sampling.py

import pandas as pd
import numpy as np
from skmultilearn.model_selection import iterative_train_test_split
from hatebr import process_row
import json
DATASET_URL = "https://raw.githubusercontent.com/franciellevargas/HateBR/2d18c5b9410c2dfdd6d5394caa54d608857dae7c/dataset/HateBR.csv"

def generate_stratified_indexes(df, y_column="offensive_language"):
    """Generates stratified train, validation, and test indexes for given DataFrame.

    Args:
        df: A pandas DataFrame.
        y_column: A string indicating the name of the column representing the target variable (default: "offensive_language").

    Returns:
        A tuple of numpy arrays containing the train, validation, and test indexes for the input DataFrame:
        X_train_indexes: An array of indexes representing the train data.
        X_dev_indexes: An array of indexes representing the validation data.
        X_test_indexes: An array of indexes representing the test data.
        y_train_indexes: An array of indexes representing the train target values.
        y_dev_indexes: An array of indexes representing the validation target values.
        y_test_indexes: An array of indexes representing the test target values.
    """
    records = df.to_dict("records")
    processed_records = [ process_row(row, None) for row in records ]
    processed_df = pd.DataFrame(processed_records)

    y = processed_df[[y_column]].to_numpy().astype(np.int32)

    indices = np.arange(y.shape[0])
    indices = indices.reshape(indices.shape[0], 1)

    y = np.append(y, indices, axis=1)

    processed_df.drop(columns=[y_column], inplace=True)
    
    X = processed_df.to_numpy().astype(np.int32)
    X = np.append(X, indices, axis=1)

    X_train_dev, y_train_dev, X_test, y_test = iterative_train_test_split(X, y, test_size = 0.2)
    X_train, y_train, X_dev, y_dev = iterative_train_test_split(X_train_dev, y_train_dev, test_size = 0.2)

    X_train_indexes = X_train[:, -1]
    X_dev_indexes = X_dev[:, -1]
    X_test_indexes = X_test[:, -1]

    y_train_indexes = y_train[:, -1]
    y_dev_indexes = y_dev[:, -1]
    y_test_indexes = y_test[:, -1]

    return X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes

def main():
    df = pd.read_csv(DATASET_URL)
    df.drop(columns=["instagram_comments"], inplace=True)
    X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes = generate_stratified_indexes(df)

    final_indexes = {
        "train": [int(x) for x in X_train_indexes],
        "validation": [int(x) for x in X_dev_indexes],
        "test": [int(x) for x in X_test_indexes]
    }

    with open("indexes.json", "w") as f:
        json.dump(final_indexes, f)


if __name__ == "__main__":
    main()