def very_basic_xgboost_test(): X, y, w = generate_classification_data(n_classes=2) clf = XGBoostClassifier(n_estimators=10).fit(X, y) clf.predict(X) clf.predict_proba(X) # testing that returned features in importances are correct and in the same order assert numpy.all(clf.features == clf.get_feature_importances().index)
def test_basic_xgboost(): X, y, w = generate_classification_data(n_classes=2) clf = XGBoostClassifier(n_estimators=10).fit(X, y) clf.predict(X) clf.predict_proba(X) # testing that returned features in importances are correct and in the same order assert numpy.all(clf.features == clf.get_feature_importances().index)
def test_xgboost_feature_importance(): X, y, weights = generate_classification_data(n_classes=2, distance=5) clf = XGBoostClassifier(n_estimators=1, max_depth=1) clf.fit(X, y) importances = clf.get_feature_importances() original_features = set(X.columns) importances_features = set(importances.index) print(original_features, importances_features) assert original_features == importances_features, 'feature_importances_ return something wrong' assert len(original_features) == len(clf.feature_importances_)