Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

adagrad_opt_res_dense_batch.py

1 # file: adagrad_opt_res_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 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 Adagrad algorithm
19 # !*****************************************************************************
20 
21 #
22 ## <a name="DAAL-EXAMPLE-PY-ADAGRAD_OPT_RES_DENSE_BATCH"></a>
23 ## \example adagrad_opt_res_dense_batch.py
24 #
25 
26 import os
27 import sys
28 
29 import numpy as np
30 
31 import daal.algorithms.optimization_solver as optimization_solver
32 import daal.algorithms.optimization_solver.mse
33 import daal.algorithms.optimization_solver.adagrad
34 import daal.algorithms.optimization_solver.iterative_solver
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 
46 nFeatures = 3
47 accuracyThreshold = 0.0000001
48 halfNIterations = 500
49 nIterations = halfNIterations * 2
50 batchSize = 1
51 learningRate = 1.0
52 
53 startPoint = np.array([[8], [2], [1], [4]], dtype=np.float64)
54 
55 if __name__ == "__main__":
56 
57  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
58  dataSource = FileDataSource(datasetFileName,
59  DataSourceIface.notAllocateNumericTable,
60  DataSourceIface.doDictionaryFromContext)
61 
62  # Create Numeric Tables for data and values for dependent variable
63  data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
64  dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
65  mergedData = MergedNumericTable(data, dependentVariables)
66 
67  # Retrieve the data from the input file
68  dataSource.loadDataBlock(mergedData)
69 
70  nVectors = data.getNumberOfRows()
71 
72  mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
73  mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
74  mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
75 
76  # Create objects to compute the Adagrad result using the default method
77  adagradAlgorithm = optimization_solver.adagrad.Batch(mseObjectiveFunction)
78 
79  # Set input objects for the the Adagrad algorithm
80  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, HomogenNumericTable(startPoint))
81  adagradAlgorithm.parameter.learningRate = HomogenNumericTable(1, 1, NumericTableIface.doAllocate, learningRate)
82  adagradAlgorithm.parameter.nIterations = halfNIterations
83  adagradAlgorithm.parameter.accuracyThreshold = accuracyThreshold
84  adagradAlgorithm.parameter.batchSize = batchSize
85  adagradAlgorithm.parameter.optionalResultRequired = True
86 
87  # Compute the Adagrad result
88  # Result class from daal.algorithms.optimization_solver.iterative_solver
89  res = adagradAlgorithm.compute()
90 
91  # Print computed the Adagrad result
92  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after first compute():")
93  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")
94 
95  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, res.getResult(optimization_solver.iterative_solver.minimum))
96  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.optionalArgument, res.getResult(optimization_solver.iterative_solver.optionalResult))
97 
98  res = adagradAlgorithm.compute()
99 
100  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after second compute():")
101  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")

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