Individual Variable Effect

Individual Variable Effect

individual_variable_effect(x, ...)

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

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

shap(x, ...)

Arguments

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

Value

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.

Examples

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.')
}
#> Welcome to DALEX (version: 2.2.1).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Additional features will be available after installation of: ggpubr.
#> Use 'install_dependencies()' to get all suggested dependencies
#> randomForest 4.6-14
#> Type rfNews() to see new features/changes/bug fixes.
#>     gender      age    hours evaluation salary _id_ _ylevel_ _yhat_ _yhat_mean_
#> 1     male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.3   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.4   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.5   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.6   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.1   male 32.58267 41.88626          3      1    1       ok    0.1   0.2730292
#>        _vname_ _attribution_ _sign_      _label_
#> 1       gender   -0.02056803      - randomForest
#> 1.3        age    0.03448379      + randomForest
#> 1.4      hours    0.32636349      + randomForest
#> 1.5 evaluation    0.11472954      + randomForest
#> 1.6     salary    0.06627323      + randomForest
#> 1.1     gender    0.01840784      + randomForest