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evaluation.py
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evaluation.py
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# This file is adopted from IMN (https://github.com/ruidan/IMN-E2E-ABSA) by Ruidan He.
# We slightly modify the part for OE, because AE and OE are separate tasks in RACL.
import numpy as np
def convert_to_list(y_aspect, y_sentiment, mask):
y_aspect_list = []
y_sentiment_list = []
for seq_aspect, seq_sentiment, seq_mask in zip(y_aspect, y_sentiment, mask):
l_a = []
l_s = []
for label_dist_a, label_dist_s, m in zip(seq_aspect, seq_sentiment, seq_mask):
if m == 0:
break
else:
l_a.append(np.argmax(label_dist_a))
### all entries are zeros means that it is a background word or word with conflict sentiment
### which are not counted for training SC
### also when evaluating, we do not count conflict examples
if not np.any(label_dist_s):
l_s.append(0)
else:
l_s.append(np.argmax(label_dist_s)+1)
y_aspect_list.append(l_a)
y_sentiment_list.append(l_s)
return y_aspect_list, y_sentiment_list
def score(true_aspect, predict_aspect, true_sentiment, predict_sentiment, train_op):
if train_op:
begin = 1
inside = 2
else:
begin = 1
inside = 2
# predicted sentiment distribution for aspect terms that are correctly extracted
pred_count = {'pos':0, 'neg':0, 'neu':0}
# gold sentiment distribution for aspect terms that are correctly extracted
rel_count = {'pos':0, 'neg':0, 'neu':0}
# sentiment distribution for terms that get both span and sentiment predicted correctly
correct_count = {'pos':0, 'neg':0, 'neu':0}
# sentiment distribution in original data
total_count = {'pos':0, 'neg':0, 'neu':0}
polarity_map = {1: 'pos', 2: 'neg', 3: 'neu'}
# count of predicted conflict aspect term
predicted_conf = 0
correct, predicted, relevant = 0, 0, 0
for i in range(len(true_aspect)):
true_seq = true_aspect[i]
predict = predict_aspect[i]
for num in range(len(true_seq)):
# print('num', true_seq[num])
if true_seq[num] == begin:
relevant += 1
if not train_op:
if true_sentiment[i][num]!=0:
total_count[polarity_map[true_sentiment[i][num]]]+=1
if predict[num] == begin:
match = True
for j in range(num+1, len(true_seq)):
if true_seq[j] == inside and predict[j] == inside:
continue
elif true_seq[j] != inside and predict[j] != inside:
break
else:
match = False
break
if match:
correct += 1
if not train_op:
# do not count conflict examples
if true_sentiment[i][num]!=0:
rel_count[polarity_map[true_sentiment[i][num]]]+=1
pred_count[polarity_map[predict_sentiment[i][num]]]+=1
if true_sentiment[i][num] == predict_sentiment[i][num]:
correct_count[polarity_map[true_sentiment[i][num]]]+=1
else:
predicted_conf += 1
for pred in predict:
if pred == begin:
predicted += 1
p_aspect = correct / (predicted + 1e-6)
r_aspect = correct / (relevant + 1e-6)
# F1 score for aspect (opinion) extraction
f_aspect = 2 * p_aspect * r_aspect / (p_aspect + r_aspect + 1e-6)
acc_s, f_s, f_absa = 0, 0, 0
if not train_op:
num_correct_overall = correct_count['pos']+correct_count['neg']+correct_count['neu']
num_correct_aspect = rel_count['pos']+rel_count['neg']+rel_count['neu']
num_total = total_count['pos']+total_count['neg']+total_count['neu']
acc_s = num_correct_overall/(num_correct_aspect+1e-6)
p_pos = correct_count['pos'] / (pred_count['pos']+1e-6)
r_pos = correct_count['pos'] / (rel_count['pos']+1e-6)
p_neg = correct_count['neg'] / (pred_count['neg']+1e-6)
r_neg = correct_count['neg'] / (rel_count['neg']+1e-6)
p_neu = correct_count['neu'] / (pred_count['neu']+1e-6)
r_neu= correct_count['neu'] / (rel_count['neu']+1e-6)
pr_s = (p_pos+p_neg+p_neu)/3.0
re_s = (r_pos+r_neg+r_neu)/3.0
# For calculating the F1 Score for SC, we have discussed with Ruidan at https://github.com/ruidan/IMN-E2E-ABSA/issues?q=is%3Aissue+is%3Aclosed.
# We provide the correct formula as follow, but we still adopt the calculation in IMN to conduct a fair comparison.
# f_pos = 2*p_pos*r_pos /(p_pos+r_pos+1e-6)
# f_neg = 2*p_neg*r_neg /(p_neg+r_neg+1e-6)
# f_neu = 2*p_neu*r_neu /(p_neu+r_neu+1e-6)
# f_s = (f_pos+f_neg+f_neu)/3.0
# F1 score for SC only (in IMN)
f_s = 2*pr_s*re_s/(pr_s+re_s+1e-6)
precision_absa = num_correct_overall/(predicted+1e-6 - predicted_conf)
recall_absa = num_correct_overall/(num_total+1e-6)
# F1 score of the end-to-end task
f_absa = 2*precision_absa*recall_absa/(precision_absa+recall_absa+1e-6)
return f_aspect, acc_s, f_s, f_absa
def get_metric(y_true_aspect, y_predict_aspect, y_true_opinion, y_predict_opinion, y_true_sentiment, y_predict_sentiment, mask, train_op):
f_a, f_o = 0, 0
true_aspect, true_sentiment = convert_to_list(y_true_aspect, y_true_sentiment, mask)
predict_aspect, predict_sentiment = convert_to_list(y_predict_aspect, y_predict_sentiment, mask)
true_opinion, _ = convert_to_list(y_true_opinion, y_true_sentiment, mask)
predict_opinion, _ = convert_to_list(y_predict_opinion, y_predict_sentiment, mask)
f_aspect, acc_s, f_s, f_absa = score(true_aspect, predict_aspect, true_sentiment, predict_sentiment, 0)
if train_op:
f_opinion, _, _, _ = score(true_opinion, predict_opinion, true_sentiment, predict_sentiment, 1)
return f_aspect, f_opinion, acc_s, f_s, f_absa