ridge_reg_norm_eq_dense_distr.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: ridge_reg_norm_eq_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.
 #===============================================================================
 
 #
 # !  Content:
 # !    Python example of ridge regression in the distributed processing mode.
 # !
 # !    The program trains the multiple ridge regression model on a training
 # !    datasetFileName with the normal equations method and computes regression
 # !    for the test data.
 # !*****************************************************************************
 
 #
 ## <a name="DAAL-EXAMPLE-PY-RIDGE_REGRESSION_NORM_EQ_DISTRIBUTED"></a>
 ## \example ridge_reg_norm_eq_dense_distr.py
 #
 
 import os
 import sys
 
 from daal import step1Local, step2Master
 from daal.algorithms.ridge_regression import training, prediction
 from daal.data_management import DataSource, FileDataSource, NumericTable, 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 printNumericTable
 
 trainDatasetFileNames = [
     os.path.join("..", "data", "distributed", "linear_regression_train_1.csv"),
     os.path.join("..", "data", "distributed", "linear_regression_train_2.csv"),
     os.path.join("..", "data", "distributed", "linear_regression_train_3.csv"),
     os.path.join("..", "data", "distributed", "linear_regression_train_4.csv"),
 ]
 
 
 testDatasetFileName = os.path.join("..", "data", "distributed", "linear_regression_test.csv")
 
 nBlocks = 4
 nFeatures           = 10    # Number of features in training and testing data sets
 nDependentVariables = 2     # Number of dependent variables that correspond to each observation
 
 
 def trainModel():
     # Create an algorithm object to build the final ridge regression model on the master node
     masterAlgorithm = training.Distributed(step=step2Master)
 
     for i in range(nBlocks):
         # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
         trainDataSource = FileDataSource(trainDatasetFileNames[i],
                                          DataSource.notAllocateNumericTable,
                                          DataSource.doDictionaryFromContext)
 
         # Create Numeric Tables for training data and variables
         trainData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
         trainDependentVariables = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
         mergedData = MergedNumericTable(trainData, trainDependentVariables)
 
         # Retrieve the data from input file
         trainDataSource.loadDataBlock(mergedData)
 
         # Create an algorithm object to train the ridge regression model based on the local-node data
         localAlgorithm = training.Distributed(step=step1Local)
 
         # Pass a training data set and dependent values to the algorithm
         localAlgorithm.input.set(training.data, trainData)
         localAlgorithm.input.set(training.dependentVariables, trainDependentVariables)
 
         # Train the ridge regression model on the local-node data
         presult = localAlgorithm.compute()
 
         # Set the local ridge regression model as input for the master-node algorithm
         masterAlgorithm.input.add(training.partialModels, presult)
 
 
     # Merge and finalize the ridge regression model on the master node
     masterAlgorithm.compute()
 
     # Retrieve the algorithm results
     trainingResult = masterAlgorithm.finalizeCompute()
 
     printNumericTable(trainingResult.get(training.model).getBeta(), "Ridge Regression coefficients:")
     return trainingResult
 
 
 def testModel(trainingResult):
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     testDataSource = FileDataSource(testDatasetFileName,
                                     DataSource.doAllocateNumericTable,
                                     DataSource.doDictionaryFromContext)
 
     # Create Numeric Tables for testing data and ground truth values
     testData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
     testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
     mergedData = MergedNumericTable(testData, testGroundTruth)
 
     # Load the data from the data file
     testDataSource.loadDataBlock(mergedData)
 
     # Create an algorithm object to predict values of ridge regression
     algorithm = prediction.Batch()
 
     # Pass a testing data set and the trained model to the algorithm
     algorithm.input.setTable(prediction.data, testData)
     algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
 
     # Predict values of ridge regression and retrieve the algorithm results
     predictionResult = algorithm.compute()
 
     printNumericTable(predictionResult.get(prediction.prediction), "Ridge Regression prediction results: (first 10 rows):", 10)
     printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
 
 
 if __name__ == "__main__":
     trainingResult = trainModel()
     testModel(trainingResult)
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)