315 lines
7.8 KiB
R
315 lines
7.8 KiB
R
# Random Forests are the best models for both types finalize grid searchs
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rm(list = ls(all.names = TRUE)) # Clear the memory of variables from previous run.
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cat("\014") # Clear the console
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# load packages -----------------------------------------------------------
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box::use(
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magrittr[`%>%`]
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,here[here]
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,readr
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,gp2 = ggplot2[ggplot, aes]
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,rsample
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,r = recipes
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,wf = workflows
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,p = parsnip[tune]
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,ys = yardstick
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,d = dials
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,rsamp = rsample
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,tune
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)
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require(ggplot2) #this is needed for autoplot to work with workflows
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# globals -----------------------------------------------------------------
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set.seed(070823) #set seed for reproducible research
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# load-data ---------------------------------------------------------------
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model_data <- readr$read_rds(here("ML","data-unshared","model_data.RDS")) %>%
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dplyr::select(-subject_id, -charttime)
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# split data --------------------------------------------------------------
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model_data_split <- rsample$initial_split(
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model_data
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,prop = 0.80
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,strata = ft4_dia
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)
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ds_train <- rsample$training(model_data_split)
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ds_test <- rsample$testing(model_data_split)
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# verify distribution of data
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table(ds_train$ft4_dia) %>% prop.table()
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table(ds_test$ft4_dia) %>% prop.table()
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# random forest classification -----------------------------------------------------------
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rf_recipe <- r$recipe(ft4_dia ~ . , data = ds_train) %>%
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r$step_rm(FT4) %>%
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r$step_impute_bag(r$all_predictors())
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rf_tuning_model <- p$rand_forest(trees = tune(), mtry = tune(), min_n = tune()) %>%
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p$set_engine("ranger") %>% p$set_mode("classification")
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rf_workflow <- wf$workflow() %>%
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wf$add_model(rf_tuning_model) %>%
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wf$add_recipe(rf_recipe)
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rf_param <- p$extract_parameter_set_dials(rf_tuning_model)
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rf_param <- rf_param %>% update(mtry = d$finalize(d$mtry(), ds_train))
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data_fold <- rsamp$vfold_cv(ds_train, v = 5)
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# takes around 1 hr to run grid search. saving best params manaually
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# rf_tune <- rf_workflow %>%
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# tune::tune_grid(
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# data_fold
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# ,grid = rf_param %>% d$grid_regular()
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# )
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rf_best_params <- tibble::tibble(
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mtry = 8
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,trees = 2000
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,min_n = 2
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)
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final_rf_workflow <- rf_workflow %>%
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tune::finalize_workflow(rf_best_params)
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# Final Fit training data
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final_rf_fit <- p$fit(final_rf_workflow, ds_train)
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final_rf_predict <- ds_train %>%
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dplyr::select(ft4_dia) %>%
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dplyr::bind_cols(
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predict(final_rf_fit, ds_train)
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,predict(final_rf_fit, ds_train, type = "prob")
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)
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ys$accuracy(final_rf_predict,truth = ft4_dia, estimate = .pred_class )
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final_conf_rf <- ys$conf_mat(final_rf_predict, ft4_dia, .pred_class)
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# fitting test data
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class_test_results <-
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final_rf_fit %>%
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tune::last_fit(split = model_data_split)
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class_test_result_conf_matrix <- ys$conf_mat(
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class_test_results %>% tune::collect_predictions()
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,truth = ft4_dia
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,estimate = .pred_class
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)
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# x-boost- class ----------------------------------------------------------
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x_boost_rec <- r$recipe(ft4_dia ~ . , data = ds_train) %>%
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r$step_rm(FT4) %>%
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r$step_impute_bag(r$all_predictors()) %>%
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r$step_dummy(gender)
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xgb_spec <-
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p$boost_tree(tree_depth = tune(), learn_rate = tune(), loss_reduction = tune(),
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min_n = tune(), sample_size = tune(), trees = tune()) %>%
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p$set_engine("xgboost") %>%
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p$set_mode("classification")
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xboost_wf <- wf$workflow() %>%
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wf$add_model(xgb_spec) %>%
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wf$add_recipe(x_boost_rec)
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xboost_parms <- p$extract_parameter_set_dials(xgb_spec)
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# takes around 6 hours to tune
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# xboost_tune <- xboost_wf %>%
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# tune::tune_grid(
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# data_fold
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# ,grid = xboost_parms%>% d$grid_regular()
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# ,control = tune::control_grid(verbose = TRUE)
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# )
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xboost_best_params <- readRDS(here::here("ML", "outputs", "xboosttune_class.rds")) %>%
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tune::select_best(metric = "accuracy")
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final_xboost_wf <- xboost_wf %>%
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tune::finalize_workflow(xboost_best_params)
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# fit training data to best model
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final_xboost_fit <- p$fit(final_xboost_wf, ds_train)
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final_xboost_predict <- ds_train %>%
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dplyr::select(ft4_dia) %>%
