stress_at_work_analysis/exploration/ml_pipeline.py

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# ---
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# %%
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import recall_score, f1_score
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
from machine_learning.cross_validation import CrossValidation
from machine_learning.preprocessing import Preprocessing
from machine_learning.feature_selection import FeatureSelection
# %%
df = pd.read_csv("../data/stressfulness_event_with_speech/input_appraisal_stressfulness_event_mean.csv")
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
df.set_index(index_columns, inplace=True)
# Create binary target
bins = [-1, 0, 4] # bins for stressfulness (0-4) target
df['target'], edges = pd.cut(df.target, bins=bins, labels=[0, 1], retbins=True, right=True) #['low', 'medium', 'high']
nan_cols = df.columns[df.isna().any()].tolist()
df[nan_cols] = df[nan_cols].fillna(round(df[nan_cols].median(), 0))
cv = CrossValidation(data=df, cv_method="logo")
categorical_columns = ["gender", "startlanguage", "mostcommonactivity", "homelabel"]
interval_feature_list, other_feature_list = [], []
# %%
for split in cv.get_splits():
train_X, train_y, test_X, test_y = cv.get_train_test_sets(split)
pre = Preprocessing(train_X, train_y, test_X, test_y)
pre.one_hot_encode_train_and_test_sets(categorical_columns)
train_X, train_y, test_X, test_y = pre.get_train_test_sets()
print(train_X.shape, test_X.shape)
# Predict before feature selection
rfc = RandomForestClassifier(n_estimators=10)
rfc.fit(train_X, train_y)
predictions = rfc.predict(test_X)
print("Recall:", recall_score(test_y, predictions))
print("F1:", f1_score(test_y, predictions))
# Feature selection on train set
train_groups, test_groups = cv.get_groups_sets(split)
fs = FeatureSelection(train_X, train_y, train_groups)
selected_features = fs.select_features(n_min=20, n_max=29, k=40,
ml_type="classification_bin",
metric="recall", n_tolerance=20)
train_X = train_X[selected_features]
test_X = test_X[selected_features]
print(selected_features)
print(len(selected_features))
# Predict after feature selection
rfc = RandomForestClassifier(n_estimators=500)
rfc.fit(train_X, train_y)
predictions = rfc.predict(test_X)
print("Recall:", recall_score(test_y, predictions))
print("F1:", f1_score(test_y, predictions))
break
# %%