dt_cls_traverse_model.py

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: dt_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 tree classification model traversal.
 # !
 # !    The program trains the decision tree classification model on a training
 # !    datasetFileName and prints the trained model by its depth-first traversing.
 # !*****************************************************************************
 
 #
 ## <a name = "DAAL-EXAMPLE-PY-DT_CLS_TRAVERSE_MODEL"></a>
 ## \example dt_cls_traverse_model.py
 #
 from __future__ import print_function
 
 from daal.algorithms import classifier
 from daal.algorithms import decision_tree
 import daal.algorithms.decision_tree.classification
 import daal.algorithms.decision_tree.classification.training
 
 from daal.data_management import (
     DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable, FileDataSource
 )
 
 # Input data set parameters
 trainDatasetFileName = "../data/batch/decision_tree_train.csv"
 pruneDatasetFileName = "../data/batch/decision_tree_prune.csv"
 
 nFeatures = 5
 nClasses = 5
 
 
 def trainModel():
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     trainDataSource = FileDataSource(
         trainDatasetFileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
     )
 
     # Create Numeric Tables for training data and labels
     trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
     trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
     mergedData = MergedNumericTable(trainData, trainGroundTruth)
 
     # Retrieve the data from the input file
     trainDataSource.loadDataBlock(mergedData)
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the pruning input data from a .csv file
     pruneDataSource = FileDataSource(
         pruneDatasetFileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
     )
 
     # Create Numeric Tables for pruning data and labels
     pruneData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
     pruneGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
     pruneMergedData = MergedNumericTable(pruneData, pruneGroundTruth)
 
     # Retrieve the data from the pruning input file
     pruneDataSource.loadDataBlock(pruneMergedData)
 
     # Create an algorithm object to train the Decision tree model
     algorithm = decision_tree.classification.training.Batch(nClasses)
 
     # Pass the training data set, labels, and pruning dataset with labels to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainGroundTruth)
     algorithm.input.set(decision_tree.classification.training.dataForPruning, pruneData)
     algorithm.input.set(decision_tree.classification.training.labelsForPruning, pruneGroundTruth)
 
     # Train the Decision tree model and retrieve the results
     return algorithm.compute()
 
 
 # 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 = {:.4g}".format(level, featureIndex, featureValue))
 
         return True
 
 
 def printModel(m):
     visitor = PrintNodeVisitor()
     m.traverseDF(visitor)
 
 
 if __name__ == "__main__":
 
     trainingResult = trainModel()
     printModel(trainingResult.get(classifier.training.model))
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)