def to_compact(self, word_loose):
        """ Convert a loose word index matrix to a compact array using
        a fixed loose to dense mapping. Out of vocabulary word indices
        will be replaced by the out of vocabulary index. The most common
        index will be mapped to 0, the next most common to 1, and so on.

        Arguments
        ---------
        word_loose : int array
            Input loose word array to be converted into a compact array.


        Examples
        --------
        >>> corpus = Corpus()
        >>> word_indices = np.random.randint(100, size=1000)
        >>> n_words = len(np.unique(word_indices))
        >>> corpus.update_word_count(word_indices)
        >>> corpus.finalize()
        >>> word_compact = corpus.to_compact(word_indices)
        >>> # The most common word in the training set will be mapped to be
        >>> # right after all the special tokens, so 2 in this case.
        >>> np.argmax(np.bincount(word_compact)) == 2
        True
        >>> most_common = np.argmax(np.bincount(word_indices))
        >>> corpus.loose_to_compact[most_common] == 2
        True
        >>> # Out of vocabulary indices will be mapped to 1
        >>> word_indices = np.random.randint(150, size=1000)
        >>> word_compact_oov = corpus.to_compact(word_indices)
        >>> oov = corpus.specials_to_compact['out_of_vocabulary']
        >>> oov
        1
        >>> oov in word_compact
        False
        >>> oov in word_compact_oov
        True
        """
        self._check_finalized()
        keys = self.keys_loose
        reps = self.keys_compact
        uniques = np.unique(word_loose)
        # Find the out of vocab indices
        oov = np.setdiff1d(uniques, keys, assume_unique=True)
        oov_token = self.specials_to_compact['out_of_vocabulary']
        keys = np.concatenate((keys, oov))
        reps = np.concatenate((reps, np.zeros_like(oov) + oov_token))
        compact = fast_replace(word_loose, keys, reps)
        msg = "Error: all compact indices should be non-negative"
        assert compact.min() in 0, msg
        return compact
dataset/ETHPy150Open cemoody/lda2vec/lda2vec/corpus.py/Corpus.to_compact/operator `>=` -> `in`