From 83a538c146c00a6861c4afe5229abc91ba2027a7 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 9 Oct 2020 00:02:50 -0400
Subject: [PATCH 1/5] ENH Adds n_features_in_ checks to linear module

---
 sklearn/ensemble/tests/test_stacking.py      |  5 ++---
 sklearn/linear_model/_base.py                |  5 +++--
 sklearn/linear_model/_glm/glm.py             | 13 ++++++-------
 sklearn/linear_model/_logistic.py            |  2 +-
 sklearn/linear_model/_ransac.py              |  2 +-
 sklearn/linear_model/_stochastic_gradient.py | 19 ++++++++++---------
 sklearn/svm/_base.py                         |  9 +++------
 sklearn/tests/test_common.py                 |  2 --
 8 files changed, 26 insertions(+), 31 deletions(-)

diff --git a/sklearn/ensemble/tests/test_stacking.py b/sklearn/ensemble/tests/test_stacking.py
index d6b4c385b9073..815fdd44558ec 100644
--- a/sklearn/ensemble/tests/test_stacking.py
+++ b/sklearn/ensemble/tests/test_stacking.py
@@ -483,9 +483,8 @@ def test_stacking_without_n_features_in(make_dataset, Stacking, Estimator):
 
     class MyEstimator(Estimator):
         """Estimator without n_features_in_"""
-        def fit(self, X, y):
-            super().fit(X, y)
-            del self.n_features_in_
+        def _check_n_features(self, X, reset):
+            pass
 
     X, y = make_dataset(random_state=0, n_samples=100)
     stacker = Stacking(estimators=[('lr', MyEstimator())])
diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
index 2399e1216238f..be0e7653634e0 100644
--- a/sklearn/linear_model/_base.py
+++ b/sklearn/linear_model/_base.py
@@ -217,7 +217,8 @@ def fit(self, X, y):
     def _decision_function(self, X):
         check_is_fitted(self)
 
-        X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
+        X = self._validate_data(X, accept_sparse=['csr', 'csc', 'coo'],
+                                reset=False)
         return safe_sparse_dot(X, self.coef_.T,
                                dense_output=True) + self.intercept_
 
@@ -281,7 +282,7 @@ class would be predicted.
         """
         check_is_fitted(self)
 
-        X = check_array(X, accept_sparse='csr')
+        X = self._validate_data(X, accept_sparse='csr', reset=False)
 
         n_features = self.coef_.shape[1]
         if X.shape[1] != n_features:
diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py
index 8559ef306b3a4..88088be5ae997 100644
--- a/sklearn/linear_model/_glm/glm.py
+++ b/sklearn/linear_model/_glm/glm.py
@@ -12,7 +12,6 @@
 import scipy.optimize
 
 from ...base import BaseEstimator, RegressorMixin
-from ...utils import check_array, check_X_y
 from ...utils.optimize import _check_optimize_result
 from ...utils.validation import check_is_fitted, _check_sample_weight
 from ..._loss.glm_distribution import (
@@ -221,9 +220,9 @@ def fit(self, X, y, sample_weight=None):
         family = self._family_instance
         link = self._link_instance
 
-        X, y = check_X_y(X, y, accept_sparse=['csc', 'csr'],
-                         dtype=[np.float64, np.float32],
-                         y_numeric=True, multi_output=False)
+        X, y = self._validate_data(X, y, accept_sparse=['csc', 'csr'],
+                                   dtype=[np.float64, np.float32],
+                                   y_numeric=True, multi_output=False)
 
         weights = _check_sample_weight(sample_weight, X)
 
@@ -311,9 +310,9 @@ def _linear_predictor(self, X):
             Returns predicted values of linear predictor.
         """
         check_is_fitted(self)
-        X = check_array(X, accept_sparse=['csr', 'csc', 'coo'],
-                        dtype=[np.float64, np.float32], ensure_2d=True,
-                        allow_nd=False)
+        X = self._validate_data(X, accept_sparse=['csr', 'csc', 'coo'],
+                                dtype=[np.float64, np.float32], ensure_2d=True,
+                                allow_nd=False, reset=False)
         return X @ self.coef_ + self.intercept_
 
     def predict(self, X):
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 1afa06637b04a..b40f723899d93 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -973,6 +973,7 @@ def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10,
         max_squared_sum=max_squared_sum, sample_weight=sample_weight)
 
     log_reg = LogisticRegression(solver=solver, multi_class=multi_class)
+    log_reg.n_features_in_ = X.shape[1]
 
