API Reference

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

• ...: All parameters are passed through to JuliaCall::julia_setup

install_julia(prefix = julia_default_install_dir())

Download and install Julia automatically.

Arguments

• prefix: The directory where Julia will be installed. Defaults to a location determined by rappdirs::user_data_dir.

install_system_image(
  version = "latest",
  replace_default = F,
  prefix = sysimage_default_install_dir()
)

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.

get_machine_id()

Return the machine ID for the current computer.

This ID ties the IAI license file to your machine.

set_julia_seed(seed)

Set the random seed in Julia

Julia Equivalent: Random.seed!

Arguments

• seed: The seed to set

add_julia_processes(...)

Add additional Julia worker processes to parallelize workloads

Julia Equivalent: Distributed.addprocs!

For more information, refer to the documentation on parallelization

Arguments

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

get_rich_output_params()

Return the current global rich output parameter settings

Julia Equivalent: IAI.get_rich_output_params

set_rich_output_param(key, value)

Sets a global rich output parameter

Julia Equivalent: IAI.set_rich_output_param!

Arguments

• key: The parameter to set.
• value: The value to set

delete_rich_output_param(key)

Delete a global rich output parameter

Julia Equivalent: IAI.delete_rich_output_param!

Arguments

• key: The parameter to delete.

show_in_browser(obj, ...)

Show interactive visualization of an object (such as a learner or curve) in the default browser

Julia Equivalent: IAI.show_in_browser

Arguments

• obj: The object to visualize.
• ...: Refer to the Julia documentation for available parameters.

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

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.

fit(lnr, X, ...)

Fits a model to the training data

Julia Equivalent: IAI.fit!

Arguments

• lnr: 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.

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.

get_params(lnr)

Return the value of all parameters on a learner

Julia Equivalent: IAI.get_params

Arguments

• lnr: The learner to query.

clone(lnr)

Return an unfitted copy of a learner with the same parameters

Julia Equivalent: IAI.clone

Arguments

• lnr: The learner to copy.

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.

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.

predict(lnr, X, ...)

Return the predictions made by the model for each point in the features

Julia Equivalent: IAI.predict

Arguments

• lnr: The learner or grid to use for prediction.
• X: The features of the data.
• ...: Refer to the Julia documentation for available parameters.

score(lnr, X, ...)

Calculate the score for a model on the given data

Julia Equivalent: IAI.score

Arguments

• lnr: 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.

variable_importance(lnr)

Generate a ranking of the variables in the learner according to their importance during training. The results are normalized so that they sum to one.

Julia Equivalent: IAI.variable_importance

Arguments

• lnr: The learner to query.

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.

predict_proba(lnr, X)

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

Julia Equivalent: IAI.predict_proba

Arguments

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

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

roc_curve(obj, X, y, ...)

Construct an ROC curve using a trained model 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.
• ...: Additional arguments (unused)

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)

get_roc_curve_data(curve)

Extract the underlying data from an ROC curve (as returned by roc_curve)

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.

plot(x, ...)

Plot an ROC curve

Arguments

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

ggplot2::autoplot(x, ...)

Construct a ggplot2::ggplot object plotting the ROC curve

Arguments

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

predict_hazard(lnr, X)

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

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

Julia Equivalent: IAI.predict_hazard

Arguments

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

predict_expected_survival_time(lnr, X)

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

Julia Equivalent: IAI.predict_expected_survival_time

Arguments

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

get_survival_curve_data(curve)

Extract the underlying data from a survival curve (as returned by predict or get_survival_curve)

The data is returned as a list with two keys: times containing the time for each breakpoint on the curve, and coefs containing the probability for each breakpoint on the curve.

Julia Equivalent: IAI.get_survival_curve_data

Arguments

• curve: The curve to query.

predict_outcomes(lnr, X, ...)

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

Julia Equivalent: IAI.predict_outcomes (for prescription or policy learners as appropriate)

Arguments

• lnr: The learner or grid to use for prediction.
• X: The features of the data.
• ...: For policy learners only, the reward matrix.

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.

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.

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.

fit_transform(lnr, X, ...)

