R: Interface to 'Interpretable AI' Modules

# Interface to 'Interpretable AI' Modules

## Documentation for package ‘iai’ version 1.10.2

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

A C D E F G I L M N O P Q R S T V W X Z

## -- A --

acquire_license | Acquire an IAI license for the current session.
add_julia_processes | Add additional Julia worker processes to parallelize workloads
all_treatment_combinations | Return a dataframe containing all treatment combinations of one or more treatment vectors, ready for use as treatment candidates in 'fit_predict!' or 'predict'
apply | Return the leaf index in a tree model into which each point in the features falls
apply_nodes | Return the indices of the points in the features that fall into each node of a trained tree model
as.mixeddata | Convert a vector of values to IAI mixed data format
autoplot.grid_search | Construct a 'ggplot2::ggplot' object plotting grid search results for Optimal Feature Selection learners
autoplot.roc_curve | Construct a 'ggplot2::ggplot' object plotting the ROC curve
autoplot.similarity_comparison | Construct a 'ggplot2::ggplot' object plotting the results of the similarity comparison
autoplot.stability_analysis | Construct a 'ggplot2::ggplot' object plotting the results of the stability analysis

## -- C --

categorical_classification_reward_estimator | Learner for conducting reward estimation with categorical treatments and classification outcomes
categorical_regression_reward_estimator | Learner for conducting reward estimation with categorical treatments and regression outcomes
categorical_reward_estimator | Learner for conducting reward estimation with categorical treatments
categorical_survival_reward_estimator | Learner for conducting reward estimation with categorical treatments and survival outcomes
cleanup_installation | Remove all traces of automatic Julia/IAI installation
clone | Return an unfitted copy of a learner with the same parameters
convert_treatments_to_numeric | Convert 'treatments' from symbol/string format into numeric values.
copy_splits_and_refit_leaves | Copy the tree split structure from one learner into another and refit the models in each leaf of the tree using the supplied data

## -- D --

decision_path | Return a matrix where entry '(i, j)' is true if the 'i'th point in the features passes through the 'j'th node in a trained tree model.
delete_rich_output_param | Delete a global rich output parameter

## -- E --

equal_propensity_estimator | Learner that estimates equal propensity for all treatments.

## -- F --

fit | Generic function for fitting a learner.
fit.grid_search | Fits a 'grid_search' to the training data
fit.imputation_learner | Fits an imputation learner to the training data.
fit.learner | Fits a model to the training data
fit.optimal_feature_selection_learner | Fits an Optimal Feature Selection learner to the training data
fit_and_expand | Fit an imputation learner with training features and create adaptive indicator features to encode the missing pattern
fit_cv | Fits a grid search to the training data with cross-validation
fit_predict | Generic function for fitting a reward estimator on features, treatments and returning predicted counterfactual rewards and scores of the internal estimators.
fit_predict.categorical_reward_estimator | Fit a categorical reward estimator on features, treatments and outcomes and return predicted counterfactual rewards for each observation, under each treatment observed in the data, as well as the scores of the internal estimators.
fit_predict.numeric_reward_estimator | Fit a numeric reward estimator on features, treatments and outcomes and return predicted counterfactual rewards for each observation, under each treatment candidate, as well as the scores of the internal estimators.
fit_transform | Fit an imputation model using the given features and impute the missing values in these features
fit_transform_cv | Train a grid using cross-validation with features and impute all missing values in these features

