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new 579036d IGNITE-10145: [ML] Implement ROC AUC metric (#6394)
579036d is described below
commit 579036d2ebdae7c6372a925589f33884536eea0b
Author: Alexey Zinoviev <zaleslaw....@gmail.com>
AuthorDate: Thu Apr 4 14:24:56 2019 +0300
IGNITE-10145: [ML] Implement ROC AUC metric (#6394)
---
.../BinaryClassificationMetricValues.java | 12 +-
.../BinaryClassificationMetrics.java | 47 ++++++-
.../scoring/metric/classification/ROCAUC.java | 141 +++++++++++++++++++++
.../BinaryClassificationMetricsValuesTest.java | 3 +-
.../scoring/metric/classification/ROCAUCTest.java | 121 ++++++++++++++++++
5 files changed, 319 insertions(+), 5 deletions(-)
diff --git
a/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricValues.java
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricValues.java
index e04ddc0..841faf6 100644
---
a/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricValues.java
+++
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricValues.java
@@ -65,6 +65,9 @@ public class BinaryClassificationMetricValues implements
MetricValues {
/** F1-Score is the harmonic mean of Precision and Sensitivity. */
private double f1Score;
+ /** ROC AUC. */
+ private double rocauc;
+
/**
* Initialize an example by 4 metrics.
*
@@ -73,11 +76,12 @@ public class BinaryClassificationMetricValues implements
MetricValues {
* @param fp False Positive (FP).
* @param fn False Negative (FN).
*/
- public BinaryClassificationMetricValues(long tp, long tn, long fp, long
fn) {
+ public BinaryClassificationMetricValues(long tp, long tn, long fp, long
fn, double rocauc) {
this.tp = tp;
this.tn = tn;
this.fp = fp;
this.fn = fn;
+ this.rocauc = rocauc;
long p = tp + fn;
long n = tn + fp;
@@ -168,4 +172,10 @@ public class BinaryClassificationMetricValues implements
MetricValues {
public double f1Score() {
return f1Score;
}
+
+ /** Returns ROCAUC value. */
+ public double rocauc() {
+ return rocauc;
+ }
+
}
diff --git
a/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetrics.java
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetrics.java
index bfa1cf3..53c21a0 100644
---
a/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetrics.java
+++
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetrics.java
@@ -17,12 +17,14 @@
package org.apache.ignite.ml.selection.scoring.metric.classification;
+import java.util.Comparator;
+import java.util.Iterator;
+import java.util.PriorityQueue;
+import org.apache.commons.math3.util.Pair;
import org.apache.ignite.ml.selection.scoring.LabelPair;
import org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics;
import
org.apache.ignite.ml.selection.scoring.metric.exceptions.UnknownClassLabelException;
-import java.util.Iterator;
-
/**
* Binary classification metrics calculator.
* It could be used in two ways: to caculate all binary classification metrics
or specific metric.
@@ -34,6 +36,9 @@ public class BinaryClassificationMetrics extends
AbstractMetrics<BinaryClassific
/** Negative class label. Default value is 0.0. */
private double negativeClsLb;
+ /** This flag enabels ROC AUC calculation that is hard for perfromance due
to internal implementation. */
+ private boolean enableROCAUC;
+
{
metric = BinaryClassificationMetricValues::accuracy;
}
@@ -48,6 +53,12 @@ public class BinaryClassificationMetrics extends
AbstractMetrics<BinaryClassific
long tn = 0;
long fp = 0;
long fn = 0;
+ double rocauc = Double.NaN;
+
+ // for ROC AUC calculation
+ long pos = 0;
+ long neg = 0;
+ PriorityQueue<Pair<Double, Double>> queue = new
PriorityQueue<>(Comparator.comparingDouble(Pair::getKey));
while (iter.hasNext()) {
LabelPair<Double> e = iter.next();
@@ -64,9 +75,26 @@ public class BinaryClassificationMetrics extends
AbstractMetrics<BinaryClassific
else if (truth == positiveClsLb && prediction == negativeClsLb)
fn++;
else if (truth == negativeClsLb && prediction == negativeClsLb)
tn++;
else if (truth == negativeClsLb && prediction == positiveClsLb)
fp++;
+
+
+ if(enableROCAUC) {
+ queue.add(new Pair<>(prediction, truth));
