mn_naive_bayes_dense_batch.py

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 # file: mn_naive_bayes_dense_batch.py
 #===============================================================================
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 # the related documents without Intel's prior written permission.
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 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-MULTINOMIAL_NAIVE_BAYES_DENSE_BATCH"></a>
 ## \example mn_naive_bayes_dense_batch.py
 
 import os
 import sys
 
 from daal.algorithms.multinomial_naive_bayes import prediction, training
 from daal.algorithms import classifier
 from daal.data_management import (
     FileDataSource, HomogenNumericTable, MergedNumericTable, DataSourceIface, 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
 trainDatasetFileName = 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
 
 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 Naive Bayes model
     algorithm = training.Batch(nClasses)
 
     # 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)
 
     # Build the Naive Bayes model 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 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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