def test_multimetric_scorer_calls_method_once(scorers, expected_predict_count,
expected_predict_proba_count,
expected_decision_func_count):
X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
mock_est = Mock()
fit_func = Mock(return_value=mock_est)
predict_func = Mock(return_value=y)
pos_proba = np.random.rand(X.shape[0])
proba = np.c_[1 - pos_proba, pos_proba]
predict_proba_func = Mock(return_value=proba)
decision_function_func = Mock(return_value=pos_proba)
mock_est.fit = fit_func
mock_est.predict = predict_func
mock_est.predict_proba = predict_proba_func
mock_est.decision_function = decision_function_func
scorer_dict, _ = _check_multimetric_scoring(LogisticRegression(), scorers)
multi_scorer = _MultimetricScorer(**scorer_dict)
results = multi_scorer(mock_est, X, y)
assert set(scorers) == set(results) # compare dict keys
assert predict_func.call_count == expected_predict_count
assert predict_proba_func.call_count == expected_predict_proba_count
assert decision_function_func.call_count == expected_decision_func_count
def test_multimetric_scorer_sanity_check():
# scoring dictionary returned is the same as calling each scorer seperately
scorers = {
'a1': 'accuracy',
'a2': 'accuracy',
'll1': 'neg_log_loss',
'll2': 'neg_log_loss',
'ra1': 'roc_auc',
'ra2': 'roc_auc'
}
X, y = make_classification(random_state=0)
clf = DecisionTreeClassifier()
clf.fit(X, y)
scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
multi_scorer = _MultimetricScorer(**scorer_dict)
result = multi_scorer(clf, X, y)
seperate_scores = {
name: get_scorer(name)(clf, X, y)
for name in ['accuracy', 'neg_log_loss', 'roc_auc']
}
for key, value in result.items():
score_name = scorers[key]
assert_allclose(value, seperate_scores[score_name])
def test_multimetric_scorer_calls_method_once_regressor_threshold():
predict_called_cnt = 0
class MockDecisionTreeRegressor(DecisionTreeRegressor):
def predict(self, X):
nonlocal predict_called_cnt
predict_called_cnt += 1
return super().predict(X)
X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
# no decision function
clf = MockDecisionTreeRegressor()
clf.fit(X, y)
scorers = {'neg_mse': 'neg_mean_squared_error', 'r2': 'roc_auc'}
scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
scorer = _MultimetricScorer(**scorer_dict)
scorer(clf, X, y)
assert predict_called_cnt == 1
def test_multimetric_scorer_calls_method_once_classifier_no_decision():
predict_proba_call_cnt = 0
class MockKNeighborsClassifier(KNeighborsClassifier):
def predict_proba(self, X):
nonlocal predict_proba_call_cnt
predict_proba_call_cnt += 1
return super().predict_proba(X)
X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
# no decision function
clf = MockKNeighborsClassifier(n_neighbors=1)
clf.fit(X, y)
scorers = ['roc_auc', 'neg_log_loss']
scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
scorer = _MultimetricScorer(**scorer_dict)
scorer(clf, X, y)
assert predict_proba_call_cnt == 1