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

Similar to calling fit 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.

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.

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.

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.

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.

get_best_params(grid)

Return the best parameter combination from a grid

Julia Equivalent: IAI.get_best_params

Arguments

• grid: The grid search to query.

get_grid_result_summary(grid)

Return a summary of the results from the grid search

Julia Equivalent: IAI.get_grid_result_summary

Arguments

• grid: The grid search to query.

get_grid_result_details(grid)

Return a vector of lists detailing the results of the grid search

Julia Equivalent: IAI.get_grid_result_details

Arguments

• grid: The grid search to query.

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.

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.

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.

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.

get_num_nodes(lnr)

Return the number of nodes in a trained learner

Julia Equivalent: IAI.get_num_nodes

Arguments

• lnr: The learner to query.

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.

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.

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.

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.

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.

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.

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.

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.

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.

is_mixed_parallel_split(lnr, node_index)

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

Julia Equivalent: IAI.is_mixed_parallel_split

Arguments

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

get_regression_constant(lnr, node_index, ...)

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

Julia Equivalent: IAI.get_regression_constant (for regression or prescription tree learners as appropriate)

Arguments

• lnr: The learner to query.
• node_index: The node in the tree to query.
• ...: If a prescription problem, the treatment to query.

get_regression_weights(lnr, node_index, ...)

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

Julia Equivalent: IAI.get_regression_weights (for regression or prescription tree learners as appropriate)

Arguments

• lnr: The learner to query.
• node_index: The node in the tree to query.
• ...: If a prescription problem, the treatment to query.

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.

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.

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.

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.

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.

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.

write_png(filename, lnr, ...)

Output a learner as a PNG image

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.

write_pdf(filename, lnr, ...)

Output a learner as a PDF image

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.

write_svg(filename, lnr, ...)

Output a learner as a SVG image

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.

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.

write_html(filename, lnr, ...)

Output a learner as an interactive browser visualization in HTML format

Julia Equivalent: IAI.write_html

Arguments

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

write_questionnaire(filename, lnr, ...)

Output a learner as an interactive questionnaire in HTML format

Julia Equivalent: IAI.write_questionnaire

Arguments

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

show_questionnaire(lnr, ...)

Show an interactive questionnaire based on a learner in default browser

Julia Equivalent: IAI.show_questionnaire

Arguments

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

tree_plot(lnr, ...)

Specify an interactive tree visualization of a tree learner

Julia Equivalent: IAI.TreePlot

Arguments

• lnr: The learner to visualize.
• ...: Refer to the Julia documentation on advanced tree visualization for available parameters.

questionnaire(lnr, ...)

Specify an interactive questionnaire of a tree learner

Julia Equivalent: IAI.Questionnaire

Arguments

• lnr: The learner to visualize.
• ...: Refer to the Julia documentation for available parameters.

multi_tree_plot(obj, ...)

Generic function for constructing an interactive tree visualization of multiple tree learners

Julia Equivalent: IAI.MultiTreePlot

Arguments

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

multi_tree_plot(obj, ...)

Construct an interactive tree visualization of multiple tree learners as specified by questions

Refer to the documentation on advanced tree visualization for more information.

Julia Equivalent: IAI.MultiTreePlot

Arguments

• obj: The questions to visualize. Refer to the Julia documentation on multi-learner visualizations for more information.
• ...: Additional arguments (unused)

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)

multi_questionnaire(obj, ...)

Generic function for constructing an interactive questionnaire using multiple tree learners

Julia Equivalent: IAI.MultiQuestionnaire

Arguments

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

multi_questionnaire(obj, ...)

Construct an interactive questionnaire using multiple tree learners as specified by questions

Refer to the documentation on advanced tree visualization for more information.

Julia Equivalent: IAI.MultiQuestionnaire

Arguments

• obj: The questions to visualize. Refer to the Julia documentation on multi-learner visualizations for more information.
• ...: Additional arguments (unused)

multi_questionnaire(obj, ...)

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

Julia Equivalent: IAI.MultiQuestionnaire

Arguments

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

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: get_tree

Arguments

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

stability_analysis(lnr, ...)

