiday[['type', 'locale', 'locale_name', 'description']] = holiday[['type', 'locale', 'locale_name', 'description']].astype(str)\n\n # Define a custom function to join the values\n def join_values(column):\n return ''.join(column)\n\n # Group by date and apply the custom function to each column\n holiday_agg = holiday.groupby('date', as_index=False).agg({\n 'type': join_values,\n 'locale': join_values,\n 'locale_name': join_values,\n 'description': join_values\n })\n \n \n \n for i in ['type', 'locale', 'locale_name', 'description']:\n holiday_agg[i] = holiday_agg[i].astype(str).str.replace('[^0-9]', '', regex=True)\n \n # Convert the column to int64\n holiday_agg[i] = pd.to_numeric(holiday_agg[i], errors='coerce').fillna(0).astype(int)\n \n\n \n date_range = pd.date_range(start=oil['date'].min(), end=oil['date'].max())\n complete_dates = pd.DataFrame({'date': date_range})\n\n # Merge 'complete_dates' with the 'oil' DataFrame using a left join\n oil_filled = complete_dates.merge(oil, on=['date'], how='left')\n\n # Fill missing values in the 'dcoilwtico' column with the previous available value\n oil_filled['dcoilwtico'].fillna(method='bfill', inplace=True)\n\n \n train = train.fillna(0)\n \n\n return train, oil_filled, holiday_agg, transactions\n \ntrain, oil, holiday, transactions = csv_process(\"/kaggle/input/store-sales-time-series-forecasting/oil.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/train.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/transactions.csv\")\n\nunique_store_numbers = train['store_nbr'].unique()\ntrain_store_dataframes = {}\ntransaction_store_dataframes = {}\npromotion_store_dataframes = {}\n\nfor store_number in unique_store_numbers:\n train_store_dataframe = train[train['store_nbr'] == store_number]\n train_store_dataframes[store_number] = train_store_dataframe\n \nfor store_number in unique_store_numbers:\n transaction_store_dataframe = transactions[transactions['store_nbr'] == store_number]\n transaction_store_dataframes[store_number] = transaction_store_dataframe\n\nfor store_number in unique_store_numbers:\n promotion_store_dataframe = train[train['store_nbr'] == store_number]\n promotion_store_dataframes[store_number] = promotion_store_dataframe\n\n \nfor store_number in unique_store_numbers:\n train_df = train_store_dataframes[store_number]\n\n pivot_train_df = train_df.pivot_table(index='date', columns='family', values='sales', fill_value=0)\n\n # Reset the index to make 'date' a column again\n pivot_train_df.reset_index(inplace=True)\n\n # Update the dictionary with the modified DataFrame\n train_store_dataframes[store_number] = pivot_train_df\n\n \nfor store_number in unique_store_numbers:\n promotion_df = promotion_store_dataframes[store_number]\n\n pivot_promotion_df = train_df.pivot_table(index='date', columns='family', values='onpromotion', fill_value=0)\n\n # Reset the index to make 'date' a column again\n pivot_promotion_df.reset_index(inplace=True)\n\n # Update the dictionary with the modified DataFrame\n promotion_store_dataframes[store_number] = pivot_promotion_df\n\n\nprint(promotion_store_dataframes[1])\n\n\n\n\nprint(\"done\")\n\n","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:07:13.672463Z","iopub.execute_input":"2023-12-17T07:07:13.673263Z","iopub.status.idle":"2023-12-17T07:07:49.465758Z","shell.execute_reply.started":"2023-12-17T07:07:13.673221Z","shell.execute_reply":"2023-12-17T07:07:49.464706Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:78: FutureWarning: The default value of regex will change from True to False in a future version.\n","output_type":"stream"},{"name":"stdout","text":"family date 1 2 3 4 5 6 7 8 9 ... 24 25 26 27 28 \\\n0 2013-01-01 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n1 2013-01-02 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n2 2013-01-03 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n3 2013-01-04 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n4 2013-01-05 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n... ... .. .. .. ... .. .. .. .. .. ... .. .. .. .. .. \n1679 2017-08-11 0 0 7 6 3 22 0 0 11 ... 1 68 18 24 0 \n1680 2017-08-12 0 0 10 7 1 0 0 0 7 ... 1 8 15 25 0 \n1681 2017-08-13 0 0 8 7 1 0 0 0 9 ... 1 7 19 22 0 \n1682 2017-08-14 0 0 11 7 0 0 0 0 10 ... 18 8 18 23 0 \n1683 2017-08-15 0 0 8 148 1 0 0 0 11 ... 1 7 19 25 0 \n\nfamily 29 30 31 32 33 \n0 0 0 0 0 0 \n1 0 0 0 0 0 \n2 0 0 0 0 0 \n3 0 0 0 0 0 \n4 0 0 0 0 0 \n... .. .. .. .. .. \n1679 7 0 25 1 0 \n1680 4 0 27 1 4 \n1681 70 0 31 1 0 \n1682 6 0 28 1 0 \n1683 7 0 26 1 0 \n\n[1684 rows x 34 columns]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"\n\nfor store_number in unique_store_numbers:\n transactions_df = transaction_store_dataframes[store_number]\n sales_df = train_store_dataframes[store_number]\n promotion_df = promotion_store_dataframes[store_number]\n\n \n date_range = pd.date_range(start=sales_df['date'].min(), end=sales_df['date'].max())\n complete_dates = pd.DataFrame({'date': date_range})\n\n transactions_df = complete_dates.merge(transactions_df, on=['date'], how='left')\n transactions_df = transactions_df.fillna(0)\n\n sales_df = complete_dates.merge(sales_df, on=['date'], how='left')\n sales_df = sales_df.fillna(0)\n \n promotion_df = complete_dates.merge(promotion_df, on=['date'], how='left')\n promotion_df = promotion_df.fillna(0)\n\n date_range = pd.date_range(start=transactions_df['date'].min(), end=transactions_df['date'].max())\n complete_dates = pd.DataFrame({'date': date_range})\n\n transactions_df = complete_dates.merge(transactions_df, on=['date'], how='left')\n transactions_df = transactions_df.fillna(0)\n\n sales_df = complete_dates.merge(sales_df, on=['date'], how='left')\n sales_df = sales_df.fillna(0)\n \n promotion_df = complete_dates.merge(promotion_df, on=['date'], how='left')\n promotion_df = promotion_df.fillna(0)\n\n # Update the dictionaries with the modified DataFrames\n transaction_store_dataframes[store_number] = transactions_df\n train_store_dataframes[store_number] = sales_df\n promotion_store_dataframes[store_number] = promotion_df\n\n \n \n#Sales numbers by store by date\ntrainst1 = train_store_dataframes[1]\ntrainst2 = train_store_dataframes[2]\ntrainst3 = train_store_dataframes[3]\ntrainst4 = train_store_dataframes[4]\ntrainst5 = train_store_dataframes[5]\ntrainst6 = train_store_dataframes[6]\ntrainst7 = train_store_dataframes[7]\ntrainst8 = train_store_dataframes[8]\ntrainst9 = train_store_dataframes[9]\ntrainst10 = train_store_dataframes[10]\ntrainst11 = train_store_dataframes[11]\ntrainst12 = train_store_dataframes[12]\ntrainst13 = train_store_dataframes[13]\ntrainst14 = train_store_dataframes[14]\ntrainst15 = train_store_dataframes[15]\ntrainst16 = train_store_dataframes[16]\ntrainst17 = train_store_dataframes[17]\ntrainst18 = train_store_dataframes[18]\ntrainst19 = train_store_dataframes[19]\ntrainst20 = train_store_dataframes[20]\ntrainst21 = train_store_dataframes[21]\ntrainst22 = train_store_dataframes[22]\ntrainst23 = train_store_dataframes[23]\ntrainst24 = train_store_dataframes[24]\ntrainst25 = train_store_dataframes[25]\ntrainst26 = train_store_dataframes[26]\ntrainst27 = train_store_dataframes[27]\ntrainst28 = train_store_dataframes[28]\ntrainst29 = train_store_dataframes[29]\ntrainst30 = train_store_dataframes[30]\ntrainst31 = train_store_dataframes[31]\ntrainst32 = train_store_dataframes[32]\ntrainst33 = train_store_dataframes[33]\ntrainst34 = train_store_dataframes[34]\ntrainst35 = train_store_dataframes[35]\ntrainst36 = train_store_dataframes[36]\ntrainst37 = train_store_dataframes[37]\ntrainst38 = train_store_dataframes[38]\ntrainst39 = train_store_dataframes[39]\ntrainst40 = train_store_dataframes[40]\ntrainst41 = train_store_dataframes[41]\ntrainst42 = train_store_dataframes[42]\ntrainst43 = train_store_dataframes[43]\ntrainst44 = train_store_dataframes[44]\ntrainst45 = train_store_dataframes[45]\ntrainst46 = train_store_dataframes[46]\ntrainst47 = train_store_dataframes[47]\ntrainst48 = train_store_dataframes[48]\ntrainst49 = train_store_dataframes[49]\ntrainst50 = train_store_dataframes[50]\ntrainst51 = train_store_dataframes[51]\ntrainst52 = train_store_dataframes[52]\ntrainst53 = train_store_dataframes[53]\ntrainst54 = train_store_dataframes[54]\n\n#Transaction numbers by store by date\ntransst1 = transaction_store_dataframes[1]\ntransst2 = transaction_store_dataframes[2]\ntransst3 = transaction_store_dataframes[3]\ntransst4 = transaction_store_dataframes[4]\ntransst5 = transaction_store_dataframes[5]\ntransst6 = transaction_store_dataframes[6]\ntransst7 = transaction_store_dataframes[7]\ntransst8 = transaction_store_dataframes[8]\ntransst9 = transaction_store_dataframes[9]\ntransst10 = transaction_store_dataframes[10]\ntransst11 = transaction_store_dataframes[11]\ntransst12 = transaction_store_dataframes[12]\ntransst13 = transaction_store_dataframes[13]\ntransst14 = transaction_store_dataframes[14]\ntransst15 = transaction_store_dataframes[15]\ntransst16 = transaction_store_dataframes[16]\ntransst17 = transaction_store_dataframes[17]\ntransst18 = transaction_store_dataframes[18]\ntransst19 = transaction_store_dataframes[19]\ntransst20 = transaction_store_dataframes[20]\ntransst21 = transaction_store_dataframes[21]\ntransst22 = transaction_store_dataframes[22]\ntransst23 = transaction_store_dataframes[23]\ntransst24 = transaction_store_dataframes[24]\ntransst25 = transaction_store_dataframes[25]\n\npromost1 = promotion_store_dataframes[1]\npromost2 = promotion_store_dataframes[2]\npromost3 = promotion_store_dataframes[3]\npromost4 = promotion_store_dataframes[4]\npromost5 = promotion_store_dataframes[5]\npromost6 = promotion_store_dataframes[6]\npromost7 = promotion_store_dataframes[7]\npromost8 = promotion_store_dataframes[8]\npromost9 = promotion_store_dataframes[9]\npromost10 = promotion_store_dataframes[10]\npromost11 = promotion_store_dataframes[11]\npromost12 = promotion_store_dataframes[12]\npromost13 = promotion_store_dataframes[13]\npromost14 = promotion_store_dataframes[14]\npromost15 = promotion_store_dataframes[15]\npromost16 = promotion_store_dataframes[16]\npromost17 = promotion_store_dataframes[17]\npromost18 = promotion_store_dataframes[18]\npromost19 = promotion_store_dataframes[19]\npromost20 = promotion_store_dataframes[20]\npromost21 = promotion_store_dataframes[21]\npromost22 = promotion_store_dataframes[22]\npromost23 = promotion_store_dataframes[23]\npromost24 = promotion_store_dataframes[24]\npromost25 = promotion_store_dataframes[25]\npromost26 = promotion_store_dataframes[26]\npromost27 = promotion_store_dataframes[27]\npromost28 = promotion_store_dataframes[28]\npromost29 = promotion_store_dataframes[29]\npromost30 = promotion_store_dataframes[30]\npromost31 = promotion_store_dataframes[31]\npromost32 = promotion_store_dataframes[32]\npromost33 = promotion_store_dataframes[33]\npromost34 = promotion_store_dataframes[34]\npromost35 = promotion_store_dataframes[35]\npromost36 = promotion_store_dataframes[36]\npromost37 = promotion_store_dataframes[37]\npromost38 = promotion_store_dataframes[38]\npromost39 = promotion_store_dataframes[39]\npromost40 = promotion_store_dataframes[40]\npromost41 = promotion_store_dataframes[41]\npromost42 = promotion_store_dataframes[42]\npromost43 = promotion_store_dataframes[43]\npromost44 = promotion_store_dataframes[44]\npromost45 = promotion_store_dataframes[45]\npromost46 = promotion_store_dataframes[46]\npromost47 = promotion_store_dataframes[47]\npromost48 = promotion_store_dataframes[48]\npromost49 = promotion_store_dataframes[49]\npromost50 = promotion_store_dataframes[50]\npromost51 = promotion_store_dataframes[51]\npromost52 = promotion_store_dataframes[52]\npromost53 = promotion_store_dataframes[53]\npromost54 = promotion_store_dataframes[54]\n\n\n\nprint(trainst1.iloc[[356,357,358,359,360,361,362]])\nprint(transst1.iloc[[356,357,358,359,360,361,362]])\n#print(transst1['date'].value_counts())\n\nprint(len(trainst1))\nprint(len(transst1))\n\nprint(promost1)\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:07:49.468091Z","iopub.execute_input":"2023-12-17T07:07:49.468422Z","iopub.status.idle":"2023-12-17T07:07:54.148976Z","shell.execute_reply.started":"2023-12-17T07:07:49.468388Z","shell.execute_reply":"2023-12-17T07:07:54.147901Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":" date 1 2 3 4 5 6 7 8 9 ... \\\n356 2013-12-23 1.0 0.0 0.0 2.0 83.812 775.088 0.0 0.0 146.0 ... \n357 2013-12-24 1.0 0.0 0.0 4.0 129.199 527.220 0.0 0.0 97.0 ... \n358 2013-12-25 0.0 0.0 0.0 0.0 0.000 0.000 0.0 0.0 0.0 ... \n359 2013-12-26 2.0 0.0 0.0 1.0 77.212 287.849 0.0 0.0 161.0 ... \n360 2013-12-27 6.0 1.0 0.0 9.0 61.527 591.612 0.0 0.0 140.0 ... \n361 2013-12-28 4.0 0.0 0.0 3.0 68.848 201.424 0.0 0.0 115.0 ... \n362 2013-12-29 0.0 0.0 0.0 1.0 19.923 103.545 0.0 0.0 46.0 ... \n\n 24 25 26 27 28 29 30 31 32 33 \n356 191.0 168.141 1074.0 1059.0 0.0 373.752000 0.0 1464.0 2.0 31.551 \n357 153.0 158.207 952.0 611.0 0.0 238.080000 0.0 1248.0 2.0 28.162 \n358 0.0 0.000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000 \n359 133.0 119.881 595.0 690.0 0.0 307.494000 0.0 1009.0 5.0 26.623 \n360 125.0 136.624 628.0 621.0 0.0 292.350000 0.0 1025.0 0.0 22.541 \n361 106.0 71.209 469.0 398.0 0.0 205.033000 0.0 803.0 2.0 19.537 \n362 46.0 35.560 161.0 139.0 0.0 81.604996 0.0 242.0 0.0 7.108 \n\n[7 rows x 34 columns]\n date store_nbr transactions\n356 2013-12-23 1.0 2848.0\n357 2013-12-24 1.0 2844.0\n358 2013-12-25 0.0 0.0\n359 2013-12-26 1.0 1980.0\n360 2013-12-27 1.0 2022.0\n361 2013-12-28 1.0 1070.0\n362 2013-12-29 1.0 416.0\n1688\n1688\n date 1 2 3 4 5 6 7 8 9 ... 24 \\\n0 2013-01-01 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n1 2013-01-02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n2 2013-01-03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n3 2013-01-04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n4 2013-01-05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n... ... ... ... ... ... ... ... ... ... ... ... ... \n1683 2017-08-11 0.0 0.0 7.0 6.0 3.0 22.0 0.0 0.0 11.0 ... 1.0 \n1684 2017-08-12 0.0 0.0 10.0 7.0 1.0 0.0 0.0 0.0 7.0 ... 1.0 \n1685 2017-08-13 0.0 0.0 8.0 7.0 1.0 0.0 0.0 0.0 9.0 ... 1.0 \n1686 2017-08-14 0.0 0.0 11.0 7.0 0.0 0.0 0.0 0.0 10.0 ... 18.0 \n1687 2017-08-15 0.0 0.0 8.0 148.0 1.0 0.0 0.0 0.0 11.0 ... 1.0 \n\n 25 26 27 28 29 30 31 32 33 \n0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n... ... ... ... ... ... ... ... ... ... \n1683 68.0 18.0 24.0 0.0 7.0 0.0 25.0 1.0 0.0 \n1684 8.0 15.0 25.0 0.0 4.0 0.0 27.0 1.0 4.0 \n1685 7.0 19.0 22.0 0.0 70.0 0.0 31.0 1.0 0.0 \n1686 8.0 18.0 23.0 0.0 6.0 0.0 28.0 1.0 0.0 \n1687 7.0 19.0 25.0 0.0 7.0 0.0 26.0 1.0 0.0 \n\n[1688 rows x 34 columns]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"min_date = trainst1['date'].min()\nstart_date = min_date + pd.DateOffset(days=30)\nend_date = trainst1['date'].max()\n\ndate_range = pd.date_range(start=start_date, end=end_date)\ncomplete_dates = pd.DataFrame({'date': date_range})\nholiday = complete_dates.merge(holiday, on=['date'], how='left')\nholiday = holiday.fillna(0)\n\ntypes = holiday[\"type\"]\ntypes = (types - types.min()) / (types.max() + 1 - types.min())\n\nlocale = holiday[\"locale\"]\nlocale = (locale - locale.min()) / (locale.max() + 1 - locale.min())\n\nlocale_name = holiday[\"locale_name\"]\nlocale_name = (locale_name - locale_name.min()) / (locale_name.max() + 1 - locale_name.min())\n\ndescription = holiday[\"description\"]\ndescription = (description - description.min()) / (description.max() + 1 - description.min())\n\nnum_features = oil\n\nnum_features['day_of_week'] = num_features['date'].dt.dayofweek\nnum_features['month'] = num_features['date'].dt.month\nnum_features['day_of_month'] = num_features['date'].dt.day\nnum_features['quarter'] = num_features['date'].dt.quarter\n\nnum_features = num_features.iloc[30:]\n\nnum_features.reset_index(inplace=True)\n\nnum_features = num_features.drop(columns=['date'])\n\nnum_features = (num_features - num_features.min()) / (num_features.max() + 1 - num_features.min())\n\nprint(num_features)\nprint(num_features.info())\n\n\ntypes = np.array(types)\nlocale = np.array(locale)\nlocale_name = np.array(locale_name)\ndescription = np.array(description)\nnum_features = np.array(num_features)\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:07:54.150188Z","iopub.execute_input":"2023-12-17T07:07:54.150497Z","iopub.status.idle":"2023-12-17T07:07:54.206619Z","shell.execute_reply.started":"2023-12-17T07:07:54.150466Z","shell.execute_reply":"2023-12-17T07:07:54.205612Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":" index dcoilwtico day_of_week month day_of_month quarter\n0 0.000000 0.836474 0.428571 0.000000 0.967742 0.0\n1 0.000603 0.834250 0.571429 0.083333 0.000000 0.0\n2 0.001206 0.819618 0.714286 0.083333 0.032258 0.0\n3 0.001809 0.819618 0.857143 0.083333 0.064516 0.0\n4 0.002413 0.819618 0.000000 0.083333 0.096774 0.0\n... ... ... ... ... ... ...\n1653 0.996984 0.264778 0.571429 0.583333 0.322581 0.5\n1654 0.997587 0.250497 0.714286 0.583333 0.354839 0.5\n1655 0.998191 0.250497 0.857143 0.583333 0.387097 0.5\n1656 0.998794 0.250497 0.000000 0.583333 0.419355 0.5\n1657 0.999397 0.250263 0.142857 0.583333 0.451613 0.5\n\n[1658 rows x 6 columns]\n\nRangeIndex: 1658 entries, 0 to 1657\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 index 1658 non-null float64\n 1 dcoilwtico 1658 non-null float64\n 2 day_of_week 1658 non-null float64\n 3 month 1658 non-null float64\n 4 day_of_month 1658 non-null float64\n 5 quarter 1658 non-null float64\ndtypes: float64(6)\nmemory usage: 77.8 KB\nNone\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\n\ndef prepare_data(transactions, sales, window_size):\n x, y = [], []\n for i in range(len(transactions) - window_size):\n x.append(transactions[i:i+window_size])\n y.append(sales.iloc[i+window_size])\n return np.array(x), np.array(y)\n\n\n\n\ninput_data = transst1['transactions']\ninput_data2 = promost1.drop('date', axis=1)\noutput_data = trainst1.drop('date', axis=1)\n\nprint(input_data)\nprint(output_data)\n\nx,y = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst2['transactions']\ninput_data2 = promost2.drop('date', axis=1)\noutput_data = trainst2.drop('date', axis=1)\n\nx2,y2 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst3['transactions']\ninput_data2 = promost3.drop('date', axis=1)\noutput_data = trainst3.drop('date', axis=1)\n\nx3,y3 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst4['transactions']\ninput_data2 = promost4.drop('date', axis=1)\noutput_data = trainst4.drop('date', axis=1)\n\nx4,y4 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst5['transactions']\ninput_data2 = promost5.drop('date', axis=1)\noutput_data = trainst5.drop('date', axis=1)\n\nx5,y5 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst6['transactions']\ninput_data2 = promost6.drop('date', axis=1)\noutput_data = trainst6.drop('date', axis=1)\n\nx6,y6 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst7['transactions']\ninput_data2 = promost7.drop('date', axis=1)\noutput_data = trainst7.drop('date', axis=1)\n\nx7,y7 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst8['transactions']\ninput_data2 = promost8.drop('date', axis=1)\noutput_data = trainst8.drop('date', axis=1)\n\nx8,y8 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst9['transactions']\ninput_data2 = promost9.drop('date', axis=1)\noutput_data = trainst9.drop('date', axis=1)\n\nx9,y9 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst10['transactions']\ninput_data2 = promost10.drop('date', axis=1)\noutput_data = trainst10.drop('date', axis=1)\n\nx10,y10 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst11['transactions']\ninput_data2 = promost11.drop('date', axis=1)\noutput_data = trainst11.drop('date', axis=1)\n\nx11,y11 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst12['transactions']\ninput_data2 = promost12.drop('date', axis=1)\noutput_data = trainst12.drop('date', axis=1)\n\nx12,y12 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst13['transactions']\ninput_data2 = promost13.drop('date', axis=1)\noutput_data = trainst13.drop('date', axis=1)\n\nx13,y13 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst14['transactions']\ninput_data2 = promost14.drop('date', axis=1)\noutput_data = trainst14.drop('date', axis=1)\n\nx14,y14 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst15['transactions']\ninput_data2 = promost15.drop('date', axis=1)\noutput_data = trainst15.drop('date', axis=1)\n\nx15,y15 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst16['transactions']\ninput_data2 = promost16.drop('date', axis=1)\noutput_data = trainst16.drop('date', axis=1)\n\nx16,y16 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst17['transactions']\ninput_data2 = promost17.drop('date', axis=1)\noutput_data = trainst17.drop('date', axis=1)\n\nx17,y17 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst18['transactions']\ninput_data2 = promost18.drop('date', axis=1)\noutput_data = trainst18.drop('date', axis=1)\n\nx18,y18 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst19['transactions']\ninput_data2 = promost19.drop('date', axis=1)\noutput_data = trainst19.drop('date', axis=1)\n\nx19,y19 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst20['transactions']\ninput_data2 = promost20.drop('date', axis=1)\noutput_data = trainst20.drop('date', axis=1)\n\nx20,y20 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst21['transactions']\ninput_data2 = promost21.drop('date', axis=1)\noutput_data = trainst21.drop('date', axis=1)\n\nx21,y21 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst22['transactions']\ninput_data2 = promost22.drop('date', axis=1)\noutput_data = trainst22.drop('date', axis=1)\n\nx22,y22 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst23['transactions']\ninput_data2 = promost23.drop('date', axis=1)\noutput_data = trainst23.drop('date', axis=1)\n\nx23,y23 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst24['transactions']\ninput_data2 = promost24.drop('date', axis=1)\noutput_data = trainst24.drop('date', axis=1)\n\nx24,y24 = prepare_data(input_data2, output_data, 30)\n\ninput_data = transst25['transactions']\ninput_data2 = promost25.drop('date', axis=1)\noutput_data = trainst25.drop('date', axis=1)\n\nx25,y25 = prepare_data(input_data2, output_data, 30)\n\n# Prepare data for store 26\ninput_data2 = promost26.drop('date', axis=1)\noutput_data = trainst26.drop('date', axis=1)\nx26, y26 = prepare_data(input_data2, output_data, 30)\n\n# Prepare data for store 27\ninput_data27 = promost27.drop('date', axis=1)\noutput_data27 = trainst27.drop('date', axis=1)\nx27, y27 = prepare_data(input_data27, output_data27, 30)\n\n# Prepare data for store 28\ninput_data28 = promost28.drop('date', axis=1)\noutput_data28 = trainst28.drop('date', axis=1)\nx28, y28 = prepare_data(input_data28, output_data28, 30)\n\n# Prepare data for store 29\ninput_data29 = promost29.drop('date', axis=1)\noutput_data29 = trainst29.drop('date', axis=1)\nx29, y29 = prepare_data(input_data29, output_data29, 30)\n\n# Prepare data for store 30\ninput_data30 = promost30.drop('date', axis=1)\noutput_data30 = trainst30.drop('date', axis=1)\nx30, y30 = prepare_data(input_data30, output_data30, 30)\n\n# Prepare data for store 31\ninput_data31 = promost31.drop('date', axis=1)\noutput_data31 = trainst31.drop('date', axis=1)\nx31, y31 = prepare_data(input_data31, output_data31, 30)\n\n# Prepare data for store 32\ninput_data32 = promost32.drop('date', axis=1)\noutput_data32 = trainst32.drop('date', axis=1)\nx32, y32 = prepare_data(input_data32, output_data32, 30)\n\n# Prepare data for store 33\ninput_data33 = promost33.drop('date', axis=1)\noutput_data33 = trainst33.drop('date', axis=1)\nx33, y33 = prepare_data(input_data33, output_data33, 30)\n\n# Prepare data for store 34\ninput_data34 = promost34.drop('date', axis=1)\noutput_data34 = trainst34.drop('date', axis=1)\nx34, y34 = prepare_data(input_data34, output_data34, 30)\n\n# Prepare data for store 35\ninput_data35 = promost35.drop('date', axis=1)\noutput_data35 = trainst35.drop('date', axis=1)\nx35, y35 = prepare_data(input_data35, output_data35, 30)\n\n# Prepare data for store 36\ninput_data36 = promost36.drop('date', axis=1)\noutput_data36 = trainst36.drop('date', axis=1)\nx36, y36 = prepare_data(input_data36, output_data36, 30)\n\n# Prepare data for store 37\ninput_data37 = promost37.drop('date', axis=1)\noutput_data37 = trainst37.drop('date', axis=1)\nx37, y37 = prepare_data(input_data37, output_data37, 30)\n\n# Prepare data for store 38\ninput_data38 = promost38.drop('date', axis=1)\noutput_data38 = trainst38.drop('date', axis=1)\nx38, y38 = prepare_data(input_data38, output_data38, 30)\n\n# Prepare data for store 39\ninput_data39 = promost39.drop('date', axis=1)\noutput_data39 = trainst39.drop('date', axis=1)\nx39, y39 = prepare_data(input_data39, output_data39, 30)\n\n# Prepare data for store 40\ninput_data40 = promost40.drop('date', axis=1)\noutput_data40 = trainst40.drop('date', axis=1)\nx40, y40 = prepare_data(input_data40, output_data40, 30)\n\n# Prepare data for store 41\ninput_data41 = promost41.drop('date', axis=1)\noutput_data41 = trainst41.drop('date', axis=1)\nx41, y41 = prepare_data(input_data41, output_data41, 30)\n\n# Prepare data for store 42\ninput_data42 = promost42.drop('date', axis=1)\noutput_data42 = trainst42.drop('date', axis=1)\nx42, y42 = prepare_data(input_data42, output_data42, 30)\n\n# Prepare data for store 43\ninput_data43 = promost43.drop('date', axis=1)\noutput_data43 = trainst43.drop('date', axis=1)\nx43, y43 = prepare_data(input_data43, output_data43, 30)\n\n# Prepare data for store 44\ninput_data44 = promost44.drop('date', axis=1)\noutput_data44 = trainst44.drop('date', axis=1)\nx44, y44 = prepare_data(input_data44, output_data44, 30)\n\n# Prepare data for store 45\ninput_data45 = promost45.drop('date', axis=1)\noutput_data45 = trainst45.drop('date', axis=1)\nx45, y45 = prepare_data(input_data45, output_data45, 30)\n\n# Prepare data for store 46\ninput_data46 = promost46.drop('date', axis=1)\noutput_data46 = trainst46.drop('date', axis=1)\nx46, y46 = prepare_data(input_data46, output_data46, 30)\n\n# Prepare data for store 47\ninput_data47 = promost47.drop('date', axis=1)\noutput_data47 = trainst47.drop('date', axis=1)\nx47, y47 = prepare_data(input_data47, output_data47, 30)\n\n# Prepare data for store 48\ninput_data48 = promost48.drop('date', axis=1)\noutput_data48 = trainst48.drop('date', axis=1)\nx48, y48 = prepare_data(input_data48, output_data48, 30)\n\n# Prepare data for store 49\ninput_data49 = promost49.drop('date', axis=1)\noutput_data49 = trainst49.drop('date', axis=1)\nx49, y49 = prepare_data(input_data49, output_data49, 30)\n\n# Prepare data for store 50\ninput_data50 = promost50.drop('date', axis=1)\noutput_data50 = trainst50.drop('date', axis=1)\nx50, y50 = prepare_data(input_data50, output_data50, 30)\n\n# Prepare data for store 51\ninput_data51 = promost51.drop('date', axis=1)\noutput_data51 = trainst51.drop('date', axis=1)\nx51, y51 = prepare_data(input_data51, output_data51, 30)\n\n# Prepare data for store 52\ninput_data52 = promost52.drop('date', axis=1)\noutput_data52 = trainst52.drop('date', axis=1)\nx52, y52 = prepare_data(input_data52, output_data52, 30)\n\n# Prepare data for store 53\ninput_data53 = promost53.drop('date', axis=1)\noutput_data53 = trainst53.drop('date', axis=1)\nx53, y53 = prepare_data(input_data53, output_data53, 30)\n\n# Prepare data for store 54\ninput_data54 = promost54.drop('date', axis=1)\noutput_data54 = trainst54.drop('date', axis=1)\nx54, y54 = prepare_data(input_data54, output_data54, 30)\n\n\n\nprint(x)\nprint(y)\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:07:54.209338Z","iopub.execute_input":"2023-12-17T07:07:54.209677Z","iopub.status.idle":"2023-12-17T07:08:07.985170Z","shell.execute_reply.started":"2023-12-17T07:07:54.209646Z","shell.execute_reply":"2023-12-17T07:08:07.983896Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"0 0.0\n1 2111.0\n2 1833.0\n3 1863.0\n4 1509.0\n ... \n1683 570.0\n1684 1004.0\n1685 416.0\n1686 1733.0\n1687 1693.0\nName: transactions, Length: 1688, dtype: float64\n 1 2 3 4 5 6 7 8 9 \\\n0 0.0 0.0 0.0 0.000 0.000000 0.00000 0.0 0.0 0.0 \n1 2.0 0.0 0.0 0.000 47.000000 247.29700 0.0 0.0 194.0 \n2 3.0 2.0 0.0 0.000 63.000000 187.27800 0.0 0.0 153.0 \n3 3.0 0.0 0.0 0.000 67.000000 258.02300 0.0 0.0 88.0 \n4 5.0 0.0 0.0 0.000 66.000000 212.33301 0.0 0.0 141.0 \n... ... ... ... ... ... ... ... ... ... \n1683 1.0 0.0 0.0 1115.334 24.963001 259.11800 3.0 5.0 53.0 \n1684 6.0 0.0 0.0 1762.493 48.058000 217.66400 6.0 3.0 227.0 \n1685 1.0 0.0 0.0 986.669 20.346000 115.75800 0.0 2.0 45.0 \n1686 1.0 0.0 0.0 2611.755 72.004000 270.04700 6.0 3.0 159.0 \n1687 4.0 0.0 0.0 2240.230 42.822998 234.89200 21.0 3.0 173.0 \n\n 10 ... 