Method train

optimizers/lightning_batch_gd.py:280–345

(self)

                    print(f"Run succesfully deleted from WanDB: {run.name}.")

    def train(self):
        self.lob_lightning_module = LOBLightningModule(
            self.model,
            experiment_id=self.experiment_id,
            learning_rate=self.learning_rate,
            general_hyperparameters=self.general_hyperparameters,
            model_hyperparameters=self.model_hyperparameters,
        )

        checkpoint_callback = ModelCheckpoint(
            monitor="val_loss",
            dirpath=logger.find_save_path(self.experiment_id),
            filename="best_val_model",
            save_top_k=1,
            mode="min",
        )
        early_stopping_callback = EarlyStopping("val_loss", patience=self.patience, min_delta=0.003)

        os.environ["WANDB_API_KEY"] = ""  # TODO: Insert API key
        os.environ["WANDB__SERVICE_WAIT"] = "300"
        try:
            wandb_logger = WandbLogger(
                project="Limit_Order_Book",
                name=self.experiment_id,
                save_dir=logger.find_save_path(self.experiment_id),
            )
            wandb_hyperparameters_saving(
                wandb_logger=wandb_logger,
                general_hyperparameters=self.general_hyperparameters,
                model_hyperparameters=self.model_hyperparameters,
            )
            self.trainer = pl.Trainer(
                max_epochs=self.epochs,
                callbacks=[checkpoint_callback, early_stopping_callback],
                logger=wandb_logger,
                num_sanity_val_steps=0,
            )
            self.trainer.fit(self.lob_lightning_module, self.train_loader, self.val_loader)
            wandb.finish()
        except:
            root_path = sys.path[0]
            dir_path = f"{root_path}/loggers/results/{self.experiment_id}"
            if os.path.exists(dir_path):
                shutil.rmtree(dir_path)
                print(f"Folder {self.experiment_id} deleted successfully.")
            else:
                print(f"Unable to delete folder {self.experiment_id}.")

            self.delete_run()

            model = self.general_hyperparameters['model']
            horizon = self.model_hyperparameters['prediction_horizon']
            training_stocks = self.general_hyperparameters['training_stocks']
            target_stocks = self.general_hyperparameters['target_stocks']
            errors_string = f"{model} {horizon} {training_stocks} {target_stocks} {self.deleted_run}\n"
            with open("errors.txt", 'r+') as file:
                content = file.read()