def sample_n_guesser_correct_questions(question_lookup,
guess_lookup,
n_correct_samples,
n_samples=10):
sampled_questions_by_correct = defaultdict(list)
dataset = QuizBowlDataset(guesser_train=True)
training_data = dataset.training_data()
answer_counts = defaultdict(int)
for ans in training_data[1]:
answer_counts[ans] += 1
for n_correct, keys in n_correct_samples.items():
samples = random.sample(keys, min(n_samples, len(keys)))
for key in samples:
qnum, sent, token = key
page = question_lookup[qnum].page
text = question_lookup[qnum].get_text(sent, token)
guesses = guess_lookup[key]
correct_guessers = tuple(guesses[guesses.guess == page].guesser)
wrong_guessers = tuple(guesses[guesses.guess != page].guesser)
sampled_questions_by_correct[n_correct].append(
(text, key, page, answer_counts[page], correct_guessers,
wrong_guessers))
return sampled_questions_by_correct
def evaluate(ckp_dir):
db = QuizBowlDataset(guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds(["guessdev"])
questions = {x.qnum: x for x in questions}
with open(ckp_dir, "rb") as f:
checkpoint = pickle.load(f)
scores = [0, 0, 0, 0, 0]
descriptions = [
"accuracy before",
"accuracy after",
"before after match",
"top 5 accuracy before",
"top 5 accuracy after",
]
for k, q in checkpoint.items():
page = questions[k].page
gb = sorted(q["guesses_before"].items(), key=lambda x: x[1])[::-1]
ga = sorted(q["guesses_after"].items(), key=lambda x: x[1])[::-1]
scores[0] += gb[0][0] == page # accuracy before
scores[1] += ga[0][0] == page # accuracy after
scores[2] += ga[0][0] == gb[0][0] # top 1 match before / after
scores[3] += page in [x[0] for x in gb[:5]] # top 5 accuracy before
scores[4] += page in [x[0] for x in ga[:5]] # top 5 accuracy after
scores = [x / len(questions) for x in scores]
for s, d in zip(scores, descriptions):
print(d, s)
def create_instance_of_map(wikidata_claims_instance_of_path,
output_path,
n_types=50):
ds = QuizBowlDataset(guesser_train=True)
training_data = ds.training_data()
answers = set(training_data[1])
answer_counts = Counter(training_data[1])
with open(wikidata_claims_instance_of_path) as f:
claims = defaultdict(set)
for line in f:
c = json.loads(line)
if c['title'] is not None:
title = normalize_wikipedia_title(c['title'])
if title in answers:
c_object = c['object']
if c_object in object_blacklist:
continue
if c_object in object_merge_map:
claims[title].add(object_merge_map[c_object])
elif is_god(c_object):
claims[title].add('god')
else:
claims[title].add(c_object)
for k in claims:
if 'literary work' in claims[k] and len(claims[k]) > 1:
claims[k].remove('literary work')
total_counts = defaultdict(int)
class_counts = defaultdict(int)
answer_types = {}
for a in answers:
if a in claims:
answer_types[a] = claims[a]
for obj in claims[a]:
total_counts[obj] += answer_counts[a]
class_counts[obj] += 1
sorted_total_counts = sorted(total_counts.items(),
reverse=True,
key=lambda k: k[1])
top_types = {key for key, count in sorted_total_counts[:n_types]}
instance_of_map = {}
for a in answer_types:
top_class_types = top_types.intersection(answer_types[a])
if len(top_class_types) == 0:
instance_of_map[a] = NO_MATCH
elif len(top_class_types) > 1:
frequency = 0
final_a_type = None
for a_type in top_class_types:
if class_counts[a_type] > frequency:
frequency = class_counts[a_type]
final_a_type = a_type
instance_of_map[a] = final_a_type
else:
instance_of_map[a] = next(iter(top_class_types))
with open(output_path, 'wb') as f:
pickle.dump(instance_of_map, f)