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dplyr::bind_cols(
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predict(final_xboost_fit, ds_train)
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,predict(final_xboost_fit, ds_train, type = "prob")
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)
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ys$accuracy(final_xboost_predict,truth = ft4_dia, estimate = .pred_class )
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final_conf_xboost <- ys$conf_mat(final_xboost_predict, ft4_dia, .pred_class)
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# fitting test data
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class_test_results_boost <-
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final_xboost_fit %>%
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tune::last_fit(split = model_data_split)
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ys$accuracy(class_test_results_boost %>% tune::collect_predictions()
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,truth = ft4_dia, estimate = .pred_class )
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class_test_result_conf_matrix <- ys$conf_mat(
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class_test_results_boost %>% tune::collect_predictions()
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,truth = ft4_dia
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,estimate = .pred_class
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)
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# random forest regression ------------------------------------------------
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#
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reg_metrics <- ys$metric_set(ys$rmse, ys$rsq, ys$mae)
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rf_reg_tune_model <- p$rand_forest(trees = tune(), mtry = tune(), min_n = tune()) %>%
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p$set_engine("ranger") %>% p$set_mode("regression")
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rf_reg_recipe <- r$recipe(FT4 ~ . , data = ds_train) %>%
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r$step_rm(ft4_dia) %>%
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r$step_impute_bag(r$all_predictors())
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rf_reg_workflow <- wf$workflow() %>%
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wf$add_model(rf_reg_tune_model) %>%
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wf$add_recipe(rf_reg_recipe)
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rf_reg_param <- p$extract_parameter_set_dials(rf_reg_tune_model) %>%
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update(mtry = d$finalize(d$mtry(), ds_train))
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data_fold_reg <- rsamp$vfold_cv(ds_train, v = 5)
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# takes around 1 hr to run grid search. saving best params manaually
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# rf_reg_tune <- rf_reg_workflow %>%
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# tune::tune_grid(
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# data_fold_reg
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# ,grid = rf_reg_param %>% d$grid_regular()
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# )
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rf_reg_best_params <- tibble::tibble(
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mtry = 8
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,trees = 1000
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,min_n = 2
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)
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final_rf_reg_workflow <- rf_reg_workflow %>%
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tune::finalize_workflow(rf_reg_best_params)
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final_rf_reg_fit <- p$fit(final_rf_reg_workflow, ds_train)
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# predictions for training data
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final_rf_reg_predict <- ds_train %>%
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dplyr::select(FT4, TSH, ft4_dia) %>%
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dplyr::bind_cols(
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predict(final_rf_reg_fit, ds_train)
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) %>%
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dplyr::mutate(
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ft4_dia_pred = dplyr::case_when(
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TSH > 4.2 & `.pred` < 0.93 ~ "Hypo"
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,TSH > 4.2 & `.pred` > 0.93 ~ "Non-Hypo"
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,TSH < 0.27 & `.pred` > 1.7 ~ "Hyper"
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,TSH < 0.27 & `.pred` < 1.7 ~ "Non-Hyper"
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)
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) %>%
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dplyr::mutate(dplyr::across(
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ft4_dia_pred
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, ~factor(., levels = c("Hypo", "Non-Hypo","Hyper", "Non-Hyper")
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)
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)
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)
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ys$conf_mat(final_rf_reg_predict,truth = ft4_dia ,estimate = ft4_dia_pred)
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ys$accuracy(final_rf_reg_predict,truth = ft4_dia, estimate = ft4_dia_pred)
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reg_metrics(final_rf_reg_predict, truth = FT4, estimate = .pred)
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ggplot(final_rf_reg_predict, aes(x = FT4, y = .pred)) +
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gp2$geom_abline(lty = 2) +
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gp2$geom_point(alpha = 0.5) +
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tune::coord_obs_pred()
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# fitting test data
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reg_test_results <-
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final_rf_reg_fit %>%
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tune::last_fit(split = model_data_split)
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ds_reg_class_pred <- reg_test_results %>%
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tune::collect_predictions() %>%
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dplyr::select(-id, -.config) %>%
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dplyr::bind_cols(ds_test %>% dplyr::select(TSH, ft4_dia)) %>%
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dplyr::mutate(
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ft4_dia_pred = dplyr::case_when(
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TSH > 4.2 & `.pred` < 0.93 ~ "Hypo"
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,TSH > 4.2 & `.pred` > 0.93 ~ "Non-Hypo"
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,TSH < 0.27 & `.pred` > 1.7 ~ "Hyper"
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,TSH < 0.27 & `.pred` < 1.7 ~ "Non-Hyper"
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)
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) %>%
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dplyr::mutate(dplyr::across(
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ft4_dia_pred
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, ~factor(., levels = c("Hypo", "Non-Hypo","Hyper", "Non-Hyper")
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)
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)
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)
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ys$accuracy(ds_reg_class_pred,truth = ft4_dia, estimate = ft4_dia_pred)
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ys$conf_mat(ds_reg_class_pred,truth = ft4_dia ,estimate = ft4_dia_pred)
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tune::collect_metrics(reg_test_results)
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ggplot(reg_test_results %>% tune::collect_predictions() , aes(x = FT4, y = .pred)) +
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gp2$geom_abline(lty = 2) +
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gp2$geom_point(alpha = 0.5) +
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tune::coord_obs_pred()
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# check orginal data
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model_data %>%
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dplyr::mutate(tsh_level = ifelse(TSH > 4.2, "high", "low")) %>%
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dplyr::group_by(tsh_level, ft4_dia) %>%
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dplyr::summarise(
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n = n()
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) %>%
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mutate(freq = n / sum(n))
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