This repository was archived by the owner on Jan 19, 2025. It is now read-only.
This repository was archived by the owner on Jan 19, 2025. It is now read-only.
@Optional:None is generated where @Required is expected #828
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#/sklearn/sklearn.metrics.cluster._supervised/mutual_info_score/labels_pred
Actual Annotation Type
@optional
Actual Annotation Inputs
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"authors": [
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"defaultType": "none",
"defaultValue": null
}Expected Annotation Type
@required
Expected Annotation Inputs
n/a
Minimal API Data (optional)
Minimal API Data for `sklearn/sklearn.metrics.cluster._supervised/mutual_info_score/labels_pred`
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"id": "sklearn/sklearn.metrics.cluster._supervised/mutual_info_score",
"name": "mutual_info_score",
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{
"id": "sklearn/sklearn.metrics.cluster._supervised/mutual_info_score/labels_pred",
"name": "labels_pred",
"qname": "sklearn.metrics.cluster._supervised.mutual_info_score.labels_pred",
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"type": "int array-like of shape (n_samples,)",
"description": "A clustering of the data into disjoint subsets, called :math:`V` in\nthe above formula."
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"description": "Mutual Information between two clusterings.\n\nThe Mutual Information is a measure of the similarity between two labels\nof the same data. Where :math:`|U_i|` is the number of the samples\nin cluster :math:`U_i` and :math:`|V_j|` is the number of the\nsamples in cluster :math:`V_j`, the Mutual Information\nbetween clusterings :math:`U` and :math:`V` is given as:\n\n.. math::\n\nMI(U,V)=\\sum_{i=1}^{|U|} \\sum_{j=1}^{|V|} \\frac{|U_i\\cap V_j|}{N}\n\\log\\frac{N|U_i \\cap V_j|}{|U_i||V_j|}\n\nThis metric is independent of the absolute values of the labels:\na permutation of the class or cluster label values won't change the\nscore value in any way.\n\nThis metric is furthermore symmetric: switching :math:`U` (i.e\n``label_true``) with :math:`V` (i.e. ``label_pred``) will return the\nsame score value. This can be useful to measure the agreement of two\nindependent label assignments strategies on the same dataset when the\nreal ground truth is not known.\n\nRead more in the :ref:`User Guide <mutual_info_score>`.",
"docstring": "Mutual Information between two clusterings.\n\nThe Mutual Information is a measure of the similarity between two labels\nof the same data. Where :math:`|U_i|` is the number of the samples\nin cluster :math:`U_i` and :math:`|V_j|` is the number of the\nsamples in cluster :math:`V_j`, the Mutual Information\nbetween clusterings :math:`U` and :math:`V` is given as:\n\n.. math::\n\n MI(U,V)=\\sum_{i=1}^{|U|} \\sum_{j=1}^{|V|} \\frac{|U_i\\cap V_j|}{N}\n \\log\\frac{N|U_i \\cap V_j|}{|U_i||V_j|}\n\nThis metric is independent of the absolute values of the labels:\na permutation of the class or cluster label values won't change the\nscore value in any way.\n\nThis metric is furthermore symmetric: switching :math:`U` (i.e\n``label_true``) with :math:`V` (i.e. ``label_pred``) will return the\nsame score value. This can be useful to measure the agreement of two\nindependent label assignments strategies on the same dataset when the\nreal ground truth is not known.\n\nRead more in the :ref:`User Guide <mutual_info_score>`.\n\nParameters\n----------\nlabels_true : int array, shape = [n_samples]\n A clustering of the data into disjoint subsets, called :math:`U` in\n the above formula.\n\nlabels_pred : int array-like of shape (n_samples,)\n A clustering of the data into disjoint subsets, called :math:`V` in\n the above formula.\n\ncontingency : {ndarray, sparse matrix} of shape (n_classes_true, n_classes_pred), default=None\n A contingency matrix given by the :func:`contingency_matrix` function.\n If value is ``None``, it will be computed, otherwise the given value is\n used, with ``labels_true`` and ``labels_pred`` ignored.\n\nReturns\n-------\nmi : float\n Mutual information, a non-negative value, measured in nats using the\n natural logarithm.\n\nNotes\n-----\nThe logarithm used is the natural logarithm (base-e).\n\nSee Also\n--------\nadjusted_mutual_info_score : Adjusted against chance Mutual Information.\nnormalized_mutual_info_score : Normalized Mutual Information."
}
]
}Minimal Usage Store (optional)
Minimal Usage Store for `sklearn/sklearn.metrics.cluster._supervised/mutual_info_score/labels_pred`
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}Suggested Solution (optional)
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