| import pandas as pd | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| # Set random seed for reproducibility | |
| np.random.seed(42) | |
| # Load the combined dataset | |
| df = pd.read_parquet('affinity-data-combined.parquet') | |
| # First split off test set (5%) | |
| train_val_df, test_df = train_test_split(df, test_size=0.05, random_state=42) | |
| # Split remaining data into train and validation (90% and 5% of total) | |
| train_df, val_df = train_test_split(train_val_df, test_size=0.05263158, random_state=42) # 0.0526 of 95% is 5% of total | |
| # Save the splits to parquet files | |
| train_df.to_parquet('train.parquet', index=False) | |
| val_df.to_parquet('val.parquet', index=False) | |
| test_df.to_parquet('test.parquet', index=False) | |