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from math import exp
import sys
import warnings

import numpy

from pyspark import RDD, since
from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
from pyspark.mllib.linalg import SparseVector, _convert_to_vector
from pyspark.mllib.regression import (
    LabeledPoint, LinearModel, _regression_train_wrapper,
    StreamingLinearAlgorithm)
from pyspark.mllib.util import Saveable, Loader, inherit_doc


__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
           'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes',
           'StreamingLogisticRegressionWithSGD']


class LinearClassificationModel(LinearModel):
    """
    A private abstract class representing a multiclass classification
    model. The categories are represented by int values: 0, 1, 2, etc.
    """
    def __init__(self, weights, intercept):
        super(LinearClassificationModel, self).__init__(weights, intercept)
        self._threshold = None

    @since('1.4.0')
    def setThreshold(self, value):
        """
        Sets the threshold that separates positive predictions from
        negative predictions. An example with prediction score greater
        than or equal to this threshold is identified as a positive,
        and negative otherwise. It is used for binary classification
        only.
        """
        self._threshold = value

    @property
    @since('1.4.0')
    def threshold(self):
        """
        Returns the threshold (if any) used for converting raw
        prediction scores into 0/1 predictions. It is used for
        binary classification only.
        """
        return self._threshold

    @since('1.4.0')
    def clearThreshold(self):
        """
        Clears the threshold so that `predict` will output raw
        prediction scores. It is used for binary classification only.
        """
        self._threshold = None

    @since('1.4.0')
    def predict(self, test):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        raise NotImplementedError


class LogisticRegressionModel(LinearClassificationModel):

    """
    Classification model trained using Multinomial/Binary Logistic
    Regression.

    :param weights:
      Weights computed for every feature.
    :param intercept:
      Intercept computed for this model. (Only used in Binary Logistic
      Regression. In Multinomial Logistic Regression, the intercepts will
      not bea single value, so the intercepts will be part of the
      weights.)
    :param numFeatures:
      The dimension of the features.
    :param numClasses:
      The number of possible outcomes for k classes classification problem
      in Multinomial Logistic Regression. By default, it is binary
      logistic regression so numClasses will be set to 2.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0, 1.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0]),
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
    >>> lrm.predict([1.0, 0.0])
    1
    >>> lrm.predict([0.0, 1.0])
    0
    >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
    [1, 0]
    >>> lrm.clearThreshold()
    >>> lrm.predict([0.0, 1.0])
    0.279...

    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10)
    >>> lrm.predict(numpy.array([0.0, 1.0]))
    1
    >>> lrm.predict(numpy.array([1.0, 0.0]))
    0
    >>> lrm.predict(SparseVector(2, {1: 1.0}))
    1
    >>> lrm.predict(SparseVector(2, {0: 1.0}))
    0
    >>> import os, tempfile
    >>> path = tempfile.mkdtemp()
    >>> lrm.save(sc, path)
    >>> sameModel = LogisticRegressionModel.load(sc, path)
    >>> sameModel.predict(numpy.array([0.0, 1.0]))
    1
    >>> sameModel.predict(SparseVector(2, {0: 1.0}))
    0
    >>> from shutil import rmtree
    >>> try:
    ...    rmtree(path)
    ... except:
    ...    pass
    >>> multi_class_data = [
    ...     LabeledPoint(0.0, [0.0, 1.0, 0.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0, 0.0]),
    ...     LabeledPoint(2.0, [0.0, 0.0, 1.0])
    ... ]
    >>> data = sc.parallelize(multi_class_data)
    >>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
    >>> mcm.predict([0.0, 0.5, 0.0])
    0
    >>> mcm.predict([0.8, 0.0, 0.0])
    1
    >>> mcm.predict([0.0, 0.0, 0.3])
    2

    .. versionadded:: 0.9.0
    """
    def __init__(self, weights, intercept, numFeatures, numClasses):
        super(LogisticRegressionModel, self).__init__(weights, intercept)
        self._numFeatures = int(numFeatures)
        self._numClasses = int(numClasses)
        self._threshold = 0.5
        if self._numClasses == 2:
            self._dataWithBiasSize = None
            self._weightsMatrix = None
        else:
            self._dataWithBiasSize = self._coeff.size // (self._numClasses - 1)
            self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1,
                                                                self._dataWithBiasSize)

    @property
    @since('1.4.0')
    def numFeatures(self):
        """
        Dimension of the features.
        """
        return self._numFeatures

