def f_eval(task: Task) -> List[Row]:
score_map = b_guesser_score_map.value.scores()
df = task.guess_df
result = []
if len(df) > 0:
# Refer to code in evaluate_feature_question for explanation why this is safe
first_row = df.iloc[0]
qnum = int(first_row.qnum)
sentence = int(first_row.sentence)
token = int(first_row.token)
fold = first_row.fold
for guess in df.guess:
vw_features = []
key = (qnum, sentence, token, guess)
vw_features.append(format_guess(guess))
for guesser in score_map:
if key in score_map[guesser]:
score = score_map[guesser][key]
feature = '{guesser}_score:{score} {guesser}_found:1'.format(
guesser=guesser, score=score)
vw_features.append(feature)
else:
vw_features.append('{}_found:-1'.format(guesser))
f_value = '|guessers ' + ' '.join(vw_features)
row = Row(fold, qnum, sentence, token, guess, 'guessers',
f_value)
result.append(row)
return result
def preprocess_titles():
# stop_words = set(stopwords.words('english'))
titles_file = open('data/titles-sorted.txt')
db = QuestionDatabase()
pages = {format_guess(page) for page in db.questions_with_pages().keys()}
with open('data/processed-titles-sorted.txt', 'w') as f:
for line in titles_file:
page = format_guess(line.strip().lower())
# if len(page) > 2 and re.match(r"^[a-zA-Z0-9_()']+$", page)\
# and page not in stop_words and page[0].isalnum():
if page in pages:
f.write(line.strip().lower())
else:
f.write('@')
f.write('\n')
titles_file.close()
def question_recall(guesses, qst, question_lookup):
qnum, sentence, token = qst
answer = format_guess(question_lookup[qnum].page)
sorted_guesses = sorted(guesses, reverse=True, key=lambda g: g.score)
for i, guess_row in enumerate(sorted_guesses, 1):
if answer == guess_row.guess:
return qnum, sentence, token, i
return qnum, sentence, token, None
def __getitem__(self, key: str):
key = format_guess(key)
if key in self.cache:
return self.cache[key]
if "/" in key:
filename = "%s/%s" % (self.path, key.replace("/", "---"))
else:
filename = "%s/%s" % (self.path, key)
page = None
if os.path.exists(filename):
try:
page = pickle.load(open(filename, 'rb'))
except pickle.UnpicklingError:
page = None
except AttributeError:
log.info("Error loading %s" % key)
page = None
except ImportError:
log.info("Error importing %s" % key)
page = None
if page is None:
if key in self.countries:
raw = [
self.load_page("%s%s" % (x, self.countries[key]))
for x in COUNTRY_SUB
]
raw.append(self.load_page(key))
log.info("%s is a country!" % key)
else:
raw = [self.load_page(key)]
raw = [x for x in raw if x is not None]
if raw:
if len(raw) > 1:
log.info("%i pages for %s" % (len(raw), key))
page = WikipediaPage(
"\n".join(x.content for x in raw),
seq(raw).map(lambda x: x.links).flatten().list(),
seq(raw).map(lambda x: x.categories).flatten().list())
log.info("Writing file to %s" % filename)
pickle.dump(page,
open(filename, 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
else:
log.info("Dummy page for %s" % key)
page = WikipediaPage()
if self.write_dummy:
pickle.dump(page,
open(filename, 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
self.cache[key] = page
return page
def train(self, training_data):
documents = {}
for sentences, ans in zip(training_data[0], training_data[1]):
page = format_guess(ans)
paragraph = ' '.join(sentences)
if page in documents:
documents[page] += ' ' + paragraph
else:
documents[page] = paragraph
ElasticSearchIndex.build(documents)
def train(self, training_data: TrainingData) -> None:
documents = {}
for sentence, ans in zip(training_data[0], training_data[1]):
page = format_guess(ans)
