Classification example in datumbox - Java Machine Learning AI

Description

Classification example in datumbox
Demo Code

/**//from  w  w  w . j a  v  a2  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.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.classification.SoftMaxRegression;
import com.datumbox.framework.machinelearning.datatransformation.XMinMaxNormalizer;
import com.datumbox.framework.machinelearning.featureselection.continuous.PCA;

import java.io.*;
import java.net.URISyntaxException;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.Map;
import java.util.zip.GZIPInputStream;

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

    /**
     * Example of how to use directly the algorithms of the framework in order to
     * perform classification. A similar approach can be used to perform clustering,
     * regression, build recommender system or perform topic modeling and dimensionality
     * reduction.
     * 
     * @param args the command line arguments
     * @throws FileNotFoundException
     * @throws URISyntaxException
     */
    public static void main(String[] args) throws FileNotFoundException,
            URISyntaxException, IOException {
        /**
         * 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 BufferedReader(
                new InputStreamReader(
                        new GZIPInputStream(
                                new FileInputStream(
                                        Paths.get(
                                                Classification.class
                                                        .getClassLoader()
                                                        .getResource(
                                                                "datasets/diabetes/diabetes.tsv.gz")
                                                        .toURI()).toFile()))));

        Map<String, TypeInference.DataType> headerDataTypes = new HashMap<>();
        headerDataTypes
                .put("pregnancies", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("plasma glucose",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("blood pressure",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("triceps thickness",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("serum insulin",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("bmi", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("dpf", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("age", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("test result",
                TypeInference.DataType.CATEGORICAL);

        Dataset trainingDataset = Dataset.Builder.parseCSVFile(fileReader,
                "test result", headerDataTypes, '\t', '"', "\r\n", dbConf);
        Dataset testingDataset = trainingDataset.copy();

        //Transform Dataset
        //-----------------

        //Normalize continuous variables
        XMinMaxNormalizer dataTransformer = new XMinMaxNormalizer(
                "Diabetes", dbConf);
        dataTransformer.fit_transform(trainingDataset,
                new XMinMaxNormalizer.TrainingParameters());

        //Feature Selection
        //-----------------

        //Perform dimensionality reduction using PCA

        PCA featureSelection = new PCA("Diabetes", dbConf);
        PCA.TrainingParameters featureSelectionParameters = new PCA.TrainingParameters();
        featureSelectionParameters.setMaxDimensions(trainingDataset
                .getVariableNumber() - 1); //remove one dimension
        featureSelectionParameters.setWhitened(false);
        featureSelectionParameters
                .setVariancePercentageThreshold(0.99999995);
        featureSelection.fit_transform(trainingDataset,
                featureSelectionParameters);

        //Fit the classifier
        //------------------

        SoftMaxRegression classifier = new SoftMaxRegression("Diabetes",
                dbConf);

        SoftMaxRegression.TrainingParameters param = new SoftMaxRegression.TrainingParameters();
        param.setTotalIterations(200);
        param.setLearningRate(0.1);

        classifier.fit(trainingDataset, param);

        //Denormalize trainingDataset (optional)
        dataTransformer.denormalize(trainingDataset);

        //Use the classifier
        //------------------

        //Apply the same data transformations on testingDataset 
        dataTransformer.transform(testingDataset);

        //Apply the same featureSelection transformations on testingDataset
        featureSelection.transform(testingDataset);

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

        //Denormalize testingDataset (optional)
        dataTransformer.denormalize(testingDataset);

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

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

        //Erase data transformer, featureselector and classifier.
        dataTransformer.erase();
        featureSelection.erase();
        classifier.erase();

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

}
Classification example in datumbox - Java Machine Learning AI

Description

Demo Code

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