Update svm_predict.py

svm_predict.py

@@ -4,7 +4,7 @@ from nltk import pos_tag
 import joblib
 from train import feature_vector, pos_tags
 
-model = joblib.load('ner_svm_4_withpos_kaggle.pkl')
+model = joblib.load('SVM_NEI_model.pkl')
 nltk.download('averaged_perceptron_tagger_eng')
 nltk.download('punkt_tab')
 
@@ -12,14 +12,16 @@ def predict(sentence):
     tokens = word_tokenize(sentence)
     sent_pos_tags = pos_tag(tokens)
     predictions = []
+    l = len(tokens)
     for idx, word in enumerate(tokens):
-        prev_tag = -1 if idx==0 else sent_pos_tags[idx-1][1]
-        next_tag = -1 if idx==len(tokens)-1 else sent_pos_tags[idx+1][1]
-        current_tag = sent_pos_tags[idx][1]
-        prev_idx = pos_tags.index(prev_tag) if prev_tag in pos_tags else -1
-        next_idx = pos_tags.index(next_tag) if next_tag in pos_tags else -1
-        current_idx = pos_tags.index(current_tag) if current_tag in pos_tags else -1
-        vec = feature_vector(word, prev_idx, next_idx, current_idx)
+        # prev_tag = -1 if idx==0 else sent_pos_tags[idx-1][1]
+        # next_tag = -1 if idx==len(tokens)-1 else sent_pos_tags[idx+1][1]
+        # current_tag = sent_pos_tags[idx][1]
+        # prev_idx = pos_tags.index(prev_tag) if prev_tag in pos_tags else -1
+        # next_idx = pos_tags.index(next_tag) if next_tag in pos_tags else -1
+        # current_idx = pos_tags.index(current_tag) if current_tag in pos_tags else -1
+        # vec = feature_vector(word, prev_idx, next_idx, current_idx)
+        vec = feature_vector(word, idx/l, sent_pos_tags[idx][1])
         y_pred = model.predict([vec])
-        predictions.append(y_pred[0])
+        predictions.append(round(y_pred[0]))
     return tokens, predictions