* fix(Reporting): use weighted average (with no_of_samples as weights) and only report level-1 OR level-2 model performance
* chore(Config): updated sweep config
* fix(Reporting): missing import
* fix(Evaluation): get_first_valid_return_index can deal with zero valid indexes
* fix(Training): increase threshold for skipping assets
* fix(DataLoader): target asset should be always the first column
* feat(Data): add option to predict 3 classes
* feat(Evaluation): added ability to evaluate 3 class predictions
* chore(Config): set sensible config for regression models
* feat(Data): added option to use balanced or imbalanced three-class data
* feat(Evaluate): correctly track "no_of_samples" now that we have three classes
* chore(Sweep): remove probably not useful scaler values from sweep
* feat: initial wandb configured. Sweep parameters aren't configured yet.
* feat: Wandb logs now results.
* feat: gitignore.
* fix: Took out print()
* feat: Changed default value of wandb to False.
* feat: Added wandb to turn of automatically if there is no environment variable to start it (when we push it). Added environment configuration aswell.
* fix(Dependencies): the package name seems to be python-dotenv
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
Mark Aron Szulyovszky