API Reference
Documentation for the Heuristics public interface.
Index
IAI.GLMNetCVRegressorIAI.GLMNetLearnerIAI.RandomForestClassifierIAI.RandomForestLearnerIAI.RandomForestRegressorIAI.XGBoostClassifierIAI.XGBoostLearnerIAI.XGBoostRegressorIAI.get_num_fitsIAI.get_prediction_constantIAI.get_prediction_weightsIAI.predictIAI.scoreIAI.write_booster
Random Forest
IAI.RandomForestLearner — TypeAbstract type encompassing all random forest learners.
IAI.RandomForestClassifier — TypeLearner for training random forests for classification problems.
The following parameters are supported (refer to the documentation for each):
IAI.RandomForestRegressor — TypeLearner for training random forests for regression problems.
The following parameters are supported (refer to the documentation for each):
XGBoost
IAI.XGBoostLearner — TypeAbstract type encompassing all XGBoost learners.
IAI.XGBoostClassifier — TypeLearner for training XGBoost models for classification problems.
The following parameters are supported (refer to the documentation for each):
IAI.XGBoostRegressor — TypeLearner for training XGBoost models for regression problems.
The following parameters are supported (refer to the documentation for each):
IAI.write_booster — Functionwrite_booster(filepath, lnr::XGBoostLearner)Write the internal booster saved in lnr to filepath.
This can be used to open the trained XGBoost model using the XGBoost Python wrapper in order to conduct a SHAP analysis.
If filepath ends in ".json" the booster will be written in JSON format, otherwise an internal XGBoost binary format is used.
This output file cannot be loaded back into the IAI interface. Use write_json to save the entire learner if you want to be able to read it back in future.
GLMNet
IAI.GLMNetLearner — TypeAbstract type encompassing all GLMNet learners.
IAI.GLMNetCVRegressor — TypeLearner for training GLMNet models for regression problems with cross-validation.
The following parameters are supported (refer to the documentation for each):
IAI.get_num_fits — Methodget_num_fits(lnr::GLMNetCVRegressor)Returns the number of fits along the path in lnr.
IAI.predict — Methodpredict(lnr::GLMNetCVRegressor, X::FeatureInput)Returns the prediction made by the best fit on the path for lnr for each point in the data X.
predict(lnr::GLMNetCVRegressor, X::FeatureInput, fit_index::Integer)Returns the prediction made by the fit at fit_index on the path for lnr for each point in the data X.
IAI.score — Methodscore(lnr::GLMNetCVRegressor, X::FeatureInput, y::TargetInput...;
keyword_arguments...)Calculates the score for the best fit on the path for lnr on data X and y.
score(lnr::GLMNetCVRegressor, X::FeatureInput, y::TargetInput...;
fit_index::Integer, keyword_arguments...)Calculates the score for the fit at fit_index on the path for lnr on data X and y.
IAI.get_prediction_weights — Methodget_prediction_weights(lnr::GLMNetCVRegressor)Return the weights for each feature in the prediction made by the best fit on the path in lnr.
get_prediction_weights(lnr::GLMNetCVRegressor, fit_index::Integer)Return the weights for each feature in the prediction made by the fit at fit_index on the path in lnr.
IAI.get_prediction_constant — Methodget_prediction_constant(lnr::GLMNetCVRegressor)Return the constant term in the prediction made by the best fit on the path in lnr.
get_prediction_constant(lnr::GLMNetCVRegressor, fit_index::Integer)Return the constant term in the prediction made by the fit at fit_index on the path in lnr.