## -- G --

get_best_params | Return the best parameter combination from a grid
get_classification_label | Generic function for returning the predicted label in the node of a classification tree
get_classification_label.classification_tree_learner | Return the predicted label at a node of a tree
get_classification_label.classification_tree_multi_learner | Return the predicted label at a node of a multi-task tree
get_classification_proba | Generic function for returning the probabilities of class membership at a node of a classification tree
get_classification_proba.classification_tree_learner | Return the predicted probabilities of class membership at a node of a tree
get_classification_proba.classification_tree_multi_learner | Return the predicted probabilities of class membership at a node of a multi-task tree
get_cluster_assignments | Return the indices of the trees assigned to each cluster, under the clustering of a given number of trees
get_cluster_details | Return the centroid information for each cluster, under the clustering of a given number of trees
get_cluster_distances | Return the distances between the centroids of each pair of clusters, under the clustering of a given number of trees
get_depth | Get the depth of a node of a tree
get_estimation_densities | Return the total kernel density surrounding each treatment candidate for the propensity/outcome estimation problems in a fitted learner.
get_features_used | Return the names of the features used by the learner
get_grid_results | Return a summary of the results from the grid search
get_grid_result_details | Return a vector of lists detailing the results of the grid search
get_grid_result_summary | Return a summary of the results from the grid search
get_learner | Return the fitted learner using the best parameter combination from a grid
get_lower_child | Get the index of the lower child at a split node of a tree
get_machine_id | Return the machine ID for the current computer.
get_num_fits | Generic function for returning the number of fits in a trained learner
get_num_fits.glmnetcv_learner | Return the number of fits along the path in a trained GLMNet learner
get_num_fits.optimal_feature_selection_learner | Return the number of fits along the path in a trained Optimal Feature Selection learner
get_num_nodes | Return the number of nodes in a trained learner
get_num_samples | Get the number of training points contained in a node of a tree
get_params | Return the value of all parameters on a learner
get_parent | Get the index of the parent node at a node of a tree
get_policy_treatment_outcome | Return the quality of the treatments at a node of a tree
get_policy_treatment_outcome_standard_error | Return the standard error for the quality of the treatments at a node of a tree
get_policy_treatment_rank | Return the treatments ordered from most effective to least effective at a node of a tree
get_prediction_constant | Generic function for returning the prediction constant in a trained learner
get_prediction_constant.glmnetcv_learner | Return the constant term in the prediction in a trained GLMNet learner
get_prediction_constant.optimal_feature_selection_learner | Return the constant term in the prediction in a trained Optimal Feature Selection learner
get_prediction_weights | Generic function for returning the prediction weights in a trained learner
get_prediction_weights.glmnetcv_learner | Return the weights for numeric and categoric features used for prediction in a trained GLMNet learner
get_prediction_weights.optimal_feature_selection_learner | Return the weights for numeric and categoric features used for prediction in a trained Optimal Feature Selection learner
get_prescription_treatment_rank | Return the treatments ordered from most effective to least effective at a node of a tree
get_regression_constant | Generic function for returning the constant term in the regression prediction at a node of a tree
get_regression_constant.classification_tree_learner | Return the constant term in the logistic regression prediction at a node of a classification tree
get_regression_constant.classification_tree_multi_learner | Return the constant term in the logistic regression prediction at a node of a multi-task classification tree
get_regression_constant.prescription_tree_learner | Return the constant term in the linear regression prediction at a node of a prescription tree
get_regression_constant.regression_tree_learner | Return the constant term in the linear regression prediction at a node of a regression tree
get_regression_constant.regression_tree_multi_learner | Return the constant term in the linear regression prediction at a node of a multi-task regression tree
get_regression_constant.survival_tree_learner | Return the constant term in the cox regression prediction at a node of a survival tree
get_regression_weights | Generic function for returning the weights for each feature in the regression prediction at a node of a tree
get_regression_weights.classification_tree_learner | Return the weights for each feature in the logistic regression prediction at a node of a classification tree
get_regression_weights.classification_tree_multi_learner | Return the weights for each feature in the logistic regression prediction at a node of a multi-task classification tree
get_regression_weights.prescription_tree_learner | Return the weights for each feature in the linear regression prediction at a node of a prescription tree
get_regression_weights.regression_tree_learner | Return the weights for each feature in the linear regression prediction at a node of a regression tree
get_regression_weights.regression_tree_multi_learner | Return the weights for each feature in the linear regression prediction at a node of a multi-task regression tree
get_regression_weights.survival_tree_learner | Return the weights for each feature in the cox regression prediction at a node of a survival tree
get_rich_output_params | Return the current global rich output parameter settings
get_roc_curve_data | Extract the underlying data from an ROC curve
get_split_categories | Return the categoric/ordinal information used in the split at a node of a tree
get_split_feature | Return the feature used in the split at a node of a tree
get_split_threshold | Return the threshold used in the split at a node of a tree
get_split_weights | Return the weights for numeric and categoric features used in the hyperplane split at a node of a tree
get_stability_results | Return the trained trees in order of increasing objective value, along with their variable importance scores for each feature
get_survival_curve | Return the survival curve at a node of a tree
get_survival_curve_data | Extract the underlying data from a survival curve (as returned by 'predict.survival_learner' or 'get_survival_curve')
get_survival_expected_time | Return the predicted expected survival time at a node of a tree
get_survival_hazard | Return the predicted hazard ratio at a node of a tree
get_train_errors | Extract the training objective value for each candidate tree in the comparison, where a lower value indicates a better solution
get_tree | Return a copy of the learner that uses a specific tree rather than the tree with the best training objective.
get_upper_child | Get the index of the upper child at a split node of a tree
glmnetcv_classifier | Learner for training GLMNet models for classification problems with cross-validation
glmnetcv_regressor | Learner for training GLMNet models for regression problems with cross-validation
glmnetcv_survival_learner | Learner for training GLMNet models for survival problems with cross-validation
grid_search | Controls grid search over parameter combinations

