text: #===============================================================================
  # Copyright 2021 Intel Corporation
  #
  # Licensed 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.
  #===============================================================================
  
  # daal4py Decision Forest Regression example of Hist method for shared memory systems
  
  import daal4py as d4p
  import numpy as np
  import os
  from daal4py.oneapi import sycl_buffer
  
  # let's try to use pandas' fast csv reader
  try:
      import pandas
  
      def read_csv(f, c, t=np.float64):
          return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
  except Exception:
      # fall back to numpy loadtxt
      def read_csv(f, c, t=np.float64):
          return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=t)
  
  try:
      from dpctx import device_context, device_type
      with device_context(device_type.gpu, 0):
          gpu_available = True
  except Exception:
      try:
          from daal4py.oneapi import sycl_context
          with sycl_context('gpu'):
              gpu_available = True
      except Exception:
          gpu_available = False
  
  
  # Commone code for both CPU and GPU computations
  def compute(train_data, train_labels, predict_data):
      # Configure a training object
      train_algo = d4p.decision_forest_regression_training(
          method='hist',
          maxBins=256,
          minBinSize=1,
          nTrees=100,
          fptype='float',
          varImportance='MDA_Raw',
          bootstrap=True,
          engine=d4p.engines_mt2203(seed=777),
          resultsToCompute='computeOutOfBagError|computeOutOfBagErrorPerObservation'
      )
  
      # Training result provides (depending on parameters) model,
      # outOfBagError, outOfBagErrorPerObservation and/or variableImportance
      train_result = train_algo.compute(train_data, train_labels)
  
      # now predict using the model from the training above
      predict_algo = d4p.decision_forest_regression_prediction(fptype='float')
  
      predict_result = predict_algo.compute(predict_data, train_result.model)
  
      return train_result, predict_result
  
  
  # At this moment with sycl we are working only with numpy arrays
  def to_numpy(data):
      try:
          from pandas import DataFrame
          if isinstance(data, DataFrame):
              return np.ascontiguousarray(data.values)
      except Exception:
          try:
              from scipy.sparse import csr_matrix
              if isinstance(data, csr_matrix):
                  return data.toarray()
          except Exception:
              return data
  
      return data
  
  
  def main(readcsv=read_csv):
      nFeatures = 13
      # input data file
      train_file = os.path.join('..', 'data', 'batch', 'df_regression_train.csv')
      predict_file = os.path.join('..', 'data', 'batch', 'df_regression_test.csv')
  
      # Read train data. Let's use 3 features per observation
      train_data = readcsv(train_file, range(nFeatures), t=np.float32)
      train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1), t=np.float32)
      # Read test data (with same #features)
      predict_data = readcsv(predict_file, range(nFeatures), t=np.float32)
      predict_labels = readcsv(predict_file, range(nFeatures, nFeatures + 1), t=np.float32)
  
      # Using of the classic way (computations on CPU)
      train_result, predict_result = compute(train_data, train_labels, predict_data)
      assert predict_result.prediction.shape == (predict_labels.shape[0], 1)
      assert (np.square(predict_result.prediction - predict_labels).mean() < 19).any()
  
      train_data = to_numpy(train_data)
      train_labels = to_numpy(train_labels)
      predict_data = to_numpy(predict_data)
  
      try:
          from dpctx import device_context, device_type
  
          def gpu_context():
              return device_context(device_type.gpu, 0)
      except:
          from daal4py.oneapi import sycl_context
  
          def gpu_context():
              return sycl_context('gpu')
  
      # It is possible to specify to make the computations on GPU
      if gpu_available:
          with gpu_context():
              sycl_train_data = sycl_buffer(train_data)
              sycl_train_labels = sycl_buffer(train_labels)
              sycl_predict_data = sycl_buffer(predict_data)
              train_result, predict_result = compute(sycl_train_data, sycl_train_labels,
                                                     sycl_predict_data)
              assert predict_result.prediction.shape == (predict_labels.shape[0], 1)
              assert (
                  np.square(predict_result.prediction - predict_labels).mean() < 18
              ).any()
  
      return (train_result, predict_result, predict_labels)
  
  
  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]
      )
      print("\nGround truth (first 10 rows):\n", plabels[0:10])
      print('All looks good!')
  