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

impl_als_csr_batch.py

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40 
41 
42 
43 
44 import os
45 import sys
46 
47 import daal.algorithms.implicit_als.prediction.ratings as ratings
48 import daal.algorithms.implicit_als.training as training
49 import daal.algorithms.implicit_als.training.init as init
50 
51 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
52 if utils_folder not in sys.path:
53  sys.path.insert(0, utils_folder)
54 from utils import printNumericTable, createSparseTable
55 
56 DAAL_PREFIX = os.path.join('..', 'data')
57 
58 # Input data set parameters
59 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'implicit_als_csr.csv')
60 
61 # Algorithm parameters
62 nFactors = 2
63 
64 dataTable = None
65 initialModel = None
66 trainingResult = None
67 
68 
69 def initializeModel():
70  global initialModel, dataTable
71 
72  # Read trainDatasetFileName from a file and create a numeric table to store the input data
73  dataTable = createSparseTable(trainDatasetFileName)
74 
75  # Create an algorithm object to initialize the implicit ALS model with the default method
76  initAlgorithm = init.Batch(method=init.fastCSR)
77  initAlgorithm.parameter.nFactors = nFactors
78 
79  # Pass a training data set and dependent values to the algorithm
80  initAlgorithm.input.set(init.data, dataTable)
81 
82  # Initialize the implicit ALS model
83  res = initAlgorithm.compute()
84  # (Result class from implicit_als.training.init)
85  initialModel = res.get(init.model)
86 
87 
88 def trainModel():
89  global trainingResult
90 
91  # Create an algorithm object to train the implicit ALS model with the default method
92  algorithm = training.Batch(method=training.fastCSR)
93 
94  # Pass a training data set and dependent values to the algorithm
95  algorithm.input.setTable(training.data, dataTable)
96  algorithm.input.setModel(training.inputModel, initialModel)
97 
98  algorithm.parameter.nFactors = nFactors
99 
100  # Build the implicit ALS model
101  # Retrieve the algorithm results
102  trainingResult = algorithm.compute()
103 
104 
105 def testModel():
106 
107  # Create an algorithm object to predict recommendations of the implicit ALS model
108  algorithm = ratings.Batch()
109  algorithm.parameter.nFactors = nFactors
110 
111  algorithm.input.set(ratings.model, trainingResult.get(training.model))
112 
113  res = algorithm.compute()
114 
115  predictedRatings = res.get(ratings.prediction)
116 
117  printNumericTable(predictedRatings, "Predicted ratings:")
118 
119 if __name__ == "__main__":
120 
121  initializeModel()
122  trainModel()
123  testModel()

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