distance.py

from __future__ import absolute_import
from scipy.spatial.distance import pdist as scipy_pdist

def pdist(a, b, metric):
    return scipy_pdist([a, b], metric=metric)[0]

def jaccard(a, b):
    if len(a) == 0 or len(b) == 0:
        return 0
    intersect = len(a & b)
    return  intersect / (float)(len(a) + len(b) - intersect)


# metrics = {
#     'hamming': {
#         'distance' : lambda a, b: pdist(a, b, "hamming"),
#         'distance_valid' : lambda a: True
#         },
#     # return 1 - jaccard similarity, because smaller distances are better.
#     'jaccard': {
#         'distance' : lambda a, b:  1 - jaccard(a, b),
#         'distance_valid' : lambda a: a < 1 - 1e-5
#         },
#     'euclidean': {
#         'distance' : lambda a, b: pdist(a, b, "euclidean"),
#         'distance_valid' : lambda a: True
#         },
#     'angular': {
#         'distance' : lambda a, b: pdist(a, b, "cosine"),
#         'distance_valid' : lambda a: True
#         }
# }

metrics = {
    'hamming': {
        'distance' : lambda a, b: pdist(a, b, "hamming"),
        'distance_valid' : lambda a: True
        },
    # return 1 - jaccard similarity, because smaller distances are better.
    'jaccard': {
        'distance' : lambda a, b:  pdist(a, b, "jaccard"),
        'distance_valid' : lambda a: a < 1 + 1e-5
        },
    'euclidean': {
        'distance' : lambda a, b: pdist(a, b, "euclidean"),
        'distance_valid' : lambda a: True
        },
    'angular': {
        'distance' : lambda a, b: pdist(a, b, "cosine"),
        'distance_valid' : lambda a: True
        }
}