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# The ASF licenses this file to You under the Apache License, Version 2.0
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
Python package for feature in MLlib.
"""
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.mllib.linalg import _convert_to_vector, _to_java_object_rdd

__all__ = ['Word2Vec', 'Word2VecModel']


class Word2VecModel(object):
    """
    class for Word2Vec model
    """
    def __init__(self, sc, java_model):
        """
        :param sc:  Spark context
        :param java_model:  Handle to Java model object
        """
        self._sc = sc
        self._java_model = java_model

    def __del__(self):
        self._sc._gateway.detach(self._java_model)

    def transform(self, word):
        """
        :param word: a word
        :return: vector representation of word

        Transforms a word to its vector representation

        Note: local use only
        """
        # TODO: make transform usable in RDD operations from python side
        result = self._java_model.transform(word)
        return PickleSerializer().loads(str(self._sc._jvm.SerDe.dumps(result)))

    def findSynonyms(self, x, num):
        """
        :param x: a word or a vector representation of word
        :param num: number of synonyms to find
        :return: array of (word, cosineSimilarity)

        Find synonyms of a word

        Note: local use only
        """
        # TODO: make findSynonyms usable in RDD operations from python side
        ser = PickleSerializer()
        if type(x) == str:
            jlist = self._java_model.findSynonyms(x, num)
        else:
            bytes = bytearray(ser.dumps(_convert_to_vector(x)))
            vec = self._sc._jvm.SerDe.loads(bytes)
            jlist = self._java_model.findSynonyms(vec, num)
        words, similarity = ser.loads(str(self._sc._jvm.SerDe.dumps(jlist)))
        return zip(words, similarity)


class Word2Vec(object):
    """
    Word2Vec creates vector representation of words in a text corpus.
    The algorithm first constructs a vocabulary from the corpus
    and then learns vector representation of words in the vocabulary.
    The vector representation can be used as features in
    natural language processing and machine learning algorithms.

    We used skip-gram model in our implementation and hierarchical softmax
    method to train the model. The variable names in the implementation
    matches the original C implementation.
    For original C implementation, see https://code.google.com/p/word2vec/
    For research papers, see
    Efficient Estimation of Word Representations in Vector Space
    and
    Distributed Representations of Words and Phrases and their Compositionality.

    >>> sentence = "a b " * 100 + "a c " * 10
    >>> localDoc = [sentence, sentence]
    >>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" "))
    >>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc)
    >>> syms = model.findSynonyms("a", 2)
    >>> str(syms[0][0])
    'b'
    >>> str(syms[1][0])
    'c'
    >>> len(syms)
    2
    >>> vec = model.transform("a")
    >>> len(vec)
    10
    >>> syms = model.findSynonyms(vec, 2)
    >>> str(syms[0][0])
    'b'
    >>> str(syms[1][0])
    'c'
    >>> len(syms)
    2
    """
    def __init__(self):
        """
        Construct Word2Vec instance
        """
        self.vectorSize = 100
        self.learningRate = 0.025
        self.numPartitions = 1
        self.numIterations = 1
        self.seed = 42L

    def setVectorSize(self, vectorSize):
        """
        Sets vector size (default: 100).
        """
        self.vectorSize = vectorSize
        return self

    def setLearningRate(self, learningRate):
        """
        Sets initial learning rate (default: 0.025).
        """
        self.learningRate = learningRate
        return self

    def setNumPartitions(self, numPartitions):
        """
        Sets number of partitions (default: 1). Use a small number for accuracy.
        """
        self.numPartitions = numPartitions
        return self

    def setNumIterations(self, numIterations):
        """
        Sets number of iterations (default: 1), which should be smaller than or equal to number of
        partitions.
        """
        self.numIterations = numIterations
        return self

    def setSeed(self, seed):
        """
        Sets random seed.
        """
        self.seed = seed
        return self

    def fit(self, data):
        """
        Computes the vector representation of each word in vocabulary.

        :param data: training data. RDD of subtype of Iterable[String]
        :return: python Word2VecModel instance
        """
        sc = data.context
        ser = PickleSerializer()
        vectorSize = self.vectorSize
        learningRate = self.learningRate
        numPartitions = self.numPartitions
        numIterations = self.numIterations
        seed = self.seed

        model = sc._jvm.PythonMLLibAPI().trainWord2Vec(
            _to_java_object_rdd(data), vectorSize,
            learningRate, numPartitions, numIterations, seed)
        return Word2VecModel(sc, model)


def _test():
    import doctest
    from pyspark import SparkContext
    globs = globals().copy()
    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
    globs['sc'].stop()
    if failure_count:
        exit(-1)

if __name__ == "__main__":
    _test()