# API Reference

Documentation for the interpretableai package

## Setup

installFunction
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_juliaFunction
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_imageFunction
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.
load_graphvizFunction
iai.load_graphviz()

Loads the Julia Graphviz library to permit certain visualizations.

The library will be installed if not already present.

## Data Preparation

MixedDataType
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.
split_dataFunction
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

## Scoring

scoreFunction
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

IAI Compatibility

Requires IAI version 2.1 or higher.

## Learners

### Learner

fitMethod
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.

### SurvivalLearner

predictMethod

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.

predict_hazardMethod
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

IAI Compatibility

Requires IAI version 1.2 or higher.

### PolicyLearner

predict_outcomesMethod
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

IAI Compatibility

Requires IAI version 2.0 or higher.

### ImputationLearner

ImputationLearnerType
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.

fitMethod
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!

## GridSearch

fitMethod
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.

fit_cvMethod
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.

fit_transform_cvMethod
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.

## Tree Learners

### TreeLearner

decision_pathMethod
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

variable_importanceMethod

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.

### ClassificationTreeLearner

set_thresholdMethod
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!

### Tree Stability

get_treeMethod
lnr.get_tree(index)

Return a copy of the learner that uses the tree at index rather than the tree with the best training objective.

Julia Equivalent: IAI.get_tree

IAI Compatibility

Requires IAI version 2.2 or higher.

#### Stability Analysis

StabilityAnalysisType

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

IAI Compatibility

Requires IAI version 2.2 or higher.

iai.StabilityAnalysis(lnr)

Conduct a stability analysis of the trees in lnr, using similarity scores calculated during training

iai.StabilityAnalysis(lnr, X, y, criterion='default')

Conduct a stability analysis of the trees in lnr, using similarity scores calculated with the data X, y and criterion

plotMethod
stability.plot()

Plot the stability analysis results.

Returns a matplotlib.figure.Figure containing the plotted results.

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

IAI Compatibility

Requires IAI version 2.2 or higher.

### Similarity Comparison

variable_importance_similarityMethod

Calculate similarity between this learner and another tree learner using variable importance scores.

Julia Equivalent: IAI.variable_importance_similarity

IAI Compatibility

Requires IAI version 2.2 or higher.

lnr.variable_importance_similarity(new_lnr)

Calculate similarity scores between the final tree in this learner and all trees in new_lnr

lnr.variable_importance_similarity(new_lnr, X, y,

Calculate similarity scores between the final tree in this learner and all trees in new_lnr using the data X and y with criterion

SimilarityComparisonType
iai.SimilarityComparison(orig_lnr, new_lnr, deviations)

Conduct a similarity comparison between the final tree in orig_lnr and all trees in new_lnr to consider the tradeoff between training performance and similarity to the original tree as measured by deviations.

Refer to the documentation on tree stability for more information.

Julia Equivalent: IAI.SimilarityComparison

IAI Compatibility

Requires IAI version 2.2 or higher.

get_train_errorsMethod
lnr.get_train_errors()

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

IAI Compatibility

Requires IAI version 2.2 or higher.

plotMethod
similarity.plot()

Plot the similarity comparison results.

Returns a matplotlib.figure.Figure containing the plotted results.

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

IAI Compatibility

Requires IAI version 2.2 or higher.

## Optimal Trees

OptimalTreePolicyMinimizerType
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 Compatibility

Requires IAI version 2.0 or higher.

OptimalTreePolicyMaximizerType
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 Compatibility

Requires IAI version 2.0 or higher.

refit_leavesMethod
lnr.refit_leaves(X, y)

Refit the models in the leaves of the trained learner using the supplied data.

Julia Equivalent: IAI.refit_leaves!

Parameters

Refer to the Julia documentation for available parameters.

IAI Compatibility

Requires IAI version 3.0 or higher.

copy_splits_and_refit_leavesMethod
lnr.copy_splits_and_refit_leaves(orig_lnr, X, y)

Copy the tree split structure from orig_lnr into this learner and refit the models in each leaf of the tree using the supplied data.

