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R/modelStudio.R

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#' @param eda Compute EDA plots and Residuals vs Feature plot, which adds the data to the dashboard. Default is \code{TRUE}.
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#' @param show_info Verbose a progress on the console. Default is \code{TRUE}.
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#' @param parallel Speed up the computation using \code{parallelMap::parallelMap()}.
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#' See \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html#parallel-computation}{\bold{vignette}}.
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#' See \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html#parallel-computation}{\bold{vignette}}.
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#' This might interfere with showing progress using \code{show_info}.
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#' @param options Customize \code{modelStudio}. See \code{\link{ms_options}} and
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#' \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html#additional-options-1}{\bold{vignette}}.
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#' }
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#'
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#' @seealso
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#' Vignettes: \href{https://modeloriented.github.io/modelStudio/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#' Vignettes: \href{https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#'
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#' @examples
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#' library("DALEX")
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#'
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#' # make a studio for the model
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#' modelStudio(explainer_titanic,
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#' new_observations)
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#' new_observations,
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#' N = 200, B = 5) # faster example
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#'
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#' \donttest{
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#'

R/ms_options.R

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#' }
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#'
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#' @seealso
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#' Vignettes: \href{https://modeloriented.github.io/modelStudio/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#' Vignettes: \href{https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#'
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#' @examples
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#' library("DALEX")

R/ms_update_observations.R

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#' See \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html#more-calculations-means-more-time}{\bold{vignette}}
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#' @param show_info Verbose a progress on the console. Default is \code{TRUE}.
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#' @param parallel Speed up the computation using \code{parallelMap::parallelMap()}.
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#' See \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html#parallel-computation}{\bold{vignette}}.
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#' See \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html#parallel-computation}{\bold{vignette}}.
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#' This might interfere with showing progress using \code{show_info}.
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#' @param widget_id Use an explicit element ID for the widget (rather than an automatically generated one).
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#' Useful e.g. when using \code{modelStudio} with Shiny.
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#' }
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#'
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#' @seealso
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#' Vignettes: \href{https://modeloriented.github.io/modelStudio/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#' Vignettes: \href{https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#'
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#' @examples
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#' library("DALEX")
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#' y = titanic_imputed$survived)
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#'
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#' # make a studio for the model
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#' ms <- modelStudio(explainer_titanic)
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#' ms <- modelStudio(explainer_titanic,
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#' N = 200, B = 5) # faster example
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#'
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#' \donttest{
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#'

R/ms_update_options.R

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#' }
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#'
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#' @seealso
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#' Vignettes: \href{https://modeloriented.github.io/modelStudio/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#' Vignettes: \href{https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html}{\bold{modelStudio - R & Python examples}}
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#' and \href{https://modelstudio.drwhy.ai/articles/ms-perks-features.html}{\bold{modelStudio - perks and features}}
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#'
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#' @examples
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#' library("DALEX")
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#' y = titanic_imputed$survived)
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#'
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#' # make a studio for the model
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#' ms <- modelStudio(explainer_titanic)
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#' ms <- modelStudio(explainer_titanic,
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#' N = 200, B = 5) # faster example
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#'
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#' # update the options
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#' new_ms <- ms_update_options(ms,

README.md

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modelStudio(explainer)
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```
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[Save the output](http://modelstudio.drwhy.ai/#save--share) in the form of a HTML file - [**Demo Dashboard**](https://modeloriented.github.io/modelStudio/demo.html).
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[Save the output](http://modelstudio.drwhy.ai/#save--share) in the form of a HTML file - [**Demo Dashboard**](https://modelstudio.drwhy.ai/demo.html).
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![](man/figures/long.gif)
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## R & Python Examples [more](http://modelstudio.drwhy.ai/articles/ms-r-python-examples.html)
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## R & Python Examples [more](https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html)
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-------------------------------
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install.packages("DALEXtra")
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```
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### mlr [dashboard](https://modeloriented.github.io/modelStudio/mlr.html)
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### mlr [dashboard](https://modelstudio.drwhy.ai/mlr.html)
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Make a studio for the regression `ranger` model on `apartments` data.
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</details>
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### xgboost [dashboard](https://modeloriented.github.io/modelStudio/xgboost.html)
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### xgboost [dashboard](https://modelstudio.drwhy.ai/xgboost.html)
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Make a studio for the classification `xgboost` model on `titanic` data.
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install.packages("reticulate")
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```
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### scikit-learn [dashboard](https://modeloriented.github.io/modelStudio/scikitlearn.html)
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### scikit-learn [dashboard](https://modelstudio.drwhy.ai/scikitlearn.html)
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Make a studio for the regression `Pipeline SVR` model on `fifa` data.
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</details>
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### lightgbm [dashboard](https://modeloriented.github.io/modelStudio/lightgbm.html)
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### lightgbm [dashboard](https://modelstudio.drwhy.ai/lightgbm.html)
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Make a studio for the classification `Pipeline LGBMClassifier` model on `titanic` data.
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- Theoretical introduction to the plots: [Explanatory Model Analysis: Explore, Explain and Examine Predictive Models](https://pbiecek.github.io/ema)
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- Vignettes: [modelStudio - perks and features](https://modeloriented.github.io/modelStudio/articles/ms-perks-features.html) and [modelStudio - R & Python examples](https://modeloriented.github.io/modelStudio/articles/ms-r-python-examples.html)
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- Vignettes: [modelStudio - perks and features](https://modelstudio.drwhy.ai/articles/ms-perks-features.html) and [modelStudio - R & Python examples](https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html)
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- Changelog: [NEWS](https://modeloriented.github.io/modelStudio/news/index.html)
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- Changelog: [NEWS](https://modelstudio.drwhy.ai/news/index.html)
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- Conference poster: [MLinPL2019](https://github.com/ModelOriented/modelStudio/blob/master/misc/MLinPL2019_modelStudio_poster.pdf)
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man/modelStudio.Rd

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man/ms_options.Rd

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man/ms_update_observations.Rd

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man/ms_update_options.Rd

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tests/testthat/test_2_0.R

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case2 <- testthat::expect_silent(
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modelStudio::modelStudio(exp_fifa, data_fifa[1:2,],
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max_vars = 5, rounding_funtion = signif,
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digits = 3, show_info = v))
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digits = 3,
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N = 5, B = 2, show_info = v))
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testthat::expect_true(
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all(c("mse:", "rmse:", "r2:", "mad:") %in%
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strsplit(case2$x$options$measure_text, split=" ")[[1]]))

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