API Reference

Documentation for the iai package

Index

Setup

iai::iai_setup — Function
iai_setup(...)

Initialize Julia and the IAI package.

This function is called automatically with default parameters the first time any iai function is used in an R session. If custom parameters for Julia setup are required, this function must be called in every R session before calling other iai functions.

Arguments

iai::install_julia — Function
install_julia(version = "latest", prefix = julia_default_install_dir())

Download and install Julia automatically.

Arguments

  • version: The version of Julia to install (e.g. "1.6.3"). Defaults to "latest", which will install the most recent stable release.
  • prefix: The directory where Julia will be installed. Defaults to a location determined by rappdirs::user_data_dir.
iai::install_system_image — Function
install_system_image(
  version = "latest",
  replace_default = FALSE,
  prefix = sysimage_default_install_dir(),
  accept_license = FALSE
)

Download and install the IAI system image automatically.

Arguments

  • version: The version of the IAI system image to install (e.g. "2.1.0"). Defaults to "latest", which will install the most recent release.
  • replace_default: Whether to replace the default Julia system image with the downloaded IAI system image. Defaults to FALSE.
  • prefix: The directory where the IAI system image will be installed. Defaults to a location determined by rappdirs::user_data_dir.
  • accept_license: Set to TRUE to confirm that you agree to the End User License Agreement and skip the interactive confirmation dialog.
iai::get_machine_id — Function
get_machine_id()

Return the machine ID for the current computer.

This ID ties the IAI license file to your machine.

iai::acquire_license — Function
acquire_license(...)

Acquire an IAI license for the current session.

Julia Equivalent: IAI.acquire_license

Arguments

  • ...: Refer to the Julia documentation for available parameters
IAI Compatibility

Requires IAI version 3.1 or higher.

iai::load_graphviz — Function
load_graphviz()

Loads the Julia Graphviz library to permit certain visualizations.

The library will be installed if not already present.

General Utilities

iai::resume_from_checkpoint — Function
resume_from_checkpoint(checkpoint_file)

Resume training from a checkpoint file

Julia Equivalent: IAI.resume_from_checkpoint

Arguments

  • checkpoint_file: The location of the checkpoint file.
IAI Compatibility

Requires IAI version 3.1 or higher.

Data Preparation

iai::as.mixeddata — Function
as.mixeddata(values, categorical_levels, ordinal_levels = c())

Convert a vector of values to IAI mixed data format

Julia Equivalent: IAI.make_mixed_data

Arguments

  • values: The vector of values to convert
  • categorical_levels: The values in values to treat as categoric levels
  • ordinal_levels: (optional) The values in values to treat as ordinal levels, in the order supplied
iai::split_data — Function
split_data(task, X, ...)

Split the data into training and test datasets

Julia Equivalent: IAI.split_data

Arguments

  • task: The type of problem.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.

Scoring

iai::score.default — Function
score(obj, predictions, truths, ...)

Calculate the score for a set of predictions on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The type of problem.
  • predictions: The predictions to evaluate.
  • truths: The true target values for these observations.
  • ...: Other parameters, including the criterion. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

Learners

General learners

iai::fit.learner — Function
fit(obj, X, ...)

Fits a model to the training data

Julia Equivalent: IAI.fit!

Arguments

  • obj: The learner to fit.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.
iai::variable_importance.learner — Function
variable_importance(obj, ...)

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.

Julia Equivalent: IAI.variable_importance

Arguments

  • obj: The learner to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_features_used — Function
get_features_used(lnr)

Return the names of the features used by the learner

Julia Equivalent: IAI.get_features_used

Arguments

  • lnr: The learner to query.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::set_params — Function
set_params(lnr, ...)

Set all supplied parameters on a learner

Julia Equivalent: IAI.set_params!

Arguments

  • lnr: The learner to modify.
  • ...: The parameters to set on the learner.
iai::get_params — Function
get_params(lnr)

Return the value of all parameters on a learner

Julia Equivalent: IAI.get_params

Arguments

  • lnr: The learner to query.
iai::clone — Function
clone(lnr)

Return an unfitted copy of a learner with the same parameters

Julia Equivalent: IAI.clone

Arguments

  • lnr: The learner to copy.
iai::read_json — Function
read_json(filename)

Read in a learner or grid saved in JSON format

Julia Equivalent: IAI.read_json

Arguments

  • filename: The location of the JSON file.
iai::write_json — Function
write_json(filename, obj, ...)

Output a learner or grid in JSON format

Julia Equivalent: IAI.write_json

Arguments

  • filename: Where to save the output.
  • obj: The learner or grid to output.
  • ...: Refer to the Julia documentation for available parameters.

Supervised learners

iai::predict.supervised_learner — Function
predict(obj, X, ...)

Return the predictions made by a supervised learner for each point in the features

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::score.supervised_learner — Function
score(obj, X, ...)

Calculate the score for a model on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The learner or grid to evaluate.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.

Classification learners

iai::predict_proba.classification_learner — Function
predict_proba(obj, X, ...)

Return the probabilities of class membership predicted by a classification learner for each point in the features

Julia Equivalent: IAI.predict_proba

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • ...: Additional arguments (unused)
iai::roc_curve — Function
roc_curve(obj, ...)

Generic function for constructing an ROC curve

Julia Equivalent: IAI.ROCCurve

Arguments

  • obj: The object controlling which method is used
  • ...: Arguments depending on the specific method used
iai::roc_curve.classification_learner — Function
roc_curve(obj, X, y, ...)

