#
# 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
#
#    http://www.apache.org/licenses/LICENSE-2.0
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#

import warnings

from pyspark import since
from pyspark.ml.util import keyword_only
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import *
from pyspark.mllib.common import inherit_doc


__all__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel',
           'DecisionTreeRegressor', 'DecisionTreeRegressionModel',
           'GBTRegressor', 'GBTRegressionModel',
           'IsotonicRegression', 'IsotonicRegressionModel',
           'LinearRegression', 'LinearRegressionModel',
           'RandomForestRegressor', 'RandomForestRegressionModel']


@inherit_doc
class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                       HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
                       HasStandardization, HasSolver, HasWeightCol):
    """
    Linear regression.

    The learning objective is to minimize the squared error, with regularization.
    The specific squared error loss function used is: L = 1/2n ||A coefficients - y||^2^

    This support multiple types of regularization:
     - none (a.k.a. ordinary least squares)
     - L2 (ridge regression)
     - L1 (Lasso)
     - L2 + L1 (elastic net)

    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, 2.0, Vectors.dense(1.0)),
    ...     (0.0, 2.0, Vectors.sparse(1, [], []))], ["label", "weight", "features"])
    >>> lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal", weightCol="weight")
    >>> model = lr.fit(df)
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> abs(model.transform(test0).head().prediction - (-1.0)) < 0.001
    True
    >>> abs(model.coefficients[0] - 1.0) < 0.001
    True
    >>> abs(model.intercept - 0.0) < 0.001
    True
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> abs(model.transform(test1).head().prediction - 1.0) < 0.001
    True
    >>> lr.setParams("vector")
    Traceback (most recent call last):
        ...
    TypeError: Method setParams forces keyword arguments.

    .. versionadded:: 1.4.0
    """

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                 standardization=True, solver="auto", weightCol=None):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                 standardization=True, solver="auto", weightCol=None)
        """
        super(LinearRegression, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.regression.LinearRegression", self.uid)
        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    @since("1.4.0")
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                  standardization=True, solver="auto", weightCol=None):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  standardization=True, solver="auto", weightCol=None)
        Sets params for linear regression.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return LinearRegressionModel(java_model)


class LinearRegressionModel(JavaModel):
    """
    Model fitted by LinearRegression.

    .. versionadded:: 1.4.0
    """

    @property
    @since("1.4.0")
    def weights(self):
        """
        Model weights.
        """

        warnings.warn("weights is deprecated. Use coefficients instead.")
        return self._call_java("weights")

    @property
    @since("1.6.0")
    def coefficients(self):
        """
        Model coefficients.
        """
        return self._call_java("coefficients")

    @property
    @since("1.4.0")
    def intercept(self):
        """
        Model intercept.
        """
        return self._call_java("intercept")


@inherit_doc
class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                         HasWeightCol):
    """
    .. note:: Experimental

    Currently implemented using parallelized pool adjacent violators algorithm.
    Only univariate (single feature) algorithm supported.

    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> ir = IsotonicRegression()
    >>> model = ir.fit(df)
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> model.transform(test0).head().prediction
    0.0
    >>> model.boundaries
    DenseVector([0.0, 1.0])
    """

    # a placeholder to make it appear in the generated doc
    isotonic = \
        Param(Params._dummy(), "isotonic",
              "whether the output sequence should be isotonic/increasing (true) or" +
              "antitonic/decreasing (false).")
    featureIndex = \
        Param(Params._dummy(), "featureIndex",
              "The index of the feature if featuresCol is a vector column, no effect otherwise.")

