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added type hints to custom_obj and custom_eval for Spark persistence
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jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/CustomObj.scala
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/* | ||
Copyright (c) 2014 by Contributors | ||
Licensed 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. | ||
*/ | ||
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package ml.dmlc.xgboost4j.scala.spark | ||
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import ml.dmlc.xgboost4j.java.XGBoostError | ||
import ml.dmlc.xgboost4j.scala.{DMatrix, ObjectiveTrait} | ||
import ml.dmlc.xgboost4j.scala.spark.params.CustomObjParam.addShortTypeHint | ||
import org.apache.commons.logging.LogFactory | ||
import org.json4s.ShortTypeHints | ||
import scala.collection.mutable.ListBuffer | ||
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/** | ||
* loglikelihood loss obj function | ||
*/ | ||
case class CustomObj() extends ObjectiveTrait { | ||
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val logger = LogFactory.getLog(classOf[CustomObj]) | ||
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addShortTypeHint(classOf[CustomObj]) | ||
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/** | ||
* user define objective function, return gradient and second order gradient | ||
* | ||
* @param predicts untransformed margin predicts | ||
* @param dtrain training data | ||
* @return List with two float array, correspond to first order grad and second order grad | ||
*/ | ||
override def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix) | ||
: List[Array[Float]] = { | ||
val nrow = predicts.length | ||
val gradients = new ListBuffer[Array[Float]] | ||
var labels: Array[Float] = null | ||
try { | ||
labels = dtrain.getLabel | ||
} catch { | ||
case e: XGBoostError => | ||
logger.error(e) | ||
null | ||
case _: Throwable => | ||
null | ||
} | ||
val grad = new Array[Float](nrow) | ||
val hess = new Array[Float](nrow) | ||
val transPredicts = transform(predicts) | ||
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for (i <- 0 until nrow) { | ||
val predict = transPredicts(i)(0) | ||
grad(i) = predict - labels(i) | ||
hess(i) = predict * (1 - predict) | ||
} | ||
gradients += grad | ||
gradients += hess | ||
gradients.toList | ||
} | ||
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/** | ||
* simple sigmoid func | ||
* | ||
* @param input | ||
* @return Note: this func is not concern about numerical stability, only used as example | ||
*/ | ||
def sigmoid(input: Float): Float = { | ||
(1 / (1 + Math.exp(-input))).toFloat | ||
} | ||
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def transform(predicts: Array[Array[Float]]): Array[Array[Float]] = { | ||
val nrow = predicts.length | ||
val transPredicts = Array.fill[Float](nrow, 1)(0) | ||
for (i <- 0 until nrow) { | ||
transPredicts(i)(0) = sigmoid(predicts(i)(0)) | ||
} | ||
transPredicts | ||
} | ||
} |
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