Creates object with class arena_live or arena_static depending on the first argument. This method is always first in arenar workflow and you should specify all plots' parameters there.

create_arena(
  live = FALSE,
  N = 500,
  fi_N = NULL,
  fi_B = 10,
  grid_points = 101,
  shap_B = 10,
  funnel_nbins = 5,
  funnel_cutoff = 0.01,
  funnel_factor_threshold = 7,
  fairness_cutoffs = seq(0.05, 0.95, 0.05),
  max_points_number = 150,
  distribution_bins = seq(5, 40, 5),
  cl = NULL
)

Arguments

live

Defines if arena should start live server or generate static json

N

number of observations used to calculate dependence profiles

fi_N

number of observations used in feature importance

fi_B

Number of permutation rounds to perform each variable in feature importance

grid_points

number of points for profile

shap_B

Numer of random paths in SHAP

funnel_nbins

Number of partitions for numeric columns for funnel plot

funnel_cutoff

Threshold for categorical data. Entries less frequent than specified value will be merged into one category in funnel plot.

funnel_factor_threshold

Numeric columns with lower number of unique values than value of this parameter will be treated as factors in funnel plot.

fairness_cutoffs

vector of available cutoff levels for fairness panel

max_points_number

maximum size of sample to plot scatter plots in variable against another panel

distribution_bins

vector of available bins count for histogram

cl

Cluster used to run parallel computations (Do not work in live Arena)

Value

Empty arena_static or arena_live class object.
arena_static:

  • explainer List of used explainers

  • observations_batches List of data frames added as observations

  • params Plots' parameters

  • plots_data List of generated data for plots

arena_live:

  • explainer List of used explainers

  • observations_batches List of data frames added as observations

  • params Plots' parameters

  • timestamp Timestamp of last modification

Examples

#> Welcome to DALEX (version: 2.0.1). #> Find examples and detailed introduction at: https://pbiecek.github.io/ema/ #> Additional features will be available after installation of: ggpubr. #> Use 'install_dependencies()' to get all suggested dependencies
library("arenar") library("dplyr", quietly=TRUE, warn.conflicts = FALSE) # create a model model <- glm(m2.price ~ ., data=apartments) # create a DALEX explainer explainer <- DALEX::explain(model, data=apartments, y=apartments$m2.price)
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 1000 rows 6 cols #> -> target variable : 1000 values #> -> predict function : yhat.glm will be used ( default ) #> -> predicted values : numerical, min = 1781.848 , mean = 3487.019 , max = 6176.032 #> -> model_info : package stats , ver. 4.0.2 , task regression ( default ) #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -247.4728 , mean = 2.093656e-14 , max = 469.0023 #> A new explainer has been created!
# prepare observations to be explained observations <- apartments[1:3, ] # rownames are used as labels for each observation rownames(observations) <- paste0(observations$construction.year, "-", observations$surface, "m2") # generate static arena for one model and 3 observations arena <- create_arena(live=FALSE) %>% push_model(explainer) %>% push_observations(observations) print(arena)
#> ===== Static Arena Summary ===== #> Observations: 1953-25m2, 1992-143m2, 1937-56m2 #> Variables: construction.year, surface, floor, no.rooms, district #> Models: lm #> Datasets: #> Plots count: 36
if (interactive()) upload_arena(arena)