xhluca
diff --git a/‎doc/whats_new/v0.21.rst
Lines changed: 0 additions & 6 deletions b/‎doc/whats_new/v0.21.rst
Lines changed: 0 additions & 6 deletions
diff --git a/‎sklearn/utils/estimator_checks.py
Lines changed: 0 additions & 50 deletions b/‎sklearn/utils/estimator_checks.py
Lines changed: 0 additions & 50 deletions
@@ -115,9 +115,3 @@ Changes to estimator checks
 ---------------------------
 
 These changes mostly affect library developers.
-
-- Add ``check_fit_idempotent`` to
-  :func:`~utils.estimator_checks.check_estimator`, which checks that
-  when `fit` is called twice with the same data, the ouput of
-  `predit`, `predict_proba`, `transform`, and `decision_function` does not
-  change. :issue:`12328` by :user:`Nicolas Hug<NicolasHug>`
@@ -53,8 +53,6 @@
 from sklearn.exceptions import DataConversionWarning
 from sklearn.exceptions import SkipTestWarning
 from sklearn.model_selection import train_test_split
-from sklearn.model_selection import ShuffleSplit
-from sklearn.model_selection._validation import _safe_split
 from sklearn.metrics.pairwise import (rbf_kernel, linear_kernel,
                                       pairwise_distances)
 
@@ -267,7 +265,6 @@ def _yield_all_checks(name, estimator):
     yield check_set_params
     yield check_dict_unchanged
     yield check_dont_overwrite_parameters
-    yield check_fit_idempotent
 
 
 def check_estimator(Estimator):
@@ -2333,50 +2330,3 @@ def check_outliers_fit_predict(name, estimator_orig):
         for contamination in [-0.5, 2.3]:
             estimator.set_params(contamination=contamination)
             assert_raises(ValueError, estimator.fit_predict, X)
-
-
-def check_fit_idempotent(name, estimator_orig):
-    # Check that est.fit(X) is the same as est.fit(X).fit(X). Ideally we would
-    # check that the estimated parameters during training (e.g. coefs_) are
-    # the same, but having a universal comparison function for those
-    # attributes is difficult and full of edge cases. So instead we check that
-    # predict(), predict_proba(), decision_function() and transform() return
-    # the same results.
-
-    check_methods = ["predict", "transform", "decision_function",
-                     "predict_proba"]
-    rng = np.random.RandomState(0)
-
-    estimator = clone(estimator_orig)
-    set_random_state(estimator)
-    if 'warm_start' in estimator.get_params().keys():
-        estimator.set_params(warm_start=False)
-
-    n_samples = 100
-    X = rng.normal(loc=100, size=(n_samples, 2))
-    X = pairwise_estimator_convert_X(X, estimator)
-    if is_regressor(estimator_orig):
-        y = rng.normal(size=n_samples)
-    else:
-        y = rng.randint(low=0, high=2, size=n_samples)
-    y = multioutput_estimator_convert_y_2d(estimator, y)
-
-    train, test = next(ShuffleSplit(test_size=.2, random_state=rng).split(X))
-    X_train, y_train = _safe_split(estimator, X, y, train)
-    X_test, y_test = _safe_split(estimator, X, y, test, train)
-
-    # Fit for the first time
-    estimator.fit(X_train, y_train)
-
-    result = {}
-    for method in check_methods:
-        if hasattr(estimator, method):
-            result[method] = getattr(estimator, method)(X_test)
-
-    # Fit again
-    estimator.fit(X_train, y_train)
-
-    for method in check_methods:
-        if hasattr(estimator, method):
-            new_result = getattr(estimator, method)(X_test)
-            assert_allclose_dense_sparse(result[method], new_result)