scikit-learn · maikia · Jun 25, 2020 · Jun 25, 2020 · Jun 25, 2020 · Jun 25, 2020
diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
@@ -119,6 +119,7 @@ def _preprocess_data(X, y, fit_intercept, normalize=False, copy=True,
     This is here because nearly all linear models will want their data to be
     centered. This function also systematically makes y consistent with X.dtype
     """
+
     if isinstance(sample_weight, numbers.Number):
         sample_weight = None
     if sample_weight is not None:
@@ -409,6 +410,10 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
         :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
+        .. deprecated:: 0.24
+            ``normalize`` was deprecated in version 0.24 and will be removed in
+            0.26.
+
     copy_X : bool, default=True
         If True, X will be copied; else, it may be overwritten.
 
@@ -471,8 +476,8 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
     array([16.])
     """
     @_deprecate_positional_args
-    def __init__(self, *, fit_intercept=True, normalize=False, copy_X=True,
-                 n_jobs=None):
+    def __init__(self, *, fit_intercept=True, normalize='deprecate',
+                 copy_X=True, n_jobs=None):
         self.fit_intercept = fit_intercept
         self.normalize = normalize
         self.copy_X = copy_X
@@ -501,6 +506,12 @@ def fit(self, X, y, sample_weight=None):
         self : returns an instance of self.
         """
 
+        if self.normalize != "deprecate":
+            warnings.warn("'normalize' was deprecated in version 0.24 and will"
+                          " be removed in 0.26.", FutureWarning)
+        else:
+            self.normalize = False
+
         n_jobs_ = self.n_jobs
         X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],
                                    y_numeric=True, multi_output=True)
@@ -578,6 +589,8 @@ def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy,
             check_input=check_input, sample_weight=sample_weight)
     if sample_weight is not None:
         X, y = _rescale_data(X, y, sample_weight=sample_weight)
+
+    # FIXME: 'normalize' to be removed in 0.26
     if hasattr(precompute, '__array__') and (
         fit_intercept and not np.allclose(X_offset, np.zeros(n_features)) or
             normalize and not np.allclose(X_scale, np.ones(n_features))):

diff --git a/sklearn/linear_model/_bayes.py b/sklearn/linear_model/_bayes.py
@@ -8,6 +8,7 @@
 from math import log
 import numpy as np
 from scipy import linalg
+import warnings
 
 from ._base import LinearModel, _rescale_data
 from ..base import RegressorMixin
@@ -84,6 +85,10 @@ class BayesianRidge(RegressorMixin, LinearModel):
         :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
+        .. deprecated:: 0.24
+            ``normalize`` was deprecated in version 0.24 and will be removed in
+            0.26.
+
     copy_X : bool, default=True
         If True, X will be copied; else, it may be overwritten.
 
@@ -150,7 +155,7 @@ class BayesianRidge(RegressorMixin, LinearModel):
     def __init__(self, *, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
                  lambda_1=1.e-6, lambda_2=1.e-6, alpha_init=None,
                  lambda_init=None, compute_score=False, fit_intercept=True,
-                 normalize=False, copy_X=True, verbose=False):
+                 normalize='deprecate', copy_X=True, verbose=False):
         self.n_iter = n_iter
         self.tol = tol
         self.alpha_1 = alpha_1
@@ -185,6 +190,11 @@ def fit(self, X, y, sample_weight=None):
         -------
         self : returns an instance of self.
         """
+        if self.normalize != "deprecate":
+            warnings.warn("'normalize' was deprecated in version 0.24 and will"
+                          " be removed in 0.26.", FutureWarning)
+        else:
+            self.normalize = False
 
         if self.n_iter < 1:
             raise ValueError('n_iter should be greater than or equal to 1.'
@@ -436,6 +446,10 @@ class ARDRegression(RegressorMixin, LinearModel):
         :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
+        .. deprecated:: 0.24
+            ``normalize`` was deprecated in version 0.24 and will be removed in
+            0.26.
+
     copy_X : bool, default=True
         If True, X will be copied; else, it may be overwritten.
 
@@ -493,8 +507,8 @@ class ARDRegression(RegressorMixin, LinearModel):
     @_deprecate_positional_args
     def __init__(self, *, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
                  lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False,
-                 threshold_lambda=1.e+4, fit_intercept=True, normalize=False,
-                 copy_X=True, verbose=False):
+                 threshold_lambda=1.e+4, fit_intercept=True,
+                 normalize='deprecate', copy_X=True, verbose=False):
         self.n_iter = n_iter
         self.tol = tol
         self.fit_intercept = fit_intercept
@@ -526,6 +540,12 @@ def fit(self, X, y):
         -------
         self : returns an instance of self.
         """
+        if self.normalize != "deprecate":
+            warnings.warn("'normalize' was deprecated in version 0.24 and will"
+                          " be removed in 0.26.", FutureWarning)
+        else:
+            self.normalize = False
+
         X, y = self._validate_data(X, y, dtype=np.float64, y_numeric=True,
                                    ensure_min_samples=2)