MNT simple deprecations and removals for 0.21 (scikit-learn#12238)

Part of scikit-learn#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.

Author: amueller

doc/modules/classes.rst

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

sklearn/cluster/hierarchical.py

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

sklearn/cluster/tests/test_hierarchical.py

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

sklearn/covariance/graph_lasso_.py

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

sklearn/covariance/tests/test_graph_lasso.py

@@ -9,7 +9,6 @@
 
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_array_less
-from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
 
 from sklearn.covariance import (graph_lasso, GraphLasso, GraphLassoCV,
@@ -19,8 +18,6 @@
 from sklearn.utils import check_random_state
 from sklearn import datasets
 
-from numpy.testing import assert_equal
-
 
 @ignore_warnings(category=DeprecationWarning)
 def test_graph_lasso(random_state=0):
@@ -141,25 +138,3 @@ def test_graph_lasso_cv(random_state=1):
 
  # Smoke test with specified alphas
  GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
-
-
-@ignore_warnings(category=DeprecationWarning)
-@pytest.mark.filterwarnings('ignore: You should specify a value') # 0.22
-def test_deprecated_grid_scores(random_state=1):
- dim = 5
- n_samples = 6
- random_state = check_random_state(random_state)
- prec = make_sparse_spd_matrix(dim, alpha=.96,
- random_state=random_state)
- cov = linalg.inv(prec)
- X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
- graph_lasso = GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1)
- graph_lasso.fit(X)
-
- depr_message = ("Attribute grid_scores was deprecated in version "
- "0.19 and will be removed in 0.21. Use "
- "``grid_scores_`` instead")
-
- assert_warns_message(DeprecationWarning, depr_message,
- lambda: graph_lasso.grid_scores)
- assert_equal(graph_lasso.grid_scores, graph_lasso.grid_scores_)

sklearn/covariance/tests/test_graphical_lasso.py

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

sklearn/datasets/__init__.py

@@ -17,7 +17,6 @@
 from .base import clear_data_home
 from .covtype import fetch_covtype
 from .kddcup99 import fetch_kddcup99
-from .mlcomp import load_mlcomp
 from .lfw import fetch_lfw_pairs
 from .lfw import fetch_lfw_people
 from .twenty_newsgroups import fetch_20newsgroups
@@ -75,7 +74,6 @@
  'load_iris',
  'load_breast_cancer',
  'load_linnerud',
- 'load_mlcomp',
  'load_sample_image',
  'load_sample_images',
  'load_svmlight_file',

sklearn/datasets/mlcomp.py

@@ -18,7 +18,6 @@
 from ..exceptions import ConvergenceWarning
 from ..externals import six
 from ..externals.six import moves
-from ..externals.six import string_types
 from ..utils import check_array, as_float_array, check_random_state
 from ..utils.validation import check_is_fitted
 from ..utils.validation import FLOAT_DTYPES
@@ -553,29 +552,22 @@ def fit(self, X, y=None):
  self._fit(X, compute_sources=False)
  return self
 
- def transform(self, X, y='deprecated', copy=True):
+ def transform(self, X, copy=True):
  """Recover the sources from X (apply the unmixing matrix).
 
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
  Data to transform, where n_samples is the number of samples
  and n_features is the number of features.
- y : (ignored)
- .. deprecated:: 0.19
- This parameter will be removed in 0.21.
+
  copy : bool (optional)
  If False, data passed to fit are overwritten. Defaults to True.
 
  Returns
  -------
  X_new : array-like, shape (n_samples, n_components)
  """
- if not isinstance(y, string_types) or y != 'deprecated':
- warnings.warn("The parameter y on transform() is "
- "deprecated since 0.19 and will be removed in 0.21",
- DeprecationWarning)
-
  check_is_fitted(self, 'mixing_')
 
  X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)