#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

import logging
import math
from gensim import interfaces, matutils, utils


logger = logging.getLogger('gensim.models.logentropy_model')


class LogEntropyModel(interfaces.TransformationABC):
    """
    Objects of this class realize the transformation between word-document
    co-occurence matrix (integers) into a locally/globally weighted matrix
    (positive floats).

    This is done by a log entropy normalization, optionally normalizing the
    resulting documents to unit length. The following formulas explain how
    to compute the log entropy weight for term `i` in document `j`::

      local_weight_{i,j} = log(frequency_{i,j} + 1)

      P_{i,j} = frequency_{i,j} / sum_j frequency_{i,j}

                            sum_j P_{i,j} * log(P_{i,j})
      global_weight_i = 1 + ----------------------------
                            log(number_of_documents)

      final_weight_{i,j} = local_weight_{i,j} * global_weight_i

    The main methods are:

    1. constructor, which calculates the global weighting for all terms in
        a corpus.
    2. the [] method, which transforms a simple count representation into the
        log entropy normalized space.

    >>> log_ent = LogEntropyModel(corpus)
    >>> print = log_ent[some_doc]
    >>> log_ent.save('/tmp/foo.log_ent_model')

    Model persistency is achieved via its load/save methods.
    """

    def __init__(self, corpus, id2word=None, normalize=True):
        """
        `normalize` dictates whether the resulting vectors will be
        set to unit length.
        """
        self.normalize = normalize
        self.n_docs = 0
        self.n_words = 0
        self.entr = {}
        if corpus is not None:
            self.initialize(corpus)

    def __str__(self):
        return "LogEntropyModel(n_docs=%s, n_words=%s)" % (self.n_docs,
                                                           self.n_words)

    def initialize(self, corpus):
        """
        Initialize internal statistics based on a training corpus. Called
        automatically from the constructor.
        """
        logger.info("calculating counts")
        glob_freq = {}
        glob_num_words, doc_no = 0, -1
        for doc_no, bow in enumerate(corpus):
            if doc_no % 10000 == 0:
                logger.info("PROGRESS: processing document #%i" % doc_no)
            glob_num_words += len(bow)
            for term_id, term_count in bow:
                glob_freq[term_id] = glob_freq.get(term_id, 0) + term_count

        # keep some stats about the training corpus
        self.n_docs = doc_no + 1
        self.n_words = glob_num_words

        # and finally compute the global weights
        logger.info("calculating global log entropy weights for %i "
                     "documents and %i features (%i matrix non-zeros)"
                     % (self.n_docs, len(glob_freq), self.n_words))
        logger.debug('iterating over corpus')
        for bow in corpus:
            for key, freq in bow:
                p = (float(freq) / glob_freq[key]) * math.log(float(freq) /
                                                              glob_freq[key])
                self.entr[key] = self.entr.get(key, 0.0) + p
        logger.debug('iterating over keys')
        for key in self.entr:
            self.entr[key] = 1 + self.entr[key] / math.log(self.n_docs)

    def __getitem__(self, bow):
        """
        Return log entropy representation of the input vector and/or corpus.
        """
        # if the input vector is in fact a corpus, return a transformed corpus
        is_corpus, bow = utils.is_corpus(bow)
        if is_corpus:
            return self._apply(bow)

        # unknown (new) terms will be given zero weight (NOT infinity/huge)
        vector = [(term_id, math.log(tf + 1) * self.entr.get(term_id))
                  for term_id, tf in bow if term_id in self.entr]
        if self.normalize:
            vector = matutils.unitvec(vector)
        return vector
