Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file: df_cls_traverse_model.py
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 # !  Content:
 # !    Python example of decision forest classification model traversal.
 # !
 # !    The program trains the decision forest classification model on a training
 # !    datasetFileName and prints the trained model by its depth-first traversing.
 # !*****************************************************************************
 ## \example df_cls_traverse_model.py
 from __future__ import print_function
 from daal.algorithms import classifier
 from daal.algorithms import decision_forest
 import daal.algorithms.decision_forest.classification
 import daal.algorithms.decision_forest.classification.training
 from daal.data_management import (
     FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface, DataSourceIface, features
 # Input data set parameters
 trainDatasetFileName = "../data/batch/df_classification_train.csv"
 categoricalFeaturesIndices = [2]
 nFeatures = 3  # Number of features in training and testing data sets
 # Decision forest parameters
 nTrees = 2
 minObservationsInLeafNode = 8
 maxTreeDepth = 15
 nClasses = 5  # Number of classes
 def trainModel():
     # Create Numeric Tables for training data and dependent variables
     trainData, trainDependentVariable = loadData(trainDatasetFileName)
     # Create an algorithm object to train the decision forest classification model
     algorithm = decision_forest.classification.training.Batch(nClasses)
     # Pass a training data set and dependent values to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainDependentVariable)
     algorithm.parameter.nTrees = nTrees
     algorithm.parameter.featuresPerNode = nFeatures
     algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode
     algorithm.parameter.maxTreeDepth = maxTreeDepth
     # Build the decision forest classification model and return the result
     return algorithm.compute()
 def loadData(fileName):
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     trainDataSource = FileDataSource(
         fileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
     # Create Numeric Tables for training data and dependent variables
     data = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
     dependentVar = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
     mergedData = MergedNumericTable(data, dependentVar)
     # Retrieve the data from input file
     dictionary = data.getDictionary()
     for i in range(len(categoricalFeaturesIndices)):
         dictionary[categoricalFeaturesIndices[i]].featureType = features.DAAL_CATEGORICAL
     return data, dependentVar
 # Visitor class implementing NodeVisitor interface, prints out tree nodes of the model when it is called back by model traversal method
 class PrintNodeVisitor(classifier.TreeNodeVisitor):
     def __init__(self):
         super(PrintNodeVisitor, self).__init__()
     def onLeafNode(self, level, response):
         for i in range(level):
             print("  ", end='')
         print("Level {}, leaf node. Response value = {}".format(level, response))
         return True
     def onSplitNode(self, level, featureIndex, featureValue):
         for i in range(level):
             print("  ", end='')
         print("Level {}, split node. Feature index = {}, feature value = {:.6g}".format(level, featureIndex, featureValue))
         return True
 def printModel(m):
     visitor = PrintNodeVisitor()
     print("Number of trees: {}".format(m.getNumberOfTrees()))
     for i in range(m.getNumberOfTrees()):
         print("Tree #{}".format(i))
         m.traverseDF(i, visitor)
 if __name__ == "__main__":
     trainingResult = trainModel()
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