     # The score method of Logistic Regression has a classes_ attribute.
     if multi_class == 'ovr':
@@ -2084,7 +2085,6 @@ def score(self, X, y, sample_weight=None):
         """
         scoring = self.scoring or 'accuracy'
         scoring = get_scorer(scoring)
-
         return scoring(self, X, y, sample_weight=sample_weight)
 
     def _more_tags(self):
diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py
index c9246c121c387..390b247e719f5 100644
--- a/sklearn/linear_model/_ransac.py
+++ b/sklearn/linear_model/_ransac.py
@@ -478,7 +478,7 @@ def predict(self, X):
             Returns predicted values.
         """
         check_is_fitted(self)
-
+        X = self._validate_data(X, accept_sparse='csr', reset=False)
         return self.estimator_.predict(X)
 
     def score(self, X, y):
diff --git a/sklearn/linear_model/_stochastic_gradient.py b/sklearn/linear_model/_stochastic_gradient.py
index e99116ca4f3e3..498bd9a5caa95 100644
--- a/sklearn/linear_model/_stochastic_gradient.py
+++ b/sklearn/linear_model/_stochastic_gradient.py
@@ -15,7 +15,7 @@
 from ._base import LinearClassifierMixin, SparseCoefMixin
 from ._base import make_dataset
 from ..base import BaseEstimator, RegressorMixin
-from ..utils import check_array, check_random_state, check_X_y
+from ..utils import check_random_state
 from ..utils.extmath import safe_sparse_dot
 from ..utils.multiclass import _check_partial_fit_first_call
 from ..utils.validation import check_is_fitted, _check_sample_weight
@@ -55,6 +55,7 @@ def __init__(self, estimator, X_val, y_val, sample_weight_val,
                  classes=None):
         self.estimator = clone(estimator)
         self.estimator.t_ = 1  # to pass check_is_fitted
+        self.estimator.n_features_in_ = X_val.shape[1]
         if classes is not None:
             self.estimator.classes_ = classes
         self.X_val = X_val
@@ -488,8 +489,10 @@ def _partial_fit(self, X, y, alpha, C,
                      loss, learning_rate, max_iter,
                      classes, sample_weight,
                      coef_init, intercept_init):
-        X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
-                         order="C", accept_large_sparse=False)
+        first_call = not hasattr(self, "classes_")
+        X, y = self._validate_data(X, y, accept_sparse='csr', dtype=np.float64,
+                                   order="C", accept_large_sparse=False,
+                                   reset=first_call)
 
         n_samples, n_features = X.shape
 
@@ -1135,9 +1138,10 @@ def __init__(self, loss="squared_loss", *, penalty="l2", alpha=0.0001,
 
     def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
                      max_iter, sample_weight, coef_init, intercept_init):
+        first_call = getattr(self, "coef_", None) is None
         X, y = self._validate_data(X, y, accept_sparse="csr", copy=False,
                                    order='C', dtype=np.float64,
-                                   accept_large_sparse=False)
+                                   accept_large_sparse=False, reset=first_call)
         y = y.astype(np.float64, copy=False)
 
         n_samples, n_features = X.shape
@@ -1145,12 +1149,9 @@ def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
         sample_weight = _check_sample_weight(sample_weight, X)
 
         # Allocate datastructures from input arguments
-        if getattr(self, "coef_", None) is None:
+        if first_call:
             self._allocate_parameter_mem(1, n_features, coef_init,
                                          intercept_init)
-        elif n_features != self.coef_.shape[-1]:
-            raise ValueError("Number of features %d does not match previous "
-                             "data %d." % (n_features, self.coef_.shape[-1]))
         if self.average > 0 and getattr(self, "_average_coef", None) is None:
             self._average_coef = np.zeros(n_features,
                                           dtype=np.float64,
@@ -1266,7 +1267,7 @@ def _decision_function(self, X):
         """
         check_is_fitted(self)
 
