hivemall.evaluation.LogarithmicLossUDAF Maven / Gradle / Ivy
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* 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
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package hivemall.evaluation;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDAF;
import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.serde2.io.DoubleWritable;
@SuppressWarnings("deprecation")
@Description(name = "logloss",
value = "_FUNC_(double predicted, double actual) - Return a Logrithmic Loss")
public final class LogarithmicLossUDAF extends UDAF {
public static class Evaluator implements UDAFEvaluator {
private PartialResult partial;
public Evaluator() {}
@Override
public void init() {
this.partial = null;
}
public boolean iterate(DoubleWritable predicted, DoubleWritable actual)
throws HiveException {
if (predicted == null || actual == null) {// skip
return true;
}
if (partial == null) {
this.partial = new PartialResult();
}
partial.iterate(predicted.get(), actual.get());
return true;
}
public PartialResult terminatePartial() {
return partial;
}
public boolean merge(PartialResult other) throws HiveException {
if (other == null) {
return true;
}
if (partial == null) {
this.partial = new PartialResult();
}
partial.merge(other);
return true;
}
public double terminate() {
if (partial == null) {
return 0.d;
}
return partial.getLogLoss();
}
}
public static class PartialResult {
double log_sum;
long count;
PartialResult() {
this.log_sum = 0.d;
this.count = 0L;
}
void iterate(double predicted, double actual) {
double epsilon = 1E-15d;
predicted = Math.max(epsilon, predicted);
predicted = Math.min(1.d - epsilon, predicted);
log_sum += actual * Math.log(predicted) + (1.d - actual) * Math.log(1.d - predicted);
count++;
}
void merge(PartialResult other) {
log_sum += other.log_sum;
count += other.count;
}
double getLogLoss() {
if (count == 0) {
return 0.d;
}
return -1.d * log_sum / count;
}
}
}
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