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