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.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file: mn_naive_bayes_dense_distr.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-MULTINOMIAL_NAIVE_BAYES_DENSE_DISTRIBUTED"></a>
 ## \example mn_naive_bayes_dense_distr.py
 
 import os
 import sys
 
 from daal import step1Local, step2Master
 from daal.algorithms.multinomial_naive_bayes import prediction, training
 from daal.algorithms import classifier
 from daal.data_management import (
     FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable
 )
 
 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
 trainDatasetFileNames = [
     os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv'),
     os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv'),
     os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv'),
     os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv')
 ]
 
 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_dense.csv')
 
 nFeatures = 20
 nClasses = 20
 nBlocks = 4
 
 trainingResult = None
 predictionResult = None
 testGroundTruth = None
 
 
 def trainModel():
     global trainingResult
 
     masterAlgorithm = training.Distributed(step2Master, nClasses)
 
     for i in range(nBlocks):
         # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
         trainDataSource = FileDataSource(
             trainDatasetFileNames[i], 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 Naive Bayes model on the local-node data
         localAlgorithm = training.Distributed(step1Local, nClasses)
 
         # Pass a training data set and dependent values to the algorithm
         localAlgorithm.input.set(classifier.training.data,   trainData)
         localAlgorithm.input.set(classifier.training.labels, trainGroundTruth)
 
         # Build the Naive Bayes model on the local node and
         # Set the local Naive Bayes model as input for the master-node algorithm
         masterAlgorithm.input.add(training.partialModels, localAlgorithm.compute())
 
     # Merge and finalize the Naive Bayes model on the master node
     masterAlgorithm.compute()
     trainingResult = masterAlgorithm.finalizeCompute()  # Retrieve the algorithm results
 
 
 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 testing data and labels
     testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(testData, testGroundTruth)
 
     # Retrieve the data from input file
     testDataSource.loadDataBlock(mergedData)
 
     # Create an algorithm object to predict Naive Bayes values
     algorithm = prediction.Batch(nClasses)
 
     # Pass a testing data set and the trained model to the algorithm
     algorithm.input.setTable(classifier.prediction.data,  testData)
     algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
 
     # Predict Naive Bayes values (Result class from classifier.prediction)
     predictionResult = algorithm.compute()  # Retrieve the algorithm results
 
 
 def printResults():
     printNumericTables(
         testGroundTruth, predictionResult.get(classifier.prediction.prediction),
         "Ground truth", "Classification results",
         "NaiveBayes classification results (first 20 observations):", 20, flt64=False
     )
 
 if __name__ == "__main__":
 
     trainModel()
     testModel()
     printResults()
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