/*
 Copyright (c) 2014-2024 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.example

import scala.collection.mutable

import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost}
import ml.dmlc.xgboost4j.scala.example.util.CustomEval

object PredictFirstNTree {

  def main(args: Array[String]): Unit = {
    val trainMat = new DMatrix("../../demo/data/agaricus.txt.train?format=libsvm")
    val testMat = new DMatrix("../../demo/data/agaricus.txt.test?format=libsvm")

    val params = new mutable.HashMap[String, Any]()
    params += "eta" -> 1.0
    params += "max_depth" -> 2
    params += "silent" -> 1
    params += "objective" -> "binary:logistic"

    val watches = new mutable.HashMap[String, DMatrix]
    watches += "train" -> trainMat
    watches += "test" -> testMat

    val round = 3
    // train a model
    val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)

    // predict use 1 tree
    val predicts1 = booster.predict(testMat, false, 1)
    // by default all trees are used to do predict
    val predicts2 = booster.predict(testMat)

    val eval = new CustomEval
    println("error of predicts1: " + eval.eval(predicts1, testMat))
    println("error of predicts2: " + eval.eval(predicts2, testMat))
  }

}
