/*
* 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 hivemall.xgboost.regression;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import hivemall.xgboost.XGBoostUDTF;
/**
* A XGBoost regression and the document is as follows;
* - https://github.com/dmlc/xgboost/tree/master/demo/regression
*/
@Description(
name = "train_xgboost_regr",
value = "_FUNC_(string[] features, double target [, string options]) - Returns a relation consisting of <string model_id, array<byte> pred_model>"
)
public class XGBoostRegressionUDTF extends XGBoostUDTF {
public XGBoostRegressionUDTF() {}
{
// Settings for logistic regression
params.put("objective", "reg:logistic");
params.put("eval_metric", "rmse");
}
@Override
public void checkTargetValue(double target) throws HiveException {
if(target < 0.0 || target > 1.0) {
throw new HiveException("target must be in range 0 to 1: " + target);
}
}
}