Julia Equivalent: IAI.copy_splits_and_refit_leaves!

Parameters

Refer to the Julia documentation for available parameters.

IAI Compatibility

Requires IAI version 3.0 or higher.

prune_treesMethod
lnr.prune_trees(X, y)

Use the trained trees in the learner along with the supplied validation data X and y to determine the best value for the cp parameter and then prune the trees according to this value.

Julia Equivalent: IAI.prune_trees!

Parameters

Refer to the Julia documentation for available parameters.

IAI Compatibility

Requires IAI version 3.0 or higher.

## OptImpute

imputeFunction
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.

impute_cvFunction
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.

ZeroImputationLearnerType
iai.ZeroImputationLearner(**kwargs)

Learner for conducting zero-imputation.

Julia Equivalent: IAI.ZeroImputationLearner

Parameters

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

IAI Compatibility

Requires IAI version 3.0 or higher.

fit_and_expandMethod
lnr.fit_and_expand(X, type='finite')

Fit the imputation learner with training features X and create adaptive indicator features to encode the missing pattern according to type.

Julia Equivalent: IAI.fit_and_expand!

Parameters

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

IAI Compatibility

Requires IAI version 3.0 or higher.

transform_and_expandMethod
lnr.transform_and_expand(X, type='finite')

Transform features X with the trained imputation learner and create adaptive indicator features to encode the missing pattern according to type.

Julia Equivalent: IAI.transform_and_expand

Parameters

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

IAI Compatibility

Requires IAI version 3.0 or higher.

## Optimal Feature Selection

fitMethod
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!

IAI Compatibility

Requires IAI version 1.1 or higher.

predictMethod

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

Julia Equivalent: IAI.predict

IAI Compatibility

Requires IAI version 1.1 or higher.

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.

scoreMethod

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

Julia Equivalent: IAI.score

IAI Compatibility

Requires IAI version 1.1 or higher.

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.

get_prediction_constantMethod

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

Julia Equivalent: IAI.get_prediction_constant

IAI Compatibility

Requires IAI version 1.1 or higher.

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

get_prediction_weightsMethod

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

Julia Equivalent: IAI.get_prediction_weights

IAI Compatibility

Requires IAI version 1.1 or higher.

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

plotMethod
grid.plot(type)

Plot the grid search results for Optimal Feature Selection learners.

Returns a matplotlib.figure.Figure containing the plotted results.

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

Parameters

IAI Compatibility

Requires IAI version 2.2 or higher.

## Reward Estimation

### Categorical Treatments

fit_predictMethod

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!

IAI Compatibility

Requires IAI version 2.0 or higher.

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.

predictMethod

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

Julia Equivalent: IAI.predict

IAI Compatibility

Requires IAI version 2.0 or higher.

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.

predict_rewardMethod

Return counterfactual rewards estimated using the learner parameters for each observation in the supplied data and predictions.

Julia Equivalent: IAI.predict_reward

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_reward(treatments, outcomes, predictions)

For problems with classification or regression outcomes, predict rewards for each observation in the data given by treatments and outcomes with predictions given by predictions.

lnr.predict_reward(treatments, deaths, times, predictions)

For problems with survival outcomes, predict rewards for each observation in the data given by treatments, deaths and times, with predictions given by predictions.

scoreMethod

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

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

### Numeric Treatments

fit_predictMethod

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!

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

predictMethod

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

Julia Equivalent: IAI.predict

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

predict_rewardMethod

Return counterfactual rewards estimated using the learner parameters for each observation in the supplied data and predictions.

Julia Equivalent: IAI.predict_reward

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_reward(X, treatments, outcomes, predictions)

For problems with classification or regression outcomes, predict rewards for each observation in the data given by treatments and outcomes with predictions given by predictions.

lnr.predict_reward(X, treatments, deaths, times, predictions)

For problems with survival outcomes, predict rewards for each observation in the data given by treatments, deaths and times with predictions given by predictions.

scoreMethod

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.

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

set_reward_kernel_bandwidthMethod
lnr.set_reward_kernel_bandwidth(bandwidth)

Save the new value of bandwidth as the reward kernel bandwidth inside the 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!

