# Natural Language Toolkit: Spearman Rank Correlation
#
# Copyright (C) 2001-2010 NLTK Project
# Author: Joel Nothman <jnothman@student.usyd.edu.au>
# URL: <http://nltk.org>
# For license information, see LICENSE.TXT

"""
Tools for comparing ranked lists.
"""

def _rank_dists(ranks1, ranks2):
    """Finds the difference between the values in ranks1 and ranks2 for keys
    present in both dicts. If the arguments are not dicts, they are converted
    from (key, rank) sequences.
    """
    ranks1 = dict(ranks1)
    ranks2 = dict(ranks2)
    for k, v1 in ranks1.iteritems():
        try:
            yield k, v1 - ranks2[k]
        except KeyError:
            pass


def spearman_correlation(ranks1, ranks2):
    """Returns the Spearman correlation coefficient for two rankings, which
    should be dicts or sequences of (key, rank). The coefficient ranges from
    -1.0 (ranks are opposite) to 1.0 (ranks are identical), and is only
    calculated for keys in both rankings (for meaningful results, remove keys
    present in only one list before ranking)."""
    n = 0
    res = 0
    for k, d in _rank_dists(ranks1, ranks2):
        res += d * d
        n += 1
    try:
        return 1 - (6 * float(res) / (n * (n*n - 1)))
    except ZeroDivisionError:
        # Result is undefined if only one item is ranked
        return 0.0


def ranks_from_sequence(seq):
    """Given a sequence, yields each element with an increasing rank, suitable
    for use as an argument to L{spearman_correlation}.
    """
    return ((k, i) for i, k in enumerate(seq))


def ranks_from_scores(scores, rank_gap=1e-15):
    """Given a sequence of (key, score) tuples, yields each key with an
    increasing rank, tying with previous key's rank if the difference between
    their scores is less than rank_gap. Suitable for use as an argument to
    L{spearman_correlation}.
    """
    prev_score = None
    rank = 0
    for i, (key, score) in enumerate(scores):
        try:
            if abs(score - prev_score) > rank_gap:
                rank = i
        except TypeError:
            pass
       
        yield key, rank
        prev_score = score