spark LogLoss 源码
spark LogLoss 代码
文件路径：/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
/*
* 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.spark.mllib.tree.loss
import org.apache.spark.annotation.Since
import org.apache.spark.mllib.util.MLUtils
/**
* Class for log loss calculation (for classification).
* This uses twice the binomial negative log likelihood, called "deviance" in Friedman (1999).
*
* The log loss is defined as:
* 2 log(1 + exp(-2 y F(x)))
* where y is a label in {-1, 1} and F(x) is the model prediction for features x.
*/
@Since("1.2.0")
object LogLoss extends ClassificationLoss {
/**
* Method to calculate the loss gradients for the gradient boosting calculation for binary
* classification
* The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x)))
* @param prediction Predicted label.
* @param label True label.
* @return Loss gradient
*/
@Since("1.2.0")
override def gradient(prediction: Double, label: Double): Double = {
- 4.0 * label / (1.0 + math.exp(2.0 * label * prediction))
}
override private[spark] def computeError(prediction: Double, label: Double): Double = {
val margin = 2.0 * label * prediction
// The following is equivalent to 2.0 * log(1 + exp(-margin)) but more numerically stable.
2.0 * MLUtils.log1pExp(-margin)
}
/**
* Returns the estimated probability of a label of 1.0.
*/
override private[spark] def computeProbability(margin: Double): Double = {
1.0 / (1.0 + math.exp(-2.0 * margin))
}
}
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