def main():
fold = 'guessdev'
db = QuizBowlDataset(1, guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds([fold])
first_n = lambda x: len(x)
print(guesser.guess_single(' '.join(questions[0].text.values())))
'''
def main():
fold = 'guessdev'
db = QuizBowlDataset(1, guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds([fold])
first_n = lambda x: len(x)
print(guesser.guess_single(' '.join(questions[0].text.values())))
'''
def main():
dataset = QuizBowlDataset(guesser_train=True)
questions = dataset.questions_by_fold([GUESSER_DEV_FOLD])
questions = questions[GUESSER_DEV_FOLD]
correct = 0
for question in tqdm(questions):
guess = recursive_guess(question.text[0], 3)
correct += (guess == question.page)
print(correct / len(questions))
def main():
dataset = QuizBowlDataset(guesser_train=True)
questions = dataset.questions_by_fold([GUESSER_DEV_FOLD])
questions = questions[GUESSER_DEV_FOLD]
correct = 0
for question in tqdm(questions):
guess = recursive_guess(question.text[0], 3)
correct += guess == question.page
print(correct / len(questions))
def test():
dataset = QuizBowlDataset(guesser_train=True)
questions = dataset.questions_by_fold([GUESSER_DEV_FOLD])
questions = questions[GUESSER_DEV_FOLD]
i = 10
question = questions[i]
guess = recursive_guess(question.text[0], k=1)
print(question.page)
print(question.text[0])
def get_quizbowl():
qb_dataset = QuizBowlDataset(guesser_train=True, buzzer_train=False)
training_data = qb_dataset.training_data()
train_x, train_y, dev_x, dev_y, i_to_word, class_to_i, i_to_class = preprocess_dataset(
training_data)
i_to_word = ['<unk>', '<eos>'] + sorted(i_to_word)
word_to_i = {x: i for i, x in enumerate(i_to_word)}
train = transform_to_array(zip(train_x, train_y), word_to_i)
dev = transform_to_array(zip(dev_x, dev_y), word_to_i)
return train, dev, word_to_i, i_to_class
def test():
dataset = QuizBowlDataset(guesser_train=True)
questions = dataset.questions_by_fold([GUESSER_DEV_FOLD])
questions = questions[GUESSER_DEV_FOLD]
i = 10
question = questions[i]
guess = recursive_guess(question.text[0], k=1)
print(question.page)
print(question.text[0])
def create_instance_of_map(wikidata_claims_instance_of_path, output_path, n_types=50):
ds = QuizBowlDataset(guesser_train=True)
training_data = ds.training_data()
answers = set(training_data[1])
answer_counts = Counter(training_data[1])
with open(wikidata_claims_instance_of_path) as f:
claims = defaultdict(set)
for line in f:
c = json.loads(line)
if c['title'] is not None:
title = normalize_wikipedia_title(c['title'])
if title in answers:
c_object = c['object']
if c_object in object_blacklist:
continue
if c_object in object_merge_map:
claims[title].add(object_merge_map[c_object])
elif is_god(c_object):
claims[title].add('god')
else:
claims[title].add(c_object)
for k in claims:
if 'literary work' in claims[k] and len(claims[k]) > 1:
claims[k].remove('literary work')
total_counts = defaultdict(int)
class_counts = defaultdict(int)
answer_types = {}
for a in answers:
if a in claims:
answer_types[a] = claims[a]
for obj in claims[a]:
total_counts[obj] += answer_counts[a]
class_counts[obj] += 1
sorted_total_counts = sorted(total_counts.items(), reverse=True, key=lambda k: k[1])
top_types = {key for key, count in sorted_total_counts[:n_types]}
instance_of_map = {}
for a in answer_types:
top_class_types = top_types.intersection(answer_types[a])
if len(top_class_types) == 0:
instance_of_map[a] = NO_MATCH
elif len(top_class_types) > 1:
frequency = 0
