stump_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

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 # file: stump_dense_batch.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
 #
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 #
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-STUMP_BATCH"></a>
 ## \example stump_dense_batch.py
 
 import os
 import sys
 
 from daal.algorithms import classifier
 from daal.algorithms.stump import training, prediction
 from daal.data_management import (
     FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
 )
 
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTables
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 #  Input data set parameters
 nFeatures = 20
 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'stump_train.csv')
 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'stump_test.csv')
 
 trainingResult = None
 predictionResult = None
 testGroundTruth = None
 
 
 def trainModel():
     global trainingResult
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     trainDataSource = FileDataSource(
         trainDatasetFileName,
         DataSourceIface.notAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
 
     # Create Numeric Tables for training data and labels
     trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(trainData, trainGroundTruth)
 
     # Retrieve the data from the input file
     trainDataSource.loadDataBlock(mergedData)
 
     #  Create an algorithm object to train the stump model
     algorithm = training.Batch()
 
     #  Pass a training data set and dependent values to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainGroundTruth)
 
     #  Compute and retrieve the algorithm results
     trainingResult = algorithm.compute()
 
 
 def testModel():
     global predictionResult, testGroundTruth
 
     #  Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
     testDataSource = FileDataSource(
         testDatasetFileName,
         DataSourceIface.notAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
 
     # Create Numeric Tables for training data and labels
     testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(testData, testGroundTruth)
 
     # Retrieve the data from the input file
     testDataSource.loadDataBlock(mergedData)
 
     #  Create an algorithm object to train the stump model
     algorithm = prediction.Batch()
 
     #  Pass a training data set and dependent values to the algorithm
     algorithm.input.setTable(classifier.prediction.data, testData)
     algorithm.input.setModel(classifier.prediction.model,
                              trainingResult.get(classifier.training.model))
 
     #  Compute and retrieve the algorithm Result class from classifier.prediction
     predictionResult = algorithm.compute()
 
 
 def printResults():
     printNumericTables(
         testGroundTruth,
         predictionResult.get(classifier.prediction.prediction),
         "Ground truth", "Classification results",
         "Stump classification results (first 20 observations):", 20, flt64=False)
 
 if __name__ == "__main__":
     trainModel()
     testModel()
     printResults()
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