hyper parameter search

scripts/hyper_search.py

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+import argparse
+from data_loader import Train_Test_Split, ESPDataset, ESPDataModule
+import optuna
+from optuna.integration import PyTorchLightningPruningCallback
+from pytorch_lightning.callbacks import ModelCheckpoint
+from env import *
+from train import ESPFailureModel
+from data_loader import Train_Test_Split, ESPDataset, ESPDataModule
+import pytorch_lightning as pl
+from pytorch_lightning.loggers import TensorBoardLogger
+
+
+
+def objective(trial, seed, split):
+    hidden_size = trial.suggest_categorical('hidden_size', [32, 64, 128, 256])
+    dropout = trial.suggest_float('dropout', 0.0, 0.5, step=0.1)
+    num_stack_layers = trial.suggest_int('num_stack_layers', 1, 3)
+    num_epochs = trial.suggest_categorical('num_epochs', [150, 200, 250, 300])
+    learning_rate = trial.suggest_categorical('learning_rate', [1e-3, 1e-4, 1e-5])
+    num_layers = trial.suggest_int('num_layers', 1, 3)
+    batch_size = trial.suggest_categorical('batch_size', [32, 64, 128, 256])
+
+    # set seed for reproducibility
+    pl.seed_everything(seed=seed)
+
+    tts = Train_Test_Split(f"{DAILY_OUTPUT_FOLDER}_{SLIDE_N}", split=split)
+    data_paths = tts.split_data()
+
+    # Create the dataloaders
+    data_module = ESPDataModule(train_paths=data_paths["train"],
+                                val_paths=data_paths["val"],
+                                test_paths=data_paths["test"],
+                                batch_size=batch_size)
+
+    # Load a single file to get the model dimensions
+    single_batch = next(iter(ESPDataset(data_paths["val"][:1])))
+    n_features = single_batch["features"].shape[-1]
+    n_classes= single_batch["labels"].shape[-1]
+
+    # Initialize the model
+    model = ESPFailureModel(n_features=n_features,
+                            n_classes=n_classes,
+                            lr=learning_rate,
+                            dropout=dropout,
+                            hidden_size=hidden_size,
+                            num_stack_layers=num_stack_layers,
+                            n_layers=num_layers)
+
+    # Define the model callbacks
+    checkpoint_call_back = ModelCheckpoint(
+        dirpath=f"checkpoints/{trial.number}",
+        filename="best-chckpt",
+        save_top_k=1,
+        verbose=True,
+        monitor="val_loss",
+        mode="min"
+    )
+
+    logger = TensorBoardLogger(save_dir="lightning_logs", name="JTK_Challenge")
+
+    trainer = pl.Trainer(logger=logger,
+                         callbacks=checkpoint_call_back,
+                         max_epochs=num_epochs,
+                         deterministic=True,
+                         enable_progress_bar=True)
+
+    model.save_hyperparameters({"hidden_dim": hidden_size,
+                            "learning_rate": learning_rate,
+                            "dropout": dropout,
+                            "num_stack_layers": num_stack_layers,
+                            "num_layers": num_layers,
+                            "seed": seed,
+                            "split": split,
+                            "batch_size": batch_size,
+                            "device":trainer.accelerator})
+
+    trainer.fit(model, data_module)
+
+    return trainer.callback_metrics["val_loss"].item()
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Hyperparameter search for ESP Failure model")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--split", type=float, default=0.8)
+    parser.add_argument("--n_trials", type=int, default=300)
+    args = parser.parse_args()
+
+    study = optuna.create_study(direction='minimize')
+    study.optimize(lambda trial: objective(trial, args.seed, args.split), n_trials=args.n_trials)
+
+    best_params = study.best_params
+    print("Best hyperparameters:", best_params)
+    print("Best value:", study.best_value)