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 weightCol=None, isotonic=True, featureIndex=0):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 weightCol=None, isotonic=True, featureIndex=0):
        """
        super(IsotonicRegression, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.regression.IsotonicRegression", self.uid)
        self.isotonic = \
            Param(self, "isotonic",
                  "whether the output sequence should be isotonic/increasing (true) or" +
                  "antitonic/decreasing (false).")
        self.featureIndex = \
            Param(self, "featureIndex",
                  "The index of the feature if featuresCol is a vector column, no effect " +
                  "otherwise.")
        self._setDefault(isotonic=True, featureIndex=0)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  weightCol=None, isotonic=True, featureIndex=0):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 weightCol=None, isotonic=True, featureIndex=0):
        Set the params for IsotonicRegression.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return IsotonicRegressionModel(java_model)

    def setIsotonic(self, value):
        """
        Sets the value of :py:attr:`isotonic`.
        """
        self._paramMap[self.isotonic] = value
        return self

    def getIsotonic(self):
        """
        Gets the value of isotonic or its default value.
        """
        return self.getOrDefault(self.isotonic)

    def setFeatureIndex(self, value):
        """
        Sets the value of :py:attr:`featureIndex`.
        """
        self._paramMap[self.featureIndex] = value
        return self

    def getFeatureIndex(self):
        """
        Gets the value of featureIndex or its default value.
        """
        return self.getOrDefault(self.featureIndex)


class IsotonicRegressionModel(JavaModel):
    """
    .. note:: Experimental

    Model fitted by IsotonicRegression.
    """

    @property
    def boundaries(self):
        """
        Model boundaries.
        """
        return self._call_java("boundaries")

    @property
    def predictions(self):
        """
        Predictions associated with the boundaries at the same index, monotone because of isotonic
        regression.
        """
        return self._call_java("predictions")


class TreeEnsembleParams(DecisionTreeParams):
    """
    Mixin for Decision Tree-based ensemble algorithms parameters.
    """

    # a placeholder to make it appear in the generated doc
    subsamplingRate = Param(Params._dummy(), "subsamplingRate", "Fraction of the training data " +
                            "used for learning each decision tree, in range (0, 1].")

    def __init__(self):
        super(TreeEnsembleParams, self).__init__()
        #: param for Fraction of the training data, in range (0, 1].
        self.subsamplingRate = Param(self, "subsamplingRate", "Fraction of the training data " +
                                     "used for learning each decision tree, in range (0, 1].")

    @since("1.4.0")
    def setSubsamplingRate(self, value):
        """
        Sets the value of :py:attr:`subsamplingRate`.
        """
        self._paramMap[self.subsamplingRate] = value
        return self

    @since("1.4.0")
    def getSubsamplingRate(self):
        """
        Gets the value of subsamplingRate or its default value.
        """
        return self.getOrDefault(self.subsamplingRate)


class TreeRegressorParams(Params):
    """
    Private class to track supported impurity measures.
    """

    supportedImpurities = ["variance"]
    # a placeholder to make it appear in the generated doc
    impurity = Param(Params._dummy(), "impurity",
                     "Criterion used for information gain calculation (case-insensitive). " +
                     "Supported options: " +
                     ", ".join(supportedImpurities))

    def __init__(self):
        super(TreeRegressorParams, self).__init__()
        #: param for Criterion used for information gain calculation (case-insensitive).
        self.impurity = Param(self, "impurity", "Criterion used for information " +
                              "gain calculation (case-insensitive). Supported options: " +
                              ", ".join(self.supportedImpurities))

    @since("1.4.0")
    def setImpurity(self, value):
        """
        Sets the value of :py:attr:`impurity`.
        """
        self._paramMap[self.impurity] = value
        return self

    @since("1.4.0")
    def getImpurity(self):
        """
        Gets the value of impurity or its default value.
        """
        return self.getOrDefault(self.impurity)


class RandomForestParams(TreeEnsembleParams):
    """
    Private class to track supported random forest parameters.
    """

    supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"]
    # a placeholder to make it appear in the generated doc
    numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1).")
    featureSubsetStrategy = \
        Param(Params._dummy(), "featureSubsetStrategy",
              "The number of features to consider for splits at each tree node. Supported " +
              "options: " + ", ".join(supportedFeatureSubsetStrategies))

    def __init__(self):
        super(RandomForestParams, self).__init__()
        #: param for Number of trees to train (>= 1).
        self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1).")
        #: param for The number of features to consider for splits at each tree node.
        self.featureSubsetStrategy = \
            Param(self, "featureSubsetStrategy",
                  "The number of features to consider for splits at each tree node. Supported " +
                  "options: " + ", ".join(self.supportedFeatureSubsetStrategies))