24 25 26 27 28 29 30 \\\n0 0.00000 ... 0.0 0.000 0.0 0.0 0.0 0.00000 0.0 \n1 369.10100 ... 246.0 164.069 579.0 1060.0 0.0 470.65200 0.0 \n2 272.31900 ... 203.0 151.582 453.0 836.0 0.0 310.65500 0.0 \n3 454.17200 ... 171.0 131.411 460.0 827.0 0.0 198.36600 0.0 \n4 328.94000 ... 177.0 118.613 464.0 811.0 0.0 301.05700 0.0 \n... ... ... ... ... ... ... ... ... ... \n1683 385.99402 ... 86.0 64.302 343.0 341.0 4.0 145.60700 0.0 \n1684 211.75600 ... 113.0 99.488 526.0 351.0 3.0 243.22000 0.0 \n1685 88.18200 ... 60.0 47.770 266.0 169.0 1.0 136.67900 0.0 \n1686 192.76300 ... 170.0 154.578 699.0 571.0 4.0 346.03800 0.0 \n1687 274.17600 ... 131.0 116.402 602.0 703.0 21.0 329.54102 0.0 \n\n 31 32 33 \n0 0.0 0.0 0.000000 \n1 1091.0 2.0 38.029000 \n2 919.0 0.0 17.366001 \n3 953.0 3.0 29.907001 \n4 1160.0 3.0 24.842000 \n... ... ... ... \n1683 1006.0 1.0 19.424000 \n1684 1659.0 3.0 20.150000 \n1685 803.0 1.0 11.378000 \n1686 2201.0 6.0 14.129000 \n1687 1942.0 4.0 22.487000 \n\n[1688 rows x 33 columns]\n[[[ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n ...\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]]\n\n [[ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n ...\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]]\n\n [[ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n ...\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]\n [ 0. 0. 0. ... 0. 0. 0.]]\n\n ...\n\n [[ 0. 0. 0. ... 38. 1. 1.]\n [ 0. 0. 0. ... 33. 1. 4.]\n [ 0. 0. 0. ... 35. 1. 1.]\n ...\n [ 0. 0. 10. ... 28. 1. 0.]\n [ 0. 0. 7. ... 25. 1. 0.]\n [ 0. 0. 10. ... 27. 1. 4.]]\n\n [[ 0. 0. 0. ... 33. 1. 4.]\n [ 0. 0. 0. ... 35. 1. 1.]\n [ 0. 0. 0. ... 31. 1. 1.]\n ...\n [ 0. 0. 7. ... 25. 1. 0.]\n [ 0. 0. 10. ... 27. 1. 4.]\n [ 0. 0. 8. ... 31. 1. 0.]]\n\n [[ 0. 0. 0. ... 35. 1. 1.]\n [ 0. 0. 0. ... 31. 1. 1.]\n [ 0. 0. 0. ... 33. 1. 1.]\n ...\n [ 0. 0. 10. ... 27. 1. 4.]\n [ 0. 0. 8. ... 31. 1. 0.]\n [ 0. 0. 11. ... 28. 1. 0.]]]\n[[0.0000e+00 0.0000e+00 0.0000e+00 ... 8.8900e+02 2.0000e+00 2.5860e+01]\n [3.0000e+00 0.0000e+00 0.0000e+00 ... 9.4100e+02 0.0000e+00 3.2779e+01]\n [0.0000e+00 0.0000e+00 0.0000e+00 ... 1.0050e+03 4.0000e+00 2.9006e+01]\n ...\n [1.0000e+00 0.0000e+00 0.0000e+00 ... 8.0300e+02 1.0000e+00 1.1378e+01]\n [1.0000e+00 0.0000e+00 0.0000e+00 ... 2.2010e+03 6.0000e+00 1.4129e+01]\n [4.0000e+00 0.0000e+00 0.0000e+00 ... 1.9420e+03 4.0000e+00 2.2487e+01]]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom keras.callbacks import EarlyStopping\nfrom sklearn.model_selection import train_test_split\n\nimport psutil\nimport IPython.display\nfrom tensorflow.keras.callbacks import Callback\n\nclass ClearOutputAndDisplayEpoch(Callback):\n def on_epoch_end(self, epoch, logs=None):\n IPython.display.clear_output(wait=True) # Clear the Jupyter Notebook output\n metrics_string = ' - '.join([f'{k}: {v:.4f}' for k, v in logs.items()])\n print(f\"Epoch {epoch + 1}/{self.params['epochs']} - {metrics_string}\")\n\nclass MemoryUsageCheck(Callback):\n def __init__(self, memory_limit_gb=29):\n super().__init__()\n self.memory_limit_bytes = memory_limit_gb * (1024 ** 3)\n\n def on_epoch_end(self, epoch, logs=None):\n mem_info = psutil.virtual_memory()\n used_memory = mem_info.used\n if used_memory > self.memory_limit_bytes:\n print(f\"\\nStopping training: RAM usage exceeded {self.memory_limit_bytes / (1024 ** 3):.2f} GB\")\n self.model.stop_training = True\n\nmemory_usage_check = MemoryUsageCheck(memory_limit_gb=29)\n\"\"\"\nclass ClearOutputAndDisplayEpoch(Callback):\n def on_epoch_begin(self, epoch, logs=None):\n if epoch > 0:\n IPython.display.clear_output(wait=True) # Clear the Jupyter Notebook output\n print(f\"Epoch {epoch + 1}/{self.params['epochs']}\", end='')\n \n def on_epoch_end(self, epoch, logs=None):\n metrics_string = ' - '.join([f'{k}: {v:.4f}' for k, v in logs.items()])\n print(f\" - {metrics_string}\")\n\"\"\"\n\nclear_output_progbar_logger = ClearOutputAndDisplayEpoch()\n\n\ntypes_input = tf.keras.layers.Input(shape=(1,))\nlocale_input = tf.keras.layers.Input(shape=(1,))\nlocale_name_input = tf.keras.layers.Input(shape=(1,))\ndescription_input = tf.keras.layers.Input(shape=(1,))\n\noil_and_date_input = tf.keras.layers.Input(shape=(6,)) \n\n\ntypes_embedding_layer = tf.keras.layers.Embedding(input_dim=1, output_dim=20, input_length=1)(types_input)\nlocale_embedding_layer = tf.keras.layers.Embedding(input_dim=1, output_dim=20, input_length=1)(locale_input)\nlocale_name_embedding_layer = tf.keras.layers.Embedding(input_dim=1, output_dim=20, input_length=1)(locale_name_input)\ndescription_embedding_layer = tf.keras.layers.Embedding(input_dim=1, output_dim=20, input_length=1)(description_input)\n\ntypes_flatten = tf.keras.layers.Flatten()(types_embedding_layer)\nlocale_flatten = tf.keras.layers.Flatten()(locale_embedding_layer)\nlocale_name_flatten = tf.keras.layers.Flatten()(locale_name_embedding_layer)\ndescription_flatten = tf.keras.layers.Flatten()(description_embedding_layer)\n\n\nconcatenated = tf.keras.layers.Concatenate()([types_flatten, locale_flatten, \n locale_name_flatten, description_flatten, oil_and_date_input])\n\n\nLSTM_nodes = 33\n# If i set only the first LSTM to 64 it works but if I change this to 64 it breaks\n# It seems that was just straight up a mistake because this works better\n\ndensenodes1 = 256\n# maybe 512 is good\n\ndensenodes = 256\n\ndense_activation_function = \"LeakyReLU\"\n\n#num_heads = 4\n\ndense_output_activation_function = \"relu\"\n\nRNN_activation_function = \"tanh\"\n\ninputs = []\nRNN_layers = []\n#RNN_2_layers = []\nconcs = []\ndens1 = []\ndens2 = []\ndens3 = []\noutputs = []\n\n# Create input layers for input24 to input54\nfor i in range(1, 55):\n input_layer = tf.keras.layers.Input(shape=(30, 33), name=f\"input{i}\")\n RNN_layer = tf.keras.layers.LSTM(LSTM_nodes, activation=RNN_activation_function, name=f\"RNN_layer{i}\")(input_layer)\n #RNN_2_layer = tf.keras.layers.LSTM(LSTM_nodes, activation=RNN_activation_function, name=f\"RNN_2_layer{i}\")(RNN_layer)\n conc_layer = tf.keras.layers.Concatenate(name=f\"conc{i}\")([RNN_layer, concatenated])\n dense1 = tf.keras.layers.Dense(densenodes1, activation=dense_activation_function, name=f\"dense{i}1\")(conc_layer)\n dense2 = tf.keras.layers.Dense(densenodes, activation=dense_activation_function, name=f\"dense{i}2\")(dense1)\n dense3 = tf.keras.layers.Dense(densenodes, activation=dense_activation_function, name=f\"dense{i}3\")(dense2)\n output_layer = tf.keras.layers.Dense(LSTM_nodes, activation=dense_output_activation_function, name=f\"output{i}\")(dense3)\n\n # Append the layers to their respective lists\n inputs.append(input_layer)\n RNN_layers.append(RNN_layer)\n #RNN_2_layers.append(RNN_2_layer)\n concs.append(conc_layer)\n dens1.append(dense1)\n dens2.append(dense2)\n dens3.append(dense3)\n outputs.append(output_layer)\n\n\nrnn_model = tf.keras.Model(inputs= [types_input, locale_input, locale_name_input,\n description_input, oil_and_date_input] + inputs, \n outputs= outputs)\n\ndef RMSLE(y_pred:tf.Tensor, y_true:tf.Tensor) -> tf.float64:\n \"\"\"\n The Root Mean Squared Log Error (RMSLE) metric for TensorFlow / Keras\n \n :param y_true: The ground truth labels given in the dataset\n :param y_pred: Predicted values\n :return: The RMSLE score\n \"\"\"\n y_pred = tf.cast(y_pred, tf.float64)\n y_true = tf.cast(y_true, tf.float64) \n y_pred = tf.nn.relu(y_pred) \n return tf.sqrt(tf.reduce_mean(tf.math.squared_difference(tf.math.log1p(y_pred), tf.math.log1p(y_true))))\n\nrnn_model.compile(optimizer='adam', loss=RMSLE, metrics=[tf.keras.losses.MeanAbsoluteError()])\n\n#tf.keras.utils.plot_model(rnn_model, to_file='model.png', show_shapes=True)\n\nrnn_model.summary()\n\n\n\n\ndef train_multi_input_model(x_list, y_list, model):\n\n # Split the inputs and outputs into train and validation sets\n\n x_train_list, x_val_list, y_train_list, y_val_list = [], [], [], []\n for x in x_list:\n x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.05, random_state=42)\n x_train_list.append(x_train)\n x_val_list.append(x_val)\n y_train_list.append(y_train)\n y_val_list.append(y_val)\n \n \n early_stopping = EarlyStopping(monitor='val_loss', patience=25, mode='min')\n # was 25\n\n \n # Train the model with the training data and validation data\n history = model.fit(\n x=x_train_list,\n y=y_train_list[:54],\n epochs=1000,\n batch_size=256,\n #use 1024 if training needs to be faster and 256 for more accuracy\n validation_data=(x_val_list, y_val_list[:54]),\n callbacks=[early_stopping, clear_output_progbar_logger, memory_usage_check],\n verbose=2\n )\n\n return history\n\nx_list = [types, locale, locale_name, description, num_features,\n x, x2, x3, x4, x5,\n x6, x7, x8, x9, x10,\n x11, x12, x13, x14, x15,\n x16, x17, x18, x19, x20,\n x21, x22, x23, x24, x25, x26, x27, x28, x29, x30,\n x31, x32, x33, x34, x35,\n x36, x37, x38, x39, x40,\n x41, x42, x43, x44, x45,\n x46, x47, x48, x49, x50,\n x51, x52, x53, x54]\ny_list = [y, y2, y3, y4, y5,\n y6, y7, y8, y9, y10,\n y11, y12, y13, y14, y15,\n y16, y17, y18, y19, y20,\n y21, y22, y23, y24, y25,\n y26, y27, y28, y29, y30,\n y31, y32, y33, y34, y35,\n y36, y37, y38, y39, y40,\n y41, y42, y43, y44, y45,\n y46, y47, y48, y49, y50,\n y51, y52, y53, y54]\n\n#print(\"Before train-validation split:\")\n#print(\"x_train_list length:\", len(x_list))\n#print(\"x_val_list length:\", len(x_list))\n\n#rnn_model.save(\"store_sales_predictor\")\n\nhistory = train_multi_input_model(x_list, y_list, rnn_model)\n\"\"\"\n\nrnn_model.fit([types, locale, locale_name, description, num_features,\n x, x2, x3, x4, x5,\n x6, x7, x8, x9, x10,\n x11, x12, x13, x14, x15,\n x16, x17, x18, x19, x20,\n x21, x22, x23, x24, x25], \n [y, y2, y3, y4, y5,\n y6, y7, y8, y9, y10,\n y11, y12, y13, y14, y15,\n y16, y17, y18, y19, y20,\n y21, y22, y23, y24, y25],\n epochs=1000, batch_size=1024)\n#epochs = 100 lstm = 64 dense = 128 batch_size = 1024 loss = 33.57\n#epochs = early stopping patience 25 lstm = 64 dense = 256 batch_size = 256 loss = 30.8\n#epochs = early stopping patience 25 lstm = 64 dense = 256 batch_size = 256 \n# dense_activation_function = \"LeakyReLU\" dense_output_activation_function = \"Relu\" \n# RNN_activation_function = \"tanh\" Loss = 22.88\n#epochs = early stopping patience 25 lstm = 33 dense = 256 batch_size = 256 \n# dense_activation_function = \"LeakyReLU\" dense_output_activation_function = \"Relu\" \n# RNN_activation_function = \"tanh\" Loss = 21.1055\n\"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:08:07.987168Z","iopub.execute_input":"2023-12-17T07:08:07.987476Z","iopub.status.idle":"2023-12-17T07:27:32.002349Z","shell.execute_reply.started":"2023-12-17T07:08:07.987446Z","shell.execute_reply":"2023-12-17T07:27:32.001119Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Epoch 481/1000 - loss: 47.8653 - output1_loss: 1.0882 - output2_loss: 0.7613 - output3_loss: 1.1996 - output4_loss: 0.7420 - output5_loss: 0.7435 - output6_loss: 0.7962 - output7_loss: 0.7396 - output8_loss: 0.8763 - output9_loss: 0.6791 - output10_loss: 1.0598 - output11_loss: 0.7587 - output12_loss: 0.7535 - output13_loss: 0.7961 - output14_loss: 1.2120 - output15_loss: 0.7381 - output16_loss: 1.1233 - output17_loss: 1.1550 - output18_loss: 1.4109 - output19_loss: 0.7963 - output20_loss: 0.9015 - output21_loss: 0.8694 - output22_loss: 1.2757 - output23_loss: 0.7514 - output24_loss: 0.6273 - output25_loss: 0.7618 - output26_loss: 1.4872 - output27_loss: 0.7659 - output28_loss: 0.7564 - output29_loss: 0.8805 - output30_loss: 0.7553 - output31_loss: 0.6677 - output32_loss: 0.8699 - output33_loss: 0.7404 - output34_loss: 1.1030 - output35_loss: 0.8306 - output36_loss: 0.8169 - output37_loss: 0.7375 - output38_loss: 0.8429 - output39_loss: 1.1331 - output40_loss: 0.7368 - output41_loss: 0.6839 - output42_loss: 0.8212 - output43_loss: 0.9025 - output44_loss: 1.0715 - output45_loss: 0.8041 - output46_loss: 1.3081 - output47_loss: 0.8194 - output48_loss: 0.7465 - output49_loss: 0.7500 - output50_loss: 0.7270 - output51_loss: 0.8232 - output52_loss: 0.8799 - output53_loss: 1.2106 - output54_loss: 0.5769 - output1_mean_absolute_error: 54.1169 - output2_mean_absolute_error: 50.7324 - output3_mean_absolute_error: 53.5139 - output4_mean_absolute_error: 45.9221 - output5_mean_absolute_error: 49.2026 - output6_mean_absolute_error: 47.5562 - output7_mean_absolute_error: 48.9172 - output8_mean_absolute_error: 47.0062 - output9_mean_absolute_error: 48.6200 - output10_mean_absolute_error: 54.0144 - output11_mean_absolute_error: 47.4301 - output12_mean_absolute_error: 48.3471 - output13_mean_absolute_error: 47.5924 - output14_mean_absolute_error: 57.3571 - output15_mean_absolute_error: 49.3515 - output16_mean_absolute_error: 51.8784 - output17_mean_absolute_error: 52.7759 - output18_mean_absolute_error: 65.0279 - output19_mean_absolute_error: 52.3228 - output20_mean_absolute_error: 48.5346 - output21_mean_absolute_error: 48.3821 - output22_mean_absolute_error: 64.8587 - output23_mean_absolute_error: 52.1518 - output24_mean_absolute_error: 49.9669 - output25_mean_absolute_error: 48.4165 - output26_mean_absolute_error: 105.6859 - output27_mean_absolute_error: 47.7627 - output28_mean_absolute_error: 47.9694 - output29_mean_absolute_error: 49.3523 - output30_mean_absolute_error: 47.9919 - output31_mean_absolute_error: 48.6729 - output32_mean_absolute_error: 49.9639 - output33_mean_absolute_error: 47.6015 - output34_mean_absolute_error: 53.1875 - output35_mean_absolute_error: 49.7601 - output36_mean_absolute_error: 47.5857 - output37_mean_absolute_error: 48.4976 - output38_mean_absolute_error: 48.1021 - output39_mean_absolute_error: 53.4161 - output40_mean_absolute_error: 48.4885 - output41_mean_absolute_error: 46.6945 - output42_mean_absolute_error: 47.4298 - output43_mean_absolute_error: 48.4469 - output44_mean_absolute_error: 52.6096 - output45_mean_absolute_error: 49.4355 - output46_mean_absolute_error: 84.8554 - output47_mean_absolute_error: 48.6623 - output48_mean_absolute_error: 49.2123 - output49_mean_absolute_error: 53.0859 - output50_mean_absolute_error: 48.4748 - output51_mean_absolute_error: 48.6548 - output52_mean_absolute_error: 47.9294 - output53_mean_absolute_error: 56.3551 - output54_mean_absolute_error: 48.0781 - val_loss: 55.4237 - val_output1_loss: 1.1942 - val_output2_loss: 0.9081 - val_output3_loss: 1.3060 - val_output4_loss: 0.8999 - val_output5_loss: 0.9067 - val_output6_loss: 0.9545 - val_output7_loss: 0.9138 - val_output8_loss: 1.0199 - val_output9_loss: 0.8541 - val_output10_loss: 1.1515 - val_output11_loss: 0.9222 - val_output12_loss: 0.8907 - val_output13_loss: 0.9559 - val_output14_loss: 1.3041 - val_output15_loss: 0.9068 - val_output16_loss: 1.2492 - val_output17_loss: 1.2583 - val_output18_loss: 1.4918 - val_output19_loss: 0.9317 - val_output20_loss: 1.0217 - val_output21_loss: 1.0202 - val_output22_loss: 1.3558 - val_output23_loss: 0.8974 - val_output24_loss: 0.8124 - val_output25_loss: 0.9317 - val_output26_loss: 1.5618 - val_output27_loss: 0.9131 - val_output28_loss: 0.9452 - val_output29_loss: 1.0326 - val_output30_loss: 0.8996 - val_output31_loss: 0.8346 - val_output32_loss: 0.9982 - val_output33_loss: 0.9025 - val_output34_loss: 1.2120 - val_output35_loss: 0.9711 - val_output36_loss: 0.9476 - val_output37_loss: 0.9103 - val_output38_loss: 0.9761 - val_output39_loss: 1.2392 - val_output40_loss: 0.9348 - val_output41_loss: 0.8583 - val_output42_loss: 0.9625 - val_output43_loss: 1.0380 - val_output44_loss: 1.1690 - val_output45_loss: 0.9420 - val_output46_loss: 1.3746 - val_output47_loss: 0.9797 - val_output48_loss: 0.9112 - val_output49_loss: 0.9128 - val_output50_loss: 0.8916 - val_output51_loss: 0.9617 - val_output52_loss: 1.0043 - val_output53_loss: 1.2934 - val_output54_loss: 0.7871 - val_output1_mean_absolute_error: 56.3863 - val_output2_mean_absolute_error: 58.6651 - val_output3_mean_absolute_error: 60.9772 - val_output4_mean_absolute_error: 55.6823 - val_output5_mean_absolute_error: 52.1995 - val_output6_mean_absolute_error: 55.2120 - val_output7_mean_absolute_error: 53.7020 - val_output8_mean_absolute_error: 54.7232 - val_output9_mean_absolute_error: 53.8460 - val_output10_mean_absolute_error: 56.3313 - val_output11_mean_absolute_error: 51.2044 - val_output12_mean_absolute_error: 51.7554 - val_output13_mean_absolute_error: 56.0648 - val_output14_mean_absolute_error: 60.9618 - val_output15_mean_absolute_error: 53.8265 - val_output16_mean_absolute_error: 59.6144 - val_output17_mean_absolute_error: 57.2631 - val_output18_mean_absolute_error: 68.6762 - val_output19_mean_absolute_error: 57.7052 - val_output20_mean_absolute_error: 53.3091 - val_output21_mean_absolute_error: 53.1578 - val_output22_mean_absolute_error: 68.9949 - val_output23_mean_absolute_error: 59.3342 - val_output24_mean_absolute_error: 57.8192 - val_output25_mean_absolute_error: 56.2128 - val_output26_mean_absolute_error: 107.2024 - val_output27_mean_absolute_error: 52.4053 - val_output28_mean_absolute_error: 57.7537 - val_output29_mean_absolute_error: 57.8941 - val_output30_mean_absolute_error: 52.7652 - val_output31_mean_absolute_error: 55.8542 - val_output32_mean_absolute_error: 52.3933 - val_output33_mean_absolute_error: 55.3164 - val_output34_mean_absolute_error: 60.8953 - val_output35_mean_absolute_error: 53.3304 - val_output36_mean_absolute_error: 50.9999 - val_output37_mean_absolute_error: 54.8701 - val_output38_mean_absolute_error: 50.3336 - val_output39_mean_absolute_error: 57.2122 - val_output40_mean_absolute_error: 54.2106 - val_output41_mean_absolute_error: 52.6194 - val_output42_mean_absolute_error: 58.0414 - val_output43_mean_absolute_error: 55.1713 - val_output44_mean_absolute_error: 57.7724 - val_output45_mean_absolute_error: 50.7139 - val_output46_mean_absolute_error: 86.3753 - val_output47_mean_absolute_error: 54.2036 - val_output48_mean_absolute_error: 54.4225 - val_output49_mean_absolute_error: 56.4465 - val_output50_mean_absolute_error: 55.8616 - val_output51_mean_absolute_error: 52.8510 - val_output52_mean_absolute_error: 53.2442 - val_output53_mean_absolute_error: 62.1944 - val_output54_mean_absolute_error: 54.0932\n7/7 - 2s - loss: 47.8653 - output1_loss: 1.0882 - output2_loss: 0.7613 - output3_loss: 1.1996 - output4_loss: 0.7420 - output5_loss: 0.7435 - output6_loss: 0.7962 - output7_loss: 0.7396 - output8_loss: 0.8763 - output9_loss: 0.6791 - output10_loss: 1.0598 - output11_loss: 0.7587 - output12_loss: 0.7535 - output13_loss: 0.7961 - output14_loss: 1.2120 - output15_loss: 0.7381 - output16_loss: 1.1233 - output17_loss: 1.1550 - output18_loss: 1.4109 - output19_loss: 0.7963 - output20_loss: 0.9015 - output21_loss: 0.8694 - output22_loss: 1.2757 - output23_loss: 0.7514 - output24_loss: 0.6273 - output25_loss: 0.7618 - output26_loss: 1.4872 - output27_loss: 0.7659 - output28_loss: 0.7564 - output29_loss: 0.8805 - output30_loss: 0.7553 - output31_loss: 0.6677 - output32_loss: 0.8699 - output33_loss: 0.7404 - output34_loss: 1.1030 - output35_loss: 0.8306 - output36_loss: 0.8169 - output37_loss: 0.7375 - output38_loss: 0.8429 - output39_loss: 1.1331 - output40_loss: 0.7368 - output41_loss: 0.6839 - output42_loss: 0.8212 - output43_loss: 0.9025 - output44_loss: 1.0715 - output45_loss: 0.8041 - output46_loss: 1.3081 - output47_loss: 0.8194 - output48_loss: 0.7465 - output49_loss: 0.7500 - output50_loss: 0.7270 - output51_loss: 0.8232 - output52_loss: 0.8799 - output53_loss: 1.2106 - output54_loss: 0.5769 - output1_mean_absolute_error: 54.1169 - output2_mean_absolute_error: 50.7324 - output3_mean_absolute_error: 53.5139 - output4_mean_absolute_error: 45.9221 - output5_mean_absolute_error: 49.2026 - output6_mean_absolute_error: 47.5562 - output7_mean_absolute_error: 48.9172 - output8_mean_absolute_error: 47.0062 - output9_mean_absolute_error: 48.6200 - output10_mean_absolute_error: 54.0144 - output11_mean_absolute_error: 47.4301 - output12_mean_absolute_error: 48.3471 - output13_mean_absolute_error: 47.5924 - output14_mean_absolute_error: 57.3571 - output15_mean_absolute_error: 49.3515 - output16_mean_absolute_error: 51.8784 - output17_mean_absolute_error: 52.7759 - output18_mean_absolute_error: 65.0279 - output19_mean_absolute_error: 52.3228 - output20_mean_absolute_error: 48.5346 - output21_mean_absolute_error: 48.3821 - output22_mean_absolute_error: 64.8587 - output23_mean_absolute_error: 52.1518 - output24_mean_absolute_error: 49.9669 - output25_mean_absolute_error: 48.4165 - output26_mean_absolute_error: 105.6859 - output27_mean_absolute_error: 47.7627 - output28_mean_absolute_error: 47.9694 - output29_mean_absolute_error: 49.3523 - output30_mean_absolute_error: 47.9919 - output31_mean_absolute_error: 48.6729 - output32_mean_absolute_error: 49.9639 - output33_mean_absolute_error: 47.6015 - output34_mean_absolute_error: 53.1875 - output35_mean_absolute_error: 49.7601 - output36_mean_absolute_error: 47.5857 - output37_mean_absolute_error: 48.4976 - output38_mean_absolute_error: 48.1021 - output39_mean_absolute_error: 53.4161 - output40_mean_absolute_error: 48.4885 - output41_mean_absolute_error: 46.6945 - output42_mean_absolute_error: 47.4298 - output43_mean_absolute_error: 48.4469 - output44_mean_absolute_error: 52.6096 - output45_mean_absolute_error: 49.4355 - output46_mean_absolute_error: 84.8554 - output47_mean_absolute_error: 48.6623 - output48_mean_absolute_error: 49.2123 - output49_mean_absolute_error: 53.0859 - output50_mean_absolute_error: 48.4748 - output51_mean_absolute_error: 48.6548 - output52_mean_absolute_error: 47.9294 - output53_mean_absolute_error: 56.3551 - output54_mean_absolute_error: 48.0781 - val_loss: 55.4237 - val_output1_loss: 1.1942 - val_output2_loss: 0.9081 - val_output3_loss: 1.3060 - val_output4_loss: 0.8999 - val_output5_loss: 0.9067 - val_output6_loss: 0.9545 - val_output7_loss: 0.9138 - val_output8_loss: 1.0199 - val_output9_loss: 0.8541 - val_output10_loss: 1.1515 - val_output11_loss: 0.9222 - val_output12_loss: 0.8907 - val_output13_loss: 0.9559 - val_output14_loss: 1.3041 - val_output15_loss: 0.9068 - val_output16_loss: 1.2492 - val_output17_loss: 1.2583 - val_output18_loss: 1.4918 - val_output19_loss: 0.9317 - val_output20_loss: 1.0217 - val_output21_loss: 1.0202 - val_output22_loss: 1.3558 - val_output23_loss: 0.8974 - val_output24_loss: 0.8124 - val_output25_loss: 0.9317 - val_output26_loss: 1.5618 - val_output27_loss: 0.9131 - val_output28_loss: 0.9452 - val_output29_loss: 1.0326 - val_output30_loss: 0.8996 - val_output31_loss: 0.8346 - val_output32_loss: 0.9982 - val_output33_loss: 0.9025 - val_output34_loss: 1.2120 - val_output35_loss: 0.9711 - val_output36_loss: 0.9476 - val_output37_loss: 0.9103 - val_output38_loss: 0.9761 - val_output39_loss: 1.2392 - val_output40_loss: 0.9348 - val_output41_loss: 0.8583 - val_output42_loss: 0.9625 - val_output43_loss: 1.0380 - val_output44_loss: 1.1690 - val_output45_loss: 0.9420 - val_output46_loss: 1.3746 - val_output47_loss: 0.9797 - val_output48_loss: 0.9112 - val_output49_loss: 0.9128 - val_output50_loss: 0.8916 - val_output51_loss: 0.9617 - val_output52_loss: 1.0043 - val_output53_loss: 1.2934 - val_output54_loss: 0.7871 - val_output1_mean_absolute_error: 56.3863 - val_output2_mean_absolute_error: 58.6651 - val_output3_mean_absolute_error: 60.9772 - val_output4_mean_absolute_error: 55.6823 - val_output5_mean_absolute_error: 52.1995 - val_output6_mean_absolute_error: 55.2120 - val_output7_mean_absolute_error: 53.7020 - val_output8_mean_absolute_error: 54.7232 - val_output9_mean_absolute_error: 53.8460 - val_output10_mean_absolute_error: 56.3313 - val_output11_mean_absolute_error: 51.2044 - val_output12_mean_absolute_error: 51.7554 - val_output13_mean_absolute_error: 56.0648 - val_output14_mean_absolute_error: 60.9618 - val_output15_mean_absolute_error: 53.8265 - val_output16_mean_absolute_error: 59.6144 - val_output17_mean_absolute_error: 57.2631 - val_output18_mean_absolute_error: 68.6762 - val_output19_mean_absolute_error: 57.7052 - val_output20_mean_absolute_error: 53.3091 - val_output21_mean_absolute_error: 53.1578 - val_output22_mean_absolute_error: 68.9949 - val_output23_mean_absolute_error: 59.3342 - val_output24_mean_absolute_error: 57.8192 - val_output25_mean_absolute_error: 56.2128 - val_output26_mean_absolute_error: 107.2024 - val_output27_mean_absolute_error: 52.4053 - val_output28_mean_absolute_error: 57.7537 - val_output29_mean_absolute_error: 57.8941 - val_output30_mean_absolute_error: 52.7652 - val_output31_mean_absolute_error: 55.8542 - val_output32_mean_absolute_error: 52.3933 - val_output33_mean_absolute_error: 55.3164 - val_output34_mean_absolute_error: 60.8953 - val_output35_mean_absolute_error: 53.3304 - val_output36_mean_absolute_error: 50.9999 - val_output37_mean_absolute_error: 54.8701 - val_output38_mean_absolute_error: 50.3336 - val_output39_mean_absolute_error: 57.2122 - val_output40_mean_absolute_error: 54.2106 - val_output41_mean_absolute_error: 52.6194 - val_output42_mean_absolute_error: 58.0414 - val_output43_mean_absolute_error: 55.1713 - val_output44_mean_absolute_error: 57.7724 - val_output45_mean_absolute_error: 50.7139 - val_output46_mean_absolute_error: 86.3753 - val_output47_mean_absolute_error: 54.2036 - val_output48_mean_absolute_error: 54.4225 - val_output49_mean_absolute_error: 56.4465 - val_output50_mean_absolute_error: 55.8616 - val_output51_mean_absolute_error: 52.8510 - val_output52_mean_absolute_error: 53.2442 - val_output53_mean_absolute_error: 62.1944 - val_output54_mean_absolute_error: 54.0932 - 2s/epoch - 293ms/step\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"'\\n\\nrnn_model.fit([types, locale, locale_name, description, num_features,\\n x, x2, x3, x4, x5,\\n x6, x7, x8, x9, x10,\\n x11, x12, x13, x14, x15,\\n x16, x17, x18, x19, x20,\\n x21, x22, x23, x24, x25], \\n [y, y2, y3, y4, y5,\\n y6, y7, y8, y9, y10,\\n y11, y12, y13, y14, y15,\\n y16, y17, y18, y19, y20,\\n y21, y22, y23, y24, y25],\\n epochs=1000, batch_size=1024)\\n#epochs = 100 lstm = 64 dense = 128 batch_size = 1024 loss = 33.57\\n#epochs = early stopping patience 25 lstm = 64 dense = 256 batch_size = 256 loss = 30.8\\n#epochs = early stopping patience 25 lstm = 64 dense = 256 batch_size = 256 \\n# dense_activation_function = \"LeakyReLU\" dense_output_activation_function = \"Relu\" \\n# RNN_activation_function = \"tanh\" Loss = 22.88\\n#epochs = early stopping patience 25 lstm = 33 dense = 256 batch_size = 256 \\n# dense_activation_function = \"LeakyReLU\" dense_output_activation_function = \"Relu\" \\n# RNN_activation_function = \"tanh\" Loss = 21.1055\\n'"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\ndef replace_nan_with_next_value(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n col_values = df[column_name].values\n\n for i in range(len(col_values) - 1):\n if pd.isna(col_values[i]):\n j = i + 1\n while j < len(col_values) and pd.isna(col_values[j]):\n j += 1\n if j < len(col_values):\n col_values[i] = col_values[j]\n\n df[column_name] = col_values\n return df\n \ndef delete_rows_with_true(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df = df[df[column_name] != True]\n return df\n\ndef delete_rows_with_work_day(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df[column_name] = df[column_name].astype(str)\n\n df = df[df[column_name] != 'Work Day']\n \n return