API Reference

install(**kwargs)

Install Julia packages required for interpretableai.iai.

This function must be called once after the package is installed to configure the connection between Python and Julia.

Parameters

Refer to the installation instructions for information on any additional parameters that may be required.

install_julia(**kwargs)

Download and install Julia automatically.

Parameters

• version: (string, optional) The version of Julia to install (e.g. '1.6.3'). Defaults to 'latest', which will install the most recent stable release.
• prefix: (string, optional) The directory where Julia will be installed. Defaults to a location determined by appdirs.user_data_dir.

install_system_image(**kwargs)

Download and install the IAI system image automatically.

Parameters

• version: (string, optional) 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: (bool) Whether to replace the default Julia system image with the downloaded IAI system image. Defaults to False.
• prefix: (string, optional) The directory where Julia will be installed. Defaults to a location determined by appdirs.user_data_dir.
• accept_license: (bool) Set to True to confirm that you agree to the End User License Agreement and skip the interactive confirmation dialog.

cleanup_installation()

Remove all files created by install_julia and install_system_image.

iai.get_machine_id()

Return the machine ID for the current computer.

iai.load_graphviz()

Loads the Julia Graphviz library to permit certain visualizations.

The library will be installed if not already present.

iai.set_julia_seed(seed)

Set the random seed in Julia to seed.

Julia Equivalent: Random.seed!

iai.add_julia_processes(3)

Add additional Julia worker processes to parallelize workloads.

Julia Equivalent: Distributed.addprocs

For more information, refer to the documentation on parallelization

iai.read_json(filename)

Read in a learner or grid saved in JSON format from filename.

Julia Equivalent: IAI.read_json

iai.get_rich_output_params()

Return the current global rich output parameter settings.

Julia Equivalent: IAI.get_rich_output_params

iai.set_rich_output_param(key, value)

Sets the global rich output parameter key to value.

Julia Equivalent: IAI.set_rich_output_param!

iai.delete_rich_output_param(key)

Delete the global rich output parameter key.

Julia Equivalent: IAI.delete_rich_output_param!

iai.MixedData(values, ordinal_levels=None)

Represents a mixed data feature

MixedData features can represent a feature with either numeric/categoric or ordinal/categoric values.

Julia Equivalent: IAI.make_mixed_data

Parameters

• values: (list-like) The values of the mixed feature. In a numeric/categoric mix, all numeric elements will be treated as numeric and all remaining elements as categoric. In an ordinal/categoric mix, all elements belonging to the ordinal_levels will be treated as ordinal, and all remaining elements as categoric.
• ordinal_levels: (Index-like, optional) If not supplied, the feature is treated as a numeric/categoric mix. If supplied, these are the ordered levels of the ordinal values in the ordinal/categoric mix.

iai.split_data(task, X, *y, **kwargs)

Split the data (X and y) into a tuple of training and testing data, (X_train, y_train), (X_test, y_test), for a problem of type task.

Julia Equivalent: IAI.split_data

iai.score(task, predictions, *truths, **kwargs)

Calculates the score attained by predictions against the true target truths for the problem type indicated by task.

Julia Equivalent: IAI.score

Abstract type encompassing all learners.

Julia Equivalent: IAI.Learner

lnr.fit(X, *y, sample_weight=None)

Fit a model using the parameters in learner and the data X and y.

Julia Equivalent: IAI.fit!

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

lnr.set_params(**kwargs)

Set all supplied parameters on learner.

Julia Equivalent: IAI.set_params!

lnr.get_params()

Return the value of all learner parameters.

Julia Equivalent: IAI.get_params

lnr.clone()

Return an unfitted copy of the learner with the same parameters.

Julia Equivalent: IAI.clone

lnr.write_json(filename, **kwargs)

Write learner or grid to filename in JSON format.

Julia Equivalent: IAI.write_json

lnr.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.

Julia Equivalent: IAI.variable_importance

Abstract type encompassing all learners for supervised tasks.

Julia Equivalent: IAI.SupervisedLearner

lnr.predict(X)

Return the predictions made by the learner for each point in the features X.

