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
import pickle
from qanta.guesser.abstract import AbstractGuesser
from qanta.guesser.dan import DanGuesser
from qanta.util.constants import BUZZER_TRAIN_FOLD, BUZZER_DEV_FOLD
guesser_directory = AbstractGuesser.output_path(
'qanta.guesser.dan', 'DanGuesser', 0, '')
guesser = DanGuesser.load(guesser_directory) # type: AbstractGuesser
guesser.batch_size /= 8
word_skip = 2
folds = [BUZZER_TRAIN_FOLD, BUZZER_DEV_FOLD]
for fold in folds:
df = guesser.generate_guesses(1, [fold], word_skip=word_skip)
output_path = AbstractGuesser.guess_path(guesser_directory, fold)
with open(output_path, 'wb') as f:
pickle.dump(df, f)
def output(self):
return [
LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
'guesser_report.pdf')),
LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
'guesser_report.pickle'))
]
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
reporting_directory = AbstractGuesser.reporting_path(
self.guesser_module, self.guesser_class, self.config_num, "")
# In the cases of huge parameter sweeps on SLURM its easy to accidentally run out of /fs/ storage.
# Since we only care about the results we can get them, then delete the models. We can use the regular
# GuesserReport to preserve the model
guesser_directory = AbstractGuesser.output_path(
self.guesser_module, self.guesser_class, self.config_num, "")
param_path = AbstractGuesser.output_path(
self.guesser_module,
self.guesser_class,
self.config_num,
f"guesser_params.pickle",
)
guesses_files = []
if os.path.exists(c.QANTA_EXPO_DATASET_PATH):
folds = [c.GUESSER_DEV_FOLD, c.GUESSER_TEST_FOLD, c.EXPO_FOLD]
else:
folds = [c.GUESSER_DEV_FOLD, c.GUESSER_TEST_FOLD]
for f in folds:
guesses_files.extend([
f"guesses_char_{f}.pickle",
f"guesses_full_{f}.pickle",
f"guesses_first_{f}.pickle",
])
guesses_paths = [
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class, self.config_num, f)
for f in guesses_files
]
log.info(f'Running: "cp {param_path} {reporting_directory}"')
shell(f"cp {param_path} {reporting_directory}")
for g_path in guesses_paths:
log.info(f'Running: "cp {g_path} {reporting_directory}"')
shell(f"cp {g_path} {reporting_directory}")
guesser_instance = guesser_class(self.config_num)
for f in folds:
guesser_instance.create_report(reporting_directory, f)
log.info(f'Running: "rm -rf {guesser_directory}"')
shell(f"rm -rf {guesser_directory}")
for g_path in guesses_paths:
shell(f"rm -f {g_path}")
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 main():
buzzer = RNNBuzzer()
# setup questions
questions = list(QuestionDatabase().all_questions().values())
dev_questions = [x for x in questions if x.fold == 'dev']
# setup machine agent
gspec = AbstractGuesser.list_enabled_guessers()[0]
guesser_dir = AbstractGuesser.output_path(gspec.guesser_module,
gspec.guesser_class, '')
guesser = ElasticSearchWikidataGuesser.load(guesser_dir)
guesser = ESGuesserWrapper(guesser)
machine_agent = GuesserBuzzerAgent(guesser, buzzer)
# setup human agent
human_agent = HumanAgent()
# setup hook
hooks = []
hooks.append(hook.NotifyBuzzingHook)
hooks.append(hook.GameInterfaceHook)
hooks.append(hook.VisualizeGuesserBuzzerHook(machine_agent))
hooks.append(hook.HighlightHook)
# setup game
game = Game(dev_questions, [human_agent, machine_agent], hooks)
game.run(10)
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 test():
gspec = AbstractGuesser.list_enabled_guessers()[0]
guesser_dir = AbstractGuesser.output_path(gspec.guesser_module,
gspec.guesser_class, '')
guesser = ElasticSearchWikidataGuesser.load(guesser_dir)
torch.cuda.set_device(0)
predictor = Predictor()
predictor.cuda()
dataset = BonusPairsDataset()
examples = [x for x in dataset.examples if x['start'] != -1]
guesses = []
for example in tqdm(examples):
document = example['content']
question = example['query']
answer = example['answer']
predictions = predictor.predict(document, question, top_n=1)
prediction = predictions[0][0]
gs = guesser.guess_single(example['query'])
gs = sorted(gs.items(), key=lambda x: x[1])[::-1]
guess = gs[0][0].replace('_', ' ')
guesses.append((prediction, guess, example['answer']))
with open('results.pkl', 'wb') as f:
pickle.dump(guesses, f)
def output(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_targets = [
LocalTarget(file) for file in guesser_class.files(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class, ''))
]
return [
LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class, '')),
LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
'guesser_params.pickle'))
] + guesser_targets
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_instance = guesser_class() # type: AbstractGuesser
qb_dataset = guesser_instance.qb_dataset()
start_time = time.time()
guesser_instance.train(qb_dataset.training_data())
end_time = time.time()
guesser_instance.save(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class, ''))
params = guesser_instance.parameters()
params['training_time'] = end_time - start_time
params_path = AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
'guesser_params.pickle')
with open(params_path, 'wb') as f:
pickle.dump(params, f)
def output(self):
return LocalTarget(
AbstractGuesser.output_path(
self.guesser_module,
self.guesser_class,
self.config_num,
f"guesser_report_{self.fold}.pickle",
))
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_directory = AbstractGuesser.output_path(
self.guesser_module, self.guesser_class, self.config_num, "")
guesser_instance = guesser_class.load(
guesser_directory) # type: AbstractGuesser
if self.fold in {c.GUESSER_TRAIN_FOLD, c.GUESSER_DEV_FOLD}:
char_skip = conf["guesser_char_skip"]
elif self.fold == c.EXPO_FOLD:
char_skip = conf["expo_char_skip"]
else:
char_skip = conf["buzzer_char_skip"]
log.info(
f"Generating and saving guesses for {self.fold} fold with char_skip={char_skip}..."
