/*
* Copyright (c) 2015-2016 The University Of Sheffield.
*
* This file is part of gateplugin-LearningFramework
* (see https://github.com/GateNLP/gateplugin-LearningFramework).
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 2.1 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this software. If not, see <http://www.gnu.org/licenses/>.
*/
package gate.plugin.learningframework.engines;
import gate.util.GateRuntimeException;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.net.URL;
import java.util.List;
import java.util.Objects;
import org.yaml.snakeyaml.DumperOptions;
import org.yaml.snakeyaml.Yaml;
import org.yaml.snakeyaml.constructor.CustomClassLoaderConstructor;
import org.yaml.snakeyaml.nodes.Tag;
import static gate.plugin.learningframework.LFUtils.newURL;
import java.io.IOException;
/**
* A class that represents the information stored in the info file.
* This class also has static methods for storing and loading itself.
* @author Johann Petrak
*/
public class Info {
public static final String FILENAME_INFO = "info.yaml";
public String engineClass; // this also can tell us if classifier or sequence tagging algorihtm
public String algorithmClass; // the class of the enum
public String algorithmName; // the actual value of enum
public String trainerClass;
public String modelClass;
public String task; // classification, regression or sequence tagging?
public int nrTrainingInstances;
public int nrTrainingDocuments;
public int nrTrainingDimensions;
public int nrTargetValues; // -1 for regression
public List<String> classLabels; // empty for regression
public String trainingCorpusName;
public String targetFeature;
public String classAnnotationType; // classAnnotationType for classification
public List<String> classAnnotationTypes; // for sequence tagging
public String seqEncoderClass;
public String seqEncoderOptions;
public String modelWhenTrained; // date-time of when the model finished training
public String algorithmParameters = "";
/**
* TODO: NOTE: this is incomplete!! Should contain all fields that are also in the hashCode method!
* For now we have only included the fields we need for the unit test.
*
* @param other instance to compare with
* @return equality flag
*/
@Override
public boolean equals(Object other) {
if(other == null) {
return false;
}
if (other instanceof Info) {
if(engineClass!=null && !engineClass.equals(((Info) other).engineClass)) {
return false;
}
if(trainerClass!=null && !trainerClass.equals(((Info) other).trainerClass)) {
return false;
}
}
return true;
}
@Override
public int hashCode() {
int hash = 7;
hash = 89 * hash + Objects.hashCode(this.engineClass);
hash = 89 * hash + Objects.hashCode(this.trainerClass);
hash = 89 * hash + Objects.hashCode(this.task);
hash = 89 * hash + this.nrTrainingInstances;
hash = 89 * hash + this.nrTrainingDocuments;
hash = 89 * hash + this.nrTrainingDimensions;
hash = 89 * hash + this.nrTargetValues;
hash = 89 * hash + Objects.hashCode(this.classLabels);
hash = 89 * hash + Objects.hashCode(this.trainingCorpusName);
hash = 89 * hash + Objects.hashCode(this.algorithmParameters);
return hash;
}
/**
* Save to directory.
* @param directory directory to save to.
*/
public void save(File directory) {
CustomClassLoaderConstructor constr =
new CustomClassLoaderConstructor(this.getClass().getClassLoader());
String dump =
new Yaml(constr)
.dumpAs(this,Tag.MAP,DumperOptions.FlowStyle.BLOCK);
File infoFile = new File(directory,FILENAME_INFO);
//System.err.println("Saving engine to "+infoFile);
try (OutputStreamWriter out = new OutputStreamWriter(new FileOutputStream(infoFile),"UTF-8")) {
out.append(dump);
} catch (IOException ex) {
throw new GateRuntimeException("Could not write info file "+infoFile,ex);
}
}
/**
* Load from directory.
* @param directory directory to load from
* @return Info instance
*/
public static Info load(URL directory) {
CustomClassLoaderConstructor constr =
new CustomClassLoaderConstructor(Info.class.getClassLoader());
Yaml yaml = new Yaml(constr);
Object obj;
URL infoFile = newURL(directory,FILENAME_INFO);
try (InputStream is = infoFile.openStream()) {
obj = yaml.loadAs(new InputStreamReader(is,"UTF-8"),Info.class);
} catch (IOException ex) {
throw new GateRuntimeException("Could not load info file "+infoFile,ex);
}
Info info = (Info)obj;
return info;
}
@Override
public String toString() {
return "Info{" + "engineClass=" + engineClass +
", algorithmClass=" + trainerClass +
", task=" + task +
", nrTrainingInstances=" + nrTrainingInstances +
", nrTrainingDocuments=" + nrTrainingDocuments +
", nrTrainingDimensions=" + nrTrainingDimensions +
", nrTargetValues=" + nrTargetValues +
", classLabels=" + classLabels + ", trainingCorpusName=" + trainingCorpusName + '}';
}
public String toFormattedString() {
StringBuilder sb = new StringBuilder();
sb.append("Info.engineClass: "); sb.append(engineClass); sb.append("\n");
sb.append("Info.algorithmClass: "); sb.append(trainerClass); sb.append("\n");
sb.append("Info.algorithmParameters: "); sb.append(algorithmParameters); sb.append("\n");
sb.append("Info.task: "); sb.append(task); sb.append("\n");
sb.append("Info.nrTrainingInstances: "); sb.append(nrTrainingInstances); sb.append("\n");
sb.append("Info.nrTrainingDocuments: "); sb.append(nrTrainingDocuments); sb.append("\n");
sb.append("Info.nrTrainingDimensions: "); sb.append(nrTrainingDimensions); sb.append("\n");
sb.append("Info.nrTargetValues: "); sb.append(nrTargetValues); sb.append("\n");
sb.append("Info.classLabels: "); sb.append(classLabels); sb.append("\n");
sb.append("Info.trainingCorpus: "); sb.append(trainingCorpusName); sb.append("\n");
sb.append("Info.task: "); sb.append(task); sb.append("\n");
sb.append("Info.seqEncoderClass: "); sb.append(seqEncoderClass); sb.append("\n");
sb.append("Info.seqEncoderOptions: "); sb.append(seqEncoderOptions); sb.append("\n");
sb.append("Info.modelWhenTrained: "); sb.append(modelWhenTrained); sb.append("\n");
sb.append("Info.algorithmParameters: "); sb.append(algorithmParameters); sb.append("\n");
return sb.toString();
}
}
