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 for using of data source feature extraction
 # !*****************************************************************************
 ## \example
 import os
 import sys
 from daal.data_management import FileDataSource, DataSourceIface, ColumnFilter, OneHotEncoder
 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
 # Input data set parameters
 datasetFileName = "../data/batch/kmeans_dense.csv"
 if __name__ == "__main__":
     # Initialize FileDataSource to retrieve the input data from a .csv file
     dataSource = FileDataSource(datasetFileName, DataSourceIface.doAllocateNumericTable)
     # Create data source dictionary from loading of the first .csv file
     # Filter in 3 chosen columns from a .csv file
     validList = [1, 2, 5]
     colFilter = ColumnFilter()
     filterList = colFilter.list(validList)
     # Consider column with index 1 as categorical and convert it into 3 binary categorical features
     dataSource.getFeatureManager().addModifier(OneHotEncoder(1, 3))
     # Load data from .csv file
     # Print result
     table = dataSource.getNumericTable()
     printNumericTable(table, "Loaded data", 4, 20)
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