plotD3_shap function

Plot Shap (Break Down Uncertainty) Objects in D3 with r2d3 package.

Plots Shapley values.

## S3 method for class 'shap'
plotD3(
  x,
  ...,
  baseline = NA,
  max_features = 10,
  digits = 3,
  rounding_function = round,
  bar_width = 12,
  margin = 0.2,
  scale_height = FALSE,
  min_max = NA,
  vcolors = NA,
  chart_title = NA,
  time = 0,
  max_vars = NULL,
  reload = FALSE
)

Arguments

• x: an explanation created with shap
• ...: other parameters.
• baseline: if numeric then veritical line will start in baseline.
• max_features: maximal number of features to be included in the plot. By default it's 10.
• digits: number of decimal places (round) or significant digits (signif) to be used. See the rounding_function argument.
• rounding_function: a function to be used for rounding numbers. This should be signif which keeps a specified number of significant digits or round (which is default) to have the same precision for all components.
• bar_width: width of bars in px. By default it's 12px
• margin: extend x axis domain range to adjust the plot. Usually value between 0.1 and 0.3, by default it's 0.2
• scale_height: if TRUE, the height of the plot scales with window size.
• min_max: a range of OX axis. By deafult NA therefore will be extracted from the contributions of x. But can be set to some constants, usefull if these plots are used for comparisons.
• vcolors: If NA (default), DrWhy colors are used.
• chart_title: a character. Set custom title
• time: in ms. Set the animation length
• max_vars: alias for the max_features parameter.
• reload: Reload the plot on resize. By default it's FALSE.

Returns

a r2d3 object.

Examples

library("DALEX")
library("iBreakDown")
set.seed(1313)
model_titanic_glm <- glm(survived ~ gender + age + fare,
                       data = titanic_imputed, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_imputed,
                           y = titanic_imputed$survived,
                           label = "glm")

s_glm <- shap(explain_titanic_glm, titanic_imputed[1, ])
s_glm
plotD3(s_glm)

## Not run:

## Not run:
library("randomForest")

HR_small <- HR[2:500,]
m_rf <- randomForest(status ~. , data = HR_small)
new_observation <- HR_test[1,]
new_observation

p_fun <- function(object, newdata){predict(object, newdata=newdata, type = "prob")}

s_rf <- shap(m_rf,
             data = HR_small[,-6],
             new_observation =  new_observation,
             predict_function = p_fun)

plotD3(s_rf, time = 500)
## End(Not run)

References

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://ema.drwhy.ai