feat(Transformations): replaced feature selection pre-processing step with online version (with cache) (#170)

* feat(Transformations): removed feature-selection pre-processing step completely

* fix(Core): removed unnecessary `original_X`

* fix(Transformations): use the X_expanding_window to transform subsequent data

* fix(RFE): should check for model correctly

* fix(Config): only re-train the model every 40 timestamp

* fix(MetaLabeling): pass in the correct X to meta-labeling step

* fix(Transformation): PCA should at least keep as many features as sliding_window_size

* feat(Transformations): cache transformations across the same asset

* fix(Tests): missing preloaded_transformations arg

* chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys

models/sklearn.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+from typing import Literal
 from models.base import Model
 import numpy as np
 from sklearn.base import clone
@@ -6,14 +7,15 @@ from sklearn.base import clone
 
 class SKLearnModel(Model):
 
+    method: Literal["regression", "classification"]
     data_transformation = 'transformed'
     only_column = None
-    feature_selection = 'on'
     model_type = 'ml'
     predict_window_size = 'single_timestamp'
 
-    def __init__(self, model):
+    def __init__(self, model, method: Literal['regression', 'classification']):
         self.model = model
+        self.method = method
     
     def fit(self, X: np.ndarray, y: np.ndarray) -> None:
         self.model.fit(X, y)
@@ -24,7 +26,7 @@ class SKLearnModel(Model):
         return (pred, probability)
     
     def clone(self) -> SKLearnModel:
-        return SKLearnModel(clone(self.model))
+        return SKLearnModel(clone(self.model), self.method)
 
     def get_name(self) -> str:
         return self.model.__class__.__name__