final_a_type = None
for a_type in top_class_types:
if class_counts[a_type] > frequency:
frequency = class_counts[a_type]
final_a_type = a_type
instance_of_map[a] = final_a_type
else:
instance_of_map[a] = next(iter(top_class_types))
with open(output_path, 'wb') as f:
pickle.dump(instance_of_map, f)
def load_quizbowl(folds=c.BUZZER_INPUT_FOLDS) \
-> Tuple[Dict[str, int], Dict[str, list]]:
# merge_dfs()
log.info('Loading data')
question_db = QuestionDatabase()
quizbowl_db = QuizBowlDataset(bc.MIN_ANSWERS,
guesser_train=True,
buzzer_train=True)
all_questions = question_db.all_questions()
if not os.path.isfile(bc.OPTIONS_DIR):
log.info('Loading the set of options')
all_options = set(quizbowl_db.training_data()[1])
id2option = list(all_options)
with open(safe_path(bc.OPTIONS_DIR), 'wb') as outfile:
pickle.dump(id2option, outfile)
else:
with open(safe_path(bc.OPTIONS_DIR), 'rb') as infile:
id2option = pickle.load(infile)
option2id = {o: i for i, o in enumerate(id2option)}
num_options = len(id2option)
log.info('Number of options {0}'.format(len(id2option)))
guesses_by_fold = dict()
for fold in folds:
save_dir = '%s_processed.pickle' % (os.path.join(bc.GUESSES_DIR, fold))
if os.path.isfile(save_dir):
with open(safe_path(save_dir), 'rb') as infile:
guesses_by_fold[fold] = pickle.load(infile)
log.info('Loading {0} guesses'.format(fold))
continue
log.info('Processing {0} guesses'.format(fold))
guesses = AbstractGuesser.load_guesses(bc.GUESSES_DIR, folds=[fold])
worker = partial(_process_question, option2id, all_questions)
inputs = guesses.groupby('qnum')
guesses_by_fold[fold] = _multiprocess(worker,
inputs,
info='df data',
multi=True)
guesses_by_fold[fold] = [
x for x in guesses_by_fold[fold] if x is not None
]
print(len(guesses_by_fold[fold]))
with open(safe_path(save_dir), 'wb') as outfile:
pickle.dump(guesses_by_fold[fold], outfile)
log.info('Processed {0} guesses saved to {1}'.format(fold, save_dir))
return option2id, guesses_by_fold
def compute_question_stats(question_db_path: str):
dataset = QuizBowlDataset(5, qb_question_db=question_db_path)
train_dev_questions = dataset.questions_in_folds(('train', 'dev'))
question_lengths = [
len(q.flatten_text().split()) for q in train_dev_questions
]
mean = np.mean(question_lengths)
std = np.std(question_lengths)
stats = (mean, std)
with safe_open(SENTENCE_STATS, 'wb') as f:
pickle.dump(stats, f)
def create_memory_index():
dataset = QuizBowlDataset(guesser_train=True)
training_data = dataset.training_data()
answers = set(training_data[1])
cw = CachedWikipedia()
try:
Index('mem').delete()
except:
pass
Answer.init()
all_wiki_pages = [cw[page] for page in answers]
wiki_pages = [p for p in all_wiki_pages if p.content != '']
sc = create_spark_context()
sc.parallelize(wiki_pages, 1000).foreach(index_page)
def protobowl(model, fold=BUZZER_DEV_FOLD):
buzzes = get_buzzes(model, fold)
"""eval"""
guesses_dir = AbstractGuesser.output_path("qanta.guesser.rnn", "RnnGuesser", 0, "")
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, "char")
with open(guesses_dir, "rb") as f:
guesses = pickle.load(f)
guesses = guesses.groupby("qanta_id")
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = questions[fold]
df = load_protobowl()
df = df.groupby("qid")
worker = partial(simulate_game, guesses, buzzes, df)
possibility = []
outcome = []
for question in tqdm(questions):
pos, out = worker(question)
possibility += pos
outcome += out
result_df = pd.DataFrame({"Possibility": possibility, "Outcome": outcome,})