Julia Equivalent: IAI.predict

lnr.score(X, *y, **kwargs)

Calculates the score for the learner on data X and y.

Julia Equivalent: IAI.score

Abstract type encompassing all learners for unsupervised tasks.

Julia Equivalent: IAI.UnsupervisedLearner

Abstract type encompassing all learners for classification tasks.

Julia Equivalent: IAI.ClassificationLearner

lnr.predict_proba(X)

Return the probabilities of class membership predicted by the learner for each point in the features X.

Julia Equivalent: IAI.predict_proba

lnr.ROCCurve(X, y)

Construct an ROCCurve using the trained learner on the features X and labels y

Julia Equivalent: IAI.ROCCurve

Abstract type encompassing all learners for regression tasks.

Julia Equivalent: IAI.RegressionLearner

Abstract type encompassing all learners for survival tasks.

Julia Equivalent: IAI.SurvivalLearner

Return the predictions made by the learner for each point in the features X (see predict)..

Julia Equivalent: IAI.predict

lnr.predict(X)

Return the SurvivalCurve predicted by the trained learner for each point in the data.

lnr.predict(X, t=t)

Return the probability that death occurs at or before time t as predicted by the trained learner for each point.

lnr.predict_hazard(X)

Return the fitted hazard coefficient estimate made by the learner for each point in the data X.

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

Julia Equivalent: IAI.predict_hazard

lnr.predict_expected_survival_time(X)

Return the expected survival time estimate made by the learner for each point in the data X.

Julia Equivalent: IAI.predict_expected_survival_time

Abstract type encompassing all learners for prescription tasks.

Julia Equivalent: IAI.PrescriptionLearner

lnr.predict_outcomes(X)

Return the the predicted outcome for each treatment made by the learner for each point in the features X.

Julia Equivalent: IAI.predict_outcomes

Abstract type encompassing all learners for policy tasks.

Julia Equivalent: IAI.PolicyLearner

lnr.predict_outcomes(X, rewards)

Return the outcome from rewards for each point in the features X under the prescriptions made by the learner.

Julia Equivalent: IAI.predict_outcomes

lnr.predict_treatment_rank(X)

Return the treatments in ranked order of effectiveness for each point in the features X as predicted by the learner.

Julia Equivalent: IAI.predict_treatment_rank

lnr.predict_treatment_outcome(X)

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

Julia Equivalent: IAI.predict_treatment_outcome

iai.ImputationLearner(method='opt_knn', **kwargs)

Abstract type containing all imputation learners.

Julia Equivalent: IAI.ImputationLearner

Parameters

Can be used to construct instances of imputation learners using the method keyword argument.

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

lnr.fit(X, **kwargs)

Fit a model using the parameters in learner and the data X(see [fit`](@ref fit(::Learner))).

Additional keyword arguments are available for fitting imputation learners - please refer to the Julia documentation.

Julia Equivalent: IAI.fit!

lnr.fit_transform(X, **kwargs)

Fit the imputation learner using the training data X and impute the missing values in the training data.

Julia Equivalent: IAI.fit_transform!

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

lnr.transform(X)

Impute missing values in X using the fitted imputation model in the learner.

Julia Equivalent: IAI.transform

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

iai.GridSearch(lnr, params)

Controls grid search over parameter combinations in params for lnr.

Julia Equivalent: IAI.GridSearch

grid.fit(X, *y, **kwargs)

Fit a grid with data X and y.

Julia Equivalent: IAI.fit!

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

grid.fit_cv(X, *y, **kwargs)

Fit a grid with data X and y using k-fold cross-validation.

Julia Equivalent: IAI.fit_cv!

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

grid.fit_transform_cv(X, **kwargs)

For imputation learners, fit a grid with features X using k-fold cross-validation and impute missing values in X.

Julia Equivalent: IAI.fit_transform_cv!

Parameters

Refer to the documentation on data preparation for information on how to format and supply the data.

grid.get_learner()

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

Julia Equivalent: IAI.get_learner

grid.get_best_params()

Return the best parameter combination from the grid.

Julia Equivalent: IAI.get_best_params

grid.get_grid_result_summary()

Return a summary of the results from the grid search.

