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Title:

Make Static HTML Website for Predictive Models

Version:

1.1

Description:

Website generator with HTML summaries for predictive models. This package uses 'DALEX' explainers to describe global model behavior. We can see how well models behave (tabs: Model Performance, Auditor), how much each variable contributes to predictions (tabs: Variable Response) and which variables are the most important for a given model (tabs: Variable Importance). We can also compare Concept Drift for pairs of models (tabs: Drifter). Additionally, data available on the website can be easily recreated in current R session. Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 at Warsaw University of Technology, Faculty of Mathematics and Information Science.

Depends:

R (≥ 3.4.0)

License:

Apache License 2.0

Encoding:

UTF-8

LazyData:

true

Imports:

DALEX (≥ 1.0), auditor (≥ 0.3.0), ggplot2 (≥ 3.1.0), whisker (≥ 0.3-2), DT (≥ 0.4), kableExtra (≥ 0.9.0), psych (≥ 1.8.4), archivist (≥ 2.1.0), svglite (≥ 1.2.1), devtools (≥ 2.0.1), breakDown (≥ 0.1.6), drifter (≥ 0.2.1)

Suggests:

ranger, testthat, useful

RoxygenNote:

7.1.0

URL:

https://github.com/ModelOriented/modelDown

BugReports:

https://github.com/ModelOriented/modelDown/issues

NeedsCompilation:

Packaged:

2020-04-11 16:08:24 UTC; kromash

Author:

Przemysław Biecek [aut], Magda Tatarynowicz [aut], Kamil Romaszko [aut, cre], Mateusz Urbański [aut]

Maintainer:

Kamil Romaszko <kamil.romaszko@gmail.com>

Repository:

CRAN

Date/Publication:

2020-04-15 00:30:03 UTC

Generates a website with HTML summaries for predictive models

Description

Generates a website with HTML summaries for predictive models

Usage

modelDown(..., modules = c("auditor", "drifter", "model_performance",
  "variable_importance", "variable_response"), output_folder = "output",
  repository_name = "repository", should_open_website = interactive())

Arguments

...

one or more explainers created with DALEX::explain() function. Pair of explainer could be provided to check drift of models

modules

modules that should be included in the website

output_folder

folder where the website will be saved

repository_name

name of local archivist repository that will be created

should_open_website

should generated website be automatically opened in default browser

Details

Additional arguments that could by passed by name:

remote_repository_path Path to remote repository that stores folder with archivist repository. If not provided, links to local repository will be shown.
device Device to use. Tested for "png" and "svg", but values from ggplot2::ggsave function should be working fine. Defaults to "png".
vr.vars variables which will be examined in Variable Response module. Defaults to all variables. Example vr.vars = c("var1", "var2")
vr.type types of examinations which will be conducteed in Variable Response module. Defaults to "pdp". Example vr.type = c("ale", "pdp")

Author(s)

Przemysław Biecek, Magda Tatarynowicz, Kamil Romaszko, Mateusz Urbański

Examples


require("ranger")
require("breakDown")
require("DALEX")


# Generate simple modelDown page
HR_data_selected <- HR_data[1000:3000,]
HR_glm_model <- glm(left~., HR_data_selected, family = "binomial")
explainer_glm <- explain(HR_glm_model, data=HR_data_selected, y = HR_data_selected$left)

modelDown::modelDown(explainer_glm,
                     modules = c("model_performance", "variable_importance",
                                 "variable_response"),
                     output_folder = tempdir(),
                     repository_name = "HR",
                     device = "png",
                     vr.vars= c("average_montly_hours"),
                     vr.type = "ale")

# More complex example with all modules
HR_ranger_model <- ranger(as.factor(left) ~ .,
                      data = HR_data, num.trees = 500, classification = TRUE, probability = TRUE)
explainer_ranger <- explain(HR_ranger_model,
                      data = HR_data, y = HR_data$left, function(model, data) {
 return(predict(model, data)$prediction[,2])
}, na.rm=TRUE)

# Two glm models used for drift detection
HR_data1 <- HR_data[1:4000,]
HR_data2 <- HR_data[4000:nrow(HR_data),]
HR_glm_model1 <- glm(left~., HR_data1, family = "binomial")
HR_glm_model2 <- glm(left~., HR_data2, family = "binomial")
explainer_glm1 <- explain(HR_glm_model1, data=HR_data1, y = HR_data1$left)
explainer_glm2 <- explain(HR_glm_model2, data=HR_data2, y = HR_data2$left)

modelDown::modelDown(list(explainer_glm1, explainer_glm2),
  modules = c("auditor", "drifter", "model_performance", "variable_importance",
              "variable_response"),
  output_folder = tempdir(),
  repository_name = "HR",
  remote_repository_path = "some_user/remote_repo_name",
  device = "png",
  vr.vars= c("average_montly_hours", "time_spend_company"),
  vr.type = "ale")