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/run_pipeline.py

@@ -0,0 +1,41 @@
+
+#%%
+from create_dataset import load_format_data, create_dataloaders
+from train_predict import train_model, predict
+from models.built_in_models import create_TemporalFusionTransformer
+from models.custom_model import create_FullyConnectedModel
+from options import training_options, model_options_tft, model_options_fcn, dataset_options_tft, dataset_options_fcn
+
+import warnings
+warnings.filterwarnings("ignore")
+
+#%%
+def run_pipeline(model_name, data_dir):
+    data = load_format_data(data_dir)  
+    
+    dataset_options, model_options, _create_model = select_model(model_name)
+    training_dataset, train_dataloader, val_dataloader = create_dataloaders(data, dataset_options)
+    
+    model = _create_model( training_dataset, model_options )
+    trainer = train_model(model, train_dataloader, val_dataloader, training_options)
+    predict(trainer, model, val_dataloader)
+    
+#%%
+
+def select_model(model_name):
+    if model_name == "FullyConnectedLayer":
+        return dataset_options_fcn, model_options_fcn, create_FullyConnectedModel
+    elif model_name == "TemporalFusionTransformer":
+        return dataset_options_tft, model_options_tft, create_TemporalFusionTransformer
+    else:
+        assert False, "No such model exists."
+    
+
+#%%
+run_pipeline("FullyConnectedLayer", '../data/')
+
+# #%%
+# if __name__ == '__main__':
+#     run_pipeline("FullyConnectedLayer", '../data/')
+
+