diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
index 49107f9d487492b76154aeb517859920849e9002..ea3b275b9d2966439f68a8704adda3178291c005 100644
--- a/sklearn/metrics/scorer.py
+++ b/sklearn/metrics/scorer.py
@@ -34,14 +34,15 @@ from ..base import is_regressor
class _BaseScorer(six.with_metaclass(ABCMeta, object)):
- def __init__(self, score_func, sign, kwargs):
+ def __init__(self, score_func, sign, kwargs, deprecation_msg=None):
self._kwargs = kwargs
self._score_func = score_func
self._sign = sign
+ self._deprecation_msg = deprecation_msg
- @abstractmethod
def __call__(self, estimator, X, y, sample_weight=None):
- pass
+ if self._deprecation_msg is not None:
+ warnings.warn(self._deprecation_msg, category=DeprecationWarning)
def __repr__(self):
kwargs_string = "".join([", %s=%s" % (str(k), str(v))
@@ -80,6 +81,8 @@ class _PredictScorer(_BaseScorer):
score : float
Score function applied to prediction of estimator on X.
"""
+ super(_PredictScorer, self).__call__(estimator, X, y_true,
+ sample_weight=sample_weight)
y_pred = estimator.predict(X)
if sample_weight is not None:
return self._sign * self._score_func(y_true, y_pred,
@@ -115,6 +118,8 @@ class _ProbaScorer(_BaseScorer):
score : float
Score function applied to prediction of estimator on X.
"""
+ super(_ProbaScorer, self).__call__(clf, X, y,
+ sample_weight=sample_weight)
y_pred = clf.predict_proba(X)
if sample_weight is not None:
return self._sign * self._score_func(y, y_pred,
@@ -154,6 +159,8 @@ class _ThresholdScorer(_BaseScorer):
score : float
Score function applied to prediction of estimator on X.
"""
+ super(_ThresholdScorer, self).__call__(clf, X, y,
+ sample_weight=sample_weight)
y_type = type_of_target(y)
if y_type not in ("binary", "multilabel-indicator"):
raise ValueError("{0} format is not supported".format(y_type))
@@ -189,14 +196,6 @@ class _ThresholdScorer(_BaseScorer):
def get_scorer(scoring):
if isinstance(scoring, six.string_types):
- if scoring in ('mean_squared_error', 'mean_absolute_error',
- 'median_absolute_error', 'log_loss'):
- warnings.warn('Scoring method %s was renamed to '
- 'neg_%s in version 0.18 and will be '
- 'removed in 0.20.' % (scoring, scoring),
- category=DeprecationWarning)
- scoring = 'neg_' + scoring
-
try:
scorer = SCORERS[scoring]
except KeyError:
@@ -255,7 +254,7 @@ def check_scoring(estimator, scoring=None, allow_none=False):
def make_scorer(score_func, greater_is_better=True, needs_proba=False,
- needs_threshold=False, **kwargs):
+ needs_threshold=False, deprecation_msg=None, **kwargs):
"""Make a scorer from a performance metric or loss function.
This factory function wraps scoring functions for use in GridSearchCV
@@ -317,17 +316,36 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
cls = _ThresholdScorer
else:
cls = _PredictScorer
- return cls(score_func, sign, kwargs)
+ return cls(score_func, sign, kwargs, deprecation_msg=deprecation_msg)
# Standard regression scores
r2_scorer = make_scorer(r2_score)
neg_mean_squared_error_scorer = make_scorer(mean_squared_error,
greater_is_better=False)
+deprecation_msg = ('Scoring method mean_squared_error was renamed to '
+ 'neg_mean_squared_error in version 0.18 and will '
+ 'be removed in 0.20.')
+mean_squared_error_scorer = make_scorer(mean_squared_error,
+ greater_is_better=False,
+ deprecation_msg=deprecation_msg)
neg_mean_absolute_error_scorer = make_scorer(mean_absolute_error,
greater_is_better=False)
+deprecation_msg = ('Scoring method mean_absolute_error was renamed to '
+ 'neg_mean_absolute_error in version 0.18 and will '
+ 'be removed in 0.20.')
+mean_absolute_error_scorer = make_scorer(mean_absolute_error,
+ greater_is_better=False,
+ deprecation_msg=deprecation_msg)
neg_median_absolute_error_scorer = make_scorer(median_absolute_error,
greater_is_better=False)
+deprecation_msg = ('Scoring method median_absolute_error was renamed to '
+ 'neg_median_absolute_error in version 0.18 and will '
+ 'be removed in 0.20.')
+median_absolute_error_scorer = make_scorer(median_absolute_error,
+ greater_is_better=False
+ deprecation_msg=deprecation_msg)
+
# Standard Classification Scores
accuracy_scorer = make_scorer(accuracy_score)
@@ -344,6 +362,11 @@ recall_scorer = make_scorer(recall_score)
# Score function for probabilistic classification
neg_log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
needs_proba=True)
+deprecation_msg = ('Scoring method log_loss was renamed to '
+ 'neg_log_loss in version 0.18 and will be removed in 0.20.')
+log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
+ needs_proba=True, deprecation_msg=deprecation_msg)
+
# Clustering scores
adjusted_rand_scorer = make_scorer(adjusted_rand_score)
@@ -352,6 +375,9 @@ SCORERS = dict(r2=r2_scorer,
neg_median_absolute_error=neg_median_absolute_error_scorer,
neg_mean_absolute_error=neg_mean_absolute_error_scorer,
neg_mean_squared_error=neg_mean_squared_error_scorer,
+ median_absolute_error=median_absolute_error_scorer,
+ mean_absolute_error=mean_absolute_error_scorer,
+ mean_squared_error=mean_squared_error_scorer,
accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
average_precision=average_precision_scorer,
neg_log_loss=neg_log_loss_scorer,
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index ad076d5826ab2110bc9d09011ed7ff7b9961ff07..c8afc07fffa917e0b31a2ffefde38c80dbc25061 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -348,10 +348,10 @@ def test_cross_val_score_with_score_func_regression():
assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
# Mean squared error; this is a loss function, so "scores" are negative
- mse_scores = cross_val_score(reg, X, y, cv=5,
- scoring="neg_mean_squared_error")
- expected_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
- assert_array_almost_equal(mse_scores, expected_mse, 2)
+ neg_mse_scores = cross_val_score(reg, X, y, cv=5,
+ scoring="neg_mean_squared_error")
+ expected_neg_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
+ assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
# Explained variance
scoring = make_scorer(explained_variance_score)
diff --git a/sklearn/tests/test_cross_validation.py b/sklearn/tests/test_cross_validation.py
index 5a3c94aefb15bc01810ed15d869d76cc00300f91..4ca2b6683912e896e35ada3823d60875149231b2 100644
--- a/sklearn/tests/test_cross_validation.py
+++ b/sklearn/tests/test_cross_validation.py
@@ -905,10 +905,10 @@ def test_cross_val_score_with_score_func_regression():
assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
# Mean squared error; this is a loss function, so "scores" are negative
- mse_scores = cval.cross_val_score(reg, X, y, cv=5,
- scoring="neg_mean_squared_error")
- expected_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
- assert_array_almost_equal(mse_scores, expected_mse, 2)
+ neg_mse_scores = cval.cross_val_score(reg, X, y, cv=5,
+ scoring="neg_mean_squared_error")
+ expected_neg_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
+ assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
# Explained variance
scoring = make_scorer(explained_variance_score)