MAINT Clean up deprecated square_distances in TSNE for 1.3 (scikit-le…

…arn#26483)

sklearn/manifold/_t_sne.py

@@ -8,7 +8,6 @@
 # * Fast Optimization for t-SNE:
 # https://cseweb.ucsd.edu/~lvdmaaten/workshops/nips2010/papers/vandermaaten.pdf
 
-import warnings
 from time import time
 import numpy as np
 from scipy import linalg
@@ -21,7 +20,7 @@
 from ..utils import check_random_state
 from ..utils._openmp_helpers import _openmp_effective_n_threads
 from ..utils.validation import check_non_negative
-from ..utils._param_validation import Interval, StrOptions, Hidden
+from ..utils._param_validation import Interval, StrOptions
 from ..decomposition import PCA
 from ..metrics.pairwise import pairwise_distances, _VALID_METRICS
 
@@ -678,14 +677,6 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
 
  .. versionadded:: 0.22
 
- square_distances : True, default='deprecated'
- This parameter has no effect since distance values are always squared
- since 1.1.
-
- .. deprecated:: 1.1
- `square_distances` has no effect from 1.1 and will be removed in
- 1.3.
-
  Attributes
  ----------
  embedding_ : array-like of shape (n_samples, n_components)
@@ -778,7 +769,6 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
  "method": [StrOptions({"barnes_hut", "exact"})],
  "angle": [Interval(Real, 0, 1, closed="both")],
  "n_jobs": [None, Integral],
- "square_distances": ["boolean", Hidden(StrOptions({"deprecated"}))],
  }
 
  # Control the number of exploration iterations with early_exaggeration on
@@ -805,7 +795,6 @@ def __init__(
  method="barnes_hut",
  angle=0.5,
  n_jobs=None,
- square_distances="deprecated",
  ):
  self.n_components = n_components
  self.perplexity = perplexity
@@ -822,7 +811,6 @@ def __init__(
  self.method = method
  self.angle = angle
  self.n_jobs = n_jobs
- self.square_distances = square_distances
 
  def _check_params_vs_input(self, X):
  if self.perplexity >= X.shape[0]:
@@ -837,14 +825,7 @@ def _fit(self, X, skip_num_points=0):
  "with the sparse input matrix. Use "
  'init="random" instead.'
  )
- if self.square_distances != "deprecated":
- warnings.warn(
- (
- "The parameter `square_distances` has not effect and will be "
- "removed in version 1.3."
- ),
- FutureWarning,
- )
+
  if self.learning_rate == "auto":
  # See issue #18018
  self.learning_rate_ = X.shape[0] / self.early_exaggeration / 4

sklearn/manifold/tests/test_t_sne.py

@@ -1138,45 +1138,6 @@ def test_tsne_with_mahalanobis_distance():
  assert_allclose(X_trans, X_trans_expected)
 
 
-# FIXME: remove in 1.3 after deprecation of `square_distances`
-def test_tsne_deprecation_square_distances():
- """Check that we raise a warning regarding the removal of
- `square_distances`.
-
- Also check the parameters do not have any effect.
- """
- random_state = check_random_state(0)
- X = random_state.randn(30, 10)
- tsne = TSNE(
- n_components=2,
- init="pca",
- learning_rate="auto",
- perplexity=25.0,
- angle=0,
- n_jobs=1,
- random_state=0,
- square_distances=True,
- )
- warn_msg = (
- "The parameter `square_distances` has not effect and will be removed in"
- " version 1.3"
- )
- with pytest.warns(FutureWarning, match=warn_msg):
- X_trans_1 = tsne.fit_transform(X)
-
- tsne = TSNE(
- n_components=2,
- init="pca",
- learning_rate="auto",
- perplexity=25.0,
- angle=0,
- n_jobs=1,
- random_state=0,
- )
- X_trans_2 = tsne.fit_transform(X)
- assert_allclose(X_trans_1, X_trans_2)
-
-
 @pytest.mark.parametrize("perplexity", (20, 30))
 def test_tsne_perplexity_validation(perplexity):
  """Make sure that perplexity > n_samples results in a ValueError"""