Construct an ROC curve using a trained classification learner on the given data

Julia Equivalent: IAI.ROCCurve

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • y: The labels of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::roc_curve.default — Function
roc_curve(obj, y, positive_label = stop("`positive_label` is required"), ...)

Construct an ROC curve from predicted probabilities and true labels

Julia Equivalent: IAI.ROCCurve

Arguments

  • obj: The predicted probabilities for each point in the data.
  • y: The true labels of the data.
  • positive_label: The label for which probability is being predicted.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::get_roc_curve_data — Function
get_roc_curve_data(curve)

Extract the underlying data from an ROC curve

ROC curves are returned by roc_curve, e.g. roc_curve.classification_learner

The data is returned as a list with two keys: auc giving the area-under-the-curve, and coords containing a vector of lists representing each point on the curve, each with keys fpr (the false positive rate), tpr (the true positive rate) and threshold (the threshold).

Julia Equivalent: IAI.get_roc_curve_data

Arguments

  • curve: The curve to query.
IAI Compatibility

Requires IAI version 2.1 or higher.

plot.roc_curve — Function
plot(x, ...)

Plot an ROC curve

Arguments

  • x: The ROC curve to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.1 or higher.

ggplot2::autoplot.roc_curve — Function
ggplot2::autoplot(x, ...)

Construct a ggplot2::ggplot object plotting the ROC curve

Arguments

  • x: The ROC curve to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::write_html.roc_curve — Function
write_html(filename, obj, ...)

Output an ROC curve as an interactive browser visualization in HTML format

Julia Equivalent: IAI.write_html

Arguments

  • filename: Where to save the output.
  • obj: The curve to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::show_in_browser.roc_curve — Function
show_in_browser(obj, ...)

Show interactive visualization of a roc_curve in the default browser

Julia Equivalent: IAI.show_in_browser

Arguments

  • obj: The curve to visualize.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

Survival learners

iai::predict.survival_learner — Function
predict(obj, X, t = NULL, ...)

Return the predictions made by a survival learner for each point in the features

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • t: The time for which to predict survival probability, defaulting to returning the entire survival curve if not supplied
  • ...: Additional arguments (unused)
iai::predict_hazard.survival_learner — Function
predict_hazard(obj, X, ...)

Return the fitted hazard coefficient estimate made by a survival learner for each point in the features.

A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

Julia Equivalent: IAI.predict_hazard

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 1.2 or higher.

iai::predict_expected_survival_time.survival_learner — Function
predict_expected_survival_time(obj, X, ...)

Return the expected survival time estimate made by a survival learner for each point in the features.

Julia Equivalent: IAI.predict_expected_survival_time

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.0 or higher.

Prescription learners

iai::predict_outcomes.prescription_learner — Function
predict_outcomes(obj, X, ...)

Return the predicted outcome for each treatment made by a prescription learner for each point in the features

Julia Equivalent: IAI.predict_outcomes

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • ...: Additional arguments (unused)

Policy learners

iai::predict_outcomes.policy_learner — Function
predict_outcomes(obj, X, rewards, ...)

Return the predicted outcome for each treatment made by a policy learner for each point in the features

Julia Equivalent: IAI.predict_outcomes

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • rewards: The estimated reward matrix for the data.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.0 or higher

iai::predict_treatment_rank — Function
predict_treatment_rank(lnr, X)

Return the treatments in ranked order of effectiveness for each point in the features

Julia Equivalent: IAI.predict_treatment_rank

Arguments

  • lnr: The learner or grid to use for prediction.
  • X: The features of the data.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::predict_treatment_outcome — Function
predict_treatment_outcome(lnr, X)

Return the estimated quality of each treatment in the trained model of the learner for each point in the features

Julia Equivalent: IAI.predict_treatment_outcome

Arguments

  • lnr: The learner or grid to use for prediction.
  • X: The features of the data.
IAI Compatibility

Requires IAI version 2.1 or higher.

Imputation learners

iai::fit.imputation_learner — Function
fit(obj, X, ...)

Fits an imputation learner to the training data.

Additional keyword arguments are available for fitting imputation learners - please refer to the Julia documentation.

Julia Equivalent: IAI.fit!

Arguments

  • obj: The learner or grid to fit.
  • X: The features of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::fit_transform — Function
fit_transform(lnr, X, ...)

Fit an imputation model using the given features and impute the missing values in these features

Similar to calling fit.imputation_learner followed by transform

Julia Equivalent: IAI.fit_transform!

Arguments

  • lnr: The learner or grid to use for imputation
  • X: The features of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::transform — Function
transform(lnr, X)

Impute missing values in a dataframe using a fitted imputation model

Julia Equivalent: IAI.transform

Arguments

  • lnr: The learner or grid to use for imputation
  • X: The features of the data.

Visualization

iai::write_html.abstract_visualization — Function
write_html(filename, obj, ...)

Output an object as an interactive browser visualization in HTML format

Julia Equivalent: IAI.write_html

Arguments

  • filename: Where to save the output.
  • obj: The object to output.
  • ...: Refer to the Julia documentation for available parameters.
iai::questionnaire — Function
questionnaire(obj, ...)

Generic function for constructing an interactive questionnaire

Julia Equivalent: IAI.Questionnaire

Arguments

  • obj: The object controlling which method is used
  • ...: Arguments depending on the specific method used
iai::multi_questionnaire.grid_search — Function
multi_questionnaire(obj, ...)

Construct an interactive tree questionnaire using multiple learners from the results of a grid search

Julia Equivalent: IAI.MultiQuestionnaire

Arguments

  • obj: The grid to visualize
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.0 or higher.

