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/base.py

@@ -7,9 +7,8 @@ import numpy as np
 
 class Model(ABC):
 
+    method: Literal["regression", "classification"]
     data_transformation: Literal["transformed", "original"]
-    feature_selection: Literal["on", "off"]
-    # data_format: Literal["wide", "narrow"]
     only_column: Optional[str]
     model_type: Literal['ml', 'static']
     predict_window_size: Literal['single_timestamp', 'window_size']