mse_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: mse_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.
 #===============================================================================
 
 #
 # !  Content:
 # !    Python example of the mean squared error objective function
 # !*****************************************************************************
 
 
 #
 ## <a name="DAAL-EXAMPLE-PY-MSE_BATCH"></a>
 ##  \example mse_dense_batch.py
 #
 
 import os
 import sys
 
 import numpy as np
 
 import daal.algorithms.optimization_solver as optimization_solver
 import daal.algorithms.optimization_solver.mse
 
 from daal.data_management import (
     DataSourceIface, FileDataSource, 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 printNumericTable
 
 datasetFileName = os.path.join('..', 'data', 'batch', 'mse.csv')
 nFeatures = 3
 
 argumentValue = np.array([[-1], [0.1], [0.15], [-0.5]], dtype=np.float64)
 
 if __name__ == "__main__":
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     dataSource = FileDataSource(datasetFileName,
                                 DataSourceIface.notAllocateNumericTable,
                                 DataSourceIface.doDictionaryFromContext)
 
     # Create Numeric Tables for data and values for dependent variable
     data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(data, dependentVariables)
 
     # Retrieve the data from the input file
     dataSource.loadDataBlock(mergedData)
 
     nVectors = data.getNumberOfRows()
 
     # Create the MSE objective function objects to compute the MSE objective function result using the default method
     mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
 
     # Set input objects for the MSE objective function
     mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
     mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
     mseObjectiveFunction.input.set(optimization_solver.mse.argument, HomogenNumericTable(argumentValue))
     mseObjectiveFunction.parameter.resultsToCompute = (
         optimization_solver.objective_function.gradient |
         optimization_solver.objective_function.value |
         optimization_solver.objective_function.hessian
     )
 
     # Compute the MSE objective function result
     # Result class from optimization_solver.objective_function
     res = mseObjectiveFunction.compute()
 
     # Print computed the MSE objective function result
     printNumericTable(res.get(optimization_solver.objective_function.valueIdx),
                       "Value")
     printNumericTable(res.get(optimization_solver.objective_function.gradientIdx),
                       "Gradient")
     printNumericTable(res.get(optimization_solver.objective_function.hessianIdx),
                       "Hessian")
For more complete information about compiler optimizations, see our Optimization Notice.
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