111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
import os
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import argparse
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import pandas as pd
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from misc import DATA_DIR, REGION_MAPPING, logging
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def clean(filepath) -> pd.DataFrame:
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"""
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Clean the CSV file by removing null bytes, non-breaking spaces, and extra spaces.
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Also, it attempts to read the file with different encodings to handle potential encoding issues.
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"""
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encodings = ['utf-8', 'utf-16', 'latin1']
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for enc in encodings:
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try:
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logging.info(f"Trying to read {filepath} with encoding: {enc}")
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# Use chunked reading to handle large files
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chunks = pd.read_csv(filepath, encoding=enc, chunksize=100_000, on_bad_lines='skip')
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cleaned_chunks = []
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for chunk in chunks:
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# Drop rows with essential missing values early
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chunk = chunk.dropna(subset=['name', 'sex', 'region'])
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# Clean string columns in-place
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for col in chunk.select_dtypes(include='object').columns:
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chunk[col] = (
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chunk[col]
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.astype(str)
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.str.replace('\x00', ' ', regex=False)
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.str.replace('\u00a0', ' ', regex=False)
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.str.replace(' +', ' ', regex=True)
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.str.strip()
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.str.lower()
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)
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cleaned_chunks.append(chunk)
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df = pd.concat(cleaned_chunks, ignore_index=True)
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df.to_csv(filepath, index=False, encoding='utf-8')
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logging.info(f"Successfully read with encoding: {enc}")
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return df
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except Exception:
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continue
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raise UnicodeDecodeError(f"Unable to decode {filepath} with common encodings.")
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def process(df: pd.DataFrame) -> pd.DataFrame:
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"""
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Process the DataFrame to extract features and clean data.
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This includes counting words, calculating name length, and extracting probable native names and surnames.
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Also maps regions to provinces based on REGION_MAPPING.
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"""
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logging.info("Preprocessing names")
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df['words'] = df['name'].str.count(' ') + 1
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df['length'] = df['name'].str.replace(' ', '', regex=False).str.len()
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name_split = df['name'].str.split()
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df['probable_native'] = name_split.apply(lambda x: ' '.join(x[:-1]) if len(x) > 1 else '')
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df['probable_surname'] = name_split.apply(lambda x: x[-1] if x else '')
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df['identified_category'] = df['words'].apply(lambda x: 'compose' if x > 3 else 'simple')
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df['identified_name'] = None
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df['identified_surname'] = None
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logging.info("Mapping regions to provinces")
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df['province'] = df['region'].map(lambda r: REGION_MAPPING.get(r, ('AUTRES', 'AUTRES'))[1])
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df['province'] = df['province'].str.lower()
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df['annotated'] = 0
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return df
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def save_artifacts(df: pd.DataFrame, split_eval: bool = True, split_by_sex: bool = True) -> None:
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"""
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Splits the input DataFrame into evaluation and featured datasets, saves them as CSV files,
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and additionally saves separate CSV files for male and female entries if requested.
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"""
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if split_eval:
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logging.info("Saving evaluation and featured datasets")
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eval_idx = df.sample(frac=0.2, random_state=42).index
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df_evaluation = df.loc[eval_idx]
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df_featured = df.drop(index=eval_idx)
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df_evaluation.to_csv(os.path.join(DATA_DIR, 'names_evaluation.csv'), index=False)
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df_featured.to_csv(os.path.join(DATA_DIR, 'names_featured.csv'), index=False)
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else:
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df.to_csv(os.path.join(DATA_DIR, 'names_featured.csv'), index=False)
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if split_by_sex:
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logging.info("Saving by sex")
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df[df['sex'] == 'm'].to_csv(os.path.join(DATA_DIR, 'names_males.csv'), index=False)
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df[df['sex'] == 'f'].to_csv(os.path.join(DATA_DIR, 'names_females.csv'), index=False)
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def main(split_eval: bool = True, split_by_sex: bool = True):
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df = process(clean(os.path.join(DATA_DIR, 'names.csv')))
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save_artifacts(df, split_eval=split_eval, split_by_sex=split_by_sex)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Prepare name datasets with optional splits.")
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parser.add_argument('--split_eval', action='store_true', default=True, help="Split into evaluation and featured datasets (default: True)")
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parser.add_argument('--no-split_eval', action='store_false', dest='split_eval', help="Do not split into evaluation and featured datasets")
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parser.add_argument('--split_by_sex', action='store_true', default=True, help="Split by sex into male/female datasets (default: True)")
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parser.add_argument('--no-split_by_sex', action='store_false', dest='split_by_sex', help="Do not split by sex into male/female datasets")
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args = parser.parse_args()
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main(split_eval=args.split_eval, split_by_sex=args.split_by_sex)
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