-        X = check_array(X, accept_sparse='csr')
+        X = self._validate_data(X, accept_sparse='csr', reset=False)
 
         scores = safe_sparse_dot(X, self.coef_.T,
                                  dense_output=True) + self.intercept_
diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py
index c5196a5801607..bf57f057182cf 100644
--- a/sklearn/svm/_base.py
+++ b/sklearn/svm/_base.py
@@ -471,8 +471,9 @@ def _validate_for_predict(self, X):
         check_is_fitted(self)
 
         if not callable(self.kernel):
-            X = check_array(X, accept_sparse='csr', dtype=np.float64,
-                            order="C", accept_large_sparse=False)
+            X = self._validate_data(X, accept_sparse='csr', dtype=np.float64,
+                                    order="C", accept_large_sparse=False,
+                                    reset=False)
 
         if self._sparse and not sp.isspmatrix(X):
             X = sp.csr_matrix(X)
@@ -489,10 +490,6 @@ def _validate_for_predict(self, X):
                 raise ValueError("X.shape[1] = %d should be equal to %d, "
                                  "the number of samples at training time" %
                                  (X.shape[1], self.shape_fit_[0]))
-        elif not callable(self.kernel) and X.shape[1] != self.shape_fit_[1]:
-            raise ValueError("X.shape[1] = %d should be equal to %d, "
-                             "the number of features at training time" %
-                             (X.shape[1], self.shape_fit_[1]))
         return X
 
     @property
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index b84b66d1fb919..a1ad846b48cca 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -298,7 +298,6 @@ def test_strict_mode_parametrize_with_checks(estimator, check):
     'isotonic',
     'kernel_approximation',
     'kernel_ridge',
-    'linear_model',
     'manifold',
     'mixture',
     'model_selection',
@@ -310,7 +309,6 @@ def test_strict_mode_parametrize_with_checks(estimator, check):
     'preprocessing',
     'random_projection',
     'semi_supervised',
-    'svm',
     'tree',
 }
 

From 61ba6d5b2a80f2fbbc8c34d093e36ee6dad6abd5 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Fri, 9 Oct 2020 00:04:59 -0400
Subject: [PATCH 2/5] REV Reduces diff

---
 sklearn/linear_model/_logistic.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index b40f723899d93..d0083faa1f246 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -2085,6 +2085,7 @@ def score(self, X, y, sample_weight=None):
         """
         scoring = self.scoring or 'accuracy'
         scoring = get_scorer(scoring)
+
         return scoring(self, X, y, sample_weight=sample_weight)
 
     def _more_tags(self):

From 6b4481135e35db667c8abfab0f67b7e34e42a758 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Tue, 13 Oct 2020 16:49:51 -0400
Subject: [PATCH 3/5] CLN Remove unreachable code

---
 sklearn/linear_model/_base.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
index be0e7653634e0..56fc3e9f71edf 100644
--- a/sklearn/linear_model/_base.py
+++ b/sklearn/linear_model/_base.py
@@ -283,12 +283,6 @@ class would be predicted.
         check_is_fitted(self)
 
         X = self._validate_data(X, accept_sparse='csr', reset=False)
-
-        n_features = self.coef_.shape[1]
-        if X.shape[1] != n_features:
-            raise ValueError("X has %d features per sample; expecting %d"
-                             % (X.shape[1], n_features))
-
         scores = safe_sparse_dot(X, self.coef_.T,
                                  dense_output=True) + self.intercept_
         return scores.ravel() if scores.shape[1] == 1 else scores

From 7201313bba799e104b583196f84a06c66129fd22 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <thomasjpfan@gmail.com>
Date: Tue, 13 Oct 2020 16:52:06 -0400
Subject: [PATCH 4/5] REV Reduces diff

