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MAINT Clean up deprecations for 1.6: clean up leftovers #30216

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merged 2 commits into from
Nov 5, 2024
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MAINT Clean up deprecations for 1.6: clean up leftovers #30216

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a7da351
cln leftovers
jeremiedbb Nov 4, 2024
219de80
Merge branch 'main' into cln-deprecations-1.6-leftovers
jeremiedbb Nov 4, 2024
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6 changes: 0 additions & 6 deletions sklearn/cluster/_agglomerative.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
from ..utils._fast_dict import IntFloatDict
from ..utils._param_validation import (
HasMethods,
Hidden,
Interval,
StrOptions,
validate_params,
Expand Down Expand Up @@ -1143,10 +1142,6 @@ class FeatureAgglomeration(

.. versionadded:: 1.2

.. deprecated:: 1.4
`metric=None` is deprecated in 1.4 and will be removed in 1.6.
Let `metric` be the default value (i.e. `"euclidean"`) instead.

memory : str or object with the joblib.Memory interface, default=None
Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
Expand Down Expand Up @@ -1273,7 +1268,6 @@ class FeatureAgglomeration(
"metric": [
StrOptions(set(_VALID_METRICS) | {"precomputed"}),
callable,
Hidden(None),
],
"memory": [str, HasMethods("cache"), None],
"connectivity": ["array-like", "sparse matrix", callable, None],
Expand Down
7 changes: 0 additions & 7 deletions sklearn/tests/test_docstring_parameters.py
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Expand Up @@ -177,9 +177,6 @@ def _construct_sparse_coder(Estimator):


@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
# TODO(1.6): remove "@pytest.mark.filterwarnings" as SAMME.R will be removed
# and substituted with the SAMME algorithm as a default
@pytest.mark.filterwarnings("ignore:The SAMME.R algorithm")
@pytest.mark.parametrize("name, Estimator", all_estimators())
def test_fit_docstring_attributes(name, Estimator):
pytest.importorskip("numpydoc")
Expand Down Expand Up @@ -225,10 +222,6 @@ def test_fit_docstring_attributes(name, Estimator):
# default raises an error, perplexity must be less than n_samples
est.set_params(perplexity=2)

# TODO(1.6): remove (avoid FutureWarning)
if Estimator.__name__ in ("NMF", "MiniBatchNMF"):
est.set_params(n_components="auto")

# Low max iter to speed up tests: we are only interested in checking the existence
# of fitted attributes. This should be invariant to whether it has converged or not.
if "max_iter" in est.get_params():
Expand Down