    @since("1.4.0")
    def setNumTrees(self, value):
        """
        Sets the value of :py:attr:`numTrees`.
        """
        self._paramMap[self.numTrees] = value
        return self

    @since("1.4.0")
    def getNumTrees(self):
        """
        Gets the value of numTrees or its default value.
        """
        return self.getOrDefault(self.numTrees)

    @since("1.4.0")
    def setFeatureSubsetStrategy(self, value):
        """
        Sets the value of :py:attr:`featureSubsetStrategy`.
        """
        self._paramMap[self.featureSubsetStrategy] = value
        return self

    @since("1.4.0")
    def getFeatureSubsetStrategy(self):
        """
        Gets the value of featureSubsetStrategy or its default value.
        """
        return self.getOrDefault(self.featureSubsetStrategy)


class GBTParams(TreeEnsembleParams):
    """
    Private class to track supported GBT params.
    """
    supportedLossTypes = ["squared", "absolute"]


@inherit_doc
class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                            DecisionTreeParams, TreeRegressorParams, HasCheckpointInterval):
    """
    `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
    learning algorithm for regression.
    It supports both continuous and categorical features.

    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> dt = DecisionTreeRegressor(maxDepth=2)
    >>> model = dt.fit(df)
    >>> model.depth
    1
    >>> model.numNodes
    3
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> model.transform(test0).head().prediction
    0.0
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> model.transform(test1).head().prediction
    1.0

    .. versionadded:: 1.4.0
    """

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance"):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
        """
        super(DecisionTreeRegressor, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.regression.DecisionTreeRegressor", self.uid)
        self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                         maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                         impurity="variance")
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    @since("1.4.0")
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                  impurity="variance"):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
        Sets params for the DecisionTreeRegressor.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return DecisionTreeRegressionModel(java_model)


@inherit_doc
class DecisionTreeModel(JavaModel):
    """Abstraction for Decision Tree models.

    .. versionadded:: 1.5.0
    """

    @property
    @since("1.5.0")
    def numNodes(self):
        """Return number of nodes of the decision tree."""
        return self._call_java("numNodes")

    @property
    @since("1.5.0")
    def depth(self):
        """Return depth of the decision tree."""
        return self._call_java("depth")

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


@inherit_doc
class TreeEnsembleModels(JavaModel):
    """Represents a tree ensemble model.

    .. versionadded:: 1.5.0
    """

    @property
    @since("1.5.0")
    def treeWeights(self):
        """Return the weights for each tree"""
        return list(self._call_java("javaTreeWeights"))

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


@inherit_doc
class DecisionTreeRegressionModel(DecisionTreeModel):
    """
    Model fitted by DecisionTreeRegressor.

    .. versionadded:: 1.4.0
    """


@inherit_doc
class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
                            RandomForestParams, TreeRegressorParams, HasCheckpointInterval):
    """
    `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
    learning algorithm for regression.
    It supports both continuous and categorical features.

    >>> from numpy import allclose
    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42)
    >>> model = rf.fit(df)
    >>> allclose(model.treeWeights, [1.0, 1.0])
    True
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> model.transform(test0).head().prediction
    0.0
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> model.transform(test1).head().prediction
    0.5

    .. versionadded:: 1.4.0
    """

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                 impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
                 featureSubsetStrategy="auto"):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                 impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \
                 featureSubsetStrategy="auto")
        """
        super(RandomForestRegressor, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.regression.RandomForestRegressor", self.uid)
        self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                         maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                         impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
                         featureSubsetStrategy="auto")
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    @since("1.4.0")
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                  impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
                  featureSubsetStrategy="auto"):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                  impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \
                  featureSubsetStrategy="auto")
        Sets params for linear regression.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return RandomForestRegressionModel(java_model)


class RandomForestRegressionModel(TreeEnsembleModels):
    """
    Model fitted by RandomForestRegressor.