Grid Search

iai::grid_search — Function
grid_search(lnr, ...)

Controls grid search over parameter combinations

Julia Equivalent: IAI.GridSearch

Arguments

  • lnr: The learner to use when validating.
  • ...: The parameters to validate over.
iai::fit.grid_search — Function
fit(obj, X, ...)

Fits a grid_search to the training data

Julia Equivalent: IAI.fit!

Arguments

  • obj: The grid search to fit.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.
iai::fit_cv — Function
fit_cv(grid, X, ...)

Fits a grid search to the training data with cross-validation

Julia Equivalent: IAI.fit_cv!

Arguments

  • grid: The grid to fit.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.
iai::fit_transform_cv — Function
fit_transform_cv(grid, X, ...)

Train a grid using cross-validation with features and impute all missing values in these features

Julia Equivalent: IAI.fit_transform_cv!

Arguments

  • grid: The grid to use for imputation
  • X: The features of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_learner — Function
get_learner(grid)

Return the fitted learner using the best parameter combination from a grid

Julia Equivalent: IAI.get_learner

Arguments

  • grid: The grid to query.

Tree Learners

General tree learners

iai::apply — Function
apply(lnr, X)

Return the leaf index in a tree model into which each point in the features falls

Julia Equivalent: IAI.apply

Arguments

  • lnr: The learner or grid to query.
  • X: The features of the data.
iai::apply_nodes — Function
apply_nodes(lnr, X)

Return the indices of the points in the features that fall into each node of a trained tree model

Julia Equivalent: IAI.apply_nodes

Arguments

  • lnr: The learner or grid to query.
  • X: The features of the data.
iai::decision_path — Function
decision_path(lnr, X)

Return a matrix where entry (i, j) is true if the ith point in the features passes through the jth node in a trained tree model.

Julia Equivalent: IAI.decision_path

Arguments

  • lnr: The learner or grid to query.
  • X: The features of the data.
iai::print_path — Function
print_path(lnr, X, ...)

Print the decision path through the learner for each sample in the features

Julia Equivalent: IAI.print_path

Arguments

  • lnr: The learner or grid to query.
  • X: The features of the data.
  • ...: Refer to the Julia documentation for available parameters.
iai::variable_importance.tree_learner — Function
variable_importance(obj, ...)

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.

Julia Equivalent: IAI.variable_importance

Arguments

  • obj: The learner to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_depth — Function
get_depth(lnr, node_index)

Get the depth of a node of a tree

Julia Equivalent: IAI.get_depth

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_parent — Function
get_parent(lnr, node_index)

Get the index of the parent node at a node of a tree

Julia Equivalent: IAI.get_parent

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_lower_child — Function
get_lower_child(lnr, node_index)

Get the index of the lower child at a split node of a tree

Julia Equivalent: IAI.get_lower_child

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_upper_child — Function
get_upper_child(lnr, node_index)

Get the index of the upper child at a split node of a tree

Julia Equivalent: IAI.get_upper_child

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_num_samples — Function
get_num_samples(lnr, node_index)

Get the number of training points contained in a node of a tree

Julia Equivalent: IAI.get_num_samples

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_categoric_split — Function
is_categoric_split(lnr, node_index)

Check if a node of a tree applies a categoric split

Julia Equivalent: IAI.is_categoric_split

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_hyperplane_split — Function
is_hyperplane_split(lnr, node_index)

Check if a node of a tree applies a hyperplane split

Julia Equivalent: IAI.is_hyperplane_split

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_leaf — Function
is_leaf(lnr, node_index)

Check if a node of a tree is a leaf

Julia Equivalent: IAI.is_leaf

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_mixed_ordinal_split — Function
is_mixed_ordinal_split(lnr, node_index)

Check if a node of a tree applies a mixed ordinal/categoric split

Julia Equivalent: IAI.is_mixed_ordinal_split

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_ordinal_split — Function
is_ordinal_split(lnr, node_index)

Check if a node of a tree applies a ordinal split

Julia Equivalent: IAI.is_ordinal_split

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::is_parallel_split — Function
is_parallel_split(lnr, node_index)

Check if a node of a tree applies a parallel split

Julia Equivalent: IAI.is_parallel_split

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_split_categories — Function
get_split_categories(lnr, node_index)

Return the categoric/ordinal information used in the split at a node of a tree

Julia Equivalent: IAI.get_split_categories

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_split_feature — Function
get_split_feature(lnr, node_index)

Return the feature used in the split at a node of a tree

Julia Equivalent: IAI.get_split_feature

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_split_threshold — Function
get_split_threshold(lnr, node_index)

Return the threshold used in the split at a node of a tree

Julia Equivalent: IAI.get_split_threshold

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::get_split_weights — Function
get_split_weights(lnr, node_index)

Return the weights for numeric and categoric features used in the hyperplane split at a node of a tree

Julia Equivalent: IAI.get_split_weights

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
iai::missing_goes_lower — Function
missing_goes_lower(lnr, node_index)

Check if points with missing values go to the lower child at a split node of of a tree

Julia Equivalent: IAI.missing_goes_lower

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.

Classification tree learners

iai::get_classification_label — Function
get_classification_label(lnr, node_index, ...)

Return the predicted label at a node of a tree

Julia Equivalent: IAI.get_classification_label

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_classification_proba — Function
get_classification_proba(lnr, node_index, ...)

Return the predicted probabilities of class membership at a node of a tree

Julia Equivalent: IAI.get_classification_proba

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_regression_constant.classification_tree_learner — Function
get_regression_constant(obj, node_index, ...)

