text: #*******************************************************************************
  # Copyright 2014-2020 Intel Corporation
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  #
  # This software is licensed under the Apache License, Version 2.0 (the
  # "License"), the following terms apply:
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  # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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  #*******************************************************************************
  
  # daal4py Decision Forest Classification example for shared memory systems
  
  import daal4py as d4p
  import numpy as np
  
  # let's try to use pandas' fast csv reader
  try:
      import pandas
      read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
  except:
      # fall back to numpy loadtxt
      read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=t)
  
  # Get Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) version
  from daal4py import __daal_link_version__ as dv
  daal_version = tuple(map(int, (dv[0:4], dv[4:8])))
  
  def main(readcsv=read_csv, method='defaultDense'):
      # input data file
      infile = "./data/batch/df_classification_train.csv"
      testfile = "./data/batch/df_classification_test.csv"
  
      # Configure a training object (5 classes)
      train_algo = d4p.decision_forest_classification_training(5,
                                                               nTrees=10,
                                                               minObservationsInLeafNode=8,
                                                               featuresPerNode=3,
                                                               engine = d4p.engines_mt19937(seed=777),
                                                               varImportance='MDI',
                                                               bootstrap=True,
                                                               resultsToCompute='computeOutOfBagError')
  
      # Read data. Let's use 3 features per observation
      data   = readcsv(infile, range(3), t=np.float32)
      labels = readcsv(infile, range(3,4), t=np.float32)
      train_result = train_algo.compute(data, labels)
      # Traiing result provides (depending on parameters) model, outOfBagError, outOfBagErrorPerObservation and/or variableImportance
  
      # Now let's do some prediction
      if daal_version < (2020,1):
          predict_algo = d4p.decision_forest_classification_prediction(nClasses=5)
      else:
          predict_algo = d4p.decision_forest_classification_prediction(nClasses=5,
              resultsToEvaluate="computeClassLabels|computeClassProbabilities", votingMethod="unweighted")
      # read test data (with same #features)
      pdata = readcsv(testfile, range(3), t=np.float32)
      plabels = readcsv(testfile, range(3,4), t=np.float32)
      # now predict using the model from the training above
      predict_result = predict_algo.compute(pdata, train_result.model)
  
      # Prediction result provides prediction
      assert(predict_result.prediction.shape == (pdata.shape[0], 1))
  
      return (train_result, predict_result, plabels)
  
  
  if __name__ == "__main__":
      (train_result, predict_result, plabels) = main()
      print("\nVariable importance results:\n", train_result.variableImportance)
      print("\nOOB error:\n", train_result.outOfBagError)
      print("\nDecision forest prediction results (first 10 rows):\n", predict_result.prediction[0:10])
      if daal_version >= (2020,1):
          print("\nDecision forest probabilities results (first 10 rows):\n", predict_result.probabilities[0:10])
      print("\nGround truth (first 10 rows):\n", plabels[0:10])
      print('All looks good!')
  