22 from daal.algorithms.kdtree_knn_classification
import training, prediction
23 from daal.algorithms
import classifier
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 printNumericTables
33 DAAL_PREFIX = os.path.join(
'..',
'data')
36 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'k_nearest_neighbors_train.csv')
37 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'k_nearest_neighbors_test.csv')
42 predictionResult =
None
49 trainDataSource = FileDataSource(
50 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
51 DataSourceIface.doDictionaryFromContext
55 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
56 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
57 mergedData = MergedNumericTable(trainData, trainGroundTruth)
60 trainDataSource.loadDataBlock(mergedData)
63 algorithm = training.Batch()
66 algorithm.input.set(classifier.training.data, trainData)
67 algorithm.input.set(classifier.training.labels, trainGroundTruth)
70 trainingResult = algorithm.compute()
74 global trainingResult, predictionResult
77 testDataSource = FileDataSource(
78 testDatasetFileName, DataSourceIface.doAllocateNumericTable,
79 DataSourceIface.doDictionaryFromContext
83 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
84 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
85 mergedData = MergedNumericTable(testData, testGroundTruth)
88 testDataSource.loadDataBlock(mergedData)
91 algorithm = prediction.Batch()
94 algorithm.input.setTable(classifier.prediction.data, testData)
95 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
98 predictionResult = algorithm.compute()
100 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
101 "Ground truth",
"Classification results",
102 "KD-tree based kNN classification results (first 20 observations):", 20, flt64=
False
105 if __name__ ==
"__main__":