Refactor: consistent naming across functions, targets, and pkgdown

Functions: prepare_eda_recipe -> build_eda_recipe,
           create_efficiency_plot -> plot_efficiency,
           format_class_imbalance_tourney_gt -> format_tournament_gt

Targets: model_inputs_prefix -> baf_model_input_prefix,
         tbl_fraud_by_month_data -> fraud_by_month_summary,
         model_diag -> diag_fit, winning_params -> best_params,
         production_recipe_blueprint -> prod_recipe,
         final_eval_data -> test_predictions

pkgdown: restructured reference index into 6 logical sections,
         removed stale names and development comments.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-02-22 03:52:34 -05:00
parent f47b2e1be2
commit b38892f49e
7 changed files with 159 additions and 212 deletions

View File

@@ -19,9 +19,9 @@ tar_option_set(
"scales",
"ggplot2",
"quarto",
"corrr",
"recipes",
"themis",
"corrr",
"recipes",
"themis",
"tidyselect"
)
)
@@ -37,7 +37,7 @@ list(
bucket_name = "baf-fraud"
)
),
tar_target(
baf_primary_prefix,
clean_baf_base(
@@ -49,7 +49,7 @@ list(
verbose = TRUE
)
),
tar_target(
baf_feature_prefix,
engineer_features(
@@ -61,98 +61,60 @@ list(
verbose = TRUE
)
),
# ---- Figure objects ----
# ---- 05_model_input Generation ----
tar_target(
fig_fraud_by_month,
plot_fraud_by_month(baf_primary_prefix, bucket_name = "baf-fraud")
baf_model_input_prefix,
generate_model_inputs(
feature_prefix = baf_feature_prefix,
out_prefix = "05_model_input",
bucket_name = "baf-fraud"
)
),
# ---- Saved figure path (file target) ----
tar_target(
fig_fraud_by_month_path,
save_report_figure(
fig_fraud_by_month,
filename = "fig_fraud_by_month.png",
out_dir = "reports/figures"
),
format = "file"
),
tar_target(
tbl_fraud_by_month_data,
compute_fraud_by_month(baf_primary_prefix)
),
tar_target(
tbl_fraud_by_month_gt,
format_fraud_by_month_gt(tbl_fraud_by_month_data)
),
tar_target(
tbl_fraud_by_month_path,
save_report_table(tbl_fraud_by_month_gt, filename = "tbl_fraud_by_month.rds"),
format = "file"
),
# ---- Exploratory Data Analysis (EDA) Layer ----
# ---- EDA Layer ----
tar_target(
data_eda_m0,
connect_baf(baf_primary_prefix, use_duckdb = TRUE) |>
filter(month == 0) |>
collect()
),
tar_target(
eda_recipe,
prepare_eda_recipe(data_eda_m0)
build_eda_recipe(data_eda_m0)
),
tar_target(
data_baked_eda_m0,
bake(eda_recipe, new_data = data_eda_m0)
),
tar_target(
model_diag,
diag_fit,
train_diag_model(data_baked_eda_m0)
),
# ---- EDA Figures ----
tar_target(fig_var_imp, plot_var_imp(model_diag)),
tar_target(fig_var_imp, plot_var_imp(diag_fit)),
tar_target(fig_hexbin_interaction, plot_hexbin_interaction(data_baked_eda_m0)),
tar_target(fig_missingness, plot_missingness(data_eda_m0)),
tar_target(fig_num_cor, plot_num_cor(data_eda_m0)),
# ---- Saved EDA Figure Paths ----
tar_target(fig_missingness, plot_missingness(data_eda_m0)),
tar_target(fig_num_cor, plot_num_cor(data_eda_m0)),
# ---- Fraud Prevalence ----
tar_target(
fig_var_imp_path,
save_report_figure(fig_var_imp, "fig_var_imp.png"),
format = "file"
fig_fraud_by_month,
plot_fraud_by_month(baf_primary_prefix, bucket_name = "baf-fraud")
),
tar_target(
fig_hexbin_interaction_path,
save_report_figure(fig_hexbin_interaction, "fig_hexbin_interaction.png"),
format = "file"
fraud_by_month_summary,
compute_fraud_by_month(baf_primary_prefix)
),
tar_target(
fig_missingness_path,
save_report_figure(fig_missingness, "fig_missingness.png"),
format = "file"
),
tar_target(
fig_num_cor_path,
save_report_figure(fig_num_cor, "fig_num_cor.png"),
format = "file"
),
# ---- 05_model_input Generation ----
tar_target(
model_inputs_prefix,
generate_model_inputs(
feature_prefix = baf_feature_prefix,
out_prefix = "05_model_input",
bucket_name = "baf-fraud"
)
tbl_fraud_by_month_gt,
format_fraud_by_month_gt(fraud_by_month_summary)