Julia Equivalent: IAI.get_grid_result_summary

Abstract type encompassing objects related to visualization.

Julia Equivalent: IAI.AbstractVisualization

treeplot.write_html(filename, **kwargs)

Write interactive browser visualization to filename as HTML.

Julia Equivalent: IAI.write_html

treeplot.show_in_browser(**kwargs)

Show interactive visualization in default browser.

Julia Equivalent: IAI.show_in_browser

Specifies an interactive questionnaire.

Julia Equivalent: IAI.Questionnaire

Parameters

Refer to the Julia documentation on advanced tree visualization for available parameters.

Specify an interactive questionnaire of multiple learners

iai.MultiQuestionnaire(questions)

Constructs an interactive questionnaire using multiple learners from specified questions. Refer to the documentation on advanced tree visualization for more information.

Julia Equivalent: IAI.MultiQuestionnaire

grid.MultiQuestionnaire()

Construct a MultiQuestionnaire containing the final fitted learner from the trained grid search as well as the learner found for each parameter combination.

Julia Equivalent: IAI.MultiQuestionnaire

Abstract type encompassing all tree-based learners.

Julia Equivalent: IAI.TreeLearner

lnr.apply(X)

Return the leaf index in the learner into which each point in the features X falls.

Julia Equivalent: IAI.apply

lnr.apply_nodes(X)

Return the indices of the points in the features X that fall into each node in the learner.

Julia Equivalent: IAI.apply_nodes

lnr.decision_path(X)

Return a matrix where entry (i, j) is True if the ith point in the features X passes through the jth node in the learner.

Julia Equivalent: IAI.decision_path

lnr.print_path(X)

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

Julia Equivalent: IAI.print_path

Calculate the variable importance for the learner (see variable_importance).

Julia Equivalent: IAI.variable_importance

lnr.variable_importance(**kwargs)

Calculate the variable_importance for the learner.

lnr.variable_importance(X, **kwargs)

Calculate the variable_importance for the learner on new samples X.

lnr.variable_importance(X, *y, **kwargs)

Calculate the variable_importance for the learner on new data X and y.

lnr.get_num_nodes(node_index)

Return the number of nodes in the trained learner.

Julia Equivalent: IAI.get_num_nodes

lnr.get_depth(node_index)

Return the depth of node node_index in the trained learner.

Julia Equivalent: IAI.get_depth

lnr.get_parent(node_index)

Return the index of the parent node of node node_index in the trained learner.

Julia Equivalent: IAI.get_parent

lnr.get_lower_child(node_index)

Return the index of the lower child of node node_index in the trained learner.

Julia Equivalent: IAI.get_lower_child

lnr.get_upper_child(node_index)

Return the index of the upper child of node node_index in the trained learner.

Julia Equivalent: IAI.get_upper_child

lnr.get_num_samples(node_index)

Return the number of training points contained in node node_index in the trained learner.

Julia Equivalent: IAI.get_num_samples

lnr.is_categoric_split(node_index)

Return True if node node_index in the trained learner is a categoric split.

Julia Equivalent: IAI.is_categoric_split

lnr.is_hyperplane_split(node_index)

Return True if node node_index in the trained learner is a hyperplane split.

Julia Equivalent: IAI.is_hyperplane_split

lnr.is_leaf(node_index)

Return True if node node_index in the trained learner is a leaf.

Julia Equivalent: IAI.is_leaf

lnr.is_mixed_ordinal_split(node_index)

Return True if node node_index in the trained learner is a mixed categoric/ordinal split.

Julia Equivalent: IAI.is_mixed_ordinal_split

lnr.is_mixed_parallel_split(node_index)

Return True if node node_index in the trained learner is a mixed categoric/parallel split.

Julia Equivalent: IAI.is_mixed_parallel_split

lnr.is_ordinal_split(node_index)

Return True if node node_index in the trained learner is an ordinal split.

Julia Equivalent: IAI.is_ordinal_split

lnr.is_parallel_split(node_index)

Return True if node node_index in the trained learner is a parallel split.