df\n\ndef replace_categories_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n category_to_integer = {\n 'Holiday': 1,\n 'Event': 2,\n 'Additional': 3,\n 'Transfer': 4,\n 'Bridge': 5,\n 'Regional' : 1,\n 'Local' : 2,\n 'National' : 3\n }\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ndef replace_locale_names_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n 'Ecuador', 'Riobamba', 'Quito', 'Guaranda', 'Latacunga', 'Ambato',\n 'Guayaquil', 'Salinas', 'Loja', 'Santa Elena',\n 'Santo Domingo de los Tsachilas', 'Quevedo', 'Ibarra', 'Manta',\n 'Esmeraldas', 'Cotopaxi', 'El Carmen', 'Santo Domingo', 'Machala',\n 'Imbabura', 'Puyo', 'Libertad', 'Cuenca', 'Cayambe'\n ]\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ndef merge_categories_with_same_prefix(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n df[column_name] = df[column_name].str.replace(r'[-+]\\d+', '').str.strip()\n return df\n\ndef replace_description_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n \"Navidad\",\n \"Terremoto Manabi\",\n \"Fundacion de Quito\",\n \"Dia de la Madre\",\n \"Carnaval\",\n \"Fundacion de Guayaquil\",\n \"Primer dia del ano\",\n \"Independencia de Latacunga\",\n \"Independencia de Cuenca\",\n \"Provincializacion de Santo Domingo\",\n \"Provincializacion Santa Elena\",\n \"Independencia de Guaranda\",\n \"Fundacion de Loja\",\n \"Independencia de Ambato\",\n \"Cantonizacion de Quevedo\",\n \"Provincializacion de Cotopaxi\",\n \"Cantonizacion de Salinas\",\n \"Dia de Difuntos\",\n \"Fundacion de Manta\",\n \"Cantonizacion de Libertad\",\n \"Fundacion de Machala\",\n \"Fundacion de Ibarra\",\n \"Cantonizacion de Riobamba\",\n \"Cantonizacion del Puyo\",\n \"Cantonizacion de Guaranda\",\n \"Fundacion de Cuenca\",\n \"Cantonizacion de Latacunga\",\n \"Provincializacion de Imbabura\",\n \"Fundacion de Santo Domingo\",\n \"Cantonizacion de El Carmen\",\n \"Cantonizacion de Cayambe\",\n \"Fundacion de Esmeraldas\",\n \"Fundacion de Riobamba\",\n \"Fundacion de Ambato\",\n \"Viernes Santo\",\n \"Dia del Trabajo\",\n \"Mundial de futbol Brasil: Octavos de Final\",\n \"Primer Grito de Independencia\",\n \"Independencia de Guayaquil\",\n \"Cyber Monday\",\n \"Black Friday\",\n \"Traslado Independencia de Guayaquil\",\n \"Batalla de Pichincha\",\n \"Puente Navidad\",\n \"Mundial de futbol Brasil: Cuartos de Final\",\n \"Mundial de futbol Brasil: Semifinales\",\n \"Traslado Primer Grito de Independencia\",\n \"Traslado Batalla de Pichincha\",\n \"Puente Primer dia del ano\",\n \"Traslado Primer dia del ano\",\n \"Puente Dia de Difuntos\",\n \"Traslado Fundacion de Guayaquil\",\n \"Mundial de futbol Brasil: Ecuador-Honduras\",\n \"Mundial de futbol Brasil: Ecuador-Francia\",\n \"Inauguracion Mundial de futbol Brasil\",\n \"Mundial de futbol Brasil: Final\",\n \"Mundial de futbol Brasil: Tercer y cuarto lugar\",\n \"Mundial de futbol Brasil: Ecuador-Suiza\",\n \"Traslado Fundacion de Quito\"\n ]\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\ncounter = 0\n\n\ndef replace_family_names_with_integers(df, column_name):\n if column_name not in df.columns:\n raise ValueError(f\"Column '{column_name}' not found in DataFrame\")\n\n categories = [\n \"AUTOMOTIVE\",\n \"HOME APPLIANCES\",\n \"SCHOOL AND OFFICE SUPPLIES\",\n \"PRODUCE\",\n \"PREPARED FOODS\",\n \"POULTRY\",\n \"PLAYERS AND ELECTRONICS\",\n \"PET SUPPLIES\",\n \"PERSONAL CARE\",\n \"MEATS\",\n \"MAGAZINES\",\n \"LIQUOR,WINE,BEER\",\n \"LINGERIE\",\n \"LAWN AND GARDEN\",\n \"LADIESWEAR\",\n \"HOME CARE\",\n \"HOME AND KITCHEN II\",\n \"BABY CARE\",\n \"HOME AND KITCHEN I\",\n \"HARDWARE\",\n \"GROCERY II\",\n \"GROCERY I\",\n \"FROZEN FOODS\",\n \"EGGS\",\n \"DELI\",\n \"DAIRY\",\n \"CLEANING\",\n \"CELEBRATION\",\n \"BREAD/BAKERY\",\n \"BOOKS\",\n \"BEVERAGES\",\n \"BEAUTY\",\n \"SEAFOOD\"\n ]\n\n\n category_to_integer = {category: index+1 for index, category in enumerate(categories)}\n\n df[column_name] = df[column_name].replace(category_to_integer)\n return df\n\n\n\ndef append_rows_after_july_sixteenth_2017(train, supplement):\n # Convert the 'date' column to a datetime object in both DataFrames\n train['date'] = pd.to_datetime(train['date'])\n supplement['date'] = pd.to_datetime(supplement['date'])\n\n # Filter the 'supplement' DataFrame to only include rows after 2017-07-16\n supplement_filtered = supplement.loc[supplement['date'] > '2017-07-16']\n\n # Append the filtered 'supplement' DataFrame to the 'train' DataFrame\n train_updated = pd.concat([train, supplement_filtered], ignore_index=True)\n\n # Sort the updated 'train' DataFrame by date\n train_updated.sort_values(by='date', inplace=True)\n\n # Reset the index of the resulting dataframe\n train_updated.reset_index(drop=True, inplace=True)\n\n # Return the updated 'train' DataFrame\n return train_updated\n\n\ndef csv_process(filepath1, filepath2, filepath3, filepath4, filepath5):\n \n supplement = pd.read_csv(filepath5)\n \n supplement = supplement.drop('sales', axis=1)\n\n oil = pd.read_csv(filepath1)\n \n replace_nan_with_next_value(oil, \"dcoilwtico\")\n \n #print(oil['dcoilwtico'].value_counts())\n \n #print(oil)\n \n holiday = pd.read_csv(filepath2)\n \n holiday = delete_rows_with_true(holiday, \"transferred\")\n \n holiday = delete_rows_with_work_day(holiday, \"type\")\n \n holiday = holiday.drop('transferred', axis=1)\n \n holiday = replace_categories_with_integers(holiday, \"type\")\n \n holiday = replace_categories_with_integers(holiday, \"locale\")\n \n holiday = replace_locale_names_with_integers(holiday, \"locale_name\")\n \n holiday = merge_categories_with_same_prefix(holiday, \"description\")\n \n holiday = replace_description_with_integers(holiday, \"description\")\n \n train = pd.read_csv(filepath3)\n \n \n #this might make the difference\n \n \n train = train.sort_values(by=['store_nbr','date'])\n \n \n \n #this might make the difference\n \n \n \n ids = train['id']\n \n train = append_rows_after_july_sixteenth_2017(train, supplement)\n \n transactions = pd.read_csv(filepath4)\n \n #train = train.head()\n #train = train.tail()\n \n #transactions = transactions.head(100000)\n #transactions = transactions.tail(20)\n \n #holiday = holiday.head(1000)\n #holiday = holiday.tail(20)\n \n #oil = oil.head(1000)\n #oil = oil.tail(20)\n \n #print(\"done\")\n \n #print(train.info())\n #print(transactions.info())\n \n \n def drop_before_july_sixteenth_2017(df):\n # Convert the 'date' column to a datetime object\n df['date'] = pd.to_datetime(df['date'])\n\n # Select rows after January 1st, 2013\n df = df.loc[df['date'] >= '2017-07-16']\n\n # Reset the index of the resulting dataframe\n df = df.reset_index(drop=True)\n\n # Return the resulting dataframe\n return df\n \n train = drop_before_july_sixteenth_2017(train)\n \n transactions = drop_before_july_sixteenth_2017(transactions)\n \n holiday = drop_before_july_sixteenth_2017(holiday)\n \n oil = drop_before_july_sixteenth_2017(oil)\n \n def drop_after_august_thirtyfirst_2017(df):\n # Convert the 'date' column to a datetime object\n df['date'] = pd.to_datetime(df['date'])\n\n # Select rows after January 1st, 2013\n df = df.loc[df['date'] <= '2017-08-31']\n\n # Reset the index of the resulting dataframe\n df = df.reset_index(drop=True)\n\n # Return the resulting dataframe\n return df\n\n train = drop_after_august_thirtyfirst_2017(train)\n \n transactions = drop_after_august_thirtyfirst_2017(transactions)\n \n holiday = drop_after_august_thirtyfirst_2017(holiday)\n \n oil = drop_after_august_thirtyfirst_2017(oil)\n \n print(train['date'].min())\n \n #train = train.merge(transactions, on=['store_nbr', 'date'], how='left')\n \n train = replace_family_names_with_integers(train, \"family\")\n \n \n \n #print(train)\n #print(holiday.info())\n #print(holiday['date'].value_counts())\n holiday[['type', 'locale', 'locale_name', 'description']] = holiday[['type', 'locale', 'locale_name', 'description']].astype(str)\n\n # Define a custom function to join the values\n def join_values(column):\n return ''.join(column)\n\n # Group by date and apply the custom function to each column\n holiday_agg = holiday.groupby('date', as_index=False).agg({\n 'type': join_values,\n 'locale': join_values,\n 'locale_name': join_values,\n 'description': join_values\n })\n \n \n \n for i in ['type', 'locale', 'locale_name', 'description']:\n holiday_agg[i] = holiday_agg[i].astype(str).str.replace('[^0-9]', '', regex=True)\n \n # Convert the column to int64\n holiday_agg[i] = pd.to_numeric(holiday_agg[i], errors='coerce').fillna(0).astype(int)\n \n #train = train.merge(holiday_agg, on=[\"date\"], how=\"left\")\n \n #this is what is causing the increase in rows\n #print(train.info())\n #print(train)\n \n \n \n date_range = pd.date_range(start=oil['date'].min(), end=oil['date'].max())\n complete_dates = pd.DataFrame({'date': date_range})\n\n # Merge 'complete_dates' with the 'oil' DataFrame using a left join\n oil_filled = complete_dates.merge(oil, on=['date'], how='left')\n\n # Fill missing values in the 'dcoilwtico' column with the previous available value\n oil_filled['dcoilwtico'].fillna(method='bfill', inplace=True)\n\n \n \n # Now merge the 'train' DataFrame with the 'oil_filled' DataFrame\n #train = train.merge(oil_filled, on=[\"date\"], how=\"inner\")\n \n \n \n #print(holiday['description'].value_counts())\n \n #print(transactions)\n \n #print(train['transactions'])\n \n train = train.fillna(0)\n \n \n \n #print(train.info())\n # look up cartesian product\n \n\n return train, oil_filled, holiday_agg, transactions, ids\n \ntrain, oil, holiday, transactions, ids = csv_process(\"/kaggle/input/store-sales-time-series-forecasting/oil.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/test.csv\",\n \"/kaggle/input/store-sales-time-series-forecasting/transactions.csv\", \n \"/kaggle/input/store-sales-time-series-forecasting/train.csv\")\n\nunique_store_numbers = train['store_nbr'].unique()\ntrain_store_dataframes = {}\ntransaction_store_dataframes = {}\npromotion_store_dataframes = {}\n\n\"\"\" \nfor store_number in unique_store_numbers:\n train_store_dataframe = train[train['store_nbr'] == store_number]\n train_store_dataframes[store_number] = train_store_dataframe\n\"\"\" \n\nfor store_number in unique_store_numbers:\n transaction_store_dataframe = transactions[transactions['store_nbr'] == store_number]\n transaction_store_dataframes[store_number] = transaction_store_dataframe\n\nfor store_number in unique_store_numbers:\n promotion_store_dataframe = train[train['store_nbr'] == store_number]\n promotion_store_dataframes[store_number] = promotion_store_dataframe\n\n\"\"\" \nfor store_number in unique_store_numbers:\n train_df = train_store_dataframes[store_number]\n\n pivot_train_df = train_df.pivot_table(index='date', columns='family', values='sales', fill_value=0)\n\n # Reset the index to make 'date' a column again\n pivot_train_df.reset_index(inplace=True)\n\n # Update the dictionary with the modified DataFrame\n train_store_dataframes[store_number] = pivot_train_df\n\"\"\"\n \nfor store_number in unique_store_numbers:\n promotion_df = promotion_store_dataframes[store_number]\n\n pivot_promotion_df = promotion_df.pivot_table(index='date', columns='family', values='onpromotion', fill_value=0)\n\n # Reset the index to make 'date' a column again\n pivot_promotion_df.reset_index(inplace=True)\n\n # Update the dictionary with the modified DataFrame\n promotion_store_dataframes[store_number] = pivot_promotion_df\n\n\n#print(promotion_store_dataframes[1])\n#print(train)\n\nprint(ids)\n\nprint(\"done\")\n","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:27:32.004388Z","iopub.execute_input":"2023-12-17T07:27:32.004816Z","iopub.status.idle":"2023-12-17T07:27:36.860153Z","shell.execute_reply.started":"2023-12-17T07:27:32.004772Z","shell.execute_reply":"2023-12-17T07:27:36.859113Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:75: FutureWarning: The default value of regex will change from True to False in a future version.\n","output_type":"stream"},{"name":"stdout","text":"2017-07-17 00:00:00\n0 3000888\n1 3000889\n2 3000890\n3 3000891\n4 3000892\n ... \n28375 3029263\n28376 3029264\n28377 3029265\n28378 3029266\n28379 3029267\nName: id, Length: 28512, dtype: int64\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"\n\nfor store_number in unique_store_numbers:\n promotion_df = promotion_store_dataframes[store_number]\n\n date_range = pd.date_range(start=promotion_df['date'].min(), end=promotion_df['date'].max())\n complete_dates = pd.DataFrame({'date': date_range})\n \n promotion_df = complete_dates.merge(promotion_df, on=['date'], how='left')\n promotion_df = promotion_df.fillna(0)\n\n # Update the dictionaries with the modified DataFrames\n promotion_store_dataframes[store_number] = promotion_df\n\n \n\"\"\" \n#Sales numbers by store by date\ntrainst1 = train_store_dataframes[1]\ntrainst2 = train_store_dataframes[2]\ntrainst3 = train_store_dataframes[3]\ntrainst4 = train_store_dataframes[4]\ntrainst5 = train_store_dataframes[5]\ntrainst6 = train_store_dataframes[6]\ntrainst7 = train_store_dataframes[7]\ntrainst8 = train_store_dataframes[8]\ntrainst9 = train_store_dataframes[9]\ntrainst10 = train_store_dataframes[10]\ntrainst11 = train_store_dataframes[11]\ntrainst12 = train_store_dataframes[12]\ntrainst13 = train_store_dataframes[13]\ntrainst14 = train_store_dataframes[14]\ntrainst15 = train_store_dataframes[15]\ntrainst16 = train_store_dataframes[16]\ntrainst17 = train_store_dataframes[17]\ntrainst18 = train_store_dataframes[18]\ntrainst19 = train_store_dataframes[19]\ntrainst20 = train_store_dataframes[20]\ntrainst21 = train_store_dataframes[21]\ntrainst22 = train_store_dataframes[22]\ntrainst23 = train_store_dataframes[23]\ntrainst24 = train_store_dataframes[24]\ntrainst25 = train_store_dataframes[25]\n\n\n#Transaction numbers by store by date\ntransst1 = transaction_store_dataframes[1]\ntransst2 = transaction_store_dataframes[2]\ntransst3 = transaction_store_dataframes[3]\ntransst4 = transaction_store_dataframes[4]\ntransst5 = transaction_store_dataframes[5]\ntransst6 = transaction_store_dataframes[6]\ntransst7 = transaction_store_dataframes[7]\ntransst8 = transaction_store_dataframes[8]\ntransst9 = transaction_store_dataframes[9]\ntransst10 = transaction_store_dataframes[10]\ntransst11 = transaction_store_dataframes[11]\ntransst12 = transaction_store_dataframes[12]\ntransst13 = transaction_store_dataframes[13]\ntransst14 = transaction_store_dataframes[14]\ntransst15 = transaction_store_dataframes[15]\ntransst16 = transaction_store_dataframes[16]\ntransst17 = transaction_store_dataframes[17]\ntransst18 = transaction_store_dataframes[18]\ntransst19 = transaction_store_dataframes[19]\ntransst20 = transaction_store_dataframes[20]\ntransst21 = transaction_store_dataframes[21]\ntransst22 = transaction_store_dataframes[22]\ntransst23 = transaction_store_dataframes[23]\ntransst24 = transaction_store_dataframes[24]\ntransst25 = transaction_store_dataframes[25]\n\n\"\"\"\n\n#Promotions by family and store number\n#Promotions by family and store number\n#Promotions by family and store number\n#Promotions by family and store number\n#Promotions by family and store number\n#Promotions by family and store number\n\npromost1 = promotion_store_dataframes[1]\npromost2 = promotion_store_dataframes[2]\npromost3 = promotion_store_dataframes[3]\npromost4 = promotion_store_dataframes[4]\npromost5 = promotion_store_dataframes[5]\npromost6 = promotion_store_dataframes[6]\npromost7 = promotion_store_dataframes[7]\npromost8 = promotion_store_dataframes[8]\npromost9 = promotion_store_dataframes[9]\npromost10 = promotion_store_dataframes[10]\npromost11 = promotion_store_dataframes[11]\npromost12 = promotion_store_dataframes[12]\npromost13 = promotion_store_dataframes[13]\npromost14 = promotion_store_dataframes[14]\npromost15 = promotion_store_dataframes[15]\npromost16 = promotion_store_dataframes[16]\npromost17 = promotion_store_dataframes[17]\npromost18 = promotion_store_dataframes[18]\npromost19 = promotion_store_dataframes[19]\npromost20 = promotion_store_dataframes[20]\npromost21 = promotion_store_dataframes[21]\npromost22 = promotion_store_dataframes[22]\npromost23 = promotion_store_dataframes[23]\npromost24 = promotion_store_dataframes[24]\npromost25 = promotion_store_dataframes[25]\npromost26 = promotion_store_dataframes[26]\npromost27 = promotion_store_dataframes[27]\npromost28 = promotion_store_dataframes[28]\npromost29 = promotion_store_dataframes[29]\npromost30 = promotion_store_dataframes[30]\npromost31 = promotion_store_dataframes[31]\npromost32 = promotion_store_dataframes[32]\npromost33 = promotion_store_dataframes[33]\npromost34 = promotion_store_dataframes[34]\npromost35 = promotion_store_dataframes[35]\npromost36 = promotion_store_dataframes[36]\npromost37 = promotion_store_dataframes[37]\npromost38 = promotion_store_dataframes[38]\npromost39 = promotion_store_dataframes[39]\npromost40 = promotion_store_dataframes[40]\npromost41 = promotion_store_dataframes[41]\npromost42 = promotion_store_dataframes[42]\npromost43 = promotion_store_dataframes[43]\npromost44 = promotion_store_dataframes[44]\npromost45 = promotion_store_dataframes[45]\npromost46 = promotion_store_dataframes[46]\npromost47 = promotion_store_dataframes[47]\npromost48 = promotion_store_dataframes[48]\npromost49 = promotion_store_dataframes[49]\npromost50 = promotion_store_dataframes[50]\npromost51 = promotion_store_dataframes[51]\npromost52 = promotion_store_dataframes[52]\npromost53 = promotion_store_dataframes[53]\npromost54 = promotion_store_dataframes[54]\n\n\n\"\"\"\nprint(trainst1.iloc[[356,357,358,359,360,361,362]])\nprint(transst1.iloc[[356,357,358,359,360,361,362]])\n#print(transst1['date'].value_counts())\n\nprint(len(trainst1))\nprint(len(transst1))\n\"\"\"\n\nprint(promost1)\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:27:36.861330Z","iopub.execute_input":"2023-12-17T07:27:36.861632Z","iopub.status.idle":"2023-12-17T07:27:37.071549Z","shell.execute_reply.started":"2023-12-17T07:27:36.861602Z","shell.execute_reply":"2023-12-17T07:27:37.070585Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":" date 1 2 3 4 5 6 7 8 9 ... 24 25 26 27 28 29 \\\n0 2017-07-17 0 0 0 3 0 0 0 0 4 ... 0 13 17 15 0 0 \n1 2017-07-18 0 0 0 3 0 0 0 0 5 ... 0 13 18 19 0 0 \n2 2017-07-19 0 0 0 210 0 0 0 0 4 ... 0 11 19 22 0 0 \n3 2017-07-20 0 0 0 10 0 0 0 0 5 ... 0 10 17 19 0 0 \n4 2017-07-21 0 0 0 9 0 38 0 0 4 ... 29 45 18 17 0 0 \n5 2017-07-22 0 0 0 10 0 0 0 0 5 ... 0 11 19 15 0 0 \n6 2017-07-23 0 0 0 9 0 0 0 0 5 ... 0 4 16 10 0 0 \n7 2017-07-24 0 0 0 11 0 0 0 0 5 ... 0 10 18 12 0 0 \n8 2017-07-25 0 0 0 10 0 0 0 0 6 ... 0 11 18 10 0 0 \n9 2017-07-26 0 0 0 211 0 0 0 0 4 ... 0 15 17 11 0 0 \n10 2017-07-27 0 0 0 13 0 0 0 0 6 ... 0 8 8 15 0 0 \n11 2017-07-28 0 0 0 13 0 37 0 0 6 ... 25 54 7 15 0 0 \n12 2017-07-29 0 0 0 14 0 0 0 0 4 ... 0 4 7 15 0 0 \n13 2017-07-30 0 0 0 14 0 0 0 0 3 ... 0 2 6 13 0 0 \n14 2017-07-31 0 0 0 13 0 0 0 0 3 ... 0 6 7 17 0 0 \n15 2017-08-01 0 0 0 11 0 0 0 0 5 ... 0 5 3 18 0 0 \n16 2017-08-02 0 0 0 219 0 0 0 0 12 ... 0 5 3 17 0 0 \n17 2017-08-03 0 0 0 10 0 0 0 0 8 ... 0 3 12 7 0 7 \n18 2017-08-04 0 0 0 12 0 37 0 0 10 ... 27 52 20 10 0 8 \n19 2017-08-05 0 0 0 8 0 0 0 0 9 ... 0 6 21 10 0 6 \n20 2017-08-06 0 0 0 6 0 0 0 0 5 ... 0 1 14 6 0 5 \n21 2017-08-07 0 0 0 6 0 0 0 0 10 ... 0 4 18 10 0 6 \n22 2017-08-08 0 0 0 7 0 0 0 0 9 ... 0 4 20 11 0 9 \n23 2017-08-09 0 0 0 212 0 0 0 0 11 ... 0 6 17 10 0 9 \n24 2017-08-10 0 0 0 9 0 0 0 0 9 ... 0 7 21 13 0 10 \n25 2017-08-11 0 0 0 7 0 36 0 0 5 ... 24 35 14 7 0 8 \n26 2017-08-12 0 0 0 7 0 0 0 0 7 ... 0 3 19 8 0 6 \n27 2017-08-13 0 0 0 5 0 0 0 0 5 ... 0 4 15 5 0 6 \n28 2017-08-14 0 0 0 9 0 0 0 0 9 ... 0 7 23 11 0 9 \n29 2017-08-15 0 0 0 7 0 0 1 0 9 ... 0 5 19 10 0 8 \n30 2017-08-16 0 0 14 256 0 0 0 0 18 ... 1 18 45 25 0 12 \n31 2017-08-17 0 0 0 6 0 1 0 0 7 ... 0 7 13 7 0 7 \n32 2017-08-18 0 0 0 5 0 41 1 0 9 ... 25 52 18 6 0 10 \n33 2017-08-19 0 0 0 6 0 0 1 0 9 ... 0 6 20 11 0 9 \n34 2017-08-20 0 0 0 5 0 0 0 0 7 ... 0 6 17 4 0 5 \n35 2017-08-21 0 0 0 3 0 0 1 0 11 ... 0 7 20 9 0 10 \n36 2017-08-22 0 0 0 3 0 1 2 0 9 ... 0 7 18 10 0 6 \n37 2017-08-23 0 0 0 211 0 0 0 0 8 ... 0 11 22 11 0 14 \n38 2017-08-24 0 0 0 3 0 0 0 0 7 ... 0 11 13 12 0 1 \n39 2017-08-25 0 0 0 1 0 36 0 0 7 ... 26 52 12 8 0 2 \n40 2017-08-26 0 0 0 2 0 0 0 0 8 ... 0 8 12 10 0 3 \n41 2017-08-27 0 0 0 0 0 0 0 0 6 ... 0 6 7 7 0 5 \n42 2017-08-28 0 0 0 2 0 0 0 0 6 ... 0 10 12 11 0 1 \n43 2017-08-29 0 0 0 1 0 0 1 0 5 ... 0 7 13 13 0 5 \n44 2017-08-30 0 0 0 208 0 0 1 0 6 ... 0 5 16 11 0 7 \n45 2017-08-31 0 0 0 3 0 0 0 0 8 ... 0 5 11 12 0 4 \n\n 30 31 32 33 \n0 0 39 0 0 \n1 0 42 0 0 \n2 0 34 0 0 \n3 0 32 0 0 \n4 0 30 0 6 \n5 0 22 1 0 \n6 0 19 1 0 \n7 0 22 0 0 \n8 0 23 1 0 \n9 0 21 0 0 \n10 0 27 0 0 \n11 0 26 0 5 \n12 0 22 0 0 \n13 0 16 0 0 \n14 0 24 0 0 \n15 0 26 0 0 \n16 0 25 1 0 \n17 0 7 1 0 \n18 0 10 1 6 \n19 0 7 1 0 \n20 0 5 1 0 \n21 0 7 1 0 \n22 0 7 1 0 \n23 0 7 1 0 \n24 0 6 1 0 \n25 0 3 1 4 \n26 0 7 1 0 \n27 0 5 0 0 \n28 0 9 1 0 \n29 0 11 1 0 \n30 0 20 2 0 \n31 0 17 1 0 \n32 0 12 1 7 \n33 0 11 1 0 \n34 0 10 1 0 \n35 0 14 1 0 \n36 0 9 0 0 \n37 0 27 1 0 \n38 0 26 0 1 \n39 0 32 0 5 \n40 0 32 1 1 \n41 0 23 1 0 \n42 0 31 1 1 \n43 0 31 1 0 \n44 0 35 0 1 \n45 0 33 1 0 \n\n[46 rows x 34 columns]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"min_date = promost1['date'].min()\nstart_date = min_date + pd.DateOffset(days=30)\nend_date = promost1['date'].max()\n\ndate_range = pd.date_range(start=start_date, end=end_date)\ncomplete_dates = pd.DataFrame({'date': date_range})\nholiday = complete_dates.merge(holiday, on=['date'], how='left')\nholiday = holiday.fillna(0)\n\ntypes = holiday[\"type\"]\ntypes = (types - types.min()) / (types.max() + 1 - types.min())\n\nlocale = holiday[\"locale\"]\nlocale = (locale - locale.min()) / (locale.max() + 1 - locale.min())\n\nlocale_name = holiday[\"locale_name\"]\nlocale_name = (locale_name - locale_name.min()) / (locale_name.max() + 1 - locale_name.min())\n\ndescription = holiday[\"description\"]\ndescription = (description - description.min()) / (description.max() + 1 - description.min())\n\nnum_features = oil\n\nnum_features['day_of_week'] = num_features['date'].dt.dayofweek\nnum_features['month'] = num_features['date'].dt.month\nnum_features['day_of_month'] = num_features['date'].dt.day\nnum_features['quarter'] = num_features['date'].dt.quarter\n\nnum_features = num_features.iloc[30:]\n\nnum_features.reset_index(inplace=True)\n\nnum_features = num_features.drop(columns=['date'])\n\nnum_features = (num_features - num_features.min()) / (num_features.max() + 1 - num_features.min())\n\nprint(num_features)\nprint(num_features.info())\n\n\ntypes = np.array(types)\nlocale = np.array(locale)\nlocale_name = np.array(locale_name)\ndescription = np.array(description)\nnum_features = np.array(num_features)\n\nprint(num_features)\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:27:37.073148Z","iopub.execute_input":"2023-12-17T07:27:37.073660Z","iopub.status.idle":"2023-12-17T07:27:37.117682Z","shell.execute_reply.started":"2023-12-17T07:27:37.073613Z","shell.execute_reply":"2023-12-17T07:27:37.116609Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":" index dcoilwtico day_of_week month day_of_month quarter\n0 0.0000 0.231405 0.285714 0.0 0.0000 0.0\n1 0.0625 0.305785 0.428571 0.0 0.0625 0.0\n2 0.1250 0.724518 0.571429 0.0 0.1250 0.0\n3 0.1875 0.393939 0.714286 0.0 0.1875 0.0\n4 0.2500 0.393939 0.857143 0.0 0.2500 0.0\n5 0.3125 0.393939 0.000000 0.0 0.3125 0.0\n6 0.3750 0.465565 0.142857 0.0 0.3750 0.0\n7 0.4375 0.685950 0.285714 0.0 0.4375 0.0\n8 0.5000 0.352617 0.428571 0.0 0.5000 0.0\n9 0.5625 0.465565 0.571429 0.0 0.5625 0.0\n10 0.6250 0.121212 0.714286 0.0 0.6250 0.0\n11 0.6875 0.121212 0.857143 0.0 0.6875 0.0\n12 0.7500 0.121212 0.000000 0.0 0.7500 0.0\n13 0.8125 0.137741 0.142857 0.0 0.8125 0.0\n14 0.8750 0.000000 0.285714 0.0 0.8750 0.0\n15 0.9375 0.358127 0.428571 0.0 0.9375 0.0\n\nRangeIndex: 16 entries, 0 to 15\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 index 16 non-null float64\n 1 dcoilwtico 16 non-null float64\n 2 day_of_week 16 non-null float64\n 3 month 16 non-null float64\n 4 day_of_month 16 non-null float64\n 5 quarter 16 non-null float64\ndtypes: float64(6)\nmemory usage: 896.0 bytes\nNone\n[[0. 0.23140496 0.28571429 0. 0. 0. ]\n [0.0625 0.30578512 0.42857143 0. 0.0625 0. ]\n [0.125 0.72451791 0.57142857 0. 0.125 0. ]\n [0.1875 0.39393939 0.71428571 0. 0.1875 0. ]\n [0.25 0.39393939 0.85714286 0. 0.25 0. ]\n [0.3125 0.39393939 0. 0. 0.3125 0. ]\n [0.375 0.46556474 0.14285714 0. 0.375 0. ]\n [0.4375 0.68595041 0.28571429 0. 0.4375 0. ]\n [0.5 0.35261708 0.42857143 0. 0.5 0. ]\n [0.5625 0.46556474 0.57142857 0. 0.5625 0. ]\n [0.625 0.12121212 0.71428571 0. 0.625 0. ]\n [0.6875 0.12121212 0.85714286 0. 0.6875 0. ]\n [0.75 0.12121212 0. 0. 0.75 0. ]\n [0.8125 0.13774105 0.14285714 0. 0.8125 0. ]\n [0.875 0. 0.28571429 0. 0.875 0. ]\n [0.9375 0.35812672 0.42857143 0. 0.9375 0. ]]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\n\ndef prepare_data(promost, window_size):\n x = []\n for i in range(len(promost) - window_size):\n combined_data = promost[i:i+window_size]\n x.append(combined_data)\n return np.array(x)\n\ninput_data2 = promost1.drop('date', axis=1)\nx = prepare_data(input_data2, 30)\n\ninput_data2 = promost2.drop('date', axis=1)\nx2 = prepare_data(input_data2, 30)\n\ninput_data2 = promost3.drop('date', axis=1)\nx3 = prepare_data(input_data2, 30)\n\ninput_data2 = promost4.drop('date', axis=1)\nx4 = prepare_data(input_data2, 30)\n\ninput_data2 = promost5.drop('date', axis=1)\nx5 = prepare_data(input_data2, 30)\n\ninput_data2 = promost6.drop('date', axis=1)\nx6 = prepare_data(input_data2, 30)\n\ninput_data2 = promost7.drop('date', axis=1)\nx7 = prepare_data(input_data2, 30)\n\ninput_data2 = promost8.drop('date', axis=1)\nx8 = prepare_data(input_data2, 30)\n\ninput_data2 = promost9.drop('date', axis=1)\nx9 = prepare_data(input_data2, 30)\n\ninput_data2 = promost10.drop('date', axis=1)\nx10 = prepare_data(input_data2, 30)\n\ninput_data2 = promost11.drop('date', axis=1)\nx11 = prepare_data(input_data2, 30)\n\ninput_data2 = promost12.drop('date', axis=1)\nx12 = prepare_data(input_data2, 30)\n\ninput_data2 = promost13.drop('date', axis=1)\nx13 = prepare_data(input_data2, 30)\n\ninput_data2 = promost14.drop('date', axis=1)\nx14 = prepare_data(input_data2, 30)\n\ninput_data2 = promost15.drop('date', axis=1)\nx15 = prepare_data(input_data2, 30)\n\ninput_data2 = promost16.drop('date', axis=1)\nx16 = prepare_data(input_data2, 30)\n\ninput_data2 = promost17.drop('date', axis=1)\nx17 = prepare_data(input_data2, 30)\n\ninput_data2 = promost18.drop('date', axis=1)\nx18 = prepare_data(input_data2, 30)\n\ninput_data2 = promost19.drop('date', axis=1)\nx19 = prepare_data(input_data2, 30)\n\ninput_data2 = promost20.drop('date', axis=1)\nx20 = prepare_data(input_data2, 30)\n\ninput_data2 = promost21.drop('date', axis=1)\nx21 = prepare_data(input_data2, 30)\n\ninput_data2 = promost22.drop('date', axis=1)\nx22 = prepare_data(input_data2, 30)\n\ninput_data2 = promost23.drop('date', axis=1)\nx23 = prepare_data(input_data2, 30)\n\ninput_data2 = promost24.drop('date', axis=1)\nx24 = prepare_data(input_data2, 30)\n\ninput_data2 = promost25.drop('date', axis=1)\nx25 = prepare_data(input_data2, 30)\n\ninput_data2 = promost26.drop('date', axis=1)\nx26 = prepare_data(input_data2, 30)\n\ninput_data2 = promost27.drop('date', axis=1)\nx27 = prepare_data(input_data2, 30)\n\ninput_data2 = promost28.drop('date', axis=1)\nx28 = prepare_data(input_data2, 30)\n\ninput_data2 = promost29.drop('date', axis=1)\nx29 = prepare_data(input_data2, 30)\n\ninput_data2 = promost30.drop('date', axis=1)\nx30 = prepare_data(input_data2, 30)\n\ninput_data2 = promost31.drop('date', axis=1)\nx31 = prepare_data(input_data2, 30)\n\ninput_data2 = promost32.drop('date', axis=1)\nx32 = prepare_data(input_data2, 30)\n\ninput_data2 = promost33.drop('date', axis=1)\nx33 = prepare_data(input_data2, 30)\n\ninput_data2 = promost34.drop('date', axis=1)\nx34 = prepare_data(input_data2, 30)\n\ninput_data2 = promost35.drop('date', axis=1)\nx35 = prepare_data(input_data2, 30)\n\ninput_data2 = promost36.drop('date', axis=1)\nx36 = prepare_data(input_data2, 30)\n\ninput_data2 = promost37.drop('date', axis=1)\nx37 = prepare_data(input_data2, 30)\n\ninput_data2 = promost38.drop('date', axis=1)\nx38 = prepare_data(input_data2, 30)\n\ninput_data2 = promost39.drop('date', axis=1)\nx39 = prepare_data(input_data2, 30)\n\ninput_data2 = promost40.drop('date', axis=1)\nx40 = prepare_data(input_data2, 30)\n\ninput_data2 = promost41.drop('date', axis=1)\nx41 = prepare_data(input_data2, 30)\n\ninput_data2 = promost42.drop('date', axis=1)\nx42 = prepare_data(input_data2, 30)\n\ninput_data2 = promost43.drop('date', axis=1)\nx43 = prepare_data(input_data2, 30)\n\ninput_data2 = promost44.drop('date', axis=1)\nx44 = prepare_data(input_data2, 30)\n\ninput_data2 = promost45.drop('date', axis=1)\nx45 = prepare_data(input_data2, 30)\n\ninput_data2 = promost46.drop('date', axis=1)\nx46 = prepare_data(input_data2, 30)\n\ninput_data2 = promost47.drop('date', axis=1)\nx47 = prepare_data(input_data2, 30)\n\ninput_data2 = promost48.drop('date', axis=1)\nx48 = prepare_data(input_data2, 30)\n\ninput_data2 = promost49.drop('date', axis=1)\nx49 = prepare_data(input_data2, 30)\n\ninput_data2 = promost50.drop('date', axis=1)\nx50 = prepare_data(input_data2, 30)\n\ninput_data2 = promost51.drop('date', axis=1)\nx51 = prepare_data(input_data2, 30)\n\ninput_data2 = promost52.drop('date', axis=1)\nx52 = prepare_data(input_data2, 30)\n\ninput_data2 = promost53.drop('date', axis=1)\nx53 = prepare_data(input_data2, 30)\n\ninput_data2 = promost54.drop('date', axis=1)\nx54 = prepare_data(input_data2, 30)\n\n\n\n\nprint(x)\n\n\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:27:37.119224Z","iopub.execute_input":"2023-12-17T07:27:37.119665Z","iopub.status.idle":"2023-12-17T07:27:37.225351Z","shell.execute_reply.started":"2023-12-17T07:27:37.119623Z","shell.execute_reply":"2023-12-17T07:27:37.224309Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"[[[ 0 0 0 ... 