87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
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from __future__ import division
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import numpy as np
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from sklearn.utils.linear_assignment_ import linear_assignment
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from sklearn.utils.validation import check_consistent_length, check_array
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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() *
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(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(list(list(similarity(a_rows[i], a_cols[i],
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b_rows[j], b_cols[j])
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for j in range(n_b))
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for i in range(n_a)))
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return result
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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 : (rows, columns)
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Tuple of row and column indicators for a set of biclusters.
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b : (rows, columns)
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Another set of biclusters like ``a``.
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similarity : string or function, optional, 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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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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"""
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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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indices = linear_assignment(1. - 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[indices[:, 0], indices[:, 1]].sum() / max(n_a, n_b)
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