preprocess_marc_ja.py

"""
This code is licensed under CC-BY-4.0 from the original work by shunk031.
The code is adapted from https://huggingface.co/datasets/shunk031/JGLUE/blob/main/JGLUE.py
with minor modifications to the code structure.

This codebase provides pre-processing functionality for the MARC-ja dataset in the Japanese GLUE benchmark.
The original code can be found at https://github.com/yahoojapan/JGLUE/blob/main/preprocess/marc-ja/scripts/marc-ja.py.
"""

import random
import warnings
from typing import Dict, List, Optional, Union
import string

import datasets as ds
import pandas as pd


class MarcJaConfig(ds.BuilderConfig):
    def __init__(
        self,
        name: str = "MARC-ja",
        is_han_to_zen: bool = False,
        max_instance_num: Optional[int] = None,
        max_char_length: int = 500,
        remove_netural: bool = True,
        train_ratio: float = 0.94,
        val_ratio: float = 0.03,
        test_ratio: float = 0.03,
        output_testset: bool = False,
        filter_review_id_list_valid: bool = True,
        label_conv_review_id_list_valid: bool = True,
        version: Optional[Union[ds.utils.Version, str]] = ds.utils.Version("0.0.0"),
        data_dir: Optional[str] = None,
        data_files: Optional[ds.data_files.DataFilesDict] = None,
        description: Optional[str] = None,
    ) -> None:
        super().__init__(
            name=name,
            version=version,
            data_dir=data_dir,
            data_files=data_files,
            description=description,
        )
        if train_ratio + val_ratio + test_ratio != 1.0:
            raise ValueError(
                "train_ratio + val_ratio + test_ratio should be 1.0, "
                f"but got {train_ratio} + {val_ratio} + {test_ratio} = {train_ratio + val_ratio + test_ratio}"
            )

        self.train_ratio = train_ratio
        self.val_ratio = val_ratio
        self.test_ratio = test_ratio

        self.is_han_to_zen = is_han_to_zen
        self.max_instance_num = max_instance_num
        self.max_char_length = max_char_length
        self.remove_netural = remove_netural
        self.output_testset = output_testset

        self.filter_review_id_list_valid = filter_review_id_list_valid
        self.label_conv_review_id_list_valid = label_conv_review_id_list_valid


def get_label(rating: int, remove_netural: bool = False) -> Optional[str]:
    if rating >= 4:
        return "positive"
    elif rating <= 2:
        return "negative"
    else:
        if remove_netural:
            return None
        else:
            return "neutral"


def is_filtered_by_ascii_rate(text: str, threshold: float = 0.9) -> bool:
    ascii_letters = set(string.printable)
    rate = sum(c in ascii_letters for c in text) / len(text)
    return rate >= threshold


def shuffle_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    instances = df.to_dict(orient="records")
    random.seed(1)
    random.shuffle(instances)
    return pd.DataFrame(instances)


def get_filter_review_id_list(
    filter_review_id_list_paths: Dict[str, str],
) -> Dict[str, List[str]]:
    filter_review_id_list_valid = filter_review_id_list_paths.get("valid")
    filter_review_id_list_test = filter_review_id_list_paths.get("test")

    filter_review_id_list = {}

    if filter_review_id_list_valid is not None:
        with open(filter_review_id_list_valid, "r") as rf:
            filter_review_id_list["valid"] = [line.rstrip() for line in rf]

    if filter_review_id_list_test is not None:
        with open(filter_review_id_list_test, "r") as rf:
            filter_review_id_list["test"] = [line.rstrip() for line in rf]

    return filter_review_id_list


def get_label_conv_review_id_list(
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, Dict[str, str]]:
    import csv

    label_conv_review_id_list_valid = label_conv_review_id_list_paths.get("valid")
    label_conv_review_id_list_test = label_conv_review_id_list_paths.get("test")

    label_conv_review_id_list: Dict[str, Dict[str, str]] = {}

    if label_conv_review_id_list_valid is not None:
        with open(label_conv_review_id_list_valid, "r", encoding="utf-8") as rf:
            label_conv_review_id_list["valid"] = {row[0]: row[1] for row in csv.reader(rf)}

    if label_conv_review_id_list_test is not None:
        with open(label_conv_review_id_list_test, "r", encoding="utf-8") as rf:
            label_conv_review_id_list["test"] = {row[0]: row[1] for row in csv.reader(rf)}