paragraph = ' '.join(sentence)
if page in documents:
documents[page] += ' ' + paragraph
else:
documents[page] = paragraph
WhooshWikiIndex.build(documents, index_path=WHOOSH_WIKI_INDEX_PATH)
def initialize_cache(path):
"""
This function iterates over all pages and accessing them in the cache. This forces a
prefetch of all wiki pages
"""
db = QuestionDatabase(QB_QUESTION_DB)
pages = db.questions_with_pages()
cw = CachedWikipedia(path)
pool = Pool()
input_data = [(format_guess(title), cw) for title in pages.keys()]
pool.starmap(access_page, input_data)
def run(self):
db = QuestionDatabase(QB_QUESTION_DB)
questions = db.all_questions()
with open(safe_path(EXPO_QUESTIONS), 'w', newline='') as f:
f.write('id,answer,sent,text\n')
writer = csv.writer(f, delimiter=',')
for q in questions.values():
if q.fold != 'test':
continue
max_sent = max(q.text.keys())
for i in range(max_sent + 1):
writer.writerow(
[q.qnum, format_guess(q.page), i, q.text[i]])
def create_output(path: str):
df = read_dfs(path).cache()
question_db = QuestionDatabase()
answers = question_db.all_answers()
for qnum in answers:
answers[qnum] = format_guess(answers[qnum])
sc = SparkContext.getOrCreate() # type: SparkContext
b_answers = sc.broadcast(answers)
def generate_string(group):
rows = group[1]
result = ""
feature_values = []
meta = None
qnum = None
sentence = None
token = None
guess = None
for name in FEATURE_NAMES:
named_feature_list = list(
filter(lambda r: r.feature_name == name, rows))
if len(named_feature_list) != 1:
raise ValueError(
'Encountered more than one row when there should be exactly one row'
)
named_feature = named_feature_list[0]
if meta is None:
qnum = named_feature.qnum
sentence = named_feature.sentence
token = named_feature.token
guess = named_feature.guess
meta = '{} {} {} {}'.format(qnum, named_feature.sentence,
named_feature.token, guess)
feature_values.append(named_feature.feature_value)
assert '@' not in result, \
'@ is a special character that is split on and not allowed in the feature line'
vw_features = ' '.join(feature_values)
if guess == b_answers.value[qnum]:
vw_label = "1 '{}_{}_{} ".format(qnum, sentence, token)
else:
vw_label = "-1 '{}_{}_{} ".format(qnum, sentence, token)
return vw_label + vw_features + '@' + meta
for fold in VW_FOLDS:
group_features(df.filter(df.fold == fold))\
.map(generate_string)\
.saveAsTextFile('output/vw_input/{0}.vw'.format(fold))
sc.stop()
def create_line(group):
question = group[0]
elements = group[1]
st_groups = seq(elements).group_by(
lambda x: (x[0].sentence, x[0].token)).sorted()
st_lines = []
for st, v in st_groups:
scored_guesses = seq(v)\
.map(lambda x: ScoredGuess(x[0].score, x[1].guess)).sorted(reverse=True).list()
st_lines.append(
Line(question, st[0], st[1],
scored_guesses[0].score > 0, scored_guesses[0].guess,
format_guess(answers[question]), scored_guesses))
return question, st_lines
def normalize_title(corpus, title):
norm_title = corpus + format_guess(title)
return norm_title
def create_report(self, directory: str):
with open(os.path.join(directory, 'guesser_params.pickle'), 'rb') as f:
params = pickle.load(f)
all_guesses = AbstractGuesser.load_guesses(directory)
dev_guesses = all_guesses[all_guesses.fold == 'dev']
test_guesses = all_guesses[all_guesses.fold == 'test']
qdb = QuestionDatabase()
questions = qdb.all_questions()
# Compute recall and accuracy
dev_recall = compute_fold_recall(dev_guesses, questions)
test_recall = compute_fold_recall(test_guesses, questions)
dev_questions = {qnum: q for qnum, q in questions.items() if q.fold == 'dev'}
