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iai: Interpretable AI R Interface

iai is a package providing an interface to the algorithms of Interpretable AI from the R programming language, including:

Optimal Trees for classification, regression, prescription and survival analysis
Optimal Imputation for missing data imputation and outlier detection
Optimal Feature Selection for exact sparse regression

Installation and Usage

Please refer to the official Interpretable AI documentation for information on setting up and using the package.

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Version

Install

install.packages('iai')

Monthly Downloads

399

Version

1.3.0

License

MIT + file LICENSE

Maintainer

Jack Dunn

Last Published

August 5th, 2020

Functions in iai (1.3.0)

Fit a reward estimation model on features, treatments and outcomes and return predicted counterfactual rewards for each observation.

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.

Impute missing values using cross validation

delete_rich_output_param

Delete a global rich output parameter

multi_tree_plot.grid_search

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

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

Get the depth of a node of a tree

Return the value of all parameters on a learner

get_grid_results

Return a summary of the results from the grid search

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

Fits a model to the training data

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

fit_transform_cv

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

optimal_tree_policy_minimizer

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

Return the number of nodes in a trained learner

get_classification_label

Return the predicted label at a node of a tree

get_upper_child

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

get_rich_output_params

Return the current global rich output parameter settings

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

imputation_learner

Generic learner for imputing missing values

Impute missing values using either a specified method or through validation

predict_expected_survival_time

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

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

multi_tree_plot.default

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

optimal_feature_selection_regressor

Learner for conducting Optimal Feature Selection on regression problems

Specify an interactive tree visualization of a tree learner

is_categoric_split

Check if a node of a tree applies a categoric split

optimal_tree_classifier

Learner for training Optimal Classification Trees

get_survival_curve

Return the survival curve at a node of a tree

get_split_feature

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

get_policy_treatment_rank

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

get_prediction_constant

Return the constant term in the prediction in the trained learner

get_split_weights

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

Convert a vector of values to IAI mixed data format

get_best_params

Return the best parameter combination from a grid

get_prediction_weights

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

optimal_tree_regressor

Learner for training Optimal Regression Trees

is_mixed_ordinal_split

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

is_mixed_parallel_split

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

roc_curve.learner

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

get_classification_proba

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

multi_questionnaire

Generic function for constructing an interactive questionnaire using multiple tree learners

get_survival_curve_data

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

missing_goes_lower

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

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

mean_imputation_learner

Learner for conducting mean imputation

Return an unfitted copy of a learner with the same parameters

Initialize Julia and the IAI package.

get_lower_child

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

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

reset_display_label

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

optimal_tree_policy_maximizer

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

get_split_categories

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

Controls grid search over parameter combinations

optimal_feature_selection_classifier

Learner for conducting Optimal Feature Selection on classification problems

opt_tree_imputation_learner

Learner for conducting optimal tree-based imputation

optimal_tree_prescription_maximizer

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

optimal_tree_prescription_minimizer

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

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

Specify an interactive questionnaire of a tree learner

variable_importance

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.

reward_estimator

Learner for conducting Reward Estimation

show_questionnaire

Show an interactive questionnaire based on a learner in default browser

write_questionnaire

Output a learner as an interactive questionnaire in HTML format

Output a learner in .dot format

predict_outcomes

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

multi_questionnaire.default

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

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

get_num_samples

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

get_regression_constant

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

Output a learner or grid in JSON format

get_regression_weights

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

Calculate the score for a model on the given data

optimal_tree_survivor

Learner for training Optimal Survival Trees

set_rich_output_param

Sets a global rich output parameter

Split the data into training and test datasets

get_split_threshold

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

Set all supplied parameters on a learner

is_ordinal_split

Check if a node of a tree applies a ordinal split

Impute missing values in a dataframe using a fitted imputation model

get_prescription_treatment_rank

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

Generic function for constructing an ROC curve

is_hyperplane_split

Check if a node of a tree applies a hyperplane split

set_display_label

Show the probability of a specified label when visualizing a learner

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

Check if a node of a tree is a leaf

single_knn_imputation_learner

Learner for conducting heuristic k-NN imputation

Set the random seed in Julia

is_parallel_split

Check if a node of a tree applies a parallel split

multi_tree_plot

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

multi_questionnaire.grid_search

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

show_in_browser

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

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.

opt_svm_imputation_learner

Learner for conducting optimal SVM imputation

opt_knn_imputation_learner

Learner for conducting optimal k-NN imputation

roc_curve.default

Construct an ROC curve from predicted probabilities and true labels

optimal_tree_survival_learner

Learner for training Optimal Survival Trees

rand_imputation_learner

Learner for conducting random imputation

Output a learner as a PNG image

Output a learner as an interactive browser visualization in HTML format

Read in a learner or grid saved in JSON format