47 from daal.algorithms.adaboost
import prediction, training
48 from daal.algorithms
import classifier
49 from daal.data_management
import (
50 FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
53 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
54 if utils_folder
not in sys.path:
55 sys.path.insert(0, utils_folder)
56 from utils
import printNumericTables
58 DAAL_PREFIX = os.path.join(
'..',
'data')
61 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'adaboost_train.csv')
63 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'adaboost_test.csv')
68 predictionResult =
None
69 testGroundTruth =
None
76 trainDataSource = FileDataSource(
77 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
78 DataSourceIface.doDictionaryFromContext
82 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
83 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
84 mergedData = MergedNumericTable(trainData, trainGroundTruth)
87 trainDataSource.loadDataBlock(mergedData)
90 algorithm = training.Batch()
93 algorithm.input.set(classifier.training.data, trainData)
94 algorithm.input.set(classifier.training.labels, trainGroundTruth)
97 trainingResult = algorithm.compute()
101 global predictionResult, testGroundTruth
104 testDataSource = FileDataSource(
105 testDatasetFileName, DataSourceIface.notAllocateNumericTable,
106 DataSourceIface.doDictionaryFromContext
110 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
111 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
112 mergedData = MergedNumericTable(testData, testGroundTruth)
115 testDataSource.loadDataBlock(mergedData)
118 algorithm = prediction.Batch()
121 algorithm.input.setTable(classifier.prediction.data, testData)
122 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
126 predictionResult = algorithm.compute()
130 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
131 "Ground truth",
"Classification results",
132 "AdaBoost classification results (first 20 observations):", 20, flt64=
False
135 if __name__ ==
"__main__":