47 from daal.algorithms
import gbt
48 from daal.algorithms.gbt.classification
import prediction, training
49 from daal.algorithms
import classifier
50 from daal.data_management
import (
51 FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable,
52 MergedNumericTable, data_feature_utils
55 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
56 if utils_folder
not in sys.path:
57 sys.path.insert(0, utils_folder)
58 from utils
import printNumericTable, printNumericTables
60 DAAL_PREFIX = os.path.join(
'..',
'data')
63 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'df_classification_train.csv')
64 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'df_classification_test.csv')
71 minObservationsInLeafNode = 8
75 predictionResult =
None
76 testGroundTruth =
None
83 trainDataSource = FileDataSource(
85 DataSourceIface.notAllocateNumericTable,
86 DataSourceIface.doDictionaryFromContext
90 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
91 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
92 mergedData = MergedNumericTable(trainData, trainGroundTruth)
95 trainDataSource.loadDataBlock(mergedData)
98 dict = trainData.getDictionary()
101 dict[0].featureType = data_feature_utils.DAAL_CONTINUOUS
102 dict[1].featureType = data_feature_utils.DAAL_CONTINUOUS
103 dict[2].featureType = data_feature_utils.DAAL_CATEGORICAL
106 algorithm = training.Batch(nClasses)
107 algorithm.parameter().maxIterations = maxIterations
108 algorithm.parameter().minObservationsInLeafNode = minObservationsInLeafNode
109 algorithm.parameter().featuresPerNode = nFeatures
112 algorithm.input.set(classifier.training.data, trainData)
113 algorithm.input.set(classifier.training.labels, trainGroundTruth)
116 trainingResult = algorithm.compute()
117 model = trainingResult.get(classifier.training.model)
120 global testGroundTruth, predictionResult
123 testDataSource = FileDataSource(
125 DataSourceIface.notAllocateNumericTable,
126 DataSourceIface.doDictionaryFromContext
130 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
131 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
132 mergedData = MergedNumericTable(testData, testGroundTruth)
135 testDataSource.loadDataBlock(mergedData)
138 dict = testData.getDictionary()
141 dict[0].featureType = data_feature_utils.DAAL_CONTINUOUS
142 dict[1].featureType = data_feature_utils.DAAL_CONTINUOUS
143 dict[2].featureType = data_feature_utils.DAAL_CATEGORICAL
146 algorithm = prediction.Batch(nClasses)
149 algorithm.input.setTable(classifier.prediction.data, testData)
150 algorithm.input.setModel(classifier.prediction.model, model)
154 predictionResult = algorithm.compute()
161 predictionResult.get(classifier.prediction.prediction),
162 "Ground truth",
"Classification results",
163 "gradient boosted trees classification results (first 20 observations):", 20
166 if __name__ ==
"__main__":