es.ehu.si.ixa.ixa.pipe.nerc.train.Flags Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of ixa-pipe-nerc Show documentation
Show all versions of ixa-pipe-nerc Show documentation
ixa pipes sequence labeler (ixa2.si.ehu.es/ixa-pipes).
package es.ehu.si.ixa.ixa.pipe.nerc.train;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
import opennlp.tools.util.TrainingParameters;
import es.ehu.si.ixa.ixa.pipe.nerc.features.XMLFeatureDescriptor;
public class Flags {
public static final String DEFAULT_FEATURE_FLAG = "no";
public static final String CHAR_NGRAM_RANGE = "2:5";
public static final String DEFAULT_WINDOW = "2:2";
/**
* Default beam size for decoding.
*/
public static final int DEFAULT_BEAM_SIZE = 3;
public static final int DEFAULT_FOLDS_VALUE = 10;
public static final String DEFAULT_EVALUATE_MODEL = "off";
public static final String DEFAULT_NE_TYPES = "off";
public static final String DEFAULT_LEXER = "off";
public static final String DEFAULT_DICT_OPTION = "off";
public static final String DEFAULT_DICT_PATH = "off";
public static final String DEFAULT_OUTPUT_FORMAT="naf";
public static final String DEFAULT_SEQUENCE_CODEC = "BILOU";
public static final String DEFAULT_EVAL_FORMAT = "opennlp";
private Flags() {
}
public static String getLanguage(TrainingParameters params) {
String lang = null;
if (params.getSettings().get("Language") == null) {
langException();
} else {
lang = params.getSettings().get("Language");
}
return lang;
}
public static String getDataSet(String dataset, TrainingParameters params) {
String trainSet = null;
if (params.getSettings().get(dataset) == null) {
datasetException();
} else {
trainSet = params.getSettings().get(dataset);
}
return trainSet;
}
public static String getModel(TrainingParameters params) {
String model = null;
if (params.getSettings().get("OutputModel") == null) {
modelException();
} else if (params.getSettings().get("OutputModel") != null
&& params.getSettings().get("OutputModel").length() == 0) {
modelException();
} else {
model = params.getSettings().get("OutputModel");
}
return model;
}
public static String getCorpusFormat(TrainingParameters params) {
String corpusFormat = null;
if (params.getSettings().get("CorpusFormat") == null) {
corpusFormatException();
} else {
corpusFormat = params.getSettings().get("CorpusFormat");
}
return corpusFormat;
}
public static String getOutputFormat(TrainingParameters params) {
String outFormatOption = null;
if (params.getSettings().get("OutputFormat") != null) {
outFormatOption = params.getSettings().get("OutputFormat");
} else {
outFormatOption = Flags.DEFAULT_OUTPUT_FORMAT;
}
return outFormatOption;
}
public static Integer getBeamsize(TrainingParameters params) {
Integer beamsize = null;
if (params.getSettings().get("BeamSize") == null) {
beamsize = Flags.DEFAULT_BEAM_SIZE;
} else {
beamsize = Integer.parseInt(params.getSettings().get("BeamSize"));
}
return beamsize;
}
public static Integer getFolds(TrainingParameters params) {
Integer beamsize = null;
if (params.getSettings().get("Folds") == null) {
beamsize = Flags.DEFAULT_FOLDS_VALUE;
} else {
beamsize = Integer.parseInt(params.getSettings().get("Folds"));
}
return beamsize;
}
public static String getSequenceCodec(TrainingParameters params) {
String seqCodec = null;
if (params.getSettings().get("SequenceCodec") == null) {
seqCodec = Flags.DEFAULT_SEQUENCE_CODEC;
} else {
seqCodec = params.getSettings().get("SequenceCodec");
}
return seqCodec;
}
public static String getWindow(TrainingParameters params) {
String windowFlag = null;
if (params.getSettings().get("Window") == null) {
windowFlag = Flags.DEFAULT_WINDOW;
} else {
windowFlag = params.getSettings().get("Window");
}
return windowFlag;
}
public static String getTokenFeatures(TrainingParameters params) {
String tokenFlag = null;
if (params.getSettings().get("TokenFeatures") != null) {
tokenFlag = params.getSettings().get("TokenFeatures");
} else {
tokenFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return tokenFlag;
}
public static String getTokenClassFeatures(TrainingParameters params) {
String tokenClassFlag = null;
if (params.getSettings().get("TokenClassFeatures") != null) {
tokenClassFlag = params.getSettings().get("TokenClassFeatures");
} else {
tokenClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return tokenClassFlag;
}
public static String getTokenPatternFeatures(TrainingParameters params) {
String tokenClassFlag = null;
if (params.getSettings().get("TokenPatternFeatures") != null) {
tokenClassFlag = params.getSettings().get("TokenPatternFeatures");
} else {
tokenClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return tokenClassFlag;
}
public static String getOutcomePriorFeatures(TrainingParameters params) {
String outcomePriorFlag = null;
if (params.getSettings().get("OutcomePriorFeatures") != null) {