## -- I --

iai_setup | Initialize Julia and the IAI package.
imputation_learner | Generic learner for imputing missing values
impute | Impute missing values using either a specified method or through validation
impute_cv | Impute missing values using cross validation
install_julia | Download and install Julia automatically.
install_system_image | Download and install the IAI system image automatically.
is_categoric_split | Check if a node of a tree applies a categoric split
is_hyperplane_split | Check if a node of a tree applies a hyperplane split
is_leaf | Check if a node of a tree is a leaf
is_mixed_ordinal_split | Check if a node of a tree applies a mixed ordinal/categoric split
is_mixed_parallel_split | Check if a node of a tree applies a mixed parallel/categoric split
is_ordinal_split | Check if a node of a tree applies a ordinal split
is_parallel_split | Check if a node of a tree applies a parallel split

## -- L --

load_graphviz | Loads the Julia Graphviz library to permit certain visualizations.

## -- M --

mean_imputation_learner | Learner for conducting mean imputation
missing_goes_lower | Check if points with missing values go to the lower child at a split node of of a tree
multi_questionnaire | Generic function for constructing an interactive questionnaire with multiple learners
multi_questionnaire.default | Construct an interactive questionnaire from multiple specified learners
multi_questionnaire.grid_search | Construct an interactive tree questionnaire using multiple learners from the results of a grid search
multi_tree_plot | Generic function for constructing an interactive tree visualization of multiple tree learners
multi_tree_plot.default | Construct an interactive tree visualization of multiple tree learners as specified by questions
multi_tree_plot.grid_search | Construct an interactive tree visualization of multiple tree learners from the results of a grid search

## -- N --

numeric_classification_reward_estimator | Learner for conducting reward estimation with numeric treatments and classification outcomes
numeric_regression_reward_estimator | Learner for conducting reward estimation with numeric treatments and regression outcomes
numeric_reward_estimator | Learner for conducting reward estimation with numeric treatments
numeric_survival_reward_estimator | Learner for conducting reward estimation with numeric treatments and survival outcomes

## -- O --

optimal_feature_selection_classifier | Learner for conducting Optimal Feature Selection on classification problems
optimal_feature_selection_regressor | Learner for conducting Optimal Feature Selection on regression problems
optimal_tree_classifier | Learner for training Optimal Classification Trees
optimal_tree_multi_classifier | Learner for training multi-task Optimal Classification Trees
optimal_tree_multi_regressor | Learner for training multi-task Optimal Regression Trees
optimal_tree_policy_maximizer | Learner for training Optimal Policy Trees where the policy should aim to maximize outcomes
optimal_tree_policy_minimizer | Learner for training Optimal Policy Trees where the policy should aim to minimize outcomes
optimal_tree_prescription_maximizer | Learner for training Optimal Prescriptive Trees where the prescriptions should aim to maximize outcomes
optimal_tree_prescription_minimizer | Learner for training Optimal Prescriptive Trees where the prescriptions should aim to minimize outcomes
optimal_tree_regressor | Learner for training Optimal Regression Trees
optimal_tree_survival_learner | Learner for training Optimal Survival Trees
optimal_tree_survivor | Learner for training Optimal Survival Trees
opt_knn_imputation_learner | Learner for conducting optimal k-NN imputation
opt_svm_imputation_learner | Learner for conducting optimal SVM imputation
opt_tree_imputation_learner | Learner for conducting optimal tree-based imputation

