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.
28 from __future__
import print_function
30 from daal.algorithms
import classifier
31 from daal.algorithms
import decision_tree
32 import daal.algorithms.decision_tree.classification
33 import daal.algorithms.decision_tree.classification.training
35 from daal.data_management
import (
36 DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable, FileDataSource
40 trainDatasetFileName =
"../data/batch/decision_tree_train.csv"
41 pruneDatasetFileName =
"../data/batch/decision_tree_prune.csv"
50 trainDataSource = FileDataSource(
51 trainDatasetFileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
55 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
56 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
57 mergedData = MergedNumericTable(trainData, trainGroundTruth)
60 trainDataSource.loadDataBlock(mergedData)
63 pruneDataSource = FileDataSource(
64 pruneDatasetFileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
68 pruneData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
69 pruneGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
70 pruneMergedData = MergedNumericTable(pruneData, pruneGroundTruth)
73 pruneDataSource.loadDataBlock(pruneMergedData)
76 algorithm = decision_tree.classification.training.Batch(nClasses)
79 algorithm.input.set(classifier.training.data, trainData)
80 algorithm.input.set(classifier.training.labels, trainGroundTruth)
81 algorithm.input.set(decision_tree.classification.training.dataForPruning, pruneData)
82 algorithm.input.set(decision_tree.classification.training.labelsForPruning, pruneGroundTruth)
85 return algorithm.compute()
90 class PrintNodeVisitor(classifier.TreeNodeVisitor):
93 super(PrintNodeVisitor, self).__init__()
95 def onLeafNode(self, level, response):
97 for i
in range(level):
99 print(
"Level {}, leaf node. Response value = {}".format(level, response))
103 def onSplitNode(self, level, featureIndex, featureValue):
105 for i
in range(level):
107 print(
"Level {}, split node. Feature index = {}, feature value = {:.4g}".format(level, featureIndex, featureValue))
113 visitor = PrintNodeVisitor()
114 m.traverseDF(visitor)
117 if __name__ ==
"__main__":
119 trainingResult = trainModel()
120 printModel(trainingResult.get(classifier.training.model))