Julia Equivalent: IAI.is_parallel_split

lnr.get_split_categories(node_index)

Return the categoric/ordinal information used in the split at node node_index in the trained learner.

Julia Equivalent: IAI.get_split_categories

lnr.get_split_feature(node_index)

Return the feature used in the split at node node_index in the trained learner.

Julia Equivalent: IAI.get_split_feature

lnr.get_split_threshold(node_index)

Return the threshold used in the split at node node_index in the trained learner.

Julia Equivalent: IAI.get_split_threshold

lnr.get_split_weights(node_index)

Return the weights for numeric and categoric features used in the hyperplane split at node node_index in the trained learner.

Julia Equivalent: IAI.get_split_weights

lnr.missing_goes_lower(node_index)

Return True if missing values take the lower branch at node node_index in the trained learner.

Julia Equivalent: IAI.missing_goes_lower

lnr.write_png(filename, **kwargs)

Write learner to filename 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

lnr.write_pdf(filename, **kwargs)

Write learner to filename 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

lnr.write_svg(filename, **kwargs)

Write learner to filename as an 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

lnr.write_dot(filename, **kwargs)

Write learner to filename in .dot format.

Julia Equivalent: IAI.write_dot

lnr.write_html(filename, **kwargs)

Write interactive browser visualization of learner to filename as HTML.

Julia Equivalent: IAI.write_html

lnr.show_in_browser(**kwargs)

Show interactive visualization of learner in default browser.

Julia Equivalent: IAI.show_in_browser

lnr.write_questionnaire(filename, **kwargs)

Write interactive questionnaire based on learner to filename as HTML.

Julia Equivalent: IAI.write_questionnaire

lnr.show_questionnaire(**kwargs)

Show interactive questionnaire based on learner in default browser.

Julia Equivalent: IAI.show_questionnaire

Specifies an interactive tree visualization.

Julia Equivalent: IAI.TreePlot

Parameters

Refer to the Julia documentation on advanced tree visualization for available parameters.

iai.TreePlot(lnr, **kwargs)

Construct a TreePlot based on the trained learner.

Julia Equivalent: IAI.TreePlot

Specify an interactive tree visualization of multiple tree learners

iai.MultiTreePlot(questions)

Constructs an interactive tree visualization using multiple tree learners from specified questions. Refer to the documentation on advanced tree visualization for more information.

Julia Equivalent: IAI.MultiTreePlot

grid.MultiTreePlot()

Construct a MultiTreePlot containing the final fitted learner from the trained grid search as well as the learner found for each parameter combination.

Julia Equivalent: IAI.MultiTreePlot

iai.Questionnaire(lnr, **kwargs)

Construct a Questionnaire based on the trained learner.

Julia Equivalent: IAI.Questionnaire

Abstract type encompassing all tree-based learners with classification leaves.

Julia Equivalent: IAI.ClassificationTreeLearner

lnr.get_classification_label(node_index)

Return the predicted label at node node_index in the trained learner.

Julia Equivalent: IAI.get_classification_label

lnr.get_classification_proba(node_index)

Return the predicted probabilities of class membership at node node_index in the trained learner.

Julia Equivalent: IAI.get_classification_proba

lnr.set_threshold(label, threshold, simplify=False)

For a binary classification problem, update the the predicted labels in the leaves of the learner to predict label only if the predicted probability is at least threshold. If simplify is True, the tree will be simplified after all changes have been made.

Julia Equivalent: IAI.set_threshold!

lnr.set_display_label(display_label)

Show the probability of display_label when visualizing learner.

Julia Equivalent: IAI.set_display_label!

lnr.reset_display_label(display_label)

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

Julia Equivalent: IAI.reset_display_label!

Abstract type encompassing all tree-based learners with regression leaves.

Julia Equivalent: IAI.RegressionTreeLearner

lnr.get_regression_constant(node_index)

Return the constant term in the regression prediction at node node_index in the trained learner.

Julia Equivalent: IAI.get_regression_constant

lnr.get_regression_weights(node_index)

Return the weights for each feature in the regression prediction at node node_index in the trained learner.

Julia Equivalent: IAI.get_regression_weights

Abstract type encompassing all tree-based learners with survival leaves.

