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_forest
32 import daal.algorithms.decision_forest.classification
33 import daal.algorithms.decision_forest.classification.training
35 from daal.data_management
import (
36 FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface, DataSourceIface, features
40 trainDatasetFileName =
"../data/batch/df_classification_train.csv"
41 categoricalFeaturesIndices = [2]
46 minObservationsInLeafNode = 8
55 trainData, trainDependentVariable = loadData(trainDatasetFileName)
58 algorithm = decision_forest.classification.training.Batch(nClasses)
61 algorithm.input.set(classifier.training.data, trainData)
62 algorithm.input.set(classifier.training.labels, trainDependentVariable)
64 algorithm.parameter.nTrees = nTrees
65 algorithm.parameter.featuresPerNode = nFeatures
66 algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode
67 algorithm.parameter.maxTreeDepth = maxTreeDepth
70 return algorithm.compute()
73 def loadData(fileName):
76 trainDataSource = FileDataSource(
77 fileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
81 data = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
82 dependentVar = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
83 mergedData = MergedNumericTable(data, dependentVar)
86 trainDataSource.loadDataBlock(mergedData)
88 dictionary = data.getDictionary()
89 for i
in range(len(categoricalFeaturesIndices)):
90 dictionary[categoricalFeaturesIndices[i]].featureType = features.DAAL_CATEGORICAL
92 return data, dependentVar
96 class PrintNodeVisitor(classifier.TreeNodeVisitor):
99 super(PrintNodeVisitor, self).__init__()
101 def onLeafNode(self, level, response):
103 for i
in range(level):
105 print(
"Level {}, leaf node. Response value = {}".format(level, response))
108 def onSplitNode(self, level, featureIndex, featureValue):
110 for i
in range(level):
112 print(
"Level {}, split node. Feature index = {}, feature value = {:.6g}".format(level, featureIndex, featureValue))
117 visitor = PrintNodeVisitor()
118 print(
"Number of trees: {}".format(m.getNumberOfTrees()))
119 for i
in range(m.getNumberOfTrees()):
120 print(
"Tree #{}".format(i))
121 m.traverseDF(i, visitor)
124 if __name__ ==
"__main__":
126 trainingResult = trainModel()
127 printModel(trainingResult.get(classifier.training.model))