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#504 - Refactor Ngram Features for quicker processing
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dkpro-tc-features-ngram/src/main/java/org/dkpro/tc/features/ngram/AbstractNgram.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,74 @@ | ||
| /******************************************************************************* | ||
| * Copyright 2018 | ||
| * Ubiquitous Knowledge Processing (UKP) Lab | ||
| * Technische Universität Darmstadt | ||
| * | ||
| * 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 org.dkpro.tc.features.ngram; | ||
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| import java.util.HashSet; | ||
| import java.util.Set; | ||
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| import org.dkpro.tc.api.exception.TextClassificationException; | ||
| import org.dkpro.tc.api.features.Feature; | ||
| import org.dkpro.tc.api.features.FeatureExtractor; | ||
| import org.dkpro.tc.api.features.FeatureType; | ||
| import org.dkpro.tc.features.ngram.meta.base.LuceneFeatureExtractorBase; | ||
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| import de.tudarmstadt.ukp.dkpro.core.api.frequency.util.FrequencyDistribution; | ||
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| public abstract class AbstractNgram extends LuceneFeatureExtractorBase | ||
| implements FeatureExtractor | ||
| { | ||
| protected Set<Feature> prepFeatSet; | ||
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| protected Set<Feature> getFeatureSet(FrequencyDistribution<String> fd) throws TextClassificationException { | ||
| /* | ||
| * Instead of iterating all top-k ngrams comparing them to all document ngrams for each | ||
| * iteration (expensive for large top-Ks),we build all features that might be created only once. | ||
| * We copy this feature map then for each call, which is cheaper and update only the values of those ngrams that are found. | ||
| * (TH 2018-09-23) | ||
| */ | ||
| Set<Feature> features = new HashSet<>(prepFeatSet); | ||
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| for (String ng : fd.getKeys()) { | ||
| if (topKSet.contains(ng)) { | ||
| // remove default value from set, i.e. feature name and value are part of the | ||
| // features identity. Thus, remove feature with value 0 and add new one with value | ||
| // 1. Just adding the same feature with a new value will NOT override the existing | ||
| // entry. | ||
| Feature feature = new Feature(getFeaturePrefix() + "_" + ng, 0, true, FeatureType.BOOLEAN); | ||
| features.remove(feature); | ||
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| //Set value to 1, i.e. feature found and mark the feature value as non-default value | ||
| feature.setValue(1); | ||
| feature.setDefault(false); | ||
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|
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| //add to set | ||
| features.add(feature); | ||
| } | ||
| } | ||
| return features; | ||
| } | ||
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| protected void prepare() throws TextClassificationException | ||
| { | ||
| prepFeatSet = new HashSet<>(1024); | ||
| //Iterate once all topK and init features | ||
| for(String topNgram : topKSet.getKeys()) { | ||
| Feature feature = new Feature(getFeaturePrefix() + "_" + topNgram, 0, true, FeatureType.BOOLEAN); | ||
| prepFeatSet.add(feature); | ||
| } | ||
| } | ||
| } |
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