scikit-learn · GaelVaroquaux · Jul 16, 2018 · Oct 25, 2016 · Dec 14, 2017 · Dec 14, 2017
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -856,10 +856,18 @@ These changes mostly affect library developers.
   that accept pairwise data.
   :issue:`9701` by :user:`Kyle Johnson <gkjohns>`
 
-- Allow :func:`~utils.estimator_checks.check_estimator` to check that there is no
+- Allow :func:`utils.estimator_checks.check_estimator` to check that there is no
   private settings apart from parameters during estimator initialization.
   :issue:`9378` by :user:`Herilalaina Rakotoarison <herilalaina>`
 
+- The set of checks in :func:`utils.estimator_checks.check_estimator` now includes a
+  ``check_set_params`` test which checks that ``set_params`` is equivalent to
+  passing parameters in ``__init__`` and warns if it encounters parameter
+  validation. :issue:`7738` by :user:`Alvin Chiang <absolutelyNoWarranty>`
+
+- Add invariance tests for clustering metrics. :issue:`8102` by :user:`Ankita
+  Sinha <anki08>` and :user:`Guillaume Lemaitre <glemaitre>`.
+
 - Add ``check_methods_subset_invariance`` to
   :func:`~utils.estimator_checks.check_estimator`, which checks that
   estimator methods are invariant if applied to a data subset.  :issue:`10420`

diff --git a/sklearn/utils/_unittest_backport.py b/sklearn/utils/_unittest_backport.py
@@ -149,7 +149,7 @@ def __exit__(self, exc_type, exc_value, tb):
 
 
 class TestCase(unittest.TestCase):
-    longMessage = False
+    longMessage = True
     failureException = AssertionError
 
     def _formatMessage(self, msg, standardMsg):

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -261,6 +261,7 @@ def _yield_all_checks(name, estimator):
     yield check_fit2d_1feature
     yield check_fit1d
     yield check_get_params_invariance
+    yield check_set_params
     yield check_dict_unchanged
     yield check_dont_overwrite_parameters
 
@@ -2180,6 +2181,59 @@ def transform(self, X):
                     shallow_params.items()))
 
 
+@ignore_warnings(category=(DeprecationWarning, FutureWarning))
+def check_set_params(name, estimator_orig):
+    # Check that get_params() returns the same thing
+    # before and after set_params() with some fuzz
+    estimator = clone(estimator_orig)
+
+    orig_params = estimator.get_params(deep=False)
+    msg = ("get_params result does not match what was passed to set_params")
+
+    estimator.set_params(**orig_params)
+    curr_params = estimator.get_params(deep=False)
+    assert_equal(set(orig_params.keys()), set(curr_params.keys()), msg)
+    for k, v in curr_params.items():
+        assert orig_params[k] is v, msg
+
+    # some fuzz values
+    test_values = [-np.inf, np.inf, None]
+
+    test_params = deepcopy(orig_params)
+    for param_name in orig_params.keys():
+        default_value = orig_params[param_name]
+        for value in test_values:
+            test_params[param_name] = value
+            try:
+                estimator.set_params(**test_params)
+            except (TypeError, ValueError) as e:
+                e_type = e.__class__.__name__
+                # Exception occurred, possibly parameter validation
+                warnings.warn("{} occurred during set_params. "
+                              "It is recommended to delay parameter "
+                              "validation until fit.".format(e_type))
+
+                change_warning_msg = "Estimator's parameters changed after " \
+                                     "set_params raised {}".format(e_type)
+                params_before_exception = curr_params
+                curr_params = estimator.get_params(deep=False)
+                try:
+                    assert_equal(set(params_before_exception.keys()),
+                                 set(curr_params.keys()))
+                    for k, v in curr_params.items():
+                        assert params_before_exception[k] is v
+                except AssertionError:
+                    warnings.warn(change_warning_msg)
+            else:
+                curr_params = estimator.get_params(deep=False)
+                assert_equal(set(test_params.keys()),
+                             set(curr_params.keys()),
+                             msg)
+                for k, v in curr_params.items():
+                    assert test_params[k] is v, msg
+        test_params[param_name] = default_value
+
+
 @ignore_warnings(category=(DeprecationWarning, FutureWarning))
 def check_classifiers_regression_target(name, estimator_orig):
     # Check if classifier throws an exception when fed regression targets

diff --git a/sklearn/utils/tests/test_estimator_checks.py b/sklearn/utils/tests/test_estimator_checks.py
@@ -10,7 +10,8 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.utils import deprecated
 from sklearn.utils.testing import (assert_raises_regex, assert_true,
-                                   assert_equal, ignore_warnings)
+                                   assert_equal, ignore_warnings,
+                                   assert_warns)
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.estimator_checks import set_random_state
 from sklearn.utils.estimator_checks import set_checking_parameters
@@ -86,6 +87,61 @@ def fit(self, X, y=None):
         return self
 
 
+class RaisesErrorInSetParams(BaseEstimator):
+    def __init__(self, p=0):
+        self.p = p
+
+    def set_params(self, **kwargs):
+        if 'p' in kwargs:
+            p = kwargs.pop('p')
+            if p < 0:
+                raise ValueError("p can't be less than 0")
+            self.p = p
+        return super(RaisesErrorInSetParams, self).set_params(**kwargs)
+
+    def fit(self, X, y=None):
+        X, y = check_X_y(X, y)
+        return self
+
+
+class ModifiesValueInsteadOfRaisingError(BaseEstimator):
+    def __init__(self, p=0):
+        self.p = p
+
+    def set_params(self, **kwargs):
+        if 'p' in kwargs:
+            p = kwargs.pop('p')
+            if p < 0:
+                p = 0
+            self.p = p
+        return super(ModifiesValueInsteadOfRaisingError,
+                     self).set_params(**kwargs)
+
+    def fit(self, X, y=None):
+        X, y = check_X_y(X, y)
+        return self
+
+
+class ModifiesAnotherValue(BaseEstimator):
+    def __init__(self, a=0, b='method1'):
+        self.a = a
+        self.b = b
+
+    def set_params(self, **kwargs):
+        if 'a' in kwargs:
+            a = kwargs.pop('a')
+            self.a = a
+            if a is None:
+                kwargs.pop('b')
+                self.b = 'method2'
+        return super(ModifiesAnotherValue,
+                     self).set_params(**kwargs)
+
+    def fit(self, X, y=None):
+        X, y = check_X_y(X, y)
+        return self
+
+
 class NoCheckinPredict(BaseBadClassifier):
     def fit(self, X, y):
         X, y = check_X_y(X, y)
@@ -219,6 +275,13 @@ def test_check_estimator():
     msg = "it does not implement a 'get_params' methods"
     assert_raises_regex(TypeError, msg, check_estimator, object)
     assert_raises_regex(TypeError, msg, check_estimator, object())
+    # check that values returned by get_params match set_params
+    msg = "get_params result does not match what was passed to set_params"
+    assert_raises_regex(AssertionError, msg, check_estimator,
+                        ModifiesValueInsteadOfRaisingError())
+    assert_warns(UserWarning, check_estimator, RaisesErrorInSetParams())
+    assert_raises_regex(AssertionError, msg, check_estimator,
+                        ModifiesAnotherValue())
     # check that we have a fit method
     msg = "object has no attribute 'fit'"
     assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)