def run(which="all"):
backup()
if which == "all":
bases = _bases
else:
bases = [which]
for base in bases:
compute_diff("mpl_%s_raw.xml" % base, "mpl_%s.xml" % base, "mpl_%s_diff.xml" % base)
run_parse(base)
apply_diff("mpl_%s_raw.xml" % base, "mpl_%s_diff.xml" % base, "mpl_%s.xml" % base)
run_generate(base)
def run(which="all"):
backup()
if which == "all":
bases = _bases
else:
bases = [which]
for base in bases:
compute_diff("mpl_%s_raw.xml" % base, "mpl_%s.xml" % base,
"mpl_%s_diff.xml" % base)
run_parse(base)
apply_diff("mpl_%s_raw.xml" % base, "mpl_%s_diff.xml" % base,
"mpl_%s.xml" % base)
run_generate(base)
def generate_text_with_our_methods_sentiment(sentiment_tokenizer,
sentiment_classifier):
for condition in ["negative", "positive"]:
for prefix1 in [
"The chiken", "The house", "The pizza", "The potato",
"The lake"
]:
prefix2 = "The following is an article about " + condition + ". " + prefix1
for prefix in [prefix1, prefix2]:
print('Condition:', condition)
print('Prefix:', prefix)
perplexit_scores = []
diversity_scores = []
sentiment_scores = []
for k in range(20): # generate 100 samples per combination
a_lst = run_generate(prefix=prefix,
condition=condition,
length=100,
device=device)
a_str = [''.join(a for a in a_lst)]
#print(a_str)
perplexit_scores.append(
np.mean([
perplexity_score(text, per_model, tokenizer)
for text in a_str
]))
diversity_scores.append(diversity(a_str))
sentiment_scores.append([
sentiment_predict(
text, sentiment_tokenizer,
sentiment_classifier)['label'] == condition
for text in a_str
][0])
print('Perplexity score %.3f' % np.mean(perplexit_scores))
print('Sentiment acc. %.3f' %
(sum(sentiment_scores) / len(sentiment_scores)))
print('Diversity score:')
print('\t Dist-1: %.3f' %
np.mean([a[0] for a in diversity_scores]))
print('\t Dist-2: %.3f' %
np.mean([a[1] for a in diversity_scores]))
print('\t Dist-3: %.3f' %
np.mean([a[2] for a in diversity_scores]))
print('********\n')
def generate_text_with_our_methods_topic():
# loop over prefix and conditions and log the generated sentences and evaluation scores
for condition in [
"Religion", "Military", "Politics", "Science", "Legal", "Space",
"Computers", "Technology"
]:
for prefix1 in [
"The chiken", "The house", "The pizza", "The potato",
"The lake"
]:
prefix2 = "The following is an article about " + condition + ". " + prefix1
# print(prefix2) # added the following
for prefix in [prefix1, prefix2]:
print('Condition:', condition)
print('Prefix:', prefix)
perplexit_scores = []
diversity_scores = []
for k in range(20): # generate 100 samples per combination
a_lst = run_generate(prefix=prefix,
condition=condition,
length=100,
device=device)
a_str = [''.join(a for a in a_lst)]
#print(a_str)
perplexit_scores.append(
np.mean([
perplexity_score(text, per_model, tokenizer)
for text in a_str
]))
diversity_scores.append(diversity(a_str))
#evaluate_all(all_text)
print('Perplexity score %.3f' % np.mean(perplexit_scores))
print('Diversity score:')
print('\t Dist-1: %.3f' %
np.mean([a[0] for a in diversity_scores]))
print('\t Dist-2: %.3f' %
np.mean([a[1] for a in diversity_scores]))
print('\t Dist-3: %.3f' %
np.mean([a[2] for a in diversity_scores]))
print('********\n')