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"""Here we detect PII: Emails, IP addresses, and keys (SSH/API) and redact/anonymize them
* we use one regex for emails and one for IP addresses
* for keys we use detect-secrets tool, which is a combination of multiple plugins (regexes, entropy..)
* we also add some filters on top of each tool to decrease the number of false positives
This script is adapted from https://github.com/bigscience-workshop/data-preparation/blob/main/preprocessing/training/02_pii/pii_processor.py
"""
import argparse
import random
import json
import logging
from pprint import pformat
from functools import partial
from datasets.utils.logging import set_verbosity_info
from datasets import load_dataset
from pii_detection import scan_pii_batch
from pii_redaction import redact_pii_batch, random_replacements
from utils.manual_sharding import save_manual_shards
def parseArgs():
parser = argparse.ArgumentParser(description="PII detection and redaction")
parser.add_argument(
"--dataset_name",
default="bigcode/pii-for-code",
type=str,
help="HF repo name/path of the dataset.",
)
parser.add_argument(
"--subset",
default="data/",
type=str,
help="Data subset to use.",
)
parser.add_argument(
"--text_column",
default="content",
type=str,
help="Text column to use, if will be renamed to content",
)
parser.add_argument(
"--split",
default="train",
type=str,
help="Dataset split to process",
)
parser.add_argument(
"--batch_size",
default=100,
type=int,
help="Batch size for the PII detection/redaction",
)
parser.add_argument(
"--seed",
default=0,
type=int,
help="Seed for random",
)
parser.add_argument(
"--num_proc",
default=96,
type=int,
help="Number of processes to use for the PII detection/redaction",
)
parser.add_argument(
"--no_redaction",
action="store_true",
help="If set, we don't perform redaction",
)
parser.add_argument(
"--load_replacements",
default=True,
help="If set, we load the replacements from file replacements.json",
)
parser.add_argument(
"--add_reference_text",
default=True,
type=bool,
help="If True we add the reference text with PII between delimiters \
in the redacted text -used for visualization-",
)
parser.add_argument(
"--check_all_files",
action="store_true",
help="If set, we check all files, not only the ones that contain PII",
)
parser.add_argument(
"--check_sampling_size",
default=0,
type=int,
help="Number of samples to check for PII",
)
# for saving the dataset: either push to HF or save locally with datasets or save manual shards
parser.add_argument(
"--save_mode",
default="manual_shards",
type=str,
choices=["hub", "local", "manual_shards"],
help="How to save the dataset",
)
parser.add_argument(
"--save_mode_checks",
default="hub",
type=str,
choices=["hub", "local", "manual_shards"],
help="How to save the checks dataset",
)
# add argument for name of dataset on the hub
parser.add_argument(
"--target_dataset",
default="bigcode-pii-pjj",
type=str,
help="HF repo name of the target dataset in save_mode=hub.",
)
parser.add_argument(
"--hub_username",
default="loubnabnl",
type=str,
help="Username for the hub",
)
parser.add_argument(
"--save_path_disk",
default="bigcode-pii-pjj-local",
type=str,
help="Path to save the dataset on disk in save_mode=local.",
)
parser.add_argument(
# TODO: investigate issue to remove this arg
"--remove_columns_the_stack",
default=True,
type=bool,
help="The Stack v1.1 has many columns and this can cause an issue during processing of large subsets.",
)
# add an option of evaluating the pipeline on the PII benchmark we built
return parser.parse_args()
def get_check_ds(ds, args):
if not args.check_all_files:
ds_checks = ds.filter(
lambda exs: exs["modified"],
batched=True,
batch_size=args.batch_size,
num_proc=args.num_proc
)
else:
ds_checks = ds
if not args.check_sampling_size:
sampling_size = len(ds_checks)
idx_samples = random.sample(range(len(ds_checks)), min(len(ds_checks), sampling_size))
ds_checks = ds_checks.select(idx_samples)
return ds_checks
def main():
set_verbosity_info()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
handlers=[
logging.FileHandler("pii.log"),
logging.StreamHandler()
]
)
args = parseArgs()
logger.info(f"** The job is running with the following arguments: **\n{args}\n **** ")
logger.info(f" ===== Loading {args.dataset_name} =====")
ds = load_dataset(args.dataset_name, data_dir=args.subset, split=args.split, use_auth_token=True)
if args.text_column != "content":
