8000 [MRG+1] Do not transform y (#9180) · raghavrv/scikit-learn@763d93b · GitHub 8000
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[MRG+1] Do not transform y (scikit-learn#9180)
* we do not transform y * more * added Deprecation Warning to transform() to remove Y parameter * more * ENH ensure FunctionTransformer's transform/inverse_transform doesn't permit y * Undo changes to pls_. It will be done in a separate PR (see scikit-learn#9160) * flake8 * Update whatsnew * Fully undo PLS changes
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‎doc/whats_new.rst

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@@ -549,6 +549,12 @@ API changes summary
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- ``utils.stats.rankdata``
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- ``neighbors.approximate.LSHForest``
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- Deprecate the ``y`` parameter in `transform` and `inverse_transform`.
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The method should not accept ``y`` parameter, as it's used at the prediction time.
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:issue:`8174` by :user:`Tahar Zanouda <tzano>`, `Alexandre Gramfort`_
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and `Raghav RV`_.
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.. _changes_0_18_1:
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Version 0.18.1

‎sklearn/decomposition/base.py

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@@ -96,8 +96,7 @@ def fit(X, y=None):
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Returns the instance itself.
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"""
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def transform(self, X, y=None):
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def transform(self, X):
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"""Apply dimensionality reduction to X.
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X is projected on the first principal components previously extracted
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X_transformed /= np.sqrt(self.explained_variance_)
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return X_transformed
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def inverse_transform(self, X, y=None):
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def inverse_transform(self, X):
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"""Transform data back to its original space.
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In other words, return an input X_original whose transform would be X.

‎sklearn/decomposition/fastica_.py

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@@ -8,13 +8,16 @@
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# Authors: Pierre Lafaye de Micheaux, Stefan van der Walt, Gael Varoquaux,
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# Bertrand Thirion, Alexandre Gramfort, Denis A. Engemann
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# License: BSD 3 clause
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import warnings
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import numpy as np
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from scipy import linalg
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from ..base import BaseEstimator, TransformerMixin
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from ..externals import six
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from ..externals.six import moves
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from ..externals.six import string_types
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from ..utils import check_array, as_float_array, check_random_state
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from ..utils.validation import check_is_fitted
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from ..utils.validation import FLOAT_DTYPES
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self._fit(X, compute_sources=False)
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return self
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def transform(self, X, y=None, copy=True):
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def transform(self, X, y='deprecated', copy=True):
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"""Recover the sources from X (apply the unmixing matrix).
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Data to transform, where n_samples is the number of samples
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and n_features is the number of features.
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copy : bool (optional)
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If False, data passed to fit are overwritten. Defaults to True.
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y : (ignored)
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.. deprecated:: 0.19
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This parameter will be removed in 0.21.
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Returns
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-------
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X_new : array-like, shape (n_samples, n_components)
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"""
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if not isinstance(y, string_types) or y != 'deprecated':
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warnings.warn("The parameter y on transform() is "
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"deprecated since 0.19 and will be removed in 0.21",
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DeprecationWarning)
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check_is_fitted(self, 'mixing_')
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X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)

‎sklearn/decomposition/pca.py

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@@ -764,7 +764,7 @@ def fit_transform(self, X, y=None):
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X = self._fit(X)
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return np.dot(X, self.components_.T)
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def inverse_transform(self, X, y=None):
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def inverse_transform(self, X):
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"""Transform data back to its original space.
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Returns an array X_original whose transform would be X.

‎sklearn/feature_extraction/dict_vectorizer.py

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@@ -270,7 +270,7 @@ def inverse_transform(self, X, dict_type=dict):
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return dicts
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def transform(self, X, y=None):
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def transform(self, X):
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"""Transform feature->value dicts to array or sparse matrix.
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Named features not encountered during fit or fit_transform will be
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X : Mapping or iterable over Mappings, length = n_samples
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Dict(s) or Mapping(s) from feature names (arbitrary Python
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objects) to feature values (strings or convertible to dtype).
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y : (ignored)
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Returns
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-------

‎sklearn/feature_extraction/hashing.py

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@@ -126,7 +126,7 @@ def fit(self, X=None, y=None):
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self._validate_params(self.n_features, self.input_type)
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return self
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def transform(self, raw_X, y=None):
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def transform(self, raw_X):
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"""Transform a sequence of instances to a scipy.sparse matrix.
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Parameters
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the input_type constructor argument) which will be hashed.
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raw_X need not support the len function, so it can be the result
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of a generator; n_samples is determined on the fly.
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y : (ignored)
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Returns
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-------

‎sklearn/feature_extraction/text.py

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-------
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X : scipy.sparse matrix, shape = (n_samples, self.n_features)
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Document-term matrix.
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"""
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if isinstance(X, six.string_types):
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raise ValueError(

‎sklearn/kernel_approximation.py

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size=self.n_components)
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return self
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def transform(self, X, y=None):
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def transform(self, X):
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"""Apply the approximate feature map to X.
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Parameters
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size=self.n_components)
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return self
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def transform(self, X, y=None):
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def transform(self, X):
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"""Apply the approximate feature map to X.
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Parameters
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self.sample_interval_ = self.sample_interval
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return self
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def transform(self, X, y=None):
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def transform(self, X):
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"""Apply approximate feature map to X.
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Parameters

‎sklearn/mixture/base.py

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"""
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return self.score_samples(X).mean()
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def predict(self, X, y=None):
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def predict(self, X):
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"""Predict the labels for the data samples in X using trained model.
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Parameters

‎sklearn/neighbors/approximate.py

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self.fit(X)
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return self.transform(X)
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def transform(self, X, y=None):
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def transform(self, X):
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return self._to_hash(super(ProjectionToHashMixin, self).transform(X))
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