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.classification import LogisticRegressionWithLBFGS
  from pyspark.mllib.util import MLUtils
  from pyspark.mllib.evaluation import MulticlassMetrics
  # $example off$
  
  from pyspark import SparkContext
  
  if __name__ == "__main__":
      sc = SparkContext(appName="MultiClassMetricsExample")
  
      # Several of the methods available in scala are currently missing from pyspark
      # $example on$
      # Load training data in LIBSVM format
      data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
  
      # Split data into training (60%) and test (40%)
      training, test = data.randomSplit([0.6, 0.4], seed=11)
      training.cache()
  
      # Run training algorithm to build the model
      model = LogisticRegressionWithLBFGS.train(training, numClasses=3)
  
      # Compute raw scores on the test set
      predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp.label))
  
      # Instantiate metrics object
      metrics = MulticlassMetrics(predictionAndLabels)
  
      # Overall statistics
      precision = metrics.precision(1.0)
      recall = metrics.recall(1.0)
      f1Score = metrics.fMeasure(1.0)
      print("Summary Stats")
      print("Precision = %s" % precision)
      print("Recall = %s" % recall)
      print("F1 Score = %s" % f1Score)
  
      # Statistics by class
      labels = data.map(lambda lp: lp.label).distinct().collect()
      for label in sorted(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.fMeasure(label, beta=1.0)))
  
      # Weighted stats
      print("Weighted recall = %s" % metrics.weightedRecall)
      print("Weighted precision = %s" % metrics.weightedPrecision)
      print("Weighted F(1) Score = %s" % metrics.weightedFMeasure())
      print("Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5))
      print("Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate)
      # $example off$
      
      sc.stop()
  