39 0 0]\n [ 0 0 0 ... 42 0 0]\n [ 0 0 0 ... 34 0 0]\n ...\n [ 0 0 0 ... 5 0 0]\n [ 0 0 0 ... 9 1 0]\n [ 0 0 0 ... 11 1 0]]\n\n [[ 0 0 0 ... 42 0 0]\n [ 0 0 0 ... 34 0 0]\n [ 0 0 0 ... 32 0 0]\n ...\n [ 0 0 0 ... 9 1 0]\n [ 0 0 0 ... 11 1 0]\n [ 0 0 14 ... 20 2 0]]\n\n [[ 0 0 0 ... 34 0 0]\n [ 0 0 0 ... 32 0 0]\n [ 0 0 0 ... 30 0 6]\n ...\n [ 0 0 0 ... 11 1 0]\n [ 0 0 14 ... 20 2 0]\n [ 0 0 0 ... 17 1 0]]\n\n ...\n\n [[ 0 0 0 ... 16 0 0]\n [ 0 0 0 ... 24 0 0]\n [ 0 0 0 ... 26 0 0]\n ...\n [ 0 0 0 ... 32 1 1]\n [ 0 0 0 ... 23 1 0]\n [ 0 0 0 ... 31 1 1]]\n\n [[ 0 0 0 ... 24 0 0]\n [ 0 0 0 ... 26 0 0]\n [ 0 0 0 ... 25 1 0]\n ...\n [ 0 0 0 ... 23 1 0]\n [ 0 0 0 ... 31 1 1]\n [ 0 0 0 ... 31 1 0]]\n\n [[ 0 0 0 ... 26 0 0]\n [ 0 0 0 ... 25 1 0]\n [ 0 0 0 ... 7 1 0]\n ...\n [ 0 0 0 ... 31 1 1]\n [ 0 0 0 ... 31 1 0]\n [ 0 0 0 ... 35 0 1]]]\ndone\n","output_type":"stream"}]},{"cell_type":"code","source":"print(ids)\n\npredictions = rnn_model.predict([types, locale, locale_name, description, num_features,\n x, x2, x3, x4, x5,\n x6, x7, x8, x9, x10,\n x11, x12, x13, x14, x15,\n x16, x17, x18, x19, x20,\n x21, x22, x23, x24, x25,\n x26, x27, x28, x29, x30,\n x31, x32, x33, x34, x35,\n x36, x37, x38, x39, x40,\n x41, x42, x43, x44, x45,\n x46, x47, x48, x49, x50,\n x51, x52, x53, x54])\n\npredictions = predictions\n\nprint(predictions)\n\nprediction_df = pd.DataFrame({'id': ids,\n 'sales': np.squeeze(predictions).flatten()})\n\nprint(prediction_df)\n\nprediction_df.to_csv(\"predictions.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T07:27:37.228578Z","iopub.execute_input":"2023-12-17T07:27:37.228935Z","iopub.status.idle":"2023-12-17T07:27:57.360007Z","shell.execute_reply.started":"2023-12-17T07:27:37.228903Z","shell.execute_reply":"2023-12-17T07:27:57.358919Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"0 3000888\n1 3000889\n2 3000890\n3 3000891\n4 3000892\n ... \n28375 3029263\n28376 3029264\n28377 3029265\n28378 3029266\n28379 3029267\nName: id, Length: 28512, dtype: int64\n1/1 [==============================] - 19s 19s/step\n[array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.40921967e+02,\n 2.95501556e+01, 1.05944344e+02, 8.21373984e-02, 0.00000000e+00,\n 4.84081001e+01, 1.01550522e+02, 0.00000000e+00, 3.33934212e+01,\n 0.00000000e+00, 5.57240963e+00, 4.91218281e+00, 4.30027733e+01,\n 5.74804783e-01, 0.00000000e+00, 4.04346561e+00, 0.00000000e+00,\n 1.13186131e+01, 7.45866516e+02, 0.00000000e+00, 5.03217239e+01,\n 4.24430199e+01, 2.28471359e+02, 2.19997330e+02, 0.00000000e+00,\n 1.16949791e+02, 0.00000000e+00, 5.77226257e+02, 0.00000000e+00,\n 9.64849567e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.90820935e+03,\n 8.61390762e+01, 3.13898346e+02, 8.10021877e+00, 0.00000000e+00,\n 1.50371170e+02, 2.99884186e+02, 0.00000000e+00, 1.04696175e+02,\n 0.00000000e+00, 1.28314476e+01, 1.44660645e+01, 1.65438431e+02,\n 1.35426798e+01, 0.00000000e+00, 1.81455688e+01, 0.00000000e+00,\n 2.82398071e+01, 2.40371606e+03, 0.00000000e+00, 1.43156601e+02,\n 1.31018280e+02, 7.36345642e+02, 6.79400208e+02, 1.29780865e+01,\n 3.79120667e+02, 0.00000000e+00, 2.00942676e+03, 0.00000000e+00,\n 2.54895306e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.06511108e+02,\n 3.26420212e+01, 1.15840675e+02, 6.08839810e-01, 0.00000000e+00,\n 5.48680077e+01, 1.05672928e+02, 0.00000000e+00, 1.75799389e+01,\n 0.00000000e+00, 5.40933228e+00, 6.40843058e+00, 5.54091110e+01,\n 0.00000000e+00, 0.00000000e+00, 1.76435161e+00, 0.00000000e+00,\n 1.11343384e+01, 8.94087830e+02, 0.00000000e+00, 5.89514618e+01,\n 4.71985588e+01, 2.73907623e+02, 2.44890732e+02, 0.00000000e+00,\n 1.35736298e+02, 0.00000000e+00, 7.45165100e+02, 0.00000000e+00,\n 9.35525417e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.42302124e+02,\n 1.92323017e+01, 5.82021370e+01, 0.00000000e+00, 0.00000000e+00,\n 2.72220345e+01, 5.04710236e+01, 0.00000000e+00, 6.10961020e-02,\n 0.00000000e+00, 0.00000000e+00, 1.49914265e+00, 3.04361668e+01,\n 1.14032817e+00, 0.00000000e+00, 2.27677178e+00, 0.00000000e+00,\n 3.89843988e+00, 4.71920441e+02, 0.00000000e+00, 2.82272511e+01,\n 2.65639744e+01, 1.45771194e+02, 1.20474113e+02, 0.00000000e+00,\n 7.01872330e+01, 0.00000000e+00, 4.15491333e+02, 0.00000000e+00,\n 4.98824120e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.73519531e+02,\n 2.19890194e+01, 6.74355164e+01, 0.00000000e+00, 0.00000000e+00,\n 3.24845581e+01, 5.17969971e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 3.31751776e+00, 2.99639845e+00, 3.81002731e+01,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 7.19063282e+00, 5.71297791e+02, 0.00000000e+00, 4.10753517e+01,\n 2.98488178e+01, 1.75334854e+02, 1.37179581e+02, 0.00000000e+00,\n 8.02688828e+01, 0.00000000e+00, 5.11884521e+02, 0.00000000e+00,\n 6.45509768e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.98571472e+02,\n 4.13466949e+01, 1.49150284e+02, 1.94239998e+00, 0.00000000e+00,\n 6.95115738e+01, 1.42873596e+02, 0.00000000e+00, 5.22711411e+01,\n 0.00000000e+00, 7.90301275e+00, 7.01816702e+00, 6.78816910e+01,\n 4.68251181e+00, 0.00000000e+00, 7.90676308e+00, 0.00000000e+00,\n 1.52059555e+01, 1.07145557e+03, 0.00000000e+00, 6.86280365e+01,\n 6.09305687e+01, 3.28964996e+02, 3.13873413e+02, 1.23952389e+00,\n 1.69613586e+02, 0.00000000e+00, 8.49995911e+02, 0.00000000e+00,\n 1.30995884e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.53209351e+02,\n 3.59762497e+01, 1.29262512e+02, 9.54716265e-01, 0.00000000e+00,\n 5.95127182e+01, 1.23830368e+02, 0.00000000e+00, 4.28588562e+01,\n 0.00000000e+00, 7.33198357e+00, 5.82398367e+00, 5.51000061e+01,\n 2.64827394e+00, 0.00000000e+00, 5.92703390e+00, 0.00000000e+00,\n 1.32657528e+01, 9.12778259e+02, 0.00000000e+00, 6.04191628e+01,\n 5.22177162e+01, 2.80154755e+02, 2.69364166e+02, 0.00000000e+00,\n 1.43834991e+02, 0.00000000e+00, 7.11018555e+02, 0.00000000e+00,\n 1.18228226e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.50821167e+02,\n 3.45902786e+01, 1.24050148e+02, 1.04971170e+00, 0.00000000e+00,\n 5.84611740e+01, 1.15060280e+02, 0.00000000e+00, 3.41097412e+01,\n 0.00000000e+00, 6.49647713e+00, 5.21288824e+00, 5.91403198e+01,\n 3.17153931e+00, 0.00000000e+00, 4.65342236e+00, 0.00000000e+00,\n 1.23185759e+01, 9.14054749e+02, 0.00000000e+00, 5.89366722e+01,\n 5.12560959e+01, 2.81006622e+02, 2.60679199e+02, 0.00000000e+00,\n 1.42215576e+02, 0.00000000e+00, 7.41710266e+02, 0.00000000e+00,\n 1.10265827e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.11740039e+03,\n 8.21257858e+01, 2.90895447e+02, 9.20197392e+00, 0.00000000e+00,\n 1.42805771e+02, 2.66190338e+02, 0.00000000e+00, 6.89077301e+01,\n 0.00000000e+00, 9.68801498e+00, 1.50506191e+01, 1.70432938e+02,\n 1.75785446e+01, 0.00000000e+00, 1.46058140e+01, 0.00000000e+00,\n 2.48009224e+01, 2.33276562e+03, 0.00000000e+00, 1.36752304e+02,\n 1.26117638e+02, 7.16062744e+02, 6.33691467e+02, 1.09339085e+01,\n 3.63059692e+02, 0.00000000e+00, 2.03146313e+03, 0.00000000e+00,\n 2.42269478e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.08037390e+03,\n 4.06355553e+01, 1.46747025e+02, 3.63824272e+00, 0.00000000e+00,\n 7.21400833e+01, 1.30998993e+02, 0.00000000e+00, 3.03280201e+01,\n 0.00000000e+00, 5.43442154e+00, 7.05182123e+00, 8.51367111e+01,\n 5.78924370e+00, 0.00000000e+00, 4.44007158e+00, 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0.00000000e+00,\n 1.41531830e+02, 3.29430206e+02, 0.00000000e+00, 1.14045876e+02,\n 0.00000000e+00, 0.00000000e+00, 1.51957073e+01, 1.72347778e+02,\n 1.85899582e+01, 0.00000000e+00, 2.27203121e+01, 0.00000000e+00,\n 2.17691441e+01, 2.34106982e+03, 0.00000000e+00, 1.50687195e+02,\n 1.39232346e+02, 7.18774048e+02, 6.97803040e+02, 1.37323189e+01,\n 3.85490417e+02, 0.00000000e+00, 1.88255847e+03, 0.00000000e+00,\n 3.09455299e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.52685217e+03,\n 3.97085342e+01, 1.38537216e+02, 0.00000000e+00, 0.00000000e+00,\n 7.72730026e+01, 1.06844643e+02, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 7.20905972e+00, 1.01877686e+02,\n 5.82152700e+00, 0.00000000e+00, 4.98005581e+00, 0.00000000e+00,\n 1.00986223e+01, 1.27671375e+03, 0.00000000e+00, 7.07197266e+01,\n 6.09480629e+01, 3.61638672e+02, 2.94211121e+02, 1.60045052e+00,\n 1.81560654e+02, 0.00000000e+00, 1.16219165e+03, 0.00000000e+00,\n 1.40627003e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.93429932e+03,\n 6.77461166e+01, 2.50485733e+02, 0.00000000e+00, 0.00000000e+00,\n 1.18977448e+02, 2.29975418e+02, 0.00000000e+00, 8.13297348e+01,\n 0.00000000e+00, 0.00000000e+00, 1.33189945e+01, 1.57244003e+02,\n 1.62343521e+01, 0.00000000e+00, 1.47509861e+01, 0.00000000e+00,\n 1.97590446e+01, 1.99898999e+03, 0.00000000e+00, 1.11460938e+02,\n 1.07804298e+02, 5.97302124e+02, 5.44552307e+02, 1.09063244e+01,\n 3.16086304e+02, 0.00000000e+00, 1.73122998e+03, 0.00000000e+00,\n 2.19496231e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.07759351e+03,\n 7.23092728e+01, 2.60527435e+02, 0.00000000e+00, 0.00000000e+00,\n 1.28017883e+02, 2.34268661e+02, 0.00000000e+00, 5.81122398e+01,\n 0.00000000e+00, 0.00000000e+00, 1.46954584e+01, 1.66150070e+02,\n 1.74991302e+01, 0.00000000e+00, 1.41523352e+01, 0.00000000e+00,\n 2.03101635e+01, 2.11528564e+03, 0.00000000e+00, 1.22175888e+02,\n 1.13646217e+02, 6.28940430e+02, 5.62492126e+02, 9.43473434e+00,\n 3.27843689e+02, 0.00000000e+00, 1.83422107e+03, 0.00000000e+00,\n 2.29576721e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.17976709e+03,\n 7.43440323e+01, 2.66476654e+02, 0.00000000e+00, 0.00000000e+00,\n 1.31826523e+02, 2.36066208e+02, 0.00000000e+00, 5.47231255e+01,\n 0.00000000e+00, 0.00000000e+00, 1.43221760e+01, 1.72473038e+02,\n 1.84237671e+01, 0.00000000e+00, 1.57737408e+01, 0.00000000e+00,\n 2.10527363e+01, 2.17662109e+03, 0.00000000e+00, 1.24610214e+02,\n 1.16029991e+02, 6.45258789e+02, 5.73236633e+02, 1.08116665e+01,\n 3.35760193e+02, 0.00000000e+00, 1.89764307e+03, 0.00000000e+00,\n 2.40547695e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.06668066e+03,\n 9.96527481e+01, 3.52026581e+02, 0.00000000e+00, 0.00000000e+00,\n 1.74973282e+02, 3.05604919e+02, 0.00000000e+00, 5.61930771e+01,\n 0.00000000e+00, 0.00000000e+00, 1.84265919e+01, 2.23797836e+02,\n 2.23301392e+01, 0.00000000e+00, 2.39291573e+01, 0.00000000e+00,\n 2.98560505e+01, 2.95156836e+03, 0.00000000e+00, 1.66212677e+02,\n 1.51898438e+02, 8.63973511e+02, 7.58149963e+02, 1.75092926e+01,\n 4.46499451e+02, 0.00000000e+00, 2.59038599e+03, 0.00000000e+00,\n 3.18114758e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.52964258e+03,\n 6.63522644e+01, 2.38072403e+02, 4.05875301e+00, 0.00000000e+00,\n 1.01445564e+02, 2.38796341e+02, 0.00000000e+00, 1.04312553e+02,\n 0.00000000e+00, 4.74178267e+00, 1.00331182e+01, 1.10543518e+02,\n 6.30224085e+00, 0.00000000e+00, 8.32034111e+00, 0.00000000e+00,\n 1.83383465e+01, 1.83820435e+03, 9.91219025e+01, 1.08563942e+02,\n 9.97075195e+01, 5.48449524e+02, 5.06255341e+02, 8.25140858e+00,\n 2.90245941e+02, 0.00000000e+00, 1.50678735e+03, 0.00000000e+00,\n 2.02787991e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.11502246e+03,\n 7.35604858e+01, 2.40149536e+02, 4.15253925e+00, 0.00000000e+00,\n 1.19070694e+02, 2.06259369e+02, 0.00000000e+00, 5.42398834e+01,\n 0.00000000e+00, 7.17814970e+00, 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1.04069223e+01,\n 2.85996552e+02, 0.00000000e+00, 1.32787170e+03, 0.00000000e+00,\n 2.02175713e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.83515063e+03,\n 8.37007294e+01, 3.14785217e+02, 0.00000000e+00, 0.00000000e+00,\n 1.52926193e+02, 2.75583191e+02, 0.00000000e+00, 8.39312210e+01,\n 0.00000000e+00, 0.00000000e+00, 1.50188856e+01, 1.80307663e+02,\n 2.20728951e+01, 0.00000000e+00, 1.95117702e+01, 0.00000000e+00,\n 2.12256680e+01, 2.59445801e+03, 1.07516769e+02, 1.39735626e+02,\n 1.31658112e+02, 7.78527527e+02, 6.79577271e+02, 1.07237043e+01,\n 3.92675323e+02, 0.00000000e+00, 2.28865649e+03, 0.00000000e+00,\n 2.65060158e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.04949976e+03,\n 7.87760468e+01, 2.96230408e+02, 0.00000000e+00, 0.00000000e+00,\n 1.34752106e+02, 2.81499390e+02, 0.00000000e+00, 1.10485489e+02,\n 0.00000000e+00, 0.00000000e+00, 1.35143461e+01, 1.64670517e+02,\n 2.21539726e+01, 0.00000000e+00, 2.06778526e+01, 0.00000000e+00,\n 1.93961964e+01, 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1.05629168e+01, 1.76080032e+02,\n 2.37382240e+01, 0.00000000e+00, 2.24765530e+01, 0.00000000e+00,\n 1.88819122e+01, 2.40511963e+03, 1.19620476e+02, 1.33761307e+02,\n 1.28387604e+02, 7.27114441e+02, 6.59004456e+02, 1.38846874e+01,\n 3.74599182e+02, 0.00000000e+00, 2.07407642e+03, 0.00000000e+00,\n 2.58091564e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.92773413e+03,\n 7.65238190e+01, 2.77949188e+02, 6.93721485e+00, 0.00000000e+00,\n 1.23686325e+02, 2.66056427e+02, 0.00000000e+00, 1.00651283e+02,\n 5.31559992e+00, 6.46887970e+00, 1.54104366e+01, 1.58285446e+02,\n 1.78122063e+01, 0.00000000e+00, 1.79749718e+01, 0.00000000e+00,\n 1.81360359e+01, 2.18797949e+03, 0.00000000e+00, 1.19539162e+02,\n 1.16079880e+02, 6.64964355e+02, 6.10181702e+02, 1.29638262e+01,\n 3.46757294e+02, 0.00000000e+00, 1.85307178e+03, 0.00000000e+00,\n 2.30915985e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.23910718e+03,\n 9.24983215e+01, 3.40090057e+02, 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2.92489319e+02, 0.00000000e+00, 1.03207016e+02,\n 3.64719772e+00, 6.59585285e+00, 1.65663395e+01, 1.84858444e+02,\n 2.11232758e+01, 0.00000000e+00, 2.02910938e+01, 0.00000000e+00,\n 1.70894032e+01, 2.47879932e+03, 0.00000000e+00, 1.36167404e+02,\n 1.30029846e+02, 7.58129761e+02, 6.79952454e+02, 1.26665974e+01,\n 3.91876099e+02, 0.00000000e+00, 2.13459497e+03, 0.00000000e+00,\n 2.78671856e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.25752771e+03,\n 4.49374275e+01, 1.53047821e+02, 2.12077117e+00, 0.00000000e+00,\n 7.49558640e+01, 1.34728592e+02, 0.00000000e+00, 3.11004677e+01,\n 1.10490286e+00, 3.84630156e+00, 8.50089264e+00, 9.73548660e+01,\n 9.39621353e+00, 0.00000000e+00, 8.33450317e+00, 0.00000000e+00,\n 8.51066685e+00, 1.21764417e+03, 0.00000000e+00, 7.28224182e+01,\n 6.40590134e+01, 3.78106018e+02, 3.21558411e+02, 3.63733697e+00,\n 1.90553162e+02, 0.00000000e+00, 1.08087500e+03, 0.00000000e+00,\n 1.50434227e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.43840527e+03,\n 9.61514664e+01, 3.49776703e+02, 8.05351257e+00, 0.00000000e+00,\n 1.56738281e+02, 3.31638885e+02, 0.00000000e+00, 1.26412537e+02,\n 5.74094629e+00, 9.26713085e+00, 1.93594017e+01, 2.04149124e+02,\n 2.35329247e+01, 0.00000000e+00, 2.36025848e+01, 0.00000000e+00,\n 2.19906578e+01, 2.73818823e+03, 0.00000000e+00, 1.49296890e+02,\n 1.45663757e+02, 8.34948120e+02, 7.62919312e+02, 1.57856493e+01,\n 4.35503174e+02, 0.00000000e+00, 2.32691235e+03, 0.00000000e+00,\n 2.98775501e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.08438428e+03,\n 7.85124207e+01, 2.80641693e+02, 6.61991692e+00, 0.00000000e+00,\n 1.29257126e+02, 2.59541473e+02, 0.00000000e+00, 9.33915253e+01,\n 3.77641249e+00, 7.47355270e+00, 1.56150627e+01, 1.71015381e+02,\n 2.03578110e+01, 0.00000000e+00, 1.82060242e+01, 0.00000000e+00,\n 1.76860161e+01, 2.20657373e+03, 0.00000000e+00, 1.21488129e+02,\n 1.17424957e+02, 6.77637207e+02, 6.06056824e+02, 1.10759840e+01,\n 3.50890503e+02, 0.00000000e+00, 1.90778882e+03, 0.00000000e+00,\n 2.47448940e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.58412524e+03,\n 1.07220963e+02, 3.97026154e+02, 8.51448250e+00, 0.00000000e+00,\n 1.76600571e+02, 3.79773285e+02, 0.00000000e+00, 1.49314667e+02,\n 6.37748480e+00, 1.20717268e+01, 1.97436676e+01, 2.28288269e+02,\n 2.77493420e+01, 0.00000000e+00, 3.01082745e+01, 0.00000000e+00,\n 2.51851673e+01, 3.06597070e+03, 0.00000000e+00, 1.66127945e+02,\n 1.64723602e+02, 9.32412354e+02, 8.65973450e+02, 1.84720135e+01,\n 4.90229858e+02, 0.00000000e+00, 2.56599487e+03, 0.00000000e+00,\n 3.38664017e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.62951758e+03,\n 1.05269852e+02, 3.84648987e+02, 8.05007362e+00, 0.00000000e+00,\n 1.72174377e+02, 3.65851257e+02, 0.00000000e+00, 1.39233856e+02,\n 6.04299307e+00, 1.11768761e+01, 2.00896816e+01, 2.23620987e+02,\n 2.63396435e+01, 0.00000000e+00, 2.71172600e+01, 0.00000000e+00,\n 2.34162102e+01, 3.00232031e+03, 0.00000000e+00, 1.63662567e+02,\n 1.59825745e+02, 9.14657471e+02, 8.39514832e+02, 1.76871738e+01,\n 4.77884155e+02, 0.00000000e+00, 2.53927051e+03, 0.00000000e+00,\n 3.33962669e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.99460364e+03,\n 6.19755135e+01, 2.14169800e+02, 0.00000000e+00, 0.00000000e+00,\n 1.11981216e+02, 1.81530518e+02, 1.82881439e+00, 3.67204323e+01,\n 0.00000000e+00, 1.16401405e+01, 7.60237122e+00, 1.38694275e+02,\n 1.38204985e+01, 0.00000000e+00, 9.71253109e+00, 0.00000000e+00,\n 1.56291304e+01, 1.81560388e+03, 7.56510849e+01, 1.03776611e+02,\n 9.60623398e+01, 5.52450317e+02, 4.69906006e+02, 4.57973576e+00,\n 2.73046356e+02, 0.00000000e+00, 1.64326538e+03, 0.00000000e+00,\n 1.97423363e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.22178296e+03,\n 7.34254150e+01, 2.56195007e+02, 0.00000000e+00, 0.00000000e+00,\n 1.28377869e+02, 2.24811661e+02, 2.73844051e+00, 6.85181503e+01,\n 0.00000000e+00, 8.82831573e+00, 1.12800293e+01, 1.55361420e+02,\n 1.72769432e+01, 0.00000000e+00, 1.46679945e+01, 0.00000000e+00,\n 1.85452690e+01, 2.13428516e+03, 9.49201279e+01, 1.17235672e+02,\n 1.11874680e+02, 6.43611084e+02, 5.61456055e+02, 8.73502827e+00,\n 3.23108307e+02, 0.00000000e+00, 1.89216626e+03, 0.00000000e+00,\n 2.17835464e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.85904395e+03,\n 7.15866241e+01, 2.58460785e+02, 0.00000000e+00, 0.00000000e+00,\n 1.17467163e+02, 2.47870148e+02, 2.77169323e+00, 9.15613174e+01,\n 0.00000000e+00, 1.04542580e+01, 6.97643089e+00, 1.46428909e+02,\n 1.55279694e+01, 0.00000000e+00, 1.37706957e+01, 0.00000000e+00,\n 1.49306803e+01, 2.11808276e+03, 1.04415825e+02, 1.18354240e+02,\n 1.10897560e+02, 6.30654480e+02, 5.76262146e+02, 9.48676777e+00,\n 3.25112000e+02, 0.00000000e+00, 1.77704761e+03, 0.00000000e+00,\n 2.56573544e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.07919385e+03,\n 7.29681473e+01, 2.66447998e+02, 0.00000000e+00, 0.00000000e+00,\n 1.26392960e+02, 2.40807892e+02, 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6.77617569e+01, 2.30818481e+02, 0.00000000e+00, 0.00000000e+00,\n 1.22518494e+02, 1.98651306e+02, 1.30156791e+00, 3.85711479e+01,\n 0.00000000e+00, 6.66828871e+00, 1.12932434e+01, 1.48547501e+02,\n 1.85180187e+01, 0.00000000e+00, 9.51412582e+00, 0.00000000e+00,\n 1.65662670e+01, 1.95259619e+03, 8.42729721e+01, 1.12406425e+02,\n 1.05029198e+02, 5.93555054e+02, 5.08520752e+02, 5.14392376e+00,\n 2.95773438e+02, 0.00000000e+00, 1.75064917e+03, 0.00000000e+00,\n 1.85129051e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.98198865e+03,\n 6.84298553e+01, 2.46580154e+02, 0.00000000e+00, 0.00000000e+00,\n 1.22883224e+02, 2.22149292e+02, 1.95406425e+00, 6.73135757e+01,\n 0.00000000e+00, 9.93266678e+00, 1.03843708e+01, 1.55623947e+02,\n 1.98558292e+01, 0.00000000e+00, 1.27241526e+01, 0.00000000e+00,\n 1.72100964e+01, 2.04615930e+03, 9.32698288e+01, 1.15059547e+02,\n 1.09972755e+02, 6.17876526e+02, 5.44863525e+02, 7.51833057e+00,\n 3.13700897e+02, 0.00000000e+00, 1.77440222e+03, 0.00000000e+00,\n 2.05396385e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.69305469e+03,\n 8.99586029e+01, 3.21997070e+02, 0.00000000e+00, 0.00000000e+00,\n 1.58327713e+02, 2.88165833e+02, 2.89641476e+00, 9.06071930e+01,\n 0.00000000e+00, 1.60388756e+01, 1.08885498e+01, 1.97691467e+02,\n 2.15468464e+01, 0.00000000e+00, 1.75920887e+01, 0.00000000e+00,\n 2.23492622e+01, 2.68353613e+03, 1.20118599e+02, 1.49056046e+02,\n 1.41167053e+02, 8.08462036e+02, 7.10847168e+02, 1.05287733e+01,\n 4.07825226e+02, 0.00000000e+00, 2.35140308e+03, 0.00000000e+00,\n 2.89364319e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.10358081e+03,\n 6.88850250e+01, 2.51690598e+02, 0.00000000e+00, 0.00000000e+00,\n 1.24084679e+02, 2.18061935e+02, 3.19140720e+00, 7.79193726e+01,\n 0.00000000e+00, 1.05681038e+01, 1.11460075e+01, 1.63117264e+02,\n 1.93770580e+01, 0.00000000e+00, 1.56610022e+01, 0.00000000e+00,\n 1.87674980e+01, 2.05994604e+03, 9.09046402e+01, 1.10327919e+02,\n 1.09973663e+02, 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0.00000000e+00,\n 1.33074331e+01, 1.72505298e+03, 7.33669205e+01, 9.06740265e+01,\n 9.17376022e+01, 5.25695007e+02, 4.53613403e+02, 5.67117691e+00,\n 2.65758484e+02, 0.00000000e+00, 1.53745483e+03, 0.00000000e+00,\n 1.90500069e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.26594482e+02,\n 1.55897141e+01, 7.16570129e+01, 0.00000000e+00, 0.00000000e+00,\n 3.71365089e+01, 5.08110619e+01, 9.98025835e-01, 0.00000000e+00,\n 0.00000000e+00, 7.78229189e+00, 1.49548244e+00, 7.09025879e+01,\n 1.45847178e+00, 0.00000000e+00, 1.07615137e+00, 0.00000000e+00,\n 6.33143330e+00, 6.01624756e+02, 2.53084335e+01, 3.76502457e+01,\n 3.49669228e+01, 1.93608810e+02, 1.50139267e+02, 0.00000000e+00,\n 9.14837723e+01, 0.00000000e+00, 5.44379883e+02, 0.00000000e+00,\n 7.49334526e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.09445923e+03,\n 6.44053040e+01, 2.16304932e+02, 0.00000000e+00, 0.00000000e+00,\n 1.21028809e+02, 1.75305099e+02, 1.02442634e+00, 1.61056061e+01,\n 0.00000000e+00, 7.92497969e+00, 1.08008327e+01, 1.50166245e+02,\n 2.08288822e+01, 0.00000000e+00, 9.03554058e+00, 0.00000000e+00,\n 1.51951151e+01, 1.83333752e+03, 7.35392227e+01, 1.07816811e+02,\n 1.01377693e+02, 5.64258850e+02, 4.68194153e+02, 3.47625256e+00,\n 2.77266754e+02, 0.00000000e+00, 1.68692883e+03, 0.00000000e+00,\n 1.78228779e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13695605e+03,\n 7.77044525e+01, 2.88592163e+02, 0.00000000e+00, 0.00000000e+00,\n 1.38903961e+02, 2.64585541e+02, 2.46339703e+00, 9.49816895e+01,\n 0.00000000e+00, 1.46024723e+01, 1.25892429e+01, 1.79925644e+02,\n 2.40994053e+01, 0.00000000e+00, 1.79627247e+01, 0.00000000e+00,\n 2.05043678e+01, 2.34330298e+03, 1.09279793e+02, 1.30272751e+02,\n 1.27856621e+02, 7.06015686e+02, 6.32100403e+02, 1.02141237e+01,\n 3.62408447e+02, 0.00000000e+00, 1.99348975e+03, 0.00000000e+00,\n 2.49149036e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.82747363e+03,\n 9.28351822e+01, 3.36482788e+02, 0.00000000e+00, 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2.17515796e+03, 0.00000000e+00,\n 2.66210785e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.01422125e+03,\n 3.74119377e+01, 1.24113548e+02, 2.75226974e+00, 0.00000000e+00,\n 6.27056923e+01, 1.13252419e+02, 0.00000000e+00, 2.91451416e+01,\n 1.66191375e+00, 6.38105822e+00, 5.79425716e+00, 7.76373291e+01,\n 8.03512955e+00, 0.00000000e+00, 5.35032177e+00, 0.00000000e+00,\n 6.81184578e+00, 1.03644202e+03, 4.65936775e+01, 6.23288040e+01,\n 5.64764290e+01, 3.10464386e+02, 2.68548004e+02, 0.00000000e+00,\n 1.58236710e+02, 0.00000000e+00, 9.10153870e+02, 0.00000000e+00,\n 1.37111826e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.04695190e+03,\n 6.65760574e+01, 2.20516693e+02, 6.92028332e+00, 0.00000000e+00,\n 1.18210579e+02, 1.95581772e+02, 0.00000000e+00, 4.34890862e+01,\n 4.86120319e+00, 9.21639824e+00, 1.26800728e+01, 1.47908096e+02,\n 1.96941223e+01, 0.00000000e+00, 8.01438332e+00, 0.00000000e+00,\n 1.26563044e+01, 1.89376135e+03, 8.06815414e+01, 1.11268539e+02,\n 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1.87843647e+01, 0.00000000e+00,\n 1.81598549e+01, 2.52621509e+03, 1.11053818e+02, 1.41013672e+02,\n 1.35024094e+02, 7.59152527e+02, 6.50142578e+02, 0.00000000e+00,\n 3.87318817e+02, 0.00000000e+00, 2.27643994e+03, 0.00000000e+00,\n 2.70652275e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.21941943e+03,\n 8.03162079e+01, 2.82642853e+02, 8.18714809e+00, 0.00000000e+00,\n 1.35571106e+02, 2.65025421e+02, 0.00000000e+00, 1.06731575e+02,\n 5.39922428e+00, 9.89437675e+00, 1.51831074e+01, 1.79849731e+02,\n 2.31534214e+01, 0.00000000e+00, 2.19796104e+01, 0.00000000e+00,\n 1.95983810e+01, 2.31424048e+03, 1.09060997e+02, 1.23615524e+02,\n 1.22739822e+02, 6.91550476e+02, 6.08980286e+02, 0.00000000e+00,\n 3.58039856e+02, 0.00000000e+00, 2.02549023e+03, 0.00000000e+00,\n 2.35893707e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.18257153e+03,\n 6.07806473e+01, 2.20775070e+02, 0.00000000e+00, 0.00000000e+00,\n 1.08360535e+02, 1.90319305e+02, 0.00000000e+00, 5.30935669e+01,\n 0.00000000e+00, 6.79654694e+00, 1.11505585e+01, 1.36304779e+02,\n 9.49431705e+00, 0.00000000e+00, 9.00548267e+00, 0.00000000e+00,\n 1.88325214e+01, 1.87043115e+03, 7.54348373e+01, 1.04901276e+02,\n 9.62603302e+01, 5.68790771e+02, 4.83644989e+02, 4.55736828e+00,\n 2.81250244e+02, 0.00000000e+00, 1.71662390e+03, 0.00000000e+00,\n 1.81409760e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.89898096e+03,\n 9.32705383e+01, 3.38750732e+02, 0.00000000e+00, 0.00000000e+00,\n 1.61567261e+02, 3.13286011e+02, 0.00000000e+00, 1.01310837e+02,\n 0.00000000e+00, 8.24939823e+00, 1.68188972e+01, 1.95818512e+02,\n 1.69490929e+01, 0.00000000e+00, 1.91554565e+01, 0.00000000e+00,\n 2.77378941e+01, 2.81256519e+03, 1.27183792e+02, 1.55806442e+02,\n 1.45395172e+02, 8.48976257e+02, 7.55783813e+02, 1.19087524e+01,\n 4.29389069e+02, 0.00000000e+00, 2.47176147e+03, 0.00000000e+00,\n 2.79709225e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.95948010e+03,\n 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0.00000000e+00,\n 4.90267944e+01, 7.66498108e+01, 0.00000000e+00, 1.17939568e+01,\n 0.00000000e+00, 6.24012041e+00, 4.81073570e+00, 6.56130600e+01,\n 4.78885508e+00, 0.00000000e+00, 4.46152020e+00, 0.00000000e+00,\n 7.50909567e+00, 7.71152588e+02, 3.34381599e+01, 4.74599419e+01,\n 4.14584465e+01, 2.38876465e+02, 1.99168396e+02, 0.00000000e+00,\n 1.16915474e+02, 0.00000000e+00, 7.02696899e+02, 0.00000000e+00,\n 1.05769243e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.37508447e+03,\n 7.29866943e+01, 2.67547058e+02, 0.00000000e+00, 0.00000000e+00,\n 1.31875427e+02, 2.31538315e+02, 0.00000000e+00, 7.42321320e+01,\n 0.00000000e+00, 