#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from pyspark import SparkContext
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.rdd import RDD
from pyspark.mllib.linalg import _to_java_object_rdd

__all__ = ['MatrixFactorizationModel', 'ALS']


class Rating(object):
    def __init__(self, user, product, rating):
        self.user = int(user)
        self.product = int(product)
        self.rating = float(rating)

    def __reduce__(self):
        return Rating, (self.user, self.product, self.rating)

    def __repr__(self):
        return "Rating(%d, %d, %d)" % (self.user, self.product, self.rating)


class MatrixFactorizationModel(object):

    """A matrix factorisation model trained by regularized alternating
    least-squares.

    >>> r1 = (1, 1, 1.0)
    >>> r2 = (1, 2, 2.0)
    >>> r3 = (2, 1, 2.0)
    >>> ratings = sc.parallelize([r1, r2, r3])
    >>> model = ALS.trainImplicit(ratings, 1)
    >>> model.predict(2,2) is not None
    True

    >>> testset = sc.parallelize([(1, 2), (1, 1)])
    >>> model = ALS.train(ratings, 1)
    >>> model.predictAll(testset).count() == 2
    True
    """

    def __init__(self, sc, java_model):
        self._context = sc
        self._java_model = java_model

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

    def predict(self, user, product):
        return self._java_model.predict(user, product)

    def predictAll(self, user_product):
        assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
        first = user_product.first()
        if isinstance(first, list):
            user_product = user_product.map(tuple)
            first = tuple(first)
        assert type(first) is tuple and len(first) == 2, \
            "user_product should be RDD of (user, product)"
        if any(isinstance(x, str) for x in first):
            user_product = user_product.map(lambda (u, p): (int(x), int(p)))
            first = tuple(map(int, first))
        assert all(type(x) is int for x in first), "user and product in user_product shoul be int"
        sc = self._context
        tuplerdd = sc._jvm.SerDe.asTupleRDD(_to_java_object_rdd(user_product).rdd())
        jresult = self._java_model.predict(tuplerdd).toJavaRDD()
        return RDD(sc._jvm.SerDe.javaToPython(jresult), sc,
                   AutoBatchedSerializer(PickleSerializer()))


class ALS(object):

    @classmethod
    def _prepare(cls, ratings):
        assert isinstance(ratings, RDD), "ratings should be RDD"
        first = ratings.first()
        if not isinstance(first, Rating):
            if isinstance(first, (tuple, list)):
                ratings = ratings.map(lambda x: Rating(*x))
            else:
                raise ValueError("rating should be RDD of Rating or tuple/list")
        # serialize them by AutoBatchedSerializer before cache to reduce the
        # objects overhead in JVM
        cached = ratings._reserialize(AutoBatchedSerializer(PickleSerializer())).cache()
        return _to_java_object_rdd(cached)

    @classmethod
    def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
        sc = ratings.context
        jrating = cls._prepare(ratings)
        mod = sc._jvm.PythonMLLibAPI().trainALSModel(jrating, rank, iterations, lambda_, blocks)
        return MatrixFactorizationModel(sc, mod)

    @classmethod
    def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
        sc = ratings.context
        jrating = cls._prepare(ratings)
        mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
            jrating, rank, iterations, lambda_, blocks, alpha)
        return MatrixFactorizationModel(sc, mod)


def _test():
    import doctest
    import pyspark.mllib.recommendation
    globs = pyspark.mllib.recommendation.__dict__.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()