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
char_skip=char_skip)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f"Guessing on {self.fold} fold took {elapsed}s, saving guesses...")
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
"char")
log.info("Done saving guesses")
log.info(
f"Generating and saving guesses for {self.fold} fold with full question..."
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
full_question=True)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f"Guessing on {self.fold} fold took {elapsed}s, saving guesses...")
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
"full")
log.info("Done saving guesses")
log.info(
f"Generating and saving guesses for {self.fold} fold with first sentence"
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
first_sentence=True)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f"Guessing on {self.fold} fold took {elapsed}s, saving guesses...")
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
"first")
log.info("Done saving guesses")
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_directory = AbstractGuesser.output_path(
self.guesser_module, self.guesser_class, self.config_num, '')
guesser_instance = guesser_class.load(
guesser_directory) # type: AbstractGuesser
if self.fold in {c.GUESSER_TRAIN_FOLD, c.GUESSER_DEV_FOLD}:
char_skip = conf['guesser_char_skip']
else:
char_skip = conf['buzzer_char_skip']
log.info(
f'Generating and saving guesses for {self.fold} fold with char_skip={char_skip}...'
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
char_skip=char_skip)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f'Guessing on {self.fold} fold took {elapsed}s, saving guesses...')
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
'char')
log.info('Done saving guesses')
log.info(
f'Generating and saving guesses for {self.fold} fold with full question...'
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
full_question=True)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f'Guessing on {self.fold} fold took {elapsed}s, saving guesses...')
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
'full')
log.info('Done saving guesses')
log.info(
f'Generating and saving guesses for {self.fold} fold with first sentence'
)
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
first_sentence=True)
end_time = time.time()
elapsed = end_time - start_time
log.info(
f'Guessing on {self.fold} fold took {elapsed}s, saving guesses...')
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold],
'first')
log.info('Done saving guesses')
def output(self):
files = [
f'guesses_char_{self.fold}.pickle',
f'guesses_full_{self.fold}.pickle',
f'guesses_first_{self.fold}.pickle'
]
return [
LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
self.config_num, f)) for f in files
]
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_instance = guesser_class(
self.config_num) # type: AbstractGuesser
qb_dataset = guesser_instance.qb_dataset()
start_time = time.time()
guesser_instance.train(qb_dataset.training_data())
end_time = time.time()
guesser_instance.save(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class, self.config_num,
""))
params = guesser_instance.parameters()
params["training_time"] = end_time - start_time
params_path = AbstractGuesser.output_path(
self.guesser_module,
self.guesser_class,
self.config_num,
"guesser_params.pickle",
)
with open(params_path, "wb") as f:
pickle.dump(params, f)
def test_buzzer():
questions = QuestionDatabase().all_questions()
buzzer = RNNBuzzer(word_skip=conf['buzzer_word_skip'])
# setup machine agent
gspec = AbstractGuesser.list_enabled_guessers()[0]
guesser_dir = AbstractGuesser.output_path(gspec.guesser_module,
gspec.guesser_class, '')
guesser = ElasticSearchWikidataGuesser.load(guesser_dir)
guesser = ESGuesserWrapper(guesser)
key = list(questions.keys())[4]
question = questions[key].flatten_text().split()
for i, word in enumerate(question):
clue = ' '.join(question[:i])
guesses = guesser.guess(clue)
buzz = buzzer.buzz(guesses)
print(buzz)
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 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 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 run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_directory = AbstractGuesser.output_path(
self.guesser_module, self.guesser_class, '')
guesser_instance = guesser_class.load(
guesser_directory) # type: AbstractGuesser
if self.fold in {c.GUESSER_TRAIN_FOLD, c.GUESSER_DEV_FOLD}:
word_skip = conf['guesser_word_skip']
else:
word_skip = conf['buzzer_word_skip']
log.info(
'Generating and saving guesses for {} fold with word_skip={}...'.