## -- P --

plot.grid_search | Plot a grid search results for Optimal Feature Selection learners
plot.roc_curve | Plot an ROC curve
plot.similarity_comparison | Plot a similarity comparison
plot.stability_analysis | Plot a stability analysis
predict | Generic function for returning the predictions of a model
predict.categorical_reward_estimator | Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data
predict.glmnetcv_learner | Return the predictions made by a GLMNet learner for each point in the features
predict.numeric_reward_estimator | Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data
predict.optimal_feature_selection_learner | Return the predictions made by an Optimal Feature Selection learner for each point in the features
predict.supervised_learner | Return the predictions made by a supervised learner for each point in the features
predict.supervised_multi_learner | Return the predictions made by a multi-task supervised learner for each point in the features
predict.survival_learner | Return the predictions made by a survival learner for each point in the features
predict_expected_survival_time | Generic function for returning the expected survival time predicted by a model
predict_expected_survival_time.glmnetcv_survival_learner | Return the expected survival time estimate made by a 'glmnetcv_survival_learner' for each point in the features.
predict_expected_survival_time.survival_curve | Return the expected survival time estimate made by a survival curve (as returned by 'predict.survival_learner' or 'get_survival_curve')
predict_expected_survival_time.survival_learner | Return the expected survival time estimate made by a survival learner for each point in the features.
predict_hazard | Generic function for returning the hazard coefficient predicted by a model
predict_hazard.glmnetcv_survival_learner | Return the fitted hazard coefficient estimate made by a 'glmnetcv_survival_learner' for each point in the features.
predict_hazard.survival_learner | Return the fitted hazard coefficient estimate made by a survival learner for each point in the features.
predict_outcomes | Generic function for returning the outcomes predicted by a model under each treatment
predict_outcomes.policy_learner | Return the predicted outcome for each treatment made by a policy learner for each point in the features
predict_outcomes.prescription_learner | Return the predicted outcome for each treatment made by a prescription learner for each point in the features
predict_proba | Generic function for returning the probabilities of class membership predicted by a model
predict_proba.classification_learner | Return the probabilities of class membership predicted by a classification learner for each point in the features
predict_proba.classification_multi_learner | Return the probabilities of class membership predicted by a multi-task classification learner for each point in the features
predict_proba.glmnetcv_classifier | Return the probabilities of class membership predicted by a 'glmnetcv_classifier' learner for each point in the features
predict_reward | Generic function for returning the counterfactual rewards estimated by a model under each treatment
predict_reward.categorical_reward_estimator | Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data and predictions
predict_reward.numeric_reward_estimator | Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data and predictions
predict_shap | Calculate SHAP values for all points in the features using the learner
predict_treatment_outcome | Return the estimated quality of each treatment in the trained model of the learner for each point in the features
predict_treatment_outcome_standard_error | Return the standard error for the estimated quality of each treatment in the trained model of the learner for each point in the features
predict_treatment_rank | Return the treatments in ranked order of effectiveness for each point in the features
print_path | Print the decision path through the learner for each sample in the features
prune_trees | Use the trained trees in a learner along with the supplied validation data to determine the best value for the 'cp' parameter and then prune the trees according to this value

## -- Q --

questionnaire | Generic function for constructing an interactive questionnaire
questionnaire.optimal_feature_selection_learner | Specify an interactive questionnaire of an Optimal Feature Selection learner
questionnaire.tree_learner | Specify an interactive questionnaire of a tree learner

## -- R --

random_forest_classifier | Learner for training random forests for classification problems
random_forest_regressor | Learner for training random forests for regression problems
random_forest_survival_learner | Learner for training random forests for survival problems
rand_imputation_learner | Learner for conducting random imputation
read_json | Read in a learner or grid saved in JSON format
refit_leaves | Refit the models in the leaves of a trained learner using the supplied data
release_license | Release any IAI license held by the current session.
reset_display_label | Reset the predicted probability displayed to be that of the predicted label when visualizing a learner
resume_from_checkpoint | Resume training from a checkpoint file
reward_estimator | Learner for conducting reward estimation with categorical treatments
roc_curve | Generic function for constructing an ROC curve
roc_curve.classification_learner | Construct an ROC curve using a trained classification learner on the given data
roc_curve.classification_multi_learner | Construct an ROC curve using a trained multi-task classification learner on the given data
roc_curve.default | Construct an ROC curve from predicted probabilities and true labels
roc_curve.glmnetcv_classifier | Construct an ROC curve using a trained 'glmnetcv_classifier' on the given data

