8000 MAINT Clean up leftovers from previous deprecations clean up PRs (#26… · scikit-learn/scikit-learn@168b4e7 · GitHub
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MAINT Clean up leftovers from previous deprecations clean up PRs (#26545)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
1 parent 99c9f41 commit 168b4e7

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+10
-17
lines changed

4 files changed

+10
-17
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‎sklearn/decomposition/_dict_learning.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2412,7 +2412,7 @@ def fit(self, X, y=None):
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self.n_steps_ = i + 1
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self.n_iter_ = np.ceil(self.n_steps_ / n_steps_per_iter)
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else:
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# TODO remove this branch in 1.3
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# TODO remove this branch in 1.4
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n_iter = 1000 if self.n_iter == "deprecated" else self.n_iter
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batches = gen_batches(n_samples, self._batch_size)

‎sklearn/metrics/tests/test_pairwise.py

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -301,8 +301,6 @@ def callable_rbf_kernel(x, y, **kwds):
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return K
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# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
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@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
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@pytest.mark.parametrize(
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"func, metric, kwds",
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[

‎sklearn/neighbors/tests/test_neighbors.py

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -2120,8 +2120,6 @@ def test_auto_algorithm(X, metric, metric_params, expected_algo):
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assert model._fit_method == expected_algo
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# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
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@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
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@pytest.mark.parametrize(
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"metric", sorted(set(neighbors.VALID_METRICS["brute"]) - set(["precomputed"]))
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)

‎sklearn/tests/test_docstring_parameters.py

Lines changed: 9 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -197,6 +197,7 @@ def _construct_sparse_coder(Estimator):
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return Estimator(dictionary=dictionary)
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@ignore_warnings(category=sklearn.exceptions.ConvergenceWarning)
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@pytest.mark.parametrize("name, Estimator", all_estimators())
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def test_fit_docstring_attributes(name, Estimator):
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pytest.importorskip("numpydoc")
@@ -240,18 +241,6 @@ def test_fit_docstring_attributes(name, Estimator):
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# default raises an error, perplexity must be less than n_samples
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est.set_params(perplexity=2)
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "SequentialFeatureSelector":
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est.set_params(n_features_to_select="auto")
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "FastICA":
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est.set_params(whiten="unit-variance")
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "MiniBatchDictionaryLearning":
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est.set_params(batch_size=5)
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# TODO(1.4): TO BE REMOVED for 1.4 (avoid FutureWarning)
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if Estimator.__name__ in ("KMeans", "MiniBatchKMeans"):
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est.set_params(n_init="auto")
@@ -277,6 +266,14 @@ def test_fit_docstring_attributes(name, Estimator):
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if Estimator.__name__ == "MDS":
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est.set_params(normalized_stress="auto")
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# Low max iter to speed up tests: we are only interested in checking the existence
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# of fitted attributes. This should be invariant to whether it has converged or not.
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if "max_iter" in est.get_params():
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est.set_params(max_iter=2)
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if "random_state" in est.get_params():
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est.set_params(random_state=0)
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# In case we want to deprecate some attributes in the future
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skipped_attributes = {}
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