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LUCENE-7305 - use macro average in confusion matrix metrics, removed unused import in datasplitter 

(cherry picked from commit dc50b79)
This commit is contained in:
Tommaso Teofili 2016-05-26 16:13:46 +02:00
parent 8c64931517
commit 55d854566e
1 changed files with 16 additions and 33 deletions
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49
lucene/classification/src/java/org/apache/lucene/classification/utils/ConfusionMatrixGenerator.java
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@ -175,7 +175,7 @@ public class ConfusionMatrixGenerator {
public double getPrecision(String klass) {
Map<String, Long> classifications = linearizedMatrix.get(klass);
double tp = 0;
double fp = 0;
double den = 0; // tp + fp
if (classifications != null) {
for (Map.Entry<String, Long> entry : classifications.entrySet()) {
if (klass.equals(entry.getKey())) {
@ -184,11 +184,11 @@ public class ConfusionMatrixGenerator {
}
for (Map<String, Long> values : linearizedMatrix.values()) {
if (values.containsKey(klass)) {
fp += values.get(klass);
den += values.get(klass);
}
}
}
return tp > 0 ? tp / (tp + fp) : 0;
return tp > 0 ? tp / den : 0;
}
/**
@ -246,7 +246,7 @@ public class ConfusionMatrixGenerator {
if (this.accuracy == -1) {
double tp = 0d;
double tn = 0d;
double fp = 0d;
double tfp = 0d; // tp + fp
double fn = 0d;
for (Map.Entry<String, Map<String, Long>> classification : linearizedMatrix.entrySet()) {
String klass = classification.getKey();
@ -259,63 +259,46 @@ public class ConfusionMatrixGenerator {
}
for (Map<String, Long> values : linearizedMatrix.values()) {
if (values.containsKey(klass)) {
fp += values.get(klass);
tfp += values.get(klass);
} else {
tn++;
}
}
}
this.accuracy = (tp + tn) / (fp + fn + tp + tn);
this.accuracy = (tp + tn) / (tfp + fn + tn);
}
return this.accuracy;
}
/**
* get the precision (see {@link #getPrecision(String)}) over all the classes.
* get the macro averaged precision (see {@link #getPrecision(String)}) over all the classes.
*
* @return the precision as computed from the whole confusion matrix
* @return the macro averaged precision as computed from the confusion matrix
*/
public double getPrecision() {
double tp = 0;
double fp = 0;
double p = 0;
for (Map.Entry<String, Map<String, Long>> classification : linearizedMatrix.entrySet()) {
String klass = classification.getKey();
for (Map.Entry<String, Long> entry : classification.getValue().entrySet()) {
if (klass.equals(entry.getKey())) {
tp += entry.getValue();
}
}
for (Map<String, Long> values : linearizedMatrix.values()) {
if (values.containsKey(klass)) {
fp += values.get(klass);
}
}
p += getPrecision(klass);
}
return tp > 0 ? tp / (tp + fp) : 0;
return p / linearizedMatrix.size();
}
/**
* get the recall (see {@link #getRecall(String)}) over all the classes
* get the macro averaged recall (see {@link #getRecall(String)}) over all the classes
*
* @return the recall as computed from the whole confusion matrix
* @return the recall as computed from the confusion matrix
*/
public double getRecall() {
double tp = 0;
double fn = 0;
double r = 0;
for (Map.Entry<String, Map<String, Long>> classification : linearizedMatrix.entrySet()) {
String klass = classification.getKey();
for (Map.Entry<String, Long> entry : classification.getValue().entrySet()) {
if (klass.equals(entry.getKey())) {
tp += entry.getValue();
} else {
fn += entry.getValue();
}
}
r += getRecall(klass);
}
return tp + fn > 0 ? tp / (tp + fn) : 0;
return r / linearizedMatrix.size();
}
@Override