def download_panx_data_and_write_config(task_data_base_path: str, task_config_base_path: str):
def _process_one_file(infile, outfile):
lines = open(infile, "r").readlines()
if lines[-1].strip() == "":
lines = lines[:-1]
with open(outfile, "w") as fout:
for line in lines:
items = line.strip().split("\t")
if len(items) == 2:
label = items[1].strip()
idx = items[0].find(":")
if idx != -1:
token = items[0][idx + 1 :].strip()
fout.write(f"{token}\t{label}\n")
else:
fout.write("\n")
panx_temp_path = os.path.join(task_data_base_path, "panx_temp")
zip_path = os.path.join(panx_temp_path, "AmazonPhotos.zip")
assert os.path.exists(zip_path), (
"Download AmazonPhotos.zip from"
" https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN"
f" and save it to {zip_path}"
)
download_utils.unzip_file(zip_path=zip_path, extract_location=panx_temp_path)
languages = (
"af ar bg bn de el en es et eu fa fi fr he hi hu id it ja jv ka "
"kk ko ml mr ms my nl pt ru sw ta te th tl tr ur vi yo zh"
).split()
for lang in languages:
task_name = f"panx_{lang}"
untar_path = os.path.join(panx_temp_path, "panx_dataset", lang)
os.makedirs(untar_path, exist_ok=True)
download_utils.untar_file(
tar_path=os.path.join(panx_temp_path, "panx_dataset", f"{lang}.tar.gz"),
extract_location=untar_path,
delete=True,
)
task_data_path = os.path.join(task_data_base_path, task_name)
os.makedirs(task_data_path, exist_ok=True)
filename_dict = {"train": "train", "val": "dev", "test": "test"}
paths_dict = {}
for phase, filename in filename_dict.items():
in_path = os.path.join(untar_path, filename)
out_path = os.path.join(task_data_path, f"{phase}.tsv")
if not os.path.exists(in_path):
continue
_process_one_file(infile=in_path, outfile=out_path)
paths_dict[phase] = out_path
py_io.write_json(
data={
"task": "panx",
"paths": paths_dict,
"name": task_name,
"kwargs": {"language": lang},
},
path=os.path.join(task_config_base_path, f"{task_name}_config.json"),
)
shutil.rmtree(os.path.join(panx_temp_path, "panx_dataset"))
def download_newsqa_data_and_write_config(
task_name: str, task_data_path: str, task_config_path: str
):
def get_consensus_answer(row_):
answer_char_start, answer_char_end = None, None
if row_.validated_answers:
validated_answers_ = json.loads(row.validated_answers)
answer_, max_count = max(validated_answers_.items(), key=itemgetter(1))
total_count = sum(validated_answers_.values())
if max_count >= total_count / 2.0:
if answer_ != "none" and answer_ != "bad_question":
answer_char_start, answer_char_end = map(int, answer_.split(":"))
else:
# No valid answer.
pass
else:
# Check row_.answer_char_ranges for most common answer.
# No validation was done so there must be an answer with consensus.
answers = Counter()
for user_answer in row_.answer_char_ranges.split("|"):
for ans in user_answer.split(","):
answers[ans] += 1
top_answer = answers.most_common(1)
if top_answer:
top_answer, _ = top_answer[0]
if ":" in top_answer:
answer_char_start, answer_char_end = map(int, top_answer.split(":"))
return answer_char_start, answer_char_end
def load_combined(path):
result = pd.read_csv(
path,
encoding="utf-8",
dtype=dict(is_answer_absent=float),
na_values=dict(question=[], story_text=[], validated_answers=[]),
keep_default_na=False,
)
if "story_text" in result.keys():
for row_ in display.tqdm(
result.itertuples(), total=len(result), desc="Adjusting story texts"
):
story_text_ = row_.story_text.replace("\r\n", "\n")
result.at[row_.Index, "story_text"] = story_text_
return result
def _map_answers(answers):
result = []
for a in answers.split("|"):
user_answers = []
result.append(dict(sourcerAnswers=user_answers))
for r in a.split(","):
if r == "None":
user_answers.append(dict(noAnswer=True))
else:
start_, end_ = map(int, r.split(":"))
user_answers.append(dict(s=start_, e=end_))
return result
def strip_empty_strings(strings):
while strings and strings[-1] == "":
del strings[-1]
return strings
# Require: cnn_stories.tgz
cnn_stories_path = os.path.join(task_data_path, "cnn_stories.tgz")
assert os.path.exists(cnn_stories_path), (
"Download CNN Stories from https://cs.nyu.edu/~kcho/DMQA/ and save to " + cnn_stories_path
)
# Require: newsqa-data-v1/newsqa-data-v1.csv
dataset_path = os.path.join(task_data_path, "newsqa-data-v1", "newsqa-data-v1.csv")
