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.
22 from daal
import step1Local, step2Master
23 from daal.algorithms.linear_regression
import training, prediction
24 from daal.data_management
import (
25 DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable,NumericTableIface
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder
not in sys.path:
30 sys.path.insert(0, utils_folder)
31 from utils
import printNumericTable
33 DAAL_PREFIX = os.path.join(
'..',
'data')
35 trainDatasetFileNames = [
36 os.path.join(DAAL_PREFIX,
'distributed',
'linear_regression_train_1.csv'),
37 os.path.join(DAAL_PREFIX,
'distributed',
'linear_regression_train_2.csv'),
38 os.path.join(DAAL_PREFIX,
'distributed',
'linear_regression_train_3.csv'),
39 os.path.join(DAAL_PREFIX,
'distributed',
'linear_regression_train_4.csv')
42 testDatasetFileName = os.path.join(DAAL_PREFIX,
'distributed',
'linear_regression_test.csv')
47 nDependentVariables = 2
50 predictionResult =
None
57 masterAlgorithm = training.Distributed(step2Master)
59 for i
in range(nBlocks):
61 trainDataSource = FileDataSource(
62 trainDatasetFileNames[i], DataSourceIface.notAllocateNumericTable,
63 DataSourceIface.doDictionaryFromContext
67 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
68 trainDependentVariables = HomogenNumericTable(
69 nDependentVariables, 0, NumericTableIface.doNotAllocate
71 mergedData = MergedNumericTable(trainData, trainDependentVariables)
74 trainDataSource.loadDataBlock(mergedData)
77 localAlgorithm = training.Distributed(step1Local)
80 localAlgorithm.input.set(training.data, trainData)
81 localAlgorithm.input.set(training.dependentVariables, trainDependentVariables)
85 masterAlgorithm.input.add(training.partialModels, localAlgorithm.compute())
88 masterAlgorithm.compute()
91 trainingResult = masterAlgorithm.finalizeCompute()
92 printNumericTable(trainingResult.get(training.model).getBeta(),
"Linear Regression coefficients:")
96 global trainingResult, predictionResult
99 testDataSource = FileDataSource(
100 testDatasetFileName, DataSourceIface.doAllocateNumericTable,
101 DataSourceIface.doDictionaryFromContext
105 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
106 testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
107 mergedData = MergedNumericTable(testData, testGroundTruth)
110 testDataSource.loadDataBlock(mergedData)
113 algorithm = prediction.Batch()
116 algorithm.input.setTable(prediction.data, testData)
117 algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
120 predictionResult = algorithm.compute()
121 printNumericTable(predictionResult.get(prediction.prediction),
"Linear Regression prediction results: (first 10 rows):", 10)
122 printNumericTable(testGroundTruth,
"Ground truth (first 10 rows):", 10)
124 if __name__ ==
"__main__":