Add scripts
Browse files- .gitignore +8 -0
- rehydrate.py +47 -0
- upload.py +85 -0
- util.py +8 -0
.gitignore
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# Virtual environment
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.env
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# Local arrow copy of data
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arrow_data
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# pycache
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*.pyc
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rehydrate.py
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import os
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import json
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from datasets import load_from_disk, load_dataset, DatasetDict
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from util import PARTITIONING_CATS
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def download_dir(repo_name: str = "persius/hicric", output_dir="./arrow_data"):
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"""Download the dir from HF hub without cloning, if you like, and save locally."""
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ds_dict = DatasetDict()
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for split in PARTITIONING_CATS:
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ds = load_dataset(repo_name, name=split)
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ds_dict[split] = ds
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ds_dict.save_to_disk(output_dir)
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return None
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def repopulate_dir(
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hf_data_dir: str = "./arrow_data", rehydrate_target_dir: str = "./data/processed"
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):
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"""Rehydrate the HICRIC processed data dir from the HF Dataset.
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This hydrates the data in the same format in which it was/is originally produced in
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the HICRIC repository's code.
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"""
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for split in PARTITIONING_CATS:
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dataset = load_from_disk(os.path.join(hf_data_dir, split, "train"))
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# Get individual lines
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for instance in dataset:
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# Extract the output file/directory associated with line
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rel_path = instance["relative_path"]
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output_file_path = os.path.join(rehydrate_target_dir, rel_path)
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output_directory = os.path.join(
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rehydrate_target_dir, os.path.dirname(rel_path)
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)
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os.makedirs(output_directory, exist_ok=True)
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with open(output_file_path, "a") as writer:
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writer.write(json.dumps(instance) + "\n")
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print(f"Repopulated data saved to {rehydrate_target_dir}")
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return None
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if __name__ == "__main__":
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download_dir()
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repopulate_dir()
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upload.py
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import json
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from datasets import Dataset, DatasetDict
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from util import PARTITIONING_CATS
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def construct_hf_dataset(metadata_file: str = "processed_sources.jsonl"):
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"""Construct a HF DatasetDict class from the HICRIC processed data dir, and push to hub."""
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def data_generator(cat: str):
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def validate_tags(tags, partitioning_cats=PARTITIONING_CATS):
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# Find the intersection of the two lists
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matches = [tag for tag in tags if tag in partitioning_cats]
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# Raise an exception if there are none or two or more matches
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if len(matches) == 0 or len(matches) >= 2:
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raise ValueError(
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f"The list of tags must contain exactly one key from the partitioning categories: {partitioning_cats}."
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)
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return True # If the tags are valid
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# Open metadata file
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with open(metadata_file, "r") as metadata_f:
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for idx, line in enumerate(metadata_f):
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obj = json.loads(line)
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local_processed_path = obj["local_processed_path"]
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file_tags = obj["tags"]
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date_accessed = obj["date_accessed"]
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url = obj["url"]
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raw_md5 = obj["md5"]
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# Only proceed for relevant partition cat
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_valid = validate_tags(file_tags)
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if cat not in file_tags:
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continue
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# Read the JSONL file pointed to by the `local_processed_path`
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with open(local_processed_path, "r") as data_file:
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for _idx, data_line in enumerate(data_file):
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data_obj = json.loads(data_line, strict=False)
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# Get line specific data
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text = data_obj.get("text", "")
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line_tags = data_obj.get("tags", [])
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if len(text) == 0:
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continue
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if len(line_tags) > 0:
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tags = file_tags + line_tags
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else:
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tags = file_tags
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rec = {
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"text": data_obj.get("text", ""),
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"tags": tags,
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"date_accessed": date_accessed,
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"source_url": url,
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"source_md5": raw_md5,
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"relative_path": local_processed_path,
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}
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# Add some specific partition keys
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if cat == "case-description":
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rec["decision"] = data_obj.get("decision", "unknown")
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rec["appeal_type"] = data_obj.get("appeal_type", "unknown")
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yield rec
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# Create a DatasetDict to store sub-directory datasets
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dataset_dict = DatasetDict()
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for cat in PARTITIONING_CATS:
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sub_dataset = Dataset.from_generator(
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generator=data_generator, gen_kwargs={"cat": cat}
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)
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dataset_dict[cat] = sub_dataset
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# Save each sub-directory dataset as a separate dataset within a DatasetDict
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for k, v in dataset_dict.items():
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v.push_to_hub("persius/hicric", k, private=True)
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# dataset_dict.save_to_disk("./arrow_data")
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return None
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if __name__ == "__main__":
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construct_hf_dataset()
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util.py
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PARTITIONING_CATS = [
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"legal",
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"regulatory-guidance",
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"contract-coverage-rule-medical-policy",
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"opinion-policy-summary",
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"case-description",
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"clinical-guidelines",
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]
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