if os.path.exists(dataset_path):
pass
elif os.path.exists(os.path.join(task_data_path, "newsqa-data-v1.zip")):
download_utils.unzip_file(
zip_path=os.path.join(task_data_path, "newsqa-data-v1.zip"),
extract_location=task_data_path,
delete=False,
)
else:
raise AssertionError(
"Download https://www.microsoft.com/en-us/research/project/newsqa-dataset/#!download"
" and save to " + os.path.join(task_data_path, "newsqa-data-v1.zip")
)
# Download auxiliary data
os.makedirs(task_data_path, exist_ok=True)
file_name_list = [
"train_story_ids.csv",
"dev_story_ids.csv",
"test_story_ids.csv",
"stories_requiring_extra_newline.csv",
"stories_requiring_two_extra_newlines.csv",
"stories_to_decode_specially.csv",
]
for file_name in file_name_list:
download_utils.download_file(
f"https://raw.githubusercontent.com/Maluuba/newsqa/master/maluuba/newsqa/{file_name}",
os.path.join(task_data_path, file_name),
)
dataset = load_combined(dataset_path)
remaining_story_ids = set(dataset["story_id"])
with open(
os.path.join(task_data_path, "stories_requiring_extra_newline.csv"), "r", encoding="utf-8"
) as f:
stories_requiring_extra_newline = set(f.read().split("\n"))
with open(
os.path.join(task_data_path, "stories_requiring_two_extra_newlines.csv"),
"r",
encoding="utf-8",
) as f:
stories_requiring_two_extra_newlines = set(f.read().split("\n"))
with open(
os.path.join(task_data_path, "stories_to_decode_specially.csv"), "r", encoding="utf-8"
) as f:
stories_to_decode_specially = set(f.read().split("\n"))
# Start combining data files
story_id_to_text = {}
with tarfile.open(cnn_stories_path, mode="r:gz", encoding="utf-8") as t:
highlight_indicator = "@highlight"
copyright_line_pattern = re.compile(
"^(Copyright|Entire contents of this article copyright, )"
)
with display.tqdm(total=len(remaining_story_ids), desc="Getting story texts") as pbar:
for member in t.getmembers():
story_id = member.name
if story_id in remaining_story_ids:
remaining_story_ids.remove(story_id)
story_file = t.extractfile(member)
# Correct discrepancies in stories.
# Problems are caused by using several programming languages and libraries.
# When ingesting the stories, we started with Python 2.
# After dealing with unicode issues, we tried switching to Python 3.
# That caused inconsistency problems so we switched back to Python 2.
# Furthermore, when crowdsourcing, JavaScript and HTML templating perturbed
# the stories.
# So here we map the text to be compatible with the indices.
lines = map(lambda s_: s_.strip().decode("utf-8"), story_file.readlines())
story_file.close()
lines = list(lines)
highlights_start = lines.index(highlight_indicator)
story_lines = lines[:highlights_start]
story_lines = strip_empty_strings(story_lines)
while len(story_lines) > 1 and copyright_line_pattern.search(story_lines[-1]):
story_lines = strip_empty_strings(story_lines[:-2])
if story_id in stories_requiring_two_extra_newlines:
story_text = "\n\n\n".join(story_lines)
elif story_id in stories_requiring_extra_newline:
story_text = "\n\n".join(story_lines)
else:
story_text = "\n".join(story_lines)
story_text = story_text.replace("\xe2\x80\xa2", "\xe2\u20ac\xa2")
story_text = story_text.replace("\xe2\x82\xac", "\xe2\u201a\xac")
story_text = story_text.replace("\r", "\n")
if story_id in stories_to_decode_specially:
story_text = story_text.replace("\xe9", "\xc3\xa9")
story_id_to_text[story_id] = story_text
pbar.update()
if len(remaining_story_ids) == 0:
break
for row in display.tqdm(dataset.itertuples(), total=len(dataset), desc="Setting story texts"):
# Set story_text since we cannot include it in the dataset.
story_text = story_id_to_text[row.story_id]
dataset.at[row.Index, "story_text"] = story_text
# Handle endings that are too large.
answer_char_ranges = row.answer_char_ranges.split("|")
updated_answer_char_ranges = []
ranges_updated = False
for user_answer_char_ranges in answer_char_ranges:
updated_user_answer_char_ranges = []
for char_range in user_answer_char_ranges.split(","):
if char_range != "None":
start, end = map(int, char_range.split(":"))
if end > len(story_text):
ranges_updated = True
end = len(story_text)
if start < end:
updated_user_answer_char_ranges.append("%d:%d" % (start, end))
else:
