Update svm_predict.py

svm_predict.py

@@ -2,26 +2,24 @@ import nltk
 from nltk import word_tokenize
 from nltk import pos_tag
 import joblib
+import numpy as np
 from train import feature_vector, pos_tags
 
-model = joblib.load('SVM_NEI_model.pkl')
+model = joblib.load('model.pkl')
+scaler = joblib.load('scaler.pkl')
 nltk.download('averaged_perceptron_tagger_eng')
 nltk.download('punkt_tab')
 
 def predict(sentence):
     tokens = word_tokenize(sentence)
     sent_pos_tags = pos_tag(tokens)
-    predictions = []
+    sent_features = []
     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)
-        vec = feature_vector(word, idx/l, sent_pos_tags[idx][1])
-        y_pred = model.predict([vec])
-        predictions.append(round(y_pred[0]))
+        current_tag = sent_pos_tags[idx][1]
+        current_idx = pos_tags.index(current_tag) if current_tag in pos_tags else -1
+        word_features = feature_vector(word, (1+idx)/l, current_idx)
+        sent_features.append(word_features)
+        # scaled_features = scaler.transform(sent_features)
+    predictions = model.predict(sent_features)
     return tokens, predictions