stress_at_work_analysis/exploration/ml_pipeline_classification.py

124 lines
3.6 KiB
Python

# ---
# jupyter:
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# text_representation:
# extension: .py
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# language: python
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# ---
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# from IPython.core.interactiveshell import InteractiveShell
from pathlib import Path
# matplotlib inline
# import os
# import sys
import pandas as pd
from machine_learning.helper import (
impute_encode_categorical_features,
prepare_cross_validator,
prepare_sklearn_data_format,
run_all_classification_models,
)
# InteractiveShell.ast_node_interactivity = "all"
#
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
# sys.path.append(nb_dir)
# %%
CV_METHOD = "logo" # logo, half_logo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
print("CV_METHOD: " + CV_METHOD)
N_SL = 3 # Number of largest/smallest accuracies (of particular CV) outputs
UNDERSAMPLING = False
# (bool) If True this will train and test data on balanced dataset
# (using undersampling method)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
PATH_BASE = Path("E:/STRAWresults/20230415")
SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
TARGET_VARIABLE = "appraisal_stressfulness"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
PATH_FULL = (
PATH_BASE
/ SEGMENT_LENGTH
/ ("input_" + TARGET_VARIABLE + "_" + SEGMENT_TYPE + "_mean.csv")
)
model_input = pd.read_csv(PATH_FULL)
if SEGMENT_LENGTH == "daily":
DAY_LENGTH = "daily" # or "working"
print(DAY_LENGTH)
model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input["target"].value_counts()
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets
BINS = [-1, 0, 4] # bins for stressfulness (0-4) target
print("BINS: ", BINS)
model_input["target"], edges = pd.cut(
model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True
) # ['low', 'medium', 'high']
print(model_input["target"].value_counts())
REMOVE_MEDIUM = True
if ("medium" in model_input["target"]) and REMOVE_MEDIUM:
model_input = model_input[model_input["target"] != "medium"]
print(model_input["target"].value_counts())
model_input["target"] = (
model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# UnderSampling
if UNDERSAMPLING:
no_stress = model_input[model_input["target"] == 0]
stress = model_input[model_input["target"] == 1]
no_stress = no_stress.sample(n=len(stress))
model_input = pd.concat([stress, no_stress], axis=0)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
model_input_encoded, CV_METHOD
)
cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
# %%
data_y.head()
# %%
data_y.tail()
# %%
data_y.shape
# %%
scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
TARGET_VARIABLE + SEGMENT_LENGTH + "_classification_" + CV_METHOD + ".csv"
)
scores.to_csv(path_output_full, index=False)