Return the constant term in the logistic regression prediction at a node of a classification tree

Julia Equivalent: IAI.get_regression_constant

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::get_regression_weights.classification_tree_learner — Function
get_regression_weights(obj, node_index, ...)

Return the weights for each feature in the logistic regression prediction at a node of a classification tree

Julia Equivalent: IAI.get_regression_weights

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::set_threshold — Function
set_threshold(lnr, label, 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.

Julia Equivalent: IAI.set_threshold!

Arguments

  • lnr: The learner to modify.
  • label: The referenced label.
  • threshold: The probability threshold above which label will be be predicted.
  • ...: Refer to the Julia documentation for available parameters.
iai::set_display_label — Function
set_display_label(lnr, display_label)

Show the probability of a specified label when visualizing a learner

Julia Equivalent: IAI.set_display_label!

Arguments

  • lnr: The learner to modify.
  • display_label: The label for which to show probabilities.
iai::reset_display_label — Function
reset_display_label(lnr)

Reset the predicted probability displayed to be that of the predicted label when visualizing a learner

Julia Equivalent: IAI.reset_display_label!

Arguments

  • lnr: The learner to modify.

Regression tree learners

iai::get_regression_weights.regression_tree_learner — Function
get_regression_weights(obj, node_index, ...)

Return the weights for each feature in the linear regression prediction at a node of a regression tree

Julia Equivalent: IAI.get_regression_weights

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.

Survival tree learners

iai::get_survival_curve — Function
get_survival_curve(lnr, node_index, ...)

Return the survival curve at a node of a tree

Julia Equivalent: IAI.get_survival_curve

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_survival_expected_time — Function
get_survival_expected_time(lnr, node_index, ...)

Return the predicted expected survival time at a node of a tree

Julia Equivalent: IAI.get_survival_expected_time

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::get_survival_hazard — Function
get_survival_hazard(lnr, node_index, ...)

Return the predicted hazard ratio at a node of a tree

Julia Equivalent: IAI.get_survival_hazard

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::get_regression_constant.survival_tree_learner — Function
get_regression_constant(obj, node_index, ...)

Return the constant term in the cox regression prediction at a node of a survival tree

Julia Equivalent: IAI.get_regression_constant

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::get_regression_weights.survival_tree_learner — Function
get_regression_weights(obj, node_index, ...)

Return the weights for each feature in the cox regression prediction at a node of a survival tree

Julia Equivalent: IAI.get_regression_weights

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

Prescription tree learners

iai::get_prescription_treatment_rank — Function
get_prescription_treatment_rank(lnr, node_index, ...)

Return the treatments ordered from most effective to least effective at a node of a tree

Julia Equivalent: IAI.get_prescription_treatment_rank

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_regression_constant.prescription_tree_learner — Function
get_regression_constant(obj, node_index, treatment, ...)

Return the constant term in the linear regression prediction at a node of a prescription tree

Julia Equivalent: IAI.get_regression_constant

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • treatment: The treatment to query.
  • ...: Refer to the Julia documentation for available parameters.
iai::get_regression_weights.prescription_tree_learner — Function
get_regression_weights(obj, node_index, treatment, ...)

Return the weights for each feature in the linear regression prediction at a node of a prescription tree

Julia Equivalent: IAI.get_regression_weights

Arguments

  • obj: The learner to query.
  • node_index: The node in the tree to query.
  • treatment: The treatment to query.
  • ...: Refer to the Julia documentation for available parameters.

Policy tree learners

iai::get_policy_treatment_rank — Function
get_policy_treatment_rank(lnr, node_index, ...)

Return the treatments ordered from most effective to least effective at a node of a tree

Julia Equivalent: IAI.get_policy_treatment_rank

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::get_policy_treatment_outcome — Function
get_policy_treatment_outcome(lnr, node_index, ...)

Return the quality of the treatments at a node of a tree

Julia Equivalent: IAI.get_policy_treatment_outcome

Arguments

  • lnr: The learner to query.
  • node_index: The node in the tree to query.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

Tree learner visualization

iai::write_png — Function
write_png(filename, lnr, ...)

Output a learner as a PNG image

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Julia Equivalent: IAI.write_png

Arguments

  • filename: Where to save the output.
  • lnr: The learner to output.
  • ...: Refer to the Julia documentation for available parameters.
iai::write_pdf — Function
write_pdf(filename, lnr, ...)

Output a learner as a PDF image

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Julia Equivalent: IAI.write_pdf

Arguments

  • filename: Where to save the output.
  • lnr: The learner to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::write_svg — Function
write_svg(filename, lnr, ...)

Output a learner as a SVG image

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Julia Equivalent: IAI.write_svg

Arguments

  • filename: Where to save the output.
  • lnr: The learner to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::write_dot — Function
write_dot(filename, lnr, ...)

Output a learner in .dot format

Julia Equivalent: IAI.write_dot

Arguments

  • filename: Where to save the output.
  • lnr: The learner to output.
  • ...: Refer to the Julia documentation for available parameters.
iai::write_html.tree_learner — Function
write_html(filename, obj, ...)

Output a tree learner as an interactive browser visualization in HTML format

Julia Equivalent: IAI.write_html

Arguments

  • filename: Where to save the output.
  • obj: The learner or grid to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Outputting a grid search requires IAI version 2.0 or higher.

iai::show_in_browser.tree_learner — Function
show_in_browser(obj, ...)

