iBreakDown: plotD3_break_down – R documentation

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iBreakDown

plotD3_break_down

Plot Break Down Objects in D3 with r2d3 package.

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

Model Agnostic Instance Level Variable Attributions

v2.0.1

GPL-3

Authors

Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Alicja Gosiewska [aut] (<https://orcid.org/0000-0001-6563-5742>), Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>), Adam Izdebski [aut], Dariusz Komosinski [ctb]

Initial release

plotD3_break_down

Description

Usage

Arguments

Value

References

Examples

iBreakDown

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