Julia Equivalent: IAI.SurvivalTreeLearner

lnr.get_survival_curve(node_index)

Return the SurvivalCurve at node node_index in the trained learner.

Julia Equivalent: IAI.get_survival_curve

lnr.get_survival_expected_time(node_index)

Return the predicted expected survival time at node node_index in the trained learner.

Julia Equivalent: IAI.get_survival_expected_time

lnr.get_survival_hazard(node_index)

Return the predicted hazard ratio at node node_index in the trained learner.

Julia Equivalent: IAI.get_survival_hazard

Abstract type encompassing all tree-based learners with prescription leaves.

Julia Equivalent: IAI.PrescriptionTreeLearner

lnr.get_prescription_treatment_rank(node_index)

Return the treatments ordered from most effective to least effective at node node_index in the trained learner.

Julia Equivalent: IAI.get_prescription_treatment_rank

lnr.get_regression_constant(node_index, treatment)

Return the constant in the regression prediction for treatment at node node_index in the trained learner.

Julia Equivalent: IAI.get_regression_constant

lnr.get_regression_weights(node_index, treatment)

Return the weights for each feature in the regression prediction for treatment at node node_index in the trained learner.

Julia Equivalent: IAI.get_regression_weights

Abstract type encompassing all tree-based learners with policy leaves.

Julia Equivalent: IAI.PolicyTreeLearner

lnr.get_policy_treatment_rank(node_index)

Return the treatments ordered from most effective to least effective at node node_index in the trained learner.

Julia Equivalent: IAI.get_policy_treatment_rank

lnr.get_policy_treatment_outcome(node_index)

Return the quality of the treatments at node node_index in the trained learner.

Julia Equivalent: IAI.get_policy_treatment_outcome

Abstract type encompassing all optimal tree learners.

Julia Equivalent: IAI.OptimalTreeLearner

iai.OptimalTreeClassifier(**kwargs)

Learner for training Optimal Classification Trees.

Julia Equivalent: IAI.OptimalTreeClassifier

Parameters

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

iai.OptimalTreeRegressor(**kwargs)

Learner for training Optimal Regression Trees.

Julia Equivalent: IAI.OptimalTreeRegressor

Parameters

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

iai.OptimalTreeSurvivalLearner(**kwargs)

Learner for training Optimal Survival Trees.

Julia Equivalent: IAI.OptimalTreeSurvivalLearner

Parameters

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

iai.OptimalTreePrescriptionMinimizer(**kwargs)

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

Julia Equivalent: IAI.OptimalTreePrescriptionMinimizer

Parameters

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

iai.OptimalTreePrescriptionMaximizer(**kwargs)

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

Julia Equivalent: IAI.OptimalTreePrescriptionMaximizer

Parameters

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

iai.OptimalTreePolicyMinimizer(**kwargs)

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

Julia Equivalent: IAI.OptimalTreePolicyMinimizer

Parameters

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

iai.OptimalTreePolicyMaximizer(**kwargs)

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

Julia Equivalent: IAI.OptimalTreePolicyMaximizer

Parameters

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

iai.impute(X, *args, **kwargs)

Impute the missing values in X using either a specified method or through grid search validation.

Julia Equivalent: IAI.impute

This method was deprecated in interpretableai 2.9.0. This is for consistency with the IAI v3.0.0 Julia release.

Parameters

Refer to the Julia documentation for available parameters.

iai.impute_cv(X, *args, **kwargs)

Impute the missing values in X using cross validation.

Julia Equivalent: IAI.impute_cv

This method was deprecated in interpretableai 2.9.0. This is for consistency with the IAI v3.0.0 Julia release.

Parameters

Refer to the Julia documentation for available parameters.

iai.OptKNNImputationLearner(**kwargs)

Learner for conducting optimal k-NN imputation.

Julia Equivalent: IAI.OptKNNImputationLearner

Parameters

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

iai.OptSVMImputationLearner(**kwargs)

Learner for conducting optimal SVM imputation.

Julia Equivalent: IAI.OptSVMImputationLearner

Parameters

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

iai.OptTreeImputationLearner(**kwargs)

Learner for conducting optimal tree-based imputation.

