* feat: Added base functions for Neural Net.
* feat: Added function to handle Neural Nets.
* fix: Fixed fit loop
* feat: Neural Net trains now, need to test it.
* feat: Prediction now works on the neural net.
* fix: Put back config and run_pipeline.py
* fix: Took out import from run_pipeline.
* fix(Models): added get_name(), adjusted pytorch model output size
* fix(Tests): fixed tests
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
* fix(FeatureExtractor): apply log to transform some series to normality
* feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features
* feat(FeatureExtractors): added standard scaling for exogenous data
* feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection
* fix(Config): sweep config
* feat(Models): output probability, store it
* feat(Core): added caching to select_features() and load_data()
* fix(Dependencies): added diskcache
* fix(Training): error when creating results DF
* feat(Models): added xgboost, fixed tests
* refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching
* fix(Tests): new syntax
* fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow
* fix(Model): XGBoost config
* feat(Cache): add run_clear_cache script
* fix(Pipeline) accidentally re-instatiating all_predictions for each asset
Daniel Szemerey