#
# Licensed to the Apache Software Foundation (ASF) under one or more
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# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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import numpy

from numpy import array, shape
from pyspark import SparkContext
from pyspark.mllib._common import \
    _dot, _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
    _serialize_double_matrix, _deserialize_double_matrix, \
    _serialize_double_vector, _deserialize_double_vector, \
    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
    _linear_predictor_typecheck, _get_unmangled_labeled_point_rdd
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint, LinearModel
from math import exp, log


class LogisticRegressionModel(LinearModel):
    """A linear binary classification model derived from logistic regression.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0]),
    ...     LabeledPoint(1.0, [1.0]),
    ...     LabeledPoint(1.0, [2.0]),
    ...     LabeledPoint(1.0, [3.0])
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
    >>> lrm.predict(array([1.0])) > 0
    True
    >>> lrm.predict(array([0.0])) <= 0
    True
    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data))
    >>> lrm.predict(array([0.0, 1.0])) > 0
    True
    >>> lrm.predict(array([0.0, 0.0])) <= 0
    True
    >>> lrm.predict(SparseVector(2, {1: 1.0})) > 0
    True
    >>> lrm.predict(SparseVector(2, {1: 0.0})) <= 0
    True
    """
    def predict(self, x):
        _linear_predictor_typecheck(x, self._coeff)
        margin = _dot(x, self._coeff) + self._intercept
        if margin > 0:
            prob = 1 / (1 + exp(-margin))
        else:
            prob = 1 - 1 / (1 + exp(margin))
        return 1 if prob > 0.5 else 0


class LogisticRegressionWithSGD(object):
    @classmethod
    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None):
        """Train a logistic regression model on the given data."""
        sc = data.context
        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
            d._jrdd, iterations, step, miniBatchFraction, i)
        return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
                                         initialWeights)


class SVMModel(LinearModel):
    """A support vector machine.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0]),
    ...     LabeledPoint(1.0, [1.0]),
    ...     LabeledPoint(1.0, [2.0]),
    ...     LabeledPoint(1.0, [3.0])
    ... ]
    >>> svm = SVMWithSGD.train(sc.parallelize(data))
    >>> svm.predict(array([1.0])) > 0
    True
    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
    ... ]
    >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data))
    >>> svm.predict(SparseVector(2, {1: 1.0})) > 0
    True
    >>> svm.predict(SparseVector(2, {0: -1.0})) <= 0
    True
    """
    def predict(self, x):
        _linear_predictor_typecheck(x, self._coeff)
        margin = _dot(x, self._coeff) + self._intercept
        return 1 if margin >= 0 else 0


class SVMWithSGD(object):
    @classmethod
    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
              miniBatchFraction=1.0, initialWeights=None):
        """Train a support vector machine on the given data."""
        sc = data.context
        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
        return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)


class NaiveBayesModel(object):
    """
    Model for Naive Bayes classifiers.

    Contains two parameters:
    - pi: vector of logs of class priors (dimension C)
    - theta: matrix of logs of class conditional probabilities (CxD)

    >>> data = [
    ...     LabeledPoint(0.0, [0.0, 0.0]),
    ...     LabeledPoint(0.0, [0.0, 1.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0]),
    ... ]
    >>> model = NaiveBayes.train(sc.parallelize(data))
    >>> model.predict(array([0.0, 1.0]))
    0.0
    >>> model.predict(array([1.0, 0.0]))
    1.0
    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
    ... ]
    >>> model = NaiveBayes.train(sc.parallelize(sparse_data))
    >>> model.predict(SparseVector(2, {1: 1.0}))
    0.0
    >>> model.predict(SparseVector(2, {0: 1.0}))
    1.0
    """

    def __init__(self, labels, pi, theta):
        self.labels = labels
        self.pi = pi
        self.theta = theta

    def predict(self, x):
        """Return the most likely class for a data vector x"""
        return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]


class NaiveBayes(object):
    @classmethod
    def train(cls, data, lambda_=1.0):
        """
        Train a Naive Bayes model given an RDD of (label, features) vectors.

        This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
        handle all kinds of discrete data.  For example, by converting
        documents into TF-IDF vectors, it can be used for document
        classification.  By making every vector a 0-1 vector, it can also be
        used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).

        @param data: RDD of NumPy vectors, one per element, where the first
               coordinate is the label and the rest is the feature vector
               (e.g. a count vector).
        @param lambda_: The smoothing parameter
        """
        sc = data.context
        dataBytes = _get_unmangled_labeled_point_rdd(data)
        ans = sc._jvm.PythonMLLibAPI().trainNaiveBayes(dataBytes._jrdd, lambda_)
        return NaiveBayesModel(
            _deserialize_double_vector(ans[0]),
            _deserialize_double_vector(ans[1]),
            _deserialize_double_matrix(ans[2]))


def _test():
    import doctest
    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()