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.

 # file:
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 # !  Content:
 # !    Python example of modifiers usage with file data source
 # !*****************************************************************************
 ## \example
 from daal.data_management import FileDataSource, CsvDataSourceOptions, modifiers
 from daal.data_management.modifiers.csv import FeatureModifier
 import os,  sys
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTable
 # User-defined feature modifier that computes a square for every feature
 class MySquaringModifier(FeatureModifier):
     def apply(self, tokens):
         return [[float(x)*float(x) for x in t] for t in tokens]
 # User-defined feature modifier that selects max element among all features
 class MyMaxFeatureModifier(FeatureModifier):
     def __init__(self):
         super(MyMaxFeatureModifier, self).__init__(1,4)
     # This method is called for every row in CSV file
     def apply(self, tokens):
             return [[float(max(t))] for t in tokens]
 if __name__ == "__main__":
     # Path to the CSV to be read
     csvFileName = "../data/batch/mixed_text_and_numbers.csv"
     # Define options for CSV data source
     csvOptions = CsvDataSourceOptions(CsvDataSourceOptions.allocateNumericTable | CsvDataSourceOptions.createDictionaryFromContext | CsvDataSourceOptions.parseHeader)
     # Define CSV file data source
     ds = FileDataSource(csvFileName, csvOptions)
     # Configure format of output numeric table by applying modifiers.
     # Output numeric table will have the following format:
     # | Numeric1 | Numeric2 ^ 2 | Numeric5 ^ 2 | max(Numeric0, Numeric5) |
     fm = ds.getFeatureManager()
     fm.addModifier(["Numeric1"], modifiers.csv.continuous())
     fm.addModifier(["Numeric2", "Numeric5"], MySquaringModifier())
     fm.addModifier(["Numeric0", "Numeric5"], MyMaxFeatureModifier())
     # Load and parse CSV file
     printNumericTable(ds.getNumericTable(), "Loaded numeric table:")
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