path: root/python/pyspark/ml/classification.py

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# The ASF licenses this file to You under the Apache License, Version 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.ml.regression import (
    RandomForestParams, TreeEnsembleParams, DecisionTreeModel, TreeEnsembleModels)
from pyspark.mllib.common import inherit_doc


__all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier',
           'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel',
           'RandomForestClassifier', 'RandomForestClassificationModel', 'NaiveBayes',
           'NaiveBayesModel', 'MultilayerPerceptronClassifier',
           'MultilayerPerceptronClassificationModel']


@inherit_doc
class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
                         HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds):
    """
    Logistic regression.
    Currently, this class only supports binary classification.

    >>> from pyspark.sql import Row
    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sc.parallelize([
    ...     Row(label=1.0, features=Vectors.dense(1.0)),
    ...     Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
    >>> lr = LogisticRegression(maxIter=5, regParam=0.01)
    >>> model = lr.fit(df)
    >>> model.weights
    DenseVector([5.5...])
    >>> model.intercept
    -2.68...
    >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
    >>> result = model.transform(test0).head()
    >>> result.prediction
    0.0
    >>> result.probability
    DenseVector([0.99..., 0.00...])
    >>> result.rawPrediction
    DenseVector([8.22..., -8.22...])
    >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
    >>> model.transform(test1).head().prediction
    1.0
    >>> lr.setParams("vector")
    Traceback (most recent call last):
        ...
    TypeError: Method setParams forces keyword arguments.
    """

    # a placeholder to make it appear in the generated doc
    threshold = Param(Params._dummy(), "threshold",
                      "Threshold in binary classification prediction, in range [0, 1]." +
                      " If threshold and thresholds are both set, they must match.")

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                 threshold=0.5, thresholds=None, probabilityCol="probability",
                 rawPredictionCol="rawPrediction", standardization=True):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                 threshold=0.5, thresholds=None, probabilityCol="probability", \
                 rawPredictionCol="rawPrediction", standardization=True)
        If the threshold and thresholds Params are both set, they must be equivalent.
        """
        super(LogisticRegression, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.classification.LogisticRegression", self.uid)
        #: param for threshold in binary classification, in range [0, 1].
        self.threshold = Param(self, "threshold",
                               "Threshold in binary classification prediction, in range [0, 1]." +
                               " If threshold and thresholds are both set, they must match.")
        self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)
        self._checkThresholdConsistency()

    @keyword_only
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                  threshold=0.5, thresholds=None, probabilityCol="probability",
                  rawPredictionCol="rawPrediction", standardization=True):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  threshold=0.5, thresholds=None, probabilityCol="probability", \
                  rawPredictionCol="rawPrediction", standardization=True)
        Sets params for logistic regression.
        If the threshold and thresholds Params are both set, they must be equivalent.
        """
        kwargs = self.setParams._input_kwargs
        self._set(**kwargs)
        self._checkThresholdConsistency()
        return self

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

    def setThreshold(self, value):
        """
        Sets the value of :py:attr:`threshold`.
        Clears value of :py:attr:`thresholds` if it has been set.
        """
        self._paramMap[self.threshold] = value
        if self.isSet(self.thresholds):
            del self._paramMap[self.thresholds]
        return self

    def getThreshold(self):
        """
        Gets the value of threshold or its default value.
        """
        self._checkThresholdConsistency()
        if self.isSet(self.thresholds):
            ts = self.getOrDefault(self.thresholds)
            if len(ts) != 2:
                raise ValueError("Logistic Regression getThreshold only applies to" +
                                 " binary classification, but thresholds has length != 2." +
                                 "  thresholds: " + ",".join(ts))
            return 1.0/(1.0 + ts[0]/ts[1])
        else:
            return self.getOrDefault(self.threshold)

    def setThresholds(self, value):
        """
        Sets the value of :py:attr:`thresholds`.
        Clears value of :py:attr:`threshold` if it has been set.
        """
        self._paramMap[self.thresholds] = value
        if self.isSet(self.threshold):
            del self._paramMap[self.threshold]
        return self

    def getThresholds(self):
        """
        If :py:attr:`thresholds` is set, return its value.
        Otherwise, if :py:attr:`threshold` is set, return the equivalent thresholds for binary
        classification: (1-threshold, threshold).
        If neither are set, throw an error.
        """
        self._checkThresholdConsistency()
        if not self.isSet(self.thresholds) and self.isSet(self.threshold):
            t = self.getOrDefault(self.threshold)
            return [1.0-t, t]
        else:
            return self.getOrDefault(self.thresholds)

    def _checkThresholdConsistency(self):
        if self.isSet(self.threshold) and self.isSet(self.thresholds):
            ts = self.getParam(self.thresholds)
            if len(ts) != 2:
                raise ValueError("Logistic Regression getThreshold only applies to" +
                                 " binary classification, but thresholds has length != 2." +
                                 " thresholds: " + ",".join(ts))
            t = 1.0/(1.0 + ts[0]/ts[1])
            t2 = self.getParam(self.threshold)
            if abs(t2 - t) >= 1E-5:
                raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
                                 " threshold (%g) and thresholds (equivalent to %g)" % (t2, t))


class LogisticRegressionModel(JavaModel):
    """
    Model fitted by LogisticRegression.
    """

