Compute mean reciprocal rank between two sets of feature vectors

mrr.py

def mean_reciprocal_rank(X, Y, indices, metric='hamming'):
    ''' Computes the mean reciprocal rank of the correct match
    Assumes that X[n] should be closest to Y[n]
    Default uses hamming distance
    :parameters:
        - X : np.ndarray, shape=(n_examples, n_features)
            Data matrix in X modality
        - Y : np.ndarray, shape=(n_examples, n_features)
            Data matrix in Y modality
        - indices : np.ndarray
            Denotes which rows to use in MRR calculation
        - metric : str
            Which metric to use to compare feature vectors
    :returns:
        - mrr_pessimist : float
            Mean reciprocal rank, where ties are resolved pessimistically
            That is, rank = # of distances <= dist(X[:, n], Y[:, n])
        - mrr_optimist : float
            Mean reciprocal rank, where ties are resolved optimistically
            That is, rank = # of distances < dist(X[:, n], Y[:, n]) + 1
    '''
    # Compute distances between each codeword and each other codeword
    distance_matrix = scipy.spatial.distance.cdist(X, Y, metric=metric)
    # Rank is the number of distances smaller than the correct distance, as
    # specified by the indices arg
    n_le = distance_matrix.T <= distance_matrix[np.arange(X.shape[0]), indices]
    n_lt = distance_matrix.T < distance_matrix[np.arange(X.shape[0]), indices]
    return (np.mean(1./n_le.sum(axis=0)),
            np.mean(1./(n_lt.sum(axis=0) + 1)))