Clustering example in datumbox - Java Machine Learning AI

Description

Clustering example in datumbox
Demo Code

/**/*  w  w w  .j  a  v a 2s  .  c o  m*/
 * 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.clustering.Kmeans;
import com.datumbox.framework.machinelearning.datatransformation.DummyXMinMaxNormalizer;

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;

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

    /**
     * Example of how to use directly the algorithms of the framework in order to
     * perform clustering. A similar approach can be used to perform classification,
     * 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 {
        /**
         * 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(
                Clustering.class.getClassLoader()
                        .getResource("datasets/heart-desease/heart.csv")
                        .toURI()).toFile());

        Map<String, TypeInference.DataType> headerDataTypes = new HashMap<>();
        headerDataTypes.put("Age", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Sex", TypeInference.DataType.CATEGORICAL);
        headerDataTypes
                .put("ChestPain", TypeInference.DataType.CATEGORICAL);
        headerDataTypes.put("RestBP", TypeInference.DataType.NUMERICAL);
        headerDataTypes
                .put("Cholesterol", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("BloodSugar", TypeInference.DataType.BOOLEAN);
        headerDataTypes.put("ECG", TypeInference.DataType.CATEGORICAL);
        headerDataTypes.put("MaxHeartRate",
                TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Angina", TypeInference.DataType.BOOLEAN);
        headerDataTypes.put("OldPeak", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("STSlope", TypeInference.DataType.ORDINAL);
        headerDataTypes.put("Vessels", TypeInference.DataType.NUMERICAL);
        headerDataTypes.put("Thal", TypeInference.DataType.CATEGORICAL);
        headerDataTypes.put("Class", TypeInference.DataType.CATEGORICAL);
        Dataset trainingDataset = Dataset.Builder.parseCSVFile(fileReader,
                "Class", headerDataTypes, ',', '"', "\r\n", dbConf);
        Dataset testingDataset = trainingDataset.copy();

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

        //Convert Categorical variables to dummy variables (boolean) and normalize continuous variables
        DummyXMinMaxNormalizer dataTransformer = new DummyXMinMaxNormalizer(
                "HeartDesease", dbConf);
        dataTransformer.fit_transform(trainingDataset,
                new DummyXMinMaxNormalizer.TrainingParameters());

        //Fit the clusterer
        //-----------------

        Kmeans clusterer = new Kmeans("HeartDesease", dbConf);

        Kmeans.TrainingParameters param = new Kmeans.TrainingParameters();
        param.setK(2);
        param.setMaxIterations(200);
        param.setInitializationMethod(Kmeans.TrainingParameters.Initialization.FORGY);
        param.setDistanceMethod(Kmeans.TrainingParameters.Distance.EUCLIDIAN);
        param.setWeighted(false);
        param.setCategoricalGamaMultiplier(1.0);
        param.setSubsetFurthestFirstcValue(2.0);

        clusterer.fit(trainingDataset, param);

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

        System.out.println("Cluster assignments (Record Ids):");
        for (Map.Entry<Integer, Kmeans.Cluster> entry : clusterer
                .getClusters().entrySet()) {
            Integer clusterId = entry.getKey();
            Kmeans.Cluster cl = entry.getValue();

            System.out.println("Cluster " + clusterId + ": "
                    + cl.getRecordIdSet());
        }

        //Use the clusterer
        //-----------------

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

        //Get validation metrics on the training set
        Kmeans.ValidationMetrics vm = clusterer.validate(testingDataset);
        clusterer.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 + " - Original Y: "
                    + r.getY() + ", Predicted Cluster Id: "
                    + r.getYPredicted());
        }

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

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

        //Erase data transformer, clusterer.
        dataTransformer.erase();
        clusterer.erase();

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

}
Clustering example in datumbox - Java Machine Learning AI

Description

Demo Code

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