Show interactive tree visualization of a tree learner in the default browser

Julia Equivalent: IAI.show_in_browser

Arguments

  • obj: The learner or grid to visualize.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Showing a grid search requires IAI version 2.0 or higher.

iai::write_questionnaire.tree_learner — Function
write_questionnaire(filename, obj, ...)

Output a tree learner as an interactive questionnaire in HTML format

Julia Equivalent: IAI.write_questionnaire

Arguments

  • filename: Where to save the output.
  • obj: The learner or grid to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Outputting a grid search requires IAI version 2.0 or higher.

iai::show_questionnaire.tree_learner — Function
show_questionnaire(obj, ...)

Show an interactive questionnaire based on a tree learner in default browser

Julia Equivalent: IAI.show_questionnaire

Arguments

  • obj: The learner or grid to visualize.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Showing a grid search requires IAI version 2.0 or higher.

iai::multi_tree_plot.grid_search — Function
multi_tree_plot(obj, ...)

Construct an interactive tree visualization of multiple tree learners from the results of a grid search

Julia Equivalent: IAI.MultiTreePlot

Arguments

  • obj: The grid to visualize
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.0 or higher.

Tree Stability

iai::get_tree — Function
get_tree(lnr, index)

Return a copy of the learner that uses a specific tree rather than the tree with the best training objective.

Julia Equivalent: IAI.get_tree

Arguments

  • lnr: The original learner
  • index: The index of the tree to use
IAI Compatibility

Requires IAI version 2.2 or higher.

Stability Analysis

iai::get_stability_results — Function
get_stability_results(stability)

Return the trained trees in order of increasing objective value, along with their variable importance scores for each feature

Julia Equivalent: IAI.get_stability_results

Arguments

  • stability: The stability analysis to query
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_cluster_distances — Function
get_cluster_distances(stability, num_trees)

Return the distances between the centroids of each pair of clusters, under the clustering of a given number of trees

Julia Equivalent: IAI.get_cluster_distances

Arguments

  • stability: The stability analysis to query
  • num_trees: The number of trees to include in the clustering
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_cluster_assignments — Function
get_cluster_assignments(stability, num_trees)

Return the indices of the trees assigned to each cluster, under the clustering of a given number of trees

Julia Equivalent: IAI.get_cluster_assignments

Arguments

  • stability: The stability analysis to query
  • num_trees: The number of trees to include in the clustering
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_cluster_details — Function
get_cluster_details(stability, num_trees)

Return the centroid information for each cluster, under the clustering of a given number of trees

Julia Equivalent: IAI.get_cluster_details

Arguments

  • stability: The stability analysis to query
  • num_trees: The number of trees to include in the clustering
IAI Compatibility

Requires IAI version 2.2 or higher.

plot.stability_analysis — Function
plot(x, ...)

Plot a stability analysis

Arguments

  • x: The stability analysis to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.2 or higher.

ggplot2::autoplot.stability_analysis — Function
ggplot2::autoplot(x, ...)

Construct a ggplot2::ggplot object plotting the results of the stability analysis

Arguments

  • x: The stability analysis to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.2 or higher.

Similarity Comparison

iai::variable_importance_similarity — Function
variable_importance_similarity(lnr, new_lnr, ...)

Calculate similarity between the final tree in a tree learner with all trees in new tree learner using variable importance scores.

Julia Equivalent: IAI.variable_importance_similarity

Arguments

  • lnr: The original learner
  • new_lnr: The new learner
  • ...: Additional arguments (refer to Julia documentation)
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::similarity_comparison — Function
similarity_comparison(lnr, new_lnr, deviations)

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

Refer to the documentation on tree stability for more information.

Julia Equivalent: IAI.SimilarityComparison

Arguments

  • lnr: The original learner
  • new_lnr: The new learner
  • deviations: The deviation between the original tree and each tree in the new learner
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_train_errors — Function
get_train_errors(similarity)

Extract the training objective value for each candidate tree in the comparison, where a lower value indicates a better solution

Julia Equivalent: IAI.get_train_errors

Arguments

  • similarity: The similarity comparison
IAI Compatibility

Requires IAI version 2.2 or higher.

plot.similarity_comparison — Function
plot(x, ...)

Plot a similarity comparison

Arguments

  • x: The similarity comparison to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.2 or higher.

ggplot2::autoplot.similarity_comparison — Function
ggplot2::autoplot(x, ...)

Construct a ggplot2::ggplot object plotting the results of the similarity comparison

Arguments

  • x: The similarity comparison to plot
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.2 or higher.

Optimal Trees

iai::optimal_tree_classifier — Function
optimal_tree_classifier(...)

Learner for training Optimal Classification Trees

Julia Equivalent: IAI.OptimalTreeClassifier

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::optimal_tree_regressor — Function
optimal_tree_regressor(...)

Learner for training Optimal Regression Trees

Julia Equivalent: IAI.OptimalTreeRegressor

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::optimal_tree_prescription_minimizer — Function
optimal_tree_prescription_minimizer(...)

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to minimize outcomes

Julia Equivalent: IAI.OptimalTreePrescriptionMinimizer

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::optimal_tree_prescription_maximizer — Function
optimal_tree_prescription_maximizer(...)

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to maximize outcomes

Julia Equivalent: IAI.OptimalTreePrescriptionMaximizer

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::optimal_tree_policy_minimizer — Function
optimal_tree_policy_minimizer(...)

Learner for training Optimal Policy Trees where the policy should aim to minimize outcomes

Julia Equivalent: IAI.OptimalTreePolicyMinimizer

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::optimal_tree_policy_maximizer — Function
optimal_tree_policy_maximizer(...)

