Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

impl_als_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.

1 # file: impl_als_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 ## <a name="DAAL-EXAMPLE-PY-IMPLICIT_ALS_DENSE_BATCH"></a>
17 ## \example impl_als_dense_batch.py
18 
19 import os
20 import sys
21 
22 import daal.algorithms.implicit_als.training.init
23 import daal.algorithms.implicit_als.prediction.ratings
24 from daal.algorithms.implicit_als import training, prediction
25 from daal.data_management import FileDataSource, DataSourceIface
26 
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder not in sys.path:
29  sys.path.insert(0, utils_folder)
30 from utils import printNumericTable
31 
32 DAAL_PREFIX = os.path.join('..', 'data')
33 
34 # Input data set parameters
35 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'implicit_als_dense.csv')
36 
37 # Algorithm parameters
38 nFactors = 2
39 
40 dataTable = None
41 initialModel = None
42 trainingResult = None
43 
44 
45 def initializeModel():
46  global dataTable, initialModel
47 
48  # Read trainDatasetFileName from a file and create a numeric table to store the input data
49  dataSource = FileDataSource(
50  trainDatasetFileName, DataSourceIface.doAllocateNumericTable,
51  DataSourceIface.doDictionaryFromContext
52  )
53 
54  # Retrieve the input data
55  dataSource.loadDataBlock()
56 
57  dataTable = dataSource.getNumericTable()
58  # Create an algorithm object to initialize the implicit ALS model with the default method
59  initAlgorithm = training.init.Batch()
60  initAlgorithm.parameter.nFactors = nFactors
61 
62  # Pass a training data set and dependent values to the algorithm
63  initAlgorithm.input.set(training.init.data, dataTable)
64  res = initAlgorithm.compute()
65 
66  # Initialize the implicit ALS model
67  initialModel = res.get(training.init.model)
68 
69 
70 def trainModel():
71  global trainingResult
72 
73  # Create an algorithm object to train the implicit ALS model with the default method
74  algorithm = training.Batch()
75 
76  # Pass a training data set and dependent values to the algorithm
77  algorithm.input.setTable(training.data, dataTable)
78  algorithm.input.setModel(training.inputModel, initialModel)
79 
80  algorithm.parameter.nFactors = nFactors
81 
82  # Build the implicit ALS model and retrieve the algorithm results
83  trainingResult = algorithm.compute()
84 
85 
86 def testModel():
87 
88  # Create an algorithm object to predict recommendations of the implicit ALS model
89  algorithm = prediction.ratings.Batch()
90  algorithm.parameter.nFactors = nFactors
91 
92  algorithm.input.set(prediction.ratings.model, trainingResult.get(training.model))
93 
94  res = algorithm.compute()
95  predictedRatings = res.get(prediction.ratings.prediction)
96 
97  printNumericTable(predictedRatings, "Predicted ratings:")
98 
99 if __name__ == "__main__":
100 
101  initializeModel()
102  trainModel()
103  testModel()

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