1.02471876e+01, 1.38579712e+01, 1.69612534e+02,\n 1.99046669e+01, 0.00000000e+00, 1.49498110e+01, 0.00000000e+00,\n 2.00302715e+01, 2.18122314e+03, 9.21442719e+01, 1.21560242e+02,\n 1.17007088e+02, 6.65932312e+02, 5.79641724e+02, 7.10512972e+00,\n 3.35921265e+02, 0.00000000e+00, 1.95780798e+03, 0.00000000e+00,\n 2.15767174e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.19336694e+03,\n 7.93551712e+01, 2.91388550e+02, 0.00000000e+00, 0.00000000e+00,\n 1.38273239e+02, 2.68872925e+02, 0.00000000e+00, 9.25694351e+01,\n 0.00000000e+00, 1.05856581e+01, 1.24913960e+01, 1.72153168e+02,\n 2.07952595e+01, 0.00000000e+00, 1.90897980e+01, 0.00000000e+00,\n 2.03907032e+01, 2.32124805e+03, 1.09844505e+02, 1.30185196e+02,\n 1.24707146e+02, 7.03996643e+02, 6.40286499e+02, 8.15972233e+00,\n 3.61957611e+02, 0.00000000e+00, 1.98673621e+03, 0.00000000e+00,\n 2.50841789e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.68397314e+03,\n 8.79069977e+01, 3.21270081e+02, 0.00000000e+00, 0.00000000e+00,\n 1.59161499e+02, 2.82545105e+02, 0.00000000e+00, 7.86678848e+01,\n 0.00000000e+00, 1.37918510e+01, 1.35303688e+01, 1.99317429e+02,\n 2.34481716e+01, 0.00000000e+00, 2.20676537e+01, 0.00000000e+00,\n 2.39604816e+01, 2.59222485e+03, 1.17282310e+02, 1.47191498e+02,\n 1.38251938e+02, 7.91725403e+02, 6.95029785e+02, 8.41766930e+00,\n 3.99598480e+02, 0.00000000e+00, 2.28649194e+03, 0.00000000e+00,\n 2.79287529e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.04220630e+03,\n 9.13403931e+01, 3.31419586e+02, 0.00000000e+00, 0.00000000e+00,\n 1.51967255e+02, 3.19485535e+02, 0.00000000e+00, 1.11482414e+02,\n 0.00000000e+00, 1.18071089e+01, 1.39131050e+01, 1.86447083e+02,\n 2.18871880e+01, 0.00000000e+00, 2.79351807e+01, 0.00000000e+00,\n 2.64185429e+01, 2.53029541e+03, 1.36685043e+02, 1.46303146e+02,\n 1.40430878e+02, 7.67167603e+02, 7.23782532e+02, 1.37659721e+01,\n 3.99967743e+02, 0.00000000e+00, 2.06470117e+03, 0.00000000e+00,\n 2.77374573e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.81598926e+03,\n 7.08377991e+01, 0.00000000e+00, 7.11548042e+00, 0.00000000e+00,\n 1.14131432e+02, 2.24789978e+02, 2.22460866e+00, 6.51220093e+01,\n 2.84138346e+00, 9.13465977e+00, 1.07873526e+01, 1.40811584e+02,\n 1.50295725e+01, 0.00000000e+00, 1.27521353e+01, 0.00000000e+00,\n 1.27516365e+01, 2.01382642e+03, 9.48392487e+01, 1.15222076e+02,\n 1.05711502e+02, 5.90967407e+02, 5.22872986e+02, 0.00000000e+00,\n 2.99520721e+02, 0.00000000e+00, 1.71354358e+03, 0.00000000e+00,\n 2.34494209e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.10422461e+03,\n 7.83841629e+01, 0.00000000e+00, 9.11080933e+00, 0.00000000e+00,\n 1.28719620e+02, 2.36930145e+02, 3.24904561e+00, 6.07819748e+01,\n 3.54539919e+00, 8.33918285e+00, 1.28272543e+01, 1.62477097e+02,\n 1.96154022e+01, 0.00000000e+00, 1.55224771e+01, 0.00000000e+00,\n 1.50556068e+01, 2.21592871e+03, 1.00442421e+02, 1.25503845e+02,\n 1.16528206e+02, 6.51128052e+02, 5.63849060e+02, 0.00000000e+00,\n 3.26718384e+02, 0.00000000e+00, 1.91675867e+03, 0.00000000e+00,\n 2.47122860e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.66407251e+03,\n 6.33155708e+01, 0.00000000e+00, 5.75378895e+00, 0.00000000e+00,\n 1.02828888e+02, 2.06396088e+02, 2.33213735e+00, 6.95734177e+01,\n 1.81747580e+00, 8.79208279e+00, 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0.00000000e+00, 2.19368237e+03, 0.00000000e+00,\n 2.94773159e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.06891333e+03,\n 8.05940933e+01, 0.00000000e+00, 9.34455585e+00, 0.00000000e+00,\n 1.29214691e+02, 2.65398529e+02, 4.19395781e+00, 1.11540657e+02,\n 4.15331125e+00, 9.23799992e+00, 1.51710577e+01, 1.71725006e+02,\n 1.90604744e+01, 0.00000000e+00, 2.05870018e+01, 0.00000000e+00,\n 1.91621456e+01, 2.30449512e+03, 1.11039452e+02, 1.23836487e+02,\n 1.22592911e+02, 6.78268494e+02, 6.08342224e+02, 0.00000000e+00,\n 3.52802094e+02, 0.00000000e+00, 1.95317761e+03, 0.00000000e+00,\n 2.36198349e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.17548291e+03,\n 8.58232117e+01, 0.00000000e+00, 8.08778667e+00, 0.00000000e+00,\n 1.39613953e+02, 2.83501160e+02, 3.60981297e+00, 1.01750206e+02,\n 2.41693354e+00, 1.23276339e+01, 1.13499022e+01, 1.77722824e+02,\n 2.06975384e+01, 0.00000000e+00, 2.03568058e+01, 0.00000000e+00,\n 1.44144993e+01, 2.46402637e+03, 1.17995033e+02, 1.36883636e+02,\n 1.31644363e+02, 7.24594116e+02, 6.47585022e+02, 0.00000000e+00,\n 3.72665741e+02, 0.00000000e+00, 2.08207690e+03, 0.00000000e+00,\n 2.92290936e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.11634473e+03,\n 8.00366516e+01, 0.00000000e+00, 7.88818836e+00, 0.00000000e+00,\n 1.29794464e+02, 2.53386795e+02, 4.37629652e+00, 8.96838226e+01,\n 2.64289331e+00, 9.75432873e+00, 1.35645857e+01, 1.68515366e+02,\n 1.76355190e+01, 0.00000000e+00, 1.86490784e+01, 0.00000000e+00,\n 1.49321270e+01, 2.28006152e+03, 1.06798790e+02, 1.26082397e+02,\n 1.20689590e+02, 6.70406494e+02, 5.90470520e+02, 0.00000000e+00,\n 3.42283478e+02, 0.00000000e+00, 1.95532922e+03, 0.00000000e+00,\n 2.59607239e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.33240356e+02,\n 3.13947620e+01, 0.00000000e+00, 2.20398951e+00, 0.00000000e+00,\n 5.26402550e+01, 8.18307343e+01, 1.62318921e+00, 9.38786685e-01,\n 1.04916081e-01, 5.59027147e+00, 5.02952766e+00, 6.64945221e+01,\n 4.59126663e+00, 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9.22562714e+01,\n 2.53226519e+00, 1.17786970e+01, 1.15075388e+01, 1.62725082e+02,\n 1.97202511e+01, 0.00000000e+00, 1.66914692e+01, 0.00000000e+00,\n 1.35916176e+01, 2.22248706e+03, 1.03432579e+02, 1.22381653e+02,\n 1.18557404e+02, 6.53347595e+02, 5.79154358e+02, 0.00000000e+00,\n 3.35728302e+02, 0.00000000e+00, 1.89811072e+03, 0.00000000e+00,\n 2.58126431e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.61950928e+03,\n 9.59330902e+01, 0.00000000e+00, 1.09521208e+01, 0.00000000e+00,\n 1.60313202e+02, 2.86474152e+02, 5.14559507e+00, 7.24393997e+01,\n 3.96610117e+00, 1.23259230e+01, 1.58661108e+01, 2.02825653e+02,\n 2.57535305e+01, 0.00000000e+00, 2.09011269e+01, 0.00000000e+00,\n 1.75129166e+01, 2.71027661e+03, 1.20864342e+02, 1.53390717e+02,\n 1.43753769e+02, 7.97458862e+02, 6.84831482e+02, 0.00000000e+00,\n 3.99980621e+02, 0.00000000e+00, 2.35775146e+03, 0.00000000e+00,\n 3.09928703e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.16890039e+03,\n 8.34511261e+01, 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1.89546875e+02, 0.00000000e+00, 1.02567920e+03, 0.00000000e+00,\n 1.53847027e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.06893042e+03,\n 8.81929626e+01, 3.31804535e+02, 5.75163698e+00, 0.00000000e+00,\n 1.50892441e+02, 3.12393890e+02, 0.00000000e+00, 1.33976959e+02,\n 0.00000000e+00, 1.01128569e+01, 1.60442238e+01, 1.91242294e+02,\n 2.61487274e+01, 0.00000000e+00, 2.06279831e+01, 0.00000000e+00,\n 2.06799793e+01, 2.54701660e+03, 1.24677307e+02, 1.39738754e+02,\n 1.41619553e+02, 7.68578491e+02, 7.12693970e+02, 1.17092161e+01,\n 4.05766052e+02, 0.00000000e+00, 2.13896484e+03, 0.00000000e+00,\n 2.75114746e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.05012524e+03,\n 8.63691330e+01, 3.22029388e+02, 6.13228750e+00, 0.00000000e+00,\n 1.47363449e+02, 3.04639435e+02, 0.00000000e+00, 1.25291183e+02,\n 0.00000000e+00, 9.21502018e+00, 1.44924259e+01, 1.84291214e+02,\n 2.65152206e+01, 0.00000000e+00, 1.76797142e+01, 0.00000000e+00,\n 1.94253197e+01, 2.49289478e+03, 1.22083504e+02, 1.37471573e+02,\n 1.37650314e+02, 7.50303955e+02, 6.93450439e+02, 1.24821157e+01,\n 3.94847137e+02, 0.00000000e+00, 2.09918701e+03, 0.00000000e+00,\n 2.74560795e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.95264355e+03,\n 1.02238892e+02, 3.66453979e+02, 7.74868727e+00, 0.00000000e+00,\n 1.80019363e+02, 3.20182220e+02, 0.00000000e+00, 1.01226250e+02,\n 0.00000000e+00, 1.49757738e+01, 1.54324360e+01, 2.24362122e+02,\n 2.62546005e+01, 0.00000000e+00, 2.35479031e+01, 0.00000000e+00,\n 2.59053650e+01, 2.98045288e+03, 1.27758125e+02, 1.62416153e+02,\n 1.58700409e+02, 8.93915771e+02, 7.74126953e+02, 1.38796682e+01,\n 4.59750641e+02, 0.00000000e+00, 2.63861377e+03, 0.00000000e+00,\n 3.19878101e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.27783862e+03,\n 8.32808075e+01, 3.06496582e+02, 6.16469526e+00, 0.00000000e+00,\n 1.45567139e+02, 2.76215179e+02, 0.00000000e+00, 9.94291840e+01,\n 0.00000000e+00, 1.08504362e+01, 1.24361820e+01, 1.81050873e+02,\n 2.31367741e+01, 0.00000000e+00, 2.16274147e+01, 0.00000000e+00,\n 2.07246094e+01, 2.44138306e+03, 1.10378006e+02, 1.31229645e+02,\n 1.30723236e+02, 7.32945190e+02, 6.51485352e+02, 1.49120131e+01,\n 3.79520477e+02, 0.00000000e+00, 2.12584912e+03, 0.00000000e+00,\n 2.61800327e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.22767505e+03,\n 8.17331772e+01, 2.74180939e+02, 0.00000000e+00, 0.00000000e+00,\n 1.37027008e+02, 2.48099442e+02, 0.00000000e+00, 7.47267532e+01,\n 6.38873291e+00, 1.02036638e+01, 1.63462982e+01, 1.58656509e+02,\n 1.54303408e+01, 0.00000000e+00, 1.67488785e+01, 0.00000000e+00,\n 2.19750862e+01, 2.26366187e+03, 1.05216835e+02, 0.00000000e+00,\n 1.19173431e+02, 6.74751892e+02, 5.93619324e+02, 1.21866045e+01,\n 3.42628693e+02, 0.00000000e+00, 1.98121680e+03, 0.00000000e+00,\n 2.42983055e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.33893066e+03,\n 1.02180496e+02, 3.49287628e+02, 0.00000000e+00, 0.00000000e+00,\n 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6.64359970e+01,\n 2.78167915e+00, 6.44615412e+00, 1.45288401e+01, 1.57367111e+02,\n 2.18088398e+01, 0.00000000e+00, 1.46067114e+01, 0.00000000e+00,\n 1.51442003e+01, 2.10991724e+03, 9.51888962e+01, 0.00000000e+00,\n 1.11705185e+02, 6.27514282e+02, 5.49231079e+02, 1.01098204e+01,\n 3.19759308e+02, 0.00000000e+00, 1.84882092e+03, 0.00000000e+00,\n 2.37551327e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.08385803e+02,\n 2.99001141e+01, 1.00505875e+02, 0.00000000e+00, 0.00000000e+00,\n 5.13141785e+01, 7.94344101e+01, 0.00000000e+00, 8.74463749e+00,\n 1.35249782e+00, 5.45443773e+00, 4.69547701e+00, 6.96078262e+01,\n 5.15042210e+00, 0.00000000e+00, 3.93083739e+00, 0.00000000e+00,\n 7.06607866e+00, 8.30607300e+02, 3.67340851e+01, 0.00000000e+00,\n 4.29349632e+01, 2.48481873e+02, 2.04512619e+02, 6.88630581e-01,\n 1.20575851e+02, 0.00000000e+00, 7.53617798e+02, 0.00000000e+00,\n 1.12259932e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28494824e+03,\n 7.70074768e+01, 2.60646423e+02, 0.00000000e+00, 0.00000000e+00,\n 1.33971313e+02, 2.21882645e+02, 0.00000000e+00, 5.60483818e+01,\n 3.35151958e+00, 7.94207048e+00, 1.62212524e+01, 1.62244507e+02,\n 2.54048462e+01, 0.00000000e+00, 1.36612043e+01, 0.00000000e+00,\n 1.67433739e+01, 2.16224438e+03, 9.28620377e+01, 0.00000000e+00,\n 1.14902733e+02, 6.45217346e+02, 5.51764954e+02, 1.02545691e+01,\n 3.27088928e+02, 0.00000000e+00, 1.93658057e+03, 0.00000000e+00,\n 2.36603718e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.36538599e+03,\n 8.38353806e+01, 2.88390564e+02, 0.00000000e+00, 0.00000000e+00,\n 1.44072311e+02, 2.54369919e+02, 0.00000000e+00, 7.54087372e+01,\n 4.09775066e+00, 8.35275555e+00, 1.58777380e+01, 1.77204910e+02,\n 2.58924351e+01, 0.00000000e+00, 1.71429672e+01, 0.00000000e+00,\n 1.85450706e+01, 2.36952734e+03, 1.05601402e+02, 0.00000000e+00,\n 1.25881241e+02, 7.05828186e+02, 6.16707214e+02, 1.25922308e+01,\n 3.60205688e+02, 0.00000000e+00, 2.08250464e+03, 0.00000000e+00,\n 2.61620216e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.62593433e+03,\n 9.82411575e+01, 3.36549164e+02, 0.00000000e+00, 0.00000000e+00,\n 1.66485825e+02, 3.03933411e+02, 0.00000000e+00, 8.83645859e+01,\n 5.10880756e+00, 1.31058149e+01, 1.60680809e+01, 1.98742035e+02,\n 2.53489513e+01, 0.00000000e+00, 2.26146984e+01, 0.00000000e+00,\n 2.17998142e+01, 2.74508618e+03, 1.25958412e+02, 0.00000000e+00,\n 1.45440582e+02, 8.17546448e+02, 7.23086670e+02, 1.49177561e+01,\n 4.16458679e+02, 0.00000000e+00, 2.37926929e+03, 0.00000000e+00,\n 3.14331398e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.20596216e+03,\n 9.08652649e+01, 3.22304016e+02, 0.00000000e+00, 0.00000000e+00,\n 1.50353806e+02, 3.01613953e+02, 0.00000000e+00, 1.15208160e+02,\n 5.37791729e+00, 1.54678392e+01, 1.36001482e+01, 1.91848572e+02,\n 2.39315071e+01, 0.00000000e+00, 2.40634575e+01, 0.00000000e+00,\n 2.09032669e+01, 2.55769971e+03, 1.25248711e+02, 0.00000000e+00,\n 1.37289154e+02, 7.59732483e+02, 6.94374695e+02, 1.56505766e+01,\n 3.91912994e+02, 0.00000000e+00, 2.14709058e+03, 0.00000000e+00,\n 3.06310234e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.23759668e+03,\n 7.58267822e+01, 2.49352112e+02, 4.79995394e+00, 0.00000000e+00,\n 1.30247238e+02, 2.19000870e+02, 0.00000000e+00, 2.41352673e+01,\n 0.00000000e+00, 8.00291061e+00, 0.00000000e+00, 1.47832550e+02,\n 1.40131998e+01, 0.00000000e+00, 1.23243437e+01, 0.00000000e+00,\n 1.67775555e+01, 2.09560107e+03, 9.50133438e+01, 1.30407806e+02,\n 0.00000000e+00, 6.24993347e+02, 5.22879578e+02, 6.41768312e+00,\n 3.10305603e+02, 0.00000000e+00, 1.90165332e+03, 0.00000000e+00,\n 1.94167213e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.24375244e+03,\n 7.48027115e+01, 2.49474701e+02, 5.21684313e+00, 0.00000000e+00,\n 1.31164337e+02, 2.15717850e+02, 8.59711111e-01, 2.67172966e+01,\n 0.00000000e+00, 8.27351189e+00, 0.00000000e+00, 1.53651154e+02,\n 1.68565903e+01, 0.00000000e+00, 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1.12453508e+01,\n 3.15248871e+02, 0.00000000e+00, 1.76040857e+03, 0.00000000e+00,\n 1.99649258e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.12657446e+03,\n 7.72462158e+01, 2.73436890e+02, 3.99749422e+00, 0.00000000e+00,\n 1.29929199e+02, 2.51732040e+02, 2.08490419e+00, 7.31234207e+01,\n 0.00000000e+00, 9.79896736e+00, 0.00000000e+00, 1.62514771e+02,\n 1.71298447e+01, 0.00000000e+00, 1.35408764e+01, 0.00000000e+00,\n 1.35619440e+01, 2.24737866e+03, 1.03402298e+02, 1.28777298e+02,\n 0.00000000e+00, 6.69656128e+02, 5.92378357e+02, 9.05746174e+00,\n 3.41744476e+02, 0.00000000e+00, 1.95585132e+03, 0.00000000e+00,\n 2.51482525e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.26348315e+03,\n 7.39785538e+01, 2.53887238e+02, 4.75776339e+00, 0.00000000e+00,\n 1.31848465e+02, 2.15533188e+02, 3.32286501e+00, 3.22854424e+01,\n 0.00000000e+00, 6.25426626e+00, 0.00000000e+00, 1.67454895e+02,\n 1.94062977e+01, 0.00000000e+00, 1.08250914e+01, 0.00000000e+00,\n 1.31302881e+01, 2.12025098e+03, 9.14851532e+01, 1.23650749e+02,\n 0.00000000e+00, 6.34859741e+02, 5.28054810e+02, 4.59653616e+00,\n 3.16063873e+02, 0.00000000e+00, 1.92574158e+03, 0.00000000e+00,\n 1.98053131e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.31969543e+03,\n 4.49061584e+01, 1.53039291e+02, 1.42292058e+00, 0.00000000e+00,\n 7.64427261e+01, 1.32275330e+02, 1.02429247e+00, 1.37319117e+01,\n 0.00000000e+00, 5.99132109e+00, 0.00000000e+00, 9.93038025e+01,\n 8.07058620e+00, 0.00000000e+00, 6.72440720e+00, 0.00000000e+00,\n 7.11957645e+00, 1.27545532e+03, 5.63312950e+01, 7.82976532e+01,\n 0.00000000e+00, 3.81445862e+02, 3.19012482e+02, 3.18651199e+00,\n 1.88852921e+02, 0.00000000e+00, 1.14878101e+03, 0.00000000e+00,\n 1.46837711e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.53104077e+03,\n 8.78504410e+01, 3.05611572e+02, 6.25902987e+00, 0.00000000e+00,\n 1.52954498e+02, 2.74054443e+02, 2.07792711e+00, 6.94356995e+01,\n 0.00000000e+00, 8.20887756e+00, 0.00000000e+00, 1.84847504e+02,\n 2.20314369e+01, 0.00000000e+00, 1.62185593e+01, 0.00000000e+00,\n 1.81194248e+01, 2.53346069e+03, 1.13130600e+02, 1.45532608e+02,\n 0.00000000e+00, 7.55984741e+02, 6.54433777e+02, 8.37794018e+00,\n 3.82869507e+02, 0.00000000e+00, 2.24386353e+03, 0.00000000e+00,\n 2.30626240e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.67067700e+03,\n 9.17833939e+01, 3.26685455e+02, 7.21511126e+00, 0.00000000e+00,\n 1.62069244e+02, 2.91237946e+02, 2.87636995e+00, 8.22167358e+01,\n 0.00000000e+00, 7.86217260e+00, 0.00000000e+00, 1.99779266e+02,\n 2.67419357e+01, 0.00000000e+00, 1.86067715e+01, 0.00000000e+00,\n 1.83200245e+01, 2.69089331e+03, 1.19881149e+02, 1.49580063e+02,\n 0.00000000e+00, 8.03686707e+02, 6.97558289e+02, 1.08577003e+01,\n 4.08033722e+02, 0.00000000e+00, 2.37651660e+03, 0.00000000e+00,\n 2.44644928e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.84283130e+03,\n 9.84853516e+01, 3.42258881e+02, 7.20228243e+00, 0.00000000e+00,\n 1.71840591e+02, 3.05644073e+02, 2.36630678e+00, 7.06423264e+01,\n 0.00000000e+00, 1.24631538e+01, 0.00000000e+00, 2.06983322e+02,\n 2.51047382e+01, 0.00000000e+00, 1.93706970e+01, 0.00000000e+00,\n 2.14123249e+01, 2.83654370e+03, 1.27098625e+02, 1.65114944e+02,\n 0.00000000e+00, 8.47965332e+02, 7.30111328e+02, 9.87101746e+00,\n 4.27602203e+02, 0.00000000e+00, 2.51605420e+03, 0.00000000e+00,\n 2.68581791e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.19367285e+03,\n 8.13031616e+01, 2.91319702e+02, 6.07189608e+00, 0.00000000e+00,\n 1.38589478e+02, 2.67657440e+02, 1.70883226e+00, 8.92059937e+01,\n 0.00000000e+00, 8.75187492e+00, 0.00000000e+00, 1.69811371e+02,\n 2.19873505e+01, 0.00000000e+00, 1.95438900e+01, 0.00000000e+00,\n 1.88187847e+01, 2.36283398e+03, 1.10460907e+02, 1.30532562e+02,\n 0.00000000e+00, 7.05260071e+02, 6.29186890e+02, 1.23743238e+01,\n 3.62591034e+02, 0.00000000e+00, 2.03986670e+03, 0.00000000e+00,\n 2.26586018e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.10166162e+03,\n 7.23153915e+01, 2.44351273e+02, 0.00000000e+00, 0.00000000e+00,\n 1.22119507e+02, 2.22262894e+02, 0.00000000e+00, 5.41218033e+01,\n 0.00000000e+00, 6.12077141e+00, 1.11353464e+01, 1.41465363e+02,\n 1.40141802e+01, 0.00000000e+00, 1.43162489e+01, 0.00000000e+00,\n 2.02751369e+01, 2.03287354e+03, 9.27603531e+01, 1.18765869e+02,\n 1.08212448e+02, 6.02880859e+02, 0.00000000e+00, 0.00000000e+00,\n 3.06650452e+02, 0.00000000e+00, 1.78122888e+03, 0.00000000e+00,\n 1.90567989e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.99190601e+03,\n 7.49097366e+01, 2.73207184e+02, 0.00000000e+00, 0.00000000e+00,\n 1.24307289e+02, 2.60553497e+02, 0.00000000e+00, 1.11941132e+02,\n 0.00000000e+00, 8.66860676e+00, 1.02614975e+01, 1.56698288e+02,\n 1.56656656e+01, 0.00000000e+00, 2.00449219e+01, 0.00000000e+00,\n 2.26257458e+01, 2.16913965e+03, 1.06447304e+02, 1.17252281e+02,\n 1.16838470e+02, 6.44276245e+02, 0.00000000e+00, 0.00000000e+00,\n 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0.00000000e+00,\n 2.47331276e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00935120e+03,\n 2.62470322e+01, 9.82380829e+01, 0.00000000e+00, 0.00000000e+00,\n 5.15412102e+01, 7.28904190e+01, 0.00000000e+00, 1.10763988e+01,\n 0.00000000e+00, 6.37829399e+00, 3.56015849e+00, 6.96946411e+01,\n 5.89460087e+00, 0.00000000e+00, 4.98430777e+00, 0.00000000e+00,\n 6.94577932e+00, 8.10847473e+02, 3.06668568e+01, 4.66725540e+01,\n 4.11563110e+01, 2.41483322e+02, 0.00000000e+00, 0.00000000e+00,\n 1.19779549e+02, 0.00000000e+00, 7.61342407e+02, 0.00000000e+00,\n 1.01697884e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28590137e+03,\n 7.15296249e+01, 2.51954666e+02, 0.00000000e+00, 0.00000000e+00,\n 1.27909325e+02, 2.12869293e+02, 0.00000000e+00, 5.01941566e+01,\n 0.00000000e+00, 6.42803097e+00, 1.09562397e+01, 1.60773331e+02,\n 2.39735546e+01, 0.00000000e+00, 1.23839560e+01, 0.00000000e+00,\n 1.49408312e+01, 2.08612207e+03, 8.94615250e+01, 1.18801193e+02,\n 1.13077538e+02, 6.23098999e+02, 0.00000000e+00, 0.00000000e+00,\n 3.14776245e+02, 0.00000000e+00, 1.86617578e+03, 0.00000000e+00,\n 1.93752098e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.08060645e+03,\n 7.17641373e+01, 2.71909149e+02, 0.00000000e+00, 0.00000000e+00,\n 1.26297081e+02, 2.43099213e+02, 0.00000000e+00, 1.04182243e+02,\n 0.00000000e+00, 8.86627007e+00, 1.01433182e+01, 1.71777191e+02,\n 2.58261089e+01, 0.00000000e+00, 1.71772995e+01, 0.00000000e+00,\n 1.66885719e+01, 2.14263330e+03, 9.92972260e+01, 1.13726555e+02,\n 1.18254761e+02, 6.41304016e+02, 0.00000000e+00, 0.00000000e+00,\n 3.33709412e+02, 0.00000000e+00, 1.84444141e+03, 0.00000000e+00,\n 2.22984715e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.91743555e+03,\n 9.08425980e+01, 3.29091522e+02, 0.00000000e+00, 0.00000000e+00,\n 1.65175644e+02, 2.80522217e+02, 0.00000000e+00, 8.50396729e+01,\n 0.00000000e+00, 1.31482306e+01, 1.24694042e+01, 2.06317688e+02,\n 2.72629642e+01, 0.00000000e+00, 2.09404716e+01, 0.00000000e+00,\n 2.27469902e+01, 2.69479639e+03, 1.15485893e+02, 1.49369141e+02,\n 1.44791077e+02, 8.05139465e+02, 0.00000000e+00, 0.00000000e+00,\n 4.09926392e+02, 0.00000000e+00, 2.40113501e+03, 0.00000000e+00,\n 2.56963291e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.51079639e+03,\n 8.59453888e+01, 3.20304779e+02, 0.00000000e+00, 0.00000000e+00,\n 1.51277084e+02, 2.87100067e+02, 0.00000000e+00, 1.16407700e+02,\n 0.00000000e+00, 1.22210608e+01, 1.19073925e+01, 1.98256607e+02,\n 2.67596836e+01, 0.00000000e+00, 2.23551369e+01, 0.00000000e+00,\n 2.26372986e+01, 2.54659302e+03, 1.17635780e+02, 1.37253143e+02,\n 1.39022430e+02, 7.61243896e+02, 0.00000000e+00, 0.00000000e+00,\n 3.94915009e+02, 0.00000000e+00, 2.20155591e+03, 0.00000000e+00,\n 2.52153587e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.29243359e+03,\n 8.27692566e+01, 2.90457306e+02, 8.29749489e+00, 0.00000000e+00,\n 1.43591965e+02, 2.69650208e+02, 0.00000000e+00, 1.03631958e+02,\n 3.77214789e+00, 0.00000000e+00, 1.36339855e+01, 1.72853134e+02,\n 2.49956779e+01, 0.00000000e+00, 1.68983994e+01, 0.00000000e+00,\n 1.68856850e+01, 2.32195117e+03, 1.11558441e+02, 1.29208084e+02,\n 1.26132507e+02, 7.16342896e+02, 6.45127380e+02, 1.02481766e+01,\n 3.77231232e+02, 0.00000000e+00, 2.03096143e+03, 0.00000000e+00,\n 2.29846077e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.43302759e+03,\n 8.27174377e+01, 2.90829620e+02, 9.07927895e+00, 0.00000000e+00,\n 1.46787628e+02, 2.63586975e+02, 0.00000000e+00, 1.02555428e+02,\n 3.88667274e+00, 0.00000000e+00, 1.33900404e+01, 1.82008682e+02,\n 2.90236435e+01, 0.00000000e+00, 1.90037460e+01, 0.00000000e+00,\n 1.85126648e+01, 2.36929224e+03, 1.09842270e+02, 1.28093918e+02,\n 1.27219498e+02, 7.27711670e+02, 6.46760681e+02, 1.04427395e+01,\n 3.83578552e+02, 0.00000000e+00, 2.09678760e+03, 0.00000000e+00,\n 2.13309669e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.07226929e+03,\n 8.89197006e+01, 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7.66160355e+01, 1.10573151e+02, 0.00000000e+00, 5.28338289e+00,\n 3.47993895e-02, 0.00000000e+00, 7.27468300e+00, 1.14122337e+02,\n 1.07776356e+01, 0.00000000e+00, 5.73432207e+00, 0.00000000e+00,\n 5.65578556e+00, 1.13875806e+03, 4.74549103e+01, 7.07238083e+01,\n 6.02339363e+01, 3.58119476e+02, 2.81470520e+02, 2.07367444e+00,\n 1.75718658e+02, 0.00000000e+00, 1.03782812e+03, 0.00000000e+00,\n 1.25839357e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.32076196e+03,\n 8.59112854e+01, 2.97327759e+02, 8.83266449e+00, 0.00000000e+00,\n 1.49733612e+02, 2.67309631e+02, 0.00000000e+00, 8.29960632e+01,\n 1.66855407e+00, 0.00000000e+00, 1.56265755e+01, 1.93080002e+02,\n 2.67419281e+01, 0.00000000e+00, 1.48211937e+01, 0.00000000e+00,\n 1.39542046e+01, 2.35390625e+03, 1.08113235e+02, 1.36675430e+02,\n 1.28678802e+02, 7.33634033e+02, 6.45127502e+02, 1.00163898e+01,\n 3.79375061e+02, 0.00000000e+00, 2.06410425e+03, 0.00000000e+00,\n 2.29218750e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.87733691e+03,\n 7.70007629e+01, 2.68614288e+02, 7.21884584e+00, 0.00000000e+00,\n 1.32636993e+02, 2.47111099e+02, 0.00000000e+00, 7.89409485e+01,\n 2.03581405e+00, 0.00000000e+00, 1.39600277e+01, 1.67510208e+02,\n 2.29967880e+01, 0.00000000e+00, 1.44179668e+01, 0.00000000e+00,\n 1.40452633e+01, 2.04777344e+03, 9.83093414e+01, 1.19802650e+02,\n 1.15190689e+02, 6.44408203e+02, 5.79999084e+02, 8.73882008e+00,\n 3.34201721e+02, 0.00000000e+00, 1.75996216e+03, 0.00000000e+00,\n 2.14104977e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.10485303e+03,\n 1.22377922e+02, 4.34114624e+02, 1.28857613e+01, 0.00000000e+00,\n 2.07788132e+02, 4.12794464e+02, 0.00000000e+00, 1.62816376e+02,\n 5.86590576e+00, 0.00000000e+00, 2.20709457e+01, 2.43647202e+02,\n 3.79430580e+01, 0.00000000e+00, 2.93614845e+01, 0.00000000e+00,\n 2.56514435e+01, 3.35624487e+03, 1.66828079e+02, 1.87213501e+02,\n 1.86359344e+02, 1.04209509e+03, 9.62607361e+02, 1.84137840e+01,\n 5.51949951e+02, 0.00000000e+00, 2.88272510e+03, 0.00000000e+00,\n 3.48504486e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.22333911e+03,\n 9.12909088e+01, 3.28074158e+02, 9.11614609e+00, 0.00000000e+00,\n 1.53942596e+02, 3.15292053e+02, 0.00000000e+00, 1.27568375e+02,\n 3.81634498e+00, 0.00000000e+00, 1.56347084e+01, 1.78102341e+02,\n 2.83328629e+01, 0.00000000e+00, 2.47850342e+01, 0.00000000e+00,\n 1.91250877e+01, 2.48858350e+03, 1.26087601e+02, 1.37919418e+02,\n 1.39401276e+02, 7.75367554e+02, 7.25923645e+02, 1.61242294e+01,\n 4.11827332e+02, 0.00000000e+00, 2.11781641e+03, 0.00000000e+00,\n 2.70686817e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.37465601e+03,\n 7.27671890e+01, 2.67714294e+02, 6.39950037e+00, 0.00000000e+00,\n 1.31184616e+02, 2.27543442e+02, 0.00000000e+00, 8.21948242e+01,\n 0.00000000e+00, 7.71066666e+00, 9.75318718e+00, 1.67957993e+02,\n 2.00681534e+01, 0.00000000e+00, 1.45003958e+01, 0.00000000e+00,\n 0.00000000e+00, 2.24112305e+03, 9.45228653e+01, 1.16164688e+02,\n 1.16236565e+02, 6.72805054e+02, 5.87704163e+02, 8.69182873e+00,\n 3.40948090e+02, 0.00000000e+00, 1.99683447e+03, 0.00000000e+00,\n 2.08226986e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.08415454e+03,\n 7.77527847e+01, 2.79158569e+02, 6.12254000e+00, 0.00000000e+00,\n 1.30470444e+02, 2.58127411e+02, 0.00000000e+00, 1.08487808e+02,\n 0.00000000e+00, 7.71096182e+00, 9.69105721e+00, 1.61021042e+02,\n 1.92327957e+01, 0.00000000e+00, 1.84704380e+01, 0.00000000e+00,\n 0.00000000e+00, 2.26593213e+03, 1.07801170e+02, 1.21502594e+02,\n 1.20057083e+02, 6.75115784e+02, 6.18909119e+02, 1.01192017e+01,\n 3.49661102e+02, 0.00000000e+00, 1.92369946e+03, 0.00000000e+00,\n 2.24933491e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.06177551e+03,\n 5.42202644e+01, 1.94642868e+02, 2.73588610e+00, 0.00000000e+00,\n 8.37591248e+01, 1.98054214e+02, 0.00000000e+00, 9.87297363e+01,\n 0.00000000e+00, 5.70073891e+00, 3.54327369e+00, 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8.10826397e+00, 0.00000000e+00,\n 1.47551865e+02, 2.79539734e+02, 0.00000000e+00, 1.18880829e+02,\n 0.00000000e+00, 9.51065350e+00, 1.04580021e+01, 1.90937668e+02,\n 2.70114021e+01, 0.00000000e+00, 2.18948364e+01, 0.00000000e+00,\n 0.00000000e+00, 2.51854639e+03, 1.17526291e+02, 1.32460785e+02,\n 1.35189682e+02, 7.54612549e+02, 6.86481812e+02, 1.41733847e+01,\n 3.90261993e+02, 0.00000000e+00, 2.15077344e+03, 0.00000000e+00,\n 2.52910061e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.05511206e+03,\n 9.57758026e+01, 3.22922180e+02, 9.98534775e+00, 0.00000000e+00,\n 1.68401993e+02, 2.72990112e+02, 0.00000000e+00, 7.37377396e+01,\n 0.00000000e+00, 1.50388889e+01, 1.56458292e+01, 1.94563889e+02,\n 2.32677612e+01, 0.00000000e+00, 2.08059311e+01, 0.00000000e+00,\n 2.24871979e+01, 2.73683228e+03, 1.11438385e+02, 1.57163589e+02,\n 1.46040436e+02, 8.18925537e+02, 7.12949707e+02, 0.00000000e+00,\n 4.16177094e+02, 0.00000000e+00, 2.43068774e+03, 0.00000000e+00,\n 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1.70290756e+01, 2.19533249e+02,\n 2.84700394e+01, 0.00000000e+00, 2.20549259e+01, 0.00000000e+00,\n 2.15521793e+01, 2.95550244e+03, 1.33475632e+02, 1.68744522e+02,\n 1.58436249e+02, 8.86959717e+02, 7.90675781e+02, 1.37061501e+01,\n 4.53606323e+02, 0.00000000e+00, 2.58660889e+03, 0.00000000e+00,\n 2.98507004e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.84505066e+02,\n 6.34462051e+01, 2.51685913e+02, 4.06151867e+00, 0.00000000e+00,\n 1.01844917e+02, 2.58430695e+02, 0.00000000e+00, 1.24952362e+02,\n 5.13820410e+00, 1.13496876e+01, 1.06548023e+01, 1.23131462e+02,\n 1.27263546e+01, 0.00000000e+00, 2.12054596e+01, 0.00000000e+00,\n 1.98415813e+01, 0.00000000e+00, 1.01510391e+02, 1.00278152e+02,\n 1.07457588e+02, 5.19931946e+02, 5.29650452e+02, 9.14328575e+00,\n 2.92385101e+02, 0.00000000e+00, 1.23493164e+03, 0.00000000e+00,\n 