24 from daal.algorithms.kdtree_knn_classification
import training, prediction
25 from daal.algorithms
import classifier
26 from daal.data_management
import (
27 DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder
not in sys.path:
32 sys.path.insert(0, utils_folder)
33 from utils
import printNumericTables
35 DAAL_PREFIX = os.path.join(
'..',
'data')
38 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'k_nearest_neighbors_train.csv')
39 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'k_nearest_neighbors_test.csv')
45 predictionResult =
None
52 trainDataSource = FileDataSource(
53 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
54 DataSourceIface.doDictionaryFromContext
58 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
59 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
60 mergedData = MergedNumericTable(trainData, trainGroundTruth)
63 trainDataSource.loadDataBlock(mergedData)
66 algorithm = training.Batch()
69 algorithm.input.set(classifier.training.data, trainData)
70 algorithm.input.set(classifier.training.labels, trainGroundTruth)
71 algorithm.parameter.nClasses = nClasses
74 trainingResult = algorithm.compute()
78 global trainingResult, predictionResult
81 testDataSource = FileDataSource(
82 testDatasetFileName, DataSourceIface.doAllocateNumericTable,
83 DataSourceIface.doDictionaryFromContext
87 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
88 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
89 mergedData = MergedNumericTable(testData, testGroundTruth)
92 testDataSource.loadDataBlock(mergedData)
95 algorithm = prediction.Batch()
98 algorithm.input.setTable(classifier.prediction.data, testData)
99 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
102 predictionResult = algorithm.compute()
104 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
105 "Ground truth",
"Classification results",
106 "KD-tree based kNN classification results (first 20 observations):", 20, flt64=
False
109 if __name__ ==
"__main__":