outcomePriorFlag = params.getSettings().get("OutcomePriorFeatures");
} else {
outcomePriorFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return outcomePriorFlag;
}
public static String getPreviousMapFeatures(TrainingParameters params) {
String previousMapFlag = null;
if (params.getSettings().get("PreviousMapFeatures") != null) {
previousMapFlag = params.getSettings().get("PreviousMapFeatures");
} else {
previousMapFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return previousMapFlag;
}
public static String getSentenceFeatures(TrainingParameters params) {
String sentenceFlag = null;
if (params.getSettings().get("SentenceFeatures") != null) {
sentenceFlag = params.getSettings().get("SentenceFeatures");
} else {
sentenceFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return sentenceFlag;
}
public static String getPreffixFeatures(TrainingParameters params) {
String prefixFlag = null;
if (params.getSettings().get("PrefixFeatures") != null) {
prefixFlag = params.getSettings().get("PrefixFeatures");
} else {
prefixFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return prefixFlag;
}
public static String getSuffixFeatures(TrainingParameters params) {
String suffixFlag = null;
if (params.getSettings().get("SuffixFeatures") != null) {
suffixFlag = params.getSettings().get("SuffixFeatures");
} else {
suffixFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return suffixFlag;
}
public static String getBigramClassFeatures(TrainingParameters params) {
String bigramClassFlag = null;
if (params.getSettings().get("BigramClassFeatures") != null) {
bigramClassFlag = params.getSettings().get("BigramClassFeatures");
} else {
bigramClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return bigramClassFlag;
}
public static String getTrigramClassFeatures(TrainingParameters params) {
String trigramClassFlag = null;
if (params.getSettings().get("TrigramClassFeatures") != null) {
trigramClassFlag = params.getSettings().get("TrigramClassFeatures");
} else {
trigramClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return trigramClassFlag;
}
public static String getFourgramClassFeatures(TrainingParameters params) {
String fourgramClassFlag = null;
if (params.getSettings().get("FourgramClassFeatures") != null) {
fourgramClassFlag = params.getSettings().get("FourgramClassFeatures");
} else {
fourgramClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return fourgramClassFlag;
}
public static String getFivegramClassFeatures(TrainingParameters params) {
String fivegramClassFlag = null;
if (params.getSettings().get("FivegramClassFeatures") != null) {
fivegramClassFlag = params.getSettings().get("FivegramClassFeatures");
} else {
fivegramClassFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return fivegramClassFlag;
}
public static String getCharNgramFeatures(TrainingParameters params) {
String charNgramFlag = null;
if (params.getSettings().get("CharNgramFeatures") != null) {
charNgramFlag = params.getSettings().get("CharNgramFeatures");
} else {
charNgramFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return charNgramFlag;
}
public static String getCharNgramFeaturesRange(TrainingParameters params) {
String charNgramRangeFlag = null;
if (params.getSettings().get("CharNgramFeaturesRange") != null) {
charNgramRangeFlag = params.getSettings().get("CharNgramFeaturesRange");
} else {
charNgramRangeFlag = Flags.CHAR_NGRAM_RANGE;
}
return charNgramRangeFlag;
}
public static String getDictionaryFeatures(TrainingParameters params) {
String dictionaryFlag = null;
if (params.getSettings().get("DictionaryFeatures") != null) {
dictionaryFlag = params.getSettings().get("DictionaryFeatures");
} else {
dictionaryFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return dictionaryFlag;
}
public static String getClarkFeatures(TrainingParameters params) {
String distSimFlag = null;
if (params.getSettings().get("ClarkClusterFeatures") != null) {
distSimFlag = params.getSettings().get("ClarkClusterFeatures");
} else {
distSimFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return distSimFlag;
}
public static String getWord2VecClusterFeatures(TrainingParameters params) {
String word2vecFlag = null;
if (params.getSettings().get("Word2VecClusterFeatures") != null) {
word2vecFlag = params.getSettings().get("Word2VecClusterFeatures");
} else {
word2vecFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return word2vecFlag;
}
public static String getBrownFeatures(TrainingParameters params) {
String brownFlag = null;
if (params.getSettings().get("BrownClusterFeatures") != null) {
brownFlag = params.getSettings().get("BrownClusterFeatures");
} else {
brownFlag = Flags.DEFAULT_FEATURE_FLAG;
}
return brownFlag;
}
/**
* Get a parameter in trainParams.prop file consisting of a list of
* clustering lexicons separated by comma "," and return a list of
* files, one for each lexicon.
