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import numpy as np
import pandas as pd
from pathlib import Path
# Import your project's modules
from data.data_handling import prepare_and_load_data, GP_FEATURIZERS
from training.training_pipeline import k_fold_tuned_eval
from models.gp import TanimotoGP as GPModel
from data.loaders import load_dataset
from training.train_eval import (
k_fold_eval as gp_k_fold_eval,
train_gp_model,
eval_gp_model,
)
def run_gnn_experiment(args):
"""
Orchestrates a full, tuned GNN experiment using the EFFICIENT pipeline.
"""
print(
f"--- Starting GNN Experiment: {args.model} | {args.rep} | {args.dataset} ---"
)
# 1. Prepare data ONCE. This function handles caching.
train_graphs, test_graphs = prepare_and_load_data(args)
# 2. Run the rigorous evaluation pipeline with the pre-featurized data.
k_fold_tuned_eval(args, train_graphs, test_graphs)
print(f"---GNN Experiment Finished. Results saved---")
def run_gp_experiment(args):
"""Orchestrates a GP experiment, preserving the original logic."""
print(f"--- Starting GP Experiment: {args.rep} | {args.dataset} ---")
X_train, X_test, y_train, y_test = load_dataset(args.dataset)
gp_feat = GP_FEATURIZERS[args.rep]
X_train_feat = np.stack([gp_feat(x) for x in X_train]).astype(np.float32)
X_test_feat = np.stack([gp_feat(x) for x in X_test]).astype(np.float32)
def train_fn(X_tr, y_tr, log_file):
model = GPModel()
return train_gp_model(model, X_tr, y_tr, log_file)
def eval_fn(model, X_te, y_te, log_file, scaler, return_preds=False):
return eval_gp_model(
model, X_te, y_te, log_file, scaler, return_preds=return_preds
)
_, test_metrics = gp_k_fold_eval(
train_fn=train_fn,
eval_fn=eval_fn,
X_train=X_train_feat,
y_train=y_train,
model_name="gp",
rep_name=args.rep,
dataset_name=args.dataset,
X_test=X_test_feat,
y_test=y_test,
)
print(f"--- GP Experiment Finished. Final Test Metrics: {test_metrics} ---")
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