/**
* Copyright 2013-2015 Pierre Merienne
*
* 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 com.github.pmerienne.trident.ml.regression;
import java.util.List;
import com.github.pmerienne.trident.ml.core.Instance;
import com.github.pmerienne.trident.ml.regression.Regressor;
import com.github.pmerienne.trident.ml.testing.data.DatasetUtils;
public abstract class RegressorTest {
private final static Integer FOLD_NB = 10;
/**
* Cross validation with 10 folds
*
* @param <L>
* @param <F>
* @param classifier
* @param samples
* @return
*/
protected double eval(Regressor regressor, List<Instance<Double>> samples) {
double error = 0.0;
for (int i = 0; i < FOLD_NB; i++) {
List<Instance<Double>> training = DatasetUtils.getTrainingFolds(i, FOLD_NB, samples);
List<Instance<Double>> eval = DatasetUtils.getEvalFold(i, FOLD_NB, samples);
error += this.eval(regressor, training, eval);
}
return error / FOLD_NB;
}
protected double eval(Regressor regressor, List<Instance<Double>> training, List<Instance<Double>> eval) {
regressor.reset();
// Train
for (Instance<Double> sample : training) {
regressor.update(sample.label, sample.features);
}
// Evaluate
double rmse = 0.0;
Double actualPrediction;
for (Instance<Double> sample : eval) {
actualPrediction = regressor.predict(sample.features);
rmse += Math.pow(actualPrediction - sample.label, 2);
// System.out.println("Was " + sample.label + ", Found " +
// actualPrediction);
}
return Math.sqrt(rmse / eval.size());
}
}