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MAINT Parameters validation for cluster.estimate_bandwidth #24869

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Nov 30, 2022
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MAINT Parameters validation for cluster.estimate_bandwidth #24869

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MAINT Parameters validation for cluster.estimate_bandwidth
OmarManzoor Nov 9, 2022
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MAINT Parameters validation for decomposition.dict_learning
OmarManzoor Nov 9, 2022
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Revert "MAINT Parameters validation for decomposition.dict_learning"
OmarManzoor Nov 9, 2022
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Addressed PR suggestions
OmarManzoor Nov 10, 2022
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Merge main into branch
OmarManzoor Nov 18, 2022
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Merge branch 'main' into validate_params_for_estimate_bandwidth
jeremiedbb Nov 30, 2022
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Resolve conflicts
OmarManzoor Nov 30, 2022
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17 changes: 14 additions & 3 deletions sklearn/cluster/_mean_shift.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
from numbers import Integral, Real

from collections import defaultdict
from ..utils._param_validation import Interval
from ..utils._param_validation import Interval, validate_params
from ..utils.validation import check_is_fitted
from ..utils.fixes import delayed
from ..utils import check_random_state, gen_batches, check_array
Expand All @@ -30,11 +30,22 @@
from .._config import config_context


@validate_params(
{
"X": ["array-like"],
"quantile": [Interval(Real, 0, 1, closed="both")],
"n_samples": [Interval(Integral, 1, None, closed="left"), None],
"random_state": ["random_state"],
"n_jobs": [Integral, None],
}
)
def estimate_bandwidth(X, *, quantile=0.3, n_samples=None, random_state=0, n_jobs=None):
"""Estimate the bandwidth to use with the mean-shift algorithm.

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@glemaitre glemaitre Nov 10, 2022 •
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I think that we should update the comment below:

This function takes time at least quadratic in `n_samples`. For large
datasets, it is wise to subsample by setting `n_samples`. Alternatively,
the parameter `bandwidth` can be set to a small value without estimating
it.

That this function takes time at least quadratic in n_samples. For large
datasets, it's wise to set that parameter to a small value.
This function takes time at least quadratic in `n_samples`. For large
datasets, it is wise to subsample by setting `n_samples`. Alternatively,
the parameter `bandwidth` can be set to a small value without estimating
it.

Parameters
----------
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10 changes: 0 additions & 10 deletions sklearn/cluster/tests/test_mean_shift.py
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Expand Up @@ -7,8 +7,6 @@
import warnings
import pytest

from scipy import sparse

from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_allclose

Expand Down Expand Up @@ -76,14 +74,6 @@ def test_mean_shift(
assert cluster_centers.dtype == global_dtype


def test_estimate_bandwidth_with_sparse_matrix():
# Test estimate_bandwidth with sparse matrix
X = sparse.lil_matrix((1000, 1000))
msg = "A sparse matrix was passed, but dense data is required."
with pytest.raises(TypeError, match=msg):
estimate_bandwidth(X)


def test_parallel(global_dtype):
centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
X, _ = make_blobs(
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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -9,6 +9,7 @@


PARAM_VALIDATION_FUNCTION_LIST = [
"sklearn.cluster.estimate_bandwidth",
"sklearn.cluster.kmeans_plusplus",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.mean_absolute_error",
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