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


class TreeClassifierParams(object):
    """
    Private class to track supported impurity measures.
    """
    supportedImpurities = ["entropy", "gini"]

    # 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(TreeClassifierParams, 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))

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

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


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


@inherit_doc
class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                             HasProbabilityCol, HasRawPredictionCol, DecisionTreeParams,
                             TreeClassifierParams, HasCheckpointInterval):
    """
    `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
    learning algorithm for classification.
    It supports both binary and multiclass labels, as well as both continuous and categorical
    features.

    >>> from pyspark.mllib.linalg import Vectors
    >>> from pyspark.ml.feature import StringIndexer
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
    >>> si_model = stringIndexer.fit(df)
    >>> td = si_model.transform(df)
    >>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
    >>> model = dt.fit(td)
    >>> model.numNodes
    3
    >>> model.depth
    1
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> result = model.transform(test0).head()
    >>> result.prediction
    0.0
    >>> result.probability
    DenseVector([1.0, 0.0])
    >>> result.rawPrediction
    DenseVector([1.0, 0.0])
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> model.transform(test1).head().prediction
    1.0
    """

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

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

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


@inherit_doc
class DecisionTreeClassificationModel(DecisionTreeModel):
    """
    Model fitted by DecisionTreeClassifier.
    """


@inherit_doc
class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
                             HasRawPredictionCol, HasProbabilityCol,
                             RandomForestParams, TreeClassifierParams, HasCheckpointInterval):
    """
    `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
    learning algorithm for classification.
    It supports both binary and multiclass labels, as well as both continuous and categorical
    features.

    >>> import numpy
    >>> from numpy import allclose
    >>> from pyspark.mllib.linalg import Vectors
    >>> from pyspark.ml.feature import StringIndexer
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
    >>> si_model = stringIndexer.fit(df)
    >>> td = si_model.transform(df)
    >>> rf = RandomForestClassifier(numTrees=3, maxDepth=2, labelCol="indexed", seed=42)
    >>> model = rf.fit(td)
    >>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
    True
    >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
    >>> result = model.transform(test0).head()
    >>> result.prediction
    0.0
    >>> numpy.argmax(result.probability)
    0
    >>> numpy.argmax(result.rawPrediction)
    0
    >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
    >>> model.transform(test1).head().prediction
    1.0
    """

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

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

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


class RandomForestClassificationModel(TreeEnsembleModels):
    """
    Model fitted by RandomForestClassifier.
    """


@inherit_doc
class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                    GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
    """
    `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
    learning algorithm for classification.
    It supports binary labels, as well as both continuous and categorical features.
    Note: Multiclass labels are not currently supported.

    >>> from numpy import allclose
    >>> from pyspark.mllib.linalg import Vectors
    >>> from pyspark.ml.feature import StringIndexer
    >>> df = sqlContext.createDataFrame([
    ...     (1.0, Vectors.dense(1.0)),
    ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
    >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
    >>> si_model = stringIndexer.fit(df)
    >>> td = si_model.transform(df)
    >>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed")
    >>> model = gbt.fit(td)
    >>> 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
    """

    # 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, checkpointInterval=10, lossType="logistic",
                 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, checkpointInterval=10, \
                 lossType="logistic", maxIter=20, stepSize=0.1)
        """
        super(GBTClassifier, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.classification.GBTClassifier", 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, checkpointInterval=10,
                         lossType="logistic", maxIter=20, stepSize=0.1)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

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

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

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

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


class GBTClassificationModel(TreeEnsembleModels):
    """
    Model fitted by GBTClassifier.
    """


@inherit_doc
class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol,
                 HasRawPredictionCol):
    """
    Naive Bayes Classifiers.
    It supports both Multinomial and Bernoulli NB. Multinomial NB
    (`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`)
    can handle finitely supported discrete data. For example, by converting documents into
    TF-IDF vectors, it can be used for document classification. By making every vector a
    binary (0/1) data, it can also be used as Bernoulli NB
    (`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`).
    The input feature values must be nonnegative.

    >>> from pyspark.sql import Row
    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     Row(label=0.0, features=Vectors.dense([0.0, 0.0])),
    ...     Row(label=0.0, features=Vectors.dense([0.0, 1.0])),
    ...     Row(label=1.0, features=Vectors.dense([1.0, 0.0]))])
    >>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial")
    >>> model = nb.fit(df)
    >>> model.pi
    DenseVector([-0.51..., -0.91...])
    >>> model.theta
    DenseMatrix(2, 2, [-1.09..., -0.40..., -0.40..., -1.09...], 1)
    >>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
    >>> result = model.transform(test0).head()
    >>> result.prediction
    1.0
    >>> result.probability
    DenseVector([0.42..., 0.57...])
    >>> result.rawPrediction
    DenseVector([-1.60..., -1.32...])
    >>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
    >>> model.transform(test1).head().prediction
    1.0
    """

    # a placeholder to make it appear in the generated doc
    smoothing = Param(Params._dummy(), "smoothing", "The smoothing parameter, should be >= 0, " +
                      "default is 1.0")
    modelType = Param(Params._dummy(), "modelType", "The model type which is a string " +
                      "(case-sensitive). Supported options: multinomial (default) and bernoulli.")