test_questions = {qnum: q for qnum, q in questions.items() if q.fold == 'test'}
dev_recall_stats = compute_recall_at_positions(dev_recall)
test_recall_stats = compute_recall_at_positions(test_recall)
dev_summary_accuracy = compute_summary_accuracy(dev_questions, dev_recall_stats)
test_summary_accuracy = compute_summary_accuracy(test_questions, test_recall_stats)
dev_summary_recall = compute_summary_recall(dev_questions, dev_recall_stats)
test_summary_recall = compute_summary_recall(test_questions, test_recall_stats)
accuracy_plot('/tmp/dev_accuracy.png', dev_summary_accuracy, 'Dev')
accuracy_plot('/tmp/test_accuracy.png', test_summary_accuracy, 'Test')
recall_plot('/tmp/dev_recall.png', dev_questions, dev_summary_recall, 'Dev')
recall_plot('/tmp/test_recall.png', test_questions, test_summary_recall, 'Test')
# Obtain metrics on number of answerable questions based on the dataset requested
all_answers = {format_guess(g) for g in qdb.all_answers().values()}
all_questions = list(qdb.all_questions().values())
answer_lookup = {qnum: format_guess(guess) for qnum, guess in qdb.all_answers().items()}
dataset = self.qb_dataset()
training_data = dataset.training_data()
min_n_answers = {format_guess(g) for g in training_data[1]}
train_questions = [q for q in all_questions if q.fold == 'train']
train_answers = {format_guess(q.page) for q in train_questions}
dev_questions = [q for q in all_questions if q.fold == 'dev']
dev_answers = {format_guess(q.page) for q in dev_questions}
test_questions = [q for q in all_questions if q.fold == 'test']
test_answers = [format_guess(q.page) for q in test_questions]
min_n_train_questions = [q for q in train_questions if
format_guess(q.page) in min_n_answers]
all_common_train_dev = train_answers.intersection(dev_answers)
all_common_train_test = train_answers.intersection(test_answers)
min_common_train_dev = min_n_answers.intersection(dev_answers)
min_common_train_test = min_n_answers.intersection(test_answers)
all_train_answerable_questions = [q for q in train_questions
if format_guess(q.page) in train_answers]
all_dev_answerable_questions = [q for q in dev_questions
if format_guess(q.page) in train_answers]
all_test_answerable_questions = [q for q in test_questions
if format_guess(q.page) in train_answers]
min_train_answerable_questions = [q for q in train_questions
if format_guess(q.page) in min_n_answers]
min_dev_answerable_questions = [q for q in dev_questions
if format_guess(q.page) in min_n_answers]
min_test_answerable_questions = [q for q in test_questions
if format_guess(q.page) in min_n_answers]
# The next section of code generates the percent of questions correct by the number
# of training examples.
Row = namedtuple('Row', [
'fold', 'guess', 'guesser',
'qnum', 'score', 'sentence', 'token',
'correct', 'answerable_1', 'answerable_2',
'n_examples'
])
train_example_count_lookup = seq(train_questions) \
.group_by(lambda q: format_guess(q.page)) \
.smap(lambda page, group: (page, len(group))) \
.dict()
def guess_to_row(*args):
guess = args[1]
qnum = args[3]
answer = answer_lookup[qnum]
return Row(
*args,
answer == guess,
answer in train_answers,
answer in min_n_answers,
train_example_count_lookup[answer] if answer in train_example_count_lookup else 0
)
dev_data = seq(dev_guesses) \
.smap(guess_to_row) \
.group_by(lambda r: (r.qnum, r.sentence)) \
.smap(lambda key, group: seq(group).max_by(lambda q: q.sentence)) \
.to_pandas(columns=Row._fields)
dev_data['correct_int'] = dev_data['correct'].astype(int)
dev_data['ones'] = 1
