DataSourceFeatureExtraction.java

/* file: DataSourceFeatureExtraction.java */
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/*
 //  Content:
 //    Java example for using of data source feature extraction
 */

package com.intel.daal.examples.datasource;

import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.*;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;

class DataSourceFeatureExtraction {

    /* Input data set parameters */
    private static final String dataset = "../data/batch/kmeans_dense.csv";

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {

        /* Retrieve the input data */
        FileDataSource dataSource = new FileDataSource(context, dataset,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);

        /* Filter in 3 chosen columns from a .csv file */
        dataSource.getFeatureManager().addModifier(new ColumnFilter(context).list(new long[]{1, 2, 5}));

        /* Consider column with index 1 as categorical and convert it into 3 binary categorical features */
        dataSource.getFeatureManager().addModifier(new OneHotEncoder(context, 1, 3));

        /* Load data from .csv file */
        dataSource.loadDataBlock();
        NumericTable table = dataSource.getNumericTable();

        /* Print result */
        Service.printNumericTable("Loaded data", table, 4, 20);

        context.dispose();
    }
}
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