* @param clusterPath the clustering parameter in the prop file
* @return a list of files one for each lexicon
*/
public static List getClusterLexiconFiles(String clusterPath) {
List clusterLexicons = new ArrayList();
String[] clusterPaths = clusterPath.split(",");
for (String clusterName : clusterPaths) {
clusterLexicons.add(new File(clusterName));
}
return clusterLexicons;
}
public static void devSetException() {
System.err
.println("UseDevSet options in the parameters file if CrossEval is activated!");
System.exit(1);
}
public static void modelException() {
System.err
.println("Please provide a model in the OutputModel field in the parameters file!");
System.exit(1);
}
public static void langException() {
System.err
.println("Please fill in the Language field in the parameters file!");
System.exit(1);
}
public static void datasetException() {
System.err
.println("Please specify your training/testing sets in the TrainSet and TestSet fields in the parameters file!");
System.exit(1);
}
public static void corpusFormatException() {
System.err
.println("Please fill in CorpusFormat field in the parameters file!");
System.exit(1);
}
public static void dictionaryException() {
System.err
.println("You need to set the --dictPath option to the dictionaries directory to use the dictTag option!");
System.exit(1);
}
public static void dictionaryFeaturesException() {
System.err
.println("You need to specify the DictionaryFeatures in the parameters file to use the DictionaryPath!");
System.exit(1);
}
/**
* @param params
* @return whether the word2vecClusterfeatures are activated or not
*/
public static boolean isWord2VecClusterFeatures(TrainingParameters params) {
String word2vecClusterFeatures = getWord2VecClusterFeatures(params);
return !word2vecClusterFeatures
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isClarkFeatures(TrainingParameters params) {
String clarkFeatures = getClarkFeatures(params);
return !clarkFeatures
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isBrownFeatures(TrainingParameters params) {
String brownFeatures = getBrownFeatures(params);
return !brownFeatures
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isDictionaryFeatures(TrainingParameters params) {
String dictFeatures = getDictionaryFeatures(params);
return !dictFeatures
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isCharNgramClassFeature(TrainingParameters params) {
XMLFeatureDescriptor.setNgramRange(params);
String charngramParam = getCharNgramFeatures(params);
return !charngramParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isFivegramClassFeature(TrainingParameters params) {
String fivegramParam = getFivegramClassFeatures(params);
return !fivegramParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isFourgramClassFeature(TrainingParameters params) {
String fourgramParam = getFourgramClassFeatures(params);
return !fourgramParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isTrigramClassFeature(TrainingParameters params) {
String trigramParam = getTrigramClassFeatures(params);
return !trigramParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isBigramClassFeature(TrainingParameters params) {
String bigramParam = getBigramClassFeatures(params);
return !bigramParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isSuffixFeature(TrainingParameters params) {
String suffixParam = getSuffixFeatures(params);
return !suffixParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isPrefixFeature(TrainingParameters params) {
String prefixParam = getPreffixFeatures(params);
return !prefixParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isSentenceFeature(TrainingParameters params) {
String sentenceParam = getSentenceFeatures(params);
return !sentenceParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isPreviousMapFeature(TrainingParameters params) {
String previousMapParam = getPreviousMapFeatures(params);
return !previousMapParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isOutcomePriorFeature(TrainingParameters params) {
String outcomePriorParam = getOutcomePriorFeatures(params);
return !outcomePriorParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isTokenClassFeature(TrainingParameters params) {
String tokenParam = getTokenClassFeatures(params);
return !tokenParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
public static boolean isTokenFeature(TrainingParameters params) {
String tokenParam = getTokenFeatures(params);
return !tokenParam
.equalsIgnoreCase(Flags.DEFAULT_FEATURE_FLAG);
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy