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
import algorithms
31 from daal.algorithms
import decision_forest
32 import daal.algorithms.decision_forest.regression
33 import daal.algorithms.decision_forest.regression.training
35 from daal.data_management
import (
36 FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable, features
40 trainDatasetFileName =
"../data/batch/df_regression_train.csv"
41 categoricalFeaturesIndices = [3]
51 trainData, trainDependentVariable = loadData(trainDatasetFileName)
54 algorithm = decision_forest.regression.training.Batch()
57 algorithm.input.set(decision_forest.regression.training.data, trainData)
58 algorithm.input.set(decision_forest.regression.training.dependentVariable, trainDependentVariable)
60 algorithm.parameter.nTrees = nTrees
63 return algorithm.compute()
66 def loadData(fileName):
69 trainDataSource = FileDataSource(
70 fileName, DataSourceIface.notAllocateNumericTable, DataSourceIface.doDictionaryFromContext
74 data = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
75 dependentVar = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
76 mergedData = MergedNumericTable(data, dependentVar)
79 trainDataSource.loadDataBlock(mergedData)
81 dictionary = data.getDictionary()
82 for i
in range(len(categoricalFeaturesIndices)):
83 dictionary[categoricalFeaturesIndices[i]].featureType = features.DAAL_CATEGORICAL
85 return data, dependentVar
89 class PrintNodeVisitor(algorithms.regression.TreeNodeVisitor):
92 super(PrintNodeVisitor, self).__init__()
94 def onLeafNode(self, level, response):
96 for i
in range(level):
98 print(
"Level {}, leaf node. Response value = {:.4g}".format(level, response))
102 def onSplitNode(self, level, featureIndex, featureValue):
104 for i
in range(level):
106 print(
"Level {}, split node. Feature index = {}, feature value = {:.4g}".format(level, featureIndex, featureValue))
111 visitor = PrintNodeVisitor()
112 print(
"Number of trees: {}".format(m.getNumberOfTrees()))
113 for i
in range(m.getNumberOfTrees()):
114 print(
"Tree #{}".format(i))
115 m.traverseDF(i, visitor)
117 if __name__ ==
"__main__":
119 trainingResult = trainModel()
120 printModel(trainingResult.get(decision_forest.regression.training.model))