这是indexloc提供的服务，不要输入任何密码
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Merged
merged 1 commit into from
May 27, 2025
Merged

https://jira.baeldung.com/browse/BAEL-9295 #18565

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31 changes: 31 additions & 0 deletions libraries-ai/pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,18 @@
<modelVersion>4.0.0</modelVersion>
<artifactId>libraries-ai</artifactId>
<name>libraries-ai</name>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>21</source>
<target>21</target>
</configuration>
</plugin>
</plugins>
</build>

<parent>
<groupId>com.baeldung</groupId>
Expand Down Expand Up @@ -90,6 +102,22 @@
<artifactId>openai-java</artifactId>
<version>${openai.version}</version>
</dependency>
<dependency>
<groupId>org.tribuo</groupId>
<artifactId>tribuo-all</artifactId>
<version>${tribuo-all.version}</version>
<type>pom</type>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${common-lang3.version}</version>
</dependency>
<dependency>
<groupId>com.opencsv</groupId>
<artifactId>opencsv</artifactId>
<version>${opencsv.version}</version>
</dependency>
</dependencies>

<properties>
Expand All @@ -99,6 +127,9 @@
<theokanning.gpt>0.18.2</theokanning.gpt>
<h2o-genmodel.version>3.46.0.6</h2o-genmodel.version>
<openai.version>0.22.0</openai.version>
<tribuo-all.version>4.3.2</tribuo-all.version>
<common-lang3.version>3.17.0</common-lang3.version>
<opencsv.version>5.11</opencsv.version>
</properties>

</project>
47 changes: 47 additions & 0 deletions libraries-ai/src/main/java/com/baeldung/tribuo/WineQualityPredictor.java
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package com.baeldung.tribuo;

import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.ObjectInputStream;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.tribuo.Model;
import org.tribuo.Prediction;
import org.tribuo.impl.ArrayExample;
import org.tribuo.regression.Regressor;

public class WineQualityPredictor {

private static final Logger log = LoggerFactory.getLogger(WineQualityPredictor.class);

public static void main(String[] args) throws IOException, ClassNotFoundException {
File modelFile = new File("src/main/resources/model/winequality-red-regressor.ser");
Model<Regressor> loadedModel = null;

try (ObjectInputStream objectInputStream = new ObjectInputStream(new FileInputStream(modelFile))) {
loadedModel = (Model<Regressor>) objectInputStream.readObject();
}

ArrayExample<Regressor> wineAttribute = new ArrayExample<Regressor>(new Regressor("quality", Double.NaN));
wineAttribute.add("fixed acidity", 7.4f);
wineAttribute.add("volatile acidity", 0.7f);
wineAttribute.add("citric acid", 0.47f);
wineAttribute.add("residual sugar", 1.9f);
wineAttribute.add("chlorides", 0.076f);
wineAttribute.add("free sulfur dioxide", 11.0f);
wineAttribute.add("total sulfur dioxide", 34.0f);
wineAttribute.add("density", 0.9978f);
wineAttribute.add("pH", 3.51f);
wineAttribute.add("sulphates", 0.56f);
wineAttribute.add("alcohol", 9.4f);

Prediction<Regressor> prediction = loadedModel.predict(wineAttribute);
double predictQuality = prediction.getOutput()
.getValues()[0];
log.info("Predicted wine quality: " + predictQuality);

}

}
108 changes: 108 additions & 0 deletions libraries-ai/src/main/java/com/baeldung/tribuo/WineQualityRegression.java
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package com.baeldung.tribuo;

