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 # 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 sys
 
 from pyspark import since
 from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
 from pyspark.sql import SQLContext
 from pyspark.sql.types import ArrayType, StructField, StructType, DoubleType
 
 __all__ = ['BinaryClassificationMetrics', 'RegressionMetrics',
            'MulticlassMetrics', 'RankingMetrics']
 
 
 class BinaryClassificationMetrics(JavaModelWrapper):
     """
     Evaluator for binary classification.
 
     :param scoreAndLabels: an RDD of score, label and optional weight.
 
     >>> 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()
     >>> scoreAndLabelsWithOptWeight = sc.parallelize([
     ...     (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0, 0.6), (0.6, 1.0, 0.9),
     ...     (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2)
     >>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight)
     >>> metrics.areaUnderROC
     0.79...
     >>> metrics.areaUnderPR
     0.88...
 
     .. versionadded:: 1.4.0
     """
 
     def __init__(self, scoreAndLabels):
         sc = scoreAndLabels.ctx
         sql_ctx = SQLContext.getOrCreate(sc)
         numCol = len(scoreAndLabels.first())
         schema = StructType([
             StructField("score", DoubleType(), nullable=False),
             StructField("label", DoubleType(), nullable=False)])
         if numCol == 3:
             schema.add("weight", DoubleType(), False)
         df = sql_ctx.createDataFrame(scoreAndLabels, schema=schema)
         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 and optional weight.
 
     >>> 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...
     >>> predictionAndObservationsWithOptWeight = sc.parallelize([
     ...     (2.5, 3.0, 0.5), (0.0, -0.5, 1.0), (2.0, 2.0, 0.3), (8.0, 7.0, 0.9)])
     >>> metrics = RegressionMetrics(predictionAndObservationsWithOptWeight)
     >>> metrics.rootMeanSquaredError
     0.68...
 
     .. versionadded:: 1.4.0
     """
 
     def __init__(self, predictionAndObservations):
         sc = predictionAndObservations.ctx
         sql_ctx = SQLContext.getOrCreate(sc)
         numCol = len(predictionAndObservations.first())
         schema = StructType([
             StructField("prediction", DoubleType(), nullable=False),
             StructField("observation", DoubleType(), nullable=False)])
         if numCol == 3:
             schema.add("weight", DoubleType(), False)
         df = sql_ctx.createDataFrame(predictionAndObservations, schema=schema)
         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):
         r"""
         Returns the explained variance regression score.
         explainedVariance = :math:`1 - \frac{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, optional weight
      and optional probability.
 
     >>> 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.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...
     >>> predAndLabelsWithOptWeight = sc.parallelize([(0.0, 0.0, 1.0), (0.0, 1.0, 1.0),
     ...      (0.0, 0.0, 1.0), (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0),
     ...      (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)])
     >>> metrics = MulticlassMetrics(predAndLabelsWithOptWeight)
     >>> 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.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...
     >>> predictionAndLabelsWithProbabilities = sc.parallelize([
     ...      (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]),
     ...      (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])])
     >>> metrics = MulticlassMetrics(predictionAndLabelsWithProbabilities)
     >>> metrics.logLoss()
     0.9682...
 
     .. versionadded:: 1.4.0
     """
 
     def __init__(self, predictionAndLabels):
         sc = predictionAndLabels.ctx
         sql_ctx = SQLContext.getOrCreate(sc)
         numCol = len(predictionAndLabels.first())
         schema = StructType([
             StructField("prediction", DoubleType(), nullable=False),
             StructField("label", DoubleType(), nullable=False)])
         if numCol >= 3:
             schema.add("weight", DoubleType(), False)
         if numCol == 4:
             schema.add("probability", ArrayType(DoubleType(), False), False)
         df = sql_ctx.createDataFrame(predictionAndLabels, schema)
         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):
         """
         Returns precision.
         """
         return self.call("precision", float(label))
 
     @since('1.4.0')
     def recall(self, label):
         """
         Returns recall.
         """
         return self.call("recall", float(label))
 
     @since('1.4.0')
     def fMeasure(self, label, beta=None):
         """
         Returns f-measure.
         """
         if beta is None:
             return self.call("fMeasure", label)
         else:
             return self.call("fMeasure", label, beta)
 
     @property
     @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)
 
     @since('3.0.0')
     def logLoss(self, eps=1e-15):
         """
         Returns weighted logLoss.
         """
         return self.call("logLoss", eps)
 
 
 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.meanAveragePrecisionAt(1)
     0.3333333333333333...
     >>> metrics.meanAveragePrecisionAt(2)
     0.25...
     >>> metrics.ndcgAt(3)
     0.33...
     >>> metrics.ndcgAt(10)
     0.48...
     >>> metrics.recallAt(1)
     0.06...
     >>> metrics.recallAt(5)
     0.35...
     >>> metrics.recallAt(15)
     0.66...
 
     .. 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('3.0.0')
     def meanAveragePrecisionAt(self, k):
         """
         Returns the mean average precision (MAP) at first k ranking 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("meanAveragePrecisionAt", int(k))
 
     @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))
 
     @since('3.0.0')
     def recallAt(self, k):
         """
         Compute the average recall of all the queries, truncated at ranking position k.
 
         If for a query, the ranking algorithm returns n results, the recall value
         will be computed as #(relevant items retrieved) / #(ground truth set).
         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 recall together
         with a log warning.
         """
         return self.call("recallAt", 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
     import numpy
     from pyspark.sql import SparkSession
     import pyspark.mllib.evaluation
     try:
         # Numpy 1.14+ changed it's string format.
         numpy.set_printoptions(legacy='1.13')
     except TypeError:
         pass
     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:
         sys.exit(-1)
 
 
 if __name__ == "__main__":
     _test()

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