feat(WalkForward): added regression/classification switch, archived old experiments, wrapped the process into run_whole_pipeline() (#10)

* refactor(WalkForward): cleaned up training & evaluation code

* refactor: added run_whole_pipeline(), moved all previous models to archive

archive/lightning/models/built_in_models.py

@@ -0,0 +1,19 @@
+from pytorch_forecasting import TemporalFusionTransformer
+from pytorch_forecasting.metrics import QuantileLoss
+
+def create_TemporalFusionTransformer(training_dataset, model_options):
+    # create the model
+    tft = TemporalFusionTransformer.from_dataset(
+        training_dataset,
+        learning_rate=0.03,
+        hidden_size=32,
+        attention_head_size=1,
+        dropout=0.1,
+        hidden_continuous_size=16,
+        output_size=7,
+        loss=QuantileLoss(),
+        log_interval=2,
+        reduce_on_plateau_patience=4
+    )
+    print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
+    return tft