iBreakDown (version 0.9.6)

plotD3: Plot Break Down Objects in D3 with r2d3 package.

Description

Experimental interactive explainer created with 'D3.js' library.

Usage

plotD3(x, ...)
# S3 method for break_down
plotD3(x, ..., baseline = NA, max_features = 10,
  min_max = NA, vcolors = DALEX::theme_drwhy_colors_break_down(),
  digits = 3, rounding_function = round)

Arguments

x

the model model of `break_down` class.

...

other parameters.

baseline

if numeric then veritical line will start in baseline.

max_features

maximal number of features to be included in the plot. default value is 10.

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

named vector with colors.

digits

number of decimal places (round) or significant digits (signif) to be used. See the rounding_function argument.

rounding_function

function that is to used for rounding numbers. It may be signif which keeps a specified number of significant digits. Or the default round to have the same precision for all components.

Value

an `r2d3` object.

References

Predictive Models: Visual Exploration, Explanation and Debugging https://pbiecek.github.io/PM_VEE

Examples

# NOT RUN {
library("DALEX")
library("iBreakDown")
# Toy examples, because CRAN angels ask for them
titanic <- na.omit(titanic)
set.seed(1313)
titanic_small <- titanic[sample(1:nrow(titanic), 500), c(1,2,6,9)]
model_titanic_glm <- glm(survived == "yes" ~ gender + age + fare,
                       data = titanic_small, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_small[,-9],
                           y = titanic_small$survived == "yes")
bd_rf <- local_attributions(explain_titanic_glm, titanic_small[1, ])
bd_rf
plotD3(bd_rf)

# }
# NOT RUN {
library("randomForest")
titanic <- na.omit(titanic)
model_titanic_rf <- randomForest(survived == "yes" ~ gender + age + class + embarked +
                                   fare + sibsp + parch,  data = titanic)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic[,-9],
                              y = titanic$survived == "yes",
                              label = "Random Forest v7")

new_passanger <- data.frame(
  class = factor("1st", levels = c("1st", "2nd", "3rd", "deck crew", "engineering crew",
                                    "restaurant staff", "victualling crew")),
  gender = factor("male", levels = c("female", "male")),
  age = 8,
  sibsp = 0,
  parch = 0,
  fare = 72,
  embarked = factor("Southampton",
                  levels = c("Belfast", "Cherbourg", "Queenstown", "Southampton")))

  rf_la <- local_attributions(explain_titanic_rf, new_passanger)
  rf_la

  plotD3(rf_la)
  plotD3(rf_la, max_features = 3)
  plotD3(rf_la, max_features = 3, min_max = c(0,1))
# }