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4_predict_plots.R
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4_predict_plots.R
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source("4_predict/src/select_features.R")
source("4_predict/src/train_models.R")
source("4_predict/src/plot_diagnostics.R")
source("1_fetch/src/generate_credentials.R")
#Predict phase
p4_plot_targets_list <- list(
# Visualize Model Diagnostics:
# see note at top of 1_fetch.R re why these are 'local' file targets
# Boruta screening
# tar_target(p4_Boruta_static_png,
# plot_Boruta(p4_Boruta_static$brf_All,
# model_name = 'daily_SC_RF',
# out_dir = '4_predict/out/Boruta'),
# deployment = 'main',
# format = 'file'
# ),
# RF variable importance plot
#Should add error bars over X random seeds
tar_target(p4_vip_static_png,
plot_vip(RF_model = p4_train_RF_static$best_fit,
model_name = 'daily_SC_RF_static',
num_features = 20,
out_dir = '4_predict/out/random/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_static_dynamic_png,
plot_vip(RF_model = p4_train_RF_static_dynamic$best_fit,
model_name = 'daily_SC_RF_static_dynamic',
num_features = 20,
out_dir = '4_predict/out/random/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_min_static_png,
plot_vip(RF_model = p4_train_RF_min_static$best_fit,
model_name = 'daily_SC_RF_min_static',
num_features = 20,
out_dir = '4_predict/out/random/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_min_static_dynamic_png,
plot_vip(RF_model = p4_train_RF_min_static_dynamic$best_fit,
model_name = 'daily_SC_RF_min_static_dynamic',
num_features = 20,
out_dir = '4_predict/out/random/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_dynamic_png,
plot_vip(RF_model = p4_train_RF_dynamic$best_fit,
model_name = 'daily_SC_RF_dynamic',
num_features = 20,
out_dir = '4_predict/out/random/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_static_dynamic_temporal_png,
plot_vip(RF_model = p4_train_RF_static_dynamic_temporal$best_fit,
model_name = 'daily_SC_RF_static_dynamic_temporal',
num_features = 20,
out_dir = '4_predict/out/temporal/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_min_static_dynamic_temporal_png,
plot_vip(RF_model = p4_train_RF_min_static_dynamic_temporal$best_fit,
model_name = 'daily_SC_RF_min_static_dynamic_temporal',
num_features = 20,
out_dir = '4_predict/out/temporal/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_dynamic_temporal_png,
plot_vip(RF_model = p4_train_RF_dynamic_temporal$best_fit,
model_name = 'daily_SC_RF_dynamic_temporal',
num_features = 20,
out_dir = '4_predict/out/temporal/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_static_dynamic_spatial_png,
plot_vip(RF_model = p4_train_RF_static_dynamic_spatial$best_fit,
model_name = 'daily_SC_RF_static_dynamic_spatial',
num_features = 20,
out_dir = '4_predict/out/spatial/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_min_static_dynamic_spatial_png,
plot_vip(RF_model = p4_train_RF_min_static_dynamic_spatial$best_fit,
model_name = 'daily_SC_RF_min_static_dynamic_spatial',
num_features = 20,
out_dir = '4_predict/out/spatial/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_vip_dynamic_spatial_png,
plot_vip(RF_model = p4_train_RF_dynamic_spatial$best_fit,
model_name = 'daily_SC_RF_dynamic_spatial',
num_features = 20,
out_dir = '4_predict/out/spatial/vip'),
deployment = 'main',
format = 'file',
repository = 'local'
),
# RF hyperparameter optimization
tar_target(p4_hypopt_static_png,
plot_hyperparam_opt_results_RF(p4_train_RF_static$grid_params,
model_name = 'daily_SC_RF_static',
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_static_png,
plot_hyperparam_opt_marginals(p4_train_RF_static$grid_params,
model_name = 'daily_SC_RF_static',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_static_dynamic_png,
plot_hyperparam_opt_results_RF(p4_train_RF_static_dynamic$grid_params,
model_name = 'daily_SC_RF_static_dynamic',
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_static_dynamic_png,
plot_hyperparam_opt_marginals(p4_train_RF_static_dynamic$grid_params,
model_name = 'daily_SC_RF_static_dynamic',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_min_static_png,
plot_hyperparam_opt_results_RF(p4_train_RF_min_static$grid_params,
model_name = 'daily_SC_RF_min_static',
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_min_static_png,
plot_hyperparam_opt_marginals(p4_train_RF_min_static$grid_params,
model_name = 'daily_SC_RF_min_static',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_min_static_dynamic_png,
plot_hyperparam_opt_results_RF(p4_train_RF_min_static_dynamic$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic',
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_min_static_dynamic_png,
plot_hyperparam_opt_marginals(p4_train_RF_min_static_dynamic$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_dynamic_png,
plot_hyperparam_opt_results_RF(p4_train_RF_dynamic$grid_params,
model_name = 'daily_SC_RF_dynamic',
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_dynamic_png,
plot_hyperparam_opt_marginals(p4_train_RF_dynamic$grid_params,
model_name = 'daily_SC_RF_dynamic',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/random/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_static_dynamic_temporal_png,
plot_hyperparam_opt_results_RF(p4_train_RF_static_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_static_dynamic_temporal',
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_static_dynamic_temporal_png,
plot_hyperparam_opt_marginals(p4_train_RF_static_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_static_dynamic_temporal',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_min_static_dynamic_temporal_png,
