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.

archive/run_lightning.py

@@ -0,0 +1,48 @@
+from load_data import load_files
+import pandas as pd
+# from tensorflow import keras
+from utils.normalize import normalize
+# import tensorflow as tf
+from utils.visualize import visualize_loss
+
+from torch.utils.data import DataLoader, random_split
+from model_lightning import LitManualAutoEncoder 
+import pytorch_lightning as pl
+
+#%%
+data = load_files('data/', False)
+data.reset_index(drop=True, inplace=True)
+data = data[[column for column in data.columns if not column.endswith('volume')]]
+
+data.head()
+
+#%%
+ticker_to_predict = 'ETH_returns'
+
+learning_rate = 0.002
+batch_size = 64
+epochs =  100
+
+split_fraction = 0.715
+train_split = int(split_fraction * int(data.shape[0]))
+
+past = 10
+future = 1
+
+start = past + future
+end = start + train_split
+
+
+# train = DataLoader(train, batch_size=32)
+# test = DataLoader(test, batch_size=32)
+# val = DataLoader(val, batch_size=32)
+
+
+# init model
+ae = LitManualAutoEncoder()
+
+# Initialize a trainer
+trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)
+
+# Train the model ⚡
+# trainer.fit(ae, train, val)