0.6.0

• Add Metric class to easily build your own value-driven and cost-sensitive metrics

• Add support for LightGBM and Catboost models in CSBoostClassifier and B2BoostClassifier

• make_objective_churn and make_objective_acquisition
  now take a model argument to calculate the objective for either XGBoost, LightGBM or Catboost models.

• XGBoost is now an optional dependency together with LightGBM and Catboost. To install the package with
  XGBoost, LightGBM and Catboost support, use the following command: pip install empulse[optional]

• Renamed y_pred_baseline and y_proba_baseline to baseline in savings_score
  and expected_savings_score. It now accepts the following arguments:

  • If 'zero_one', the baseline model is a naive model that predicts all zeros or all ones
    depending on which is better.
  • If 'prior', the baseline model is a model that predicts the prior probability of
    the majority or minority class depending on which is better (not available for savings score).
  • If array-like, target probabilities of the baseline model.

• Add parameter validation for all models and samplers

• Make all arguments of dataset loaders keyword-only

• Update the descriptions attached to each dataset to match information found in the user guide

• Improve type hints for functions and classes

Full Changelog: 0.5.2...0.6.0