plot_hyperparam_opt_results_RF(p4_train_RF_min_static_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic_temporal',
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_min_static_dynamic_temporal_png,
plot_hyperparam_opt_marginals(p4_train_RF_min_static_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic_temporal',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_dynamic_temporal_png,
plot_hyperparam_opt_results_RF(p4_train_RF_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_dynamic_temporal',
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_dynamic_temporal_png,
plot_hyperparam_opt_marginals(p4_train_RF_dynamic_temporal$grid_params,
model_name = 'daily_SC_RF_dynamic_temporal',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/temporal/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_static_dynamic_spatial_png,
plot_hyperparam_opt_results_RF(p4_train_RF_static_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_static_dynamic_spatial',
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_static_dynamic_spatial_png,
plot_hyperparam_opt_marginals(p4_train_RF_static_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_static_dynamic_spatial',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_min_static_dynamic_spatial_png,
plot_hyperparam_opt_results_RF(p4_train_RF_min_static_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic_spatial',
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_min_static_dynamic_spatial_png,
plot_hyperparam_opt_marginals(p4_train_RF_min_static_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_min_static_dynamic_spatial',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_dynamic_spatial_png,
plot_hyperparam_opt_results_RF(p4_train_RF_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_dynamic_spatial',
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_hypopt_marginals_dynamic_spatial_png,
plot_hyperparam_opt_marginals(p4_train_RF_dynamic_spatial$grid_params,
model_name = 'daily_SC_RF_dynamic_spatial',
plt_type = "marginals",
perf_metric = NULL,
out_dir = '4_predict/out/spatial/hypopt'),
deployment = 'main',
format = 'file',
repository = 'local'
),
# RF predicted vs. observed y
#Should be for the mean over X random seeds
tar_target(p4_pred_obs_static_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static$pred,
model_name = 'daily_SC_RF_static',
out_dir = '4_predict/out/random/pred_obs/RF_static',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_test$pred,
model_name = 'daily_SC_RF_static_test',
out_dir = '4_predict/out/random/pred_obs/RF_static',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic$pred,
model_name = 'daily_SC_RF_static_dynamic',
out_dir = '4_predict/out/random/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic_test$pred,
model_name = 'daily_SC_RF_static_dynamic_test',
out_dir = '4_predict/out/random/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static$pred,
model_name = 'daily_SC_RF_min_static',
out_dir = '4_predict/out/random/pred_obs/RF_min_static',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_test$pred,
model_name = 'daily_SC_RF_min_static_test',
out_dir = '4_predict/out/random/pred_obs/RF_min_static',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic$pred,
model_name = 'daily_SC_RF_min_static_dynamic',
out_dir = '4_predict/out/random/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic_test$pred,
model_name = 'daily_SC_RF_min_static_dynamic_test',
out_dir = '4_predict/out/random/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic$pred,
model_name = 'daily_SC_RF_dynamic',
out_dir = '4_predict/out/random/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic_test$pred,
model_name = 'daily_SC_RF_dynamic_test',
out_dir = '4_predict/out/random/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_temporal_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic_temporal$pred,
model_name = 'daily_SC_RF_static_dynamic_temporal',
out_dir = '4_predict/out/temporal/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic_temporal_test$pred,
model_name = 'daily_SC_RF_static_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_temporal_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic_temporal$pred,
model_name = 'daily_SC_RF_min_static_dynamic_temporal',
out_dir = '4_predict/out/temporal/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic_temporal_test$pred,
model_name = 'daily_SC_RF_min_static_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_temporal_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic_temporal$pred,
model_name = 'daily_SC_RF_dynamic_temporal',
out_dir = '4_predict/out/temporal/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic_temporal_test$pred,
model_name = 'daily_SC_RF_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_spatial_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic_spatial$pred,
model_name = 'daily_SC_RF_static_dynamic_spatial',
out_dir = '4_predict/out/spatial/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_static_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_static_dynamic_spatial_test$pred,
model_name = 'daily_SC_RF_static_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RF_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_spatial_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic_spatial$pred,
model_name = 'daily_SC_RF_min_static_dynamic_spatial',
out_dir = '4_predict/out/spatial/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_min_static_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_min_static_dynamic_spatial_test$pred,
