8000 MNT simple deprecations and removals for 0.21 (#12238) · scikit-learn/scikit-learn@0f94f29 · GitHub
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MNT simple deprecations and removals for 0.21 (#12238)
Part of #11992. These were all the things that seemed pretty straight-forward. It's actually a bit bulky but should still be easy to review, hopefully.
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‎doc/modules/classes.rst

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@@ -1513,23 +1513,4 @@ To be removed in 0.22
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:template: deprecated_function.rst
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covariance.graph_lasso
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datasets.fetch_mldata
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To be removed in 0.21
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---------------------
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.. autosummary::
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:toctree: generated/
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:template: deprecated_class.rst
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linear_model.RandomizedLasso
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linear_model.RandomizedLogisticRegression
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neighbors.LSHForest
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.. autosummary::
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:toctree: generated/
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:template: deprecated_function.rst
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datasets.load_mlcomp
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linear_model.lasso_stability_path
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datasets.fetch_mldata

‎sklearn/cluster/hierarchical.py

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@@ -339,9 +339,8 @@ def ward_tree(X, connectivity=None, n_clusters=None, return_distance=False):
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# single average and complete linkage
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def linkage_tree(X, connectivity=None, n_components='deprecated',
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n_clusters=None, linkage='complete', affinity="euclidean",
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return_distance=False):
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def linkage_tree(X, connectivity=None, n_clusters=None, linkage='complete',
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affinity="euclidean", return_distance=False):
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"""Linkage agglomerative clustering based on a Feature matrix.
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The inertia matrix uses a Heapq-based representation.
@@ -362,9 +361,6 @@ def linkage_tree(X, connectivity=None, n_components='deprecated',
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be symmetric and only the upper triangular half is used.
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Default is None, i.e, the Ward algorithm is unstructured.
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n_components : int (optional)
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The number of connected components in the graph.
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n_clusters : int (optional)
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Stop early the construction of the tree at n_clusters. This is
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useful to decrease computation time if the number of clusters is
@@ -420,10 +416,6 @@ def linkage_tree(X, connectivity=None, n_components='deprecated',
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--------
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ward_tree : hierarchical clustering with ward linkage
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"""
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if n_components != 'deprecated':
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warnings.warn("n_components was deprecated in 0.19"
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"will be removed in 0.21", DeprecationWarning)
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X = np.asarray(X)
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if X.ndim == 1:
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X = np.reshape(X, (-1, 1))

‎sklearn/cluster/tests/test_hierarchical.py

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@@ -38,21 +38,6 @@
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from sklearn.datasets import make_moons, make_circles
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def test_deprecation_of_n_components_in_linkage_tree():
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rng = np.random.RandomState(0)
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X = rng.randn(50, 100)
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# Test for warning of deprecation of n_components in linkage_tree
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children, n_nodes, n_leaves, parent = assert_warns(DeprecationWarning,
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linkage_tree,
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X.T,
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n_components=10)
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children_t, n_nodes_t, n_leaves_t, parent_t = linkage_tree(X.T)
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assert_array_equal(children, children_t)
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assert_equal(n_nodes, n_nodes_t)
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assert_equal(n_leaves, n_leaves_t)
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assert_equal(parent, parent_t)
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def test_linkage_misc():
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# Misc tests on linkage
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rng = np.random.RandomState(42)

‎sklearn/covariance/graph_lasso_.py

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@@ -586,12 +586,6 @@ def __init__(self, alphas=4, n_refinements=4, cv='warn', tol=1e-4,
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self.cv = cv
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self.n_jobs = n_jobs
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@property
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@deprecated("Attribute grid_scores was deprecated in version 0.19 and "
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"will be removed in 0.21. Use ``grid_scores_`` instead")
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def grid_scores(self):
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return self.grid_scores_
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def fit(self, X, y=None):
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"""Fits the GraphicalLasso covariance model to X.
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‎sklearn/covariance/tests/test_graph_lasso.py