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
                 modelType="multinomial"):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
                 modelType="multinomial")
        """
        super(NaiveBayes, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.classification.NaiveBayes", self.uid)
        #: param for the smoothing parameter.
        self.smoothing = Param(self, "smoothing", "The smoothing parameter, should be >= 0, " +
                               "default is 1.0")
        #: param for the model type.
        self.modelType = Param(self, "modelType", "The model type which is a string " +
                               "(case-sensitive). Supported options: multinomial (default) " +
                               "and bernoulli.")
        self._setDefault(smoothing=1.0, modelType="multinomial")
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
                  modelType="multinomial"):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
                  modelType="multinomial")
        Sets params for Naive Bayes.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)

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

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

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

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

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


class NaiveBayesModel(JavaModel):
    """
    Model fitted by NaiveBayes.
    """

    @property
    def pi(self):
        """
        log of class priors.
        """
        return self._call_java("pi")

    @property
    def theta(self):
        """
        log of class conditional probabilities.
        """
        return self._call_java("theta")


@inherit_doc
class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                                     HasMaxIter, HasTol, HasSeed):
    """
    Classifier trainer based on the Multilayer Perceptron.
    Each layer has sigmoid activation function, output layer has softmax.
    Number of inputs has to be equal to the size of feature vectors.
    Number of outputs has to be equal to the total number of labels.

    >>> from pyspark.mllib.linalg import Vectors
    >>> df = sqlContext.createDataFrame([
    ...     (0.0, Vectors.dense([0.0, 0.0])),
    ...     (1.0, Vectors.dense([0.0, 1.0])),
    ...     (1.0, Vectors.dense([1.0, 0.0])),
    ...     (0.0, Vectors.dense([1.0, 1.0]))], ["label", "features"])
    >>> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[2, 5, 2], blockSize=1, seed=11)
    >>> model = mlp.fit(df)
    >>> model.layers
    [2, 5, 2]
    >>> model.weights.size
    27
    >>> testDF = sqlContext.createDataFrame([
    ...     (Vectors.dense([1.0, 0.0]),),
    ...     (Vectors.dense([0.0, 0.0]),)], ["features"])
    >>> model.transform(testDF).show()
    +---------+----------+
    | features|prediction|
    +---------+----------+
    |[1.0,0.0]|       1.0|
    |[0.0,0.0]|       0.0|
    +---------+----------+
    ...
    """

    # a placeholder to make it appear in the generated doc
    layers = Param(Params._dummy(), "layers", "Sizes of layers from input layer to output layer " +
                   "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with 100 " +
                   "neurons and output layer of 10 neurons, default is [1, 1].")
    blockSize = Param(Params._dummy(), "blockSize", "Block size for stacking input data in " +
                      "matrices. Data is stacked within partitions. If block size is more than " +
                      "remaining data in a partition then it is adjusted to the size of this " +
                      "data. Recommended size is between 10 and 1000, default is 128.")

    @keyword_only
    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                 maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
        """
        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                 maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
        """
        super(MultilayerPerceptronClassifier, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.classification.MultilayerPerceptronClassifier", self.uid)
        self.layers = Param(self, "layers", "Sizes of layers from input layer to output layer " +
                            "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with " +
                            "100 neurons and output layer of 10 neurons, default is [1, 1].")
        self.blockSize = Param(self, "blockSize", "Block size for stacking input data in " +
                               "matrices. Data is stacked within partitions. If block size is " +
                               "more than remaining data in a partition then it is adjusted to " +
                               "the size of this data. Recommended size is between 10 and 1000, " +
                               "default is 128.")
        self._setDefault(maxIter=100, tol=1E-4, layers=[1, 1], blockSize=128)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
        """
        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
        Sets params for MultilayerPerceptronClassifier.
        """
        kwargs = self.setParams._input_kwargs
        if layers is None:
            return self._set(**kwargs).setLayers([1, 1])
        else:
            return self._set(**kwargs)

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

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

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

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

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


class MultilayerPerceptronClassificationModel(JavaModel):
    """
    Model fitted by MultilayerPerceptronClassifier.
    """

    @property
    def layers(self):
        """
        array of layer sizes including input and output layers.
        """
        return self._call_java("javaLayers")

    @property
    def weights(self):
        """
        vector of initial weights for the model that consists of the weights of layers.
        """
        return self._call_java("weights")


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.classification 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)