model_name = 'daily_SC_RF_min_static_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RF_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_spatial_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic_spatial$pred,
model_name = 'daily_SC_RF_dynamic_spatial',
out_dir = '4_predict/out/spatial/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_pred_obs_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_pred_RF_dynamic_spatial_test$pred,
model_name = 'daily_SC_RF_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RF_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
# RGCN predicted vs. observed y
tar_target(p4_RGCN_pred_obs_static_dynamic_temporal_train,
read_csv('4_predict/out/temporal/RGCN/static_dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_temporal_val,
read_csv('4_predict/out/temporal/RGCN/static_dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_temporal_test,
read_csv('4_predict/out/temporal/RGCN/static_dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_temporal_full,
rbind(p4_RGCN_pred_obs_static_dynamic_temporal_train,
p4_RGCN_pred_obs_static_dynamic_temporal_val,
p4_RGCN_pred_obs_static_dynamic_temporal_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_static_dynamic_temporal_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_static_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RGCN_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_temporal_train,
read_csv('4_predict/out/temporal/RGCN/min_static_dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_temporal_val,
read_csv('4_predict/out/temporal/RGCN/min_static_dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_temporal_test,
read_csv('4_predict/out/temporal/RGCN/min_static_dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_temporal_full,
rbind(p4_RGCN_pred_obs_min_static_dynamic_temporal_train,
p4_RGCN_pred_obs_min_static_dynamic_temporal_val,
p4_RGCN_pred_obs_min_static_dynamic_temporal_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_min_static_dynamic_temporal_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_min_static_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RGCN_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_RGCN_pred_obs_dynamic_temporal_train,
read_csv('4_predict/out/temporal/RGCN/dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_temporal_val,
read_csv('4_predict/out/temporal/RGCN/dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_temporal_test,
read_csv('4_predict/out/temporal/RGCN/dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_temporal_full,
rbind(p4_RGCN_pred_obs_dynamic_temporal_train,
p4_RGCN_pred_obs_dynamic_temporal_val,
p4_RGCN_pred_obs_dynamic_temporal_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_temporal_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_dynamic_temporal_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_dynamic_temporal_test',
out_dir = '4_predict/out/temporal/pred_obs/RGCN_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_spatial_train,
read_csv('4_predict/out/spatial/RGCN/static_dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_spatial_val,
read_csv('4_predict/out/spatial/RGCN/static_dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_spatial_test,
read_csv('4_predict/out/spatial/RGCN/static_dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_spatial_full,
rbind(p4_RGCN_pred_obs_static_dynamic_spatial_train,
p4_RGCN_pred_obs_static_dynamic_spatial_val,
p4_RGCN_pred_obs_static_dynamic_spatial_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_static_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_static_dynamic_spatial_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_static_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RGCN_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_spatial_train,
read_csv('4_predict/out/spatial/RGCN/min_static_dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_spatial_val,
read_csv('4_predict/out/spatial/RGCN/min_static_dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_spatial_test,
read_csv('4_predict/out/spatial/RGCN/min_static_dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_spatial_full,
rbind(p4_RGCN_pred_obs_min_static_dynamic_spatial_train,
p4_RGCN_pred_obs_min_static_dynamic_spatial_val,
p4_RGCN_pred_obs_min_static_dynamic_spatial_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_min_static_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_min_static_dynamic_spatial_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_min_static_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RGCN_min_static_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_RGCN_pred_obs_dynamic_spatial_train,
read_csv('4_predict/out/spatial/RGCN/dynamic_test1/trn_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_spatial_val,
read_csv('4_predict/out/spatial/RGCN/dynamic_test1/val_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_spatial_test,
read_csv('4_predict/out/spatial/RGCN/dynamic_test1/tst_preds_obs.csv') %>%
rename(.pred = pred) %>%
mutate(Month = month(Date)) %>%
mutate(Year = year(Date)) %>%
mutate(err = (.pred - obs)) %>%
mutate(errsq = err^2),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_spatial_full,
rbind(p4_RGCN_pred_obs_dynamic_spatial_train,
p4_RGCN_pred_obs_dynamic_spatial_val,
p4_RGCN_pred_obs_dynamic_spatial_test) %>%
arrange(Date, PRMS_segid),
deployment = 'main'
),
tar_target(p4_RGCN_pred_obs_dynamic_spatial_test_png,
plot_pred_obs(df_pred_obs = p4_RGCN_pred_obs_dynamic_spatial_test %>%