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@@ -9,7 +9,6 @@
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_less
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from sklearn.utils.testing import assert_warns_message
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from sklearn.utils.testing import ignore_warnings
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from sklearn.covariance import (graph_lasso, GraphLasso, GraphLassoCV,
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from sklearn.utils import check_random_state
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from sklearn import datasets
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from numpy.testing import assert_equal
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@ignore_warnings(category=DeprecationWarning)
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def test_graph_lasso(random_state=0):
@@ -141,25 +138,3 @@ def test_graph_lasso_cv(random_state=1):
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# Smoke test with specified alphas
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GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
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146-
@ignore_warnings(category=DeprecationWarning)
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@pytest.mark.filterwarnings('ignore: You should specify a value') # 0.22
148-
def test_deprecated_grid_scores(random_state=1):
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dim = 5
150-
n_samples = 6
151-
random_state = check_random_state(random_state)
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prec = make_sparse_spd_matrix(dim, alpha=.96,
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random_state=random_state)
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cov = linalg.inv(prec)
155-
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
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graph_lasso = GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1)
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graph_lasso.fit(X)
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depr_message = ("Attribute grid_scores was deprecated in version "
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"``grid_scores_`` instead")
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assert_warns_message(DeprecationWarning, depr_message,
164-
lambda: graph_lasso.grid_scores)
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assert_equal(graph_lasso.grid_scores, graph_lasso.grid_scores_)

‎sklearn/covariance/tests/test_graphical_lasso.py

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@@ -8,17 +8,13 @@
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_less
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from sklearn.utils.testing import assert_warns_message
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1312
from sklearn.covariance import (graphical_lasso, GraphicalLasso,
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GraphicalLassoCV, empirical_covariance)
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from sklearn.datasets.samples_generator import make_sparse_spd_matrix
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from sklearn.externals.six.moves import StringIO
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from sklearn.utils import check_random_state
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from sklearn import datasets
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from sklearn.utils.fixes import PY3_OR_LATER
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from numpy.testing import assert_equal
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2420
def test_graphical_lasso(random_state=0):
@@ -137,25 +133,3 @@ def test_graphical_lasso_cv(random_state=1):
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138134
# Smoke test with specified alphas
139135
GraphicalLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
140-
141-
142-
@pytest.mark.filterwarnings('ignore: You should specify a value') # 0.22
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@pytest.mark.skipif(not PY3_OR_LATER,
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reason='On Python 2 DeprecationWarning is not issued for some unkown reason.')
145-
def test_deprecated_grid_scores(random_state=1):
146-
dim = 5
147-
n_samples = 6
148-
random_state = check_random_state(random_state)
149-
prec = make_sparse_spd_matrix(dim, alpha=.96,
150-
random_state=random_state)
151-
cov = linalg.inv(prec)
152-
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
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graphical_lasso = GraphicalLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1)
154-
graphical_lasso.fit(X)
155-
156-
depr_message = ("Attribute grid_scores was deprecated in version "
157-
"0.19 and will be removed in 0.21. Use "
158-
"``grid_scores_`` instead")
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160-
with pytest.warns(DeprecationWarning, match=depr_message):
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assert_equal(graphical_lasso.grid_scores, graphical_lasso.grid_scores_)

‎sklearn/datasets/__init__.py

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from .base import clear_data_home
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from .covtype import fetch_covtype
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from .kddcup99 import fetch_kddcup99
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from .mlcomp import load_mlcomp
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from .lfw import fetch_lfw_pairs
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from .lfw import fetch_lfw_people
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from .twenty_newsgroups import fetch_20newsgroups
@@ -75,7 +74,6 @@
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'load_iris',
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'load_breast_cancer',
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'load_linnerud',
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'load_mlcomp',
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'load_sample_image',
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'load_sample_images',
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'load_svmlight_file',

‎sklearn/datasets/mlcomp.py

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This file was deleted.

‎sklearn/decomposition/fastica_.py

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from ..exceptions import ConvergenceWarning
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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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553552
self._fit(X, compute_sources=False)
554553
return self
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def transform(self, X, y='deprecated', copy=True):
555+
def transform(self, X, copy=True):
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"""Recover the sources from X (apply the unmixing matrix).
558557
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Parameters
560559
----------
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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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y : (ignored)
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.. deprecated:: 0.19
566-
This parameter will be removed in 0.21.
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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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Returns
571568
-------
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X_new : array-like, shape (n_samples, n_components)
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"""
574-
if not isinstance(y, string_types) or y != 'deprecated':
575-
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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579571
check_is_fitted(self, 'mixing_')
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581573
X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)

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