dev_counts = dev_data\
.groupby('n_examples')\
.agg({'correct_int': np.mean, 'ones': np.sum})\
.reset_index()
correct_by_n_count_plot('/tmp/dev_correct_by_count.png', dev_counts, 'dev')
n_train_vs_fold_plot('/tmp/n_train_vs_dev.png', dev_counts, 'dev')
test_data = seq(test_guesses) \
.smap(guess_to_row) \
.group_by(lambda r: (r.qnum, r.sentence)) \
.smap(lambda key, group: seq(group).max_by(lambda q: q.sentence)) \
.to_pandas(columns=Row._fields)
test_data['correct_int'] = test_data['correct'].astype(int)
test_data['ones'] = 1
test_counts = dev_data \
.groupby('n_examples') \
.agg({'correct_int': np.mean, 'ones': np.sum}) \
.reset_index()
correct_by_n_count_plot('/tmp/test_correct_by_count.png', test_counts, 'test')
n_train_vs_fold_plot('/tmp/n_train_vs_test.png', test_counts, 'test')
report = ReportGenerator({
'dev_recall_plot': '/tmp/dev_recall.png',
'test_recall_plot': '/tmp/test_recall.png',
'dev_accuracy_plot': '/tmp/dev_accuracy.png',
'test_accuracy_plot': '/tmp/test_accuracy.png',
'dev_accuracy': dev_summary_accuracy,
'test_accuracy': test_summary_accuracy,
'guesser_name': self.display_name(),
'guesser_params': pformat(params),
'n_answers_all_folds': len(all_answers),
'n_total_train_questions': len(train_questions),
'min_class_examples': dataset.min_class_examples,
'n_train_questions': len(min_n_train_questions),
'n_dev_questions': len(dev_questions),
'n_test_questions': len(test_questions),
'n_total_train_answers': len(train_answers),
'n_train_answers': len(min_n_answers),
'n_dev_answers': len(dev_answers),
'n_test_answers': len(test_answers),
'all_n_common_train_dev': len(all_common_train_dev),
'all_n_common_train_test': len(all_common_train_test),
'all_p_common_train_dev': len(all_common_train_dev) / max(1, len(dev_answers)),
'all_p_common_train_test': len(all_common_train_test) / max(1, len(test_answers)),
'min_n_common_train_dev': len(min_common_train_dev),
'min_n_common_train_test': len(min_common_train_test),
'min_p_common_train_dev': len(min_common_train_dev) / max(1, len(dev_answers)),
'min_p_common_train_test': len(min_common_train_test) / max(1, len(test_answers)),
'all_n_answerable_train': len(all_train_answerable_questions),
'all_p_answerable_train': len(all_train_answerable_questions) / len(train_questions),
'all_n_answerable_dev': len(all_dev_answerable_questions),
'all_p_answerable_dev': len(all_dev_answerable_questions) / len(dev_questions),
'all_n_answerable_test': len(all_test_answerable_questions),
'all_p_answerable_test': len(all_test_answerable_questions) / len(test_questions),
'min_n_answerable_train': len(min_train_answerable_questions),
'min_p_answerable_train': len(min_train_answerable_questions) / len(train_questions),
'min_n_answerable_dev': len(min_dev_answerable_questions),
'min_p_answerable_dev': len(min_dev_answerable_questions) / len(dev_questions),
'min_n_answerable_test': len(min_test_answerable_questions),
'min_p_answerable_test': len(min_test_answerable_questions) / len(test_questions),
'dev_correct_by_count_plot': '/tmp/dev_correct_by_count.png',
'test_correct_by_count_plot': '/tmp/test_correct_by_count.png',
'n_train_vs_dev_plot': '/tmp/n_train_vs_dev.png',
'n_train_vs_test_plot': '/tmp/n_train_vs_test.png'
}, 'guesser.md')
output = safe_path(os.path.join(directory, 'guesser_report.pdf'))
report.create(output)
with open(os.path.join(directory, 'guesser_report.pickle'), 'wb') as f:
pickle.dump({
'dev_accuracy': dev_summary_accuracy,
'test_accuracy': test_summary_accuracy,
'guesser_name': self.display_name(),
'guesser_params': params
}, f)