Learner for training Optimal Policy Trees where the policy should aim to maximize outcomes

Julia Equivalent: IAI.OptimalTreePolicyMaximizer

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::refit_leaves — Function
refit_leaves(lnr, ...)

Refit the models in the leaves of a trained learner using the supplied data

Julia Equivalent: IAI.refit_leaves!

Arguments

  • lnr: The learner to refit
  • ...: Refer to the Julia documentation for available parameters
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::copy_splits_and_refit_leaves — Function
copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)

Copy the tree split structure from one learner into another and refit the models in each leaf of the tree using the supplied data

Julia Equivalent: IAI.copy_splits_and_refit_leaves!

Arguments

  • new_lnr: The learner to modify and refit
  • orig_lnr: The learner from which to copy the tree split structure
  • ...: Refer to the Julia documentation for available parameters
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::prune_trees — Function
prune_trees(lnr, ...)

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

Julia Equivalent: IAI.prune_trees!

Arguments

  • lnr: The learner to prune
  • ...: Refer to the Julia documentation for available parameters
IAI Compatibility

Requires IAI version 3.0 or higher.

OptImpute

iai::impute — Function
impute(X, ...)

Impute missing values using either a specified method or through validation

Julia Equivalent: IAI.impute

This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.

Arguments

  • X: The dataframe in which to impute missing values.
  • ...: Refer to the Julia documentation for available parameters.
iai::impute_cv — Function
impute_cv(X, ...)

Impute missing values using cross validation

Julia Equivalent: IAI.impute_cv

This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.

Arguments

  • X: The dataframe in which to impute missing values.
  • ...: Refer to the Julia documentation for available parameters.
iai::imputation_learner — Function
imputation_learner(method = "opt_knn", ...)

Generic learner for imputing missing values

Julia Equivalent: IAI.ImputationLearner

Arguments

  • method: (optional) Specifies the imputation method to use.
  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::opt_knn_imputation_learner — Function
opt_knn_imputation_learner(...)

Learner for conducting optimal k-NN imputation

Julia Equivalent: IAI.OptKNNImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::opt_svm_imputation_learner — Function
opt_svm_imputation_learner(...)

Learner for conducting optimal SVM imputation

Julia Equivalent: IAI.OptSVMImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::opt_tree_imputation_learner — Function
opt_tree_imputation_learner(...)

Learner for conducting optimal tree-based imputation

Julia Equivalent: IAI.OptTreeImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::mean_imputation_learner — Function
mean_imputation_learner(...)

Learner for conducting mean imputation

Julia Equivalent: IAI.MeanImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::rand_imputation_learner — Function
rand_imputation_learner(...)

Learner for conducting random imputation

Julia Equivalent: IAI.RandImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
iai::zero_imputation_learner — Function
zero_imputation_learner(...)

Learner for conducting zero-imputation

Julia Equivalent: IAI.ZeroImputationLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::fit_and_expand — Function
fit_and_expand(lnr, X, ...)

Fit an imputation learner with training features and create adaptive indicator features to encode the missing pattern

Julia Equivalent: IAI.fit_and_expand!

Arguments

  • lnr: The learner to use for imputation.
  • X: The dataframe in which to impute missing values.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::transform_and_expand — Function
transform_and_expand(lnr, X, ...)

Transform features with a trained imputation learner and create adaptive indicator features to encode the missing pattern

Julia Equivalent: IAI.transform_and_expand

Arguments

  • lnr: The learner to use for imputation.
  • X: The dataframe in which to impute missing values.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

Optimal Feature Selection

iai::optimal_feature_selection_classifier — Function
optimal_feature_selection_classifier(...)

Learner for conducting Optimal Feature Selection on classification problems

Julia Equivalent: IAI.OptimalFeatureSelectionClassifier

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::optimal_feature_selection_regressor — Function
optimal_feature_selection_regressor(...)

Learner for conducting Optimal Feature Selection on regression problems

Julia Equivalent: IAI.OptimalFeatureSelectionRegressor

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::fit.optimal_feature_selection_learner — Function
fit(obj, X, ...)

Fits an Optimal Feature Selection learner to the training data

When the coordinated_sparsity parameter of the learner is TRUE, additional keyword arguments are required - please refer to the Julia documentation.

Julia Equivalent: IAI.fit!

Arguments

  • obj: The learner or grid to fit.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::predict.optimal_feature_selection_learner — Function
predict(obj, X, fit_index = NULL, ...)

Return the predictions made by an Optimal Feature Selection learner for each point in the features

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • fit_index: The index of the cluster to use for prediction, if the coordinated_sparsity parameter on the learner is TRUE.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::score.optimal_feature_selection_learner — Function
score(obj, X, ...)

Calculate the score for an Optimal Feature Selection learner on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The learner or grid to evaluate.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. If the coordinated_sparsity parameter on the learner is TRUE, then fit_index must be used to specify which cluster should be used. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::variable_importance.optimal_feature_selection_learner — Function
variable_importance(obj, fit_index = NULL, ...)

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.

Julia Equivalent: IAI.variable_importance

Arguments

  • obj: The learner to query.
  • fit_index: The index of the cluster to use for prediction, if the coordinated_sparsity parameter on the learner is TRUE.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::get_prediction_constant.optimal_feature_selection_learner — Function
get_prediction_constant(obj, fit_index = NULL, ...)

Return the constant term in the prediction in a trained Optimal Feature Selection learner

Julia Equivalent: IAI.get_prediction_constant

Arguments

  • obj: The learner to query.
  • fit_index: The index of the cluster to use for prediction, if the coordinated_sparsity parameter on the learner is TRUE.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::get_prediction_weights.optimal_feature_selection_learner — Function
get_prediction_weights(obj, fit_index = NULL, ...)