result_dir = os.path.join(model.model_dir, "{}_protobowl.pkl".format(fold))
with open(result_dir, "wb") as f:
pickle.dump(result_df, f)
def protobowl(model, fold=BUZZER_DEV_FOLD):
buzzes = get_buzzes(model, fold)
'''eval'''
guesses_dir = AbstractGuesser.output_path('qanta.guesser.rnn',
'RnnGuesser', 0, '')
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, 'char')
with open(guesses_dir, 'rb') as f:
guesses = pickle.load(f)
guesses = guesses.groupby('qanta_id')
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = questions[fold]
df = load_protobowl()
df = df.groupby('qid')
worker = partial(simulate_game, guesses, buzzes, df)
possibility = []
outcome = []
for question in tqdm(questions):
pos, out = worker(question)
possibility += pos
outcome += out
result_df = pd.DataFrame({
'Possibility': possibility,
'Outcome': outcome,
})
result_dir = os.path.join(model.model_dir, '{}_protobowl.pkl'.format(fold))
with open(result_dir, 'wb') as f:
pickle.dump(result_df, f)
def generate_domain_classifier_data(weight=150):
"""
Reads all sentences from every wikipedia page corresponding to a known answer and splits them into two vowpal wabbit files,
interleaving true quiz bowl questions randomly and with higher weight specified by the weight arg.
"""
qb_data = QuizBowlDataset(guesser_train=True).training_data()
real_questions = [('1', str(weight), ans, clean_question(sent))
for q, ans, _ in zip(*qb_data) for sent in q]
pages = set(a for _, _, a, _ in real_questions)
cw = CachedWikipedia()
# Split wikipedia questions into two sets
wiki_questions = ([], [])
use_second = False
for page in pages:
for sentence in sentences_from_page(cw[page]):
q = clean_question(sentence)
wiki_questions[use_second].append(('-1', '1', page, q))
use_second = not use_second
vw_line = '{} {} \'{}|text {}\n'
for i, wiki_qs in enumerate(wiki_questions):
# Create list of True/False and shuffle to define ordering of train data
order = list(
chain(repeat(False, len(real_questions)),
repeat(True, len(wiki_qs))))
random.shuffle(order)
iters = (iter(real_questions), iter(wiki_qs))
with safe_open(DOMAIN_TARGET_PREFIX + str(i), 'w') as f:
for choice in order:
f.write(vw_line.format(*next(iters[choice])))
def read_data(fold,
output_type='char',
guesser_module='qanta.guesser.dan',
guesser_class='DanGuesser',
guesser_config_num=0,
vector_converter=vector_converter_0):
if os.path.isfile(dataset_dir.format(fold)):
with open(dataset_dir.format(fold), 'rb') as f:
return pickle.load(f)
g_dir = AbstractGuesser.output_path(guesser_module, guesser_class,
guesser_config_num, '')
g_path = AbstractGuesser.guess_path(g_dir, fold, output_type)
with open(g_path, 'rb') as f:
df = pickle.load(f)
df_groups = df.groupby('qanta_id')
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
pool = Pool(8)
worker = partial(process_question, questions, vector_converter)
dataset = pool.map(worker, df_groups)
with open(dataset_dir.format(fold), 'wb') as f:
return pickle.dump(dataset, f)
return dataset
def training_data(self):
cw = CachedWikipedia(QB_WIKI_LOCATION)
ds = QuizBowlDataset(2)
train_data = ds.training_data()
answer_classes = set(train_data[1])
train_x = []
train_y = []
for page in answer_classes:
sentences = list(wiki_sentences(cw[page].content))
sampled_sentences = random.sample(
sentences, min(len(sentences), self.max_sentences))
training_examples = []
for sentence in sampled_sentences:
training_examples.append(sentence)
train_x.append(training_examples)
train_y.append(page)
return train_x, train_y, None
