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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.
*/
package ml.dmlc.xgboost4j.scala.spark.params
import scala.collection.immutable.HashSet
import org.apache.spark.ml.param._
private[spark] trait LearningTaskParams extends Params {
/**
* Specify the learning task and the corresponding learning objective.
* options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
* multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear
*/
final val objective = new Param[String](this, "objective", "objective function used for " +
s"training, options: {${LearningTaskParams.supportedObjective.mkString(",")}",
(value: String) => LearningTaskParams.supportedObjective.contains(value))
final def getObjective: String = $(objective)
/**
* the initial prediction score of all instances, global bias. default=0.5
*/
final val baseScore = new DoubleParam(this, "baseScore", "the initial prediction score of all" +
" instances, global bias")
final def getBaseScore: Double = $(baseScore)
/**
* evaluation metrics for validation data, a default metric will be assigned according to
* objective(rmse for regression, and error for classification, mean average precision for
* ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map,
* gamma-deviance
*/
final val evalMetric = new Param[String](this, "evalMetric", "evaluation metrics for " +
"validation data, a default metric will be assigned according to objective " +
"(rmse for regression, and error for classification, mean average precision for ranking), " +
s"options: {${LearningTaskParams.supportedEvalMetrics.mkString(",")}}",
(value: String) => LearningTaskParams.supportedEvalMetrics.contains(value))
final def getEvalMetric: String = $(evalMetric)
/**
* Fraction of training points to use for testing.
*/
final val trainTestRatio = new DoubleParam(this, "trainTestRatio",
"fraction of training points to use for testing",
ParamValidators.inRange(0, 1))
final def getTrainTestRatio: Double = $(trainTestRatio)
/**
* If non-zero, the training will be stopped after a specified number
* of consecutive increases in any evaluation metric.
*/
final val numEarlyStoppingRounds = new IntParam(this, "numEarlyStoppingRounds",
"number of rounds of decreasing eval metric to tolerate before " +
"stopping the training",
(value: Int) => value == 0 || value > 1)
final def getNumEarlyStoppingRounds: Int = $(numEarlyStoppingRounds)
setDefault(objective -> "reg:linear", baseScore -> 0.5,
trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0)
}
private[spark] object LearningTaskParams {
val supportedObjective = HashSet("reg:linear", "reg:logistic", "binary:logistic",
"binary:logitraw", "count:poisson", "multi:softmax", "multi:softprob", "rank:pairwise",
"reg:gamma", "reg:tweedie")
val supportedEvalMetrics = HashSet("rmse", "mae", "logloss", "error", "merror", "mlogloss",
"auc", "aucpr", "ndcg", "map", "gamma-deviance")
}