    .. versionadded:: 1.4.0
    """


@inherit_doc
class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                   GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
    """
    `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
    learning algorithm for regression.
    It supports both continuous and categorical features.

    >>> from numpy import allclose
    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> gbt = GBTRegressor(maxIter=5, maxDepth=2)
    >>> model = gbt.fit(df)
    >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
    True
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> model.transform(test0).head().prediction
    0.0
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> model.transform(test1).head().prediction
    1.0

    .. versionadded:: 1.4.0
    """

    # a placeholder to make it appear in the generated doc
    lossType = Param(Params._dummy(), "lossType",
                     "Loss function which GBT tries to minimize (case-insensitive). " +
                     "Supported options: " + ", ".join(GBTParams.supportedLossTypes))

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                 maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
                 checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                 maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
                 checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
        """
        super(GBTRegressor, self).__init__()
        self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid)
        #: param for Loss function which GBT tries to minimize (case-insensitive).
        self.lossType = Param(self, "lossType",
                              "Loss function which GBT tries to minimize (case-insensitive). " +
                              "Supported options: " + ", ".join(GBTParams.supportedLossTypes))
        self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                         maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
                         checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    @since("1.4.0")
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
        Sets params for Gradient Boosted Tree Regression.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return GBTRegressionModel(java_model)

    @since("1.4.0")
    def setLossType(self, value):
        """
        Sets the value of :py:attr:`lossType`.
        """
        self._paramMap[self.lossType] = value
        return self

    @since("1.4.0")
    def getLossType(self):
        """
        Gets the value of lossType or its default value.
        """
        return self.getOrDefault(self.lossType)


class GBTRegressionModel(TreeEnsembleModels):
    """
    Model fitted by GBTRegressor.

    .. versionadded:: 1.4.0
    """


@inherit_doc
class AFTSurvivalRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                            HasFitIntercept, HasMaxIter, HasTol):
    """
    Accelerated Failure Time (AFT) Model Survival Regression

    Fit a parametric AFT survival regression model based on the Weibull distribution
    of the survival time.

    .. seealso:: `AFT Model <https://en.wikipedia.org/wiki/Accelerated_failure_time_model>`_

    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0), 1.0),
    ...     (0.0, Vectors.sparse(1, [], []), 0.0)], ["label", "features", "censor"])
    >>> aftsr = AFTSurvivalRegression()
    >>> model = aftsr.fit(df)
    >>> model.predict(Vectors.dense(6.3))
    1.0
    >>> model.predictQuantiles(Vectors.dense(6.3))
    DenseVector([0.0101, 0.0513, 0.1054, 0.2877, 0.6931, 1.3863, 2.3026, 2.9957, 4.6052])
    >>> model.transform(df).show()
    +-----+---------+------+----------+
    |label| features|censor|prediction|
    +-----+---------+------+----------+
    |  1.0|    [1.0]|   1.0|       1.0|
    |  0.0|(1,[],[])|   0.0|       1.0|
    +-----+---------+------+----------+
    ...

    .. versionadded:: 1.6.0
    """

    # a placeholder to make it appear in the generated doc
    censorCol = Param(Params._dummy(), "censorCol",
                      "censor column name. The value of this column could be 0 or 1. " +
                      "If the value is 1, it means the event has occurred i.e. " +
                      "uncensored; otherwise censored.")
    quantileProbabilities = \
        Param(Params._dummy(), "quantileProbabilities",
              "quantile probabilities array. Values of the quantile probabilities array " +
              "should be in the range (0, 1) and the array should be non-empty.")
    quantilesCol = Param(Params._dummy(), "quantilesCol",
                         "quantiles column name. This column will output quantiles of " +
                         "corresponding quantileProbabilities if it is set.")

