Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

mse_dense_batch.py

1 # file: mse_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 #
17 # ! Content:
18 # ! Python example of the mean squared error objective function
19 # !*****************************************************************************
20 
21 
22 #
23 
24 
25 #
26 
27 import os
28 import sys
29 
30 import numpy as np
31 
32 import daal.algorithms.optimization_solver as optimization_solver
33 import daal.algorithms.optimization_solver.mse
34 
35 from daal.data_management import (
36  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
37 )
38 
39 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
40 if utils_folder not in sys.path:
41  sys.path.insert(0, utils_folder)
42 from utils import printNumericTable
43 
44 datasetFileName = os.path.join('..', 'data', 'batch', 'mse.csv')
45 nFeatures = 3
46 
47 argumentValue = np.array([[-1], [0.1], [0.15], [-0.5]], dtype=np.float64)
48 
49 if __name__ == "__main__":
50 
51  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
52  dataSource = FileDataSource(datasetFileName,
53  DataSourceIface.notAllocateNumericTable,
54  DataSourceIface.doDictionaryFromContext)
55 
56  # Create Numeric Tables for data and values for dependent variable
57  data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
58  dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
59  mergedData = MergedNumericTable(data, dependentVariables)
60 
61  # Retrieve the data from the input file
62  dataSource.loadDataBlock(mergedData)
63 
64  nVectors = data.getNumberOfRows()
65 
66  # Create the MSE objective function objects to compute the MSE objective function result using the default method
67  mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
68 
69  # Set input objects for the MSE objective function
70  mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
71  mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
72  mseObjectiveFunction.input.set(optimization_solver.mse.argument, HomogenNumericTable(argumentValue))
73  mseObjectiveFunction.parameter().resultsToCompute = (
74  optimization_solver.objective_function.gradient |
75  optimization_solver.objective_function.value |
76  optimization_solver.objective_function.hessian
77  )
78 
79  # Compute the MSE objective function result
80  # Result class from optimization_solver.objective_function
81  res = mseObjectiveFunction.compute()
82 
83  # Print computed the MSE objective function result
84  printNumericTable(res.get(optimization_solver.objective_function.valueIdx),
85  "Value")
86  printNumericTable(res.get(optimization_solver.objective_function.gradientIdx),
87  "Gradient")
88  printNumericTable(res.get(optimization_solver.objective_function.hessianIdx),
89  "Hessian")

For more complete information about compiler optimizations, see our Optimization Notice.