An R wrapper of SHAP python library
Blog post with gentle introduction to shapper
Installation and configurationInstall shapper
R package
devtools::install_github("ModelOriented/shapper")
You can install shap Python library via
If installation didn't work for some reason. Try installing dependencies first
reticulate::py_install(c("numpy", "pandas"))
or
reticulate::conda_install(c("numpy", "pandas"))
Python library SHAP can be also installed from PyPI
or conda-forge
conda install -c conda-forge shap
For more details how to configure python paths and environments for R see reticulate.
# instal shapper # devtools::install_github("ModelOriented/shapper") # install shap python library # shapper::install_shap() # load datasets # devtools::install_github("ModelOriented/DALEX2") library("DALEX2") Y_train <- HR$status x_train <- HR[ , -6] # Let's build models library("randomForest") set.seed(123) model_rf <- randomForest(x = x_train, y = Y_train) # here shapper starts # load shapper library(shapper) p_function <- function(model, data) predict(model, newdata = data, type = "prob") ive_rf <- individual_variable_effect(model_rf, data = x_train, predict_function = p_function, new_observation = x_train[1:2,], nsamples = 50) # plot plot(ive_rf)
# filtered ive_rf_filtered <- dplyr::filter(ive_rf, `_ylevel_` =="fired") shapper:::plot.individual_variable_effect(ive_rf_filtered)
library(shapper) library("DALEX2") library("randomForest") Y_train <- dragons$life_length x_train <- dragons[ , -8] set.seed(123) model_rf <- randomForest(x = x_train, y = Y_train) ive_rf <- individual_variable_effect(model_rf, data = x_train, new_observation = x_train[1,]) plot(ive_rf)
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