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stress_at_work_analysis
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ml_pipeline
junos 2023-01-04 21:25:12 +01:00
parent 61d786b2ca
commit b0b9edccc4
1 changed files with 51 additions and 46 deletions
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exploration/ml_pipeline_classification.py
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@ -13,7 +13,7 @@
# name: straw2analysis # name: straw2analysis
# --- # ---
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
# %matplotlib inline # %matplotlib inline
import os import os
import sys import sys
@ -41,20 +41,23 @@ if nb_dir not in sys.path:
# %% [markdown] # %% [markdown]
# ## Set script's parameters # ## Set script's parameters
#
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
cv_method_str = '5kfold' # logo, half_logo, 5kfold # Cross-validation method (could be regarded as a hyperparameter) cv_method_str = '5kfold' # logo, half_logo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
undersampling = True # (bool) If True this will train and test data on balanced dataset (using undersampling method) undersampling = True # (bool) If True this will train and test data on balanced dataset (using undersampling method)
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv") model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv")
# model_input = model_input[model_input.columns.drop(list(model_input.filter(regex='empatica_temperature')))] # model_input = model_input[model_input.columns.drop(list(model_input.filter(regex='empatica_temperature')))]
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
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) model_input.set_index(index_columns, inplace=True)
model_input['target'].value_counts() model_input['target'].value_counts()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets # bins = [-10, 0, 10] # bins for z-scored targets
bins = [-1, 0, 4] # bins for stressfulness (0-4) target bins = [-1, 0, 4] # bins for stressfulness (0-4) target
model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high'] model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
@ -64,7 +67,7 @@ model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x
model_input['target'].value_counts() model_input['target'].value_counts()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
# UnderSampling # UnderSampling
if undersampling: if undersampling:
model_input.groupby("pid").count() model_input.groupby("pid").count()
@ -77,7 +80,7 @@ if undersampling:
model_input["target"].value_counts() model_input["target"].value_counts()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
if cv_method_str == 'half_logo': if cv_method_str == 'half_logo':
model_input['pid_index'] = model_input.groupby('pid').cumcount() model_input['pid_index'] = model_input.groupby('pid').cumcount()
model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count') model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
@ -90,7 +93,7 @@ else:
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"]
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
categorical_feature_colnames = ["gender", "startlanguage"] categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col] additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features categorical_feature_colnames += additional_categorical_features
@ -110,7 +113,7 @@ numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
train_x = pd.concat([numerical_features, categorical_features], axis=1) train_x = pd.concat([numerical_features, categorical_features], axis=1)
train_x.dtypes train_x.dtypes
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method
if cv_method_str == 'logo' or cv_method_str == 'half_logo': if cv_method_str == 'logo' or cv_method_str == 'half_logo':
cv_method = LeaveOneGroupOut() cv_method = LeaveOneGroupOut()
@ -120,14 +123,16 @@ if cv_method_str == 'logo' or cv_method_str == 'half_logo':
groups=data_groups, groups=data_groups,
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
imputer = SimpleImputer(missing_values=np.nan, strategy='median') imputer = SimpleImputer(missing_values=np.nan, strategy='median')
# %% [markdown] # %% [markdown]
# ### Baseline: Dummy Classifier (most frequent) # ### Baseline: Dummy Classifier (most frequent)
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
dummy_class = DummyClassifier(strategy="most_frequent") dummy_class = DummyClassifier(strategy="most_frequent")
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
dummy_classifier = cross_validate( dummy_classifier = cross_validate(
dummy_class, dummy_class,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -138,7 +143,7 @@ dummy_classifier = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(dummy_classifier['test_accuracy'])) print("Acc (median)", np.nanmedian(dummy_classifier['test_accuracy']))
print("Acc (mean)", np.mean(dummy_classifier['test_accuracy'])) print("Acc (mean)", np.mean(dummy_classifier['test_accuracy']))
print("Precision", np.mean(dummy_classifier['test_precision'])) print("Precision", np.mean(dummy_classifier['test_precision']))
@ -150,10 +155,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accur
