Java tutorial
/** * DataCleaner (community edition) * Copyright (C) 2014 Free Software Foundation, Inc. * * This copyrighted material is made available to anyone wishing to use, modify, * copy, or redistribute it subject to the terms and conditions of the GNU * Lesser General Public License, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License * for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this distribution; if not, write to: * Free Software Foundation, Inc. * 51 Franklin Street, Fifth Floor * Boston, MA 02110-1301 USA */ package org.datacleaner.components.machinelearning; import java.io.IOException; import java.io.UncheckedIOException; import java.util.ArrayList; import java.util.Collection; import java.util.List; import java.util.concurrent.ConcurrentLinkedQueue; import java.util.concurrent.atomic.AtomicInteger; import java.util.stream.Collectors; import org.apache.commons.lang.SerializationUtils; import org.apache.metamodel.util.CollectionUtils; import org.apache.metamodel.util.HasNameMapper; import org.datacleaner.api.Categorized; import org.datacleaner.api.Configured; import org.datacleaner.api.Description; import org.datacleaner.api.Initialize; import org.datacleaner.api.InputColumn; import org.datacleaner.api.InputRow; import org.datacleaner.api.NumberProperty; import org.datacleaner.components.machinelearning.api.MLFeatureModifier; import org.datacleaner.components.machinelearning.api.MLFeatureModifierBuilder; import org.datacleaner.components.machinelearning.api.MLFeatureModifierBuilderFactory; import org.datacleaner.components.machinelearning.api.MLFeatureModifierType; import org.datacleaner.components.machinelearning.api.MLRegressionRecord; import org.datacleaner.components.machinelearning.api.MLRegressor; import org.datacleaner.components.machinelearning.api.MLRegressorTrainer; import org.datacleaner.components.machinelearning.api.MLTrainerCallback; import org.datacleaner.components.machinelearning.api.MLTrainingConstraints; import org.datacleaner.components.machinelearning.api.MLTrainingOptions; import org.datacleaner.components.machinelearning.impl.MLFeatureModifierBuilderFactoryImpl; import org.datacleaner.components.machinelearning.impl.MLFeatureUtils; import org.datacleaner.components.machinelearning.impl.MLRegressionRecordImpl; import org.datacleaner.util.Percentage; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.google.common.io.Files; @Categorized(MachineLearningCategory.class) public abstract class MLRegressionTrainingAnalyzer extends MLTrainingAnalyzer<MLRegressionAnalyzerResult> { private static final Logger logger = LoggerFactory.getLogger(MLRegressionTrainingAnalyzer.class); private static final MLFeatureModifierBuilderFactory featureModifierBuilderFactory = new MLFeatureModifierBuilderFactoryImpl(); @Configured InputColumn<Number> regressionOutput; @Configured @Description("Determine how much (if any) of the records should be used for cross-validation.") @NumberProperty(negative = false) Percentage crossValidationSampleRate = new Percentage(10); private AtomicInteger recordCounter; private Collection<MLRegressionRecord> trainingRecords; private Collection<MLRegressionRecord> crossValidationRecords; private List<MLFeatureModifierBuilder> featureModifierBuilders; @Initialize public void init() { recordCounter = new AtomicInteger(); trainingRecords = new ConcurrentLinkedQueue<>(); crossValidationRecords = new ConcurrentLinkedQueue<>(); featureModifierBuilders = new ArrayList<>(featureModifierTypes.length); final int maxFeatures = maxFeaturesGeneratedPerColumn == null ? -1 : maxFeaturesGeneratedPerColumn; final MLTrainingConstraints constraints = new MLTrainingConstraints(maxFeatures, includeUniqueValueFeatures); for (MLFeatureModifierType featureModifierType : featureModifierTypes) { final MLFeatureModifierBuilder featureModifierBuilder = featureModifierBuilderFactory .create(featureModifierType, constraints); featureModifierBuilders.add(featureModifierBuilder); } } @Override public void run(InputRow row, int distinctCount) { final MLRegressionRecord record = MLRegressionRecordImpl.forTraining(row, regressionOutput, featureColumns); if (record == null) { return; } final Object[] recordValues = record.getRecordValues(); for (int i = 0; i < recordValues.length; i++) { final MLFeatureModifierBuilder featureModifierBuilder = featureModifierBuilders.get(i); featureModifierBuilder.addRecordValue(recordValues[i]); } final int recordNumber = recordCounter.incrementAndGet(); if (recordNumber % 100 > crossValidationSampleRate.getNominator()) { trainingRecords.add(record); } else { crossValidationRecords.add(record); } } @Override public MLRegressionAnalyzerResult getResult() { final List<MLFeatureModifier> featureModifiers = featureModifierBuilders.stream() .map(MLFeatureModifierBuilder::build).collect(Collectors.toList()); final List<String> columnNames = CollectionUtils.map(featureColumns, new HasNameMapper()); final MLTrainingOptions options = new MLTrainingOptions(Double.class, columnNames, featureModifiers); final MLRegressorTrainer trainer = createTrainer(options); log("Training model starting. Records=" + trainingRecords.size() + ", Columns=" + columnNames.size() + ", Features=" + MLFeatureUtils.getFeatureCount(featureModifiers) + "."); final MLRegressor regressor = trainer.train(trainingRecords, featureModifiers, new MLTrainerCallback() { @Override public void epochDone(int epochNo, int expectedEpochs) { if (expectedEpochs > 1) { log("Training progress: Epoch " + epochNo + " of " + expectedEpochs + " done."); } } }); if (saveModelToFile != null) { logger.info("Saving model to file: {}", saveModelToFile); try { final byte[] bytes = SerializationUtils.serialize(regressor); Files.write(bytes, saveModelToFile); } catch (IOException e) { throw new UncheckedIOException("Failed to save model to file: " + saveModelToFile, e); } } log("Trained model. Creating evaluation matrices."); return new MLRegressionAnalyzerResult(regressor); } protected abstract MLRegressorTrainer createTrainer(MLTrainingOptions options); }