I am trying to create a Partial Dependence Plot for tidymodels using the R package DALEXtra, but an error occurs: Error in scream(): Can't convert from data$sqft to sqft due to loss of precision.
library(tidymodels)
data(Sacramento, package = "modeldata")
Sacramento <- Sacramento %>%
mutate_if(is.character, as.factor)
set.seed(123)
data_split <- initial_split(Sacramento, prop = 0.75, strata = price)
Sac_train <- training(data_split)
Sac_test <- testing(data_split)
rf_mod <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_engine("ranger", importance = "permutation", keep.inbag = TRUE) %>%
set_mode("regression")
Sac_recipe <- recipe(price ~ ., data = Sac_train) %>%
step_rm(zip, latitude, longitude) %>%
step_normalize(all_numeric_predictors()) %>%
step_dummy(all_nominal_predictors())
rf_workflow <- workflow() %>%
add_model(rf_mod) %>%
add_recipe(Sac_recipe)
set.seed(123)
Sac_folds <- vfold_cv(Sac_train, v = 10, repeats = 2, strata = price)
set.seed(123)
rf_res <- rf_workflow %>%
tune_grid(grid = 3,
resamples = Sac_folds,
control = control_grid(save_pred = TRUE),
metrics = metric_set(rmse))
rf_best <- rf_res %>%
select_best(metric = "rmse")
last_wf <- rf_workflow %>%
finalize_workflow(rf_best)
last_fit <- last_wf %>%
last_fit(data_split)
final_model <- extract_workflow(last_fit)
library(DALEXtra)
rf_explanier <- explain_tidymodels(model = final_model,
data = select(Sac_train, -price),
y = Sac_train$price)
pdp_sqft <- model_profile(explainer = rf_explanier, variables = "sqft",
N = NULL, groups = "type")
# Error
# Error in `scream()`: Can't convert from `data$sqft` <double> to `sqft` <integer> due to loss of precision.
You problem appears to come from using out-of-date packages. Updating the tidymodels packages should fix your problem.
library(tidymodels)
data(Sacramento, package = "modeldata")
Sacramento <- Sacramento %>%
mutate_if(is.character, as.factor)
set.seed(123)
data_split <- initial_split(Sacramento, prop = 0.75, strata = price)
Sac_train <- training(data_split)
Sac_test <- testing(data_split)
rf_mod <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_engine("ranger", importance = "permutation", keep.inbag = TRUE) %>%
set_mode("regression")
Sac_recipe <- recipe(price ~ ., data = Sac_train) %>%
step_rm(zip, latitude, longitude) %>%
step_normalize(all_numeric_predictors()) %>%
step_dummy(all_nominal_predictors())
rf_workflow <- workflow() %>%
add_model(rf_mod) %>%
add_recipe(Sac_recipe)
set.seed(123)
Sac_folds <- vfold_cv(Sac_train, v = 10, repeats = 2, strata = price)
set.seed(123)
rf_res <- rf_workflow %>%
tune_grid(grid = 3,
resamples = Sac_folds,
control = control_grid(save_pred = TRUE),
metrics = metric_set(rmse))
#> i Creating pre-processing data to finalize unknown parameter: mtry
rf_best <- rf_res %>%
select_best(metric = "rmse")
last_wf <- rf_workflow %>%
finalize_workflow(rf_best)
last_fit <- last_wf %>%
last_fit(data_split)
final_model <- extract_workflow(last_fit)
library(DALEXtra)
rf_explanier <- explain_tidymodels(model = final_model,
data = select(Sac_train, -price),
y = Sac_train$price)
#> Preparation of a new explainer is initiated
#> -> model label : workflow ( default )
#> -> data : 698 rows 8 cols
#> -> data : tibble converted into a data.frame
#> -> target variable : 698 values
#> -> predict function : yhat.workflow will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package tidymodels , ver. 1.0.0 , task regression ( default )
#> -> predicted values : numerical, min = 100683.5 , mean = 245393.1 , max = 692722.4
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -133229.2 , mean = 834.6177 , max = 300979.8
#> A new explainer has been created!
pdp_sqft <- model_profile(explainer = rf_explanier, variables = "sqft",
N = NULL, groups = "type")
#> Warning in FUN(X[[i]], ...): Variable: < sqft > has more than 201 unique
#> values and all of them will be used as variable splits in calculating
#> variable profiles. Use the `variable_splits` parameter to mannualy change this
#> behaviour. If you believe this warning to be a false positive, raise issue at
#> <https://github.com/ModelOriented/ingredients/issues>.
pdp_sqft
#> Top profiles :
#> _vname_ _label_ _x_ _groups_ _yhat_ _ids_
#> 1 sqft workflow_Condo 484 Condo 146246.5 0
#> 2 sqft workflow_Condo 539 Condo 148405.1 0
#> 3 sqft workflow_Condo 610 Condo 121844.8 0
#> 4 sqft workflow_Condo 611 Condo 121768.2 0
#> 5 sqft workflow_Condo 623 Condo 122501.8 0
#> 6 sqft workflow_Condo 625 Condo 123659.1 0
sessioninfo::session_info()
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