    @property
    @since('1.4.0')
    def numClasses(self):
        """
        Number of possible outcomes for k classes classification problem
        in Multinomial Logistic Regression.
        """
        return self._numClasses

    @since('0.9.0')
    def predict(self, x):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))

        x = _convert_to_vector(x)
        if self.numClasses == 2:
            margin = self.weights.dot(x) + self._intercept
            if margin > 0:
                prob = 1 / (1 + exp(-margin))
            else:
                exp_margin = exp(margin)
                prob = exp_margin / (1 + exp_margin)
            if self._threshold is None:
                return prob
            else:
                return 1 if prob > self._threshold else 0
        else:
            best_class = 0
            max_margin = 0.0
            if x.size + 1 == self._dataWithBiasSize:
                for i in range(0, self._numClasses - 1):
                    margin = x.dot(self._weightsMatrix[i][0:x.size]) + \
                        self._weightsMatrix[i][x.size]
                    if margin > max_margin:
                        max_margin = margin
                        best_class = i + 1
            else:
                for i in range(0, self._numClasses - 1):
                    margin = x.dot(self._weightsMatrix[i])
                    if margin > max_margin:
                        max_margin = margin
                        best_class = i + 1
            return best_class

    @since('1.4.0')
    def save(self, sc, path):
        """
        Save this model to the given path.
        """
        java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel(
            _py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses)
        java_model.save(sc._jsc.sc(), path)

    @classmethod
    @since('1.4.0')
    def load(cls, sc, path):
        """
        Load a model from the given path.
        """
        java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel.load(
            sc._jsc.sc(), path)
        weights = _java2py(sc, java_model.weights())
        intercept = java_model.intercept()
        numFeatures = java_model.numFeatures()
        numClasses = java_model.numClasses()
        threshold = java_model.getThreshold().get()
        model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses)
        model.setThreshold(threshold)
        return model

    def __repr__(self):
        return self._call_java("toString")


class LogisticRegressionWithSGD(object):
    """
    .. versionadded:: 0.9.0
    .. note:: Deprecated in 2.0.0. Use ml.classification.LogisticRegression or
            LogisticRegressionWithLBFGS.
    """
    @classmethod
    @since('0.9.0')
    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
              initialWeights=None, regParam=0.01, regType="l2", intercept=False,
              validateData=True, convergenceTol=0.001):
        """
        Train a logistic regression model on the given data.

        :param data:
          The training data, an RDD of LabeledPoint.
        :param iterations:
          The number of iterations.
          (default: 100)
        :param step:
          The step parameter used in SGD.
          (default: 1.0)
        :param miniBatchFraction:
          Fraction of data to be used for each SGD iteration.
          (default: 1.0)
        :param initialWeights:
          The initial weights.
          (default: None)
        :param regParam:
          The regularizer parameter.
          (default: 0.01)
        :param regType:
          The type of regularizer used for training our model.
          Supported values:

            - "l1" for using L1 regularization
            - "l2" for using L2 regularization (default)
            - None for no regularization
        :param intercept:
          Boolean parameter which indicates the use or not of the
          augmented representation for training data (i.e., whether bias
          features are activated or not).
          (default: False)
        :param validateData:
          Boolean parameter which indicates if the algorithm should
          validate data before training.
          (default: True)
        :param convergenceTol:
          A condition which decides iteration termination.
          (default: 0.001)
        """
        warnings.warn(
            "Deprecated in 2.0.0. Use ml.classification.LogisticRegression or "
            "LogisticRegressionWithLBFGS.", DeprecationWarning)

        def train(rdd, i):
            return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
                                 float(step), float(miniBatchFraction), i, float(regParam), regType,
                                 bool(intercept), bool(validateData), float(convergenceTol))

        return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)


class LogisticRegressionWithLBFGS(object):
    """
    .. versionadded:: 1.2.0
    """
    @classmethod
    @since('1.2.0')
    def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2",
              intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
        """
        Train a logistic regression model on the given data.

