changed metric prints

classifier/nn.py

@@ -25,7 +25,7 @@ class BiLstmClassifier(Model):
         self.elmo = elmo
         self.use_elmo = use_input_elmo
         self.text_field_embedder = text_field_embedder
-        self.num_classes = self.vocab.get_vocab_size("label")
+        self.num_classes = self.vocab.get_vocab_size("labels")
         self.encoder = encoder
         self.classifier_feed_forward = classifier_feedforward
         self.label_accuracy = CategoricalAccuracy()
@@ -33,7 +33,7 @@ class BiLstmClassifier(Model):
         self.label_f1_metrics = {}
 
         for i in range(self.num_classes):
-            self.label_f1_metrics[vocab.get_token_from_index(index=i, namespace="label")] = \
+            self.label_f1_metrics[vocab.get_token_from_index(index=i, namespace="labels")] = \
                 F1Measure(positive_label=i)
 
         self.loss = torch.nn.CrossEntropyLoss()
@@ -86,7 +86,7 @@ class BiLstmClassifier(Model):
 
             # compute F1 per label
             for i in range(self.num_classes):
-                metric = self.label_f1_metrics[self.vocab.get_token_from_index(index=i, namespace="label")]
+                metric = self.label_f1_metrics[self.vocab.get_token_from_index(index=i, namespace="labels")]
                 metric(class_probabilities, label)
             output_dict['label'] = label
 
@@ -99,7 +99,7 @@ class BiLstmClassifier(Model):
         class_probabilities = torch.nn.functional.softmax(output_dict['logits'], dim=-1)
         predictions = class_probabilities.cpu().data.numpy()
         argmax_indices = np.argmax(predictions, axis=-1)
-        label = [self.vocab.get_token_from_index(x, namespace="label")
+        label = [self.vocab.get_token_from_index(x, namespace="labels")
                  for x in argmax_indices]
         output_dict['probabilities'] = class_probabilities
         output_dict['positive_label'] = label
@@ -118,8 +118,6 @@ class BiLstmClassifier(Model):
         sum_f1 = 0.0
         for name, metric in self.label_f1_metrics.items():
             metric_val = metric.get_metric(reset)
-            metric_dict[name + '_P'] = metric_val[0]
-            metric_dict[name + '_R'] = metric_val[1]
             metric_dict[name + '_F1'] = metric_val[2]
             if name != 'none':  # do not consider `none` label in averaging F1
                 sum_f1 += metric_val[2]
@@ -127,7 +125,7 @@ class BiLstmClassifier(Model):
         names = list(self.label_f1_metrics.keys())
         total_len = len(names) if 'none' not in names else len(names) - 1
         average_f1 = sum_f1 / total_len
-        metric_dict['average_F1'] = average_f1
+        metric_dict['AVG_F1_Score'] = average_f1
 
         return metric_dict
 

testing/intent_predictor.py

@@ -0,0 +1,17 @@
+from allennlp.common import JsonDict
+from allennlp.data import Instance
+from allennlp.predictors import Predictor
+from overrides import overrides
+
+
+@Predictor.register('citation_intent_predictor')
+class IntentClassificationPredictor(Predictor):
+    """"Predictor for Citation Intent Classifier"""
+
+    @overrides
+    def _json_to_instance(self, json_dict: JsonDict) -> Instance:
+        pass
+
+    @overrides
+    def predict_json(self, inputs: JsonDict) -> JsonDict:
+        pass