Julia Equivalent: IAI.OptTreeImputationLearner

Parameters

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

iai.SingleKNNImputationLearner(**kwargs)

Learner for conducting heuristic k-NN imputation.

Julia Equivalent: IAI.SingleKNNImputationLearner

Parameters

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

iai.MeanImputationLearner(**kwargs)

Learner for conducting mean imputation.

Julia Equivalent: IAI.MeanImputationLearner

Parameters

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

iai.RandImputationLearner(**kwargs)

Learner for conducting random imputation.

Julia Equivalent: IAI.RandImputationLearner

Parameters

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

Abstract type encompassing all Optimal Feature Selection learners.

Julia Equivalent: IAI.OptimalFeatureSelectionLearner

iai.OptimalFeatureSelectionClassifier(**kwargs)

Learner for conducting Optimal Feature Selection on classification problems.

Julia Equivalent: IAI.OptimalFeatureSelectionClassifier

Parameters

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

iai.OptimalFeatureSelectionRegressor(**kwargs)

Learner for conducting Optimal Feature Selection on regression problems.

Julia Equivalent: IAI.OptimalFeatureSelectionRegressor

Parameters

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

lnr.fit(X, *y, sample_weight=None, **kwargs)

Fit a model using the parameters in learner and the data X and y (see fit).

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!

Return the prediction made by the learner for each point in the data X (see predict).

Julia Equivalent: IAI.predict

lnr.predict(X)

Return the prediction made by the learner.

lnr.predict(X, fit_index=fit_index)

Return the prediction made by cluster fit_index in the learner.

Calculates the score for the learner on data X and y (see score).

Julia Equivalent: IAI.score

lnr.score(X, *y, **kwargs)

Calculates the score for the learner.

lnr.score(X, *y, **kwargs, fit_index=fit_index)

Calculates the score for cluster fit_index in the learner.

Calculates the variable importance for the learner (see variable_importance).

Julia Equivalent: IAI.variable_importance

lnr.variable_importance()

Return the variable_importance for the learner.

lnr.variable_importance(fit_index=fit_index)

Return the variableimportance for cluster `fitindex` in the learner.

Return the constant term in the prediction in the trained learner.

Julia Equivalent: IAI.get_prediction_constant

lnr.get_prediction_constant()

Return the constant term in the prediction

lnr.get_prediction_constant(fit_index=fit_index)

Return the constant term in the prediction for cluster fit_index

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

Julia Equivalent: IAI.get_prediction_weights

lnr.get_prediction_weights()

Return the weights in the prediction

lnr.get_prediction_weights(fit_index=fit_index)

Return the weights in the prediction for cluster fit_index

iai.Questionnaire(lnr, **kwargs)

Construct a Questionnaire based on the trained learner.

Julia Equivalent: IAI.Questionnaire

lnr.write_questionnaire(filename, **kwargs)

Write interactive questionnaire based on learner to filename as HTML.

Julia Equivalent: IAI.write_questionnaire

lnr.show_questionnaire(**kwargs)

Show interactive questionnaire based on learner in default browser.

Julia Equivalent: IAI.show_questionnaire

Abstract type encompassing all learners for reward estimation.

Julia Equivalent: IAI.RewardEstimationLearner

Fit a reward estimation model and return predicted counterfactual rewards for each observation along with the scores of the internal estimators during training.

Julia Equivalent: IAI.fit_predict!

Abstract type encompassing all learners for reward estimation with categorical treatments.

Julia Equivalent: IAI.CategoricalRewardEstimationLearner

Fit a reward estimation model and return predicted counterfactual rewards for each observation along with the scores of the internal estimators during training.

Julia Equivalent: IAI.fit_predict!

lnr.fit_predict(X, treatments, outcomes)

For problems with classification or regression outcomes, fit reward estimation model on features X, treatments treatments, and outcomes outcomes and predict rewards for each observation.

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

For problems with survival outcomes, fit reward estimation model on features X, treatments treatments, death indicator deaths and event times times and predict rewards for each observation.

Return counterfactual rewards estimated by the learner for each observation in the supplied data.