2.42027016e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.20979565e+03,\n 6.30327759e+01, 2.58866302e+02, 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0.00000000e+00,\n 3.08415127e+01, 3.67167944e+03, 2.02271042e+02, 2.16216034e+02,\n 2.02261505e+02, 1.10204980e+03, 1.05869080e+03, 2.31112862e+01,\n 5.84064453e+02, 0.00000000e+00, 3.02186450e+03, 0.00000000e+00,\n 4.30906219e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.93545740e+03,\n 7.44758224e+01, 2.53861099e+02, 5.35222435e+00, 0.00000000e+00,\n 1.20937340e+02, 2.30397659e+02, 0.00000000e+00, 5.38375854e+01,\n 5.33952188e+00, 1.09809752e+01, 0.00000000e+00, 1.43480621e+02,\n 1.43951921e+01, 0.00000000e+00, 1.65948277e+01, 0.00000000e+00,\n 1.59523668e+01, 1.98435730e+03, 9.58244171e+01, 1.23193443e+02,\n 1.06541054e+02, 6.04354370e+02, 5.31378723e+02, 7.02008295e+00,\n 3.07360291e+02, 0.00000000e+00, 1.71443103e+03, 0.00000000e+00,\n 2.29120789e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.56021436e+03,\n 7.81893845e+01, 2.85560181e+02, 7.17497826e+00, 0.00000000e+00,\n 1.17435051e+02, 2.85039093e+02, 0.00000000e+00, 1.24053574e+02,\n 4.53900957e+00, 1.02481213e+01, 0.00000000e+00, 1.40037659e+02,\n 1.63813820e+01, 0.00000000e+00, 2.04217739e+01, 0.00000000e+00,\n 1.85364170e+01, 2.08510034e+03, 1.18492531e+02, 1.21509239e+02,\n 1.18374237e+02, 6.27459351e+02, 6.16165222e+02, 1.35152969e+01,\n 3.35713440e+02, 0.00000000e+00, 1.68012292e+03, 0.00000000e+00,\n 2.49453430e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.58875940e+03,\n 7.63474579e+01, 2.76809204e+02, 7.25089073e+00, 0.00000000e+00,\n 1.16159805e+02, 2.73652802e+02, 0.00000000e+00, 1.12744453e+02,\n 3.65922022e+00, 9.22698402e+00, 0.00000000e+00, 1.39552292e+02,\n 1.70684681e+01, 0.00000000e+00, 1.93457813e+01, 0.00000000e+00,\n 1.66588917e+01, 2.04180969e+03, 1.13715645e+02, 1.19932777e+02,\n 1.15368065e+02, 6.15346924e+02, 5.96192688e+02, 1.29528427e+01,\n 3.27124237e+02, 0.00000000e+00, 1.66031848e+03, 0.00000000e+00,\n 2.48800430e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.54130969e+03,\n 7.06748352e+01, 2.56577637e+02, 6.81179237e+00, 0.00000000e+00,\n 1.09809875e+02, 2.49075974e+02, 0.00000000e+00, 9.88242493e+01,\n 2.95794153e+00, 8.61510468e+00, 0.00000000e+00, 1.33913956e+02,\n 1.72238331e+01, 0.00000000e+00, 1.74971600e+01, 0.00000000e+00,\n 1.45744638e+01, 1.90271545e+03, 1.02825233e+02, 1.12005173e+02,\n 1.07228310e+02, 5.75558105e+02, 5.48503662e+02, 1.09349594e+01,\n 3.03910034e+02, 0.00000000e+00, 1.56333484e+03, 0.00000000e+00,\n 2.32510777e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.35475903e+03,\n 1.15777733e+02, 4.02356445e+02, 1.11676617e+01, 0.00000000e+00,\n 1.93739349e+02, 3.62772430e+02, 0.00000000e+00, 1.27631226e+02,\n 5.92384958e+00, 1.23627787e+01, 0.00000000e+00, 2.21919205e+02,\n 2.27749462e+01, 0.00000000e+00, 3.01947193e+01, 0.00000000e+00,\n 2.70584545e+01, 3.23637915e+03, 1.51284286e+02, 1.80121521e+02,\n 1.71142761e+02, 9.81716675e+02, 8.72937622e+02, 1.97477112e+01,\n 5.11952209e+02, 0.00000000e+00, 2.84115747e+03, 0.00000000e+00,\n 3.32388649e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.51228296e+03,\n 7.24911346e+01, 2.59423248e+02, 0.00000000e+00, 2.04584479e+00,\n 1.27855942e+02, 2.20120773e+02, 0.00000000e+00, 8.70737991e+01,\n 0.00000000e+00, 6.75321484e+00, 9.32890129e+00, 1.51311676e+02,\n 1.81433945e+01, 0.00000000e+00, 1.58548651e+01, 0.00000000e+00,\n 1.77495270e+01, 2.23419043e+03, 8.76951447e+01, 1.16193695e+02,\n 1.10639297e+02, 6.72079529e+02, 5.68210022e+02, 9.66612244e+00,\n 3.41954651e+02, 0.00000000e+00, 2.02275684e+03, 0.00000000e+00,\n 2.01304417e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.53784106e+03,\n 7.84104614e+01, 2.82568390e+02, 0.00000000e+00, 2.66811299e+00,\n 1.36926117e+02, 2.45417831e+02, 0.00000000e+00, 1.02347954e+02,\n 0.00000000e+00, 6.52995777e+00, 1.10379696e+01, 1.61040726e+02,\n 2.30273075e+01, 0.00000000e+00, 1.92276993e+01, 0.00000000e+00,\n 1.90171661e+01, 2.37979004e+03, 9.85503464e+01, 1.22775604e+02,\n 1.20131012e+02, 7.16229065e+02, 6.20778625e+02, 1.26445198e+01,\n 3.69274078e+02, 0.00000000e+00, 2.12008105e+03, 0.00000000e+00,\n 2.27686272e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.06019263e+03,\n 8.44372177e+01, 3.05352722e+02, 0.00000000e+00, 2.48293805e+00,\n 1.35022049e+02, 3.00180878e+02, 0.00000000e+00, 1.28416168e+02,\n 0.00000000e+00, 6.77029896e+00, 1.01173954e+01, 1.58970779e+02,\n 1.96874790e+01, 0.00000000e+00, 2.02181664e+01, 0.00000000e+00,\n 1.66219311e+01, 2.42022900e+03, 1.24533585e+02, 1.32799591e+02,\n 1.29200134e+02, 7.22424377e+02, 6.82337219e+02, 1.69826069e+01,\n 3.80440308e+02, 0.00000000e+00, 2.01558911e+03, 0.00000000e+00,\n 2.88464336e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.35702197e+03,\n 7.58374252e+01, 2.65006348e+02, 0.00000000e+00, 3.89436412e+00,\n 1.30179993e+02, 2.28722549e+02, 0.00000000e+00, 7.82027206e+01,\n 0.00000000e+00, 4.35446262e+00, 1.32969303e+01, 1.57521103e+02,\n 1.88505116e+01, 0.00000000e+00, 1.65138893e+01, 0.00000000e+00,\n 1.53126049e+01, 2.21808203e+03, 9.43009338e+01, 1.21744881e+02,\n 1.13181488e+02, 6.68352722e+02, 5.73476990e+02, 7.75262642e+00,\n 3.39748230e+02, 0.00000000e+00, 1.97098767e+03, 0.00000000e+00,\n 2.22841015e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.52122742e+02,\n 6.32968521e+00, 1.64786663e+01, 0.00000000e+00, 0.00000000e+00,\n 1.61562939e+01, 4.33047116e-01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 5.67128062e-01, 0.00000000e+00, 2.22397366e+01,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 3.63802838e+00, 2.85873291e+02, 4.67240667e+00, 1.75910969e+01,\n 1.24567261e+01, 8.41173630e+01, 2.30489254e+01, 0.00000000e+00,\n 3.18509502e+01, 0.00000000e+00, 3.62054626e+02, 0.00000000e+00,\n 2.89088631e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.09867065e+03,\n 5.63965263e+01, 1.99065903e+02, 0.00000000e+00, 1.81225705e+00,\n 1.04116890e+02, 1.57014542e+02, 0.00000000e+00, 4.44207954e+01,\n 0.00000000e+00, 5.28291607e+00, 7.76407051e+00, 1.24710976e+02,\n 1.40261440e+01, 0.00000000e+00, 1.07185373e+01, 0.00000000e+00,\n 1.43609905e+01, 1.74020752e+03, 6.39472313e+01, 9.40043945e+01,\n 8.69101028e+01, 5.25032043e+02, 4.21657257e+02, 3.33197021e+00,\n 2.60912354e+02, 0.00000000e+00, 1.60975684e+03, 0.00000000e+00,\n 1.29575663e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.21287354e+03,\n 5.73014030e+01, 2.04008072e+02, 0.00000000e+00, 1.17134452e+00,\n 1.06722092e+02, 1.59419754e+02, 0.00000000e+00, 4.67564316e+01,\n 0.00000000e+00, 5.41907406e+00, 6.81205177e+00, 1.27754829e+02,\n 1.37778530e+01, 0.00000000e+00, 1.19066343e+01, 0.00000000e+00,\n 1.55310946e+01, 1.80067749e+03, 6.46480408e+01, 9.56649246e+01,\n 8.85966949e+01, 5.43148071e+02, 4.32688721e+02, 3.49469566e+00,\n 2.69569946e+02, 0.00000000e+00, 1.67638416e+03, 0.00000000e+00,\n 1.31547804e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.86363452e+03,\n 8.47428207e+01, 3.02136414e+02, 0.00000000e+00, 3.28741956e+00,\n 1.49028030e+02, 2.54770370e+02, 0.00000000e+00, 9.10785904e+01,\n 0.00000000e+00, 1.08466272e+01, 1.17880802e+01, 1.79500671e+02,\n 1.96769276e+01, 0.00000000e+00, 1.93780537e+01, 0.00000000e+00,\n 2.05226460e+01, 2.57448926e+03, 1.02525101e+02, 1.37760147e+02,\n 1.28989792e+02, 7.75283875e+02, 6.54571167e+02, 1.13142586e+01,\n 3.92877075e+02, 0.00000000e+00, 2.32238892e+03, 0.00000000e+00,\n 2.45940514e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00760461e+03,\n 6.18430824e+01, 2.26215546e+02, 0.00000000e+00, 2.50889850e+00,\n 1.07686844e+02, 1.93906082e+02, 0.00000000e+00, 8.85061417e+01,\n 0.00000000e+00, 4.64988804e+00, 9.49786949e+00, 1.35335876e+02,\n 1.73368931e+01, 0.00000000e+00, 1.70492039e+01, 0.00000000e+00,\n 1.54713678e+01, 1.87519507e+03, 7.90447235e+01, 9.56556015e+01,\n 9.56226807e+01, 5.65655823e+02, 4.90465302e+02, 1.06932774e+01,\n 2.92537567e+02, 0.00000000e+00, 1.67430664e+03, 0.00000000e+00,\n 1.78488560e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.14618872e+03,\n 9.42503738e+01, 3.46265411e+02, 0.00000000e+00, 3.22507405e+00,\n 1.51006744e+02, 3.42005463e+02, 0.00000000e+00, 1.54114624e+02,\n 0.00000000e+00, 9.68152332e+00, 1.15133343e+01, 1.84088120e+02,\n 2.59510117e+01, 0.00000000e+00, 2.41858559e+01, 0.00000000e+00,\n 1.80110989e+01, 2.67072070e+03, 1.42070541e+02, 1.46535919e+02,\n 1.46610413e+02, 7.98578003e+02, 7.66366272e+02, 1.91975956e+01,\n 4.23424805e+02, 0.00000000e+00, 2.19163696e+03, 0.00000000e+00,\n 3.35769615e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.35433057e+03,\n 7.88873138e+01, 2.83751404e+02, 0.00000000e+00, 4.55237818e+00,\n 1.36141632e+02, 2.47377563e+02, 0.00000000e+00, 1.00838417e+02,\n 0.00000000e+00, 6.67960072e+00, 1.33011990e+01, 1.72849503e+02,\n 2.35013065e+01, 0.00000000e+00, 1.85085678e+01, 0.00000000e+00,\n 1.52891712e+01, 2.31123535e+03, 1.01781715e+02, 1.24519424e+02,\n 1.21136765e+02, 6.98037109e+02, 6.11638733e+02, 9.34632874e+00,\n 3.59161469e+02, 0.00000000e+00, 2.03044775e+03, 0.00000000e+00,\n 2.48662663e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.93597168e+02,\n 2.30375519e+01, 8.08233566e+01, 0.00000000e+00, 1.15727329e+00,\n 4.31659851e+01, 5.68826408e+01, 0.00000000e+00, 1.15512190e+01,\n 0.00000000e+00, 5.49891281e+00, 2.40077853e+00, 6.39354630e+01,\n 3.58198738e+00, 0.00000000e+00, 4.31645155e+00, 0.00000000e+00,\n 6.40732861e+00, 7.14155457e+02, 2.57879887e+01, 4.20027390e+01,\n 3.75401001e+01, 2.17751556e+02, 1.58043411e+02, 0.00000000e+00,\n 1.03922127e+02, 0.00000000e+00, 6.96353149e+02, 0.00000000e+00,\n 8.56982613e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.39747778e+03,\n 7.40282822e+01, 2.60336151e+02, 0.00000000e+00, 4.13998175e+00,\n 1.33125610e+02, 2.13466400e+02, 0.00000000e+00, 5.54307289e+01,\n 0.00000000e+00, 8.78941250e+00, 1.28678074e+01, 1.62266937e+02,\n 2.15828495e+01, 0.00000000e+00, 1.49348679e+01, 0.00000000e+00,\n 1.71026249e+01, 2.17644287e+03, 8.81053696e+01, 1.22212982e+02,\n 1.13351471e+02, 6.58015442e+02, 5.48551331e+02, 6.96658707e+00,\n 3.30448151e+02, 0.00000000e+00, 1.95590527e+03, 0.00000000e+00,\n 2.00809994e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.60040259e+03,\n 8.29363480e+01, 2.94789581e+02, 0.00000000e+00, 4.46519899e+00,\n 1.46412476e+02, 2.49399155e+02, 0.00000000e+00, 8.21483994e+01,\n 0.00000000e+00, 7.49324894e+00, 1.46760006e+01, 1.78918381e+02,\n 2.48838253e+01, 0.00000000e+00, 1.83715324e+01, 0.00000000e+00,\n 1.90574169e+01, 2.44309058e+03, 1.02246796e+02, 1.33454636e+02,\n 1.26739029e+02, 7.37723206e+02, 6.30440735e+02, 1.03796501e+01,\n 3.75548157e+02, 0.00000000e+00, 2.17373560e+03, 0.00000000e+00,\n 2.30608463e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.19242920e+03,\n 9.69554901e+01, 3.46863647e+02, 0.00000000e+00, 4.69595718e+00,\n 1.70778152e+02, 2.90150452e+02, 0.00000000e+00, 1.01955795e+02,\n 0.00000000e+00, 1.37826033e+01, 1.47327366e+01, 2.11289886e+02,\n 2.45571041e+01, 0.00000000e+00, 2.41797066e+01, 0.00000000e+00,\n 2.28797626e+01, 2.91109692e+03, 1.17742790e+02, 1.56590286e+02,\n 1.48323715e+02, 8.78444763e+02, 7.43748108e+02, 1.35638962e+01,\n 4.45643127e+02, 0.00000000e+00, 2.61557593e+03, 0.00000000e+00,\n 2.88236103e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.21550146e+03,\n 7.36232376e+01, 2.66418976e+02, 0.00000000e+00, 3.96926808e+00,\n 1.27359665e+02, 2.31966690e+02, 0.00000000e+00, 9.43984375e+01,\n 0.00000000e+00, 6.94701290e+00, 1.24458027e+01, 1.59261597e+02,\n 2.28295708e+01, 0.00000000e+00, 2.02861156e+01, 0.00000000e+00,\n 1.73783474e+01, 2.16955005e+03, 9.51755600e+01, 1.14702278e+02,\n 1.13532104e+02, 6.55017273e+02, 5.74806091e+02, 1.30924435e+01,\n 3.38843628e+02, 0.00000000e+00, 1.90882593e+03, 0.00000000e+00,\n 2.22759972e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.34841138e+03,\n 7.56351318e+01, 2.61206055e+02, 0.00000000e+00, 0.00000000e+00,\n 1.28371704e+02, 2.36574326e+02, 0.00000000e+00, 8.70709763e+01,\n 0.00000000e+00, 0.00000000e+00, 1.41099138e+01, 1.51167847e+02,\n 1.76565170e+01, 0.00000000e+00, 1.99741726e+01, 0.00000000e+00,\n 2.12440643e+01, 2.23693823e+03, 1.01361435e+02, 1.20738983e+02,\n 1.16204109e+02, 6.63736450e+02, 5.81749268e+02, 1.39008608e+01,\n 3.36072083e+02, 0.00000000e+00, 2.00909167e+03, 0.00000000e+00,\n 2.41035595e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00493750e+03,\n 6.68011322e+01, 2.27720413e+02, 0.00000000e+00, 0.00000000e+00,\n 1.19791405e+02, 1.99891632e+02, 0.00000000e+00, 4.03729630e+01,\n 0.00000000e+00, 0.00000000e+00, 1.31903563e+01, 1.39985184e+02,\n 1.77289925e+01, 0.00000000e+00, 1.51942501e+01, 0.00000000e+00,\n 1.74774933e+01, 2.02925244e+03, 8.79833374e+01, 1.13119537e+02,\n 1.02595604e+02, 5.95742981e+02, 5.12480835e+02, 9.55310535e+00,\n 2.88369202e+02, 0.00000000e+00, 1.78034106e+03, 0.00000000e+00,\n 2.15295448e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.74751892e+03,\n 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9.53469772e+01,\n 4.69994640e+00, 1.09768505e+01, 1.62532310e+01, 1.93848633e+02,\n 2.68729362e+01, 0.00000000e+00, 1.91774445e+01, 0.00000000e+00,\n 1.95964546e+01, 2.58859644e+03, 1.22924522e+02, 1.48328094e+02,\n 1.43853912e+02, 7.82185547e+02, 6.94510925e+02, 0.00000000e+00,\n 4.02572632e+02, 0.00000000e+00, 2.23237744e+03, 0.00000000e+00,\n 2.77799072e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.07094189e+03,\n 1.02937035e+02, 3.59934631e+02, 9.43717575e+00, 0.00000000e+00,\n 1.80844666e+02, 3.02850342e+02, 0.00000000e+00, 1.00079117e+02,\n 4.70380974e+00, 1.48222532e+01, 1.58622065e+01, 2.15937988e+02,\n 2.66497555e+01, 0.00000000e+00, 2.13476715e+01, 0.00000000e+00,\n 2.05153866e+01, 2.92361133e+03, 1.26865135e+02, 1.63788147e+02,\n 1.59028046e+02, 8.84779663e+02, 7.67561157e+02, 0.00000000e+00,\n 4.48771759e+02, 0.00000000e+00, 2.58195093e+03, 0.00000000e+00,\n 3.04827614e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00542480e+03,\n 6.10097084e+01, 2.12909180e+02, 4.84907436e+00, 0.00000000e+00,\n 1.09234261e+02, 1.71049271e+02, 0.00000000e+00, 5.29467812e+01,\n 1.67761874e+00, 7.29392147e+00, 7.07692003e+00, 1.39182693e+02,\n 1.59897995e+01, 0.00000000e+00, 8.03940296e+00, 0.00000000e+00,\n 1.12536039e+01, 1.77363403e+03, 7.17278824e+01, 9.80964813e+01,\n 9.61938095e+01, 5.38997864e+02, 4.51175232e+02, 0.00000000e+00,\n 2.67898834e+02, 0.00000000e+00, 1.60422302e+03, 0.00000000e+00,\n 1.82740459e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.01083484e+03,\n 7.50582962e+01, 2.65191772e+02, 5.35554695e+00, 0.00000000e+00,\n 1.23148399e+02, 2.48901520e+02, 0.00000000e+00, 9.52804260e+01,\n 7.20574522e+00, 1.25560331e+01, 9.50587559e+00, 1.39303787e+02,\n 1.24180450e+01, 0.00000000e+00, 1.32551937e+01, 0.00000000e+00,\n 1.70289879e+01, 2.15434058e+03, 1.05228722e+02, 1.23329384e+02,\n 1.17255554e+02, 6.45765564e+02, 5.83430420e+02, 1.09386330e+01,\n 3.30073059e+02, 0.00000000e+00, 1.85906042e+03, 0.00000000e+00,\n 2.30916519e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.07812817e+03,\n 7.97827988e+01, 2.83732788e+02, 5.91927719e+00, 0.00000000e+00,\n 1.31854553e+02, 2.67277222e+02, 0.00000000e+00, 1.02307159e+02,\n 7.30843019e+00, 1.30003557e+01, 1.05697336e+01, 1.49367950e+02,\n 1.52271585e+01, 0.00000000e+00, 1.50675344e+01, 0.00000000e+00,\n 1.74395161e+01, 2.28846069e+03, 1.13191048e+02, 1.30267700e+02,\n 1.25486549e+02, 6.85042175e+02, 6.23612549e+02, 1.15822906e+01,\n 3.51850067e+02, 0.00000000e+00, 1.95857385e+03, 0.00000000e+00,\n 2.48339691e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.46989587e+03,\n 5.79206734e+01, 2.11071808e+02, 2.00162292e+00, 0.00000000e+00,\n 9.28516541e+01, 2.12103958e+02, 0.00000000e+00, 8.84234238e+01,\n 5.80999422e+00, 1.19603720e+01, 4.02075672e+00, 9.71757278e+01,\n 8.21184444e+00, 0.00000000e+00, 8.93264771e+00, 0.00000000e+00,\n 1.00959034e+01, 1.72435193e+03, 8.83918533e+01, 9.75264435e+01,\n 9.18186188e+01, 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0.00000000e+00,\n 1.14514801e+02, 2.33505402e+02, 0.00000000e+00, 9.21322784e+01,\n 6.37560892e+00, 1.16400919e+01, 8.98393917e+00, 1.34315155e+02,\n 1.36998663e+01, 0.00000000e+00, 1.68906269e+01, 0.00000000e+00,\n 1.60747356e+01, 1.98650061e+03, 9.97972794e+01, 1.11320465e+02,\n 1.08331612e+02, 5.95051636e+02, 5.43249756e+02, 1.19701042e+01,\n 3.06564789e+02, 0.00000000e+00, 1.69425574e+03, 0.00000000e+00,\n 2.13899975e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.02470667e+03,\n 8.28871307e+01, 2.99566833e+02, 3.80038619e+00, 0.00000000e+00,\n 1.33936462e+02, 2.94930542e+02, 0.00000000e+00, 1.21483795e+02,\n 6.04196930e+00, 1.40370064e+01, 6.62441158e+00, 1.46245956e+02,\n 1.53281403e+01, 0.00000000e+00, 1.73803577e+01, 0.00000000e+00,\n 1.44664164e+01, 2.40140015e+03, 1.23797050e+02, 1.34299957e+02,\n 1.29404282e+02, 7.13233826e+02, 6.66167053e+02, 1.47346954e+01,\n 3.70334625e+02, 0.00000000e+00, 2.00689880e+03, 0.00000000e+00,\n 2.94743919e+01],\n [0.00000000e+00, 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8.09679565e+01, 0.00000000e+00, 6.03552979e+02, 0.00000000e+00,\n 8.07512760e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.32756714e+03,\n 8.14012146e+01, 2.81051208e+02, 7.10063744e+00, 0.00000000e+00,\n 1.40795944e+02, 2.46123871e+02, 0.00000000e+00, 7.25640793e+01,\n 4.99580717e+00, 1.13304644e+01, 1.34431696e+01, 1.67254715e+02,\n 2.01741428e+01, 0.00000000e+00, 1.44929495e+01, 0.00000000e+00,\n 1.69544582e+01, 2.28155444e+03, 1.05451653e+02, 1.34611908e+02,\n 1.28484406e+02, 6.93643616e+02, 5.97972717e+02, 8.44365883e+00,\n 3.48253571e+02, 0.00000000e+00, 2.02984973e+03, 0.00000000e+00,\n 2.20595055e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.26646094e+03,\n 8.45215378e+01, 2.98705078e+02, 7.35837364e+00, 0.00000000e+00,\n 1.44703171e+02, 2.72436890e+02, 0.00000000e+00, 8.82068329e+01,\n 6.99096298e+00, 1.37840681e+01, 1.27173643e+01, 1.70707932e+02,\n 2.01109238e+01, 0.00000000e+00, 1.60005665e+01, 0.00000000e+00,\n 1.90870228e+01, 2.41188794e+03, 1.17418694e+02, 1.40206467e+02,\n 1.34349075e+02, 7.26445068e+02, 6.47288940e+02, 1.18605118e+01,\n 3.69104645e+02, 0.00000000e+00, 2.08847949e+03, 0.00000000e+00,\n 2.46083755e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.62810889e+03,\n 9.86098251e+01, 3.47946075e+02, 8.35140800e+00, 0.00000000e+00,\n 1.65917572e+02, 3.16711426e+02, 0.00000000e+00, 1.11620697e+02,\n 6.67215824e+00, 1.50558233e+01, 1.31002741e+01, 1.95524994e+02,\n 2.28516998e+01, 0.00000000e+00, 2.19499836e+01, 0.00000000e+00,\n 2.10617085e+01, 2.78861157e+03, 1.35803696e+02, 1.58873138e+02,\n 1.53401245e+02, 8.39511658e+02, 7.49885010e+02, 1.42904606e+01,\n 4.28605042e+02, 0.00000000e+00, 2.41557983e+03, 0.00000000e+00,\n 2.97630920e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.12736108e+03,\n 8.33883057e+01, 2.96770874e+02, 7.18008614e+00, 0.00000000e+00,\n 1.38317413e+02, 2.73131317e+02, 0.00000000e+00, 1.02167847e+02,\n 6.51415157e+00, 1.33599157e+01, 1.11689253e+01, 1.64986679e+02,\n 1.88205128e+01, 0.00000000e+00, 2.13036690e+01, 0.00000000e+00,\n 1.87383823e+01, 2.34985913e+03, 1.17656029e+02, 1.31944489e+02,\n 1.28924606e+02, 7.05535583e+02, 6.39399048e+02, 1.39760151e+01,\n 3.62716095e+02, 0.00000000e+00, 2.01081848e+03, 0.00000000e+00,\n 2.52152977e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.98301770e+03,\n 7.76274872e+01, 2.62700806e+02, 5.47294950e+00, 0.00000000e+00,\n 1.28486542e+02, 2.42168579e+02, 0.00000000e+00, 8.37041321e+01,\n 0.00000000e+00, 9.10253048e+00, 1.34653597e+01, 1.54229385e+02,\n 1.62217216e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.82917080e+01, 2.15750659e+03, 1.05291893e+02, 1.23508133e+02,\n 1.15966042e+02, 6.42349731e+02, 5.77153137e+02, 1.08993549e+01,\n 3.29468414e+02, 0.00000000e+00, 1.85033337e+03, 0.00000000e+00,\n 2.23657570e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.07088232e+03,\n 7.68689423e+01, 2.55488586e+02, 5.61541891e+00, 0.00000000e+00,\n 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2.08953659e+02,\n 2.68038292e+01, 0.00000000e+00, 1.48643579e+01, 0.00000000e+00,\n 2.05773640e+01, 2.64939062e+03, 1.11275879e+02, 1.49680679e+02,\n 1.42109787e+02, 7.95338440e+02, 6.78152100e+02, 9.82942867e+00,\n 3.99524811e+02, 0.00000000e+00, 2.37292554e+03, 0.00000000e+00,\n 2.58430519e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.68474731e+03,\n 9.08360596e+01, 3.23686127e+02, 1.22604790e+01, 0.00000000e+00,\n 1.61793777e+02, 2.85881317e+02, 0.00000000e+00, 1.09119736e+02,\n 0.00000000e+00, 7.79154634e+00, 1.57685452e+01, 2.17203491e+02,\n 3.05272102e+01, 0.00000000e+00, 1.81678047e+01, 0.00000000e+00,\n 2.14235630e+01, 2.69625293e+03, 1.20454559e+02, 1.46292374e+02,\n 1.47065170e+02, 8.11811951e+02, 7.19145569e+02, 1.26980438e+01,\n 4.09727142e+02, 0.00000000e+00, 2.36365649e+03, 0.00000000e+00,\n 2.81309376e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.14938354e+03,\n 1.08161224e+02, 3.72887909e+02, 1.27218781e+01, 0.00000000e+00,\n 1.85984085e+02, 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0.00000000e+00, 0.00000000e+00, 2.27434961e+03,\n 8.62162476e+01, 2.95280518e+02, 0.00000000e+00, 0.00000000e+00,\n 1.44193069e+02, 2.69695465e+02, 2.94042873e+00, 0.00000000e+00,\n 6.12409115e+00, 6.22974396e+00, 1.26800756e+01, 1.69885742e+02,\n 1.77782326e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.89488697e+01, 2.45442261e+03, 1.16141792e+02, 1.39207001e+02,\n 1.30023758e+02, 7.34930786e+02, 6.46995239e+02, 1.20997467e+01,\n 3.74970306e+02, 0.00000000e+00, 2.13944141e+03, 0.00000000e+00,\n 2.44826927e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.86375854e+03,\n 6.58217239e+01, 2.17068634e+02, 0.00000000e+00, 0.00000000e+00,\n 1.10059669e+02, 1.90395996e+02, 2.01112652e+00, 0.00000000e+00,\n 5.42234707e+00, 7.07389641e+00, 8.98185635e+00, 1.30468277e+02,\n 1.07962732e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.66634140e+01, 1.85112354e+03, 8.45160828e+01, 1.08972031e+02,\n 9.70200195e+01, 5.52328186e+02, 4.70340240e+02, 7.83288288e+00,\n 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0.00000000e+00,\n 3.05221252e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.16893311e+03,\n 4.47246513e+01, 1.46393509e+02, 0.00000000e+00, 0.00000000e+00,\n 7.35012054e+01, 1.28728516e+02, 1.26850045e+00, 0.00000000e+00,\n 2.77324605e+00, 6.14838219e+00, 6.57743359e+00, 8.72362747e+01,\n 8.36293697e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 7.61253500e+00, 1.21322229e+03, 5.62856064e+01, 7.28579330e+01,\n 6.50630951e+01, 3.64444702e+02, 3.12457886e+02, 2.17849088e+00,\n 1.82221985e+02, 0.00000000e+00, 1.07386987e+03, 0.00000000e+00,\n 1.47312088e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.23473535e+03,\n 8.30548477e+01, 2.82131439e+02, 0.00000000e+00, 0.00000000e+00,\n 1.40948715e+02, 2.51828369e+02, 3.27228832e+00, 0.00000000e+00,\n 4.64610195e+00, 5.36566353e+00, 1.47537651e+01, 1.75605927e+02,\n 2.20984707e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.69250488e+01, 2.35157764e+03, 1.06602303e+02, 1.31159592e+02,\n 1.27196854e+02, 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0.00000000e+00,\n 2.38173523e+01, 3.23706787e+03, 1.50871597e+02, 1.78541061e+02,\n 1.75570435e+02, 9.73053345e+02, 8.60452881e+02, 1.59622307e+01,\n 4.97850189e+02, 0.00000000e+00, 2.80645898e+03, 0.00000000e+00,\n 3.18897133e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.40283203e+03,\n 8.94016647e+01, 3.13027161e+02, 0.00000000e+00, 0.00000000e+00,\n 1.52330017e+02, 2.83118439e+02, 3.75409985e+00, 0.00000000e+00,\n 6.17441463e+00, 7.53475952e+00, 1.44420605e+01, 1.96611374e+02,\n 2.63240986e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 2.01430454e+01, 2.57831079e+03, 1.18529503e+02, 1.39303452e+02,\n 1.41041016e+02, 7.76575806e+02, 6.82204529e+02, 1.47122107e+01,\n 3.96322052e+02, 0.00000000e+00, 2.25149707e+03, 0.00000000e+00,\n 2.42369995e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.60983545e+03,\n 9.23693390e+01, 3.24035400e+02, 1.09238682e+01, 0.00000000e+00,\n 1.58209290e+02, 2.92989685e+02, 0.00000000e+00, 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0.00000000e+00, 1.00113928e+03, 0.00000000e+00,\n 1.35707092e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.79488049e+03,\n 6.13302498e+01, 2.28476547e+02, 8.43961430e+00, 0.00000000e+00,\n 1.08181931e+02, 2.05748169e+02, 0.00000000e+00, 8.36096420e+01,\n 0.00000000e+00, 1.08723679e+01, 6.90680313e+00, 1.40929077e+02,\n 1.92028637e+01, 0.00000000e+00, 1.20086126e+01, 0.00000000e+00,\n 1.50832090e+01, 1.81547144e+03, 8.26425705e+01, 1.02257690e+02,\n 1.00059402e+02, 5.47173706e+02, 4.88005554e+02, 8.12105083e+00,\n 2.85785522e+02, 0.00000000e+00, 1.55562122e+03, 0.00000000e+00,\n 1.94072151e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.92304651e+03,\n 7.07527542e+01, 2.60952026e+02, 8.99030972e+00, 0.00000000e+00,\n 1.22761513e+02, 2.37797226e+02, 0.00000000e+00, 8.65591354e+01,\n 0.00000000e+00, 1.13969984e+01, 7.48016357e+00, 1.56051163e+02,\n 2.31595097e+01, 0.00000000e+00, 1.50321817e+01, 0.00000000e+00,\n 1.60959072e+01, 2.05667212e+03, 9.68818054e+01, 1.15277802e+02,\n 1.13192856e+02, 6.17989624e+02, 5.56443237e+02, 9.77114773e+00,\n 3.22453308e+02, 0.00000000e+00, 1.72614868e+03, 0.00000000e+00,\n 2.23375874e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.83084192e+03,\n 6.29302483e+01, 2.29642456e+02, 7.54458618e+00, 0.00000000e+00,\n 1.11215340e+02, 2.00331161e+02, 0.00000000e+00, 6.75122910e+01,\n 0.00000000e+00, 1.11030197e+01, 6.23740005e+00, 1.43465210e+02,\n 2.13025703e+01, 0.00000000e+00, 1.18825541e+01, 0.00000000e+00,\n 1.34578352e+01, 1.82235742e+03, 8.28504181e+01, 1.04935730e+02,\n 1.00722351e+02, 5.52097656e+02, 4.80820801e+02, 6.89208174e+00,\n 2.84838135e+02, 0.00000000e+00, 1.57276392e+03, 0.00000000e+00,\n 2.07178917e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.86029932e+03,\n 9.75529175e+01, 3.31921661e+02, 1.10321398e+01, 0.00000000e+00,\n 1.64651489e+02, 2.89379181e+02, 0.00000000e+00, 8.54099655e+01,\n 0.00000000e+00, 1.44330349e+01, 1.14662437e+01, 2.03571381e+02,\n 2.48743076e+01, 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0.00000000e+00, 0.00000000e+00,\n 1.07341667e+02, 2.10622620e+02, 0.00000000e+00, 9.52631989e+01,\n 0.00000000e+00, 0.00000000e+00, 1.11495686e+01, 1.29709763e+02,\n 1.31601219e+01, 0.00000000e+00, 1.45215826e+01, 0.00000000e+00,\n 1.78462467e+01, 1.85412512e+03, 8.52160797e+01, 1.01794884e+02,\n 9.97246704e+01, 5.46439392e+02, 4.97175568e+02, 9.52791023e+00,\n 2.82400757e+02, 0.00000000e+00, 1.61020679e+03, 0.00000000e+00,\n 1.95162506e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.91790381e+03,\n 5.70951729e+01, 2.02338760e+02, 0.00000000e+00, 0.00000000e+00,\n 1.04914200e+02, 1.77308044e+02, 0.00000000e+00, 5.88217430e+01,\n 0.00000000e+00, 0.00000000e+00, 1.01090240e+01, 1.20651207e+02,\n 1.17430964e+01, 0.00000000e+00, 1.20521364e+01, 0.00000000e+00,\n 1.69492245e+01, 1.71870105e+03, 7.18883896e+01, 9.74662323e+01,\n 9.30844727e+01, 5.13122925e+02, 4.41665436e+02, 6.97871637e+00,\n 2.59927551e+02, 0.00000000e+00, 1.55929773e+03, 0.00000000e+00,\n 1.69021721e+01],\n 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1.12247505e+01,\n 3.85338165e+02, 0.00000000e+00, 2.34985132e+03, 0.00000000e+00,\n 2.53051033e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.85163586e+03,\n 7.39183121e+01, 2.47864365e+02, 4.70827198e+00, 0.00000000e+00,\n 1.17547668e+02, 2.33335693e+02, 0.00000000e+00, 8.28840485e+01,\n 0.00000000e+00, 1.20133905e+01, 1.10750437e+01, 1.35465408e+02,\n 6.89489508e+00, 0.00000000e+00, 1.43524303e+01, 0.00000000e+00,\n 1.92450047e+01, 1.98995264e+03, 9.90233765e+01, 0.00000000e+00,\n 1.05053841e+02, 6.02531860e+02, 5.43811401e+02, 9.39707851e+00,\n 3.11555054e+02, 0.00000000e+00, 1.70756421e+03, 0.00000000e+00,\n 2.07803364e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.28369482e+03,\n 7.34902649e+01, 2.55031876e+02, 5.13826895e+00, 0.00000000e+00,\n 1.07241608e+02, 2.51204315e+02, 0.00000000e+00, 1.21206520e+02,\n 0.00000000e+00, 1.14257927e+01, 1.13047333e+01, 1.16807907e+02,\n 