CHANGELOG.rst

Unreleased

• Feature Type hints are now available for all functions and classes.
• Enhancement Add support for more than one stochastic variable when building maximum profit metrics with :class:`~empulse.metrics.Metric`
• Enhancement Allow :class:`~empulse.metrics.Metric` to be used as a context manager. This ensures the metric is always built after defining the cost-benefit elements.
• Fix Fix datasets not properly being packaged together with the package
• Fix Fix :class:`~empulse.models.RobustCSClassifier` when array-like parameters are passed to fit method.

0.7.0 (05-02-2025)

• Major Feature Add :class:`~empulse.models.CSTreeClassifier`, :class:`~empulse.models.CSForestClassifier`, and :class:`~empulse.models.CSBaggingClassifier` to support cost-sensitive decision tree and ensemble models
• Enhancement Add support for scikit-learn 1.5.2 (previously Empulse only supported scikit-learn 1.6.0 and above).
• API Change Removed the emp_score and emp functions from the :mod:`~empulse.metrics` module. Use the :func:`~empulse.metrics.Metric` class instead to define custom expected maximum profit measures. For more information, read the :ref:`User Guide <user_defined_value_metric>`.
• API Change Removed numba as a dependency for Empulse. This will reduce the installation time and the size of the package.
• Fix Fix :func:`~empulse.metrics.Metric` when defining stochastic variable with fixed values.
• Fix Fix :func:`~empulse.metrics.Metric` when stochastic variable has infinite bounds.
• Fix Fix :func:`~empulse.models.CSThresholdClassifier` when costs of predicting positive and negative classes are equal.
• Fix Fix documentation linking issues to sklearn

0.6.0 (28-01-2025)

• Major Feature Add :class:`~empulse.metrics.Metric` to easily build your own value-driven and cost-sensitive metrics
• Feature Add support for LightGBM and Catboost models in :class:`~empulse.models.CSBoostClassifier` and :class:`~empulse.models.B2BoostClassifier`
• API Change :func:`~empulse.metrics.make_objective_churn` and :func:`~empulse.metrics.make_objective_acquisition` now take a model argument to calculate the objective for either XGBoost, LightGBM or Catboost models.
• API Change 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]
• API Change Renamed y_pred_baseline and y_proba_baseline to baseline in :func:`~empulse.metrics.savings_score` and :func:`~empulse.metrics.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.
• Feature Add parameter validation for all models and samplers
• API Change Make all arguments of dataset loaders keyword-only
• Fix Update the descriptions attached to each dataset to match information found in the user guide
• Fix Improve type hints for functions and classes

0.5.2 (12-01-2025)

• Feature Allow :func:`~empulse.metrics.savings_score` and :func:`~empulse.metrics.expected_savings_score` to calculate the savings score over the baseline model instead of a naive model, by setting the y_pred_baseline and y_proba_baseline parameters, respectively.
• Enhancement Reworked the user guide documentation to better explain the usage of value-driven and cost-sensitive models, samplers and metrics
• API Change :class:`~empulse.models.CSLogitClassifier` and :class:`~empulse.models.ProfLogitClassifier` by default do not perform soft-thresholding on the regression coefficients. This can be enabled by setting the soft_threshold parameter to True.
• Fix Prevent division by zero errors in :func:`~empulse.metrics.expected_cost_loss`

0.5.1 (05-01-2025)

• Fix Fixed documentation build issue

0.5.0 (05-01-2025)

• Major Feature Added supported for python 3.13

• Major Feature Added cost-sensitive models

  • :class:`~empulse.models.CSLogitClassifier`
  • :class:`~empulse.models.CSBoostClassifier`
  • :class:`~empulse.models.RobustCSClassifier`
  • :class:`~empulse.models.CSThresholdClassifier`

• Major Feature Added cost-sensitive metrics

  • :func:`~empulse.metrics.cost_loss`
  • :func:`~empulse.metrics.expected_cost_loss`
  • :func:`~empulse.metrics.expected_log_cost_loss`
  • :func:`~empulse.metrics.savings_score`
  • :func:`~empulse.metrics.expected_savings_score`

• Major Feature Added :mod:`empulse.datasets` module
• Feature Added :class:`~empulse.samplers.CostSensitiveSampler`
• Enhancement Allow all cost-sensitive models and samplers to accept cost parameters during initialization
• API Change Renamed metric arguments which expect target score from y_pred to y_score and target probabilities from y_pred to y_proba