Julia Equivalent: IAI.predict

lnr.predict(X, treatments, outcomes)

For problems with classification or regression outcomes, predict rewards for each observation in the data given by X, treatments and outcomes. If using the direct method, treatments and outcomes can be omitted.

lnr.predict(X, treatments, deaths, times)

For problems with survival outcomes, predict rewards for each observation in the data given by X, treatments, deaths and times. If using the direct method, treatments, deaths and times can be omitted.

Calculate the scores of the internal estimators in the learner on the supplied data.

Returns a dict with the following entries:

• 'propensity': the score for the propensity estimator
• ':outcome': a dict where the keys are the possible treatments, and the values are the scores of the outcome estimator corresponding to each treatment

Julia Equivalent: IAI.score

lnr.score(X, treatments, outcomes)

For problems with classification or regression outcomes, calculate the scores of the internal estimators using the data given by X, treatments and outcomes.

lnr.score(X, treatments, deaths, times)

For problems with survival outcomes, calculate the scores of the internal estimators using the data given by X, treatments, deaths and times.

iai.CategoricalRewardEstimator(**kwargs)

Learner for reward estimation with categorical treatments.

This class was deprecated in interpretableai 2.6.0, and CategoricalClassificationRewardEstimator or CategoricalRegressionRewardEstimator should be used instead. This is for consistency with the IAI v2.1.0 Julia release. This deprecation is no longer supported as of the IAI v3 release.

Parameters

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

iai.EqualPropensityEstimator(**kwargs)

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

Abstract type encompassing all learners for reward estimation with numeric treatments.

Julia Equivalent: IAI.NumericRewardEstimationLearner

Fit a reward estimation model and return predicted counterfactual rewards for each observation under each treatment option in treatment_candidates, as well as the score of the internal outcome estimator.

Julia Equivalent: IAI.fit_predict!

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

For problems with classification or regression outcomes, fit reward estimation model on features X, treatments treatments, and outcomes outcomes and predict rewards for each observation under each treatment option in treatment_candidates.

lnr.fit_predict(X, treatments, deaths, times, treatment_candidates)

For problems with survival outcomes, fit reward estimation model on features X, treatments treatments, death indicator deaths and event times times and predict rewards for each observation under each treatment option in treatment_candidates.

Return counterfactual rewards estimated by the learner for each observation in the supplied data.

Julia Equivalent: IAI.predict

lnr.predict(X, treatments, outcomes)

IAI versions 2.2 and greater: For problems with classification or regression outcomes, predict rewards for each observation in the data given by X, treatments and outcomes. If using the direct method, treatments and outcomes can be omitted.

lnr.predict(X, treatments, deaths, times)

IAI versions 2.2 and greater: For problems with survival outcomes, predict rewards for each observation in the data given by X, treatments, deaths and times. If using the direct method, treatments, deaths and times can be omitted.

lnr.predict(X, treatments, outcomes, treatment_candidates)

IAI version 2.1: Predicted reward for each observation in the data given by X, treatments and outcomes under each treatment option in treatment_candidates. If using the direct method, treatments, deaths and times can be omitted.

Calculate the scores of the internal estimator in the learner on the supplied data.

Julia Equivalent: IAI.score

On IAI versions 2.2 and greater, returns a dict with the following entries:

• 'propensity': a dict where the keys are the treatment candidates, and the values are the scores of the propensity estimator corresponding to each candidate
• ':outcome': a dict where the keys are the treatment candidates, and the values are the scores of the outcome estimator corresponding to each candidate

On IAI version 2.1, returns a float giving the score of the outcome estimator.

lnr.score(X, treatments, outcomes)

For problems with classification or regression outcomes, calculate the scores of the internal estimators using the data given by X, treatments and outcomes.

lnr.score(X, treatments, deaths, times)

For problems with survival outcomes, calculate the scores of the internal estimators using the data given by X, treatments, deaths and times.

iai.NumericRewardEstimator(**kwargs)

Learner for reward estimation with numeric treatments.