# %% [markdown] # %% [markdown]
# ### Logistic Regression # ### Logistic Regression
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
logistic_regression = linear_model.LogisticRegression() logistic_regression = linear_model.LogisticRegression()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
log_reg_scores = cross_validate( log_reg_scores = cross_validate(
logistic_regression, logistic_regression,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -163,7 +168,7 @@ log_reg_scores = cross_validate(
n_jobs=-1, n_jobs=-1,
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(log_reg_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(log_reg_scores['test_accuracy']))
print("Acc (mean)", np.mean(log_reg_scores['test_accuracy'])) print("Acc (mean)", np.mean(log_reg_scores['test_accuracy']))
print("Precision", np.mean(log_reg_scores['test_precision'])) print("Precision", np.mean(log_reg_scores['test_precision']))
@ -175,10 +180,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(log_reg_scores['test_accurac
# %% [markdown] # %% [markdown]
# ### Support Vector Machine # ### Support Vector Machine
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
svc = svm.SVC() svc = svm.SVC()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
svc_scores = cross_validate( svc_scores = cross_validate(
svc, svc,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -188,7 +193,7 @@ svc_scores = cross_validate(
n_jobs=-1, n_jobs=-1,
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(svc_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(svc_scores['test_accuracy']))
print("Acc (mean)", np.mean(svc_scores['test_accuracy'])) print("Acc (mean)", np.mean(svc_scores['test_accuracy']))
print("Precision", np.mean(svc_scores['test_precision'])) print("Precision", np.mean(svc_scores['test_precision']))
@ -200,10 +205,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(svc_scores['test_accuracy'],
# %% [markdown] # %% [markdown]
# ### Gaussian Naive Bayes # ### Gaussian Naive Bayes
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
gaussian_nb = naive_bayes.GaussianNB() gaussian_nb = naive_bayes.GaussianNB()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
gaussian_nb_scores = cross_validate( gaussian_nb_scores = cross_validate(
gaussian_nb, gaussian_nb,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -214,7 +219,7 @@ gaussian_nb_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(gaussian_nb_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(gaussian_nb_scores['test_accuracy']))
print("Acc (mean)", np.mean(gaussian_nb_scores['test_accuracy'])) print("Acc (mean)", np.mean(gaussian_nb_scores['test_accuracy']))
print("Precision", np.mean(gaussian_nb_scores['test_precision'])) print("Precision", np.mean(gaussian_nb_scores['test_precision']))
@ -226,10 +231,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gaussian_nb_scores['test_acc
# %% [markdown] # %% [markdown]
# ### Stochastic Gradient Descent Classifier # ### Stochastic Gradient Descent Classifier
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
sgdc = linear_model.SGDClassifier() sgdc = linear_model.SGDClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
sgdc_scores = cross_validate( sgdc_scores = cross_validate(
sgdc, sgdc,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -240,7 +245,7 @@ sgdc_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(sgdc_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(sgdc_scores['test_accuracy']))
print("Acc (mean)", np.mean(sgdc_scores['test_accuracy'])) print("Acc (mean)", np.mean(sgdc_scores['test_accuracy']))
print("Precision", np.mean(sgdc_scores['test_precision'])) print("Precision", np.mean(sgdc_scores['test_precision']))
@ -252,10 +257,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(sgdc_scores['test_accuracy']
# %% [markdown] # %% [markdown]
# ### K-nearest neighbors # ### K-nearest neighbors
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
knn = neighbors.KNeighborsClassifier() knn = neighbors.KNeighborsClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
knn_scores = cross_validate( knn_scores = cross_validate(
knn, knn,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -266,7 +271,7 @@ knn_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(knn_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(knn_scores['test_accuracy']))
print("Acc (mean)", np.mean(knn_scores['test_accuracy'])) print("Acc (mean)", np.mean(knn_scores['test_accuracy']))
print("Precision", np.mean(knn_scores['test_precision'])) print("Precision", np.mean(knn_scores['test_precision']))
@ -278,10 +283,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(knn_scores['test_accuracy'],
# %% [markdown] # %% [markdown]
# ### Decision Tree # ### Decision Tree
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
dtree = tree.DecisionTreeClassifier() dtree = tree.DecisionTreeClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
dtree_scores = cross_validate( dtree_scores = cross_validate(
dtree, dtree,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -292,7 +297,7 @@ dtree_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(dtree_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(dtree_scores['test_accuracy']))
