join_datasets.py

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "pandas",
#     "pyarrow",
# ]
# ///
"""
Runs with uv
"""

import pandas as pd
from pathlib import Path


def parse_txt_file(txt_path: str):
    texts = []
    labels = []

    with open(txt_path, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue

            if line.startswith("__label__ham"):
                label = 0
                text = line.replace("__label__ham", "", 1).strip()
            elif line.startswith("__label__spam"):
                label = 1
                text = line.replace("__label__spam", "", 1).strip()
            else:
                continue

            if text:
                texts.append(text)
                labels.append(bool(label))

    return pd.DataFrame({"text": texts, "label": labels})


def join_datasets(
    txt_files: list[str],
    parquet_files: list[str],
    output_path: str,
):
    dfs = []

    for txt_file in txt_files:
        if Path(txt_file).exists():
            df = parse_txt_file(txt_file)
            dfs.append(df)
            print(f"Loaded {len(df)} rows from {txt_file}")

    for parquet_file in parquet_files:
        if Path(parquet_file).exists():
            df = pd.read_parquet(parquet_file)
            if "message" in df.columns:
                df = df.rename(columns={"message": "text"})
            if "text" not in df.columns or "label" not in df.columns:
                print(f"Skipping {parquet_file}: missing 'text' or 'label' column")
                continue
            df = df[["text", "label"]]
            df["label"] = df["label"].astype("bool")
            dfs.append(df)
            print(f"Loaded {len(df)} rows from {parquet_file}")

    if not dfs:
        print("No data to combine")
        return

    combined_df = pd.concat(dfs, ignore_index=True)

    combined_df.to_parquet(output_path, index=False)
    print(f"\nCombined dataset saved to {output_path}")
    print(f"Total rows: {len(combined_df)}")
    print(f"Label column dtype: {combined_df['label'].dtype}")
    print(f"Label distribution:\n{combined_df['label'].value_counts().sort_index()}")


if __name__ == "__main__":
    txt_files = [
        "train_data.txt",
        "labeled_dataset_from_hf.txt",
    ]

    parquet_files = [
        "processed_combined.parquet",
        # hf://datasets/darkQibit/russian-spam-detection/processed_combined.parquet
    ]

    output_path = "processed_combined.parquet"

    join_datasets(txt_files, parquet_files, output_path)