def dump(**kwargs):
common = common_args(**kwargs)
t = Terminal()
ranking_line = " {prob:6.2f}% → {color}{text}{t.normal}"
actual_line = "{t.red}Actual{t.normal}: {t.bold}{actual_text}{t.normal}"
sent_forwards = Sentences(common.file_vector,
size=SENTENCE_LENGTH,
backwards=False)
sent_backwards = Sentences(common.file_vector,
size=SENTENCE_LENGTH,
backwards=True)
least_agreement = []
forwards_predictions = []
backwards_predictions = []
ranks = []
contexts = enumerate(zip(chop_prefix(common.tokens, PREFIX_LENGTH),
sent_forwards, chop_prefix(sent_backwards)))
# Note, the index is offset from the true start; i.e., when
# index == 0, the true index is SENTENCE_LENGTH
for index, (token, (prefix, x1), (suffix, x2)) in contexts:
assert x1 == x2
actual = x1
print(unvocabularize(prefix[-5:]),
t.bold_underline(token.value),
unvocabularize(suffix[:5]))
prefix_pred = common.forwards_model.predict(prefix)
suffix_pred = common.backwards_model.predict(suffix)
# harmonic mean
mean = consensus(suffix_pred, prefix_pred)
forwards_predictions.append(index_of_max(prefix_pred))
backwards_predictions.append(index_of_max(suffix_pred))
paired_rankings = rank(mean)
ranked_vocab = list(tuple(zip(*paired_rankings))[0])
top_5 = paired_rankings[:5]
top_5_words = ranked_vocab[:5]
for token_id, weight in top_5:
color = t.green if token_id == actual else ''
text = vocabulary.to_text(token_id)
prob = weight * 100.0
print(ranking_line.format_map(locals()))
ranks.append(ranked_vocab.index(actual) + 1)
min_token_id, min_prob = paired_rankings[0]
least_agreement.append(Agreement(min_prob, index))
if actual not in top_5_words:
actual_text = vocabulary.to_text(actual)
print(actual_line.format_map(locals()))
print()
if not ranks:
print(t.red("Could not analyze file!"), file=sys.stderr)
return
print("MRR: ", mean_reciprocal_rank(ranks))
print("Lowest rank:", max(ranks))
print("Time at #1: {:.2f}%".format(
100 * sum(1 for rank in ranks if rank == 1) / len(ranks)))
print()
forwards_text = [vocabulary.to_text(num) for num in forwards_predictions]
backwards_text = [vocabulary.to_text(num) for num in backwards_predictions]
least_agreement.sort()
# Compensate for offset indices
file_vector = common.file_vector[SENTENCE_LENGTH:]
tokens_text = [tok.value for tok in common.tokens[PREFIX_LENGTH:]]
for disagreement in least_agreement[:5]:
print(disagreement.probability)
prefix = ' '.join(disagreement.prefix(tokens_text))
suffix = ' '.join(disagreement.suffix(tokens_text))
print(" ", prefix, t.yellow(forwards_text @ disagreement), suffix)
print(" ", prefix, t.underline(tokens_text @ disagreement), suffix)
print(" ", prefix, t.blue(backwards_text @ disagreement), suffix)
print()