Conduct a stability analysis of the trees in a tree learner

Refer to the documentation on tree stability for more information.

Julia Equivalent: StabilityAnalysis

Arguments

• lnr: The original learner
• ...: Additional arguments (refer to Julia documentation)

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: get_stability_results

Arguments

• stability: The stability analysis to query

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: get_cluster_distances

Arguments

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

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: get_cluster_assignments

Arguments

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

get_cluster_details(stability, num_trees)

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

Julia Equivalent: get_cluster_details

Arguments

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

plot(x, ...)

Plot a stability analysis

Arguments

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

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)

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: variable_importance_similarity

Arguments

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

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: 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

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: get_train_errors

Arguments

• similarity: The similarity comparison

plot(x, ...)

Plot a similarity comparison

Arguments

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

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)

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.

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.

optimal_tree_survival_learner(...)

Learner for training Optimal Survival Trees

Julia Equivalent: IAI.OptimalTreeSurvivalLearner

Arguments

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

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.

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.

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.

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.

impute(X, ...)

Impute missing values using either a specified method or through validation

Julia Equivalent: IAI.impute

Arguments

• X: The dataframe in which to impute missing values.
• ...: Refer to the Julia documentation for available parameters.

impute_cv(X, ...)

Impute missing values using cross validation

Julia Equivalent: IAI.impute_cv

Arguments

• X: The dataframe in which to impute missing values.
• ...: Refer to the Julia documentation for available parameters.

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.

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.

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.

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.

single_knn_imputation_learner(...)

Learner for conducting heuristic k-NN imputation

Julia Equivalent: IAI.SingleKNNImputationLearner

Arguments

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

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.

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.

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.

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.

get_prediction_constant(lnr, ...)

Return the constant term in the prediction in the trained learner

Julia Equivalent: IAI.get_prediction_constant

Arguments

• lnr: The learner to query.
• ...: If a GLMNet learner, the index of the fit in the path to query, defaulting to the best fit if not supplied.

get_prediction_weights(lnr, ...)

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

Julia Equivalent: IAI.get_prediction_weights

Arguments

• lnr: The learner to query.
• ...: If a GLMNet learner, the index of the fit in the path to query, defaulting to the best fit if not supplied.

plot(x, ...)

Plot a grid search results for Optimal Feature Selection learners

Arguments

• x: The grid search to plot
• ...: Additional arguments (passed to ggplot2::autoplot.grid_search)

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

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

Arguments

• x: The grid search to plot
• type: The type of plot to construct (either "validation" or "importance", for more information refer to the Julia documentation for plotting grid search results )
• ...: Additional arguments (unused)

fit_predict(lnr, X, treatments, ...)

Fit a reward estimation model on features, treatments and outcomes and return predicted counterfactual rewards for each observation, as well as the score of the internal estimators.

For categorical treatments, returns the estimated reward under each treatment observed in the data. For numeric treatments, returns the estimated reward under each treatment candidate.

Julia Equivalent: IAI.fit_predict!

Arguments

• lnr: 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.

categorical_classification_reward_estimator(...)

Learner for conducting reward estimation with categorical treatments and classification outcomes

Julia Equivalent: IAI.CategoricalClassificationRewardEstimator

Arguments

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

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.

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.

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.

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.

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.

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.

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

tune_reward_kernel_bandwidth(lnr, input_bandwidths)

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
• input_bandwidths: The starting bandwidth values to use when tuning

set_reward_kernel_bandwidth(lnr, 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.

Julia Equivalent: IAI.set_reward_kernel_bandwidth!

Arguments

• lnr: The learner to modify
• bandwidth: The new reward kernel bandwidth

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

convert_treatments_to_numeric(treatments)

Convert treatments from symbol/string format into numeric values.

Julia Equivalent: IAI.convert_treatments_to_numeric

Arguments

• treatments: The treatments to convert

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.

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.

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.

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.

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.

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.

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.

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.

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.

get_num_fits(lnr)

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

Julia Equivalent: IAI.get_num_fits

Arguments

• lnr: The GLMNet learner to query.