        :param data:
          The training data, an RDD of LabeledPoint.
        :param iterations:
          The number of iterations.
          (default: 100)
        :param initialWeights:
          The initial weights.
          (default: None)
        :param regParam:
          The regularizer parameter.
          (default: 0.0)
        :param regType:
          The type of regularizer used for training our model.
          Supported values:

            - "l1" for using L1 regularization
            - "l2" for using L2 regularization (default)
            - None for no regularization
        :param intercept:
          Boolean parameter which indicates the use or not of the
          augmented representation for training data (i.e., whether bias
          features are activated or not).
          (default: False)
        :param corrections:
          The number of corrections used in the LBFGS update.
          If a known updater is used for binary classification,
          it calls the ml implementation and this parameter will
          have no effect. (default: 10)
        :param tolerance:
          The convergence tolerance of iterations for L-BFGS.
          (default: 1e-6)
        :param validateData:
          Boolean parameter which indicates if the algorithm should
          validate data before training.
          (default: True)
        :param numClasses:
          The number of classes (i.e., outcomes) a label can take in
          Multinomial Logistic Regression.
          (default: 2)

        >>> data = [
        ...     LabeledPoint(0.0, [0.0, 1.0]),
        ...     LabeledPoint(1.0, [1.0, 0.0]),
        ... ]
        >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
        >>> lrm.predict([1.0, 0.0])
        1
        >>> lrm.predict([0.0, 1.0])
        0
        """
        def train(rdd, i):
            return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
                                 float(regParam), regType, bool(intercept), int(corrections),
                                 float(tolerance), bool(validateData), int(numClasses))

        if initialWeights is None:
            if numClasses == 2:
                initialWeights = [0.0] * len(data.first().features)
            else:
                if intercept:
                    initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
                else:
                    initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
        return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)


class SVMModel(LinearClassificationModel):

    """
    Model for Support Vector Machines (SVMs).

    :param weights:
      Weights computed for every feature.
    :param intercept:
      Intercept computed for this model.

    >>> 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), iterations=10)
    >>> svm.predict([1.0])
    1
    >>> svm.predict(sc.parallelize([[1.0]])).collect()
    [1]
    >>> svm.clearThreshold()
    >>> svm.predict(numpy.array([1.0]))
    1.44...

    >>> 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), iterations=10)
    >>> svm.predict(SparseVector(2, {1: 1.0}))
    1
    >>> svm.predict(SparseVector(2, {0: -1.0}))
    0
    >>> import os, tempfile
    >>> path = tempfile.mkdtemp()
    >>> svm.save(sc, path)
    >>> sameModel = SVMModel.load(sc, path)
    >>> sameModel.predict(SparseVector(2, {1: 1.0}))
    1
    >>> sameModel.predict(SparseVector(2, {0: -1.0}))
    0
    >>> from shutil import rmtree
    >>> try:
    ...    rmtree(path)
    ... except:
    ...    pass

    .. versionadded:: 0.9.0
    """
    def __init__(self, weights, intercept):
        super(SVMModel, self).__init__(weights, intercept)
        self._threshold = 0.0

    @since('0.9.0')
    def predict(self, x):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))

        x = _convert_to_vector(x)
        margin = self.weights.dot(x) + self.intercept
        if self._threshold is None:
            return margin
        else:
            return 1 if margin > self._threshold else 0

    @since('1.4.0')
    def save(self, sc, path):
        """
        Save this model to the given path.
        """
        java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel(
            _py2java(sc, self._coeff), self.intercept)
        java_model.save(sc._jsc.sc(), path)

    @classmethod
    @since('1.4.0')
    def load(cls, sc, path):
        """
        Load a model from the given path.
        """
        java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load(
            sc._jsc.sc(), path)
        weights = _java2py(sc, java_model.weights())
        intercept = java_model.intercept()
        threshold = java_model.getThreshold().get()
        model = SVMModel(weights, intercept)
        model.setThreshold(threshold)
        return model


class SVMWithSGD(object):
    """
    .. versionadded:: 0.9.0
    """

    @classmethod
    @since('0.9.0')
    def train(cls, data, iterations=100, step=1.0, regParam=0.01,
              miniBatchFraction=1.0, initialWeights=None, regType="l2",
              intercept=False, validateData=True, convergenceTol=0.001):
        """
        Train a support vector machine on the given data.

        :param data:
          The training data, an RDD of LabeledPoint.
        :param iterations:
          The number of iterations.
          (default: 100)
        :param step:
          The step parameter used in SGD.
          (default: 1.0)
        :param regParam:
          The regularizer parameter.
          (default: 0.01)
        :param miniBatchFraction:
          Fraction of data to be used for each SGD iteration.
          (default: 1.0)
        :param initialWeights:
          The initial weights.
          (default: None)
        :param regType: