smartdata

Package that integrates preprocessing algorithms for oversampling, instance/feature selection, normalization, discretization, space transformation, and outliers/missing values/noise cleaning.

Installation

You can install smartdata from github with:

# install.packages("devtools")
devtools::install_github("ncordon/smartdata")

and load it into an R session with:

library("smartdata")

Examples

smartdata provides the following wrappers:

instance_selection
feature_selection
normalize
discretize
space_transformation
clean_outliers
impute_missing
clean_noise

To get the possible methods available for a certain wrapper, we can do:

which_options("instance_selection")
#> Possible methods are: 'CNN', 'ENN', 'multiedit', 'FRIS'

To get information about the parameters available for a method:

which_options("instance_selection", "multiedit")
#> For more information do: ?class::multiedit 
#> Parameters for multiedit are: 
#>   * k: Number of neighbors used in KNN 
#>        Default value: 1 
#>   * num_folds: Number of partitions the train set is split in 
#>                Default value: 3 
#>   * null_passes: Number of null passes to use in the algorithm 
#>                  Default value: 5

First let’s load a bunch of datasets:

data(iris0,  package = "imbalance")
data(ecoli1, package = "imbalance")
data(nhanes, package = "mice")

Oversampling

super_iris <- iris0 %>% oversample(method = "MWMOTE", ratio = 0.8, filtering = TRUE)

Instance selection

super_iris <- iris %>% instance_selection("multiedit", k = 3, num_folds = 2, 
                                          null_passes = 10, class_attr = "Species")

Feature selection

super_ecoli <- ecoli1 %>% feature_selection("Boruta", class_attr = "Class")

Normalization

super_iris <- iris %>% normalize("min_max", exclude = c("Sepal.Length", "Species"))

Discretization

super_iris <- iris %>% discretize("ameva", class_attr = "Species")

Space transformation

super_ecoli <- ecoli1 %>% space_transformation("lle_knn", k = 3, num_features = 2)

Outliers

super_iris <- iris %>% clean_outliers("multivariate", type = "adj")

Missing values

super_nhanes <- nhanes %>% impute_missing("gibbs_sampling")

Noise

super_iris <- iris %>% clean_noise("hybrid", class_attr = "Species", 
                                   consensus = FALSE, action = "repair")

acguidoum / smartdata