Return the weights for numeric and categoric features used for prediction in a trained Optimal Feature Selection learner

Julia Equivalent: IAI.get_prediction_weights

Arguments

  • obj: The learner to query.
  • fit_index: The index of the cluster to use for prediction, if the coordinated_sparsity parameter on the learner is TRUE.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 1.1 or higher.

iai::write_questionnaire.optimal_feature_selection_learner — Function
write_questionnaire(filename, obj, ...)

Output an Optimal Feature Selection learner as an interactive questionnaire in HTML format

Julia Equivalent: IAI.write_questionnaire

Arguments

  • filename: Where to save the output.
  • obj: The learner or grid to output.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::show_questionnaire.optimal_feature_selection_learner — Function
show_questionnaire(obj, ...)

Show an interactive questionnaire based on an Optimal Feature Selection learner in default browser

Julia Equivalent: IAI.show_questionnaire

Arguments

  • obj: The learner or grid to visualize.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

plot.grid_search — Function
plot(x, ...)

Plot a grid search results for Optimal Feature Selection learners

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

ggplot2::autoplot.grid_search — Function
ggplot2::autoplot(x, type = stop("`type` is required"), ...)

Construct a ggplot2::ggplot object plotting grid search results for Optimal Feature Selection learners

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_num_fits.optimal_feature_selection_learner — Function
get_num_fits(obj, ...)

Return the number of fits along the path in a trained Optimal Feature Selection learner

Julia Equivalent: IAI.get_num_fits

Arguments

  • obj: The Optimal Feature Selection learner to query.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 3.0 or higher.

Reward Estimation

iai::fit_predict — Function
fit_predict(obj, ...)

Generic function for fitting a reward estimator on features, treatments and returning predicted counterfactual rewards and scores of the internal estimators.

Julia Equivalent: IAI.fit_predict!

Arguments

  • obj: The object controlling which method is used
  • ...: Arguments depending on the specific method used

Categorical Treatments

iai::categorical_regression_reward_estimator — Function
categorical_regression_reward_estimator(...)

Learner for conducting reward estimation with categorical treatments and regression outcomes

Julia Equivalent: IAI.CategoricalRegressionRewardEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::categorical_survival_reward_estimator — Function
categorical_survival_reward_estimator(...)

Learner for conducting reward estimation with categorical treatments and survival outcomes

Julia Equivalent: IAI.CategoricalSurvivalRewardEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::fit_predict.categorical_reward_estimator — Function
fit_predict(obj, X, treatments, ...)

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.

Julia Equivalent: IAI.fit_predict!

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • treatments: The treatment applied to each point in the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::predict.categorical_reward_estimator — Function
predict(obj, X, ...)

Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 2.0 or higher.

iai::score.categorical_reward_estimator — Function
score(obj, X, ...)

Calculate the scores for a categorical reward estimator on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The learner or grid to evaluate.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for other available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::predict_reward.categorical_reward_estimator — Function
predict_reward(obj, X, ...)

Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data and predictions

Julia Equivalent: IAI.predict_reward

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::equal_propensity_estimator — Function
equal_propensity_estimator(...)

Learner that estimates equal propensity for all treatments.

For use with data from randomized experiments where treatments are known to be randomly assigned.

Julia Equivalent: IAI.EqualPropensityEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

Numeric Treatments

iai::numeric_classification_reward_estimator — Function
numeric_classification_reward_estimator(...)

Learner for conducting reward estimation with numeric treatments and classification outcomes

Julia Equivalent: IAI.NumericClassificationRewardEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::numeric_regression_reward_estimator — Function
numeric_regression_reward_estimator(...)

Learner for conducting reward estimation with numeric treatments and regression outcomes

Julia Equivalent: IAI.NumericRegressionRewardEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::numeric_survival_reward_estimator — Function
numeric_survival_reward_estimator(...)

Learner for conducting reward estimation with numeric treatments and survival outcomes

Julia Equivalent: IAI.NumericSurvivalRewardEstimator

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::get_estimation_densities — Function
get_estimation_densities(lnr, ...)

Return the total kernel density surrounding each treatment candidate for the propensity/outcome estimation problems in a fitted learner.

Julia Equivalent: IAI.get_estimation_densities

Arguments

  • lnr: The learner from which to extract densities
  • ...: Refer to the Julia documentation for other parameters
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::tune_reward_kernel_bandwidth — Function
tune_reward_kernel_bandwidth(lnr, ...)

Conduct the reward kernel bandwidth tuning procedure for a range of starting bandwidths and return the final tuned values.

Julia Equivalent: IAI.tune_reward_kernel_bandwidth

Arguments

  • lnr: The learner to use for tuning the bandwidth
  • ...: Refer to the Julia documentation for other parameters
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::set_reward_kernel_bandwidth — Function
set_reward_kernel_bandwidth(lnr, ...)

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.

Julia Equivalent: IAI.set_reward_kernel_bandwidth!

Arguments

  • lnr: The learner to modify
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::fit_predict.numeric_reward_estimator — Function
fit_predict(obj, X, treatments, ...)

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.

Julia Equivalent: IAI.fit_predict!

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • treatments: The treatment applied to each point in the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::predict.numeric_reward_estimator — Function
predict(obj, X, ...)

Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::score.numeric_reward_estimator — Function
score(obj, X, ...)

