0.7.0

Full Changelog: 0.6.0...0.7.0

• MajorFeature: Add empulse.models.CSTreeClassifier, empulse.models.CSForestClassifier,
  and 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: Removed the emp_score and emp functions from the empulse.metrics module.
  Use the empulse.metrics.Metric class instead to define custom expected maximum profit measures.
  For more information, read the User Guide.
• API: Removed numba as a dependency for Empulse. This will reduce the installation time and the size of the package.
• Fix: Fix empulse.metrics.Metric when defining stochastic variable with fixed values.
• Fix: Fix empulse.metrics.Metric when stochastic variable has infinite bounds.
• Fix: Fix empulse.models.CSThresholdClassifier
  when costs of predicting positive and negative classes are equal.
• Fix: Fix documentation linking issues to sklearn

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

0.5.2

• Allow savings_score and 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.
• Reworked the user guide documentation to better explain the usage of value-driven
  and cost-sensitive models, samplers and metrics
• CSLogitClassifier and ProfLogitClassifier
  by default do not perform soft-thresholding on the regression coefficients.
  This can be enabled by setting the soft_threshold parameter to True.
• Prevent division by zero errors in expected_cost_loss

Full Changelog: 0.5.1...0.5.2

0.5.1

• Fix documentation build issue

Full Changelog: 0.5.0...0.5.1

0.5.0

• Added supported for python 3.13
• Added cost-sensitive models
  • CSLogitClassifier
  • CSBoostClassifier
  • RobustCSClassifier
  • CSThresholdClassifier
• Added cost-sensitive metrics
  • cost_loss
  • expected_cost_loss
  • expected_log_cost_loss
  • savings_score
  • expected_savings_score
• Added cost-sensitive sampler
  • CostSensitiveSampler
• Added datasets module
• rename metric arguments which expect target score from y_pred to y_score and
  target probabilities from y_pred to y_proba.
• Allow all cost-sensitive models and samplers to accept cost parameters during initialization

Full Changelog: 0.4.6...0.5.0

0.4.6

Full Changelog: 0.4.0...0.4.6

Empulse 0.4.0

Added the Area Under the Expected Profit Curve metric.

Empulse 0.3.1

Bump to version 0.3.1