df_cls_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.

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

 # file: df_cls_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-DF_CLS_DENSE_BATCH"></a>
 ## \example df_cls_dense_batch.py
 
 import os
 import sys
 
 from daal.algorithms import decision_forest
 from daal.algorithms.decision_forest.classification import prediction, training
 from daal.algorithms import classifier
 from daal.data_management import (
     FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable,
     MergedNumericTable, features
 )
 
 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 printNumericTable, printNumericTables
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 # Input data set parameters
 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_train.csv')
 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_test.csv')
 
 nFeatures = 3
 nClasses = 5
 
 # Decision forest parameters
 nTrees = 10
 minObservationsInLeafNode = 8
 
 # Model object for the decision forest classification algorithm
 model = None
 predictionResult = None
 testGroundTruth = None
 
 
 def trainModel():
     global model
 
     # 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.notAllocate)
     trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
     mergedData = MergedNumericTable(trainData, trainGroundTruth)
 
     # Retrieve the data from the input file
     trainDataSource.loadDataBlock(mergedData)
 
     #  Get the dictionary and update it with additional information about data
     dict = trainData.getDictionary()
 
     #  Add a feature type to the dictionary
     dict[0].featureType = features.DAAL_CONTINUOUS
     dict[1].featureType = features.DAAL_CONTINUOUS
     dict[2].featureType = features.DAAL_CATEGORICAL
 
     # Create an algorithm object to train the decision forest classification model
     algorithm = training.Batch(nClasses)
     algorithm.parameter.nTrees = nTrees
     algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode
     algorithm.parameter.featuresPerNode = nFeatures
     algorithm.parameter.varImportance = decision_forest.training.MDI
     algorithm.parameter.resultsToCompute = decision_forest.training.computeOutOfBagError
 
     # Pass the training data set and dependent values to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainGroundTruth)
 
     # Train the decision forest classification model and retrieve the results of the training algorithm
     trainingResult = algorithm.compute()
     model = trainingResult.get(classifier.training.model)
     printNumericTable(trainingResult.getTable(training.variableImportance), "Variable importance results: ")
     printNumericTable(trainingResult.getTable(training.outOfBagError), "OOB error: ")
 
 def testModel():
     global testGroundTruth, predictionResult
 
     # 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.notAllocate)
     testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
     mergedData = MergedNumericTable(testData, testGroundTruth)
 
     # Retrieve the data from input file
     testDataSource.loadDataBlock(mergedData)
 
     #  Get the dictionary and update it with additional information about data
     dict = testData.getDictionary()
 
     #  Add a feature type to the dictionary
     dict[0].featureType = features.DAAL_CONTINUOUS
     dict[1].featureType = features.DAAL_CONTINUOUS
     dict[2].featureType = features.DAAL_CATEGORICAL
 
     # Create algorithm objects for decision forest classification prediction with the default method
     algorithm = prediction.Batch(nClasses)
 
     # Pass the testing data set and trained model to the algorithm
     algorithm.input.setTable(classifier.prediction.data,  testData)
     algorithm.input.setModel(classifier.prediction.model, model)
 
     # Compute prediction results and retrieve algorithm results
     # (Result class from classifier.prediction)
     predictionResult = algorithm.compute()
 
 
 def printResults():
     printNumericTable(predictionResult.get(classifier.prediction.prediction),"Decision forest prediction results (first 10 rows):",10)
     printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
 
 if __name__ == "__main__":
 
     trainModel()
     testModel()
     printResults()
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