10000 DEP Deprecates 'normalize' in _bayes.py (#17746) · scikit-learn/scikit-learn@a45c0c9 · GitHub
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DEP Deprecates 'normalize' in _bayes.py (#17746)
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‎doc/whats_new/v1.0.rst

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@@ -269,6 +269,8 @@ Changelog
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Ridge, RidgeClassifier, RidgeCV or RidgeClassifierCV were deprecated in:
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:pr:`17772` by :user:`Maria Telenczuk <maikia>` and
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:user:`Alexandre Gramfort <agramfort>`.
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BayesianRidge, ARDRegression were deprecated in:
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:pr:`17746` by :user:`Maria Telenczuk <maikia>`.
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- |Fix|: `sample_weight` are now fully taken into account in linear models
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when `normalize=True` for both feature centering and feature

‎sklearn/linear_model/_bayes.py

Lines changed: 25 additions & 7 deletions
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@@ -11,6 +11,7 @@
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from ._base import LinearModel, _rescale_data
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from ..base import RegressorMixin
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from ._base import _deprecate_normalize
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from ..utils.extmath import fast_logdet
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from scipy.linalg import pinvh
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from ..utils.validation import _check_sample_weight
@@ -84,6 +85,10 @@ class BayesianRidge(RegressorMixin, LinearModel):
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:class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
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on an estimator with ``normalize=False``.
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.. deprecated:: 1.0
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``normalize`` was deprecated in version 1.0 and will be removed in
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1.2.
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copy_X : bool, default=True
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If True, X will be copied; else, it may be overwritten.
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@@ -158,7 +163,7 @@ class BayesianRidge(RegressorMixin, LinearModel):
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def __init__(self, *, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
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lambda_1=1.e-6, lambda_2=1.e-6, alpha_init=None,
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lambda_init=None, compute_score=False, fit_intercept=True,
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normalize=False, copy_X=True, verbose=False):
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normalize='deprecated', copy_X=True, verbose=False):
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self.n_iter = n_iter
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self.tol = tol
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self.alpha_1 = alpha_1
@@ -193,6 +198,10 @@ def fit(self, X, y, sample_weight=None):
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-------
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self : returns an instance of self.
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"""
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self._normalize = _deprecate_normalize(
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self.normalize, default=False,
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estimator_name=self.__class__.__name__
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)
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if self.n_iter < 1:
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raise ValueError('n_iter should be greater than or equal to 1.'
@@ -205,7 +214,7 @@ def fit(self, X, y, sample_weight=None):
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dtype=X.dtype)
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X, y, X_offset_, y_offset_, X_scale_ = self._preprocess_data(
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X, y, self.fit_intercept, self.normalize, self.copy_X,
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X, y, self.fit_intercept, self._normalize, self.copy_X,
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sample_weight=sample_weight)
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if sample_weight is not None:
@@ -325,7 +334,7 @@ def predict(self, X, return_std=False):
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if return_std is False:
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return y_mean
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else:
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if self.normalize:
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if self._normalize:
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X = (X - self.X_offset_) / self.X_scale_
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sigmas_squared_data = (np.dot(X, self.sigma_) * X).sum(axis=1)
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y_std = np.sqrt(sigmas_squared_data + (1. / self.alpha_))
@@ -445,6 +454,10 @@ class ARDRegression(RegressorMixin, LinearModel):
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:class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
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on an estimator with ``normalize=False``.
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.. deprecated:: 1.0
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``normalize`` was deprecated in version 1.0 and will be removed in
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1.2.
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copy_X : bool, default=True
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If True, X will be copied; else, it may be overwritten.
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@@ -510,8 +523,8 @@ class ARDRegression(RegressorMixin, LinearModel):
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@_deprecate_positional_args
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def __init__(self, *, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
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lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False,
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threshold_lambda=1.e+4, fit_intercept=True, normalize=False,
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copy_X=True, verbose=False):
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threshold_lambda=1.e+4, fit_intercept=True,
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normalize='deprecated', copy_X=True, verbose=False):
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self.n_iter = n_iter
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self.tol = tol
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self.fit_intercept = fit_intercept
@@ -543,14 +556,19 @@ def fit(self, X, y):
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-------
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self : returns an instance of self.
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"""
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self._normalize = _deprecate_normalize(
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self.normalize, default=False,
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estimator_name=self.__class__.__name__
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)
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X, y = self._validate_data(X, y, dtype=np.float64, y_numeric=True,
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ensure_min_samples=2)
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n_samples, n_features = X.shape
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coef_ = np.zeros(n_features)
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X, y, X_offset_, y_offset_, X_scale_ = self._preprocess_data(
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X, y, self.fit_intercept, self.normalize, self.copy_X)
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X, y, self.fit_intercept, self._normalize, self.copy_X)
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self.X_offset_ = X_offset_
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self.X_scale_ = X_scale_
@@ -686,7 +704,7 @@ def predict(self, X, return_std=False):
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if return_std is False:
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return y_mean
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else:
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if self.normalize:
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if self._normalize:
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X = (X - self.X_offset_) / self.X_scale_
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X = X[:, self.lambda_ < self.threshold_lambda]
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sigmas_squared_data = (np.dot(X, self.sigma_) * X).sum(axis=1)

‎sklearn/linear_model/tests/test_bayes.py

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@@ -274,6 +274,8 @@ def test_update_sigma(seed):
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np.testing.assert_allclose(sigma, sigma_woodbury)
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# FIXME: 'normalize' to be removed in 1.2 in LinearRegression
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@pytest.mark.filterwarnings("ignore:'normalize' was deprecated")
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def test_ard_regression_predict_normalize_true():
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"""Check that we can predict with `normalize=True` and `return_std=True`.
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Non-regression test for:

‎sklearn/linear_model/tests/test_common.py

Lines changed: 4 additions & 1 deletion
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@@ -12,6 +12,8 @@
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from sklearn.linear_model import RidgeCV
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from sklearn.linear_model import RidgeClassifier
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from sklearn.linear_model import RidgeClassifierCV
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from sklearn.linear_model import BayesianRidge
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from sklearn.linear_model import ARDRegression
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from sklearn.utils import check_random_state
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@@ -24,7 +26,8 @@
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)
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@pytest.mark.parametrize(
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"estimator",
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[LinearRegression, Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV]
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[LinearRegression, Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV,
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BayesianRidge, ARDRegression]
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)
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# FIXME remove test in 1.2
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def test_linear_model_normalize_deprecation_message(

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