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AbstractRecommender.java
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AbstractRecommender.java
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/**
* Copyright (C) 2013-2016 Vasilis Vryniotis <bbriniotis@datumbox.com>
*
* 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.datumbox.framework.core.machinelearning.common.abstracts.modelers;
import com.datumbox.framework.common.Configuration;
import com.datumbox.framework.common.dataobjects.Dataframe;
import com.datumbox.framework.core.machinelearning.modelselection.metrics.RecommendationMetrics;
import com.datumbox.framework.core.machinelearning.modelselection.splitters.TemporaryKFold;
/**
* Abstract Class for recommender algorithms.
*
* @author Vasilis Vryniotis <bbriniotis@datumbox.com>
* @param <MP>
* @param <TP>
*/
public abstract class AbstractRecommender<MP extends AbstractRecommender.AbstractModelParameters, TP extends AbstractRecommender.AbstractTrainingParameters> extends AbstractModeler<MP, TP> {
/**
* @param dbName
* @param conf
* @param mpClass
* @param tpClass
* @see AbstractModeler#AbstractModeler(java.lang.String, Configuration, java.lang.Class, java.lang.Class)
*/
protected AbstractRecommender(String dbName, Configuration conf, Class<MP> mpClass, Class<TP> tpClass) {
super(dbName, conf, mpClass, tpClass);
}
//TODO: remove this once we create the save/load
public RecommendationMetrics validate(Dataframe testingData) {
logger.info("validate()");
predict(testingData);
return new RecommendationMetrics(testingData);
}
//TODO: remove this once we create the save/load
public RecommendationMetrics kFoldCrossValidation(Dataframe trainingData, TP trainingParameters, int k) {
logger.info("validate()");
return new TemporaryKFold<>(RecommendationMetrics.class).validate(trainingData, k, dbName, knowledgeBase.getConf(), this.getClass(), trainingParameters);
}
}