def main(questions, n_keep, ckp_dir):
db = QuizBowlDataset(guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds(['guessdev'])
questions = {x.qnum: x for x in questions}
checkpoint = defaultdict(dict)
for qnum, question in questions.items():
text_before = question.flatten_text()
guesses_before = guesser.guess_single(text_before)
text_after, guesses_after, removed = greedy_remove(
text_before, guesses_before, n_keep)
checkpoint[qnum]['text_before'] = text_before
checkpoint[qnum]['text_after'] = text_after
checkpoint[qnum]['guesses_before'] = guesses_before
checkpoint[qnum]['guesses_after'] = guesses_after
checkpoint[qnum]['removed'] = removed
checkpoint = dict(checkpoint)
with open(safe_path(ckp_dir), 'wb') as f:
pickle.dump(checkpoint, f)
evaluate(ckp_dir)
def evaluate(ckp_dir):
db = QuizBowlDataset(guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds(['guessdev'])
questions = {x.qnum: x for x in questions}
with open(ckp_dir, 'rb') as f:
checkpoint = pickle.load(f)
scores = [0, 0, 0, 0, 0]
descriptions = ['accuracy before', 'accuracy after', 'before after match',
'top 5 accuracy before', 'top 5 accuracy after']
for k, q in checkpoint.items():
page = questions[k].page
gb = sorted(q['guesses_before'].items(), key=lambda x: x[1])[::-1]
ga = sorted(q['guesses_after'].items(), key=lambda x: x[1])[::-1]
scores[0] += gb[0][0] == page # accuracy before
scores[1] += ga[0][0] == page # accuracy after
scores[2] += ga[0][0] == gb[0][0] # top 1 match before / after
scores[3] += page in [x[0] for x in gb[:5]] # top 5 accuracy before
scores[4] += page in [x[0] for x in ga[:5]] # top 5 accuracy after
scores = [x / len(questions) for x in scores]
for s, d in zip(scores, descriptions):
print(d, s)
def main(questions, n_keep, ckp_dir):
db = QuizBowlDataset(guesser_train=True, buzzer_train=True)
questions = db.questions_in_folds(["guessdev"])
questions = {x.qnum: x for x in questions}
checkpoint = defaultdict(dict)
for qnum, question in questions.items():
text_before = question.flatten_text()
guesses_before = guesser.guess_single(text_before)
text_after, guesses_after, removed = greedy_remove(
text_before, guesses_before, n_keep
)
checkpoint[qnum]["text_before"] = text_before
checkpoint[qnum]["text_after"] = text_after
checkpoint[qnum]["guesses_before"] = guesses_before
checkpoint[qnum]["guesses_after"] = guesses_after
checkpoint[qnum]["removed"] = removed
checkpoint = dict(checkpoint)
with open(safe_path(ckp_dir), "wb") as f:
pickle.dump(checkpoint, f)
evaluate(ckp_dir)
def ew(model, fold=BUZZER_DEV_FOLD):
buzzes = get_buzzes(model, fold)
guesses_dir = AbstractGuesser.output_path('qanta.guesser.rnn',
'RnnGuesser', 0, '')
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, 'char')
with open(guesses_dir, 'rb') as f:
guesses = pickle.load(f)
guesses = guesses.groupby('qanta_id')
answers = dict()
for qid, bs in buzzes.items():
answers[qid] = []
groups = guesses.get_group(qid).groupby('char_index')
for char_index, scores in zip(*bs):
guess = groups.get_group(char_index).head(1)['guess']
guess = guess.values[0]
buzz = scores[0] < scores[1]
answers[qid].append({
'char_index': char_index,
'guess': guess,
'buzz': buzz,
})
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
curve_score = CurveScore()
ew = []
ew_opt = []
for qid, answer in answers.items():
question = questions[qid]
q = {'text': question.text, 'page': question.page}
ew.append(curve_score.score(answer, q))
ew_opt.append(curve_score.score_optimal(answer, q))
