plotD3_break_down: Plot Break Down Objects in D3 with r2d3 package.

In iBreakDown: Model Agnostic Instance Level Variable Attributions

Description

Plots waterfall break down for objects of the break_down class.

Usage

plotD3(x, ...)

## S3 method for class 'break_down'
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 break_down
...	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.

Value

a r2d3 object.

References

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

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")

bd_glm <- local_attributions(explain_titanic_glm, titanic_imputed[1, ])
bd_glm
plotD3(bd_glm)

## Not run: 
## Not run:
library("randomForest")

m_rf <- randomForest(status ~ . , data = HR[2:2000,])
new_observation <- HR_test[1,]
new_observation

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

bd_rf <- local_attributions(m_rf,
                           data = HR_test,
                           new_observation =  new_observation,
                           predict_function = p_fun)

bd_rf
plotD3(bd_rf)

## End(Not run)

iBreakDown documentation built on May 7, 2021, 5:07 p.m.