df_cls_traverse_model.py

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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.
 # !*****************************************************************************
 
 #
 ## <a name = "DAAL-EXAMPLE-PY-DF_CLS_TRAVERSE_MODEL"></a>
 ## \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
     trainDataSource.loadDataBlock(mergedData)
 
     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()
     printModel(trainingResult.get(classifier.training.model))
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