format(self.fold, word_skip))
start_time = time.time()
guess_df = guesser_instance.generate_guesses(self.n_guesses,
[self.fold],
word_skip=word_skip)
end_time = time.time()
log.info('Guessing on {} fold took {}s, saving guesses...'.format(
self.fold, end_time - start_time))
guesser_class.save_guesses(guess_df, guesser_directory, [self.fold])
log.info('Done saving guesses')
import pickle
from qanta.guesser.abstract import AbstractGuesser
from qanta.guesser.dan import DanGuesser
from qanta.util.constants import BUZZER_TRAIN_FOLD, BUZZER_DEV_FOLD
guesser_directory = AbstractGuesser.output_path(
"qanta.guesser.dan", "DanGuesser", 0, ""
)
guesser = DanGuesser.load(guesser_directory) # type: AbstractGuesser
guesser.batch_size /= 8
word_skip = 2
folds = [BUZZER_TRAIN_FOLD, BUZZER_DEV_FOLD]
for fold in folds:
df = guesser.generate_guesses(1, [fold], word_skip=word_skip)
output_path = AbstractGuesser.guess_path(guesser_directory, fold)
with open(output_path, "wb") as f:
pickle.dump(df, f)
def output(self):
return LocalTarget(
AbstractGuesser.output_path(self.guesser_module,
self.guesser_class,
'guesses_{}.pickle'.format(self.fold)))
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
from tqdm import tqdm
from elasticsearch_dsl.connections import connections
from elasticsearch_dsl import DocType, Text, Keyword, Search, Index
from qanta.util.constants import GUESSER_DEV_FOLD
from qanta.guesser.abstract import AbstractGuesser
from qanta.datasets.quiz_bowl import QuizBowlDataset
from qanta.guesser.experimental.elasticsearch_instance_of import ElasticSearchWikidataGuesser
from qanta.guesser.experimental.elasticsearch_instance_of import ElasticSearchIndex
INDEX_NAME = 'qb_ir_instance_of'
gspec = AbstractGuesser.list_enabled_guessers()[0]
guesser_dir = AbstractGuesser.output_path(gspec.guesser_module,
gspec.guesser_class, '')
guesser = ElasticSearchWikidataGuesser.load(guesser_dir)
es_index = ElasticSearchIndex()
def recursive_guess(question, k=0):
p_class, p_prob = guesser.test_instance_of([question])[0]
first_guesses = search_not(question, p_class)
print('First round')
for x in first_guesses:
print(x)
print()
print('Second round')
new_guesses = []
for i in range(k):
guess = first_guesses[i][0]
question += ' ' + ' '.join(guess.split('_'))
guesses = es_index.search(question, p_class, p_prob, 0.6)
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)
import random
import pickle
from qanta.config import conf
from qanta.util.io import safe_path
from qanta.util.multiprocess import _multiprocess
from qanta.guesser.abstract import AbstractGuesser
from qanta.datasets.quiz_bowl import QuizBowlDataset, Question
from qanta.guesser.experimental.elasticsearch_instance_of import ElasticSearchWikidataGuesser
'''Randomly shuffle the word order and see if it changes the guesses.
'''
gspec = AbstractGuesser.list_enabled_guessers()[0]
guesser_dir = AbstractGuesser.output_path(gspec.guesser_module,
gspec.guesser_class, '')
guesser = ElasticSearchWikidataGuesser.load(guesser_dir)
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())))
'''
s = [0, 0, 0, 0, 0]
for q in questions:
sents = list(q.text.values())
text_before = ' '.join(sents[:first_n(sents)])
words = text.split()
def run(self):
guesser_class = get_class(self.guesser_module, self.guesser_class)
guesser_directory = AbstractGuesser.output_path(
self.guesser_module, self.guesser_class, self.config_num, '')
guesser_instance = guesser_class(self.config_num)
guesser_instance.create_report(guesser_directory, self.fold)
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 read_data(
fold,
output_type='char',
guesser_module='qanta.guesser.rnn',
guesser_class='RnnGuesser',
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:
pickle.dump(dataset, f)
return dataset
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)