scikit-learn · lesteve · Mar 7, 2017 · Jan 31, 2017 · Feb 1, 2017 · Feb 1, 2017
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -246,6 +246,13 @@ API changes summary
       selection classes to be used with tools such as
       :func:`sklearn.model_selection.cross_val_predict`.
       :issue:`2879` by :user:`Stephen Hoover <stephen-hoover>`.
+
+   - Estimators with both methods ``decision_function`` and ``predict_proba`` 
+     are now required to have a monotonic relation between them. The 
+     method ``check_decision_proba_consistency`` has been added in 
+     **sklearn.utils.estimator_checks** to check their consistency. 
+     :issue:`7578` by :user:`Shubham Bhardwaj <shubham0704>`
+
 
 .. _changes_0_18_1:
 

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -6,9 +6,9 @@
 import traceback
 import pickle
 from copy import deepcopy
-
 import numpy as np
 from scipy import sparse
+from scipy.stats import rankdata
 import struct
 
 from sklearn.externals.six.moves import zip
@@ -113,10 +113,10 @@ def _yield_classifier_checks(name, Classifier):
     # basic consistency testing
     yield check_classifiers_train
     yield check_classifiers_regression_target
-    if (name not in ["MultinomialNB", "LabelPropagation", "LabelSpreading"]
+    if (name not in
+        ["MultinomialNB", "LabelPropagation", "LabelSpreading"] and
         # TODO some complication with -1 label
-            and name not in ["DecisionTreeClassifier",
-                             "ExtraTreeClassifier"]):
+       name not in ["DecisionTreeClassifier", "ExtraTreeClassifier"]):
             # We don't raise a warning in these classifiers, as
             # the column y interface is used by the forests.
 
@@ -127,6 +127,8 @@ def _yield_classifier_checks(name, Classifier):
         yield check_class_weight_classifiers
 
     yield check_non_transformer_estimators_n_iter
+    # test if predict_proba is a monotonic transformation of decision_function
+    yield check_decision_proba_consistency
 
 
 @ignore_warnings(category=DeprecationWarning)
@@ -269,8 +271,7 @@ def set_testing_parameters(estimator):
     # set parameters to speed up some estimators and
     # avoid deprecated behaviour
     params = estimator.get_params()
-    if ("n_iter" in params
-            and estimator.__class__.__name__ != "TSNE"):
+    if ("n_iter" in params and estimator.__class__.__name__ != "TSNE"):
         estimator.set_params(n_iter=5)
     if "max_iter" in params:
         warnings.simplefilter("ignore", ConvergenceWarning)
@@ -1112,8 +1113,7 @@ def check_classifiers_train(name, Classifier):
                     assert_equal(decision.shape, (n_samples,))
                     dec_pred = (decision.ravel() > 0).astype(np.int)
                     assert_array_equal(dec_pred, y_pred)
-                if (n_classes is 3
-                        and not isinstance(classifier, BaseLibSVM)):
+                if (n_classes is 3 and not isinstance(classifier, BaseLibSVM)):
                     # 1on1 of LibSVM works differently
                     assert_equal(decision.shape, (n_samples, n_classes))
                     assert_array_equal(np.argmax(decision, axis=1), y_pred)
@@ -1574,9 +1574,9 @@ def check_parameters_default_constructible(name, Estimator):
         try:
             def param_filter(p):
                 """Identify hyper parameters of an estimator"""
-                return (p.name != 'self'
-                        and p.kind != p.VAR_KEYWORD
-                        and p.kind != p.VAR_POSITIONAL)
+                return (p.name != 'self' and
+                        p.kind != p.VAR_KEYWORD and
+                        p.kind != p.VAR_POSITIONAL)
 
             init_params = [p for p in signature(init).parameters.values()
                            if param_filter(p)]
@@ -1721,3 +1721,25 @@ def check_classifiers_regression_target(name, Estimator):
     e = Estimator()
     msg = 'Unknown label type: '
     assert_raises_regex(ValueError, msg, e.fit, X, y)
+
+
+@ignore_warnings(category=DeprecationWarning)
+def check_decision_proba_consistency(name, Estimator):
+    # Check whether an estimator having both decision_function and
+    # predict_proba methods has outputs with perfect rank correlation.
+
+    centers = [(2, 2), (4, 4)]
+    X, y = make_blobs(n_samples=100, random_state=0, n_features=4,
+                      centers=centers, cluster_std=1.0, shuffle=True)
+    X_test = np.random.randn(20, 2) + 4
+    estimator = Estimator()
+
+    set_testing_parameters(estimator)
+
+    if (hasattr(estimator, "decision_function") and
+            hasattr(estimator, "predict_proba")):
+
+        estimator.fit(X, y)
+        a = estimator.predict_proba(X_test)[:, 1]
+        b = estimator.decision_function(X_test)
+        assert_array_equal(rankdata(a), rankdata(b))