content: /*
   * Tencent is pleased to support the open source community by making Angel available.
   *
   * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
   *
   * 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
   *
   * https://opensource.org/licenses/Apache-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 com.tencent.angel.ml.GBDT.param;
  
  import org.apache.commons.logging.Log;
  import org.apache.commons.logging.LogFactory;
  
  /**
   * Description: hyper-parameter of tree model
   */
  
  public class TreeParam implements TrainParam {
  
    private static final Log LOG = LogFactory.getLog(TreeParam.class);
  
    // maximum depth of the tree
    int maxDepth;
    // number of features used for tree construction
    int numFeature;
    // minimum loss change required for a split, otherwise stop split
    double minSplitLoss;
    // ----- the rest parameters are less important ----
    // default direction choice
    int defaultDirection;
    // whether we want to do sample data
    float rowSample;
    // whether to sample columns during tree construction
    float colSample;
    // whether to use histogram for split
    boolean isHist;
    // number of histogram units
    int numBins;
    // whether to print info during training.
    boolean silent;
    // ----- the rest parameters are obtained after training ----
    // total number of nodes
    int numNodes = 0;
    // number of deleted nodes */
    int numDeleted = 0;
  
    public void printParam() {
      LOG.info(String.format("Tree hyper-parameters------"
          + "maxdepth: %d, numFeature: %d, minSplitLoss: %f, rowSample: %f, colSample: %f", maxDepth,
        numFeature, minSplitLoss, rowSample, colSample));
    }
  }