import java.io.File;
import java.io.FileOutputStream;
import java.io.ObjectOutputStream;
import java.nio.file.Paths;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.tribuo.DataSource;
import org.tribuo.Dataset;
import org.tribuo.Model;
import org.tribuo.MutableDataset;
import org.tribuo.Trainer;
import org.tribuo.common.tree.AbstractCARTTrainer;
import org.tribuo.common.tree.RandomForestTrainer;
import org.tribuo.data.csv.CSVIterator;
import org.tribuo.data.csv.CSVLoader;
import org.tribuo.evaluation.TrainTestSplitter;
import org.tribuo.regression.RegressionFactory;
import org.tribuo.regression.Regressor;
import org.tribuo.regression.ensemble.AveragingCombiner;
import org.tribuo.regression.evaluation.RegressionEvaluation;
import org.tribuo.regression.evaluation.RegressionEvaluator;
import org.tribuo.regression.rtree.CARTRegressionTrainer;
import org.tribuo.regression.rtree.impurity.MeanSquaredError;

import com.oracle.labs.mlrg.olcut.provenance.ProvenanceUtil;

public class WineQualityRegression {

public static final Logger log = LoggerFactory.getLogger(WineQualityRegression.class);

public static final String DATASET_PATH = "src/main/resources/dataset/winequality-red.csv";
public static final String MODEL_PATH = "src/main/resources/model/winequality-red-regressor.ser";

public Model<Regressor> model;
public Trainer<Regressor> trainer;
public Dataset<Regressor> trainSet;
public Dataset<Regressor> testSet;

public static void main(String[] args) throws Exception {
WineQualityRegression wineQualityRegression = new WineQualityRegression();

wineQualityRegression.createDatasets();
wineQualityRegression.createTrainer();
wineQualityRegression.evaluateModels();
wineQualityRegression.saveModel();
}

public void createTrainer() {
CARTRegressionTrainer subsamplingTree = new CARTRegressionTrainer(Integer.MAX_VALUE, AbstractCARTTrainer.MIN_EXAMPLES, 0.001f, 0.7f,
new MeanSquaredError(), Trainer.DEFAULT_SEED);

trainer = new RandomForestTrainer<>(subsamplingTree, new AveragingCombiner(), 10);
model = trainer.train(trainSet);
}

public void createDatasets() throws Exception {
RegressionFactory regressionFactory = new RegressionFactory();
CSVLoader<Regressor> csvLoader = new CSVLoader<>(';', CSVIterator.QUOTE, regressionFactory);
DataSource<Regressor> dataSource = csvLoader.loadDataSource(Paths.get(DATASET_PATH), "quality");

TrainTestSplitter<Regressor> dataSplitter = new TrainTestSplitter<>(dataSource, 0.7, 1L);

trainSet = new MutableDataset<>(dataSplitter.getTrain());
log.info(String.format("Train set size = %d, num of features = %d", trainSet.size(), trainSet.getFeatureMap()
.size()));

testSet = new MutableDataset<>(dataSplitter.getTest());
log.info(String.format("Test set size = %d, num of features = %d", testSet.size(), testSet.getFeatureMap()
.size()));
}

public void evaluateModels() throws Exception {
log.info("Training model");
evaluate(model, "trainSet", trainSet);

log.info("Testing model");
evaluate(model, "testSet", testSet);

log.info("Dataset Provenance: --------------------");
log.info(ProvenanceUtil.formattedProvenanceString(model.getProvenance()
.getDatasetProvenance()));
log.info("Trainer Provenance: --------------------");
log.info(ProvenanceUtil.formattedProvenanceString(model.getProvenance()
.getTrainerProvenance()));
}

public void evaluate(Model<Regressor> model, String datasetName, Dataset<Regressor> dataset) {
log.info("Results for " + datasetName + "---------------------");
RegressionEvaluator evaluator = new RegressionEvaluator();
RegressionEvaluation evaluation = evaluator.evaluate(model, dataset);

Regressor dimension0 = new Regressor("DIM-0", Double.NaN);

log.info("MAE: " + evaluation.mae(dimension0));
log.info("RMSE: " + evaluation.rmse(dimension0));
log.info("R^2: " + evaluation.r2(dimension0));
}

public void saveModel() throws Exception {
File modelFile = new File(MODEL_PATH);
try (ObjectOutputStream objectOutputStream = new ObjectOutputStream(new FileOutputStream(modelFile))) {
objectOutputStream.writeObject(model);
}
}
}
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