* 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
* feat: Added optional import of models.
* fix: Models weren't wrapped into abstract class, fixed it.
* chore: Deleted leftover comments.
* fix: Same merge commit as on remote.
* fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator.
* fix(Models): use the XGBoostModel wrapper
Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
* refr: Took out main primary and secondary loops and data processing.
* feat: Tidied the code up.
* feat: Saving models and results now works in a type safe way.
* fix: There was error in the saving function.
* chore: Took out some remaining comments.
* fix: Fixed the previous data checking process.
* feat: Fixed model selection method. I will continue the inference after we merged.
Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Mark Aron Szulyovszky