diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index 5b13f824280b4f9ba0f83dc623a5c32a0217b997..db7b59d6c1d3a8e05400c27a7c9e883bc473087f 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -94,7 +94,7 @@ Usage examples:
>>> model = svm.SVC()
>>> cross_val_score(model, X, y, scoring='wrong_choice')
Traceback (most recent call last):
- ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']
+ ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'completeness_score', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'mutual_info_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'normalized_mutual_info_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'v_measure_score']
.. note::
diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
index 4aeea1710d018e0fed5f3c912397276ac6a6bb89..3a163d967c542b3b48405809cf629fe8a70c4c44 100644
--- a/sklearn/metrics/scorer.py
+++ b/sklearn/metrics/scorer.py
@@ -27,7 +27,16 @@ from . import (r2_score, median_absolute_error, mean_absolute_error,
mean_squared_error, mean_squared_log_error, accuracy_score,
f1_score, roc_auc_score, average_precision_score,
precision_score, recall_score, log_loss)
+
from .cluster import adjusted_rand_score
+from .cluster import homogeneity_score
+from .cluster import completeness_score
+from .cluster import v_measure_score
+from .cluster import mutual_info_score
+from .cluster import adjusted_mutual_info_score
+from .cluster import normalized_mutual_info_score
+from .cluster import fowlkes_mallows_score
+
from ..utils.multiclass import type_of_target
from ..externals import six
from ..base import is_regressor
@@ -393,6 +402,14 @@ log_loss_scorer._deprecation_msg = deprecation_msg
# Clustering scores
adjusted_rand_scorer = make_scorer(adjusted_rand_score)
+homogeneity_scorer = make_scorer(homogeneity_score)
+completeness_scorer = make_scorer(completeness_score)
+v_measure_scorer = make_scorer(v_measure_score)
+mutual_info_scorer = make_scorer(mutual_info_score)
+adjusted_mutual_info_scorer = make_scorer(adjusted_mutual_info_score)
+normalized_mutual_info_scorer = make_scorer(normalized_mutual_info_score)
+fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)
+
SCORERS = dict(r2=r2_scorer,
neg_median_absolute_error=neg_median_absolute_error_scorer,
@@ -406,7 +423,16 @@ SCORERS = dict(r2=r2_scorer,
average_precision=average_precision_scorer,
log_loss=log_loss_scorer,
neg_log_loss=neg_log_loss_scorer,
- adjusted_rand_score=adjusted_rand_scorer)
+ # Cluster metrics that use supervised evaluation
+ adjusted_rand_score=adjusted_rand_scorer,
+ homogeneity_score=homogeneity_scorer,
+ completeness_score=completeness_scorer,
+ v_measure_score=v_measure_scorer,
+ mutual_info_score=mutual_info_scorer,
+ adjusted_mutual_info_score=adjusted_mutual_info_scorer,
+ normalized_mutual_info_score=normalized_mutual_info_scorer,
+ fowlkes_mallows_score=fowlkes_mallows_scorer)
+
for name, metric in [('precision', precision_score),
('recall', recall_score), ('f1', f1_score)]:
diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py
index 17a4811f52653908d8f2b46ddf31b9612bcced2a..461bdadf3d6e501d85fbdf9e0dc37145c5f2725b 100644
--- a/sklearn/metrics/tests/test_score_objects.py
+++ b/sklearn/metrics/tests/test_score_objects.py
@@ -18,7 +18,7 @@ from sklearn.utils.testing import assert_warns_message
from sklearn.base import BaseEstimator
from sklearn.metrics import (f1_score, r2_score, roc_auc_score, fbeta_score,
log_loss, precision_score, recall_score)
-from sklearn.metrics.cluster import adjusted_rand_score
+from sklearn.metrics import cluster as cluster_module
from sklearn.metrics.scorer import (check_scoring, _PredictScorer,
_passthrough_scorer)
from sklearn.metrics import make_scorer, get_scorer, SCORERS
@@ -47,9 +47,17 @@ CLF_SCORERS = ['accuracy', 'f1', 'f1_weighted', 'f1_macro', 'f1_micro',
'roc_auc', 'average_precision', 'precision',
'precision_weighted', 'precision_macro', 'precision_micro',
'recall', 'recall_weighted', 'recall_macro', 'recall_micro',
- 'neg_log_loss', 'log_loss',
- 'adjusted_rand_score' # not really, but works
- ]
+ 'neg_log_loss', 'log_loss']
+
+# All supervised cluster scorers (They behave like classification metric)
+CLUSTER_SCORERS = ["adjusted_rand_score",
+ "homogeneity_score",
+ "completeness_score",
+ "v_measure_score",
+ "mutual_info_score",
+ "adjusted_mutual_info_score",
+ "normalized_mutual_info_score",
+ "fowlkes_mallows_score"]
MULTILABEL_ONLY_SCORERS = ['precision_samples', 'recall_samples', 'f1_samples']
@@ -65,6 +73,7 @@ def _make_estimators(X_train, y_train, y_ml_train):
return dict(
[(name, sensible_regr) for name in REGRESSION_SCORERS] +
[(name, sensible_clf) for name in CLF_SCORERS] +
+ [(name, sensible_clf) for name in CLUSTER_SCORERS] +
[(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS]
)
@@ -330,16 +339,16 @@ def test_thresholded_scorers_multilabel_indicator_data():
assert_almost_equal(score1, score2)
-def test_unsupervised_scorers():
+def test_supervised_cluster_scorers():
# Test clustering scorers against gold standard labeling.
- # We don't have any real unsupervised Scorers yet.
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
km = KMeans(n_clusters=3)
km.fit(X_train)
- score1 = get_scorer('adjusted_rand_score')(km, X_test, y_test)
- score2 = adjusted_rand_score(y_test, km.predict(X_test))
- assert_almost_equal(score1, score2)
+ for name in CLUSTER_SCORERS:
+ score1 = get_scorer(name)(km, X_test, y_test)
+ score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
+ assert_almost_equal(score1, score2)
@ignore_warnings
@@ -445,4 +454,4 @@ def test_scoring_is_not_metric():
assert_raises_regexp(ValueError, 'make_scorer', check_scoring,
Ridge(), r2_score)
assert_raises_regexp(ValueError, 'make_scorer', check_scoring,
- KMeans(), adjusted_rand_score)
+ KMeans(), cluster_module.adjusted_rand_score)
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 49d1d566bd5085053580a8db8d2b62eb91663c89..117b81a35ae2cc82c8d11a76037e119e8bd0af59 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -542,6 +542,12 @@ def test_unsupervised_grid_search():
# ARI can find the right number :)
assert_equal(grid_search.best_params_["n_clusters"], 3)
+ grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
+ scoring='fowlkes_mallows_score')
+ grid_search.fit(X, y)
+ # So can FMS ;)
+ assert_equal(grid_search.best_params_["n_clusters"], 3)
+
# Now without a score, and without y
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))
grid_search.fit(X)