edu.byu.nlp.al.RandomMeasurementSelector.java Source code

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Here is the source code for edu.byu.nlp.al.RandomMeasurementSelector.java

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/**
 * Copyright 2015 Brigham Young University
 *
 * 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 edu.byu.nlp.al;

import java.util.Collection;
import java.util.Collections;
import java.util.List;

import org.apache.commons.math3.random.RandomAdaptor;
import org.apache.commons.math3.random.RandomGenerator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.google.common.collect.Lists;
import com.google.common.collect.Multimap;

import edu.byu.nlp.crowdsourcing.measurements.MeasurementModelBuilder;
import edu.byu.nlp.data.FlatInstance;
import edu.byu.nlp.data.types.Dataset;
import edu.byu.nlp.data.types.SparseFeatureVector;
import edu.byu.nlp.data.util.EmpiricalAnnotations;

/**
 * @author plf1
 *
 */
public class RandomMeasurementSelector implements MeasurementSelector {
    private static Logger logger = LoggerFactory.getLogger(RandomMeasurementSelector.class);

    private List<FlatInstance<SparseFeatureVector, Integer>> candidates = Lists.newArrayList();

    /**
     * @param modelBuilder
     * @param annotations 
     */
    public RandomMeasurementSelector(MeasurementModelBuilder modelBuilder, Dataset dataset,
            EmpiricalAnnotations<SparseFeatureVector, Integer> annotations, RandomGenerator rnd) {
        // we want to add all measurements that are not already taken (used as seed set contained in dataset)
        // FIXME: this is horrifically inefficient! Fix it! 
        for (FlatInstance<SparseFeatureVector, Integer> meas : annotations.getMeasurements()) {
            if (!dataset.getMeasurements().contains(meas)) {
                candidates.add(meas);
            }
        }
        for (Multimap<Integer, FlatInstance<SparseFeatureVector, Integer>> perAnnotatorAnnotations : annotations
                .getPerInstancePerAnnotatorAnnotations().values()) {
            for (FlatInstance<SparseFeatureVector, Integer> meas : perAnnotatorAnnotations.values()) {
                candidates.add(meas);
            }
        }
        Collections.shuffle(candidates, new RandomAdaptor(rnd));
    }

    public Collection<FlatInstance<SparseFeatureVector, Integer>> selectNext(int batchSize) {

        List<FlatInstance<SparseFeatureVector, Integer>> batch = Lists
                .newArrayList(candidates.subList(0, Math.min(batchSize, candidates.size())));
        candidates.removeAll(batch);
        logger.info("\n**********************************************************\n"
                + "******* Selected batch of size " + batch.size() + " *******\n"
                + "**********************************************************\n");
        return batch;
    }

}