scripts/split_dataset.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

df_positive = pd.read_csv("./intermediate/cleaned_positive.csv")  
df_negative = pd.read_csv("./intermediate/cleaned_negative.csv")

pos_train, pos_test = train_test_split(df_positive, test_size=0.2, random_state=42)
neg_train_full, neg_test = train_test_split(df_negative, test_size=0.2, random_state=42)
neg_train = neg_train_full.sample(n=len(pos_train), random_state=42) 
neg_remaining = neg_train_full.drop(neg_train.index)
neg_test = pd.concat([neg_test, neg_remaining]).reset_index(drop=True)
train_df = pd.concat([pos_train, neg_train]).sample(frac=1, random_state=42).reset_index(drop=True) 
test_df = pd.concat([pos_test, neg_test]).sample(frac=1, random_state=42).reset_index(drop=True)  
train_df.to_csv("./intermediate/train.csv", index=False)
test_df.to_csv("./intermediate/test.csv", index=False)