Revision: 17105
http://sourceforge.net/p/gate/code/17105
Author: valyt
Date: 2013-11-19 12:27:16 +0000 (Tue, 19 Nov 2013)
Log Message:
-----------
The categorization PR implemented, and it even compiles.
No warranties provided as to it actually doing anything just yet...
Modified Paths:
--------------
gate/trunk/plugins/Text_Categorization/src/gate/ml/categorization/TextCategorizationPR.java
Modified:
gate/trunk/plugins/Text_Categorization/src/gate/ml/categorization/TextCategorizationPR.java
===================================================================
---
gate/trunk/plugins/Text_Categorization/src/gate/ml/categorization/TextCategorizationPR.java
2013-11-19 11:15:12 UTC (rev 17104)
+++
gate/trunk/plugins/Text_Categorization/src/gate/ml/categorization/TextCategorizationPR.java
2013-11-19 12:27:16 UTC (rev 17105)
@@ -1,23 +1,20 @@
-/**
- *
+/*
+ * TextCategorizationPR.java
+ *
+ * Copyright (c) 1995-2013, The University of Sheffield. See the file
+ * COPYRIGHT.txt in the software or at http://gate.ac.uk/gate/COPYRIGHT.txt
+ *
+ * This file is part of GATE (see http://gate.ac.uk/), and is free
+ * software, licenced under the GNU Library General Public License,
+ * Version 2, June 1991 (in the distribution as file licence.html,
+ * and also available at http://gate.ac.uk/gate/licence.html).
+ *
+ * Valentin Tablan, 19 Nov 2013
+ *
+ * $Id$
*/
package gate.ml.categorization;
-import java.io.BufferedInputStream;
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.io.StringReader;
-import java.io.UnsupportedEncodingException;
-import java.net.URISyntaxException;
-import java.net.URL;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Map;
-import java.util.Properties;
-import java.util.Set;
-
import de.bwaldvogel.liblinear.Feature;
import de.bwaldvogel.liblinear.FeatureNode;
import de.bwaldvogel.liblinear.Linear;
@@ -29,6 +26,8 @@
import edu.ucla.sspace.vector.DoubleVector;
import gate.Annotation;
import gate.AnnotationSet;
+import gate.Factory;
+import gate.FeatureMap;
import gate.LanguageAnalyser;
import gate.Resource;
import gate.Utils;
@@ -39,6 +38,20 @@
import gate.creole.metadata.CreoleResource;
import gate.creole.metadata.RunTime;
+import java.io.BufferedInputStream;
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.io.StringReader;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Properties;
+import java.util.Set;
+
+import org.apache.log4j.Logger;
+
/**
* A simple text classification PR, using a <a
* href="https://github.com/fozziethebeat/S-Space/";>Semantic Space</a>
@@ -50,6 +63,8 @@
public class TextCategorizationPR extends AbstractLanguageAnalyser implements
LanguageAnalyser {
+ private static Logger logger = Logger.getLogger(TextCategorizationPR.class);
+
/**
* Serialisation UID.
*/
@@ -68,22 +83,34 @@
*/
protected Model libLinearModel;
+
/**
* Thresholds to be used when classifying text annotations. If the model
* classifies an input annotation as class "X", and "X"
* exists as a key in this map, then the classification is only effected if
* the classification probability emitted by the model is greater than or
* equal to the value in the map. Values in this map are expected to be
- * probabilities, i.e. positive values between 0.0 and 1.0.
+ * probabilities, i.e. positive values between 0.0 and 1.0. All negative
+ * values are ignored and the default will be used instead (the category
with
+ * the highest probability wins, regardless of what the probability actually
+ * is).
*
* Note that the liblinear model used must be able to produce classification
* probabilities. At the time of writing, only Linear Regression models are
* able to do so, while SVM-based one are not. Models that cannot supply
* probabilities will simply return 1.0 as the classification probability,
* causing values in this map to have no effect.
+ */
+ protected double[] categoryThresholds;
+
+
+ /**
+ * The String labels for the categories (the LIBLINEAR classifier simply
+ * returns integer values for the labels).
*/
- protected Map<String, Double> customClassThresholds;
+ protected String[] categoryLabels;
+
/**
* The set of stop-words to be used. The values in this list are matched
* verbatim against the values of the configured input feature on the input
@@ -147,6 +174,10 @@
*/
protected URL modelURL;
+ /**
+ * URL to a file defining the categories used by this PR.
+ */
+ protected URL categoriesURL;
@Override
public Resource init() throws ResourceInstantiationException {
@@ -192,15 +223,16 @@
// load the stop words
stopWords = null;
if(stopWordsURL != null) {
+ BufferedReader swReader = null;
try {
- BufferedReader swReader = new BufferedReader(
+ swReader = new BufferedReader(
new InputStreamReader(stopWordsURL.openStream(), "UTF-8"));
stopWords = new HashSet<String>();
String line = swReader.readLine();
while(line != null) {
line = line.trim();
- if(line.startsWith("#") || line.startsWith("//")) {
- //ignore comment
+ if(line.startsWith("#") || line.startsWith("//") || line.length() ==
0) {
+ // ignore comment and empty lines
} else {
stopWords.add(line);
}
@@ -209,8 +241,90 @@
} catch(IOException e) {
throw new ResourceInstantiationException(
"I/O error while reading the stop words.", e);
+ } finally {
+ if (swReader != null) {
+ try {
+ swReader.close();
+ } catch(IOException e) {
+ throw new ResourceInstantiationException(
+ "I/O error whilke closing the stop words file.", e);
+ }
+ }
}
}
+
+ // read the categories
+ List<String> categoryNames = new ArrayList<String>();
+ List<Double> categoryProbs = new ArrayList<Double>();
+ boolean customProbs = false;
+ BufferedReader catReader = null;
+ if(categoriesURL != null) {
+ try {
+ catReader = new BufferedReader(
+ new InputStreamReader(categoriesURL.openStream(), "UTF-8"));
+ String line = catReader.readLine();
+ while(line != null) {
+ line = line.trim();
+ if(line.startsWith("#") || line.startsWith("//") || line.length() ==
0) {
+ // ignore comment and empty lines
+ } else {
+ String[] elems = line.split(",");
+ if(elems.length == 0) {
+ logger.warn("Ignoring illegal line in categories file: \"" +
+ line + "\".");
+ } else {
+ categoryNames.add(elems[0]);
+ double prob = -1;
+ if(elems.length > 1) {
+ try {
+ prob = Double.parseDouble(elems[1]);
+ } catch(NumberFormatException e) {
+ logger.error(
+ "Illegal value for probablity in categories file \"" +
+ elems[1] + "\" was ignored.");
+ }
+ }
+ categoryProbs.add(prob);
+ if(prob > 0) customProbs = true;
+ if(elems.length > 2) {
+ logger.warn("Line in categories file has more than 2 entries."
