Change colour plot from explain_tidymodels - r

Question

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("Month", "AC")) %>% plot()
It automatically gives me plots in blue. However, how do I change the plot colour to red?
I already tried things like %>% plot(color = "red") and %>% plot(col = "red") but both do not work.
Any suggestions to change the partial dependence plots to a prefered colour? Thanks in advance!

Answer 1

I suggest that you access the underlying data and make your own plot using ggplot2, like I show here:
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$Sale_Price,
label = "random forest"
)
#> Preparation of a new explainer is initiated
#> -> model label : random forest
#> -> data : 2930 rows 3 cols
#> -> data : tibble converted into a data.frame
#> -> target variable : 2930 values
#> -> predict function : yhat.workflow will be used ( [33m default [39m )
#> -> predicted values : No value for predict function target column. ( [33m default [39m )
#> -> model_info : package tidymodels , ver. 0.1.3 , task regression ( [33m default [39m )
#> -> predicted values : numerical, min = 4.896787 , mean = 5.220582 , max = 5.51655
#> -> residual function : difference between y and yhat ( [33m default [39m )
#> -> residuals : numerical, min = -0.8021289 , mean = 5.872977e-05 , max = 0.3613971
#> [32m A new explainer has been created! [39m
pdp_rf <- model_profile(explainer_rf, N = NULL,
variables = "Gr_Liv_Area", groups = "Bldg_Type")
as_tibble(pdp_rf$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)
You can also access pdp_rf$cp_profiles.