67 lines
1.4 KiB
Python
67 lines
1.4 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.14.5
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %%
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import os
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import sys
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import pandas as pd
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from machine_learning.helper import (
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impute_encode_categorical_features,
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prepare_cross_validator,
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prepare_sklearn_data_format,
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run_all_regression_models,
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)
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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# %%
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model_input = pd.read_csv(
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"../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv"
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)
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# %%
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model_input = model_input[model_input["local_segment"].str.contains("daily")]
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# %%
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CV_METHOD = "logo" # logo, half_logo, 5kfold
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model_input_encoded = impute_encode_categorical_features(model_input)
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# %%
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data_x, data_y, data_groups = prepare_sklearn_data_format(
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model_input_encoded, CV_METHOD
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)
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cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
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# %%
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data_y.head()
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# %%
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data_y.tail()
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# %%
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data_y.shape
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# %%
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scores = run_all_regression_models(data_x, data_y, data_groups, cross_validator)
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# %%
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scores.to_csv(
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"../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
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index=False,
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)
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