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Update 2-modeling.R

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Kyle Belanger 2023-02-01 11:00:57 -05:00
parent c7aa0b2da1
commit 919e1ac1b6
1 changed files with 21 additions and 16 deletions
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37
ML/2-modeling.R
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@ -28,8 +28,6 @@ set.seed(070823) #set seed for reproducible research
model_data <- readr$read_rds(here("ML","data-unshared","model_data.RDS")) model_data <- readr$read_rds(here("ML","data-unshared","model_data.RDS"))
# split data -------------------------------------------------------------- # split data --------------------------------------------------------------
model_data_split <- rsample$initial_split( model_data_split <- rsample$initial_split(
@ -46,37 +44,44 @@ table(ds_train$ft4_dia) %>% prop.table()
table(ds_test$ft4_dia) %>% prop.table() table(ds_test$ft4_dia) %>% prop.table()
class_train <- ds_train %>% dplyr::select(-FT4) # training data for classification models
reg_train <- ds_train %>% dplyr::select(-ft4_dia) # training data for reg models predicting result
# random forest ----------------------------------------------------------- # random forest -----------------------------------------------------------
# base model - No Hyper Tuning
rf_model <- p$rand_forest(trees = 1900) %>% rf_model <- p$rand_forest(trees = 1900) %>%
p$set_engine("ranger") %>% p$set_mode("regression") p$set_engine("ranger") %>% p$set_mode("classification")
rf_recipe <- r$recipe(FT4 ~ . , data = ds_train) %>% rf_recipe <- r$recipe(ft4_dia ~ . , data = class_train) %>%
r$update_role(subject_id, new_role = "id") %>% r$update_role(subject_id, new_role = "id") %>%
r$update_role(charttime, new_role = "time") %>% r$update_role(charttime, new_role = "time") %>%
r$update_role(ft4_dia, new_role = "class") %>%
r$step_impute_bag(r$all_predictors()) r$step_impute_bag(r$all_predictors())
rf_workflow <- wf$workflow() %>% rf_workflow <- wf$workflow() %>%
wf$add_model(rf_model) %>% wf$add_model(rf_model) %>%
wf$add_recipe(rf_recipe) wf$add_recipe(rf_recipe)
rf_fit <- p$fit(rf_workflow, ds_train) rf_fit <- p$fit(rf_workflow, class_train)
rf_predict <- ds_train %>% rf_predict <- class_train %>%
dplyr::select(FT4) %>% dplyr::select(ft4_dia) %>%
dplyr::bind_cols(predict(rf_fit, ds_train)) dplyr::bind_cols(
predict(rf_fit, class_train)
,predict(rf_fit, class_train, type = "prob")
)
conf_mat_rf <- ys$conf_mat(rf_predict, ft4_dia, .pred_class)
gp2$ggplot(rf_predict, gp2$aes(x = FT4, y = .pred)) + # explainer_rf <- DALEXtra::explain_tidymodels(
gp2$geom_point() # rf_fit
# ,data = class_train
# ,y = class_train$ft4_dia
# )
ys$rmse(rf_predict, FT4, .pred) # this takes awhile to run
#vip_lm <- DALEX::model_parts(explainer_rf)
metrics <- ys$metric_set(ys$rmse, ys$rsq, ys$mae) #plot(vip_lm)
metrics(rf_predict, FT4, .pred)