IAI Compatibility

Requires IAI version 2.2 or higher.

all_treatment_combinationsFunction
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 Compatibility

Requires IAI version 2.1 or higher.

## Heuristics

### Random Forests

RandomForestClassifierType
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 Compatibility

Requires IAI version 2.1 or higher.

RandomForestRegressorType
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.

IAI Compatibility

Requires IAI version 2.1 or higher.

### XGBoost

XGBoostClassifierType
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 Compatibility

Requires IAI version 2.1 or higher.

XGBoostRegressorType
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.

IAI Compatibility

Requires IAI version 2.1 or higher.

XGBoostSurvivalLearnerType
iai.XGBoostSurvivalLearner(**kwargs)

Learner for training XGBoost models for survival problems.

Julia Equivalent: IAI.XGBoostSurvivalLearner

Parameters

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

IAI Compatibility

Requires IAI version 2.2 or higher.

### GLMNet

get_prediction_weightsMethod

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

Julia Equivalent: IAI.get_prediction_weights

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

get_prediction_constantMethod

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

Julia Equivalent: IAI.get_prediction_constant

IAI Compatibility

Requires IAI version 2.1 or higher.

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.

predictMethod

Return the prediction made by the learner for each point in the data X.

Julia Equivalent: IAI.predict

IAI Compatibility

Requires IAI version 2.1 or higher.

lnr.predict(X)

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

lnr.predict(X, fit_index)

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

scoreMethod

Calculate the score for the learner on data X and y

Julia Equivalent: IAI.score

IAI Compatibility

Requires IAI version 2.1 or higher.

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

Calculate the score for by the best fit on the path in the learner.

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

Calculate the score for by the fit at fit_index on the path in the learner.

GLMNetCVClassifierType
iai.GLMNetCVClassifier(**kwargs)

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

Julia Equivalent: IAI.GLMNetCVClassifier

Parameters

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

IAI Compatibility

Requires IAI version 3.0 or higher.

predict_probaMethod

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

Julia Equivalent: IAI.predict_proba

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_proba(X)

Return the probabilities of class membership predicted by the best fit on the path in the learner.

lnr.predict_proba(X, fit_index)

Return the probabilities of class membership predicted by the fit at fit_index on the path in the learner.

ROCCurveMethod

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

Julia Equivalent: IAI.ROCCurve

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_proba(X)

Construct an ROCCurve using by the best fit on the path in the learner.

iai.ROCCurve(lnr, X, y, fit_index=fit_index)

Construct an ROCCurve using by the fit at fit_index on the path in the learner.

GLMNetCVRegressorType
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.

IAI Compatibility

Requires IAI version 2.1 or higher.

GLMNetCVSurvivalLearnerType
iai.GLMNetCVSurvivalLearner(**kwargs)

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

Julia Equivalent: IAI.GLMNetCVSurvivalLearner

Parameters

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

IAI Compatibility

Requires IAI version 3.0 or higher.

predict_hazardMethod

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

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_hazard(X)

Return the hazard coefficient estimated made by the best fit on the path in the learner.

lnr.predict_hazard(X, fit_index)

Return the hazard coefficient estimated made by the fit at fit_index on the path in the learner.

predict_expected_survival_timeMethod

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

Julia Equivalent: IAI.predict_expected_survival_time

IAI Compatibility

Requires IAI version 3.0 or higher.

lnr.predict_expected_survival_time(X)

Return the expected survival time made by the best fit on the path in the learner.

lnr.predict_expected_survival_time(X, fit_index)

Return the expected survival time made by the fit at fit_index on the path in the learner.

## Miscellaneous Types

### ROCCurve

get_dataMethod
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

IAI Compatibility

Requires IAI version 2.1 or higher.

plotMethod
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().

IAI Compatibility

Requires IAI version 2.1 or higher.

write_htmlMethod
lnr.write_html(filename, **kwargs)

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

Julia Equivalent: IAI.write_html

IAI Compatibility

Requires IAI version 1.1 or higher.

### SurvivalCurve

get_dataMethod
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`