Uploaded Test files

venv/Lib/site-packages/sklearn/metrics/cluster/_bicluster.py

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+import numpy as np
+from scipy.optimize import linear_sum_assignment
+
+from ...utils.validation import check_consistent_length, check_array
+from ...utils.validation import _deprecate_positional_args
+
+__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(list(list(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
+
+
+@_deprecate_positional_args
+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 : (rows, columns)
+        Tuple of row and column indicators for a set of biclusters.
+
+    b : (rows, columns)
+        Another set of biclusters like ``a``.
+
+    similarity : string or function, optional, 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).
+
+    References
+    ----------
+
+    * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
+      for bicluster acquisition
+      <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.
+
+    """
+    if similarity == "jaccard":
+        similarity = _jaccard
+    matrix = _pairwise_similarity(a, b, similarity)
+    row_indices, col_indices = linear_sum_assignment(1. - matrix)
+    n_a = len(a[0])
+    n_b = len(b[0])
+    return matrix[row_indices, col_indices].sum() / max(n_a, n_b)