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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

436

Version

1.4.0

License

MIT + file LICENSE

Maintainer

Jack Dunn

Last Published

December 9th, 2020

Functions in iai (1.4.0)

delete_rich_output_param

Delete a global rich output parameter

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

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.

convert_treatments_to_numeric

Convert `treatments` from symbol/string format into numeric values.

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`

categorical_reward_estimator

Learner for conducting reward estimation with categorical treatments

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

equal_propensity_estimator

Learner that estimates equal propensity for all treatments.

Return an unfitted copy of a learner with the same parameters

Get the depth of a node of a tree

fit_transform_cv

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

get_num_samples

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

Convert a vector of values to IAI mixed data format

get_classification_label

Return the predicted label at a node of a tree

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 outcome estimator.

get_policy_treatment_rank

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

get_prediction_weights

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

get_best_params

Return the best parameter combination from a grid

get_prediction_constant

Return the constant term in the prediction in the trained learner

get_prescription_treatment_rank

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

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

Fits a model to the training data

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

get_grid_results

Return a summary of the results from the grid search

Return the value of all parameters on a learner

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

get_policy_treatment_outcome

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

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

Return the number of nodes in a trained learner

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

get_regression_constant

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

is_ordinal_split

Check if a node of a tree applies a ordinal split

is_parallel_split

Check if a node of a tree applies a parallel split

glmnetcv_regressor

Learner for training GLMNet models for regression problems

Controls grid search over parameter combinations

get_split_categories

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

get_roc_curve_data

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

get_classification_proba

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

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

Initialize Julia and the IAI package.

get_regression_weights

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

get_survival_curve

Return the survival curve at a node of a tree

imputation_learner

Generic learner for imputing missing values

Impute missing values using either a specified method or through validation

mean_imputation_learner

Learner for conducting mean imputation

multi_tree_plot.default

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

multi_tree_plot.grid_search

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

missing_goes_lower

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

optimal_tree_classifier

Learner for training Optimal Classification Trees

Check if a node of a tree is a leaf

is_hyperplane_split

Check if a node of a tree applies a hyperplane split

multi_questionnaire

Generic function for constructing an interactive questionnaire using multiple tree learners

optimal_tree_policy_maximizer

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

Specify an interactive questionnaire of a tree learner

get_split_threshold

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

get_split_weights

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

optimal_feature_selection_classifier

Learner for conducting Optimal Feature Selection on classification problems

optimal_tree_policy_minimizer

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

multi_questionnaire.default

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

get_split_feature

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

get_survival_expected_time

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

optimal_feature_selection_regressor

Learner for conducting Optimal Feature Selection on regression problems

Generic function for constructing an ROC curve

reward_estimator

Learner for conducting reward estimation with categorical treatments

Set the random seed in Julia

Calculate the score for a model on the given data

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

optimal_tree_prescription_maximizer

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

predict_outcomes

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

get_survival_hazard

Return the predicted hazard ratio at a node of a tree

is_mixed_ordinal_split

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

set_display_label

Show the probability of a specified label when visualizing a learner

numeric_reward_estimator

Learner for conducting reward estimation with numeric treatments

rand_imputation_learner

Learner for conducting random imputation

optimal_tree_survival_learner

Learner for training Optimal Survival Trees

opt_knn_imputation_learner

Learner for conducting optimal k-NN imputation

is_mixed_parallel_split

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

multi_tree_plot

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

predict_expected_survival_time

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

multi_questionnaire.grid_search

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

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

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

roc_curve.default

Construct an ROC curve from predicted probabilities and true labels

optimal_tree_survivor

Learner for training Optimal Survival Trees

Set all supplied parameters on a learner

predict_treatment_rank

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

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

roc_curve.learner

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

show_in_browser

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

show_questionnaire

Show an interactive questionnaire based on a learner in default browser

single_knn_imputation_learner

Learner for conducting heuristic k-NN imputation

Impute missing values using cross validation

is_categoric_split

Check if a node of a tree applies a categoric split

get_survival_curve_data

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

Output a learner or grid in JSON format

Split the data into training and test datasets

optimal_tree_regressor

Learner for training Optimal Regression Trees

opt_tree_imputation_learner

Learner for conducting optimal tree-based imputation

opt_svm_imputation_learner

Learner for conducting optimal SVM imputation

optimal_tree_prescription_minimizer

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

Output a learner as a PDF image

write_questionnaire

Output a learner as an interactive questionnaire in HTML format

Output a learner as a PNG image

xgboost_regressor

Learner for training XGBoost models for regression problems

Specify an interactive tree visualization of a tree learner

Impute missing values in a dataframe using a fitted imputation model

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.

Write the internal booster saved in the learner to file

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

random_forest_classifier

Learner for training random forests for classification problems

predict_treatment_outcome

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

Output a learner in .dot format

set_rich_output_param

Sets a global rich output parameter

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.

Output a learner as a SVG image

random_forest_regressor

Learner for training random forests for regression problems

xgboost_classifier

Learner for training XGBoost models for classification problems

Output a learner as an interactive browser visualization in HTML format