Calculate the scores for a numeric reward estimator on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The learner or grid to evaluate.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for other available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::predict_reward.numeric_reward_estimator — Function
predict_reward(obj, X, ...)

Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data and predictions

Julia Equivalent: IAI.predict_reward

Arguments

  • obj: The learner or grid to use for estimation
  • X: The features of the data.
  • ...: Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::all_treatment_combinations — Function
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

Julia Equivalent: IAI.all_treatment_combinations

Arguments

  • ...: A vector of possible options for each treatment

Heuristics

Random Forests

iai::random_forest_classifier — Function
random_forest_classifier(...)

Learner for training random forests for classification problems

Julia Equivalent: IAI.RandomForestClassifier

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::random_forest_regressor — Function
random_forest_regressor(...)

Learner for training random forests for regression problems

Julia Equivalent: IAI.RandomForestRegressor

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::random_forest_survival_learner — Function
random_forest_survival_learner(...)

Learner for training random forests for survival problems

Julia Equivalent: IAI.RandomForestSurvivalLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

XGBoost

iai::xgboost_classifier — Function
xgboost_classifier(...)

Learner for training XGBoost models for classification problems

Julia Equivalent: IAI.XGBoostClassifier

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::xgboost_regressor — Function
xgboost_regressor(...)

Learner for training XGBoost models for regression problems

Julia Equivalent: IAI.XGBoostRegressor

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::xgboost_survival_learner — Function
xgboost_survival_learner(...)

Learner for training XGBoost models for survival problems

Julia Equivalent: IAI.XGBoostSurvivalLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::predict_shap — Function
predict_shap(lnr, X)

Calculate SHAP values for all points in the features using the learner

Julia Equivalent: IAI.predict_shap

Arguments

  • lnr: The XGBoost learner or grid to use for prediction.
  • X: The features of the data.
IAI Compatibility

Requires IAI version 2.2 or higher.

iai::write_booster — Function
write_booster(filename, lnr)

Write the internal booster saved in the learner to file

Julia Equivalent: IAI.write_booster

Arguments

  • filename: Where to save the output.
  • lnr: The XGBoost learner with the booster to output.
IAI Compatibility

Requires IAI version 2.1 or higher.

GLMNet

iai::glmnetcv_classifier — Function
glmnetcv_classifier(...)

Learner for training GLMNet models for classification problems with cross-validation

Julia Equivalent: IAI.GLMNetCVClassifier

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::predict_proba.glmnetcv_classifier — Function
predict_proba(obj, X, fit_index = NULL, ...)

Return the probabilities of class membership predicted by a glmnetcv_classifier learner for each point in the features

Julia Equivalent: IAI.predict_proba

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::roc_curve.glmnetcv_classifier — Function
roc_curve(obj, X, y, fit_index = NULL, ...)

Construct an ROC curve using a trained glmnetcv_classifier on the given data

Julia Equivalent: IAI.ROCCurve

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • y: The labels of the data.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::glmnetcv_regressor — Function
glmnetcv_regressor(...)

Learner for training GLMNet models for regression problems with cross-validation

Julia Equivalent: IAI.GLMNetCVRegressor

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::glmnetcv_survival_learner — Function
glmnetcv_survival_learner(...)

Learner for training GLMNet models for survival problems with cross-validation

Julia Equivalent: IAI.GLMNetCVSurvivalLearner

Arguments

  • ...: Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::predict_expected_survival_time.glmnetcv_survival_learner — Function
predict_expected_survival_time(obj, X, fit_index = NULL, ...)

Return the expected survival time estimate made by a glmnetcv_survival_learner for each point in the features.

Julia Equivalent: IAI.predict_expected_survival_time

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::predict_hazard.glmnetcv_survival_learner — Function
predict_hazard(obj, X, fit_index = NULL, ...)

Return the fitted hazard coefficient estimate made by a glmnetcv_survival_learner for each point in the features.

A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

Julia Equivalent: IAI.predict_hazard

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 3.0 or higher.

iai::predict.glmnetcv_learner — Function
predict(obj, X, fit_index = NULL, ...)

Return the predictions made by a GLMNet learner for each point in the features

Julia Equivalent: IAI.predict

Arguments

  • obj: The learner or grid to use for prediction.
  • X: The features of the data.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Refer to the Julia documentation for available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::score.glmnetcv_learner — Function
score(obj, X, ...)

Calculate the score for a GLMNet learner on the given data

Julia Equivalent: IAI.score

Arguments

  • obj: The learner or grid to evaluate.
  • X: The features of the data.
  • ...: Other parameters, including zero or more target vectors as required by the problem type. fit_index can be used to specify the index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. Refer to the Julia documentation for other available parameters.
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::get_prediction_constant.glmnetcv_learner — Function
get_prediction_constant(obj, fit_index = NULL, ...)

Return the constant term in the prediction in a trained GLMNet learner

Julia Equivalent: IAI.get_prediction_constant

Arguments

  • obj: The learner to query.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::get_prediction_weights.glmnetcv_learner — Function
get_prediction_weights(obj, fit_index = NULL, ...)

Return the weights for numeric and categoric features used for prediction in a trained GLMNet learner

Julia Equivalent: IAI.get_prediction_weights

Arguments

  • obj: The learner to query.
  • fit_index: The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.1 or higher.

iai::get_num_fits.glmnetcv_learner — Function
get_num_fits(obj, ...)

Return the number of fits along the path in a trained GLMNet learner

Julia Equivalent: IAI.get_num_fits

Arguments

  • obj: The GLMNet learner to query.
  • ...: Additional arguments (unused)
IAI Compatibility

Requires IAI version 2.1 or higher.