# It's unclear why but sometimes the end is after the start.
# We'll filter these out.
ranges_updated = True
else:
updated_user_answer_char_ranges.append(char_range)
if updated_user_answer_char_ranges:
updated_user_answer_char_ranges = ",".join(updated_user_answer_char_ranges)
updated_answer_char_ranges.append(updated_user_answer_char_ranges)
if ranges_updated:
updated_answer_char_ranges = "|".join(updated_answer_char_ranges)
dataset.at[row.Index, "answer_char_ranges"] = updated_answer_char_ranges
if row.validated_answers and not pd.isnull(row.validated_answers):
updated_validated_answers = {}
validated_answers = json.loads(row.validated_answers)
for char_range, count in validated_answers.items():
if ":" in char_range:
start, end = map(int, char_range.split(":"))
if end > len(story_text):
ranges_updated = True
end = len(story_text)
if start < end:
char_range = "{}:{}".format(start, end)
updated_validated_answers[char_range] = count
else:
# It's unclear why but sometimes the end is after the start.
# We'll filter these out.
ranges_updated = True
else:
updated_validated_answers[char_range] = count
if ranges_updated:
updated_validated_answers = json.dumps(
updated_validated_answers, ensure_ascii=False, separators=(",", ":")
)
dataset.at[row.Index, "validated_answers"] = updated_validated_answers
# Process Splits
data = []
cache = dict()
train_story_ids = set(
pd.read_csv(os.path.join(task_data_path, "train_story_ids.csv"))["story_id"].values
)
dev_story_ids = set(
pd.read_csv(os.path.join(task_data_path, "dev_story_ids.csv"))["story_id"].values
)
test_story_ids = set(
pd.read_csv(os.path.join(task_data_path, "test_story_ids.csv"))["story_id"].values
)
def _get_data_type(story_id_):
if story_id_ in train_story_ids:
return "train"
elif story_id_ in dev_story_ids:
return "dev"
elif story_id_ in test_story_ids:
return "test"
else:
return ValueError("{} not found in any story ID set.".format(story_id))
for row in display.tqdm(dataset.itertuples(), total=len(dataset), desc="Building json"):
questions = cache.get(row.story_id)
if questions is None:
questions = []
datum = dict(
storyId=row.story_id,
type=_get_data_type(row.story_id),
text=row.story_text,
questions=questions,
)
cache[row.story_id] = questions
data.append(datum)
q = dict(
q=row.question,
answers=_map_answers(row.answer_char_ranges),
isAnswerAbsent=row.is_answer_absent,
)
if row.is_question_bad != "?":
q["isQuestionBad"] = float(row.is_question_bad)
if row.validated_answers and not pd.isnull(row.validated_answers):
validated_answers = json.loads(row.validated_answers)
q["validatedAnswers"] = []
for answer, count in validated_answers.items():
answer_item = dict(count=count)
if answer == "none":
answer_item["noAnswer"] = True
elif answer == "bad_question":
answer_item["badQuestion"] = True
else:
s, e = map(int, answer.split(":"))
answer_item["s"] = s
answer_item["e"] = e
q["validatedAnswers"].append(answer_item)
consensus_start, consensus_end = get_consensus_answer(row)
if consensus_start is None and consensus_end is None:
if q.get("isQuestionBad", 0) >= 0.5:
q["consensus"] = dict(badQuestion=True)
else:
q["consensus"] = dict(noAnswer=True)
else:
q["consensus"] = dict(s=consensus_start, e=consensus_end)
questions.append(q)
phase_dict = {
"train": [],
"val": [],
"test": [],
}
phase_map = {"train": "train", "dev": "val", "test": "test"}
for entry in data:
phase = phase_map[entry["type"]]
output_entry = {"text": entry["text"], "storyId": entry["storyId"], "qas": []}
for qn in entry["questions"]:
if "badQuestion" in qn["consensus"] or "noAnswer" in qn["consensus"]:
continue
output_entry["qas"].append({"question": qn["q"], "answer": qn["consensus"]})
phase_dict[phase].append(output_entry)
for phase, phase_data in phase_dict.items():
py_io.write_jsonl(phase_data, os.path.join(task_data_path, f"{phase}.jsonl"))
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "train.jsonl"),
"val": os.path.join(task_data_path, "val.jsonl"),
"test": os.path.join(task_data_path, "val.jsonl"),
},
"name": task_name,
},
path=task_config_path,
)
for file_name in file_name_list:
os.remove(os.path.join(task_data_path, file_name))