## -- S --

score | Generic function for calculating scores
score.categorical_reward_estimator | Calculate the scores for a categorical reward estimator on the given data
score.default | Calculate the score for a set of predictions on the given data
score.glmnetcv_learner | Calculate the score for a GLMNet learner on the given data
score.numeric_reward_estimator | Calculate the scores for a numeric reward estimator on the given data
score.optimal_feature_selection_learner | Calculate the score for an Optimal Feature Selection learner on the given data
score.supervised_learner | Calculate the score for a model on the given data
score.supervised_multi_learner | Calculate the score for a multi-task model on the given data
set_display_label | Show the probability of a specified label when visualizing a learner
set_julia_seed | Set the random seed in Julia
set_params | Set all supplied parameters on a learner
set_reward_kernel_bandwidth | Save a new reward kernel bandwidth inside a learner, and return new reward predictions generated using this bandwidth for the original data used to train the learner.
set_rich_output_param | Sets a global rich output parameter
set_threshold | For a binary classification problem, update the the predicted labels in the leaves of the learner to predict a label only if the predicted probability is at least the specified threshold.
show_in_browser | Generic function for showing interactive visualization in browser
show_in_browser.abstract_visualization | Show interactive visualization of an object in the default browser
show_in_browser.roc_curve | Show interactive visualization of a 'roc_curve' in the default browser
show_in_browser.tree_learner | Show interactive tree visualization of a tree learner in the default browser
show_questionnaire | Generic function for showing interactive questionnaire in browser
show_questionnaire.optimal_feature_selection_learner | Show an interactive questionnaire based on an Optimal Feature Selection learner in default browser
show_questionnaire.tree_learner | Show an interactive questionnaire based on a tree learner in default browser
similarity_comparison | Conduct a similarity comparison between the final tree in a learner and all trees in a new learner to consider the tradeoff between training performance and similarity to the original tree
single_knn_imputation_learner | Learner for conducting heuristic k-NN imputation
split_data | Split the data into training and test datasets
stability_analysis | Conduct a stability analysis of the trees in a tree learner

## -- T --

transform | Impute missing values in a dataframe using a fitted imputation model
transform_and_expand | Transform features with a trained imputation learner and create adaptive indicator features to encode the missing pattern
tree_plot | Specify an interactive tree visualization of a tree learner
tune_reward_kernel_bandwidth | Conduct the reward kernel bandwidth tuning procedure for a range of starting bandwidths and return the final tuned values.

## -- V --

variable_importance | Generic function for calculating variable importance
variable_importance.learner | Generate a ranking of the variables in a learner according to their importance during training. The results are normalized so that they sum to one.
variable_importance.optimal_feature_selection_learner | Generate a ranking of the variables in an Optimal Feature Selection learner according to their importance during training. The results are normalized so that they sum to one.
variable_importance.tree_learner | Generate a ranking of the variables in a tree learner according to their importance during training. The results are normalized so that they sum to one.
variable_importance_similarity | Calculate similarity between the final tree in a tree learner with all trees in new tree learner using variable importance scores.

## -- W --

write_booster | Write the internal booster saved in the learner to file
write_dot | Output a learner in .dot format
write_html | Generic function for writing interactive visualization to file
write_html.abstract_visualization | Output an object as an interactive browser visualization in HTML format
write_html.roc_curve | Output an ROC curve as an interactive browser visualization in HTML format
write_html.tree_learner | Output a tree learner as an interactive browser visualization in HTML format
write_json | Output a learner or grid in JSON format
write_pdf | Output a learner as a PDF image
write_png | Output a learner as a PNG image
write_questionnaire | Generic function for writing interactive questionnaire to file
write_questionnaire.optimal_feature_selection_learner | Output an Optimal Feature Selection learner as an interactive questionnaire in HTML format
write_questionnaire.tree_learner | Output a tree learner as an interactive questionnaire in HTML format
write_svg | Output a learner as a SVG image

## -- X --

xgboost_classifier | Learner for training XGBoost models for classification problems
xgboost_regressor | Learner for training XGBoost models for regression problems
xgboost_survival_learner | Learner for training XGBoost models for survival problems

## -- Z --

zero_imputation_learner | Learner for conducting zero-imputation