104 lines
2.9 KiB
Python
104 lines
2.9 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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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %matplotlib inline
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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 IPython.core.interactiveshell import InteractiveShell
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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_classification_models,
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)
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InteractiveShell.ast_node_interactivity = "all"
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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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CV_METHOD = "logo" # logo, half_logo, 5kfold
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# Cross-validation method (could be regarded as a hyperparameter)
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N_SL = 3 # Number of largest/smallest accuracies (of particular CV) outputs
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UNDERSAMPLING = False
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# (bool) If True this will train and test data on balanced dataset
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# (using undersampling method)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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model_input = pd.read_csv(
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"E:/STRAWresults/20230415/daily/input_PANAS_negative_affect_mean.csv"
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)
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# model_input =
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# model_input[model_input.columns.drop(
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# list(model_input.filter(regex='empatica_temperature'))
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# )]
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# bins = [-10, 0, 10] # bins for z-scored targets
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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model_input["target"], edges = pd.cut(
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model_input.target, bins=bins, labels=["low", "high"], retbins=True, right=True
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) # ['low', 'medium', 'high']
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model_input["target"].value_counts(), edges
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# model_input = model_input[model_input['target'] != "medium"]
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model_input["target"] = (
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model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
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)
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# UnderSampling
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if UNDERSAMPLING:
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no_stress = model_input[model_input["target"] == 0]
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stress = model_input[model_input["target"] == 1]
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no_stress = no_stress.sample(n=len(stress))
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model_input = pd.concat([stress, no_stress], axis=0)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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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_classification_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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