+
+ " Entries starting with " + elems[2] + " were ignored. " +
+ "Line was:\n" + line);
+ }
+ }
+ }
+ line = catReader.readLine();
+ }
+ categoryLabels = categoryNames.toArray(new
String[categoryNames.size()]);
+ if(customProbs) {
+ if(libLinearModel.isProbabilityModel()) {
+ categoryThresholds = new double[categoryProbs.size()];
+ for(int i = 0; i< categoryThresholds.length; i++) {
+ categoryThresholds[i] = categoryProbs.get(i);
+ }
+ } else {
+ logger.warn("The LIBLINEAR model provided cannot supply " +
+ "probabilities. Custom probability thresholds will be
ignored.");
+ categoryThresholds = null;
+ }
+ }
+ } catch (IOException ioe){
+ throw new ResourceInstantiationException(
+ "I/O error while reading the categories file.", ioe);
+ } finally {
+ if(catReader != null) try {
+ catReader.close();
+ } catch (IOException ioe) {
+ throw new ResourceInstantiationException(
+ "I/O error while closing the categories file.", ioe);
+ }
+ }
+ } else {
+ throw new ResourceInstantiationException(
+ "No categories file URL was provided.");
+ }
+
+
return super.init();
}
@@ -239,6 +353,7 @@
throw new ExecutionException("No output feature name provided.");
}
+ AnnotationSet outputAS = document.getAnnotations(outputASName);
// collect instance annotations
AnnotationSet inputAS = document.getAnnotations(inputASName);
@@ -273,12 +388,35 @@
features[i] = new FeatureNode(i, instanceVector.get(i));
}
double[] probs = new double[libLinearModel.getNrClass()];
- double label = Linear.predictValues(libLinearModel, features, probs);
+ // the value returned is always an int. Returned as a double because
+ // that's what cool C programmers do, or whatever...
+ // We're uncool Java types, so we bring it back to int.
+ int label = (int) Linear.predictValues(libLinearModel, features,
probs);
+ double probability = 1.0;
// do we need to check probabilities?
- // TODO
- if(customClassThresholds != null && customClassThresholds.size() > 0) {
-
+ if(categoryThresholds != null && categoryThresholds[label] > 0) {
+ // prob[i] is the value of $\theta_T x$
+ // Once mapped through the logistic function, this becomes the
+ // probability of the instance belonging to class i, as opposed to
+ // all other classes.
+ // LIBLINEAR would normalize this so that all probs sum up to 1,
+ // but we don't want that, as we're only interested in the confidence
+ // the model has in this particular classification.
+ probability = probs[label];
+ // convert to an actual probability, by applying the logistic
function
+ probability = 1 / (1 + Math.exp(-probability));
+ if(probability < categoryThresholds[label]) probability = -1;
}
+ if(probability > 0) {
+ // effect the classification
+ if(sameAnnotation) {
+ instAnn.getFeatures().put(outputFeatureName,
categoryLabels[label]);
+ } else {
+ FeatureMap fm = Factory.newFeatureMap();
+ fm.put(outputFeatureName, categoryLabels[label]);
+ Utils.addAnn(outputAS, instAnn, outputAnnotationType, fm);
+ }
+ }
}
}
@@ -287,8 +425,9 @@
@Override
public void cleanup() {
- // TODO Auto-generated method stub
- super.cleanup();
+ libLinearModel = null;
+ semanticSpace = null;
+ stopWords = null;
}
public String getInputASName() {
@@ -445,7 +584,19 @@
public void setStopWordsURL(URL stopWordsURL) {
this.stopWordsURL = stopWordsURL;
}
+
+ public URL getCategoriesURL() {
+ return categoriesURL;
+ }
+
+ /**
+ * Sets the URL to the file containing the categories.
+ * @param categoriesURL
+ */
+ @CreoleParameter(comment = "A comma-separated file containing the category "
+
+ "name and, optionally, a custom probability threshold for that
category.")
+ public void setCategoriesURL(URL categoriesURL) {
+ this.categoriesURL = categoriesURL;
+ }
-
-
}
This was sent by the SourceForge.net collaborative development platform, the
world's largest Open Source development site.
------------------------------------------------------------------------------
Shape the Mobile Experience: Free Subscription
Software experts and developers: Be at the forefront of tech innovation.
Intel(R) Software Adrenaline delivers strategic insight and game-changing
conversations that shape the rapidly evolving mobile landscape. Sign up now.
http://pubads.g.doubleclick.net/gampad/clk?id=63431311&iu=/4140/ostg.clktrk
_______________________________________________
GATE-cvs mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/gate-cvs
