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.
  #
  
  # $example on$
  from pyspark.mllib.evaluation import MultilabelMetrics
  # $example off$
  from pyspark import SparkContext
  
  if __name__ == "__main__":
      sc = SparkContext(appName="MultiLabelMetricsExample")
      # $example on$
      scoreAndLabels = 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])])
  
      # Instantiate metrics object
      metrics = MultilabelMetrics(scoreAndLabels)
  
      # Summary stats
      print("Recall = %s" % metrics.recall())
      print("Precision = %s" % metrics.precision())
      print("F1 measure = %s" % metrics.f1Measure())
      print("Accuracy = %s" % metrics.accuracy)
  
      # Individual label stats
      labels = scoreAndLabels.flatMap(lambda x: x[1]).distinct().collect()
      for label in labels:
          print("Class %s precision = %s" % (label, metrics.precision(label)))
          print("Class %s recall = %s" % (label, metrics.recall(label)))
          print("Class %s F1 Measure = %s" % (label, metrics.f1Measure(label)))
  
      # Micro stats
      print("Micro precision = %s" % metrics.microPrecision)
      print("Micro recall = %s" % metrics.microRecall)
      print("Micro F1 measure = %s" % metrics.microF1Measure)
  
      # Hamming loss
      print("Hamming loss = %s" % metrics.hammingLoss)
  
      # Subset accuracy
      print("Subset accuracy = %s" % metrics.subsetAccuracy)
      # $example off$
  