This class was deprecated in interpretableai 2.6.0, and RegressionClassificationRewardEstimator or RegressionRegressionRewardEstimator should be used instead. This is for consistency with the IAI v2.1.0 Julia release. This deprecation is no longer supported as of the IAI v3 release.

Parameters

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

iai.all_treatment_combinations(*args, **kwargs)

Return a pandas.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

iai.convert_treatments_to_numeric(treatments)

Convert treatments from symbol/string format into numeric values.

Julia Equivalent: IAI.convert_treatments_to_numeric

Abstract type encompassing all random forest learners.

Julia Equivalent: IAI.RandomForestLearner

iai.RandomForestClassifier(**kwargs)

Learner for training random forests for classification problems.

Julia Equivalent: IAI.RandomForestClassifier

Parameters

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

iai.RandomForestRegressor(**kwargs)

Learner for training random forests for regression problems.

Julia Equivalent: IAI.RandomForestRegressor

Parameters

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

Abstract type encompassing all XGBoost learners.

Julia Equivalent: IAI.XGBoostLearner

iai.XGBoostClassifier(**kwargs)

Learner for training XGBoost models for classification problems.

Julia Equivalent: IAI.XGBoostClassifier

Parameters

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

iai.XGBoostRegressor(**kwargs)

Learner for training XGBoost models for regression problems.

Julia Equivalent: IAI.XGBoostRegressor

Parameters

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

lnr.write_booster(filename)

Write the internal booster saved in the learner to filename.

Julia Equivalent: IAI.write_booster

Abstract type encompassing all GLMNet learners.

Julia Equivalent: IAI.GLMNetLearner

lnr.get_num_fits()

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

Julia Equivalent: IAI.get_num_fits

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

Julia Equivalent: IAI.get_prediction_weights

lnr.get_prediction_weights()

Return the weights for each feature in the prediction made by the best fit on the path in the learner.

lnr.get_prediction_weights(fit_index)

Return the weights for each feature in the prediction made by the fit at fit_index on the path in the learner.

Return the constant term in the prediction in the trained learner.

Julia Equivalent: IAI.get_prediction_constant

lnr.get_prediction_constant()

Return the constant term in the prediction made by the best fit on the path in the learner.

lnr.get_prediction_constant(fit_index)

Return the constant term in the prediction made by the fit at fit_index on the path in the learner.

iai.GLMNetCVRegressor(**kwargs)

Learner for training GLMNet models for regression problems with cross-validation.

Julia Equivalent: IAI.GLMNetCVRegressor

Parameters

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

Container for ROC curve information.

Julia Equivalent: IAI.ROCCurve

iai.ROCCurve(probs, y, positive_label=positive_label)

Construct a ROCCurve using predicted probabilities probs and true labels y, with probabilities indicating chance of predicting positive_label:

Julia Equivalent: IAI.ROCCurve

curve.get_data()

Extract the underlying data from the curve as a dict with two keys:

• coords: a dict for each point on the curve with the following keys:
  • 'fpr': false positive rate at the given threshold
  • 'tpr': true positive rate at the given threshold
  • 'threshold': the threshold
• auc: the area-under-the-curve (AUC)

Julia Equivalent: IAI.get_roc_curve_data

curve.plot()

Plot the ROC curve using matplotlib.

Returns a matplotlib.figure.Figure containing the ROC curve.

In a Jupyter Notebook, the plot will be shown automatically. In a terminal, you can show the plot with curve.plot().show().

lnr.write_html(filename, **kwargs)

Write interactive browser visualization of the ROC curve to filename as HTML.

Julia Equivalent: IAI.write_html

curve.show_in_browser()

Visualize the ROC curve in the browser.

Julia Equivalent: IAI.show_in_browser

Container for survival curve information.

Use curve[t] to get the survival probability prediction from curve at time t.

Julia Equivalent: IAI.SurvivalCurve

curve.get_data()

Extract the underlying data from the curve as a dict with two keys:

• 'times': the time for each breakpoint on the curve
• 'coefs': the probablility for each breakpoint on the curve

Julia Equivalent: IAI.get_survival_curve_data

curve.predict_expected_survival_time()

Return the expected survival time according to the curve

Julia Equivalent: IAI.predict_expected_survival_time