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/naive.py

@@ -7,9 +7,9 @@ class StaticNaiveModel(Model):
     Model that carries the last observation (from returns) to the next one, naively.
     '''
 
+    method = 'regression'
     data_transformation = 'original'
     only_column = None
-    feature_selection = 'off'
     model_type = 'static'
     predict_window_size = 'single_timestamp'