text: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # 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
  #
  # 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
  # limitations under the License.
  #
  
  import warnings
  
  from pyspark import since
  from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
  from pyspark.sql import SQLContext
  from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType, ArrayType
  
  __all__ = ['BinaryClassificationMetrics', 'RegressionMetrics',
             'MulticlassMetrics', 'RankingMetrics']
  
  
  class BinaryClassificationMetrics(JavaModelWrapper):
      """
      Evaluator for binary classification.
  
      :param scoreAndLabels: an RDD of (score, label) pairs
  
      >>> scoreAndLabels = sc.parallelize([
      ...     (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2)
      >>> metrics = BinaryClassificationMetrics(scoreAndLabels)
      >>> metrics.areaUnderROC
      0.70...
      >>> metrics.areaUnderPR
      0.83...
      >>> metrics.unpersist()
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, scoreAndLabels):
          sc = scoreAndLabels.ctx
          sql_ctx = SQLContext.getOrCreate(sc)
          df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([
              StructField("score", DoubleType(), nullable=False),
              StructField("label", DoubleType(), nullable=False)]))
          java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
          java_model = java_class(df._jdf)
          super(BinaryClassificationMetrics, self).__init__(java_model)
  
      @property
      @since('1.4.0')
      def areaUnderROC(self):
          """
          Computes the area under the receiver operating characteristic
          (ROC) curve.
          """
          return self.call("areaUnderROC")
  
      @property
      @since('1.4.0')
      def areaUnderPR(self):
          """
          Computes the area under the precision-recall curve.
          """
          return self.call("areaUnderPR")
  
      @since('1.4.0')
      def unpersist(self):
          """
          Unpersists intermediate RDDs used in the computation.
          """
          self.call("unpersist")
  
  
  class RegressionMetrics(JavaModelWrapper):
      """
      Evaluator for regression.
  
      :param predictionAndObservations: an RDD of (prediction,
                                        observation) pairs.
  
      >>> predictionAndObservations = sc.parallelize([
      ...     (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
      >>> metrics = RegressionMetrics(predictionAndObservations)
      >>> metrics.explainedVariance
      8.859...
      >>> metrics.meanAbsoluteError
      0.5...
      >>> metrics.meanSquaredError
      0.37...
      >>> metrics.rootMeanSquaredError
      0.61...
      >>> metrics.r2
      0.94...
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, predictionAndObservations):
          sc = predictionAndObservations.ctx
          sql_ctx = SQLContext.getOrCreate(sc)
          df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([
              StructField("prediction", DoubleType(), nullable=False),
              StructField("observation", DoubleType(), nullable=False)]))
          java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics
          java_model = java_class(df._jdf)
          super(RegressionMetrics, self).__init__(java_model)
  
      @property
      @since('1.4.0')
      def explainedVariance(self):
          """
          Returns the explained variance regression score.
          explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
          """
          return self.call("explainedVariance")
  
      @property
      @since('1.4.0')
      def meanAbsoluteError(self):
          """
          Returns the mean absolute error, which is a risk function corresponding to the
          expected value of the absolute error loss or l1-norm loss.
          """
          return self.call("meanAbsoluteError")
  
      @property
      @since('1.4.0')
      def meanSquaredError(self):
          """
          Returns the mean squared error, which is a risk function corresponding to the
          expected value of the squared error loss or quadratic loss.
          """
          return self.call("meanSquaredError")
  
      @property
      @since('1.4.0')
      def rootMeanSquaredError(self):
          """
          Returns the root mean squared error, which is defined as the square root of
          the mean squared error.
          """
          return self.call("rootMeanSquaredError")
  
      @property
      @since('1.4.0')
      def r2(self):
          """
          Returns R^2^, the coefficient of determination.
          """
          return self.call("r2")
  
  
  class MulticlassMetrics(JavaModelWrapper):
      """
      Evaluator for multiclass classification.
  
      :param predictionAndLabels: an RDD of (prediction, label) pairs.
  
      >>> predictionAndLabels = sc.parallelize([(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
      ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)])
      >>> metrics = MulticlassMetrics(predictionAndLabels)
      >>> metrics.confusionMatrix().toArray()
      array([[ 2.,  1.,  1.],
             [ 1.,  3.,  0.],
             [ 0.,  0.,  1.]])
      >>> metrics.falsePositiveRate(0.0)
      0.2...
      >>> metrics.precision(1.0)
      0.75...
      >>> metrics.recall(2.0)
      1.0...
      >>> metrics.fMeasure(0.0, 2.0)
      0.52...
      >>> metrics.precision()
      0.66...
      >>> metrics.recall()
      0.66...
      >>> metrics.accuracy()
      0.66...
      >>> metrics.weightedFalsePositiveRate
      0.19...
      >>> metrics.weightedPrecision
      0.68...
      >>> metrics.weightedRecall
      0.66...
      >>> metrics.weightedFMeasure()
      0.66...
      >>> metrics.weightedFMeasure(2.0)
      0.65...
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, predictionAndLabels):
          sc = predictionAndLabels.ctx
          sql_ctx = SQLContext.getOrCreate(sc)
          df = sql_ctx.createDataFrame(predictionAndLabels, schema=StructType([
              StructField("prediction", DoubleType(), nullable=False),
              StructField("label", DoubleType(), nullable=False)]))
          java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics
          java_model = java_class(df._jdf)
          super(MulticlassMetrics, self).__init__(java_model)
  
