CODEWITHSUNDEEP
rplottidymodels
Kylian
Kylian

Reputation: 329

Plotting issues -Partial dependence plots

The following explain_tidymodels is created, to to display partial dependence plots.

explainer <- explain_tidymodels(rf_vi_fit, data = Data_train, y = Data_train$Lead_week)

Now i'm creating plots by doing the following:

model_profile(explainer, variables = c( "AC", "Jaar, "Month", "Retentie")) %>% plot()

Now I'm getting the following image:

Partial dependence plots

The problem is that first of all, the text of "Created for the workflow model" blocks my AC header. Secondly, I want to change the colour from blue to red. I tried %>% plot(color = "red") and %>% plot(col = "red"), but both do not seem to work.

Anyone knows how to fix one of these plotting issues? Thanks in advance!

Upvotes: 0

Views: 970

Answers (1)

Julia Silge
Julia Silge

Reputation: 11643

You can access the data that creates these plots using the as_tibble() function, and then you can create plots in whatever custom way you prefer:

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(DALEXtra)
#> Loading required package: DALEX
#> Welcome to DALEX (version: 2.2.0).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Additional features will be available after installation of: ggpubr.
#> Use 'install_dependencies()' to get all suggested dependencies
#> 
#> Attaching package: 'DALEX'
#> The following object is masked from 'package:dplyr':
#> 
#>     explain

data(ames)
ames_train <- ames %>%
    transmute(Sale_Price = log10(Sale_Price),
              Gr_Liv_Area = as.numeric(Gr_Liv_Area), 
              Year_Built, Bldg_Type)

rf_model <- 
    rand_forest(trees = 1000) %>% 
    set_engine("ranger") %>% 
    set_mode("regression")

rf_wflow <- 
    workflow() %>% 
    add_formula(
        Sale_Price ~ Gr_Liv_Area + Year_Built + Bldg_Type) %>% 
    add_model(rf_model) 

rf_fit <- rf_wflow %>% fit(data = ames_train)
explainer_rf <- explain_tidymodels(
    rf_fit, 
    data = dplyr::select(ames_train, -Sale_Price), 
    y = ames_train$Sale_Price,
    label = "random forest"
)
#> Preparation of a new explainer is initiated
#>   -> model label       :  random forest 
#>   -> data              :  2930  rows  3  cols 
#>   -> data              :  tibble converted into a data.frame 
#>   -> target variable   :  2930  values 
#>   -> predict function  :  yhat.workflow  will be used ( [33m default [39m )
#>   -> predicted values  :  No value for predict function target column. ( [33m default [39m )
#>   -> model_info        :  package tidymodels , ver. 0.1.3 , task regression ( [33m default [39m ) 
#>   -> predicted values  :  numerical, min =  4.91122 , mean =  5.220561 , max =  5.520101  
#>   -> residual function :  difference between y and yhat ( [33m default [39m )
#>   -> residuals         :  numerical, min =  -0.8113628 , mean =  7.953836e-05 , max =  0.3598514  
#>  [32m A new explainer has been created! [39m

pdp_rf <- model_profile(explainer_rf, N = NULL, 
                        variables = "Gr_Liv_Area", groups = "Bldg_Type")

as_tibble(pdp_rf$agr_profiles) %>%
    mutate(`_label_` = stringr::str_remove(`_label_`, "random forest_")) %>%
    ggplot(aes(`_x_`, `_yhat_`, color = `_label_`)) +
    geom_line(size = 1.2, alpha = 0.8) +
    labs(x = "Gross living area", 
         y = "Sale Price (log)", 
         color = NULL,
         title = "Partial dependence profile for Ames housing sales",
         subtitle = "Predictions from a random forest model")

Created on 2021-05-27 by the reprex package (v2.0.0)

Upvotes: 3

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