def main():
save_name = "alamri_pair"
info_entries, output_d = load_from_pickle(save_name)
html = HtmlVisualizer("alamri_pairing_deletion.html", use_tooltip=True)
initial_text = load_p_h_pair_text(
at_output_dir("alamri_pilot", "true_pair_small.csv"))
per_group_summary: List[PerGroupSummary] = summarize_pair_deletion_results(
info_entries, output_d)
def float_arr_to_str_arr(float_arr):
return list(map(two_digit_float, float_arr))
def float_arr_to_cell(head, float_arr):
return [Cell(head)] + lmap(Cell, map(two_digit_float, float_arr))
def float_arr_to_cell2(head, float_arr):
return [Cell(head)] + lmap(Cell, map("{0:.4f}".format, float_arr))
num_data = len(output_d['input_ids'])
for data_idx, (p, h) in enumerate(initial_text):
group_summary = per_group_summary[data_idx]
p_tokens = p.split()
h_tokens = h.split()
base_score = group_summary.score_d[(-1, -1)]
pred_str = make_prediction_summary_str(base_score)
html.write_paragraph("Prediction: {}".format(pred_str))
keys = list(group_summary.score_d.keys())
p_idx_max = max(left(keys))
h_idx_max = max(right(keys))
def get_pair_score_by_h(key):
p_score, h_score = group_summary.effect_d[key]
return h_score
def get_pair_score_by_p(key):
p_score, h_score = group_summary.effect_d[key]
return p_score
def get_table(get_pair_score_at):
head = [Cell("")] + [Cell(t) for t in p_tokens]
rows = [head]
for h_idx in range(h_idx_max + 1):
row = [Cell(h_tokens[h_idx])]
for p_idx in range(p_idx_max + 1):
s = get_pair_score_at((p_idx, h_idx))
one_del_score = group_summary.score_d[(p_idx, -1)]
two_del_score = group_summary.score_d[(p_idx, h_idx)]
tooltip_str = "{} -> {}".format(
float_arr_to_str_arr(one_del_score),
float_arr_to_str_arr(two_del_score))
row.append(
get_tooltip_cell(two_digit_float(s), tooltip_str))
rows.append(row)
return rows
html.write_table(get_table(get_pair_score_by_p))
html.write_table(get_table(get_pair_score_by_h))
html.write_bar()
def write_deletion_score_to_html(out_file_name, summarized_table: List[Entry],
info: Dict[int, Dict]):
text_to_info = claim_text_to_info()
html = HtmlVisualizer(out_file_name)
tokenizer = get_biobert_tokenizer()
num_print = 0
for entry in summarized_table:
tokens = tokenizer.convert_ids_to_tokens(entry.input_ids)
idx_sep1, idx_sep2 = get_sep_loc(entry.input_ids)
max_change = 0
max_drop = 0
cells = cells_from_tokens(tokens)
drops = []
for idx in range(len(tokens)):
if tokens[idx] == "[PAD]":
break
if tokens[idx] == '[SEP]':
continue
if idx in entry.contribution:
raw_score = entry.contribution[idx]
e = idx, raw_score
drops.append(e)
drops.sort(key=get_second)
_, largest_drop = drops[0]
max_drop_idx = -1
max_drop_case_logit = None
for idx in range(len(tokens)):
if tokens[idx] == "[PAD]":
break
if tokens[idx] == '[SEP]':
continue
if idx in entry.contribution:
raw_score = entry.contribution[idx]
max_change = max(abs(raw_score), max_change)
if max_drop > raw_score:
max_drop = raw_score
max_drop_idx = idx
max_drop_case_logit = entry.case_logits_d[idx]
if raw_score < 0:
score = abs(raw_score / largest_drop) * 200
color = "B"
else:
score = 0
color = "B"
else:
score = 150
color = "Gray"
cells[idx].highlight_score = score
cells[idx].target_color = color
if max_change < 0.05 and False:
pass
else:
# if random.random() < 0.90:
# continue
base_probs = scipy.special.softmax(entry.base_logits)
info_entry = info[str(entry.data_id[0])]
claim1_info: Dict = text_to_info[info_entry['text1']]
claim2_info: Dict = text_to_info[info_entry['text2']]
question = claim1_info['question']
assertion1 = claim1_info['assertion']
assertion2 = claim2_info['assertion']
original_prediction_summary = make_prediction_summary_str(
base_probs)
html.write_bar()
html.write_paragraph("Question: {}".format(question))
html.write_paragraph("Original prediction: " +
original_prediction_summary)
html.write_paragraph("Max drop")
rows = []
for idx, score in drops[:5]:
row = [Cell(str(idx)), Cell(tokens[idx]), Cell(score)]
rows.append(row)
html.write_table(rows)
min_token = tokens[max_drop_idx]
html.write_paragraph("> \"{}\": {} ".format(min_token, max_drop))
max_drop_case_prob = scipy.special.softmax(max_drop_case_logit)
max_drop_prediction_summary = make_prediction_summary_str(
max_drop_case_prob)
html.write_paragraph("> " + max_drop_prediction_summary)
p = [Cell("Claim1 ({}):".format(assertion1))] + cells[1:idx_sep1]
h = [Cell("Claim2 ({}):".format(assertion2))
] + cells[idx_sep1 + 1:idx_sep2]
html.write_table([p])
html.write_table([h])
num_print += 1
print("printed {} of {}".format(num_print, len(summarized_table)))
