48 from daal.algorithms.adaboost
import prediction, training
49 from daal.algorithms
import classifier
50 from daal.data_management
import (
51 FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder
not in sys.path:
56 sys.path.insert(0, utils_folder)
57 from utils
import printNumericTables
59 DAAL_PREFIX = os.path.join(
'..',
'data')
62 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'adaboost_train.csv')
64 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'adaboost_test.csv')
69 predictionResult =
None 70 testGroundTruth =
None 77 trainDataSource = FileDataSource(
78 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
79 DataSourceIface.doDictionaryFromContext
83 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
84 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
85 mergedData = MergedNumericTable(trainData, trainGroundTruth)
88 trainDataSource.loadDataBlock(mergedData)
91 algorithm = training.Batch()
94 algorithm.input.set(classifier.training.data, trainData)
95 algorithm.input.set(classifier.training.labels, trainGroundTruth)
98 trainingResult = algorithm.compute()
102 global predictionResult, testGroundTruth
105 testDataSource = FileDataSource(
106 testDatasetFileName, DataSourceIface.notAllocateNumericTable,
107 DataSourceIface.doDictionaryFromContext
111 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
112 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
113 mergedData = MergedNumericTable(testData, testGroundTruth)
116 testDataSource.loadDataBlock(mergedData)
119 algorithm = prediction.Batch()
122 algorithm.input.setTable(classifier.prediction.data, testData)
123 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
127 predictionResult = algorithm.compute()
131 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
132 "Ground truth",
"Classification results",
133 "AdaBoost classification results (first 20 observations):", 20, flt64=
False 136 if __name__ ==
"__main__":