5.24742270e+00, 0.00000000e+00, 1.65271225e+01, 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0.00000000e+00, 0.00000000e+00, 2.50238965e+03,\n 8.95353165e+01, 2.93093781e+02, 6.31301212e+00, 0.00000000e+00,\n 1.46509048e+02, 2.63858582e+02, 0.00000000e+00, 7.47874832e+01,\n 0.00000000e+00, 1.40300913e+01, 1.44710932e+01, 1.76341202e+02,\n 1.11219196e+01, 0.00000000e+00, 1.75423946e+01, 0.00000000e+00,\n 2.10519333e+01, 2.40474438e+03, 1.10494293e+02, 0.00000000e+00,\n 1.26964867e+02, 7.32375732e+02, 6.35535828e+02, 1.14457884e+01,\n 3.70788757e+02, 0.00000000e+00, 2.13456348e+03, 0.00000000e+00,\n 2.49930954e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00829614e+03,\n 8.71751328e+01, 3.09351929e+02, 7.38716888e+00, 0.00000000e+00,\n 1.37737579e+02, 2.91922180e+02, 0.00000000e+00, 1.36529617e+02,\n 0.00000000e+00, 1.17876892e+01, 1.20596094e+01, 1.67012497e+02,\n 1.58440914e+01, 0.00000000e+00, 2.28177109e+01, 0.00000000e+00,\n 2.24741611e+01, 2.37553076e+03, 1.25074677e+02, 0.00000000e+00,\n 1.29738113e+02, 7.19390747e+02, 6.73580505e+02, 1.74538155e+01,\n 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1.12963509e+02, 0.00000000e+00,\n 1.26192612e+02, 7.03950989e+02, 6.31124390e+02, 1.18665609e+01,\n 3.64714661e+02, 0.00000000e+00, 1.99024023e+03, 0.00000000e+00,\n 2.56747246e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.14503967e+02,\n 2.81756630e+01, 9.45227509e+01, 1.49088967e+00, 0.00000000e+00,\n 4.88183022e+01, 8.21660385e+01, 0.00000000e+00, 2.38057365e+01,\n 0.00000000e+00, 5.90870714e+00, 3.72534680e+00, 7.32476959e+01,\n 2.96171141e+00, 0.00000000e+00, 4.50837374e+00, 0.00000000e+00,\n 7.39796972e+00, 7.61640137e+02, 3.73948822e+01, 0.00000000e+00,\n 3.99701500e+01, 2.34083023e+02, 1.96656326e+02, 4.42070484e-01,\n 1.16482796e+02, 0.00000000e+00, 6.88018860e+02, 0.00000000e+00,\n 9.10097694e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.45103882e+03,\n 8.56412506e+01, 2.94251892e+02, 8.00099373e+00, 0.00000000e+00,\n 1.48142715e+02, 2.63174622e+02, 0.00000000e+00, 8.21029816e+01,\n 0.00000000e+00, 1.30889826e+01, 1.45695620e+01, 1.88963959e+02,\n 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0.00000000e+00, 8.18404160e+01,\n 0.00000000e+00, 1.60505409e+01, 1.61125927e+01, 2.17330139e+02,\n 2.11662464e+01, 0.00000000e+00, 2.20690250e+01, 0.00000000e+00,\n 2.27260590e+01, 2.74798633e+03, 1.21918114e+02, 0.00000000e+00,\n 1.47683060e+02, 8.41668091e+02, 7.19658813e+02, 1.39650984e+01,\n 4.24429626e+02, 0.00000000e+00, 2.47174829e+03, 0.00000000e+00,\n 2.85735512e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.35008594e+03,\n 8.33998718e+01, 2.89386780e+02, 8.04240608e+00, 0.00000000e+00,\n 1.41313828e+02, 2.56355377e+02, 0.00000000e+00, 9.33267365e+01,\n 0.00000000e+00, 1.17838306e+01, 1.32582684e+01, 1.83042847e+02,\n 2.07840939e+01, 0.00000000e+00, 2.08160210e+01, 0.00000000e+00,\n 1.97634277e+01, 2.30660913e+03, 1.06761147e+02, 0.00000000e+00,\n 1.25372948e+02, 7.06506348e+02, 6.19950256e+02, 1.50141878e+01,\n 3.61893158e+02, 0.00000000e+00, 2.03739600e+03, 0.00000000e+00,\n 2.32648277e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.64833301e+03,\n 8.39805756e+01, 2.95811707e+02, 8.92166424e+00, 0.00000000e+00,\n 1.53908279e+02, 2.57642914e+02, 0.00000000e+00, 7.37860336e+01,\n 0.00000000e+00, 8.35558128e+00, 1.21072645e+01, 1.87741470e+02,\n 2.30958405e+01, 0.00000000e+00, 1.64529648e+01, 0.00000000e+00,\n 1.99506149e+01, 2.47724023e+03, 1.11804153e+02, 1.39071884e+02,\n 1.37562607e+02, 7.41234680e+02, 6.40198975e+02, 1.06711197e+01,\n 3.76366180e+02, 0.00000000e+00, 2.20754224e+03, 0.00000000e+00,\n 2.29488354e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.31032715e+03,\n 8.80425873e+01, 3.30453766e+02, 7.94659567e+00, 0.00000000e+00,\n 1.50937134e+02, 3.13603363e+02, 0.00000000e+00, 1.31976852e+02,\n 0.00000000e+00, 9.64085007e+00, 1.04970350e+01, 1.90762405e+02,\n 2.05531120e+01, 0.00000000e+00, 2.25834160e+01, 0.00000000e+00,\n 2.25998917e+01, 2.61866455e+03, 1.33028793e+02, 1.42569016e+02,\n 1.44368423e+02, 7.79000916e+02, 7.14783142e+02, 1.36387730e+01,\n 4.05666504e+02, 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2.40878086e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.52950684e+02,\n 3.35628510e+01, 1.18119446e+02, 2.43842435e+00, 0.00000000e+00,\n 5.93012886e+01, 1.03166924e+02, 0.00000000e+00, 2.51731091e+01,\n 0.00000000e+00, 4.60067940e+00, 4.72425365e+00, 7.84904404e+01,\n 6.22136354e+00, 0.00000000e+00, 6.34743738e+00, 0.00000000e+00,\n 6.73746395e+00, 9.54994141e+02, 4.51433716e+01, 5.86958923e+01,\n 5.46610565e+01, 2.87801056e+02, 2.48106964e+02, 1.17286551e+00,\n 1.44633057e+02, 0.00000000e+00, 8.31917480e+02, 0.00000000e+00,\n 1.15282059e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.60574414e+03,\n 8.86892700e+01, 3.03726410e+02, 1.07852793e+01, 0.00000000e+00,\n 1.57809006e+02, 2.68082642e+02, 0.00000000e+00, 6.90627747e+01,\n 0.00000000e+00, 7.57115173e+00, 1.62462063e+01, 1.88471649e+02,\n 2.43624973e+01, 0.00000000e+00, 1.66331406e+01, 0.00000000e+00,\n 1.99700794e+01, 2.51680859e+03, 1.16270767e+02, 1.46469772e+02,\n 1.41010483e+02, 7.53820068e+02, 6.54192749e+02, 9.53430176e+00,\n 3.82488129e+02, 0.00000000e+00, 2.21937598e+03, 0.00000000e+00,\n 2.30395298e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.46336768e+03,\n 8.26439514e+01, 2.91688232e+02, 1.02990026e+01, 0.00000000e+00,\n 1.47728638e+02, 2.62181335e+02, 0.00000000e+00, 8.44994202e+01,\n 0.00000000e+00, 6.96664810e+00, 1.28018217e+01, 1.81605743e+02,\n 2.34220772e+01, 0.00000000e+00, 1.50535669e+01, 0.00000000e+00,\n 1.81338253e+01, 2.41454370e+03, 1.12129265e+02, 1.35543015e+02,\n 1.34536209e+02, 7.21462097e+02, 6.32228943e+02, 1.09686489e+01,\n 3.69151794e+02, 0.00000000e+00, 2.12191992e+03, 0.00000000e+00,\n 2.31035023e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.00533447e+03,\n 1.02764954e+02, 3.69211060e+02, 1.22636251e+01, 0.00000000e+00,\n 1.83835159e+02, 3.30884918e+02, 0.00000000e+00, 1.11581116e+02,\n 0.00000000e+00, 1.08382149e+01, 1.47644072e+01, 2.30665863e+02,\n 2.86404915e+01, 0.00000000e+00, 2.22036133e+01, 0.00000000e+00,\n 2.42490215e+01, 3.01715186e+03, 1.42032333e+02, 1.68340683e+02,\n 1.68112610e+02, 9.02661682e+02, 7.95472229e+02, 1.38493671e+01,\n 4.62364746e+02, 0.00000000e+00, 2.63219531e+03, 0.00000000e+00,\n 2.90715160e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.52113159e+03,\n 8.71066818e+01, 3.16888031e+02, 1.04888315e+01, 0.00000000e+00,\n 1.54287674e+02, 2.88161743e+02, 0.00000000e+00, 1.08055222e+02,\n 0.00000000e+00, 5.94362068e+00, 1.22064581e+01, 1.94486893e+02,\n 2.53102837e+01, 0.00000000e+00, 2.19419174e+01, 0.00000000e+00,\n 2.09238529e+01, 2.57572485e+03, 1.24018524e+02, 1.38877670e+02,\n 1.43402878e+02, 7.69611450e+02, 6.85318848e+02, 1.52894764e+01,\n 3.95854431e+02, 0.00000000e+00, 2.23728516e+03, 0.00000000e+00,\n 2.37487926e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.96523462e+03,\n 6.77005539e+01, 2.24439514e+02, 4.56302023e+00, 0.00000000e+00,\n 1.13600555e+02, 2.07134308e+02, 0.00000000e+00, 7.35087128e+01,\n 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1.49158249e+02,\n 1.21884508e+01, 0.00000000e+00, 1.23689308e+01, 0.00000000e+00,\n 1.52064619e+01, 1.93395459e+03, 8.49848251e+01, 1.17834671e+02,\n 1.00654999e+02, 5.87455933e+02, 4.98622498e+02, 1.05720663e+01,\n 2.92762238e+02, 0.00000000e+00, 1.75437341e+03, 0.00000000e+00,\n 1.88938026e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.89277942e+03,\n 7.10749054e+01, 2.37632172e+02, 4.57336998e+00, 0.00000000e+00,\n 1.17033340e+02, 2.12493118e+02, 0.00000000e+00, 4.12320709e+01,\n 1.60065269e+00, 2.39914560e+00, 1.05981245e+01, 1.43033737e+02,\n 1.73212833e+01, 0.00000000e+00, 1.30716572e+01, 0.00000000e+00,\n 8.00907135e+00, 1.91542371e+03, 9.30466385e+01, 1.17944962e+02,\n 1.03314789e+02, 5.78547729e+02, 5.04912872e+02, 7.90955210e+00,\n 2.90615448e+02, 0.00000000e+00, 1.66823853e+03, 0.00000000e+00,\n 2.44669628e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.03548303e+03,\n 6.75888824e+01, 2.26698441e+02, 4.46612072e+00, 0.00000000e+00,\n 1.18940933e+02, 1.91040573e+02, 0.00000000e+00, 3.44097137e+01,\n 4.82033491e+00, 8.52929688e+00, 1.23532791e+01, 1.44423599e+02,\n 1.22771893e+01, 0.00000000e+00, 1.03264036e+01, 0.00000000e+00,\n 1.40633211e+01, 1.86869995e+03, 8.15793076e+01, 1.15464104e+02,\n 9.78034821e+01, 5.67486511e+02, 4.81281769e+02, 6.61988258e+00,\n 2.82966400e+02, 0.00000000e+00, 1.68340259e+03, 0.00000000e+00,\n 1.86423149e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00677698e+03,\n 6.80535507e+01, 2.41231186e+02, 5.70506716e+00, 0.00000000e+00,\n 1.21570724e+02, 2.11107162e+02, 0.00000000e+00, 6.59759521e+01,\n 5.61911631e+00, 9.49834824e+00, 1.23414469e+01, 1.49504105e+02,\n 1.69249191e+01, 0.00000000e+00, 1.16005774e+01, 0.00000000e+00,\n 1.55230665e+01, 1.94298767e+03, 8.52861252e+01, 1.11519516e+02,\n 1.03517105e+02, 5.89888184e+02, 5.20604980e+02, 8.98240948e+00,\n 3.02486664e+02, 0.00000000e+00, 1.71853613e+03, 0.00000000e+00,\n 1.78329277e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.84685974e+03,\n 6.60359650e+01, 2.35537582e+02, 5.24069500e+00, 0.00000000e+00,\n 1.13247757e+02, 2.17385132e+02, 0.00000000e+00, 8.27910309e+01,\n 3.75447273e+00, 5.81793165e+00, 1.20422211e+01, 1.36258743e+02,\n 1.41524868e+01, 0.00000000e+00, 9.82835960e+00, 0.00000000e+00,\n 1.57896185e+01, 1.89906091e+03, 8.87460861e+01, 1.06554214e+02,\n 1.00630203e+02, 5.72921692e+02, 5.21378418e+02, 8.11682796e+00,\n 2.97477814e+02, 0.00000000e+00, 1.64686414e+03, 0.00000000e+00,\n 1.67753010e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13376343e+03,\n 7.25704041e+01, 2.59376984e+02, 6.34899235e+00, 0.00000000e+00,\n 1.30667358e+02, 2.22346298e+02, 0.00000000e+00, 5.96606903e+01,\n 4.69266462e+00, 1.02937851e+01, 1.11826077e+01, 1.66476608e+02,\n 2.17678146e+01, 0.00000000e+00, 1.48942919e+01, 0.00000000e+00,\n 1.41036644e+01, 2.06986304e+03, 8.96474304e+01, 1.19095886e+02,\n 1.11966568e+02, 6.29549927e+02, 5.48710571e+02, 9.09946537e+00,\n 3.21853088e+02, 0.00000000e+00, 1.82856458e+03, 0.00000000e+00,\n 2.12953663e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28533301e+03,\n 7.55583496e+01, 2.60245422e+02, 5.22845554e+00, 0.00000000e+00,\n 1.37392288e+02, 2.13342667e+02, 0.00000000e+00, 2.65582771e+01,\n 2.63949156e+00, 1.05358849e+01, 1.12765169e+01, 1.71581436e+02,\n 2.02666397e+01, 0.00000000e+00, 1.18251085e+01, 0.00000000e+00,\n 1.33787689e+01, 2.11861328e+03, 8.90376511e+01, 1.32780579e+02,\n 1.13816803e+02, 6.45955811e+02, 5.40941040e+02, 4.03664541e+00,\n 3.22145416e+02, 0.00000000e+00, 1.90153125e+03, 0.00000000e+00,\n 2.25212898e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.33829382e+03,\n 4.96883240e+01, 1.85191055e+02, 0.00000000e+00, 0.00000000e+00,\n 8.69791870e+01, 1.72324692e+02, 0.00000000e+00, 7.91883163e+01,\n 4.71632528e+00, 8.96761322e+00, 0.00000000e+00, 9.56571655e+01,\n 8.45582485e+00, 0.00000000e+00, 8.51403522e+00, 0.00000000e+00,\n 1.40441761e+01, 1.40217957e+03, 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0.00000000e+00,\n 2.24491978e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.20497095e+03,\n 1.04008896e+02, 3.46532959e+02, 0.00000000e+00, 0.00000000e+00,\n 1.75092300e+02, 3.01739532e+02, 0.00000000e+00, 9.14400024e+01,\n 4.47336721e+00, 1.05186806e+01, 0.00000000e+00, 2.12408051e+02,\n 2.45864372e+01, 0.00000000e+00, 2.33802147e+01, 0.00000000e+00,\n 2.11100407e+01, 2.80181299e+03, 1.29828003e+02, 1.62100388e+02,\n 1.53817154e+02, 8.51162903e+02, 7.39626770e+02, 1.59008188e+01,\n 4.35051147e+02, 0.00000000e+00, 2.52247510e+03, 0.00000000e+00,\n 2.66496792e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28831274e+03,\n 7.76947174e+01, 2.67079193e+02, 0.00000000e+00, 0.00000000e+00,\n 1.29946213e+02, 2.38123581e+02, 0.00000000e+00, 8.51641769e+01,\n 4.34634542e+00, 9.85756493e+00, 0.00000000e+00, 1.58283234e+02,\n 1.41098185e+01, 0.00000000e+00, 1.74281425e+01, 0.00000000e+00,\n 1.65080853e+01, 2.11659668e+03, 9.97693558e+01, 1.21838478e+02,\n 1.15931297e+02, 6.43326294e+02, 5.72713684e+02, 1.07119560e+01,\n 3.32040955e+02, 0.00000000e+00, 1.87126538e+03, 0.00000000e+00,\n 2.11984005e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.54733667e+03,\n 9.81632080e+01, 3.40644653e+02, 0.00000000e+00, 0.00000000e+00,\n 1.59779465e+02, 3.17784271e+02, 0.00000000e+00, 1.14696602e+02,\n 2.71715283e+00, 4.51877165e+00, 0.00000000e+00, 1.86761078e+02,\n 2.53127041e+01, 0.00000000e+00, 1.89929199e+01, 0.00000000e+00,\n 1.48067675e+01, 2.65000928e+03, 1.38970352e+02, 1.57322647e+02,\n 1.50917633e+02, 7.97287048e+02, 7.34067749e+02, 1.45294294e+01,\n 4.18985931e+02, 0.00000000e+00, 2.25986157e+03, 0.00000000e+00,\n 2.98401642e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.55547351e+03,\n 4.98635559e+01, 1.63096115e+02, 0.00000000e+00, 0.00000000e+00,\n 8.54634857e+01, 1.40824600e+02, 0.00000000e+00, 3.31143799e+01,\n 1.73951745e+00, 6.19387102e+00, 0.00000000e+00, 1.03678055e+02,\n 9.49805355e+00, 0.00000000e+00, 6.40541697e+00, 0.00000000e+00,\n 6.70867491e+00, 1.34338171e+03, 6.03184013e+01, 8.31682968e+01,\n 7.40771561e+01, 4.07898407e+02, 3.44138000e+02, 4.60596323e+00,\n 2.05930740e+02, 0.00000000e+00, 1.21522412e+03, 0.00000000e+00,\n 1.51129541e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.88367371e+03,\n 6.84919357e+01, 2.42386520e+02, 0.00000000e+00, 0.00000000e+00,\n 1.15934219e+02, 2.20839035e+02, 0.00000000e+00, 8.64891739e+01,\n 4.92044020e+00, 9.70694542e+00, 0.00000000e+00, 1.37476181e+02,\n 1.42407179e+01, 0.00000000e+00, 1.40847292e+01, 0.00000000e+00,\n 1.54918032e+01, 1.87592676e+03, 9.11188660e+01, 1.09143578e+02,\n 1.05624474e+02, 5.69645081e+02, 5.21157898e+02, 8.25070190e+00,\n 2.98735535e+02, 0.00000000e+00, 1.62124768e+03, 0.00000000e+00,\n 1.98270950e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.17357300e+03,\n 8.15341492e+01, 2.82865753e+02, 0.00000000e+00, 0.00000000e+00,\n 1.36633087e+02, 2.56220642e+02, 0.00000000e+00, 9.10817490e+01,\n 4.40919161e+00, 7.82261992e+00, 0.00000000e+00, 1.62643555e+02,\n 1.68426228e+01, 0.00000000e+00, 1.77288971e+01, 0.00000000e+00,\n 1.80394669e+01, 2.19453906e+03, 1.08382996e+02, 1.29220581e+02,\n 1.23898949e+02, 6.64345825e+02, 6.05504211e+02, 9.10293484e+00,\n 3.47009827e+02, 0.00000000e+00, 1.89015234e+03, 0.00000000e+00,\n 2.22297230e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.93568738e+03,\n 6.83426208e+01, 2.35522552e+02, 0.00000000e+00, 0.00000000e+00,\n 1.16180695e+02, 2.08280472e+02, 0.00000000e+00, 7.30970764e+01,\n 3.39198422e+00, 8.34730530e+00, 0.00000000e+00, 1.41126038e+02,\n 1.31753531e+01, 0.00000000e+00, 1.44617014e+01, 0.00000000e+00,\n 1.37803354e+01, 1.84471521e+03, 8.66254654e+01, 1.08444260e+02,\n 1.03429893e+02, 5.61142029e+02, 5.00357147e+02, 6.98011351e+00,\n 2.91255219e+02, 0.00000000e+00, 1.61771875e+03, 0.00000000e+00,\n 1.89221725e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.44881641e+03,\n 1.14117630e+02, 3.73075531e+02, 0.00000000e+00, 0.00000000e+00,\n 1.89060974e+02, 3.25777649e+02, 0.00000000e+00, 8.80108261e+01,\n 5.23078871e+00, 1.02820969e+01, 0.00000000e+00, 2.31410294e+02,\n 2.15962410e+01, 0.00000000e+00, 2.73115654e+01, 0.00000000e+00,\n 2.32939053e+01, 3.03219287e+03, 1.40562027e+02, 1.77166336e+02,\n 1.64745819e+02, 9.20319153e+02, 7.95565613e+02, 1.53483057e+01,\n 4.66244904e+02, 0.00000000e+00, 2.72549268e+03, 0.00000000e+00,\n 2.87453804e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.32255322e+03,\n 7.62106857e+01, 2.65833527e+02, 0.00000000e+00, 0.00000000e+00,\n 1.35701324e+02, 2.34642990e+02, 0.00000000e+00, 6.00180359e+01,\n 0.00000000e+00, 0.00000000e+00, 1.25032711e+01, 1.59402344e+02,\n 1.87059822e+01, 0.00000000e+00, 1.58302193e+01, 0.00000000e+00,\n 2.18515625e+01, 2.21824512e+03, 1.00157463e+02, 1.26567780e+02,\n 1.18813400e+02, 6.62154663e+02, 5.77124756e+02, 7.70107126e+00,\n 3.34832672e+02, 0.00000000e+00, 1.95890942e+03, 0.00000000e+00,\n 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0.00000000e+00, 2.60634668e+03,\n 8.04524384e+01, 2.91483795e+02, 0.00000000e+00, 0.00000000e+00,\n 1.48541550e+02, 2.62809998e+02, 0.00000000e+00, 8.35746536e+01,\n 0.00000000e+00, 0.00000000e+00, 1.34493732e+01, 1.80424911e+02,\n 2.39661312e+01, 0.00000000e+00, 1.52654858e+01, 0.00000000e+00,\n 1.53284044e+01, 2.45813696e+03, 1.05267632e+02, 1.37653183e+02,\n 1.31204178e+02, 7.37787354e+02, 6.47932922e+02, 5.85979891e+00,\n 3.77271332e+02, 0.00000000e+00, 2.18350684e+03, 0.00000000e+00,\n 2.52072144e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.08356982e+03,\n 3.22237854e+01, 1.04370255e+02, 0.00000000e+00, 0.00000000e+00,\n 5.75564499e+01, 8.37430191e+01, 0.00000000e+00, 5.36447906e+00,\n 0.00000000e+00, 0.00000000e+00, 4.59752512e+00, 6.67285385e+01,\n 5.77079535e+00, 0.00000000e+00, 4.27688837e+00, 0.00000000e+00,\n 7.09271717e+00, 8.89864868e+02, 3.85042763e+01, 5.65986595e+01,\n 4.84020157e+01, 2.65023560e+02, 2.12248093e+02, 0.00000000e+00,\n 1.28985687e+02, 0.00000000e+00, 8.23289917e+02, 0.00000000e+00,\n 1.19247141e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.47921558e+03,\n 7.01387177e+01, 2.45341202e+02, 0.00000000e+00, 0.00000000e+00,\n 1.34452972e+02, 2.08309052e+02, 0.00000000e+00, 4.10485954e+01,\n 0.00000000e+00, 0.00000000e+00, 1.29263611e+01, 1.61371796e+02,\n 2.30796967e+01, 0.00000000e+00, 1.02586308e+01, 0.00000000e+00,\n 1.63005772e+01, 2.14540112e+03, 8.36215897e+01, 1.22331703e+02,\n 1.14804138e+02, 6.37677795e+02, 5.39188293e+02, 9.07732487e-01,\n 3.19936249e+02, 0.00000000e+00, 1.95814136e+03, 0.00000000e+00,\n 1.97421074e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.38611670e+03,\n 7.05068588e+01, 2.52771942e+02, 0.00000000e+00, 0.00000000e+00,\n 1.32852676e+02, 2.20061707e+02, 0.00000000e+00, 6.02736053e+01,\n 0.00000000e+00, 0.00000000e+00, 1.07669048e+01, 1.60966156e+02,\n 2.30785484e+01, 0.00000000e+00, 1.28932152e+01, 0.00000000e+00,\n 1.82792282e+01, 2.14871606e+03, 9.03535080e+01, 1.21233261e+02,\n 1.16561760e+02, 6.43113403e+02, 5.52821533e+02, 5.75940561e+00,\n 3.26175873e+02, 0.00000000e+00, 1.93680713e+03, 0.00000000e+00,\n 2.19280148e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.86419775e+03,\n 9.18858337e+01, 3.20491425e+02, 0.00000000e+00, 0.00000000e+00,\n 1.65500916e+02, 2.81292969e+02, 0.00000000e+00, 7.16243744e+01,\n 0.00000000e+00, 0.00000000e+00, 1.38806515e+01, 1.94889252e+02,\n 2.50072727e+01, 0.00000000e+00, 1.82826290e+01, 0.00000000e+00,\n 2.37883644e+01, 2.69327100e+03, 1.18559219e+02, 1.53779556e+02,\n 1.44669876e+02, 8.04589233e+02, 6.98588501e+02, 7.20297480e+00,\n 4.06634521e+02, 0.00000000e+00, 2.39037891e+03, 0.00000000e+00,\n 2.76593819e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28158691e+03,\n 8.13329468e+01, 2.87718719e+02, 0.00000000e+00, 0.00000000e+00,\n 1.40466492e+02, 2.62162384e+02, 0.00000000e+00, 8.82871704e+01,\n 0.00000000e+00, 0.00000000e+00, 1.26652985e+01, 1.63587036e+02,\n 2.02662735e+01, 0.00000000e+00, 1.99449196e+01, 0.00000000e+00,\n 2.25260067e+01, 2.32274927e+03, 1.11098671e+02, 1.30917221e+02,\n 1.25986351e+02, 6.98694214e+02, 6.24835632e+02, 9.44414806e+00,\n 3.58766907e+02, 0.00000000e+00, 2.01213843e+03, 0.00000000e+00,\n 2.37841988e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.13232056e+02,\n 1.45599518e+01, 3.05265274e+01, 0.00000000e+00, 0.00000000e+00,\n 2.88823299e+01, 1.10344620e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 1.68030596e+00, 0.00000000e+00, 4.25880775e+01,\n 2.69865179e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 3.88682437e+00, 3.51851929e+02, 1.52396288e+01, 3.08383045e+01,\n 2.18221817e+01, 1.14740372e+02, 5.08230324e+01, 0.00000000e+00,\n 4.83254623e+01, 0.00000000e+00, 4.23022308e+02, 0.00000000e+00,\n 3.13614178e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.98000232e+03,\n 7.54262314e+01, 2.65287567e+02, 0.00000000e+00, 0.00000000e+00,\n 1.29298309e+02, 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9.75941086e+00, 0.00000000e+00,\n 1.54671974e+01, 2.11752539e+03, 9.59930115e+01, 1.27680672e+02,\n 1.13013336e+02, 6.40502441e+02, 5.46345520e+02, 4.18997002e+00,\n 3.22190277e+02, 0.00000000e+00, 1.88184033e+03, 0.00000000e+00,\n 2.18683681e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.32193884e+03,\n 4.18603745e+01, 1.50762512e+02, 0.00000000e+00, 0.00000000e+00,\n 7.65571747e+01, 1.30617630e+02, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 8.15419960e+00, 3.98530865e+00, 1.00965240e+02,\n 1.27670937e+01, 0.00000000e+00, 4.13461876e+00, 0.00000000e+00,\n 8.13174438e+00, 1.24480750e+03, 5.55498238e+01, 7.36194229e+01,\n 6.77623215e+01, 3.77075043e+02, 3.19279938e+02, 2.49544716e+00,\n 1.89968689e+02, 0.00000000e+00, 1.11811279e+03, 0.00000000e+00,\n 1.36595573e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.85338806e+03,\n 7.63538055e+01, 2.86269257e+02, 0.00000000e+00, 0.00000000e+00,\n 1.34820129e+02, 2.69653992e+02, 0.00000000e+00, 0.00000000e+00,\n 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0.00000000e+00, 0.00000000e+00,\n 1.17689842e+02, 2.55704132e+02, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 1.16303959e+01, 7.26598835e+00, 1.45295212e+02,\n 2.15345535e+01, 0.00000000e+00, 2.26299629e+01, 0.00000000e+00,\n 1.86808014e+01, 1.90902063e+03, 1.02207100e+02, 1.05215233e+02,\n 1.10702522e+02, 5.74752380e+02, 5.53443970e+02, 1.07029676e+01,\n 3.06866852e+02, 0.00000000e+00, 1.51943860e+03, 0.00000000e+00,\n 1.94899063e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.45434839e+03,\n 6.70555573e+01, 2.70141815e+02, 0.00000000e+00, 0.00000000e+00,\n 1.22514580e+02, 2.63323486e+02, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 1.29784126e+01, 7.26997089e+00, 1.54462265e+02,\n 2.18478317e+01, 0.00000000e+00, 2.11383133e+01, 0.00000000e+00,\n 1.86485863e+01, 2.01083826e+03, 1.06440552e+02, 1.10670502e+02,\n 1.14798546e+02, 6.04594299e+02, 5.75835327e+02, 1.13452702e+01,\n 3.20961304e+02, 0.00000000e+00, 1.62493689e+03, 0.00000000e+00,\n 2.08428135e+01],\n 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7.51762199e+00,\n 3.97466766e+02, 0.00000000e+00, 2.29350293e+03, 0.00000000e+00,\n 2.70349674e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.08722816e+02,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 3.04961848e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.81609241e+03,\n 6.39244766e+01, 2.16094101e+02, 0.00000000e+00, 0.00000000e+00,\n 1.02939072e+02, 1.90573181e+02, 0.00000000e+00, 4.98693924e+01,\n 0.00000000e+00, 6.52975941e+00, 1.32690287e+01, 1.04801971e+02,\n 2.94154716e+00, 0.00000000e+00, 1.63109169e+01, 0.00000000e+00,\n 2.28085308e+01, 1.71209631e+03, 8.48575516e+01, 1.02683662e+02,\n 9.00885773e+01, 5.19317444e+02, 4.51061157e+02, 7.13182735e+00,\n 2.58754242e+02, 0.00000000e+00, 1.52322131e+03, 0.00000000e+00,\n 1.70949059e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00424149e+02,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.41460556e+02,\n 6.00495224e+01, 1.91352554e+02, 0.00000000e+00, 0.00000000e+00,\n 9.04156952e+01, 2.22968369e+02, 0.00000000e+00, 7.46483917e+01,\n 0.00000000e+00, 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2.63293640e+02, 0.00000000e+00, 1.64499426e+03, 0.00000000e+00,\n 1.87768078e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.73045471e+02,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 6.68766797e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 1.87838402e+01, 0.00000000e+00,\n 0.00000000e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.16089795e+03,\n 7.90043259e+01, 2.66131317e+02, 0.00000000e+00, 0.00000000e+00,\n 1.23291298e+02, 2.85983063e+02, 0.00000000e+00, 1.04902687e+02,\n 0.00000000e+00, 8.45354080e+00, 1.10354595e+01, 1.32241577e+02,\n 1.46557961e+01, 0.00000000e+00, 2.16456127e+01, 0.00000000e+00,\n 1.75609245e+01, 2.01299402e+03, 1.17663261e+02, 1.31045547e+02,\n 1.17772087e+02, 6.02614380e+02, 5.98133240e+02, 7.72694302e+00,\n 3.23476257e+02, 0.00000000e+00, 1.50336597e+03, 0.00000000e+00,\n 2.63255444e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.22790771e+02,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.40526611e+02,\n 2.13370609e+01, 6.42115021e+01, 0.00000000e+00, 0.00000000e+00,\n 3.64568176e+01, 4.99044113e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 4.05041635e-01, 6.20344543e+00, 4.95587158e+01,\n 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0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 3.83777022e+00, 5.00059891e+01,\n 6.19864368e+00, 0.00000000e+00, 9.57561731e-01, 0.00000000e+00,\n 8.22046947e+00, 4.56881439e+02, 1.59848757e+01, 2.92819939e+01,\n 2.69331284e+01, 1.42455917e+02, 8.37157516e+01, 0.00000000e+00,\n 5.76652679e+01, 0.00000000e+00, 4.68362976e+02, 0.00000000e+00,\n 1.09435105e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.39984570e+03,\n 8.19979095e+01, 2.79710724e+02, 0.00000000e+00, 0.00000000e+00,\n 1.40020004e+02, 2.43425201e+02, 0.00000000e+00, 5.10210495e+01,\n 0.00000000e+00, 7.24131489e+00, 1.49307022e+01, 1.56698090e+02,\n 1.70070610e+01, 0.00000000e+00, 2.03015957e+01, 0.00000000e+00,\n 2.31549416e+01, 2.22399805e+03, 1.03103989e+02, 1.31326553e+02,\n 1.18539223e+02, 6.82500061e+02, 5.82243530e+02, 7.22320271e+00,\n 3.37783051e+02, 0.00000000e+00, 2.00018225e+03, 0.00000000e+00,\n 2.33814220e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 4.68096199e+01, 1.63471237e+02, 4.88380432e+00, 3.12200713e+00,\n 8.36509247e+01, 1.40659821e+02, 5.34542131e+00, 4.07081718e+01,\n 0.00000000e+00, 8.02932167e+00, 0.00000000e+00, 1.11898369e+02,\n 1.46839066e+01, 0.00000000e+00, 1.25556879e+01, 0.00000000e+00,\n 1.22734509e+01, 1.26669946e+03, 6.10102730e+01, 7.74431458e+01,\n 7.19809723e+01, 3.96949005e+02, 3.44649353e+02, 6.53373909e+00,\n 2.01639053e+02, 0.00000000e+00, 1.04202063e+03, 0.00000000e+00,\n 1.49830160e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.06737648e+02, 3.73008636e+02, 6.32510662e+00, 4.26865673e+00,\n 1.73128922e+02, 3.60892242e+02, 7.82020426e+00, 1.22642326e+02,\n 0.00000000e+00, 1.17546539e+01, 0.00000000e+00, 1.86401108e+02,\n 2.33439388e+01, 0.00000000e+00, 2.19464474e+01, 0.00000000e+00,\n 2.36354465e+01, 2.84468213e+03, 1.49002625e+02, 1.74535736e+02,\n 1.60660919e+02, 8.73681580e+02, 8.18377747e+02, 1.25660982e+01,\n 4.60994324e+02, 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2.71181183e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 3.14621525e+01, 1.01932632e+02, 2.05923104e+00, 2.21268821e+00,\n 6.00741844e+01, 7.23215408e+01, 3.59647918e+00, 0.00000000e+00,\n 0.00000000e+00, 6.70777225e+00, 0.00000000e+00, 6.84221268e+01,\n 8.90290451e+00, 0.00000000e+00, 7.92460203e+00, 0.00000000e+00,\n 7.30222082e+00, 8.01343872e+02, 3.34953003e+01, 5.55672646e+01,\n 4.44529343e+01, 2.52703400e+02, 1.87979172e+02, 0.00000000e+00,\n 1.18697647e+02, 0.00000000e+00, 6.55272766e+02, 0.00000000e+00,\n 1.00191689e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 6.08271561e+01, 2.21299393e+02, 4.80066776e+00, 3.10294914e+00,\n 1.05836830e+02, 2.07805664e+02, 6.79996395e+00, 8.14521713e+01,\n 0.00000000e+00, 1.01815872e+01, 0.00000000e+00, 1.28552658e+02,\n 1.83705101e+01, 0.00000000e+00, 1.58231163e+01, 0.00000000e+00,\n 1.56021442e+01, 1.68857788e+03, 8.42632675e+01, 9.96558990e+01,\n 9.63075027e+01, 5.24747314e+02, 4.85955139e+02, 7.41943264e+00,\n 2.76765381e+02, 0.00000000e+00, 1.35796118e+03, 0.00000000e+00,\n 2.04626198e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 6.63772125e+01, 2.38396683e+02, 5.41119385e+00, 2.87766051e+00,\n 1.15174019e+02, 2.24177689e+02, 7.75948048e+00, 8.40064316e+01,\n 0.00000000e+00, 9.53859711e+00, 0.00000000e+00, 1.35942780e+02,\n 2.03929558e+01, 0.00000000e+00, 1.57060318e+01, 0.00000000e+00,\n 1.60689812e+01, 1.82853076e+03, 9.12094498e+01, 1.09107124e+02,\n 1.04285927e+02, 5.67258850e+02, 5.23894958e+02, 6.84325695e+00,\n 2.98932068e+02, 0.00000000e+00, 1.46587085e+03, 0.00000000e+00,\n 2.18284607e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 6.03711624e+01, 