Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

adagrad_opt_res_dense_batch.py

1 # file: adagrad_opt_res_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of the Adagrad algorithm
45 # !*****************************************************************************
46 
47 #
48 ## <a name="DAAL-EXAMPLE-PY-ADAGRAD_OPT_RES_DENSE_BATCH"></a>
49 ## \example adagrad_opt_res_dense_batch.py
50 #
51 
52 import os
53 import sys
54 
55 import numpy as np
56 
57 import daal.algorithms.optimization_solver as optimization_solver
58 import daal.algorithms.optimization_solver.mse
59 import daal.algorithms.optimization_solver.adagrad
60 import daal.algorithms.optimization_solver.iterative_solver
61 from daal.data_management import (
62  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
63 )
64 
65 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
66 if utils_folder not in sys.path:
67  sys.path.insert(0, utils_folder)
68 from utils import printNumericTable
69 
70 datasetFileName = os.path.join('..', 'data', 'batch', 'mse.csv')
71 
72 nFeatures = 3
73 accuracyThreshold = 0.0000001
74 halfNIterations = 500
75 nIterations = halfNIterations * 2
76 batchSize = 1
77 learningRate = 1.0
78 
79 startPoint = np.array([[8], [2], [1], [4]], dtype=np.float64)
80 
81 if __name__ == "__main__":
82 
83  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
84  dataSource = FileDataSource(datasetFileName,
85  DataSourceIface.notAllocateNumericTable,
86  DataSourceIface.doDictionaryFromContext)
87 
88  # Create Numeric Tables for data and values for dependent variable
89  data = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
90  dependentVariables = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
91  mergedData = MergedNumericTable(data, dependentVariables)
92 
93  # Retrieve the data from the input file
94  dataSource.loadDataBlock(mergedData)
95 
96  nVectors = data.getNumberOfRows()
97 
98  mseObjectiveFunction = optimization_solver.mse.Batch(nVectors)
99  mseObjectiveFunction.input.set(optimization_solver.mse.data, data)
100  mseObjectiveFunction.input.set(optimization_solver.mse.dependentVariables, dependentVariables)
101 
102  # Create objects to compute the Adagrad result using the default method
103  adagradAlgorithm = optimization_solver.adagrad.Batch(mseObjectiveFunction)
104 
105  # Set input objects for the the Adagrad algorithm
106  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, HomogenNumericTable(startPoint))
107  adagradAlgorithm.parameter.learningRate = HomogenNumericTable(1, 1, NumericTableIface.doAllocate, learningRate)
108  adagradAlgorithm.parameter.nIterations = halfNIterations
109  adagradAlgorithm.parameter.accuracyThreshold = accuracyThreshold
110  adagradAlgorithm.parameter.batchSize = batchSize
111  adagradAlgorithm.parameter.optionalResultRequired = True
112 
113  # Compute the Adagrad result
114  # Result class from daal.algorithms.optimization_solver.iterative_solver
115  res = adagradAlgorithm.compute()
116 
117  # Print computed the Adagrad result
118  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after first compute():")
119  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")
120 
121  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.inputArgument, res.getResult(optimization_solver.iterative_solver.minimum))
122  adagradAlgorithm.input.setInput(optimization_solver.iterative_solver.optionalArgument, res.getResult(optimization_solver.iterative_solver.optionalResult))
123 
124  res = adagradAlgorithm.compute()
125 
126  printNumericTable(res.getResult(optimization_solver.iterative_solver.minimum), "Minimum after second compute():")
127  printNumericTable(res.getResult(optimization_solver.iterative_solver.nIterations), "Number of iterations performed:")

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