feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml

models/base.py

@@ -6,10 +6,8 @@ import numpy as np
 class Model(ABC):
 
     name: str = ""
-    method: Literal["regression", "classification"]
     data_transformation: Literal["transformed", "original"]
     only_column: Optional[str]
-    model_type: Literal['ml', 'static']
     predict_window_size: Literal['single_timestamp', 'window_size']
 
     @abstractmethod
@@ -17,17 +15,10 @@ class Model(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def predict(self, X: np.ndarray) -> tuple[float, np.ndarray]:
+    def predict(self, X: np.ndarray) -> np.ndarray:
         raise NotImplementedError
 
     @abstractmethod
-    def clone(self) -> Model:
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
         raise NotImplementedError
-    
-    @abstractmethod
-    def initialize_network(self, input_dim:int, output_dim:int):
-        pass
 
-
-    
-