Xy() you can convienently simulate supervised learning data. The simulation can be
very specific, since there are many degrees of freedom for the user. For instance,
the functional shape of the nonlinearity is user-defined as well. Interactions can be formed and (co)variances altered.
There are numerous applications for the package, e.g.:
- Learning a new machine learning algorithm in a controlled environment
- Benchmarking feature selection algorithms in a controlled environment
- Create sample data to test productionized ML applications in a CI / CD pipeline.
The usage is pretty straight forward. I strongly encourage you to read the help document to explore all functionalities.
Xy() package uses the GNU scientific library. Linux and MacOs users are adviced to install this package before installing the code from github.
Install through Homebrew via
brew install gsl
sudo apt-get install libgsl-dev
All Operating systems:
Install the package with
# install.packages("remotes") # install from github remotes::install_github("andrebleier/Xy")
You can simulate regression and classification data with interactions and a user-specified non-linearity. The usage is in a tidy way. First you create a simulation recipe, which is a combination of the overall task invoked by
Xy() . Afterwards effects can be added to the recipe with the
add_* functions as can be seen in the example below. Finally the
simulate() function cooks this recipe.
# load the library library(Xy) # simulate regression data task <- Xy(task = "regression") # build the recipe recipe <- task %>% # adding linear features add_linear(p = 5, family = xy_normal()) %>% # adding non-linear cubic features add_nonlinear(p = 3, nlfun= function(x) x^3, family = xy_normal()) %>% # add uninformative effects add_uninformative(p = 3, collinearity = TRUE, family = xy_normal()) %>% # add dummy variables add_discrete(p = 3, levels = 3) %>% # add normally distributed noise, which correlates with the features add_noise(collinearity = TRUE, family = xy_normal()) %>% # add an intercept add_intercept() # cook the recipe sim <- recipe %>% simulate(n = 100, r_squared = 0.8) # fetch the data sim %>% pull_xy()
You can extract a feature importance of your simulation. For instance, to benchmark feature selection algorithms.
# Feature Importance variable_importance <- sim %>% importance()
Feel free to contact me with input and ideas.