drop_na(),
model_name = 'daily_SC_RGCN_dynamic_spatial_test',
out_dir = '4_predict/out/spatial/pred_obs/RGCN_dynamic',
count_shade = TRUE),
deployment = 'main',
format = 'file',
repository = 'local'
),
#Train test boxplot coverage of SC data
tar_target(p4_train_test_boxplot_coverage_png,
plot_metric_boxplot(data_split = p4_Boruta_static$input_data,
model_name = 'daily_SC_RF',
pred_var = 'mean_value',
out_dir = '4_predict/out/random/pred_obs'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_boxplot_coverage_temporal_png,
plot_metric_boxplot(data_split = p4_selected_static_dynamic_attrs_temporal$input_data,
model_name = 'daily_SC_RF',
pred_var = 'mean_value',
out_dir = '4_predict/out/temporal/pred_obs'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_boxplot_coverage_spatial_png,
plot_metric_boxplot(data_split = p4_selected_static_dynamic_attrs_spatial$input_data,
model_name = 'daily_SC_RF',
pred_var = 'mean_value',
out_dir = '4_predict/out/spatial/pred_obs'),
deployment = 'main',
format = 'file',
repository = 'local'
),
#Cross validation coverage barplots
tar_target(p4_train_test_CV_static_png,
barplot_compare_RF(mod = p4_train_RF_static,
model_name = 'daily_SC_RF_static',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/random/pred_obs/RF_static'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_static_dynamic_png,
barplot_compare_RF(mod = p4_train_RF_static_dynamic,
model_name = 'daily_SC_RF_static_dynamic',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/random/pred_obs/RF_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_min_static_png,
barplot_compare_RF(mod = p4_train_RF_min_static,
model_name = 'daily_SC_RF_min_static',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/random/pred_obs/RF_min_static'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_min_static_dynamic_png,
barplot_compare_RF(mod = p4_train_RF_min_static_dynamic,
model_name = 'daily_SC_RF_min_static_dynamic',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/random/pred_obs/RF_min_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_dynamic_png,
barplot_compare_RF(mod = p4_train_RF_dynamic,
model_name = 'daily_SC_RF_dynamic',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/random/pred_obs/RF_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_static_dynamic_temporal_png,
barplot_compare_RF(mod = p4_train_RF_static_dynamic_temporal,
model_name = 'daily_SC_RF_static_dynamic_temporal',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/temporal/pred_obs/RF_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_min_static_dynamic_temporal_png,
barplot_compare_RF(mod = p4_train_RF_min_static_dynamic_temporal,
model_name = 'daily_SC_RF_min_static_dynamic_temporal',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/temporal/pred_obs/RF_min_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_dynamic_temporal_png,
barplot_compare_RF(mod = p4_train_RF_dynamic_temporal,
model_name = 'daily_SC_RF_dynamic_temporal',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/temporal/pred_obs/RF_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_static_dynamic_spatial_png,
barplot_compare_RF(mod = p4_train_RF_static_dynamic_spatial,
model_name = 'daily_SC_RF_static_dynamic_spatial',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/spatial/pred_obs/RF_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_min_static_dynamic_spatial_png,
barplot_compare_RF(mod = p4_train_RF_min_static_dynamic_spatial,
model_name = 'daily_SC_RF_min_static_dynamic_spatial',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/spatial/pred_obs/RF_min_static_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
tar_target(p4_train_test_CV_dynamic_spatial_png,
barplot_compare_RF(mod = p4_train_RF_dynamic_spatial,
model_name = 'daily_SC_RF_dynamic_spatial',
pred_var = 'mean_value',
perf_metric = 'rmse',
out_dir = '4_predict/out/spatial/pred_obs/RF_dynamic'),
deployment = 'main',
format = 'file',
repository = 'local'
),
# Spatial residuals aggregated over time
tar_target(
p4_spatial_res_static_png,
plot_maps(pred_df = p4_pred_RF_static$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_static",
filename_end = '_full',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_static_test_png,
plot_maps(pred_df = p4_pred_RF_static_test$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_static",
filename_end = '_test',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_min_static_png,
plot_maps(pred_df = p4_pred_RF_min_static$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_min_static",
filename_end = '_full',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_min_static_test_png,
plot_maps(pred_df = p4_pred_RF_min_static_test$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_min_static",
filename_end = '_test',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_static_dynamic_png,
plot_maps(pred_df = p4_pred_RF_static_dynamic$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_static_dynamic",
filename_end = '_full',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_static_dynamic_test_png,
plot_maps(pred_df = p4_pred_RF_static_dynamic_test$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_static_dynamic",
filename_end = '_test',
reservoirs = p1_reservoirs_sf$dams),
format = "file",
repository = 'local'
),
tar_target(
p4_spatial_res_min_static_dynamic_png,
plot_maps(pred_df = p4_pred_RF_min_static_dynamic$pred,
network_geometry = p1_reaches_sf,
file_path = "4_predict/out/random/spatial_res/RF_min_static_dynamic",
filename_end = '_full',
reservoirs = p1_reservoirs_sf$dams),