feature(Models): Implemented a basic Neural Network with Pytorch-Lightning (#101)

* feat: Added base functions for Neural Net.

* feat: Added function to handle Neural Nets.

* fix: Fixed fit loop

* feat: Neural Net trains now, need to test it.

* feat: Prediction now works on the neural net.

* fix: Put back config and run_pipeline.py

* fix: Took out import from run_pipeline.

* fix(Models): added get_name(), adjusted pytorch model output size

* fix(Tests): fixed tests

Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>

data_loader/pytorch_dataset.py

@@ -0,0 +1,26 @@
+import os
+import pandas as pd
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+
+import numpy as np
+
+
+class TimeSeriesDataset(Dataset):
+    def __init__(self, X: np.ndarray, y: np.ndarray):
+        self.X = X
+        self.y = y
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, idx):
+        return self.X[idx].astype(float), self.y[idx].astype(float)
+
+def get_dataloader(X: np.ndarray, y: np.ndarray, batch_size: int = 32, shuffle: bool = True):
+    training_data = TimeSeriesDataset(X, y)
+    train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=shuffle)
+    
+    return train_dataloader