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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

335

Version

1.1.0

License

MIT + file LICENSE

Maintainer

Jack Dunn

Last Published

September 13th, 2019

Functions in iai (1.1.0)

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

get_grid_results

Return a summary of the results from the grid search

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

get_best_params

Return the best parameter combination from a grid

imputation_learner

Generic learner for imputing missing values

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

get_prediction_constant

Return the constant term in the prediction in the trained learner

Impute missing values using either a specified method or through validation

get_classification_label

Return the predicted label at a node of a tree

get_lower_child

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

get_regression_weights

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

multi_questionnaire

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

Return the number of nodes in a trained learner

Controls grid search over parameter combinations

Initialize Julia and the IAI package.

get_survival_curve_data

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

get_rich_output_params

Return the current global rich output parameter settings

get_split_categories

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

get_upper_child

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

is_hyperplane_split

Check if a node of a tree applies a hyperplane split

is_ordinal_split

Check if a node of a tree applies a ordinal split

mean_imputation_learner

Learner for conducting mean imputation

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

get_classification_proba

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

opt_tree_imputation_learner

Learner for conducting optimal tree-based imputation

multi_tree_plot

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

optimal_feature_selection_classifier

Learner for conducting Optimal Feature Selection on classification problems

get_split_feature

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

Return the value of all parameters on a learner

show_questionnaire

Show an interactive questionnaire based on a learner in default browser

single_knn_imputation_learner

Learner for conducting heuristic k-NN imputation

Calculate the score for a model on the given data

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

Get the depth of a node of a tree

is_parallel_split

Check if a node of a tree applies a parallel split

opt_knn_imputation_learner

Learner for conducting optimal k-NN imputation

missing_goes_lower

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

optimal_feature_selection_regressor

Learner for conducting Optimal Feature Selection on regression problems

get_split_threshold

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

optimal_tree_classifier

Learner for training Optimal Classification Trees

reset_display_label

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

Read in a learner or grid saved in JSON format

Check if a node of a tree is a leaf

Set all supplied parameters on a learner

opt_svm_imputation_learner

Learner for conducting optimal SVM imputation

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

Specify an interactive tree visualization of a tree learner

set_rich_output_param

Sets a global rich output parameter

variable_importance

Generate a ranking of the variables in the learner according to their importance when training the trees

get_prediction_weights

Return the weights for numeric and categoric features used for 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

predict_outcomes

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

write_questionnaire

Output a learner as an interactive questionnaire in HTML format

Split the data into training and test datasets

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

is_categoric_split

Check if a node of a tree applies a categoric split

is_mixed_parallel_split

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

Impute missing values using cross validation

is_mixed_ordinal_split

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

optimal_tree_prescription_maximizer

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

optimal_tree_regressor

Learner for training Optimal Regression Trees

get_survival_curve

Return the survival curve at a node of a tree

show_in_browser

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

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

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

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.

optimal_tree_survivor

Learner for training Optimal Survival Trees

Output a learner or grid in JSON format

Output a learner as a PNG image

optimal_tree_prescription_minimizer

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

Specify an interactive questionnaire of a tree learner

Set the random seed in Julia

set_display_label

Show the probability of a specified label when visualizing a learner

rand_imputation_learner

Learner for conducting random imputation

Output a learner as an interactive browser visualization in HTML format

Output a learner in .dot format

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

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

fit_transform_cv

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

Fits a model to the training data

Convert a vector of values to IAI mixed data format

Return an unfitted copy of a learner with the same parameters

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

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.

delete_rich_output_param

Delete a global rich output parameter