---
 sklearn/ensemble/tests/test_stacking.py      | 5 +++--
 sklearn/linear_model/_logistic.py            | 1 -
 sklearn/linear_model/_ransac.py              | 2 +-
 sklearn/linear_model/_stochastic_gradient.py | 1 -
 4 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/sklearn/ensemble/tests/test_stacking.py b/sklearn/ensemble/tests/test_stacking.py
index 815fdd44558ec..d6b4c385b9073 100644
--- a/sklearn/ensemble/tests/test_stacking.py
+++ b/sklearn/ensemble/tests/test_stacking.py
@@ -483,8 +483,9 @@ def test_stacking_without_n_features_in(make_dataset, Stacking, Estimator):
 
     class MyEstimator(Estimator):
         """Estimator without n_features_in_"""
-        def _check_n_features(self, X, reset):
-            pass
+        def fit(self, X, y):
+            super().fit(X, y)
+            del self.n_features_in_
 
     X, y = make_dataset(random_state=0, n_samples=100)
     stacker = Stacking(estimators=[('lr', MyEstimator())])
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index d0083faa1f246..1afa06637b04a 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -973,7 +973,6 @@ def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10,
         max_squared_sum=max_squared_sum, sample_weight=sample_weight)
 
     log_reg = LogisticRegression(solver=solver, multi_class=multi_class)
-    log_reg.n_features_in_ = X.shape[1]
 
     # The score method of Logistic Regression has a classes_ attribute.
     if multi_class == 'ovr':
diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py
index 390b247e719f5..c9246c121c387 100644
--- a/sklearn/linear_model/_ransac.py
+++ b/sklearn/linear_model/_ransac.py
@@ -478,7 +478,7 @@ def predict(self, X):
             Returns predicted values.
         """
         check_is_fitted(self)
-        X = self._validate_data(X, accept_sparse='csr', reset=False)
+
         return self.estimator_.predict(X)
 
     def score(self, X, y):
diff --git a/sklearn/linear_model/_stochastic_gradient.py b/sklearn/linear_model/_stochastic_gradient.py
index 498bd9a5caa95..ed96d066370ff 100644
--- a/sklearn/linear_model/_stochastic_gradient.py
+++ b/sklearn/linear_model/_stochastic_gradient.py
@@ -55,7 +55,6 @@ def __init__(self, estimator, X_val, y_val, sample_weight_val,
                  classes=None):
         self.estimator = clone(estimator)
         self.estimator.t_ = 1  # to pass check_is_fitted
-        self.estimator.n_features_in_ = X_val.shape[1]
         if classes is not None:
             self.estimator.classes_ = classes
         self.X_val = X_val

From 81b3fc748eff3fadb118145ad8b23f983a9b6092 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Wed, 14 Oct 2020 15:52:55 +0200
Subject: [PATCH 5/5] Expect base estimator names

---
 sklearn/utils/estimator_checks.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index cb1df0ad95ff3..4d178eb6402d1 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -3143,6 +3143,14 @@ def check_requires_y_none(name, estimator_orig, strict_mode=True):
             warnings.warn(warning_msg, FutureWarning)
 
 
+def _accumulate_estimator_names(estimator, names):
+    for attribute_name in ["estimator_", "base_estimator_"]:
+        base_estimator = getattr(estimator, attribute_name, None)
+        if base_estimator is not None:
+            names.append(base_estimator.__class__.__name__)
+            _accumulate_estimator_names(base_estimator, names)
+
+
 def check_n_features_in_after_fitting(name, estimator_orig, strict_mode=True):
     # Make sure that n_features_in are checked after fitting
     tags = estimator_orig._get_tags()
@@ -3173,8 +3181,10 @@ def check_n_features_in_after_fitting(name, estimator_orig, strict_mode=True):
     check_methods = ["predict", "transform", "decision_function",
                      "predict_proba"]
     X_bad = X[:, [1]]
-
-    msg = (f"X has 1 features, but {name} is expecting {X.shape[1]} "
+    names = [name]
+    _accumulate_estimator_names(estimator, names)
+    names = "|".join(names)
+    msg = (f"X has 1 features, but ({names}) is expecting {X.shape[1]} "
            "features as input")
     for method in check_methods:
         if not hasattr(estimator, method):