      @since('1.4.0')
      def confusionMatrix(self):
          """
          Returns confusion matrix: predicted classes are in columns,
          they are ordered by class label ascending, as in "labels".
          """
          return self.call("confusionMatrix")
  
      @since('1.4.0')
      def truePositiveRate(self, label):
          """
          Returns true positive rate for a given label (category).
          """
          return self.call("truePositiveRate", label)
  
      @since('1.4.0')
      def falsePositiveRate(self, label):
          """
          Returns false positive rate for a given label (category).
          """
          return self.call("falsePositiveRate", label)
  
      @since('1.4.0')
      def precision(self, label=None):
          """
          Returns precision or precision for a given label (category) if specified.
          """
          if label is None:
              # note:: Deprecated in 2.0.0. Use accuracy.
              warnings.warn("Deprecated in 2.0.0. Use accuracy.")
              return self.call("precision")
          else:
              return self.call("precision", float(label))
  
      @since('1.4.0')
      def recall(self, label=None):
          """
          Returns recall or recall for a given label (category) if specified.
          """
          if label is None:
              # note:: Deprecated in 2.0.0. Use accuracy.
              warnings.warn("Deprecated in 2.0.0. Use accuracy.")
              return self.call("recall")
          else:
              return self.call("recall", float(label))
  
      @since('1.4.0')
      def fMeasure(self, label=None, beta=None):
          """
          Returns f-measure or f-measure for a given label (category) if specified.
          """
          if beta is None:
              if label is None:
                  # note:: Deprecated in 2.0.0. Use accuracy.
                  warnings.warn("Deprecated in 2.0.0. Use accuracy.")
                  return self.call("fMeasure")
              else:
                  return self.call("fMeasure", label)
          else:
              if label is None:
                  raise Exception("If the beta parameter is specified, label can not be none")
              else:
                  return self.call("fMeasure", label, beta)
  
      @since('2.0.0')
      def accuracy(self):
          """
          Returns accuracy (equals to the total number of correctly classified instances
          out of the total number of instances).
          """
          return self.call("accuracy")
  
      @property
      @since('1.4.0')
      def weightedTruePositiveRate(self):
          """
          Returns weighted true positive rate.
          (equals to precision, recall and f-measure)
          """
          return self.call("weightedTruePositiveRate")
  
      @property
      @since('1.4.0')
      def weightedFalsePositiveRate(self):
          """
          Returns weighted false positive rate.
          """
          return self.call("weightedFalsePositiveRate")
  
      @property
      @since('1.4.0')
      def weightedRecall(self):
          """
          Returns weighted averaged recall.
          (equals to precision, recall and f-measure)
          """
          return self.call("weightedRecall")
  
      @property
      @since('1.4.0')
      def weightedPrecision(self):
          """
          Returns weighted averaged precision.
          """
          return self.call("weightedPrecision")
  
      @since('1.4.0')
      def weightedFMeasure(self, beta=None):
          """
          Returns weighted averaged f-measure.
          """
          if beta is None:
              return self.call("weightedFMeasure")
          else:
              return self.call("weightedFMeasure", beta)
  
  
  class RankingMetrics(JavaModelWrapper):
      """
      Evaluator for ranking algorithms.
  
      :param predictionAndLabels: an RDD of (predicted ranking,
                                  ground truth set) pairs.
  
      >>> predictionAndLabels = sc.parallelize([
      ...     ([1, 6, 2, 7, 8, 3, 9, 10, 4, 5], [1, 2, 3, 4, 5]),
      ...     ([4, 1, 5, 6, 2, 7, 3, 8, 9, 10], [1, 2, 3]),
      ...     ([1, 2, 3, 4, 5], [])])
      >>> metrics = RankingMetrics(predictionAndLabels)
      >>> metrics.precisionAt(1)
      0.33...
      >>> metrics.precisionAt(5)
      0.26...
      >>> metrics.precisionAt(15)
      0.17...
      >>> metrics.meanAveragePrecision
      0.35...
      >>> metrics.ndcgAt(3)
      0.33...
      >>> metrics.ndcgAt(10)
      0.48...
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, predictionAndLabels):
          sc = predictionAndLabels.ctx
          sql_ctx = SQLContext.getOrCreate(sc)
          df = sql_ctx.createDataFrame(predictionAndLabels,
                                       schema=sql_ctx._inferSchema(predictionAndLabels))
          java_model = callMLlibFunc("newRankingMetrics", df._jdf)
          super(RankingMetrics, self).__init__(java_model)
  
      @since('1.4.0')
      def precisionAt(self, k):
          """
          Compute the average precision of all the queries, truncated at ranking position k.
  
