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

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

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