eval_out = {
'expected_wins': sum(ew),
'n_examples': len(ew),
'expected_wins_optimal': sum(ew_opt),
}
print(json.dumps(eval_out))
return eval_out
def main():
fold = BUZZER_DEV_FOLD
# load questions
print('loading questions')
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = questions[fold]
# load guesser outputs
print('loading guesser outputs')
guesses = read_data(fold)
guesses = {x[0]: x for x in guesses}
# load buzzer outputs
print('loading buzzer outputs')
buzz_dir = os.path.join(buzzes_dir.format(fold))
with open(buzz_dir, 'rb') as f:
buzzes = pickle.load(f)
# load protobowl records
print('loading protobowl records')
df, _ = load_protobowl()
record_groups = df.groupby('qid')
metrics = [_protobowl_scores, _curve_scores]
pool = Pool(8)
worker = partial(run_all_metrics, guesses, buzzes, record_groups, metrics)
scores = pool.map(worker, questions)
all_scores = list(map(list, zip(*scores)))
protobowl_scores = all_scores[0]
protobowl_scores = list(map(list, zip(*protobowl_scores)))
protobowl_scores = [[x for x in s if x is not None]
for s in protobowl_scores]
print([np.mean(s) for s in protobowl_scores])
curve_scores = all_scores[1]
curve_scores = list(map(list, zip(*curve_scores)))
curve_scores = [[x for x in s if x is not None] for s in curve_scores]
print([np.mean(s) for s in curve_scores])
def ew(model, fold=BUZZER_DEV_FOLD):
buzzes = get_buzzes(model, fold)
guesses_dir = AbstractGuesser.output_path("qanta.guesser.rnn", "RnnGuesser", 0, "")
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, "char")
with open(guesses_dir, "rb") as f:
guesses = pickle.load(f)
guesses = guesses.groupby("qanta_id")
answers = dict()
for qid, bs in buzzes.items():
answers[qid] = []
groups = guesses.get_group(qid).groupby("char_index")
for char_index, scores in zip(*bs):
guess = groups.get_group(char_index).head(1)["guess"]
guess = guess.values[0]
buzz = scores[0] < scores[1]
answers[qid].append(
{"char_index": char_index, "guess": guess, "buzz": buzz,}
)
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
curve_score = CurveScore()
ew = []
ew_opt = []
for qid, answer in answers.items():
question = questions[qid]
q = {"text": question.text, "page": question.page}
ew.append(curve_score.score(answer, q))
ew_opt.append(curve_score.score_optimal(answer, q))
eval_out = {
"expected_wins": sum(ew),
"n_examples": len(ew),
"expected_wins_optimal": sum(ew_opt),
}
print(json.dumps(eval_out))
return eval_out
def qb_dataset(self) -> QuizBowlDataset:
return QuizBowlDataset(guesser_train=True)
import json
from qanta.util.constants import GUESSER_DEV_FOLD
from qanta.datasets.quiz_bowl import QuizBowlDataset
''' Preprocess the questions from a database (like data/naqt.db) and extracts
the text, qid and answer. Stores the data in sample_questions.json '''
dataset = QuizBowlDataset(guesser_train=True, qb_question_db='data/naqt.db')
questions = dataset.questions_by_fold([GUESSER_DEV_FOLD])[GUESSER_DEV_FOLD]
def convert(q):
return {'qid': q.qnum, 'text': ' '.join(q.text.values()), 'answer': q.page}
questions = [convert(q) for q in questions]
with open('data/sample_questions.json', 'w') as f:
f.write(json.dumps(questions))
def export(output_file: str, fold: str = "buzztest"):
fold = "buzztest"
guesses_dir = AbstractGuesser.output_path("qanta.guesser.rnn",
"RnnGuesser", 0, "")
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, "char")
with open(guesses_dir, "rb") as f:
guesses = pickle.load(f)
guesses = guesses.groupby("qanta_id")