+
+ if (truth == positiveClsLb)
+ pos++;
+ else if (truth == negativeClsLb)
+ neg++;
+ else
+ throw new UnknownClassLabelException(truth, positiveClsLb,
negativeClsLb);
+ }
+
}
- return new BinaryClassificationMetricValues(tp, tn, fp, fn);
+ if (enableROCAUC)
+ rocauc = ROCAUC.calculateROCAUC(queue, pos, neg, positiveClsLb);
+
+
+ return new BinaryClassificationMetricValues(tp, tn, fp, fn, rocauc);
}
/** */
@@ -93,6 +121,19 @@ public class BinaryClassificationMetrics extends
AbstractMetrics<BinaryClassific
return this;
}
+ /** */
+ public BinaryClassificationMetrics withEnablingROCAUC(boolean
enableROCAUC) {
+ this.enableROCAUC = this.enableROCAUC;
+ return this;
+ }
+
+
+ /** */
+ public boolean isROCAUCenabled() {
+ return enableROCAUC;
+ }
+
+
/** {@inheritDoc} */
@Override public String name() {
return "Binary classification metrics";
diff --git
a/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUC.java
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUC.java
new file mode 100644
index 0000000..5911a28
--- /dev/null
+++
b/modules/ml/src/main/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUC.java
@@ -0,0 +1,141 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.selection.scoring.metric.classification;
+
+import java.util.Comparator;
+import java.util.Iterator;
+import java.util.PriorityQueue;
+import org.apache.commons.math3.util.Pair;
+import org.apache.ignite.ml.selection.scoring.LabelPair;
+import org.apache.ignite.ml.selection.scoring.metric.Metric;
+import
org.apache.ignite.ml.selection.scoring.metric.exceptions.UnknownClassLabelException;
+
+/**
+ * ROC AUC score calculator.
+ * <p>
+ * The calculation of AUC is based on Mann-Whitney U test
+ * (https://en.wikipedia.org/wiki/Mann-Whitney_U_test).
+ */
+public class ROCAUC implements Metric<Double> {
+ /** Positive class label. */
+ private double positiveClsLb = 1.0;
+
+ /** Negative class label. Default value is 0.0. */
+ private double negativeClsLb;
+
+ /** {@inheritDoc} */
+ @Override public double score(Iterator<LabelPair<Double>> iter) {
+ //TODO: It should work with not binary values only, see IGNITE-11680.
+
+ PriorityQueue<Pair<Double, Double>> queue = new
PriorityQueue<>(Comparator.comparingDouble(Pair::getKey));
+
+ long pos = 0;
+ long neg = 0;
+
+ while (iter.hasNext()) {
+ LabelPair<Double> e = iter.next();
+
+ Double prediction = e.getPrediction();
+ Double truth = e.getTruth();
+
+ queue.add(new Pair<>(prediction, truth));
+
+ if (truth == positiveClsLb)
+ pos++;
+ else if (truth == negativeClsLb)
+ neg++;
+ else
+ throw new UnknownClassLabelException(truth, positiveClsLb,
negativeClsLb);
+
+ }
+
+ return calculateROCAUC(queue, pos, neg, positiveClsLb);
+ }
+
+ /**
+ * Calculates the ROC AUC value based on queue of pairs,
+ * amount of positive/negative cases and label of positive class.
+ */
+ public static double calculateROCAUC(PriorityQueue<Pair<Double, Double>>
queue, long pos, long neg, double positiveClsLb) {
+ double[] lb = new double[queue.size()];
+ double[] prediction = new double[queue.size()];
+ int cnt = 0;
+
+ while (!queue.isEmpty()) {
+ Pair<Double, Double> elem = queue.poll();
+ lb[cnt] = elem.getValue();
+ prediction[cnt] = elem.getKey();
+ cnt++;
+ }
+
+ double[] rank = new double[lb.length];
+ for (int i = 0; i < prediction.length; i++) {
+ if (i == prediction.length - 1 || prediction[i] != prediction[i +
1])
+ rank[i] = i + 1;
+ else {
+ int j = i + 1;
+ for (; j < prediction.length && prediction[j] ==
prediction[i]; j++);
+ double r = (i + 1 + j) / 2.0;
+ for (int k = i; k < j; k++)
+ rank[k] = r;
+ i = j - 1;
+ }
+ }
+
+ double auc = 0.0;
+ for (int i = 0; i < lb.length; i++) {
+ if (lb[i] == positiveClsLb)
+ auc += rank[i];
+ }
+
+ if (pos == 0L) return Double.NaN;
+ else if (neg == 0L) return Double.NaN;
+
+ auc = (auc - (pos * (pos + 1) / 2.0)) / (pos * neg);
+ return auc;
+ }
+
+ /** Get the positive label. */
+ public double positiveClsLb() {
+ return positiveClsLb;
+ }
+
+ /** Set the positive label. */