          If for a query, the ranking algorithm returns n (n < k) results, the precision value
          will be computed as #(relevant items retrieved) / k. This formula also applies when
          the size of the ground truth set is less than k.
  
          If a query has an empty ground truth set, zero will be used as precision together
          with a log warning.
          """
          return self.call("precisionAt", int(k))
  
      @property
      @since('1.4.0')
      def meanAveragePrecision(self):
          """
          Returns the mean average precision (MAP) of all the queries.
          If a query has an empty ground truth set, the average precision will be zero and
          a log warining is generated.
          """
          return self.call("meanAveragePrecision")
  
      @since('1.4.0')
      def ndcgAt(self, k):
          """
          Compute the average NDCG value of all the queries, truncated at ranking position k.
          The discounted cumulative gain at position k is computed as:
          sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
          and the NDCG is obtained by dividing the DCG value on the ground truth set.
          In the current implementation, the relevance value is binary.
          If a query has an empty ground truth set, zero will be used as NDCG together with
          a log warning.
          """
          return self.call("ndcgAt", int(k))
  
  
  class MultilabelMetrics(JavaModelWrapper):
      """
      Evaluator for multilabel classification.
  
      :param predictionAndLabels: an RDD of (predictions, labels) pairs,
                                  both are non-null Arrays, each with
                                  unique elements.
  
      >>> predictionAndLabels = sc.parallelize([([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]),
      ...     ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]),
      ...     ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])])
      >>> metrics = MultilabelMetrics(predictionAndLabels)
      >>> metrics.precision(0.0)
      1.0
      >>> metrics.recall(1.0)
      0.66...
      >>> metrics.f1Measure(2.0)
      0.5
      >>> metrics.precision()
      0.66...
      >>> metrics.recall()
      0.64...
      >>> metrics.f1Measure()
      0.63...
      >>> metrics.microPrecision
      0.72...
      >>> metrics.microRecall
      0.66...
      >>> metrics.microF1Measure
      0.69...
      >>> metrics.hammingLoss
      0.33...
      >>> metrics.subsetAccuracy
      0.28...
      >>> metrics.accuracy
      0.54...
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, predictionAndLabels):
          sc = predictionAndLabels.ctx
          sql_ctx = SQLContext.getOrCreate(sc)
          df = sql_ctx.createDataFrame(predictionAndLabels,
                                       schema=sql_ctx._inferSchema(predictionAndLabels))
          java_class = sc._jvm.org.apache.spark.mllib.evaluation.MultilabelMetrics
          java_model = java_class(df._jdf)
          super(MultilabelMetrics, self).__init__(java_model)
  
      @since('1.4.0')
      def precision(self, label=None):
          """
          Returns precision or precision for a given label (category) if specified.
          """
          if label is None:
              return self.call("precision")
          else:
              return self.call("precision", float(label))
  
      @since('1.4.0')
      def recall(self, label=None):
          """
          Returns recall or recall for a given label (category) if specified.
          """
          if label is None:
              return self.call("recall")
          else:
              return self.call("recall", float(label))
  
      @since('1.4.0')
      def f1Measure(self, label=None):
          """
          Returns f1Measure or f1Measure for a given label (category) if specified.
          """
          if label is None:
              return self.call("f1Measure")
          else:
              return self.call("f1Measure", float(label))
  
      @property
      @since('1.4.0')
      def microPrecision(self):
          """
          Returns micro-averaged label-based precision.
          (equals to micro-averaged document-based precision)
          """
          return self.call("microPrecision")
  
      @property
      @since('1.4.0')
      def microRecall(self):
          """
          Returns micro-averaged label-based recall.
          (equals to micro-averaged document-based recall)
          """
          return self.call("microRecall")
  
      @property
      @since('1.4.0')
      def microF1Measure(self):
          """
          Returns micro-averaged label-based f1-measure.
          (equals to micro-averaged document-based f1-measure)
          """
          return self.call("microF1Measure")
  
      @property
      @since('1.4.0')
      def hammingLoss(self):
          """
          Returns Hamming-loss.
          """
          return self.call("hammingLoss")
  
      @property
      @since('1.4.0')
      def subsetAccuracy(self):
          """
          Returns subset accuracy.
          (for equal sets of labels)
          """
          return self.call("subsetAccuracy")
  
      @property
      @since('1.4.0')
      def accuracy(self):
          """
          Returns accuracy.
          """
          return self.call("accuracy")
  
  
  def _test():
      import doctest
      from pyspark.sql import SparkSession
      import pyspark.mllib.evaluation
      globs = pyspark.mllib.evaluation.__dict__.copy()
      spark = SparkSession.builder\
          .master("local[4]")\
          .appName("mllib.evaluation tests")\
          .getOrCreate()
      globs['sc'] = spark.sparkContext
      (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
      spark.stop()
      if failure_count:
          exit(-1)
  
  
  if __name__ == "__main__":
      _test()
  