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)