Doesn't look like this is maintained, but you can add the below to matrix_distance.py to implement jaccard distance. I've tested it and it works for my own dataset but please don't use without a sanity check first! i.e. test a couple of points and compare to brute force similarity search to make sure the distances align.
class JaccardDistance(MatrixMetricSearch):
"""A matrix that implements jaccard distance search against it.
jaccard_distance = 1 - jaccard_similarity
Note: We want items that are more similar to be closer to zero so we are
going to instead return 1 - jaccard_similarity. We do this so similarity
and distance metrics can be treated the same way.
"""
def __init__(self, features, records_data):
super(JaccardDistance, self).__init__(features, records_data)
@staticmethod
def features_to_matrix(features):
"""
Args:
val: A list of features to be formatted.
Returns:
The transformed matrix.
"""
return _sparse.csr_matrix(features)
@staticmethod
def vstack(matrix_list):
"""
Args:
val: A list of features to be formatted.
Returns:
The transformed matrix.
"""
return _sparse.vstack(matrix_list)
def _transform_value(self, v):
return v
def _distance(self, a_matrix):
"""Vectorised sparse jaccard distance"""
X = _sparse.csr_matrix(a_matrix).astype(int)
Y = _sparse.csr_matrix(self.matrix).astype(int)
intersect = X.dot(Y.T)
x_sum = X.sum(axis=1).A1
y_sum = Y.sum(axis=1).A1
xx, yy = _np.meshgrid(x_sum, y_sum)
union = ((xx + yy).T - intersect)
return (1 - intersect / union).A
Doesn't look like this is maintained, but you can add the below to
matrix_distance.pyto implement jaccard distance. I've tested it and it works for my own dataset but please don't use without a sanity check first! i.e. test a couple of points and compare to brute force similarity search to make sure the distances align.usage:
to add to
matrix_distance.py: