DataModeling example via datumbox - Java Machine Learning AI

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Description

DataModeling example via datumbox

Demo Code

/**//from   w  w  w  .j av  a  2  s.  c  om
 * Copyright (C) 2013-2015 Vasilis Vryniotis <bbriniotis@datumbox.com>
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

import com.datumbox.applications.datamodeling.Modeler;
import com.datumbox.common.dataobjects.Dataset;
import com.datumbox.common.dataobjects.Record;
import com.datumbox.common.dataobjects.TypeInference;
import com.datumbox.common.persistentstorage.ConfigurationFactory;
import com.datumbox.common.persistentstorage.interfaces.DatabaseConfiguration;
import com.datumbox.common.utilities.PHPfunctions;
import com.datumbox.common.utilities.RandomGenerator;
import com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel;
import com.datumbox.framework.machinelearning.datatransformation.DummyXYMinMaxNormalizer;
import com.datumbox.framework.machinelearning.regression.NLMS;

import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.Reader;
import java.net.URISyntaxException;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.Map;

/**
 * DataModeling example.
 * 
 * @author Vasilis Vryniotis <bbriniotis@datumbox.com>
 */
public class DataModeling {

    /**
     * Example of how to use the Modeler class.
     * 
     * @param args the command line arguments
     * @throws FileNotFoundException
     * @throws URISyntaxException
     */
    public static void main(String[] args) throws FileNotFoundException,
            URISyntaxException {
        /**
         * There are two configuration files in the resources folder:
         * 
         * - datumbox.config.properties: It contains the configuration for the storage engines (required)
         * - logback.xml: It contains the configuration file for the logger (optional)
         */

        //Initialization
        //--------------
        RandomGenerator.setGlobalSeed(42L); //optionally set a specific seed for all Random objects
        DatabaseConfiguration dbConf = ConfigurationFactory.INMEMORY
                .getConfiguration(); //in-memory maps
        //DatabaseConfiguration dbConf = ConfigurationFactory.MAPDB.getConfiguration(); //mapdb maps

        //Reading Data
        //------------
        Reader fileReader = new FileReader(Paths.get(
                DataModeling.class
                        .getClassLoader()
                        .getResource(
                                "datasets/labor-statistics/longley.csv")
                        .toURI()).toFile());

        Map<String, TypeInference.DataType> headerDataTypes = new HashMap<>();
        headerDataTypes.put("Employed", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("GNP.deflator",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("GNP", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Unemployed", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Armed.Forces",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Population", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Year", TypeInference.DataType.NUMERICAL);
        Dataset trainingDataset = Dataset.Builder.parseCSVFile(fileReader,
                "Employed", headerDataTypes, ',', '"', "\r\n", dbConf);
        Dataset testingDataset = trainingDataset.copy();

        //Setup Training Parameters
        //-------------------------
        Modeler.TrainingParameters trainingParameters = new Modeler.TrainingParameters();

        //Model Configuration
        trainingParameters.setMLmodelClass(NLMS.class);
        trainingParameters
                .setMLmodelTrainingParameters(new NLMS.TrainingParameters());

        //Set data transfomation configuration
        trainingParameters
                .setDataTransformerClass(DummyXYMinMaxNormalizer.class);
        trainingParameters
                .setDataTransformerTrainingParameters(new DummyXYMinMaxNormalizer.TrainingParameters());

        //Set feature selection configuration
        trainingParameters.setFeatureSelectionClass(null);
        trainingParameters.setFeatureSelectionTrainingParameters(null);

        //Fit the modeler
        //---------------
        Modeler modeler = new Modeler("LaborStatistics", dbConf);
        modeler.fit(trainingDataset, trainingParameters);

        //Use the modeler
        //---------------

        //Get validation metrics on the training set
        BaseMLmodel.ValidationMetrics vm = modeler
                .validate(trainingDataset);
        modeler.setValidationMetrics(vm); //store them in the model for future reference

        //Predict a new dataset
        modeler.predict(testingDataset);

        System.out.println("Test Results:");
        for (Integer rId : testingDataset) {
            Record r = testingDataset.get(rId);
            System.out.println("Record " + rId + " - Real Y: " + r.getY()
                    + ", Predicted Y: " + r.getYPredicted());
        }

        System.out.println("Modeler Statistics: "
                + PHPfunctions.var_export(vm));

        //Clean up
        //--------

        //Erase the modeler. This removes all files.
        modeler.erase();

        //Erase datasets.
        trainingDataset.erase();
        testingDataset.erase();
    }

}

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