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(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.

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.

cleanup_installation()

Remove all traces of automatic Julia/IAI installation

Removes files created by install_julia and install_system_image

get_machine_id()

Return the machine ID for the current computer.

This ID ties the IAI license file to your machine.

acquire_license(...)

Acquire an IAI license for the current session.

Julia Equivalent: IAI.acquire_license

Arguments

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

release_license()

Release any IAI license held by the current session.

Julia Equivalent: IAI.release_license

load_graphviz()

Loads the Julia Graphviz library to permit certain visualizations.

The library will be installed if not already present.

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.

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.

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.

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.

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.

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.

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.

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(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.

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.

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)

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 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.

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

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.

plot(x, ...)

Plot an ROC curve

Arguments

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

ggplot2::autoplot(object, ...)

Construct a ggplot2::ggplot object plotting the ROC curve

Arguments

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

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.

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.

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)

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)

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)

get_survival_curve_data(curve)

Extract the underlying data from a survival curve (as returned by predict.survival_learner 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_expected_survival_time(obj, ...)

Return the expected survival time estimate made by a survival curve (as returned by predict.survival_learner or get_survival_curve)

Julia Equivalent: IAI.predict_expected_survival_time

Arguments

• obj: The survival curve to use for prediction.
• ...: Additional arguments (unused)

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)

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)

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.

predict_treatment_outcome_standard_error(lnr, X)

Return the standard error for 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_standard_error

Arguments

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

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.

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.

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.

predict(obj, X, ...)

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

Julia Equivalent: IAI.predict and 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.

score(obj, X, ...)

Calculate the score for a multi-task model on the given data

Julia Equivalent: IAI.score and 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.

predict_proba(obj, X, ...)

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

Julia Equivalent: IAI.predict_proba and IAI.predict_proba

Arguments

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

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

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

Julia Equivalent: IAI.ROCCurve and 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.

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.

show_in_browser(obj, ...)

Show interactive visualization of an object in the default browser

Julia Equivalent: IAI.show_in_browser

Arguments

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

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

multi_questionnaire(obj, ...)

Construct an interactive questionnaire from multiple specified learners

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 learners from the results of a grid search

Julia Equivalent: IAI.MultiQuestionnaire

Arguments

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

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(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.

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.

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.

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(obj, node_index, ...)

Return the predicted label at a node of a tree

Julia Equivalent: IAI.get_classification_label

Arguments

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

get_classification_proba(obj, node_index, ...)

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

Julia Equivalent: IAI.get_classification_proba

Arguments

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

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.

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.

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(obj, node_index, ...)

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

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.

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_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.

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.

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_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.

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.

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.

get_policy_treatment_outcome_standard_error(lnr, node_index, ...)

Return the standard error for the quality of the treatments at a node of a tree

Julia Equivalent: IAI.get_policy_treatment_outcome_standard_error

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_label(obj, node_index, ...)

Return the predicted label at a node of a multi-task tree

Julia Equivalent: IAI.get_classification_label and IAI.get_classification_label

Arguments

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

get_classification_proba(obj, node_index, ...)

Return the predicted probabilities of class membership at a node of a multi-task tree

Julia Equivalent: IAI.get_classification_proba and IAI.get_classification_proba

Arguments

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

get_regression_constant(obj, node_index, ...)

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

Julia Equivalent: IAI.get_regression_constant and 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.

get_regression_weights(obj, node_index, ...)

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

Julia Equivalent: IAI.get_regression_weights and 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.

get_regression_constant(obj, node_index, ...)

Return the constant term in the linear regression prediction at a node of a multi-task regression tree

Julia Equivalent: IAI.get_regression_constant and 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.

get_regression_weights(obj, node_index, ...)

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

Julia Equivalent: IAI.get_regression_weights and 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.

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.

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.

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.

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.

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.

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.

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.

questionnaire(obj, ...)

Specify an interactive questionnaire of a tree learner

Julia Equivalent: IAI.Questionnaire

Arguments

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

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.

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.

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)

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

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: IAI.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: IAI.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: IAI.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: IAI.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: IAI.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(object, ...)

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

Arguments

• object: 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: IAI.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: 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

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

plot(x, ...)

Plot a similarity comparison

Arguments

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

ggplot2::autoplot(object, ...)

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

Arguments

• object: 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.

optimal_tree_multi_classifier(...)

Learner for training multi-task Optimal Classification Trees

Julia Equivalent: IAI.OptimalTreeMultiClassifier

Arguments

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

optimal_tree_multi_regressor(...)

Learner for training multi-task Optimal Regression Trees

Julia Equivalent: IAI.OptimalTreeMultiRegressor

Arguments

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

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

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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)

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)

questionnaire(obj, ...)

Specify an interactive questionnaire of an Optimal Feature Selection learner

Julia Equivalent: IAI.Questionnaire

Arguments

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

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.

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.

plot(x, ...)

Plot a grid search results for Optimal Feature Selection learners

Arguments

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

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

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

Arguments

• object: 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)

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)

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_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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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_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.

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)

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.

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.

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.

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)

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)

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.

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.

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)

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)

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)