108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
import numpy as np
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from scipy.optimize import linear_sum_assignment
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from ...utils._param_validation import StrOptions, validate_params
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from ...utils.validation import check_array, check_consistent_length
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__all__ = ["consensus_score"]
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def _check_rows_and_columns(a, b):
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"""Unpacks the row and column arrays and checks their shape."""
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check_consistent_length(*a)
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check_consistent_length(*b)
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checks = lambda x: check_array(x, ensure_2d=False)
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a_rows, a_cols = map(checks, a)
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b_rows, b_cols = map(checks, b)
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return a_rows, a_cols, b_rows, b_cols
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def _jaccard(a_rows, a_cols, b_rows, b_cols):
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"""Jaccard coefficient on the elements of the two biclusters."""
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intersection = (a_rows * b_rows).sum() * (a_cols * b_cols).sum()
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a_size = a_rows.sum() * a_cols.sum()
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b_size = b_rows.sum() * b_cols.sum()
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return intersection / (a_size + b_size - intersection)
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def _pairwise_similarity(a, b, similarity):
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"""Computes pairwise similarity matrix.
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result[i, j] is the Jaccard coefficient of a's bicluster i and b's
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bicluster j.
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"""
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a_rows, a_cols, b_rows, b_cols = _check_rows_and_columns(a, b)
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n_a = a_rows.shape[0]
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n_b = b_rows.shape[0]
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result = np.array(
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[
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[similarity(a_rows[i], a_cols[i], b_rows[j], b_cols[j]) for j in range(n_b)]
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for i in range(n_a)
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]
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)
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return result
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@validate_params(
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{
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"a": [tuple],
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"b": [tuple],
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"similarity": [callable, StrOptions({"jaccard"})],
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},
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prefer_skip_nested_validation=True,
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)
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def consensus_score(a, b, *, similarity="jaccard"):
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"""The similarity of two sets of biclusters.
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Similarity between individual biclusters is computed. Then the
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best matching between sets is found using the Hungarian algorithm.
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The final score is the sum of similarities divided by the size of
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the larger set.
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Read more in the :ref:`User Guide <biclustering>`.
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Parameters
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----------
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a : tuple (rows, columns)
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Tuple of row and column indicators for a set of biclusters.
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b : tuple (rows, columns)
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Another set of biclusters like ``a``.
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similarity : 'jaccard' or callable, default='jaccard'
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May be the string "jaccard" to use the Jaccard coefficient, or
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any function that takes four arguments, each of which is a 1d
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indicator vector: (a_rows, a_columns, b_rows, b_columns).
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Returns
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-------
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consensus_score : float
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Consensus score, a non-negative value, sum of similarities
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divided by size of larger set.
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References
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----------
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* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
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for bicluster acquisition
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<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.
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Examples
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--------
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>>> from sklearn.metrics import consensus_score
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>>> a = ([[True, False], [False, True]], [[False, True], [True, False]])
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>>> b = ([[False, True], [True, False]], [[True, False], [False, True]])
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>>> consensus_score(a, b, similarity='jaccard')
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1.0
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"""
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if similarity == "jaccard":
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similarity = _jaccard
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matrix = _pairwise_similarity(a, b, similarity)
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row_indices, col_indices = linear_sum_assignment(1.0 - matrix)
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n_a = len(a[0])
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n_b = len(b[0])
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return matrix[row_indices, col_indices].sum() / max(n_a, n_b)
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