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 # 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
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 # 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.
 # See the License for the specific language governing permissions and
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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:
           The type of regularizer used for training our model.
           Allowed 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)
         """
         def train(rdd, i):
             return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, regType,
                                  bool(intercept), bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, SVMModel, data, initialWeights)
 
 
 @inherit_doc
 class NaiveBayesModel(Saveable, Loader):
 
     """
     Model for Naive Bayes classifiers.
 
     :param labels:
       List of labels.
     :param pi:
       Log of class priors, whose dimension is C, number of labels.
     :param theta:
       Log of class conditional probabilities, whose dimension is C-by-D,
       where D is number of features.
 
     >>> 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(numpy.array([0.0, 1.0]))
     0.0
     >>> model.predict(numpy.array([1.0, 0.0]))
     1.0
     >>> model.predict(sc.parallelize([[1.0, 0.0]])).collect()
     [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
     >>> import os, tempfile
     >>> path = tempfile.mkdtemp()
     >>> model.save(sc, path)
     >>> sameModel = NaiveBayesModel.load(sc, path)
     >>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
     True
     >>> from shutil import rmtree
     >>> try:
     ...     rmtree(path)
     ... except OSError:
     ...     pass
 
     .. versionadded:: 0.9.0
     """
     def __init__(self, labels, pi, theta):
         self.labels = labels
         self.pi = pi
         self.theta = theta
 
     @since('0.9.0')
     def predict(self, x):
         """
         Return the most likely class for a data vector
         or an RDD of vectors
         """
         if isinstance(x, RDD):
             return x.map(lambda v: self.predict(v))
         x = _convert_to_vector(x)
         return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]
 
     def save(self, sc, path):
         """
         Save this model to the given path.
         """
         java_labels = _py2java(sc, self.labels.tolist())
         java_pi = _py2java(sc, self.pi.tolist())
         java_theta = _py2java(sc, self.theta.tolist())
         java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel(
             java_labels, java_pi, java_theta)
         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.NaiveBayesModel.load(
             sc._jsc.sc(), path)
         # Can not unpickle array.array from Pyrolite in Python3 with "bytes"
         py_labels = _java2py(sc, java_model.labels(), "latin1")
         py_pi = _java2py(sc, java_model.pi(), "latin1")
         py_theta = _java2py(sc, java_model.theta(), "latin1")
         return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta))
 
 
 class NaiveBayes(object):
     """
     .. versionadded:: 0.9.0
     """
 
     @classmethod
     @since('0.9.0')
     def train(cls, data, lambda_=1.0):
         """
         Train a Naive Bayes model given an RDD of (label, features)
         vectors.
 
         This is the `Multinomial NB <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 <http://tinyurl.com/p7c96j6>`_.
         The input feature values must be nonnegative.
 
         :param data:
           RDD of LabeledPoint.
         :param lambda_:
           The smoothing parameter.
           (default: 1.0)
         """
         first = data.first()
         if not isinstance(first, LabeledPoint):
             raise ValueError("`data` should be an RDD of LabeledPoint")
         labels, pi, theta = callMLlibFunc("trainNaiveBayesModel", data, lambda_)
         return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
 
 
 @inherit_doc
 class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
     """
     Train or predict a logistic regression model on streaming data.
     Training uses Stochastic Gradient Descent to update the model based on
     each new batch of incoming data from a DStream.
 
     Each batch of data is assumed to be an RDD of LabeledPoints.
     The number of data points per batch can vary, but the number
     of features must be constant. An initial weight
     vector must be provided.
 
     :param stepSize:
       Step size for each iteration of gradient descent.
       (default: 0.1)
     :param numIterations:
       Number of iterations run for each batch of data.
       (default: 50)
     :param miniBatchFraction:
       Fraction of each batch of data to use for updates.
       (default: 1.0)
     :param regParam:
       L2 Regularization parameter.
       (default: 0.0)
     :param convergenceTol:
       Value used to determine when to terminate iterations.
       (default: 0.001)
 
     .. versionadded:: 1.5.0
     """
     def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.0,
                  convergenceTol=0.001):
         self.stepSize = stepSize
         self.numIterations = numIterations
         self.regParam = regParam
         self.miniBatchFraction = miniBatchFraction
         self.convergenceTol = convergenceTol
         self._model = None
         super(StreamingLogisticRegressionWithSGD, self).__init__(
             model=self._model)
 
     @since('1.5.0')
     def setInitialWeights(self, initialWeights):
         """
         Set the initial value of weights.
 
         This must be set before running trainOn and predictOn.
         """
         initialWeights = _convert_to_vector(initialWeights)
 
         # LogisticRegressionWithSGD does only binary classification.
         self._model = LogisticRegressionModel(
             initialWeights, 0, initialWeights.size, 2)
         return self
 
     @since('1.5.0')
     def trainOn(self, dstream):
         """Train the model on the incoming dstream."""
         self._validate(dstream)
 
         def update(rdd):
             # LogisticRegressionWithSGD.train raises an error for an empty RDD.
             if not rdd.isEmpty():
                 self._model = LogisticRegressionWithSGD.train(
                     rdd, self.numIterations, self.stepSize,
                     self.miniBatchFraction, self._model.weights,
                     regParam=self.regParam, convergenceTol=self.convergenceTol)
 
         dstream.foreachRDD(update)
 
 
 def _test():
     import doctest
     from pyspark.sql import SparkSession
     import pyspark.mllib.classification
     globs = pyspark.mllib.classification.__dict__.copy()
     spark = SparkSession.builder\
         .master("local[4]")\
         .appName("mllib.classification tests")\
         .getOrCreate()
     globs['sc'] = spark.sparkContext
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     spark.stop()
     if failure_count:
         sys.exit(-1)
 
 if __name__ == "__main__":
     _test()

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