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Added support for full set of XGBoost feature importance metrics #52

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Sep 30, 2020
Merged

Added support for full set of XGBoost feature importance metrics #52

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8bd16dd
Added support for full set of XGBoost feature importance metrics
JackSullivan Sep 29, 2020
3271313
Made changes requested in PR #52
JackSullivan Sep 30, 2020
c533f22
tying toString in `XGBoostFeatureImportance` to parent model and addi…
JackSullivan Sep 30, 2020
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22 changes: 22 additions & 0 deletions Classification/XGBoost/src/test/java/org/tribuo/classification/xgboost/TestXGBoost.java
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@
import org.tribuo.classification.evaluation.LabelEvaluation;
import org.tribuo.classification.evaluation.LabelEvaluator;
import org.tribuo.classification.example.LabelledDataGenerator;
import org.tribuo.common.xgboost.XGBoostFeatureImportance;
import org.tribuo.common.xgboost.XGBoostModel;
import org.tribuo.data.text.TextDataSource;
import org.tribuo.data.text.TextFeatureExtractor;
Expand Down Expand Up @@ -178,6 +179,27 @@ public void testXGBoost(Pair<Dataset<Label>,Dataset<Label>> p) {
Assertions.assertFalse(features.isEmpty());
}

@Test
public void testFeatureImportanceSmokeTest() {
// we're just testing that not actually throws an exception
XGBoostModel<Label> m = (XGBoostModel<Label>)t.train(LabelledDataGenerator.denseTrainTest().getA());

XGBoostFeatureImportance i = m.getFeatureImportance().get(0);
i.getImportances();
i.getCover();
i.getGain();
i.getWeight();
i.getTotalCover();
i.getTotalGain();

i.getImportances(5);
i.getCover(5);
i.getGain(5);
i.getWeight(5);
i.getTotalCover(5);
i.getTotalGain(5);
}

@Test
public void testDenseData() {
Pair<Dataset<Label>,Dataset<Label>> p = LabelledDataGenerator.denseTrainTest();
Expand Down
23 changes: 11 additions & 12 deletions Common/XGBoost/src/main/java/org/tribuo/common/xgboost/XGBoostFeatureImportance.java
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Expand Up @@ -41,16 +41,16 @@ public class XGBoostFeatureImportance {
* An instance of feature importance values for a single feature. See {@link XGBoostFeatureImportance} for details
* on interpreting the metrics.
*/
public static class XGBoostFeatureImportanceRecord {
public static class XGBoostFeatureImportanceInstance {

private String featureName;
private final String featureName;
private final double gain;
private final double cover;
private final double weight;
private final double totalGain;
private final double totalCover;

XGBoostFeatureImportanceRecord(String featureName, double gain, double cover, double weight, double totalGain, double totalCover) {
XGBoostFeatureImportanceInstance(String featureName, double gain, double cover, double weight, double totalGain, double totalCover) {
this.featureName = featureName;
this.gain = gain;
this.cover = cover;
Expand Down Expand Up @@ -89,8 +89,8 @@ public String toString() {
}
}

private Booster booster;
private ImmutableFeatureMap featureMap;
private final Booster booster;
private final ImmutableFeatureMap featureMap;

XGBoostFeatureImportance(Booster booster, ImmutableFeatureMap featureMap) {
this.booster = booster;
Expand Down Expand Up @@ -210,14 +210,15 @@ public LinkedHashMap<String, Double> getTotalCover(int numFeatures) {
}

/**
* @return records of all importance metrics for each feature, sorted by gain.
*/
public List<XGBoostFeatureImportanceRecord> getImportances() {
public List<XGBoostFeatureImportanceInstance> getImportances() {
Map<String, LinkedHashMap<String, Double>> importanceByType = Stream.of(GAIN, COVER, WEIGHT, TOTAL_GAIN, TOTAL_COVER)
.map(importanceType -> new AbstractMap.SimpleEntry<>(importanceType, coalesceImportanceStream(getImportanceStream(importanceType))))
.collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
// this is already sorted by gain
List<String> features = new ArrayList<>(importanceByType.get(GAIN).keySet());
return features.stream().map(featureName -> new XGBoostFeatureImportanceRecord(featureName,
return features.stream().map(featureName -> new XGBoostFeatureImportanceInstance(featureName,
importanceByType.get(GAIN).get(featureName),
importanceByType.get(COVER).get(featureName),
importanceByType.get(WEIGHT).get(featureName),
Expand All @@ -230,13 +231,13 @@ public List<XGBoostFeatureImportanceRecord> getImportances() {
* @param numFeatures number of features to return
* @return records of all importance metrics for each feature, sorted by gain.
*/
public List<XGBoostFeatureImportanceRecord> getImportances(int numFeatures) {
public List<XGBoostFeatureImportanceInstance> getImportances(int numFeatures) {
Map<String, LinkedHashMap<String, Double>> importanceByType = Stream.of(GAIN, COVER, WEIGHT, TOTAL_GAIN, TOTAL_COVER)
.map(importanceType -> new AbstractMap.SimpleEntry<>(importanceType, coalesceImportanceStream(getImportanceStream(importanceType))))
.collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
// this is already sorted by gain
List<String> features = new ArrayList<>(importanceByType.get(GAIN).keySet()).subList(0, Math.min(importanceByType.get(GAIN).keySet().size(), numFeatures));
return features.stream().map(featureName -> new XGBoostFeatureImportanceRecord(featureName,
return features.stream().map(featureName -> new XGBoostFeatureImportanceInstance(featureName,
importanceByType.get(GAIN).get(featureName),
importanceByType.get(COVER).get(featureName),
importanceByType.get(WEIGHT).get(featureName),
Expand All @@ -246,8 +247,6 @@ public List<XGBoostFeatureImportanceRecord> getImportances(int numFeatures) {
}

public String toString() {
return "XGBoostFeatureImportance(" + getImportances(5).stream()
.map(XGBoostFeatureImportanceRecord::toString)
.collect(Collectors.joining(",\n\t")) + ")";
return String.format("XGBoostFeatureImportance(booster=%s, featureIdMap=%s)", booster.toString(), featureMap.toString());
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I think it might be better to return a toString that refers to the model provenance? The booster and featureIdMap toStrings aren't very helpful.

}
}