print("Acc (mean)", np.mean(dtree_scores['test_accuracy'])) print("Acc (mean)", np.mean(dtree_scores['test_accuracy']))
print("Precision", np.mean(dtree_scores['test_precision'])) print("Precision", np.mean(dtree_scores['test_precision']))
@ -304,10 +309,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dtree_scores['test_accuracy'
# %% [markdown] # %% [markdown]
# ### Random Forest Classifier # ### Random Forest Classifier
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
rfc = ensemble.RandomForestClassifier() rfc = ensemble.RandomForestClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
rfc_scores = cross_validate( rfc_scores = cross_validate(
rfc, rfc,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -319,7 +324,7 @@ rfc_scores = cross_validate(
scoring=('accuracy', 'precision', 'recall', 'f1'), scoring=('accuracy', 'precision', 'recall', 'f1'),
return_estimator=True return_estimator=True
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(rfc_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(rfc_scores['test_accuracy']))
print("Acc (mean)", np.mean(rfc_scores['test_accuracy'])) print("Acc (mean)", np.mean(rfc_scores['test_accuracy']))
print("Precision", np.mean(rfc_scores['test_precision'])) print("Precision", np.mean(rfc_scores['test_precision']))
@ -331,7 +336,7 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(rfc_scores['test_accuracy'],
# %% [markdown] # %% [markdown]
# ### Feature importance (RFC) # ### Feature importance (RFC)
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
rfc_es_fimp = pd.DataFrame(columns=list(train_x.columns)) rfc_es_fimp = pd.DataFrame(columns=list(train_x.columns))
for idx, estimator in enumerate(rfc_scores['estimator']): for idx, estimator in enumerate(rfc_scores['estimator']):
feature_importances = pd.DataFrame(estimator.feature_importances_, feature_importances = pd.DataFrame(estimator.feature_importances_,
@ -353,10 +358,10 @@ train_x['empatica_temperature_cr_stdDev_X_SO_mean'].value_counts()
# %% [markdown] # %% [markdown]
# ### Gradient Boosting Classifier # ### Gradient Boosting Classifier
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
gbc = ensemble.GradientBoostingClassifier() gbc = ensemble.GradientBoostingClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
gbc_scores = cross_validate( gbc_scores = cross_validate(
gbc, gbc,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -367,7 +372,7 @@ gbc_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(gbc_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(gbc_scores['test_accuracy']))
print("Acc (mean)", np.mean(gbc_scores['test_accuracy'])) print("Acc (mean)", np.mean(gbc_scores['test_accuracy']))
print("Precision", np.mean(gbc_scores['test_precision'])) print("Precision", np.mean(gbc_scores['test_precision']))
@ -379,10 +384,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gbc_scores['test_accuracy'],
# %% [markdown] # %% [markdown]
# ### LGBM Classifier # ### LGBM Classifier
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
lgbm = LGBMClassifier() lgbm = LGBMClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
lgbm_scores = cross_validate( lgbm_scores = cross_validate(
lgbm, lgbm,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -393,7 +398,7 @@ lgbm_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(lgbm_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(lgbm_scores['test_accuracy']))
print("Acc (mean)", np.mean(lgbm_scores['test_accuracy'])) print("Acc (mean)", np.mean(lgbm_scores['test_accuracy']))
print("Precision", np.mean(lgbm_scores['test_precision'])) print("Precision", np.mean(lgbm_scores['test_precision']))
@ -405,10 +410,10 @@ print(f"Smallest {n_sl} ACC:", np.sort(np.partition(lgbm_scores['test_accuracy']
# %% [markdown] # %% [markdown]
# ### XGBoost Classifier # ### XGBoost Classifier
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
xgb_classifier = xg.sklearn.XGBClassifier() xgb_classifier = xg.sklearn.XGBClassifier()
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
xgb_classifier_scores = cross_validate( xgb_classifier_scores = cross_validate(
xgb_classifier, xgb_classifier,
X=imputer.fit_transform(train_x), X=imputer.fit_transform(train_x),
@ -419,7 +424,7 @@ xgb_classifier_scores = cross_validate(
error_score='raise', error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1') scoring=('accuracy', 'precision', 'recall', 'f1')
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
print("Acc (median)", np.nanmedian(xgb_classifier_scores['test_accuracy'])) print("Acc (median)", np.nanmedian(xgb_classifier_scores['test_accuracy']))
print("Acc (mean)", np.mean(xgb_classifier_scores['test_accuracy'])) print("Acc (mean)", np.mean(xgb_classifier_scores['test_accuracy']))
print("Precision", np.mean(xgb_classifier_scores['test_precision'])) print("Precision", np.mean(xgb_classifier_scores['test_precision']))
@ -428,4 +433,4 @@ print("F1", np.mean(xgb_classifier_scores['test_f1']))
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])) print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl]))
# %% # %% jupyter={"outputs_hidden": false, "source_hidden": false}