+ public ROCAUC withPositiveClsLb(double positiveClsLb) {
+ if (Double.isFinite(positiveClsLb))
+ this.positiveClsLb = positiveClsLb;
+ return this;
+ }
+
+ /** Get the negative label. */
+ public double negativeClsLb() {
+ return negativeClsLb;
+ }
+
+ /** Set the negative label. */
+ public ROCAUC withNegativeClsLb(double negativeClsLb) {
+ if (Double.isFinite(negativeClsLb))
+ this.negativeClsLb = negativeClsLb;
+ return this;
+ }
+
+ /** {@inheritDoc} */
+ @Override public String name() {
+ return "ROC AUC";
+ }
+}
diff --git
a/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricsValuesTest.java
b/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricsValuesTest.java
index f513ce3..4e7b03a 100644
---
a/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricsValuesTest.java
+++
b/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/BinaryClassificationMetricsValuesTest.java
@@ -28,7 +28,7 @@ public class BinaryClassificationMetricsValuesTest {
/** */
@Test
public void testDefaultBehaviour() {
- BinaryClassificationMetricValues metricValues = new
BinaryClassificationMetricValues(10, 10, 5, 5);
+ BinaryClassificationMetricValues metricValues = new
BinaryClassificationMetricValues(10, 10, 5, 5, 0.5);
assertEquals(10, metricValues.tp(), 1e-2);
assertEquals(10, metricValues.tn(), 1e-2);
@@ -44,5 +44,6 @@ public class BinaryClassificationMetricsValuesTest {
assertEquals(0.66, metricValues.precision(), 1e-2);
assertEquals(0.66, metricValues.recall(), 1e-2);
assertEquals(0.66, metricValues.specificity(), 1e-2);
+ assertEquals(0.5, metricValues.rocauc(), 1e-2);
}
}
diff --git
a/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUCTest.java
b/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUCTest.java
new file mode 100644
index 0000000..99451a3
--- /dev/null
+++
b/modules/ml/src/test/java/org/apache/ignite/ml/selection/scoring/metric/classification/ROCAUCTest.java
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.selection.scoring.metric.classification;
+
+import java.util.Arrays;
+import org.apache.ignite.ml.selection.scoring.TestLabelPairCursor;
+import org.apache.ignite.ml.selection.scoring.cursor.LabelPairCursor;
+import org.apache.ignite.ml.selection.scoring.metric.Metric;
+import org.junit.Test;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * Tests for {@link ROCAUC}.
+ */
+public class ROCAUCTest {
+ /** */
+ @Test
+ public void testTotalTruth() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 1.0, 1.0, 1.0),
+ Arrays.asList(1.0, 1.0, 1.0, 1.0, 1.0)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(Double.NaN, score, 1e-12);
+ }
+
+ /** */
+ @Test
+ public void testTotalUntruth() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 1.0, 1.0, 1.0),
+ Arrays.asList(0.0, 0.0, 0.0, 0.0, 0.0)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(Double.NaN, score, 1e-12);
+ }
+
+ /** */
+ @Test
+ public void testOneDifferent() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 1.0, 0.0),
+ Arrays.asList(1.0, 1.0, 1.0, 1.0)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(0.5, score, 1e-12);
+ }
+
+ /** */
+ @Test
+ public void testOneDifferentButBalanced() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 0.0, 0.0),
+ Arrays.asList(1.0, 1.0, 0.0, 1.0)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(0.75, score, 1e-12);
+ }
+
+ /** */
+ @Test
+ public void testTwoDifferentAndBalanced() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 0.0, 0.0),
+ Arrays.asList(1.0, 0.0, 0.0, 1.0)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(0.5, score, 1e-12);
+ }
+
+ /** */
+ @Test
+ public void testNotOnlyBinaryValues() {
+ Metric<Double> scoreCalculator = new ROCAUC();
+
+ LabelPairCursor<Double> cursor = new TestLabelPairCursor<>(
+ Arrays.asList(1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0),
+ Arrays.asList(0.40209054, 0.33697626, 0.5449324 , 0.13010869,
0.19019675, 0.39767829, 0.9686739 , 0.91783275, 0.7503783 , 0.5306605)
+ );
+
+ double score = scoreCalculator.score(cursor.iterator());
+
+ assertEquals(0.625, score, 1e-12);
+ }
+}
