Package org.apache.mahout.cf.taste.example.netflix

Source Code of org.apache.mahout.cf.taste.example.netflix.NetflixRecommenderEvaluatorRunner

/**
* 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 org.apache.mahout.cf.taste.example.netflix;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;
import java.io.File;

public final class NetflixRecommenderEvaluatorRunner {

  private static final Logger log = LoggerFactory.getLogger(NetflixRecommenderEvaluatorRunner.class);

  private NetflixRecommenderEvaluatorRunner() {
    // do nothing
  }

  public static void main(String... args) throws IOException, TasteException {
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    DataModel model = new NetflixDataModel(new File(args[0]), true);
    double evaluation = evaluator.evaluate(new NetflixRecommenderBuilder(), model, 0.9, 0.1);
    log.info(String.valueOf(evaluation));
  }

}
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