lbfgs_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: lbfgs_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 limited memory Broyden-Fletcher-Goldfarb-Shanno
 # !    algorithm
 # !*****************************************************************************
 
 #
 ## <a name="DAAL-EXAMPLE-PY-LBFGS_BATCH"></a>
 ##  \example lbfgs_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
 import daal.algorithms.optimization_solver.lbfgs
 import daal.algorithms.optimization_solver.iterative_solver
 
 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', 'lbfgs.csv')
 
 nFeatures = 10
 nIterations = 1000
 stepLength = 1.0e-4
 
 initialPoint = np.array([[100], [100], [100], [100], [100], [100], [100], [100], [100], [100], [100]], dtype=np.float64)
 expectedPoint = np.array([[11], [  1], [  2], [  3], [  4], [  5], [  6], [  7], [  8], [  9], [ 10]], 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 input data and dependent variables
     data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(data, dependentVariables)
 
     # Retrieve the data from input file
     dataSource.loadDataBlock(mergedData)
 
     mseObjectiveFunction = optimization_solver.mse.Batch(data.getNumberOfRows())
     mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
     mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
 
     # Create objects to compute LBFGS result using the default method
     algorithm = optimization_solver.lbfgs.Batch(mseObjectiveFunction)
     algorithm.parameter.nIterations = nIterations
     algorithm.parameter.stepLengthSequence = HomogenNumericTable(1, 1, NumericTableIface.doAllocate, stepLength)
 
     # Set input objects for LBFGS algorithm
     algorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, HomogenNumericTable(initialPoint))
 
     # Compute LBFGS result
     # Result class from daal.algorithms.optimization_solver.iterative_solver
     res = algorithm.compute()
 
     expectedCoefficients = HomogenNumericTable(expectedPoint)
 
     # Print computed LBFGS results
     printNumericTable(expectedCoefficients, "Expected coefficients:")
     printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Resulting coefficients:")
     printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)