Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)

* refactor(Training): added InferenceResult & TrainedModel types

* refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc.

* fix(Pipeline): getting it to compile

* refactor(WalkForward): separate preprocessing step

* feat(Pipeline): separate out transformations processing step

* refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step

* refactor(WalkForward): moved functions to separate folder

* fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster)

* fix(Tests): and evaluation

* fix(Tests): for realz

* fix(Inference): preloading everything now, renamed primary models to directional models

* fix(BetSizing): was running transformations on the wrong data, oops

* fix(BetSizing): concatenated on the wrong axis accidentally

* fix(Reporting): able to use the new Stats type

* fix(BetSizing): renamed int column names

* fix(Portfolio): name the column properly

* fix(Reporting): rename the correct Series, lol

* fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index

* fix(WalkForward): accidentally using the wrong index

* fix(WalkForward): use the correct indicies to fetch last model/transformations

* fix(CI): changed the name of the results

models/base.py

@@ -5,6 +5,7 @@ import numpy as np
 
 class Model(ABC):
 
+    name: str = ""
     method: Literal["regression", "classification"]
     data_transformation: Literal["transformed", "original"]
     only_column: Optional[str]
@@ -22,10 +23,6 @@ class Model(ABC):
     @abstractmethod
     def clone(self) -> Model:
         raise NotImplementedError
-
-    @abstractmethod
-    def get_name(self) -> str:
-        raise NotImplementedError
     
     @abstractmethod
     def initialize_network(self, input_dim:int, output_dim:int):