API Reference

API Reference

Documentation for the OptImpute public interface.

Index

Learners

ImputationLearner(; method=:opt_knn, keyword_arguments...)

Abstract type containing all imputation learners.

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

Additional keyword arguments can be used to specify parameters for the given learner, refer to the documentation for available parameters.

Learner for conducting optimal k-NN imputation.

The following parameters are supported (refer to the documentation for each):

Info

For optimal k-NN imputation, you should prefer using fit_transform! when imputing on the training set, instead of fit! followed by transform , as the former makes better use of the training data.

Learner for conducting optimal SVM imputation.

The following parameters are supported (refer to the documentation for each):

Learner for conducting optimal tree-based imputation.

The following parameters are supported (refer to the documentation for each):

Learner for conducting heuristic k-NN imputation.

The following parameters are supported (refer to the documentation for each):

Learner for conducting mean imputation.

The following parameters are supported (refer to the documentation for each):

Learner for conducting random imputation.

The following parameters are supported (refer to the documentation for each):

Simple Imputation Interface

IAI.imputeFunction.
impute(X::FeatureInput, method::Union{Symbol,AbstractString}=:opt_knn;
       X_warm::FeatureInput=DataFrame(), keyword_arguments...)

Impute the missing values in X using the specified imputation method.

A warmstart solution X_warm can optionally be supplied, which must be the same data as X with the missing values filled in. If not supplied, the algorithm will automatically generate and choose between multiple warm starts.

Additional keyword arguments can be used to specify parameters for the given method, refer to the documentation for available parameters.


impute(X::FeatureInput, params::Dict; train_proportion::Number=0.7,
       keyword arguments...)

Impute the missing values in X, validating over params using a grid search.

params should be a Dict specifying the learner parameters and corresponding ranges to be validated over.

train_proportion can be specified to control the proportion of data that is used for training.

Additional keyword arguments can be used to specify learner parameters that should be used for all parameter combinations in the grid, refer to the documentation for available parameters.

IAI.impute_cvFunction.
impute_cv(X::FeatureInput, params::Dict; n_folds::Int=5,
          keyword arguments...)

Like impute(X, params) but uses cross-validation to select the best parameter combination.

n_folds can be specified to control the number of folds used in the cross-validation.