wip

run.sh

@@ -12,6 +12,7 @@ MAIN="$PACKAGE_PREFIX.nytlabs.corpus.ACEEvents"
 MAIN="$PACKAGE_PREFIX.nytlabs.corpus.core.NYTAnnotations"
 MAIN="$PACKAGE_PREFIX.salience.learning.Main"
 MAIN="$PACKAGE_PREFIX.readers.LazyNYTReader"
+MAIN="$PACKAGE_PREFIX.features.MyTrainer"
 #MAIN="$PACKAGE_PREFIX.nytlabs.corpus.core.Evaluator"
 #MAIN="$PACKAGE_PREFIX.features.AllFeatures"
 

src/main/java/edu/illinois/cs/cogcomp/features/MyTrainer.java

@@ -45,20 +45,20 @@ public class MyTrainer {
         NoisyLabeler trainReader = new NoisyLabeler(d1);
         NoisyLabeler testReader = new NoisyLabeler(d2);
 
-//        NoisySupervisionReader trainReader = new NoisySupervisionReader(new NoisyLabeler(d1), true);
-//        NoisySupervisionReader testReader = new NoisySupervisionReader(new NoisyLabeler(d2), false);
+        FlatReader f1 = new FlatReader(new NoisyLabeler(d1), true);
+        FlatReader f2 = new FlatReader(new NoisyLabeler(d2), false);
 
-        getDist(trainReader);
-        getDist(testReader);
-        trainReader.reset();
-        testReader.reset();
-        getDist(trainReader);
-        getDist(testReader);
+//        getDist(trainReader);
+//        getDist(testReader);
+//        trainReader.reset();
+//        testReader.reset();
+//        getDist(trainReader);
+//        getDist(testReader);
 
 
-//        MyClassifier learner = new MyClassifier();
-//        BatchTrainer trainer = new BatchTrainer(learner, trainReader);
-//        trainer.train(1);
+        MyClassifier learner = new MyClassifier();
+        BatchTrainer trainer = new BatchTrainer(learner, trainReader);
+        trainer.train(1);
 
 //        getFeatureWeights(learner);
 
@@ -74,13 +74,14 @@ public class MyTrainer {
 //        LazyNYTReader goldReader = new LazyNYTReader(cc, alreadyCached, docs);
 //        NoisySupervisionReader gold= new NoisySupervisionReader(goldReader);
 
-//        trainReader.reset();
-//        testReader.reset();
-
+        trainReader.reset();
+        testReader.reset();
+        f1.reset();
+        f2.reset();
         // on train data
-//        test(learner, new AllFeatures(), trainReader);
+        test(learner, new AllFeatures(), f1);
         // on test data
-//        test(learner, new AllFeatures(), testReader);
+        test(learner, new AllFeatures(), f2);
 //        LazyNYTReader.main();
     }
 
@@ -144,23 +145,23 @@ public class MyTrainer {
         System.out.println("Dist: pos-"+pos+" neg-"+neg);
     }
 
-//    public static void test(MyClassifier learner, AllFeatures feat, NoisySupervisionReader gold){
-//        MyClassifier c = new MyClassifier();
-//
-////        SparseNetworkLearner.Parameters params = new SparseNetworkLearner.Parameters();
-////        c.setParameters(params);
-//
-//        TestDiscrete.testDiscrete(new TestDiscrete(), c, new MyLabel(), gold,  true, 1000);
-//
-////        EventInstance inst= (EventInstance) gold.next();
-////        while(inst!=null) {
-////            FeatureVector f = feat.classify(inst);
-////            System.out.println(f.featuresSize());
-////            String ans = learner.discreteValue(f);
-////            ScoreSet ss = learner.scores(inst);
-////            System.out.println(ss.values()+" "+ans);
-////            inst= (EventInstance) gold.next();
-////        }
-//    }
+    public static void test(MyClassifier learner, AllFeatures feat, FlatReader gold){
+        MyClassifier c = new MyClassifier();
+
+//        SparseNetworkLearner.Parameters params = new SparseNetworkLearner.Parameters();
+//        c.setParameters(params);
+
+        TestDiscrete.testDiscrete(new TestDiscrete(), c, new MyLabel(), gold,  true, 1000);
+
+//        EventInstance inst= (EventInstance) gold.next();
+//        while(inst!=null) {
+//            FeatureVector f = feat.classify(inst);
+//            System.out.println(f.featuresSize());
+//            String ans = learner.discreteValue(f);
+//            ScoreSet ss = learner.scores(inst);
+//            System.out.println(ss.values()+" "+ans);
+//            inst= (EventInstance) gold.next();
+//        }
+    }
 
 }