[MRG+1] add scorer based on explained_variance_score (scikit-learn#9259)

umitsamima · Aug 8, 2017 · 9f91ec7 · 9f91ec7
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diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
@@ -81,6 +81,7 @@ Scoring                           Function
 'v_measure_score'                 :func:`metrics.v_measure_score`
 
 **Regression**
+'explained_variance'              :func:`metrics.explained_variance_score`
 'neg_mean_absolute_error'         :func:`metrics.mean_absolute_error`
 'neg_mean_squared_error'          :func:`metrics.mean_squared_error`
 'neg_mean_squared_log_error'      :func:`metrics.mean_squared_log_error`
@@ -101,7 +102,7 @@ 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_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']
+    ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'completeness_score', 'explained_variance', '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/doc/whats_new.rst b/doc/whats_new.rst
@@ -139,6 +139,9 @@ Model selection and evaluation
   :class:`model_selection.RepeatedStratifiedKFold`.
   :issue:`8120` by `Neeraj Gangwar`_.
 
+- Added a scorer based on :class:`metrics.explained_variance_score`.
+  :issue:`9259` by `Hanmin Qin <https://github.com/qinhanmin2014>`_. 
+
 Miscellaneous
 
 - Validation that input data contains no NaN or inf can now be suppressed

diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
@@ -26,7 +26,8 @@
 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)
+               precision_score, recall_score, log_loss,
+               explained_variance_score)
 
 from .cluster import adjusted_rand_score
 from .cluster import homogeneity_score
@@ -463,6 +464,7 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
 
 
 # Standard regression scores
+explained_variance_scorer = make_scorer(explained_variance_score)
 r2_scorer = make_scorer(r2_score)
 neg_mean_squared_error_scorer = make_scorer(mean_squared_error,
                                             greater_is_better=False)
@@ -525,7 +527,8 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
 fowlkes_mallows_scorer = make_scorer(fowlkes_mallows_score)
 
 
-SCORERS = dict(r2=r2_scorer,
+SCORERS = dict(explained_variance=explained_variance_scorer,
+               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,

diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py
@@ -29,7 +29,6 @@
 from sklearn.svm import LinearSVC
 from sklearn.pipeline import make_pipeline
 from sklearn.cluster import KMeans
-from sklearn.dummy import DummyRegressor
 from sklearn.linear_model import Ridge, LogisticRegression
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.datasets import make_blobs
@@ -42,8 +41,9 @@
 from sklearn.externals import joblib
 
 
-REGRESSION_SCORERS = ['r2', 'neg_mean_absolute_error',
-                      'neg_mean_squared_error', 'neg_mean_squared_log_error',
+REGRESSION_SCORERS = ['explained_variance', 'r2',
+                      'neg_mean_absolute_error', 'neg_mean_squared_error',
+                      'neg_mean_squared_log_error',
                       'neg_median_absolute_error', 'mean_absolute_error',
                       'mean_squared_error', 'median_absolute_error']
 
@@ -68,7 +68,7 @@
 
 def _make_estimators(X_train, y_train, y_ml_train):
     # Make estimators that make sense to test various scoring methods
-    sensible_regr = DummyRegressor(strategy='median')
+    sensible_regr = DecisionTreeRegressor(random_state=0)
     sensible_regr.fit(X_train, y_train)
     sensible_clf = DecisionTreeClassifier(random_state=0)
     sensible_clf.fit(X_train, y_train)