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stress_at_work_analysis
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Add xgboost to dependencies and reformat helper.py.

master
junos 2023-04-21 21:33:06 +02:00
parent 59552c18a9
commit 583ee82e80
2 changed files with 131 additions and 138 deletions
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3
config/environment.yml
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@ -22,4 +22,5 @@ dependencies:
- scikit-learn
- sqlalchemy
- statsmodels
- tabulate
- tabulate
- xgboost

266
machine_learning/helper.py
View File

@ -1,15 +1,18 @@
from pathlib import Path
from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble, naive_bayes, neighbors, tree
from sklearn.model_selection import LeaveOneGroupOut, cross_validate, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor, DummyClassifier
from xgboost import XGBRegressor, XGBClassifier
import xgboost as xg
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import (
ensemble,
gaussian_process,
kernel_ridge,
linear_model,
naive_bayes,
svm,
)
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
from xgboost import XGBClassifier, XGBRegressor
def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
@ -65,28 +68,48 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P
full_path = folder / export_filename
return full_path
def insert_row(df, row):
return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
def prepare_regression_model_input(input_csv):
def prepare_regression_model_input(input_csv):
model_input = pd.read_csv(input_csv)
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
model_input.set_index(index_columns, inplace=True)
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
data_x, data_y, data_groups = (
model_input.drop(["target", "pid"], axis=1),
model_input["target"],
model_input["pid"],
)
categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames = [
"gender",
"startlanguage",
"limesurvey_demand_control_ratio_quartile",
]
additional_categorical_features = [
col
for col in data_x.columns
if "mostcommonactivity" in col or "homelabel" in col
]
categorical_feature_colnames += additional_categorical_features
#TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
# TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
categorical_features = data_x[categorical_feature_colnames].copy()
mode_categorical_features = categorical_features.mode().iloc[0]
# fillna with mode
categorical_features = categorical_features.fillna(mode_categorical_features)
# one-hot encoding
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
categorical_features = categorical_features.apply(
lambda col: col.astype("category")
)
if not categorical_features.empty:
categorical_features = pd.get_dummies(categorical_features)
@ -108,7 +131,7 @@ def run_all_regression_models(input_csv):
data_y,
groups=data_groups,
)
metrics = ['r2', 'neg_mean_absolute_error', 'neg_root_mean_squared_error']
metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"]
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "max", "nanmedian"])
@ -121,13 +144,13 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Dummy model:")
print("R^2: ", np.nanmedian(dummy_regr_scores['test_r2']))
print("R^2: ", np.nanmedian(dummy_regr_scores["test_r2"]))
scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "dummy"
scores = pd.concat([scores, scores_df])
@ -139,17 +162,17 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Linear regression:")
print("R^2: ", np.nanmedian(lin_reg_scores['test_r2']))
print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"]))
scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "linear_reg"
scores = pd.concat([scores, scores_df])
ridge_reg = linear_model.Ridge(alpha=.5)
ridge_reg = linear_model.Ridge(alpha=0.5)
ridge_reg_scores = cross_validate(
ridge_reg,
X=data_x,
@ -157,16 +180,15 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Ridge regression")
scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "ridge_reg"
scores = pd.concat([scores, scores_df])
lasso_reg = linear_model.Lasso(alpha=0.1)
lasso_reg_score = cross_validate(
lasso_reg,
@ -175,12 +197,12 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Lasso regression")
scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "lasso_reg"
scores = pd.concat([scores, scores_df])
@ -192,12 +214,12 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Bayesian Ridge")
scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "bayesian_ridge"
scores = pd.concat([scores, scores_df])
@ -209,29 +231,23 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("RANSAC (outlier robust regression)")
scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "RANSAC"
scores = pd.concat([scores, scores_df])
svr = svm.SVR()
svr_score = cross_validate(
svr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
svr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Support vector regression")
scores_df = pd.DataFrame(svr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "SVR"
scores = pd.concat([scores, scores_df])
@ -243,80 +259,56 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Kernel Ridge regression")
scores_df = pd.DataFrame(kridge_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "kernel_ridge"
scores = pd.concat([scores, scores_df])
gpr = gaussian_process.GaussianProcessRegressor()
gpr_score = cross_validate(
gpr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
gpr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Gaussian Process Regression")
scores_df = pd.DataFrame(gpr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "gaussian_proc"
scores = pd.concat([scores, scores_df])
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_validate(
rfr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
rfr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Random Forest Regression")
scores_df = pd.DataFrame(rfr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
xgb = XGBRegressor()
xgb_score = cross_validate(
xgb,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
xgb, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("XGBoost Regressor")
scores_df = pd.DataFrame(xgb_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "XGBoost"
scores = pd.concat([scores, scores_df])
ada = ensemble.AdaBoostRegressor()
ada_score = cross_validate(
ada,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
ada, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("ADA Boost Regressor")
scores_df = pd.DataFrame(ada_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "ADA_boost"
scores = pd.concat([scores, scores_df])
@ -324,7 +316,7 @@ def run_all_regression_models(input_csv):
def run_all_classification_models(data_x, data_y, data_groups, cv_method):
metrics = ['accuracy', 'average_precision', 'recall', 'f1']
metrics = ["accuracy", "average_precision", "recall", "f1"]
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "max", "mean"])
@ -332,127 +324,127 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
dummy_class = DummyClassifier(strategy="most_frequent")
dummy_score = cross_validate(
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score='raise',
scoring=metrics
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score="raise",
scoring=metrics,
)
print("Dummy")
scores_df = pd.DataFrame(dummy_score)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "Dummy"
scores = pd.concat([scores, scores_df])
logistic_regression = linear_model.LogisticRegression()
log_reg_scores = cross_validate(
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Logistic regression")
scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "logistic_reg"
scores = pd.concat([scores, scores_df])
svc = svm.SVC()
svc_scores = cross_validate(
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Support Vector Machine")
scores_df = pd.DataFrame(svc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "svc"
scores = pd.concat([scores, scores_df])
gaussian_nb = naive_bayes.GaussianNB()
gaussian_nb_scores = cross_validate(
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Gaussian Naive Bayes")
scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "gaussian_naive_bayes"
scores = pd.concat([scores, scores_df])
sgdc = linear_model.SGDClassifier()
sgdc_scores = cross_validate(
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Stochastic Gradient Descent")
scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "stochastic_gradient_descent"
scores = pd.concat([scores, scores_df])
rfc = ensemble.RandomForestClassifier()
rfc_scores = cross_validate(
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Random Forest")
scores_df = pd.DataFrame(rfc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
xgb_classifier = XGBClassifier()
xgb_scores = cross_validate(
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("XGBoost")
scores_df = pd.DataFrame(xgb_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "xgboost"
scores = pd.concat([scores, scores_df])