opennlp.tools.util.featuregen.CachedFeatureGenerator.java Source code

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Here is the source code for opennlp.tools.util.featuregen.CachedFeatureGenerator.java

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/*
 * 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 opennlp.tools.util.featuregen;

import java.util.ArrayList;
import java.util.List;

import opennlp.tools.util.Cache;

/**
 * Caches features of the aggregated {@link AdaptiveFeatureGenerator}s.
 */
public class CachedFeatureGenerator implements AdaptiveFeatureGenerator {

    private final AdaptiveFeatureGenerator generator;

    private String[] prevTokens;

    private Cache<Integer, List<String>> contextsCache;

    private long numberOfCacheHits;
    private long numberOfCacheMisses;

    @Deprecated
    public CachedFeatureGenerator(AdaptiveFeatureGenerator... generators) {
        this.generator = new AggregatedFeatureGenerator(generators);
        contextsCache = new Cache<>(100);
    }

    public CachedFeatureGenerator(AdaptiveFeatureGenerator generator) {
        this.generator = generator;
        contextsCache = new Cache<>(100);
    }

    public void createFeatures(List<String> features, String[] tokens, int index, String[] previousOutcomes) {

        List<String> cacheFeatures;

        if (tokens == prevTokens) {
            cacheFeatures = contextsCache.get(index);

            if (cacheFeatures != null) {
                numberOfCacheHits++;
                features.addAll(cacheFeatures);
                return;
            }

        } else {
            contextsCache.clear();
            prevTokens = tokens;
        }

        cacheFeatures = new ArrayList<>();

        numberOfCacheMisses++;

        generator.createFeatures(cacheFeatures, tokens, index, previousOutcomes);

        contextsCache.put(index, cacheFeatures);
        features.addAll(cacheFeatures);
    }

    public void updateAdaptiveData(String[] tokens, String[] outcomes) {
        generator.updateAdaptiveData(tokens, outcomes);
    }

    public void clearAdaptiveData() {
        generator.clearAdaptiveData();
    }

    /**
     * Retrieves the number of times a cache hit occurred.
     *
     * @return number of cache hits
     */
    public long getNumberOfCacheHits() {
        return numberOfCacheHits;
    }

    /**
     * Retrieves the number of times a cache miss occurred.
     *
     * @return number of cache misses
     */
    public long getNumberOfCacheMisses() {
        return numberOfCacheMisses;
    }

    @Override
    public String toString() {
        return super.toString() + ": hits=" + numberOfCacheHits + " misses=" + numberOfCacheMisses + " hit%"
                + (numberOfCacheHits > 0 ? (double) numberOfCacheHits / (numberOfCacheMisses + numberOfCacheHits)
                        : 0);
    }

    public AdaptiveFeatureGenerator getCachedFeatureGenerator() {
        return generator;
    }
}