Acquire and plot attention values for targets

ADA/SA/bert_analyzer.py

@@ -7,6 +7,7 @@ from bert_dataset import BertDataset, Instance, polarity_indices, generate_batch
 import time
 import numpy as np
 from sklearn import metrics
+import matplotlib.pyplot as plt
 
 semeval_2014_train_path = 'data/SemEval-2014/Laptop_Train_v2.xml'
 semeval_2014_test_path = 'data/SemEval-2014/Laptops_Test_Gold.xml'
@@ -93,13 +94,25 @@ class BertAnalyzer:
 
     def analyze_sentence(self, text, char_from, char_to):
         instance = Instance(text, char_from, char_to)
-        _, tg_from, tg_to = instance.get()
+        tokens, tg_from, tg_to = instance.get()
         texts, target_indices = instance.to_tensor()
 
         with torch.no_grad():
             outputs, attentions = self.net(texts, target_indices)
 
         target_attentions = torch.mean(attentions, 1)[0][tg_from+1:tg_to+2]
+        mean_target_att = torch.mean(target_attentions, 0)
+
+        # plot attention histogram
+        att_values = mean_target_att.numpy()[1:-1]
+
+        ax = plt.subplot(111)
+        width = 0.3
+        bins = [x - width/2 for x in range(1, len(att_values)+1)]
+        ax.bar(bins, att_values, width=width)
+        ax.set_xticks(list(range(1, len(att_values)+1)))
+        ax.set_xticklabels(tokens, rotation=45, rotation_mode='anchor', ha='right')
+        plt.show()
 
         _, pred = torch.max(outputs.data, 1)
         return pred
@@ -107,4 +120,5 @@ class BertAnalyzer:
 
 sentiment_analyzer = BertAnalyzer()
 sentiment_analyzer.load_saved()
-sentiment = sentiment_analyzer.analyze_sentence('I hate this laptop', 12, 18)
+sentiment = sentiment_analyzer.analyze_sentence('I will never buy another computer from HP/Compaq or do business with Circuit City again.', 39, 48)
+print('sentiment:', sentiment)
\ No newline at end of file

ADA/SA/bert_dataset.py

@@ -16,7 +16,6 @@ def generate_batch(batch):
                                         return_tensors='pt')
 
     max_tg_len = max(entry['to'] - entry['from'] for entry in batch)
-    print(max_tg_len)
     target_indices = torch.tensor([[[min(t, entry['to'])] * HIDDEN_OUTPUT_FEATURES
                                     for t in range(entry['from'], entry['from'] + max_tg_len + 1)]
                                    for entry in batch])
@@ -27,13 +26,11 @@ def generate_batch(batch):
 
 
 def token_for_char(char_idx, text, tokens):
-    compressed_idx = len(re.sub(r'\s+', '', text)[:char_idx+1]) - 1
-
+    compressed_idx = len(re.sub(r'\s+', '', text[:char_idx+1])) - 1
     token_idx = -1
     while compressed_idx >= 0:
         token_idx += 1
         compressed_idx -= len(tokens[token_idx].replace('##', ''))
-
     return token_idx
 
 
@@ -80,7 +77,7 @@ class Instance:
     def get(self):
         tokens = tokenizer.tokenize(self.text)
         idx_from = token_for_char(self.char_from, self.text, tokens)
-        idx_to = token_for_char(self.char_to, self.text, tokens)
+        idx_to = token_for_char(self.char_to-1, self.text, tokens)
         return tokens, idx_from, idx_to
 
     def to_tensor(self):