8000 Added Parameter Validation for metrics.cluster.adjusted_mutual_info_score() by ZeeshanLone · Pull Request #25898 · scikit-learn/scikit-learn · GitHub
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Added Parameter Validation for metrics.cluster.adjusted_mutual_info_score() #25898

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Merged
merged 2 commits into from
Mar 21, 2023
Merged

Added Parameter Validation for metrics.cluster.adjusted_mutual_info_score() #25898

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70bb286
Added Parameter Validation for cluster.adjusted_mutual_info_score
ZeeshanLone Mar 18, 2023
b193657
cln docstring
jeremiedbb Mar 21, 2023
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16 changes: 11 additions & 5 deletions sklearn/metrics/cluster/_supervised.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
from ...utils.multiclass import type_of_target
from ...utils.validation import check_array, check_consistent_length
from ...utils._param_validation import validate_params
from ...utils._param_validation import Interval
from ...utils._param_validation import Interval, StrOptions


def check_clusterings(labels_true, labels_pred):
Expand Down Expand Up @@ -847,6 +847,13 @@ def mutual_info_score(labels_true, labels_pred, *, contingency=None):
return np.clip(mi.sum(), 0.0, None)


@validate_params(
{
"labels_true": ["array-like"],
"labels_pred": ["array-like"],
"average_method": [StrOptions({"arithmetic", "max", "min", "geometric"})],
}
)
def adjusted_mutual_info_score(
labels_true, labels_pred, *, average_method="arithmetic"
):
Expand Down Expand Up @@ -876,17 +883,16 @@ def adjusted_mutual_info_score(

Parameters
----------
labels_true : int array, shape = [n_samples]
labels_true : int array-like of shape (n_samples,)
A clustering of the data into disjoint subsets, called :math:`U` in
the above formula.

labels_pred : int array-like of shape (n_samples,)
A clustering of the data into disjoint subsets, called :math:`V` in
the above formula.

average_method : str, default='arithmetic'
How to compute the normalizer in the denominator. Possible options
are 'min', 'geometric', 'arithmetic', and 'max'.
average_method : {'min', 'geometric', 'arithmetic', 'max'}, default='arithmetic'
How to compute the normalizer in the denominator.

.. versionadded:: 0.20

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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -152,6 +152,7 @@ def _check_function_param_validation(
"sklearn.metrics.brier_score_loss",
"sklearn.metrics.class_likelihood_ratios",
"sklearn.metrics.classification_report",
"sklearn.metrics.cluster.adjusted_mutual_info_score",
"sklearn.metrics.cluster.contingency_matrix",
"sklearn.metrics.cohen_kappa_score",
"sklearn.metrics.confusion_matrix",
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0