Model-based Selection

Model-based Selection

# Model-based Selection
from sklearn.feature_selection import SelectFromModel
select = SelectFromModel(RandomForestClassifier(n_estimators=100, random_state=42),
                        threshold="1.25*mean")
select.fit(X_train_full, y_train_full.values.ravel())
X_train_model = select.transform(X_train_full)
print(X_train_model.shape)
X_test_model = select.transform(X_test_full)
mask = select.get_support()
print(mask)
plt.matshow(mask.reshape(1, -1), cmap='gray_r')
plt.xlabel("Technical Indexes")

# GradientBoost Classifier
print('--------------------------Without Model-based Selection-------------------------------------')
pipe_gb = Pipeline([('scl', StandardScaler()), ('est', GradientBoostingClassifier(random_state=39))])
pipe_gb.fit(X_train_full, y_train_full.values.ravel())
print('Train Accuracy: {:.3f}'.format(accuracy_score(y_train_full.values.ravel(), pipe_gb.predict(X_train_full))))
print('Test Accuracy: {:.3f}'.format(accuracy_score(y_test_full.values.ravel(), pipe_gb.predict(X_test_full))))
print('Train F1 Score: {:.3f}'.format(f1_score(y_train_full.values.ravel(), pipe_gb.predict(X_train_full), average='micro')))
print('Test F1 Score: {:.3f}'.format(f1_score(y_test_full.values.ravel(), pipe_gb.predict(X_test_full), average='micro')))

# GradientBoost Classifier with Model-based Selection
print('----------------------------With Model-based Selection--------------------------------------')
pipe_gb_model = Pipeline([('scl', StandardScaler()), ('est', GradientBoostingClassifier(random_state=39))])
pipe_gb_model.fit(X_train_model, y_train_full.values.ravel())
print('Train Accuracy: {:.3f}'.format(accuracy_score(y_train_full.values.ravel(), pipe_gb_model.predict(X_train_model))))
print('Test Accuracy: {:.3f}'.format(accuracy_score(y_test_full.values.ravel(), pipe_gb_model.predict(X_test_model))))
print('Train F1 Score: {:.3f}'.format(f1_score(y_train_full.values.ravel(), pipe_gb_model.predict(X_train_model), average='micro')))
print('Test F1 Score: {:.3f}'.format(f1_score(y_test_full.values.ravel(), pipe_gb_model.predict(X_test_model), average='micro')))