8000 MAINT validate parameter `affinity_propagation` public function by Ala-Na · Pull Request #25026 · scikit-learn/scikit-learn · GitHub
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MAINT validate parameter affinity_propagation public function #25026

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Merged
merged 7 commits into from
Dec 28, 2022
+10 −5
Merged

MAINT validate parameter affinity_propagation public function #25026

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258dff1
Double validation for affinity_propagation public function
Ala-Na Nov 24, 2022
f2c1411
TST add the function/class association
glemaitre Dec 9, 2022
ff1ca3a
remove double validation
jeremiedbb Dec 27, 2022
d18aaa8
Merge remote-tracking branch 'upstream/main' into pr/Ala-Na/25026
jeremiedbb Dec 27, 2022
3db197c
lint
jeremiedbb Dec 27, 2022
1d1f4a9
Merge remote-tracking branch 'upstream/main' into pr/Ala-Na/25026
jeremiedbb Dec 27, 2022
23f4c67
Merge branch 'main' into affinity_propagation
jeremiedbb Dec 28, 2022
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14 changes: 9 additions & 5 deletions sklearn/cluster/_affinity_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,8 @@

from ..exceptions import ConvergenceWarning
from ..base import BaseEstimator, ClusterMixin
from ..utils import as_float_array, check_random_state
from ..utils._param_validation import Interval, StrOptions
from ..utils import check_random_state
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted
from ..metrics import euclidean_distances
from ..metrics import pairwise_distances_argmin
Expand Down Expand Up @@ -178,6 +178,12 @@ def _affinity_propagation(
# Public API


@validate_params(
{
"S": ["array-like"],
"return_n_iter": ["boolean"],
}
)
def affinity_propagation(
S,
*,
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The test is going to fail because we don't delegate all parameter validation to the class. We need to change the code below from

    S = as_float_array(S, copy=copy)

    estimator = AffinityPropagation(
        damping=damping,
        max_iter=max_iter,
        convergence_iter=convergence_iter,
        copy=False,
        preference=preference,
        affinity="precomputed",
        verbose=verbose,
        random_state=random_state,
    ).fit(S)

to

    estimator = AffinityPropagation(
        damping=damping,
        max_iter=max_iter,
        convergence_iter=convergence_iter,
        copy=copy,
        preference=preference,
        affinity="precomputed",
        verbose=verbose,
        random_state=random_state,
    ).fit(S)

So, we should not validate S and pass the parameter copy that will be validated by the class.

Expand Down Expand Up @@ -269,13 +275,11 @@ def affinity_propagation(
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
S = as_float_array(S, copy=copy)

estimator = AffinityPropagation(
damping=damping,
max_iter=max_iter,
convergence_iter=convergence_iter,
copy=False,
copy=copy,
preference=preference,
affinity="precomputed",
verbose=verbose,
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -131,6 +131,7 @@ def test_function_param_validation(func_module):

PARAM_VALIDATION_CLASS_WRAPPER_LIST = [
("sklearn.decomposition.non_negative_factorization", "sklearn.decomposition.NMF"),
("sklearn.cluster.affinity_propagation", "sklearn.cluster.AffinityPropagation"),
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I made this change here.
We modified the code such that we check all parameters, even the ones that are not provided. To be able to do so we need to provide the relation between the function and the class.

("sklearn.covariance.ledoit_wolf", "sklearn.covariance.LedoitWolf"),
("sklearn.covariance.oas", "sklearn.covariance.OAS"),
]
Expand Down
0