individual_variable_effect: Individual Variable Effect
In shapper: Wrapper of Python Library 'shap'

Description Usage Arguments Value Examples

View source: R/individual_variable_effect.R

Individual Variable Effect

individual_variable_effect(x, ...)

## S3 method for class 'explainer'
individual_variable_effect(
  x,
  new_observation,
  method = "KernelSHAP",
  nsamples = "auto",
  ...
)

## Default S3 method:
individual_variable_effect(
  x,
  data,
  predict_function = predict,
  new_observation,
  label = tail(class(x), 1),
  method = "KernelSHAP",
  nsamples = "auto",
  ...
)

shap(x, ...)

`x`	a model to be explained, or an explainer created with function `explain`.
`...`	other parameters.
`new_observation`	an observation/observations to be explained. Required for local/instance level explainers. Columns in should correspond to columns in the data argument. Data set should not contain any additional columns.
`method`	an estimation method of SHAP values. Currently the only availible is 'KernelSHAP'.
`nsamples`	number of samples or "auto". Note that number must be as integer. Use 'as.integer()'.
`data`	validation dataset. Used to determine univariate distributions, calculation of quantiles, correlations and so on. It will be extracted from 'x' if it’s an explainer.
`predict_function`	predict function that operates on the model 'x'. Since the model is a black box, the 'predict_function' is the only interface to access values from the model. It should be a function that takes at least a model 'x' and data and returns vector of predictions. If model response has more than a single number (like multiclass models) then this function should return a marix/data.frame of the size 'm' x 'd', where 'm' is the number of observations while 'd' is the dimensionality of model response. It will be extracted from 'x' if it’s an explainer.
`label`	name of the model. By default it’s extracted from the class attribute of the model

an object of class individual_variable_effect with shap values of each variable for each new observation. Columns:

first d columns contains variable values.
_id_ - id of observation, number of row in 'new_observation' data.
_ylevel_ - level of y
_yhat_ -predicted value for level of y
_yhat_mean_ - expected value of prediction, mean of all predictions
_vname_ - variable name
_attribution_ - attribution of variable
_sign_ a sign of attribution
_label_ a label

In order to use shapper with other python virtual environment following R command are required to execute reticulate::use_virtualenv("path_to_your_env") or for conda reticulate::use_conda("name_of_conda_env") before attaching shapper.

have_shap <- reticulate::py_module_available("shap")

if(have_shap){
  library("shapper")
  library("DALEX")
  library("randomForest")
  Y_train <- HR$status
  x_train <- HR[ , -6]
  set.seed(123)
  model_rf <- randomForest(x = x_train, y = Y_train, ntree= 50)
  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)
  ive_rf
} else{
    print('Python testing environment is required.')
}