    return label_conv_review_id_list


def output_data(
    df: pd.DataFrame,
    train_ratio: float,
    val_ratio: float,
    test_ratio: float,
    output_testset: bool,
    filter_review_id_list_paths: Dict[str, str],
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, pd.DataFrame]:
    instance_num = len(df)
    split_dfs: Dict[str, pd.DataFrame] = {}
    length1 = int(instance_num * train_ratio)
    split_dfs["train"] = df.iloc[:length1]

    length2 = int(instance_num * (train_ratio + val_ratio))
    split_dfs["valid"] = df.iloc[length1:length2]
    split_dfs["test"] = df.iloc[length2:]

    filter_review_id_list = get_filter_review_id_list(
        filter_review_id_list_paths=filter_review_id_list_paths,
    )
    label_conv_review_id_list = get_label_conv_review_id_list(
        label_conv_review_id_list_paths=label_conv_review_id_list_paths,
    )

    for eval_type in ("valid", "test"):
        if filter_review_id_list.get(eval_type):
            df = split_dfs[eval_type]
            df = df[~df["review_id"].isin(filter_review_id_list[eval_type])]
            split_dfs[eval_type] = df

    for eval_type in ("valid", "test"):
        if label_conv_review_id_list.get(eval_type):
            df = split_dfs[eval_type]
            df = df.assign(converted_label=df["review_id"].map(label_conv_review_id_list["valid"]))
            df = df.assign(
                label=df[["label", "converted_label"]].apply(
                    lambda xs: xs["label"] if pd.isnull(xs["converted_label"]) else xs["converted_label"],
                    axis=1,
                )
            )
            df = df.drop(columns=["converted_label"])
            split_dfs[eval_type] = df

    return {
        "train": split_dfs["train"],
        "valid": split_dfs["valid"],
    }


def preprocess_marc_ja(
    config: MarcJaConfig,
    data_file_path: str,
    filter_review_id_list_paths: Dict[str, str],
    label_conv_review_id_list_paths: Dict[str, str],
) -> Dict[str, pd.DataFrame]:
    try:
        import mojimoji

        def han_to_zen(text: str) -> str:
            return mojimoji.han_to_zen(text)

    except ImportError:
        warnings.warn(
            "can't import `mojimoji`, failing back to method that do nothing. "
            "We recommend running `pip install mojimoji` to reproduce the original preprocessing.",
            UserWarning,
        )

        def han_to_zen(text: str) -> str:
            return text

    try:
        from bs4 import BeautifulSoup

        def cleanup_text(text: str) -> str:
            return BeautifulSoup(text, "html.parser").get_text()

    except ImportError:
        warnings.warn(
            "can't import `beautifulsoup4`, failing back to method that do nothing."
            "We recommend running `pip install beautifulsoup4` to reproduce the original preprocessing.",
            UserWarning,
        )

        def cleanup_text(text: str) -> str:
            return text

    from tqdm import tqdm

    df = pd.read_csv(data_file_path, delimiter="\t")
    df = df[["review_body", "star_rating", "review_id"]]

    # rename columns
    df = df.rename(columns={"review_body": "text", "star_rating": "rating"})

    # convert the rating to label
    tqdm.pandas(dynamic_ncols=True, desc="Convert the rating to the label")
    df = df.assign(label=df["rating"].progress_apply(lambda rating: get_label(rating, config.remove_netural)))

    # remove rows where the label is None
    df = df[~df["label"].isnull()]

    # remove html tags from the text
    tqdm.pandas(dynamic_ncols=True, desc="Remove html tags from the text")
    df = df.assign(text=df["text"].progress_apply(cleanup_text))

    # filter by ascii rate
    tqdm.pandas(dynamic_ncols=True, desc="Filter by ascii rate")
    df = df[~df["text"].progress_apply(is_filtered_by_ascii_rate)]

    if config.max_char_length is not None:
        df = df[df["text"].str.len() <= config.max_char_length]

    if config.is_han_to_zen:
        df = df.assign(text=df["text"].apply(han_to_zen))

    df = df[["text", "label", "review_id"]]
    df = df.rename(columns={"text": "sentence"})

    # shuffle dataset
    df = shuffle_dataframe(df)

    split_dfs = output_data(
        df=df,
        train_ratio=config.train_ratio,
        val_ratio=config.val_ratio,
        test_ratio=config.test_ratio,
        output_testset=config.output_testset,
        filter_review_id_list_paths=filter_review_id_list_paths,
        label_conv_review_id_list_paths=label_conv_review_id_list_paths,
    )
    return split_dfs