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
buzzers = {}
for name in ["RNNBuzzer", "ThresholdBuzzer", "MLPBuzzer"]:
model_dir = f"output/buzzer/{name}"
buzzes_dir = os.path.join(model_dir, "{}_buzzes.pkl".format(fold))
with open(buzzes_dir, "rb") as f:
buzzers[name] = pickle.load(f)
qid_to_buzzes = defaultdict(dict)
for name, buzzes in track(buzzers.items()):
for qid, (char_indices, scores) in buzzes.items():
gs = (guesses.get_group(qid).groupby("char_index").aggregate(
lambda x: x.head(1)).to_dict()["guess"])
question = questions[qid]
q_len = len(question.text)
buzz_oracle_position = -1
buzz_model_position = -1
oracle_guess = None
buzz_guess = None
for i, char_index in enumerate(char_indices):
buzz_oracle = gs[char_index] == question.page
if buzz_oracle:
if buzz_oracle_position == -1 or char_index <= buzz_oracle_position:
oracle_guess = question.page
buzz_oracle_position = char_index
if scores[i][1] > scores[i][0]:
if buzz_model_position == -1 or char_index < buzz_model_position:
buzz_guess = gs[char_index]
buzz_model_position = char_index
qid_to_buzzes[qid][name] = {
"oracle":
buzz_oracle_position,
"oracle_fraction":
buzz_oracle_position /
q_len if buzz_oracle_position != -1 else -1,
"position":
buzz_model_position,
"position_fraction":
buzz_model_position /
q_len if buzz_model_position != -1 else -1,
"q_len":
q_len,
"oracle_guess":
oracle_guess,
"buzz_guess":
buzz_guess,
"answer":
question.page,
"impossible":
oracle_guess is None,
}
write_json(output_file, qid_to_buzzes)
def qb_dataset(self):
return QuizBowlDataset(guesser_train=True, buzzer_train=self.use_buzz_as_train)
def qb_dataset(self) -> QuizBowlDataset:
return QuizBowlDataset(2)
def qb_dataset(self):
return QuizBowlDataset(1)
def stack(model_dir, model_name, fold=BUZZER_DEV_FOLD):
guesses_dir = AbstractGuesser.output_path("qanta.guesser.rnn", "RnnGuesser", 0, "")
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, "char")
with open(guesses_dir, "rb") as f:
guesses = pickle.load(f)
guesses = guesses.groupby("qanta_id")
buzzes_dir = os.path.join(model_dir, "{}_buzzes.pkl".format(fold))
with open(buzzes_dir, "rb") as f:
buzzes = pickle.load(f)
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
stack_freq = {"Position": [], "Buzzing": []}
count = defaultdict(lambda: 0)
for qid, (char_indices, scores) in buzzes.items():
gs = guesses.get_group(qid).groupby("char_index")
gs = gs.aggregate(lambda x: x.head(1)).to_dict()["guess"]
question = questions[qid]
q_len = len(question.text)
for i, char_index in enumerate(char_indices):
buzz_oracle = gs[char_index] == question.page
buzz_buzzer = scores[i][1] > scores[i][0]
only_oracle = buzz_oracle and (not buzz_buzzer)
only_buzzer = buzz_buzzer and (not buzz_oracle)
both = buzz_buzzer and buzz_oracle
neither = (not buzz_buzzer) and (not buzz_oracle)
rel_position = np.round(char_index / q_len, decimals=1)
count[rel_position] += 1
if only_oracle:
stack_freq["Position"].append(rel_position)
stack_freq["Buzzing"].append("Only optimal")
if only_buzzer:
stack_freq["Position"].append(rel_position)
stack_freq["Buzzing"].append("Only buzzer")
if both:
stack_freq["Position"].append(rel_position)
stack_freq["Buzzing"].append("Both")
if neither:
stack_freq["Position"].append(rel_position)
stack_freq["Buzzing"].append("Neither")
df = pd.DataFrame(stack_freq)
df = df.groupby(["Position", "Buzzing"])
df = df.size().reset_index().rename(columns={0: "Frequency"})
df["Frequency"] = df.apply(
lambda row: row["Frequency"] / count[row["Position"]], axis=1
