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Fix flaky tests by making ImmutableLabelInfo ID assignment deterministic #424

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Fix flaky tests by making ImmutableLabelInfo ID assignment deterministic #424

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a14d211
Fix flaky tests by making ImmutableLabelInfo ID assignment deterministic
lbh930 Nov 29, 2025
46d6f1f
Use TreeSet to sort in ImmutableLabelInfo
lbh930 Dec 1, 2025
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20 changes: 14 additions & 6 deletions Classification/Core/src/main/java/org/tribuo/classification/ImmutableLabelInfo.java
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Expand Up @@ -29,9 +29,14 @@
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.SortedSet;
import java.util.TreeSet;
import java.util.logging.Level;
import java.util.logging.Logger;

Expand Down Expand Up @@ -61,15 +66,18 @@ private ImmutableLabelInfo(ImmutableLabelInfo info) {

ImmutableLabelInfo(LabelInfo info) {
super(info);
idLabelMap = new HashMap<>();
labelIDMap = new HashMap<>();
idLabelMap = new LinkedHashMap<>();
labelIDMap = new LinkedHashMap<>();
int counter = 0;
for (Map.Entry<String,MutableLong> e : labelCounts.entrySet()) {
idLabelMap.put(counter,e.getKey());
labelIDMap.put(e.getKey(),counter);
SortedSet<String> keys = new TreeSet<>(labelCounts.keySet());
Set<Label> domainSet = new LinkedHashSet<>();
for (String key : keys) {
idLabelMap.put(counter,key);
labelIDMap.put(key,counter);
domainSet.add(labels.get(key));
counter++;
}
domain = Collections.unmodifiableSet(new HashSet<>(labels.values()));
domain = Collections.unmodifiableSet(domainSet);
}

ImmutableLabelInfo(LabelInfo info, Map<Label,Integer> mapping) {
Expand Down
4 changes: 3 additions & 1 deletion ...cisionTree/src/test/java/org/tribuo/classification/dtree/TestClassificationEnsembles.java
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Expand Up @@ -19,6 +19,7 @@
import com.oracle.labs.mlrg.olcut.config.PropertyException;
import com.oracle.labs.mlrg.olcut.util.Pair;
import org.tribuo.Dataset;
import org.tribuo.ImmutableDataset;
import org.tribuo.Example;
import org.tribuo.Model;
import org.tribuo.Prediction;
Expand Down Expand Up @@ -250,7 +251,8 @@ public void load431ProtobufModel() throws IOException, URISyntaxException {
List<Prediction<Label>> deserOutput = deserModel.predict(p.getB());

AdaBoostTrainer trainer = new AdaBoostTrainer(t,10);
WeightedEnsembleModel<Label> model = trainer.train(p.getA());
Dataset<Label> train = new ImmutableDataset<>(p.getA(), p.getA().getProvenance(), p.getA().getOutputFactory(), deserModel.getFeatureIDMap(), deserModel.getOutputIDInfo(), false);
WeightedEnsembleModel<Label> model = trainer.train(train);
List<Prediction<Label>> output = model.predict(p.getB());

assertEquals(deserOutput.size(), p.getB().size());
Expand Down
4 changes: 3 additions & 1 deletion Classification/SGD/src/test/java/org/tribuo/classification/sgd/fm/TestFMClassification.java
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Expand Up @@ -25,6 +25,7 @@
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.tribuo.Dataset;
import org.tribuo.ImmutableDataset;
import org.tribuo.Model;
import org.tribuo.Prediction;
import org.tribuo.Trainer;
Expand Down Expand Up @@ -166,7 +167,8 @@ public void loadProtobufModel() throws IOException, URISyntaxException {
List<Prediction<Label>> deserOutput = deserModel.predict(p.getB());

FMClassificationTrainer trainer = new FMClassificationTrainer(new LogMulticlass(), new AdaGrad(0.1,0.1),5,1000, Trainer.DEFAULT_SEED,1,0.1);
Model<Label> model = trainer.train(p.getA());
Dataset<Label> train = new ImmutableDataset<>(p.getA().getData(), p.getA().getProvenance(), p.getA().getOutputFactory(), deserModel.getFeatureIDMap(), deserModel.getOutputIDInfo(), false);
Model<Label> model = trainer.train(train);
List<Prediction<Label>> output = model.predict(p.getB());

assertEquals(deserOutput.size(), p.getB().size());
Expand Down
2 changes: 1 addition & 1 deletion Classification/SGD/src/test/java/org/tribuo/classification/sgd/linear/TestSGDLinear.java
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Expand Up @@ -94,7 +94,7 @@ public void testSingleClassTraining() {
assertEquals(0.0,evaluation.accuracy(new Label("Baz")));
assertEquals(0.0,evaluation.accuracy(new Label("Quux")));
assertEquals(1.0,evaluation.recall(new Label("Foo")));
assertEquals(1.0,evaluation.recall(new Label("Bar"))); // This is due to the random init of the classifier.
assertEquals(0.0,evaluation.recall(new Label("Bar")));
}

@Test
Expand Down
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