feat(Pytorch): added custom model to pytorch-forecasting (#8)

* feat: Refractored and created new model. Pipeline not ready yet.

* feat: Implemented and refactored a data pipeline.

* ref: Refractored to make more sense.

* feat: Training works now with models that you can change.

* feat: Added predict function but without working instructions.

* feat: gitignore.

lightning/train_predict.py

@@ -0,0 +1,67 @@
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+import torch
+from torch import nn
+
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import sys
+sys.path.insert(0, '..')
+
+from load_data import load_files
+
+
+
+
+def train_model(model, train_dataloader, val_dataloader, kwargs):
+    print()
+    print("Creating Trainer ===>", end="  ") 
+    # define trainer with early stopping
+    early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
+    lr_logger = LearningRateMonitor()
+    
+    trainer = pl.Trainer(
+        **kwargs,
+        callbacks=[lr_logger, early_stop_callback],
+    )
+    print("<===  Trainer Created")
+    print("Finding Optimal LR  ===>", end="  ")
+    # find optimal learning rate (set limit_train_batches to 1.0 and log_interval = -1)
+    res = trainer.tuner.lr_find(
+        model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
+    )
+
+    print(f"<=== suggested learning rate: {res.suggestion()}")
+    fig = res.plot(show=True, suggest=True)
+    fig.show()
+
+    print("Training the model  ===>", end="  ")
+    # fit the model
+    trainer.fit(
+        model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
+    )
+    
+    print("<=== Training Finished")
+    return trainer
+    
+    
+def predict(trainer, model, val_dataloader):
+    pass
+    # best_model_path = trainer.checkpoint_callback.best_model_path
+    # best_model = model.load_from_checkpoint(best_model_path)
+    
+    # # calcualte mean absolute error on validation set
+    # actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])
+    # predictions = best_model.predict(val_dataloader)
+    # (actuals - predictions).abs().mean()
+    
+    # raw_predictions, x = best_model.predict(val_dataloader, mode="raw", return_x=True)
+    # for idx in range(10):  # plot 10 examples
+    #     best_model.plot_prediction(x, raw_predictions, idx=idx, add_loss_to_title=True)
+    
+    # predictions, x = best_model.predict(val_dataloader, return_x=True)
+    # predictions_vs_actuals = best_model.calculate_prediction_actual_by_variable(x, predictions)
+    # best_model.plot_prediction_actual_by_variable(predictions_vs_actuals)
+