ds = ds.rename_column(args.text_column, "content")
# if args.remove_columns_the_stack:
# logger.info("removing extra columns from The Stack")
# # columns = [ 'max_stars_repo_head_hexsha', 'max_stars_repo_licenses', 'max_stars_repo_stars_event_min_datetime',\
# # 'max_stars_repo_stars_event_max_datetime', 'max_issues_repo_path', 'max_issues_repo_name', 'max_issues_repo_head_hexsha',\
# # 'max_issues_repo_licenses', 'max_issues_count', 'max_issues_repo_issues_event_min_datetime', 'max_issues_repo_issues_event_max_datetime', \
# # 'max_forks_repo_path', 'max_forks_repo_name', 'max_forks_repo_head_hexsha', \
# # 'max_forks_repo_licenses', 'max_forks_count', 'max_forks_repo_forks_event_min_datetime', 'max_forks_repo_forks_event_max_datetime']
# ds = ds.remove_columns(columns)
# logger.info(f"New dataset fomat: {ds}")
# add id column to dataset
logger.info(f" ===== Adding an index column =====")
ds = ds.add_column("index", list(range(len(ds))))
logger.info(f" ===== Applying PII detection =====")
ds_pii = ds.map(
scan_pii_batch, batched=True, batch_size=args.batch_size, num_proc=args.num_proc, load_from_cache_file=False
)
logger.info(f"Dataset info after PII detection:\n{ds_pii}")
logger.info(f"Number of samples that contained PII: {sum(ds_pii['has_secrets'])}")
logger.info(f"Total number of secrets found: {sum(ds_pii['number_secrets'])}")
# redact PII in the dataset
if not args.no_redaction:
logger.info(f" ===== Applying PII redaction =====")
random.seed(args.seed)
# we use random replacements by default
if args.load_replacements:
with open("replacements.json", "r") as f:
replacements = json.load(f)
else:
replacements = random_replacements()
with open("random_replacements.json", "w") as f:
json.dump(replacements, f)
logging.info(f"Using the following replacements:\n{pformat(replacements)}")
ds_pii = ds_pii.map(
partial(redact_pii_batch, replacements=replacements, add_references=args.add_reference_text),
batched=True,
batch_size=args.batch_size,
num_proc=args.num_proc,
load_from_cache_file=False
)
logging.info(f"Dataset info after PII redaction:\n{ds_pii}")
# check the dataset
logger.info(f" ===== Checking {args.check_sampling_size} samples from those modified in the dataset =====")
ds_checks = get_check_ds(ds_pii, args)
# save checks dataset
# if len(ds_checks) == 0:
# logger.info("Dataset was empty. Not saving anything.")
# else:
# logger.info(f"Checks dataset info {ds_checks}")
# if args.save_mode_checks == "hub":
# logger.info(f"Pushing the checks dataset to the Hub as {args.target_dataset}_checks")
# ds_checks.push_to_hub(args.target_dataset + "_checks")
# elif args.save_mode_checks == "local":
# logger.info(f"Saving the checks dataset to disk")
# ds_checks.save_to_disk(args.save_path_disk + "_checks",format="json")
#elif args.save_mode == "local":
logger.info(f" ===== Saving the dataset to disk as JSON =====")
ds_pii_df = ds_pii.to_pandas()
ds_pii_df.to_json(args.save_path_disk + ".json", orient='records', lines=True)
# elif args.save_mode_checks == "manual_shards":
# logger.info(f"Saving the checks dataset in manual shards")
# save_manual_shards(ds_checks, user=args.hub_username, remote_dataset_repo=args.target_dataset + "_checks")
logger.info("Removing columns that are not needed for the final dataset")
columns = ["content", "modified", "secrets", "has_secrets", "number_secrets"]
if args.add_reference_text:
columns.append("references")
ds_pii = ds_pii.remove_columns(columns)
ds_pii = ds_pii.rename_column("new_content", "content")
logger.info(f"Dataset info after removing columns:\n{ds_pii}")
# save the final dataset
if args.save_mode == "hub":
logger.info(f" ===== Pushing the dataset to the Hub as: {args.target_dataset} =====")
ds_pii.push_to_hub(args.target_dataset)
# elif args.save_mode == "local":
# logger.info(f" ===== Saving the dataset to disk =====")
# ds_pii.save_to_disk(args.save_path_disk)
elif args.save_mode == "local":
logger.info(f" ===== Saving the dataset to disk as JSON =====")
ds_pii_df = ds_pii.to_pandas()
ds_pii_df.to_json(args.save_path_disk + ".json", orient='records', lines=True)
elif args.save_mode == "manual_shards":
logger.info(f" ===== Saving the dataset in manual shards =====")
save_manual_shards(ds_pii, user=args.hub_username, remote_dataset_repo=args.target_dataset)
logger.info(f" ===== Dataset saved successfully =====")
if __name__ == "__main__":
main()
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