)
df["Model"] = pd.Series([model_name for _ in range(len(df))])
stack_dir = os.path.join(model_dir, "{}_stack.pkl".format(fold))
with open(stack_dir, "wb") as f:
pickle.dump(df, f)
return df
def qb_dataset(self):
return QuizBowlDataset(self.min_answers)
def stack(model_dir, model_name, fold=BUZZER_DEV_FOLD):
guesses_dir = AbstractGuesser.output_path('qanta.guesser.rnn',
'RnnGuesser', 0, '')
guesses_dir = AbstractGuesser.guess_path(guesses_dir, fold, 'char')
with open(guesses_dir, 'rb') as f:
guesses = pickle.load(f)
guesses = guesses.groupby('qanta_id')
buzzes_dir = os.path.join(model_dir, '{}_buzzes.pkl'.format(fold))
with open(buzzes_dir, 'rb') as f:
buzzes = pickle.load(f)
questions = QuizBowlDataset(buzzer_train=True).questions_by_fold()
questions = {q.qanta_id: q for q in questions[fold]}
stack_freq = {'Position': [], 'Buzzing': []}
count = defaultdict(lambda: 0)
for qid, (char_indices, scores) in buzzes.items():
gs = guesses.get_group(qid).groupby('char_index')
gs = gs.aggregate(lambda x: x.head(1)).to_dict()['guess']
question = questions[qid]
q_len = len(question.text)
for i, char_index in enumerate(char_indices):
buzz_oracle = gs[char_index] == question.page
buzz_buzzer = scores[i][1] > scores[i][0]
only_oracle = buzz_oracle and (not buzz_buzzer)
only_buzzer = buzz_buzzer and (not buzz_oracle)
both = buzz_buzzer and buzz_oracle
neither = (not buzz_buzzer) and (not buzz_oracle)
rel_position = np.round(char_index / q_len, decimals=1)
count[rel_position] += 1
if only_oracle:
stack_freq['Position'].append(rel_position)
stack_freq['Buzzing'].append('Only optimal')
if only_buzzer:
stack_freq['Position'].append(rel_position)
stack_freq['Buzzing'].append('Only buzzer')
if both:
stack_freq['Position'].append(rel_position)
stack_freq['Buzzing'].append('Both')
if neither:
stack_freq['Position'].append(rel_position)
stack_freq['Buzzing'].append('Neither')
df = pd.DataFrame(stack_freq)
df = df.groupby(['Position', 'Buzzing'])
df = df.size().reset_index().rename(columns={0: 'Frequency'})
df['Frequency'] = df.apply(
lambda row: row['Frequency'] / count[row['Position']], axis=1)
df['Model'] = pd.Series([model_name for _ in range(len(df))])
stack_dir = os.path.join(model_dir, '{}_stack.pkl'.format(fold))
with open(stack_dir, 'wb') as f:
pickle.dump(df, f)
return df
parser.add_argument('--questions', type=str, default='questions.csv')
parser.add_argument('--buzzes', type=str, default="ir_buzz.csv")
parser.add_argument('--skip', type=int, default=0)
parser.add_argument('--output', type=str, default="competition.csv")
parser.add_argument('--finals', type=str, default="finals.csv")
parser.add_argument('--power', type=str, default="power.csv")
parser.add_argument('--max_questions', type=int, default=60)
parser.add_argument('--readable', type=str, default="readable.txt")
flags = parser.parse_args()
questions = Questions(flags.questions)
buzzes = Buzzes(flags.buzzes)
finals = load_finals(flags.finals)
power = PowerPositions(flags.power)
qb_dataset = QuizBowlDataset(guesser_train=True)
qb_answer_set = {g for g in qb_dataset.training_data()[1]}
print("Done loading data")
clear_screen()
current_players = set()
if True:
print("Time for a buzzer check")
players_needed = [1, 2, 3, 4]
while len(current_players) < len(players_needed):
print("Player %i, please buzz in" %
min(x for x in players_needed if x not in current_players))
press = interpret_keypress()
if press in players_needed:
os.system("afplay /System/Library/Sounds/Glass.aiff")
