def expand_sentence(text_p: Union[str, Example],
word_indices: List[int],
query=None,
additional_mask_indices: List[int] = None,
schedule_idx=[-1]) -> List[Example]:
if additional_mask_indices is None:
additional_mask_indices = []
if isinstance(text_p, Example):
text = text_p.sentence
else:
text = text_p
text = Sentence(text)
word_indices = list(word_indices)
word_indices = [
wi for wi in word_indices
if not all([s in ":,;.*" for s in text.words[wi]])
]
if len(word_indices) == 0:
return []
original_words = {i: text.words[i] for i in word_indices}
max_words = query.consider_max_words if query is not None else Query(
None).consider_max_words
masked_sentence = Sentence(
text.get_with_masked(word_indices + additional_mask_indices))
predictions = masked_sentence.calc_mask_predictions(max_words)
result = []
for word_idx in word_indices:
if not predictions[word_idx]:
continue
sentences = []
for predicted_token, score in predictions[word_idx]:
new_sen = text.replace_word(word_idx, predicted_token)
sentences.append(new_sen)
classification = calc_sentiment_batch(sentences)
for i, (predicted_token, score) in enumerate(predictions[word_idx]):
if original_words[word_idx] != predicted_token:
if isinstance(text_p, str):
e = Example(sentences[i],
classification[i], [(word_idx, score)],
pred_ind=[i],
sched_ind=[schedule_idx],
sent_ind=[0])
else:
e = Example(sentences[i],
classification[i],
text_p.changes + [(word_idx, score)],
pred_ind=text_p.prediction_indices + [i],
sched_ind=text_p.schedule_indices +
[schedule_idx],
sent_ind=text_p.sentence_indices + [0])
result.append(e)
return result
def one_mask():
max_words = 20
result = defaultdict(list)
result_dist_diff_only = defaultdict(list)
result_wdiff = defaultdict(list)
for enm in range(len(dataset)):
print(f"\nSentence {enm}: ", end="")
data = dataset[enm]
x, y = data["sentence"], data["label"]
x = model_config.tokenizer.clean_up_tokenization(x)
y = [0, 1] if y == 1 else [1, 0]
y_prime = [1 - y[0], 1 - y[1]]
s = Sentence(x)
word_gradients = s.calc_gradients(y_prime)
sorted_highest = np.argsort(word_gradients)[::-1]
for observed_idx in sorted_highest[:10]:
# observed_idx = sorted_highest[0]
print(f"{observed_idx},", end="")
sdir = 1 if len(s.words) - observed_idx > observed_idx else -1
alt_s = Sentence(s.get_with_masked([observed_idx]))
original_answer = alt_s.calc_mask_predictions()[observed_idx]
if len(original_answer) != 0:
for mask_distance in range(1, max_words):
if observed_idx + mask_distance * sdir < 0 or observed_idx + mask_distance * sdir >= len(
alt_s.words):
continue
new_sen = Sentence(
alt_s.get_with_masked([
observed_idx + mask_distance * sdir, observed_idx
]))
alt_sen_pred = new_sen.calc_mask_predictions(
)[observed_idx]
avg_distance, avg_word_diff, dist_diff_only = find_differences(
original_answer, alt_sen_pred)
# print(f"Mask offset {mask_distance}: dist={avg_distance:.3f} word_dist={avg_word_diff:.3f}")
result[mask_distance].append(avg_distance)
result_wdiff[mask_distance].append(avg_word_diff)
result_dist_diff_only[mask_distance].append(dist_diff_only)
if enm % 50 == 0 or enm == len(dataset) - 1:
fig = plt.figure(figsize=(11, 8))
plt.title(
"Relation Bewertung der Wörter zur Nähe des nächsten [MASK]-Token"
)
plt.xlabel("Entfernung zum zusätzlichen [MASK]-Token")
plt.xlim(0, max_words)
plt.ylim(0., 0.65)
plt.ylabel("Veränderung der Bewertung")
idx, mean, std = list(
zip(*[(md, np.mean(lst), np.std(lst))
for (md, lst) in result_wdiff.items()]))
mean = np.array(mean)
std = np.array(std)
plt.plot(idx, mean, color='r', label="Wort-Unterschiede")
plt.fill_between(idx, mean - std, mean + std, color='r', alpha=.2)
idx, mean, std = list(
zip(*[(md, np.mean(lst), np.std(lst))
for (md, lst) in result_dist_diff_only.items()]))
mean = np.array(mean)
std = np.array(std)
plt.plot(idx, mean, color='green', label="Distanz-Unterschiede")
plt.fill_between(idx,
mean - std,
mean + std,
color='green',
alpha=.2)
plt.xticks(idx)
plt.legend()
plt.savefig(f'{root}saved_plots/all/_besser_{enm}.png')
# plt.show()
plt.close(fig)
def two_mask():
max_words = 15
result = defaultdict(list)
result_dist_diff_only = defaultdict(list)
result_wdiff = defaultdict(list)
for enm in range(len(dataset)):
print(f"\nSentence {enm}: ", end="")
data = dataset[enm]
x, y = data["sentence"], data["label"]
x = model_config.tokenizer.clean_up_tokenization(x)
y = [0, 1] if y == 1 else [1, 0]
y_prime = [1 - y[0], 1 - y[1]]
s = Sentence(x)
word_gradients = s.calc_gradients(y_prime)
sorted_highest = np.argsort(word_gradients)[::-1]
for observed_idx in sorted_highest[:10]:
print(f"{observed_idx},", end="")
alt_s = Sentence(s.get_with_masked([observed_idx]))
original_answer = alt_s.calc_mask_predictions()[observed_idx]
if len(original_answer) != 0:
for mask_distance1 in range(-max_words, max_words + 1):
for mask_distance2 in range(-max_words, max_words + 1):
if not (0 <= observed_idx + mask_distance1 < len(
alt_s.words)):
continue
if not (0 <= observed_idx + mask_distance2 < len(
alt_s.words)):
continue
new_sen = Sentence(
alt_s.get_with_masked(
[observed_idx + mask_distance1, observed_idx]))
new_sen = Sentence(
new_sen.get_with_masked(
[observed_idx + mask_distance2, observed_idx]))
alt_sen_pred = new_sen.calc_mask_predictions(
)[observed_idx]
avg_distance, avg_word_diff, dist_diff_only = find_differences(
original_answer, alt_sen_pred)
result[(mask_distance1,
mask_distance2)].append(avg_distance)
result_wdiff[(mask_distance1,
mask_distance2)].append(avg_word_diff)
result_dist_diff_only[(
mask_distance1,
mask_distance2)].append(dist_diff_only)
if enm % 2 == 0 or enm == len(dataset) - 1:
all_variants = [(result, "result"), (result_wdiff, "wdiff"),
(result_dist_diff_only, "ddiff")]
with open('used_data.pickle', 'wb') as handle:
pickle.dump(all_variants, handle)
for res, name in all_variants:
data = [(k, np.mean(v)) for k, v in res.items()]
matrix = np.zeros(shape=(2 * max_words + 1, 2 * max_words + 1))
for (i, j), m in data:
matrix[(i + max_words), j + max_words] = m
plt.figure(figsize=(15, 12))
ax = sns.heatmap(
np.flip(matrix, axis=0),
linewidth=0.0,
xticklabels=list(range(-max_words, max_words + 1)),
yticklabels=list(reversed(range(-max_words,
max_words + 1))))
ax.set_title(
"Durchschnittliche Veränderung der Wörter bei 2 MASK-Tokens"
)
plt.savefig(f'{root}saved_plots/2d/{name}_{enm}.pdf')
plt.close()