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stress_at_work_analysis
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Add GroupKFold to feature selection CV. Start with generic metric calculation procedure.

ml_pipeline
Primoz 2023-04-20 11:20:26 +02:00
parent 1cbc743cf7
commit 0594993133
3 changed files with 40 additions and 49 deletions
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14
exploration/ml_pipeline.py
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@ -34,8 +34,8 @@ index_columns = ["local_segment", "local_segment_label", "local_segment_start_da
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']
# 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()
@ -58,10 +58,12 @@ for split in cv.get_splits():
# Feature selection on train set
# Morda se implementira GroupKfold namesto stratifiedKFold? >>
# >> Tako se bo posamezen pid pojavil ali v test ali v train setu
fs = FeatureSelection(train_X, train_y)
selected_features = fs.select_features(n_min=20, n_max=50, k=80,
ml_type="regression_",
n_tolerance=20)
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=50, k=60,
ml_type="classification_multi",
metric="f1", n_tolerance=20)
print(selected_features)
print(len(selected_features))

13
machine_learning/cross_validation.py
View File

@ -49,8 +49,8 @@ class CrossValidation():
data_X, data_y, data_groups = data.drop(["target", "pid", "pid_index", "pid_half"], axis=1), data["target"], data["pid_half"]
elif self.cv_method == "5kfold":
data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], data["pid"]
elif self.cv_method == "Stratified5kfold":
data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], None
self.X, self.y, self.groups = data_X, data_y, data_groups
@ -71,7 +71,7 @@ class CrossValidation():
if self.cv_method in ["logo", "half_logo"]:
self.cv = LeaveOneGroupOut()
elif self.cv_method == "5kfold":
elif self.cv_method == "Stratified5kfold":
self.cv = StratifiedKFold(n_splits=5, shuffle=True)
@ -118,4 +118,11 @@ class CrossValidation():
"""
return self.X.iloc[split[0]], self.y.iloc[split[0]], self.X.iloc[split[1]], self.y.iloc[split[1]]
def get_groups_sets(self, split):
if self.groups is None:
return None, None
else:
return self.groups.iloc[split[0]], self.groups.iloc[split[1]]

62
machine_learning/feature_selection.py
View File

@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, f_regression
from sklearn.model_selection import cross_validate, StratifiedKFold
from sklearn.model_selection import cross_validate, StratifiedKFold, GroupKFold
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Lasso
@ -23,9 +23,10 @@ from sklearn.linear_model import Lasso
class FeatureSelection:
def __init__(self, X, y):
def __init__(self, X, y, groups):
self.X = X
self.y = y
self.groups = groups
def select_best_feature(self, features, method="remove", ml_type="classification", metric="recall", stored_features=[]):
@ -65,55 +66,35 @@ class FeatureSelection:
X = self.X[pred_features].copy()
if self.groups is not None:
cv = GroupKFold(n_splits=5)
else:
cv = StratifiedKFold(n_splits=5, shuffle=True)
# See link about scoring for multiclassfication
# http://iamirmasoud.com/2022/06/19/understanding-micro-macro-and-weighted-averages-for-scikit-learn-metrics-in-multi-class-classification-with-example/
if ml_type == "classification":
nb = GaussianNB()
model_cv = cross_validate(
nb,
X=X,
y=self.y,
cv=StratifiedKFold(n_splits=5, shuffle=True),
cv=cv,
groups=self.groups,
n_jobs=-1,
scoring=('accuracy', 'precision', 'recall', 'f1')
scoring=(metric)
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
if metric == "accuracy":
acc = np.mean(model_cv['test_accuracy'])
acc_std = np.std(model_cv['test_accuracy'])
if not best_feature or (acc > best_metric_score):
best_feature = feat
best_metric_score = acc
best_metric_score_std = acc_std
metric_score = np.nanmean(model_cv[f'test_{metric}'])
metric_score_std = np.nanstd(model_cv[f'test_{metric}'])
elif metric == "precision":
prec = np.mean(model_cv['test_precision'])
prec_std = np.std(model_cv['test_precision'])
if not best_feature or (prec > best_metric_score):
best_feature = feat
best_metric_score = prec
best_metric_score_std = prec_std
elif metric == "recall":
rec = np.mean(model_cv['test_recall'])
rec_std = np.std(model_cv['test_recall'])
if not best_feature or (rec > best_metric_score):
best_feature = feat
best_metric_score = rec
best_metric_score_std = rec_std
else:
f1 = np.mean(model_cv['test_f1'])
f1_std = np.std(model_cv['test_f1'])
if not best_feature or (f1 > best_metric_score):
best_feature = feat
best_metric_score = f1
best_metric_score_std = f1_std
if not best_feature or (metric_score > best_metric_score):
best_feature = feat
best_metric_score = metric_score
best_metric_score_std = metric_score_std
elif ml_type == "regression":
lass = Lasso()
@ -121,7 +102,8 @@ class FeatureSelection:
lass,
X=X,
y=y,
cv=StratifiedKFold(n_splits=5, shuffle=True),
cv=cv,
groups=self.groups,
n_jobs=-1,
scoring=('r2')
)
@ -214,7 +196,7 @@ class FeatureSelection:
break
best_feature, best_metric_score, best_metric_score_std = \
self.select_best_feature(features, method=method, ml_type=ml_type[0], metric="recall")
self.select_best_feature(features, method=method, ml_type=ml_type[0], metric=metric)
feature_importance.append((i+1, best_feature, best_metric_score, best_metric_score_std))