2.15425568e+02, 5.17418957e+00, 3.38324594e+00,\n 1.05807487e+02, 1.97053253e+02, 7.15747643e+00, 6.92430801e+01,\n 0.00000000e+00, 9.81328297e+00, 0.00000000e+00, 1.30159866e+02,\n 1.86743736e+01, 0.00000000e+00, 1.47335339e+01, 0.00000000e+00,\n 1.46568499e+01, 1.65458667e+03, 8.14762802e+01, 9.94059372e+01,\n 9.41808701e+01, 5.15086792e+02, 4.66543976e+02, 6.91650724e+00,\n 2.68534119e+02, 0.00000000e+00, 1.33858105e+03, 0.00000000e+00,\n 1.99790611e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 9.01057816e+01, 3.12586853e+02, 6.61050510e+00, 4.23052263e+00,\n 1.50713898e+02, 2.90405304e+02, 7.71226740e+00, 8.92005615e+01,\n 0.00000000e+00, 1.14717503e+01, 0.00000000e+00, 1.76440338e+02,\n 2.43189659e+01, 0.00000000e+00, 1.72130909e+01, 0.00000000e+00,\n 1.93635540e+01, 2.40422827e+03, 1.22521767e+02, 1.47671021e+02,\n 1.35433807e+02, 7.43006714e+02, 6.74944519e+02, 1.22581425e+01,\n 3.86797974e+02, 0.00000000e+00, 1.92368994e+03, 0.00000000e+00,\n 3.04093838e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.26660742e+03,\n 6.28336067e+01, 2.45599670e+02, 5.74193573e+00, 0.00000000e+00,\n 9.61780014e+01, 2.60090027e+02, 0.00000000e+00, 1.26281418e+02,\n 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1.90095001e+02, 0.00000000e+00, 7.23427429e+01,\n 0.00000000e+00, 8.48577023e+00, 0.00000000e+00, 1.33374390e+02,\n 1.41571283e+01, 0.00000000e+00, 1.33615246e+01, 0.00000000e+00,\n 1.49101725e+01, 1.77562512e+03, 7.96756668e+01, 9.51557617e+01,\n 9.23144760e+01, 5.31935364e+02, 4.72743896e+02, 8.65114403e+00,\n 2.70966614e+02, 0.00000000e+00, 1.56262842e+03, 0.00000000e+00,\n 1.99230003e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.77424500e+03,\n 6.99301453e+01, 2.71162354e+02, 8.08053207e+00, 0.00000000e+00,\n 1.16540840e+02, 2.72168732e+02, 0.00000000e+00, 1.31816620e+02,\n 0.00000000e+00, 9.17749119e+00, 0.00000000e+00, 1.54525177e+02,\n 1.87173843e+01, 0.00000000e+00, 1.81617737e+01, 0.00000000e+00,\n 1.83805275e+01, 2.11840503e+03, 1.05740372e+02, 1.12699608e+02,\n 1.14788322e+02, 6.32169434e+02, 6.02148987e+02, 1.41194496e+01,\n 3.33890167e+02, 0.00000000e+00, 1.74997083e+03, 0.00000000e+00,\n 2.31956177e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.08617676e+03,\n 9.42601471e+01, 3.64768005e+02, 9.82813263e+00, 0.00000000e+00,\n 1.48651276e+02, 3.74057495e+02, 0.00000000e+00, 1.75834793e+02,\n 0.00000000e+00, 1.17565832e+01, 0.00000000e+00, 1.87870041e+02,\n 2.16623535e+01, 0.00000000e+00, 2.53571415e+01, 0.00000000e+00,\n 2.39910126e+01, 2.75624829e+03, 1.46829178e+02, 1.50619537e+02,\n 1.50625061e+02, 8.19281128e+02, 7.97515869e+02, 2.03911495e+01,\n 4.36069214e+02, 0.00000000e+00, 2.20640747e+03, 0.00000000e+00,\n 3.25849838e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.03713025e+03,\n 8.31003494e+01, 3.19181213e+02, 9.17142487e+00, 0.00000000e+00,\n 1.34846909e+02, 3.21341095e+02, 0.00000000e+00, 1.51002884e+02,\n 0.00000000e+00, 1.04376898e+01, 0.00000000e+00, 1.73875977e+02,\n 2.15501518e+01, 0.00000000e+00, 2.15513058e+01, 0.00000000e+00,\n 2.00869789e+01, 2.48290503e+03, 1.27163284e+02, 1.33126251e+02,\n 1.34044952e+02, 7.38282043e+02, 7.06685181e+02, 1.71532269e+01,\n 3.89998352e+02, 0.00000000e+00, 2.03678357e+03, 0.00000000e+00,\n 2.88768291e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.39435156e+03,\n 9.88450699e+01, 3.52225830e+02, 1.21551895e+01, 0.00000000e+00,\n 1.83221497e+02, 3.10950653e+02, 0.00000000e+00, 1.14908722e+02,\n 0.00000000e+00, 1.63366375e+01, 0.00000000e+00, 2.25058472e+02,\n 2.92021561e+01, 0.00000000e+00, 2.53068295e+01, 0.00000000e+00,\n 2.73333569e+01, 3.12522192e+03, 1.36113586e+02, 1.61673325e+02,\n 1.60976898e+02, 9.32659546e+02, 8.16489563e+02, 1.68010578e+01,\n 4.73767883e+02, 0.00000000e+00, 2.81138721e+03, 0.00000000e+00,\n 3.26731911e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.14674500e+02,\n 1.13388596e+01, 5.14310150e+01, 1.38142681e+00, 0.00000000e+00,\n 2.38030834e+01, 4.34725761e+01, 0.00000000e+00, 4.32059784e+01,\n 0.00000000e+00, 3.44798422e+00, 9.77727950e-01, 2.58245621e+01,\n 2.31968474e+00, 0.00000000e+00, 4.05846548e+00, 0.00000000e+00,\n 7.90454960e+00, 4.11986847e+02, 1.97714329e+01, 1.77582111e+01,\n 2.02769413e+01, 1.21690865e+02, 1.12773643e+02, 3.02810383e+00,\n 6.62438049e+01, 0.00000000e+00, 3.62619720e+02, 0.00000000e+00,\n 3.72611284e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.87542786e+03,\n 6.51554413e+01, 2.34740860e+02, 7.68735409e+00, 0.00000000e+00,\n 1.13687981e+02, 2.09653809e+02, 0.00000000e+00, 9.05139923e+01,\n 0.00000000e+00, 8.10368252e+00, 8.93448925e+00, 1.31865005e+02,\n 1.47481804e+01, 0.00000000e+00, 1.69873505e+01, 0.00000000e+00,\n 2.03349895e+01, 1.94816589e+03, 9.21669464e+01, 1.03100334e+02,\n 1.01139091e+02, 5.72892517e+02, 5.15723389e+02, 1.07193623e+01,\n 2.97316010e+02, 0.00000000e+00, 1.69680688e+03, 0.00000000e+00,\n 1.94538078e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.83412598e+02,\n 1.05808582e+01, 3.91759720e+01, 6.09775603e-01, 0.00000000e+00,\n 2.52594852e+01, 2.21343765e+01, 0.00000000e+00, 1.78796597e+01,\n 0.00000000e+00, 2.73432970e+00, 7.98184097e-01, 2.75556335e+01,\n 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2.54218845e+01, 1.00722504e+02, 3.49489403e+00, 0.00000000e+00,\n 4.82465248e+01, 8.39232101e+01, 0.00000000e+00, 5.82359428e+01,\n 0.00000000e+00, 3.23256254e+00, 3.73154497e+00, 5.82655716e+01,\n 7.59824085e+00, 0.00000000e+00, 5.67809343e+00, 0.00000000e+00,\n 1.01348991e+01, 8.40122986e+02, 3.70443344e+01, 3.85309067e+01,\n 4.29989891e+01, 2.47826187e+02, 2.19097473e+02, 6.12473440e+00,\n 1.29592392e+02, 0.00000000e+00, 7.52604919e+02, 0.00000000e+00,\n 7.78701830e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.04882263e+03,\n 3.28304558e+01, 1.29560196e+02, 4.17489529e+00, 0.00000000e+00,\n 6.16844101e+01, 1.10527306e+02, 0.00000000e+00, 6.86238556e+01,\n 0.00000000e+00, 6.53640509e+00, 3.75605655e+00, 7.47472458e+01,\n 9.53732014e+00, 0.00000000e+00, 8.16225624e+00, 0.00000000e+00,\n 1.20788784e+01, 1.05450232e+03, 4.87496414e+01, 5.15684700e+01,\n 5.52513695e+01, 3.12568359e+02, 2.80428223e+02, 6.57105589e+00,\n 1.64425400e+02, 0.00000000e+00, 9.26262512e+02, 0.00000000e+00,\n 1.05411549e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.08901831e+03,\n 7.47064285e+01, 2.76648895e+02, 9.29566097e+00, 0.00000000e+00,\n 1.31166367e+02, 2.47179077e+02, 0.00000000e+00, 1.12256416e+02,\n 0.00000000e+00, 1.11050673e+01, 9.67808151e+00, 1.57940414e+02,\n 2.02239342e+01, 0.00000000e+00, 2.10167580e+01, 0.00000000e+00,\n 2.20483456e+01, 2.23992944e+03, 1.07459900e+02, 1.17984627e+02,\n 1.18890884e+02, 6.63119934e+02, 6.01715820e+02, 1.29225073e+01,\n 3.46198486e+02, 0.00000000e+00, 1.93168237e+03, 0.00000000e+00,\n 2.30006943e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.06238159e+02,\n 1.02040367e+01, 3.26417236e+01, 1.06205750e+00, 0.00000000e+00,\n 2.74405308e+01, 1.74450684e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 2.36692190e+00, 7.50095069e-01, 3.94530563e+01,\n 5.04602242e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 6.43173504e+00, 3.66316742e+02, 6.73395061e+00, 1.71191292e+01,\n 1.90348816e+01, 1.07411896e+02, 5.08301849e+01, 0.00000000e+00,\n 4.27652168e+01, 0.00000000e+00, 4.12594025e+02, 0.00000000e+00,\n 3.10858178e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.03900098e+03,\n 3.59470100e+01, 1.36358383e+02, 3.19239140e+00, 0.00000000e+00,\n 6.39132004e+01, 1.14781784e+02, 0.00000000e+00, 4.70411034e+01,\n 0.00000000e+00, 3.64607382e+00, 2.52003074e+00, 8.78956985e+01,\n 1.39419622e+01, 0.00000000e+00, 1.02878304e+01, 0.00000000e+00,\n 6.28174686e+00, 1.07717993e+03, 4.97265930e+01, 5.57216911e+01,\n 5.95937653e+01, 3.22729187e+02, 2.82140228e+02, 4.05325031e+00,\n 1.64742844e+02, 0.00000000e+00, 9.39923035e+02, 0.00000000e+00,\n 1.29094925e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.22560486e+02,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.10869141e+01,\n 3.80873656e+00, 0.00000000e+00, 0.00000000e+00, 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0.00000000e+00,\n 1.55298737e+02, 2.38877441e+02, 0.00000000e+00, 6.23829994e+01,\n 0.00000000e+00, 1.28728342e+01, 1.15905123e+01, 1.93954819e+02,\n 2.63734818e+01, 0.00000000e+00, 2.12644444e+01, 0.00000000e+00,\n 2.31018372e+01, 2.47275562e+03, 1.06810562e+02, 1.35867508e+02,\n 1.33714066e+02, 7.32865784e+02, 6.19855835e+02, 1.04720230e+01,\n 3.67814148e+02, 0.00000000e+00, 2.21597827e+03, 0.00000000e+00,\n 2.58639793e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.73500595e+02,\n 1.55928326e+01, 5.85901947e+01, 0.00000000e+00, 0.00000000e+00,\n 2.42087593e+01, 6.27567558e+01, 0.00000000e+00, 2.43047943e+01,\n 0.00000000e+00, 5.93620300e+00, 9.21276748e-01, 2.57332935e+01,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 5.93961477e+00, 3.71986420e+02, 2.58188896e+01, 3.19630165e+01,\n 2.43371868e+01, 1.20548683e+02, 1.25187225e+02, 0.00000000e+00,\n 6.56700974e+01, 0.00000000e+00, 2.88583221e+02, 0.00000000e+00,\n 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0.00000000e+00, 7.60694885e+02, 0.00000000e+00,\n 1.36330109e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.19688318e+03,\n 5.35903625e+01, 2.19446533e+02, 5.07005692e+00, 0.00000000e+00,\n 9.73643494e+01, 2.06126999e+02, 0.00000000e+00, 9.03420029e+01,\n 0.00000000e+00, 1.21683979e+01, 6.87112093e+00, 1.26074417e+02,\n 1.70419140e+01, 0.00000000e+00, 1.32266598e+01, 0.00000000e+00,\n 1.51704998e+01, 1.44478516e+03, 8.28469315e+01, 9.29600067e+01,\n 9.14237213e+01, 4.83633453e+02, 4.55486633e+02, 7.04093122e+00,\n 2.59992737e+02, 0.00000000e+00, 1.26687134e+03, 0.00000000e+00,\n 1.95380096e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.03365881e+03,\n 4.44239655e+01, 1.82191986e+02, 3.78951335e+00, 0.00000000e+00,\n 8.18823471e+01, 1.64211502e+02, 0.00000000e+00, 6.74643021e+01,\n 0.00000000e+00, 1.28775578e+01, 5.09215498e+00, 1.11541672e+02,\n 1.42612915e+01, 0.00000000e+00, 9.08949852e+00, 0.00000000e+00,\n 1.25123196e+01, 1.20364111e+03, 6.71190033e+01, 7.83068542e+01,\n 7.57164154e+01, 4.02196564e+02, 3.70485962e+02, 2.83611631e+00,\n 2.14150909e+02, 0.00000000e+00, 1.06447388e+03, 0.00000000e+00,\n 1.69312325e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.17285315e+03,\n 4.55622940e+01, 1.84069489e+02, 5.11460400e+00, 0.00000000e+00,\n 8.58970413e+01, 1.58680923e+02, 0.00000000e+00, 5.70675697e+01,\n 0.00000000e+00, 1.16329746e+01, 4.92083502e+00, 1.17049210e+02,\n 1.67064762e+01, 0.00000000e+00, 9.88065147e+00, 0.00000000e+00,\n 1.21719799e+01, 1.25167383e+03, 6.66565399e+01, 7.91860046e+01,\n 7.72646637e+01, 4.15568481e+02, 3.70168365e+02, 4.04779196e+00,\n 2.18621445e+02, 0.00000000e+00, 1.12960608e+03, 0.00000000e+00,\n 1.64989319e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.28344775e+03,\n 9.33809204e+01, 3.42289124e+02, 8.68115044e+00, 0.00000000e+00,\n 1.58016876e+02, 3.19580170e+02, 0.00000000e+00, 1.21806633e+02,\n 0.00000000e+00, 1.50310478e+01, 1.03875551e+01, 1.86073746e+02,\n 2.01992321e+01, 0.00000000e+00, 1.90813503e+01, 0.00000000e+00,\n 2.42691364e+01, 2.43259912e+03, 1.36685562e+02, 1.52546173e+02,\n 1.44462494e+02, 7.89602783e+02, 7.26611938e+02, 9.89018917e+00,\n 4.19496277e+02, 0.00000000e+00, 2.18780884e+03, 0.00000000e+00,\n 3.07389908e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.49184601e+02,\n 4.67587757e+00, 2.62726903e+00, 0.00000000e+00, 0.00000000e+00,\n 5.33836603e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.73898768e+00, 1.25818298e+02, 0.00000000e+00, 7.25271940e+00,\n 6.28071928e+00, 3.70601044e+01, 1.05134945e+01, 0.00000000e+00,\n 1.18741484e+01, 0.00000000e+00, 1.76665573e+02, 0.00000000e+00,\n 4.05644566e-01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.97288977e+03,\n 7.62322083e+01, 2.26901611e+02, 0.00000000e+00, 0.00000000e+00,\n 1.16793793e+02, 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0.00000000e+00, 0.00000000e+00,\n 2.13968296e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.38481073e+01,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 3.33224535e+00, 3.08868896e+02, 3.61221838e+00, 1.51268320e+01,\n 1.69657993e+01, 9.54097443e+01, 4.28087044e+01, 0.00000000e+00,\n 3.75884361e+01, 0.00000000e+00, 3.91611816e+02, 0.00000000e+00,\n 2.30661464e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.05389771e+02,\n 1.94898033e+01, 4.99269180e+01, 0.00000000e+00, 0.00000000e+00,\n 3.71048203e+01, 2.67269936e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.39806938e+01,\n 0.00000000e+00, 0.00000000e+00, 8.39495718e-01, 0.00000000e+00,\n 4.00451612e+00, 5.48759888e+02, 1.63538990e+01, 3.12991924e+01,\n 2.90533848e+01, 1.66627487e+02, 1.05806602e+02, 0.00000000e+00,\n 7.34649048e+01, 0.00000000e+00, 5.93723145e+02, 0.00000000e+00,\n 5.07215452e+00],\n 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8.74715042e+00,\n 3.82943817e+02, 0.00000000e+00, 2.28141357e+03, 0.00000000e+00,\n 2.41452236e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.12965845e+03,\n 6.72043381e+01, 2.53175797e+02, 5.56324291e+00, 0.00000000e+00,\n 1.01855370e+02, 2.67714600e+02, 0.00000000e+00, 1.15671738e+02,\n 0.00000000e+00, 1.11774702e+01, 0.00000000e+00, 1.18346222e+02,\n 4.88543844e+00, 0.00000000e+00, 2.01805534e+01, 0.00000000e+00,\n 2.12446842e+01, 1.83667322e+03, 1.10298973e+02, 1.05063820e+02,\n 1.02085327e+02, 5.40686829e+02, 5.52071411e+02, 9.70447540e+00,\n 2.93981689e+02, 0.00000000e+00, 1.40510938e+03, 0.00000000e+00,\n 2.17932873e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.59221692e+03,\n 5.81707115e+01, 2.05497147e+02, 4.11889458e+00, 0.00000000e+00,\n 9.90064163e+01, 1.85190918e+02, 0.00000000e+00, 5.36696777e+01,\n 0.00000000e+00, 1.23687191e+01, 0.00000000e+00, 1.18544815e+02,\n 4.01192999e+00, 0.00000000e+00, 1.37277908e+01, 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0.00000000e+00,\n 2.18327694e+01, 3.31531372e+01, 0.00000000e+00, 2.23797059e+00,\n 0.00000000e+00, 4.08083010e+00, 0.00000000e+00, 3.53823814e+01,\n 0.00000000e+00, 0.00000000e+00, 3.89626789e+00, 0.00000000e+00,\n 7.81106329e+00, 2.53539169e+02, 1.63877888e+01, 1.75677128e+01,\n 1.59551458e+01, 8.14266968e+01, 7.36914291e+01, 0.00000000e+00,\n 4.22274361e+01, 0.00000000e+00, 1.69486771e+02, 0.00000000e+00,\n 4.03181028e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.67683014e+02,\n 1.09591084e+01, 5.43358192e+01, 0.00000000e+00, 0.00000000e+00,\n 2.56980515e+01, 4.12361984e+01, 0.00000000e+00, 9.24052715e+00,\n 0.00000000e+00, 5.83108521e+00, 0.00000000e+00, 4.37144547e+01,\n 0.00000000e+00, 0.00000000e+00, 3.02453899e+00, 0.00000000e+00,\n 8.30662727e+00, 3.59874359e+02, 2.01350842e+01, 2.28669472e+01,\n 2.01652470e+01, 1.13109261e+02, 9.62948227e+01, 0.00000000e+00,\n 5.70803528e+01, 0.00000000e+00, 2.93905701e+02, 0.00000000e+00,\n 5.44125462e+00],\n [0.00000000e+00, 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2.91849274e+02, 0.00000000e+00, 1.72620813e+03, 0.00000000e+00,\n 1.98730621e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.36543555e+03,\n 5.07095413e+01, 1.75456192e+02, 3.48021126e+00, 0.00000000e+00,\n 8.81773376e+01, 1.52885529e+02, 0.00000000e+00, 3.08647785e+01,\n 0.00000000e+00, 1.12876577e+01, 0.00000000e+00, 1.06171753e+02,\n 6.82970762e+00, 0.00000000e+00, 1.19102306e+01, 0.00000000e+00,\n 1.46281538e+01, 1.43978613e+03, 6.68909531e+01, 8.68928070e+01,\n 7.61348877e+01, 4.29912231e+02, 3.71561951e+02, 1.22141814e+00,\n 2.14170242e+02, 0.00000000e+00, 1.24046948e+03, 0.00000000e+00,\n 1.56100063e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.91713074e+02,\n 2.43692684e+01, 9.02391891e+01, 1.40075493e+00, 0.00000000e+00,\n 5.13376198e+01, 6.45811462e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 9.62460518e+00, 0.00000000e+00, 6.51945038e+01,\n 4.56952524e+00, 0.00000000e+00, 9.89264011e+00, 0.00000000e+00,\n 7.51607180e+00, 7.05480530e+02, 3.48130226e+01, 4.87190933e+01,\n 3.89913788e+01, 2.12209229e+02, 1.62274292e+02, 0.00000000e+00,\n 9.56757431e+01, 0.00000000e+00, 5.87002380e+02, 0.00000000e+00,\n 1.11238451e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.98945984e+02,\n 2.66432457e+01, 8.88701859e+01, 2.48705864e+00, 0.00000000e+00,\n 5.46278725e+01, 5.32030487e+01, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 9.44252586e+00, 0.00000000e+00, 7.36620789e+01,\n 3.92125058e+00, 0.00000000e+00, 4.85012436e+00, 0.00000000e+00,\n 1.06557236e+01, 8.01627563e+02, 3.00312252e+01, 5.25434647e+01,\n 4.29966469e+01, 2.43819046e+02, 1.67164597e+02, 0.00000000e+00,\n 1.07224602e+02, 0.00000000e+00, 7.57950500e+02, 0.00000000e+00,\n 8.94636440e+00],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.27565625e+03,\n 5.99731560e+01, 2.32435898e+02, 7.51527643e+00, 0.00000000e+00,\n 1.01898491e+02, 2.22445404e+02, 0.00000000e+00, 9.66252899e+01,\n 0.00000000e+00, 1.26237106e+01, 0.00000000e+00, 1.33111145e+02,\n 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0.00000000e+00, 7.98875122e+01,\n 0.00000000e+00, 1.26880732e+01, 0.00000000e+00, 1.41385422e+02,\n 1.84908161e+01, 0.00000000e+00, 1.89843197e+01, 0.00000000e+00,\n 1.92496567e+01, 1.78076331e+03, 8.69109573e+01, 9.72106857e+01,\n 9.95759888e+01, 5.30059631e+02, 4.88440613e+02, 7.05109501e+00,\n 2.79021698e+02, 0.00000000e+00, 1.49093872e+03, 0.00000000e+00,\n 1.93416214e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.40435327e+03,\n 7.96160812e+01, 2.74709412e+02, 7.28865623e+00, 0.00000000e+00,\n 1.40223221e+02, 2.30946396e+02, 0.00000000e+00, 5.67420120e+01,\n 0.00000000e+00, 1.59636965e+01, 0.00000000e+00, 1.71977173e+02,\n 1.44842997e+01, 0.00000000e+00, 1.76037769e+01, 0.00000000e+00,\n 2.41355286e+01, 2.31455688e+03, 9.92690811e+01, 1.33758759e+02,\n 1.21699852e+02, 6.91006836e+02, 5.84414795e+02, 5.76593876e+00,\n 3.44562256e+02, 0.00000000e+00, 2.05047095e+03, 0.00000000e+00,\n 2.35584011e+01]], dtype=float32), array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.81645117e+03,\n 5.73262672e+01, 2.08653137e+02, 0.00000000e+00, 0.00000000e+00,\n 9.69578476e+01, 1.98941696e+02, 0.00000000e+00, 9.04911575e+01,\n 0.00000000e+00, 7.91587877e+00, 0.00000000e+00, 1.13878319e+02,\n 1.01404676e+01, 0.00000000e+00, 1.04818840e+01, 0.00000000e+00,\n 1.68267937e+01, 1.68323462e+03, 7.99212952e+01, 9.12102509e+01,\n 9.08490982e+01, 5.14901794e+02, 4.74845154e+02, 7.28929234e+00,\n 2.68592896e+02, 0.00000000e+00, 1.49202295e+03, 0.00000000e+00,\n 1.54818077e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.19972119e+03,\n 7.23656235e+01, 2.60661316e+02, 0.00000000e+00, 0.00000000e+00,\n 1.17487267e+02, 2.56921143e+02, 0.00000000e+00, 1.12919205e+02,\n 0.00000000e+00, 1.30426912e+01, 0.00000000e+00, 1.35755280e+02,\n 8.04864025e+00, 0.00000000e+00, 1.74657192e+01, 0.00000000e+00,\n 2.24858055e+01, 2.10634644e+03, 1.03828117e+02, 1.13918213e+02,\n 1.10273598e+02, 6.43429871e+02, 5.99453308e+02, 1.11681604e+01,\n 3.35767426e+02, 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3.21543922e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.30467554e+03,\n 4.58726578e+01, 1.62805161e+02, 0.00000000e+00, 0.00000000e+00,\n 7.72052307e+01, 1.45803238e+02, 0.00000000e+00, 5.82353058e+01,\n 0.00000000e+00, 4.84441042e+00, 0.00000000e+00, 8.91455536e+01,\n 1.82029228e+01, 0.00000000e+00, 9.19785500e+00, 0.00000000e+00,\n 6.87362719e+00, 1.24801648e+03, 5.76685295e+01, 6.98249817e+01,\n 7.05576401e+01, 3.88858765e+02, 3.48087158e+02, 4.87508583e+00,\n 2.02986679e+02, 0.00000000e+00, 1.09717163e+03, 0.00000000e+00,\n 1.67342720e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.76897144e+03,\n 7.75588531e+01, 2.83945801e+02, 0.00000000e+00, 0.00000000e+00,\n 1.19718163e+02, 2.85278290e+02, 0.00000000e+00, 1.46296814e+02,\n 0.00000000e+00, 7.47533846e+00, 0.00000000e+00, 1.28417953e+02,\n 2.71251774e+01, 0.00000000e+00, 2.02679501e+01, 0.00000000e+00,\n 1.58585253e+01, 2.06654053e+03, 1.12170059e+02, 1.11980583e+02,\n 1.19979462e+02, 6.39072083e+02, 6.26412415e+02, 1.52780809e+01,\n 3.46791656e+02, 0.00000000e+00, 1.71264941e+03, 0.00000000e+00,\n 2.57154770e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.84675476e+03,\n 7.62052231e+01, 2.76101685e+02, 0.00000000e+00, 0.00000000e+00,\n 1.21747505e+02, 2.68747375e+02, 0.00000000e+00, 1.24793381e+02,\n 0.00000000e+00, 6.64080095e+00, 0.00000000e+00, 1.34668976e+02,\n 2.77964001e+01, 0.00000000e+00, 2.00332260e+01, 0.00000000e+00,\n 1.59632750e+01, 2.04365491e+03, 1.06216385e+02, 1.12330215e+02,\n 1.18139275e+02, 6.33433411e+02, 6.04659485e+02, 1.48398619e+01,\n 3.39701111e+02, 0.00000000e+00, 1.71923755e+03, 0.00000000e+00,\n 2.41960220e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.31125635e+03,\n 9.77032394e+01, 3.49557495e+02, 0.00000000e+00, 0.00000000e+00,\n 1.54182236e+02, 3.42095032e+02, 0.00000000e+00, 1.51119568e+02,\n 0.00000000e+00, 7.58272600e+00, 0.00000000e+00, 1.68813568e+02,\n 3.34695396e+01, 0.00000000e+00, 2.62258816e+01, 0.00000000e+00,\n 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1.53528249e+00, 3.13668785e+01,\n 1.60912359e+00, 3.83430481e+00, 5.70812130e+00, 8.40927124e+01,\n 9.65860271e+00, 0.00000000e+00, 7.17352200e+00, 0.00000000e+00,\n 6.94558001e+00, 1.19466833e+03, 5.40232925e+01, 6.93822327e+01,\n 6.20420303e+01, 3.55627197e+02, 3.02623535e+02, 4.04065418e+00,\n 1.76660980e+02, 0.00000000e+00, 1.07020374e+03, 0.00000000e+00,\n 1.32747297e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.20485913e+03,\n 8.94616013e+01, 2.98075653e+02, 9.01750278e+00, 4.81408501e+00,\n 1.43100525e+02, 2.81365784e+02, 2.86670136e+00, 9.05313950e+01,\n 6.19439936e+00, 7.83031273e+00, 1.57268543e+01, 1.62794388e+02,\n 2.20580101e+01, 0.00000000e+00, 1.73655567e+01, 0.00000000e+00,\n 1.71516743e+01, 2.42056543e+03, 1.20006226e+02, 1.38516525e+02,\n 1.29799377e+02, 7.22889160e+02, 6.51418274e+02, 1.32570620e+01,\n 3.69805847e+02, 0.00000000e+00, 2.08616675e+03, 0.00000000e+00,\n 2.35801353e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.32967163e+03,\n 9.33370667e+01, 3.03875854e+02, 9.32527828e+00, 4.71862221e+00,\n 1.49477982e+02, 2.82822754e+02, 3.68166947e+00, 7.80091171e+01,\n 4.56242418e+00, 6.55079651e+00, 1.51214571e+01, 1.69134628e+02,\n 2.38574352e+01, 0.00000000e+00, 1.75318375e+01, 0.00000000e+00,\n 1.64108906e+01, 2.49745605e+03, 1.21720428e+02, 1.43537689e+02,\n 1.33183731e+02, 7.45275024e+02, 6.59391174e+02, 1.19996042e+01,\n 3.77851074e+02, 0.00000000e+00, 2.16966577e+03, 0.00000000e+00,\n 2.41019936e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.78613501e+03,\n 1.08582001e+02, 3.63910980e+02, 1.03614893e+01, 4.90665340e+00,\n 1.74116272e+02, 3.41402466e+02, 4.12953615e+00, 1.15733849e+02,\n 7.76174593e+00, 1.26160469e+01, 1.59932318e+01, 2.04240143e+02,\n 2.52804775e+01, 0.00000000e+00, 2.12841148e+01, 0.00000000e+00,\n 2.11202869e+01, 2.98339258e+03, 1.45563156e+02, 1.69458893e+02,\n 1.58712585e+02, 8.91316284e+02, 7.97236145e+02, 1.67210960e+01,\n 4.54958496e+02, 0.00000000e+00, 2.59270923e+03, 0.00000000e+00,\n 2.98211937e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.31319165e+03,\n 9.24594345e+01, 3.14707672e+02, 1.05166435e+01, 3.86792469e+00,\n 1.45321732e+02, 2.94264557e+02, 2.98040795e+00, 1.17556236e+02,\n 6.44337320e+00, 7.44614506e+00, 1.48631115e+01, 1.77273438e+02,\n 2.37289925e+01, 0.00000000e+00, 2.27019825e+01, 0.00000000e+00,\n 2.00425930e+01, 2.51580713e+03, 1.24382195e+02, 1.36017944e+02,\n 1.34419586e+02, 7.53216064e+02, 6.85080505e+02, 1.87775650e+01,\n 3.89999451e+02, 0.00000000e+00, 2.17579395e+03, 0.00000000e+00,\n 2.51110172e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.40525269e+03,\n 1.01580826e+02, 3.54755615e+02, 9.69995689e+00, 4.49277306e+00,\n 1.58360352e+02, 3.42842865e+02, 4.87362146e+00, 1.36574844e+02,\n 5.64500666e+00, 1.03485851e+01, 1.28236609e+01, 1.84426941e+02,\n 2.62529945e+01, 0.00000000e+00, 2.41691036e+01, 0.00000000e+00,\n 1.60922585e+01, 2.79414648e+03, 1.45052811e+02, 1.54090851e+02,\n 1.51724396e+02, 8.35002686e+02, 7.77643982e+02, 1.74662971e+01,\n 4.35870544e+02, 0.00000000e+00, 2.36559204e+03, 0.00000000e+00,\n 3.21159058e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.43998462e+03,\n 9.67326508e+01, 3.30661652e+02, 9.03832531e+00, 4.98828411e+00,\n 1.54551315e+02, 3.12575745e+02, 4.93091631e+00, 1.15017883e+02,\n 6.01265192e+00, 1.06902637e+01, 1.38668947e+01, 1.81420105e+02,\n 2.35774250e+01, 0.00000000e+00, 1.96707268e+01, 0.00000000e+00,\n 1.66154156e+01, 2.67396606e+03, 1.32990280e+02, 1.50907257e+02,\n 1.43710205e+02, 7.99198975e+02, 7.24952637e+02, 1.38479433e+01,\n 4.11448212e+02, 0.00000000e+00, 2.30596411e+03, 0.00000000e+00,\n 2.89955673e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.15509058e+03,\n 4.10639687e+01, 1.30876862e+02, 3.69008708e+00, 2.74098158e+00,\n 7.22309723e+01, 1.05258118e+02, 1.75011671e+00, 0.00000000e+00,\n 2.34703588e+00, 8.45115376e+00, 6.07798147e+00, 8.79005814e+01,\n 7.39343929e+00, 0.00000000e+00, 5.07752514e+00, 0.00000000e+00,\n 7.85224915e+00, 1.11482471e+03, 4.87029610e+01, 7.14661179e+01,\n 5.92576752e+01, 3.35763550e+02, 2.65435364e+02, 9.82453108e-01,\n 1.60491135e+02, 0.00000000e+00, 1.00811359e+03, 0.00000000e+00,\n 1.25818090e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.42785913e+03,\n 9.27930679e+01, 3.10073761e+02, 9.62946033e+00, 5.41628456e+00,\n 1.54844986e+02, 2.81278320e+02, 3.53425622e+00, 7.73458176e+01,\n 6.97336626e+00, 1.33786049e+01, 1.56919012e+01, 1.83693420e+02,\n 2.39909668e+01, 0.00000000e+00, 1.61777248e+01, 0.00000000e+00,\n 1.84632874e+01, 2.54570215e+03, 1.20022408e+02, 1.48579956e+02,\n 1.36690186e+02, 7.64891235e+02, 6.66124146e+02, 1.24369020e+01,\n 3.86026123e+02, 0.00000000e+00, 2.23052515e+03, 0.00000000e+00,\n 2.40123444e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.42075439e+03,\n 9.12796173e+01, 3.00158539e+02, 9.69894505e+00, 4.80454683e+00,\n 1.50141769e+02, 2.70470703e+02, 4.41983652e+00, 7.30851669e+01,\n 4.47027063e+00, 9.79296494e+00, 1.38986731e+01, 1.77959595e+02,\n 2.55324516e+01, 0.00000000e+00, 1.61369934e+01, 0.00000000e+00,\n 1.56969519e+01, 2.48755347e+03, 1.15610863e+02, 1.42207306e+02,\n 1.32256912e+02, 7.45504883e+02, 6.44422791e+02, 1.15329180e+01,\n 3.75174591e+02, 0.00000000e+00, 2.19317334e+03, 0.00000000e+00,\n 2.39019547e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.22074365e+03,\n 1.24839195e+02, 4.19735138e+02, 1.28833017e+01, 5.78775883e+00,\n 2.01895782e+02, 3.87238922e+02, 6.22731066e+00, 1.28106857e+02,\n 7.10016298e+00, 1.35485191e+01, 1.75238132e+01, 2.41701996e+02,\n 3.34723511e+01, 0.00000000e+00, 2.57486382e+01, 0.00000000e+00,\n 2.29078102e+01, 3.42033643e+03, 1.64766891e+02, 1.91643768e+02,\n 1.82769730e+02, 1.02492590e+03, 9.08766907e+02, 1.93724861e+01,\n 5.22870117e+02, 0.00000000e+00, 2.98231787e+03, 0.00000000e+00,\n 3.38729210e+01],\n [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.68631030e+03,\n 1.02514137e+02, 3.46440491e+02, 1.17200270e+01, 4.44264126e+00,\n 1.64948853e+02, 3.14904236e+02, 3.75663972e+00, 1.15573288e+02,\n 6.20845842e+00, 9.98964691e+00, 1.50610037e+01, 2.05948090e+02,\n 2.81718235e+01, 0.00000000e+00, 2.37946434e+01, 0.00000000e+00,\n 2.06741734e+01, 2.80548096e+03, 1.32743881e+02, 1.52443115e+02,\n 1.49292175e+02, 8.42329590e+02, 7.46525635e+02, 1.93508720e+01,\n 4.31399841e+02, 0.00000000e+00, 2.46058691e+03, 0.00000000e+00,\n 2.73313580e+01]], dtype=float32)]\n id sales\n0 3000888 0.000000\n1 3000889 0.000000\n2 3000890 0.000000\n3 3000891 440.921967\n4 3000892 29.550156\n... ... ...\n28375 3029263 431.399841\n28376 3029264 0.000000\n28377 3029265 2460.586914\n28378 3029266 0.000000\n28379 3029267 27.331358\n\n[28512 rows x 2 columns]\n","output_type":"stream"}]}]} \ No newline at end of file diff --git a/Store Sales Prediction Using Deep Learning/Requirements.txt b/Store Sales Prediction Using Deep Learning/Requirements.txt new file mode 100644 index 000000000..5da87ee15 --- /dev/null +++ b/Store Sales Prediction Using Deep Learning/Requirements.txt @@ -0,0 +1,7 @@ +tensorflow +keras +numpy +scipy +pandas +matplotlib +seaborn \ No newline at end of file