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
                 quantileProbabilities=None, quantilesCol=None):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", \
                 quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \
                 quantilesCol=None)
        """
        super(AFTSurvivalRegression, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.regression.AFTSurvivalRegression", self.uid)
        #: Param for censor column name
        self.censorCol = Param(self,  "censorCol",
                               "censor column name. The value of this column could be 0 or 1. " +
                               "If the value is 1, it means the event has occurred i.e. " +
                               "uncensored; otherwise censored.")
        #: Param for quantile probabilities array
        self.quantileProbabilities = \
            Param(self, "quantileProbabilities",
                  "quantile probabilities array. Values of the quantile probabilities array " +
                  "should be in the range (0, 1) and the array should be non-empty.")
        #: Param for quantiles column name
        self.quantilesCol = Param(self, "quantilesCol",
                                  "quantiles column name. This column will output quantiles of " +
                                  "corresponding quantileProbabilities if it is set.")
        self._setDefault(censorCol="censor",
                         quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99])
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    @since("1.6.0")
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
                  quantileProbabilities=None, quantilesCol=None):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", \
                  quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \
                  quantilesCol=None):
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

    def _create_model(self, java_model):
        return AFTSurvivalRegressionModel(java_model)

    @since("1.6.0")
    def setCensorCol(self, value):
        """
        Sets the value of :py:attr:`censorCol`.
        """
        self._paramMap[self.censorCol] = value
        return self

    @since("1.6.0")
    def getCensorCol(self):
        """
        Gets the value of censorCol or its default value.
        """
        return self.getOrDefault(self.censorCol)

    @since("1.6.0")
    def setQuantileProbabilities(self, value):
        """
        Sets the value of :py:attr:`quantileProbabilities`.
        """
        self._paramMap[self.quantileProbabilities] = value
        return self

    @since("1.6.0")
    def getQuantileProbabilities(self):
        """
        Gets the value of quantileProbabilities or its default value.
        """
        return self.getOrDefault(self.quantileProbabilities)

    @since("1.6.0")
    def setQuantilesCol(self, value):
        """
        Sets the value of :py:attr:`quantilesCol`.
        """
        self._paramMap[self.quantilesCol] = value
        return self

    @since("1.6.0")
    def getQuantilesCol(self):
        """
        Gets the value of quantilesCol or its default value.
        """
        return self.getOrDefault(self.quantilesCol)


class AFTSurvivalRegressionModel(JavaModel):
    """
    Model fitted by AFTSurvivalRegression.

    .. versionadded:: 1.6.0
    """

    @property
    @since("1.6.0")
    def coefficients(self):
        """
        Model coefficients.
        """
        return self._call_java("coefficients")

    @property
    @since("1.6.0")
    def intercept(self):
        """
        Model intercept.
        """
        return self._call_java("intercept")

    @property
    @since("1.6.0")
    def scale(self):
        """
        Model scale paramter.
        """
        return self._call_java("scale")

    def predictQuantiles(self, features):
        """
        Predicted Quantiles
        """
        return self._call_java("predictQuantiles", features)

    def predict(self, features):
        """
        Predicted value
        """
        return self._call_java("predict", features)


if __name__ == "__main__":
    import doctest
    from pyspark.context import SparkContext
    from pyspark.sql import SQLContext
    globs = globals().copy()
    # The small batch size here ensures that we see multiple batches,
    # even in these small test examples:
    sc = SparkContext("local[2]", "ml.regression tests")
    sqlContext = SQLContext(sc)
    globs['sc'] = sc
    globs['sqlContext'] = sqlContext
    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
    sc.stop()
    if failure_count:
        exit(-1)