),
# ---- Tournament Inputs ----
@@ -161,14 +123,14 @@ list(
tibble::tribble(
~recipe_name, ~data_folder, ~scale_pos_weight,
"Standard", "baseline", 1,
"Weighted", "baseline", 4,
"Weighted", "baseline", 4,
"Under", "under", 1,
"Smote", "smote", 1,
"Adasyn", "adasyn", 1,
"Tomek", "tomek", 1
)
),
tar_target(
imbalance_windows,
tibble::tribble(
@@ -178,110 +140,112 @@ list(
"Window 3", c(2, 3, 4), 5
)
),
# ---- 1. Data Layer (The Tournament Results) ----
# ---- Hyperparameter Tuning ----
tar_target(
best_params,
tune_lgbm(imbalance_windows)
),
# ---- Tournament Results ----
tar_target(
tbl_strategy_showdown,
{
# Force DAG to wait for the folders to be generated
force(model_inputs_prefix)
# Pass baf_feature_prefix so it tracks the latest layer
force(baf_model_input_prefix)
run_imbalance_tournament(imbalance_tasks, imbalance_windows, baf_feature_prefix)
}
),
# ---- 2. Figure Layer ----
tar_target(
fig_strategy_showdown,
create_efficiency_plot(tbl_strategy_showdown)
plot_efficiency(tbl_strategy_showdown)
),
tar_target(
fig_strategy_showdown_path,
save_report_figure(
fig_strategy_showdown,
filename = "fig_strategy_showdown.png",
out_dir = "reports/figures"
),
format = "file"
),
# ---- 3. Table Layer (gt object) ----
tar_target(
tbl_strategy_showdown_gt,
format_class_imbalance_tourney_gt(tbl_strategy_showdown)
format_tournament_gt(tbl_strategy_showdown)
),
# ---- Final Evaluation (Months 6-7) ----
tar_target(
tbl_strategy_showdown_path,
save_report_table(
tbl_strategy_showdown_gt,
filename = "tbl_strategy_showdown.rds",
out_dir = "reports/tables"
),
format = "file"
test_predictions,
evaluate_final_model(params = best_params)
),
# ---- Final Production Evaluation ----
tar_target(
final_eval_data,
evaluate_final_model(params = winning_params)
final_conf_mat,
yardstick::conf_mat(test_predictions, truth, pred_class)
),
tar_target(
final_conf_mat,
yardstick::conf_mat(final_eval_data, truth, pred_class)
final_roc_curve,
yardstick::roc_curve(test_predictions, truth, prob)
),
tar_target(
final_roc_curve,
yardstick::roc_curve(final_eval_data, truth, prob)
final_pr_curve,
yardstick::pr_curve(test_predictions, truth, prob)
),
tar_target(
final_pr_curve,
yardstick::pr_curve(final_eval_data, truth, prob)
),
# ---- Save Final Assets ----
tar_target(
fig_final_curves,
{
p1 <- ggplot2::autoplot(final_roc_curve) + ggplot2::labs(title = "ROC Curve (Months 6-7)")
p2 <- ggplot2::autoplot(final_pr_curve) + ggplot2::labs(title = "PR Curve (Months 6-7)")
p2 <- ggplot2::autoplot(final_pr_curve) + ggplot2::labs(title = "PR Curve (Months 6-7)")
cowplot::plot_grid(p1, p2)
}
),
tar_target(
fig_final_curves_path,
save_report_figure(fig_final_curves, "fig_final_curves.png"),
format = "file"
),
tar_target(
tbl_final_conf_mat_path,
save_report_table(final_conf_mat, "tbl_final_conf_mat.rds", out_dir = "reports/tables"),
format = "file"
),
# ---- Generate and Save Heatmap ----
tar_target(
fig_final_conf_mat,
plot_conf_mat_heatmap(final_conf_mat)
),
# ---- Production Deployment ----
tar_target(
fig_final_conf_mat_path,
save_report_figure(fig_final_conf_mat, "fig_final_conf_mat.png"),
format = "file"
data_full,
connect_baf(baf_feature_prefix, use_duckdb = TRUE) |>
collect()
),
# ---- Report Dependency Update ----
tar_target(
prod_recipe,
build_baf_recipe(data_full)
),
tar_target(
production_model_uri,
train_production_model(
data = data_full,
recipe = prod_recipe,
best_params = best_params,
model_filename = "baf_lgbm_prod_v1.txt"
),
format = "rds"
),
# ---- Saved Figure Paths ----
tar_target(fig_fraud_by_month_path, save_report_figure(fig_fraud_by_month, "fig_fraud_by_month.png"), format = "file"),
tar_target(fig_var_imp_path, save_report_figure(fig_var_imp, "fig_var_imp.png"), format = "file"),
tar_target(fig_hexbin_interaction_path, save_report_figure(fig_hexbin_interaction, "fig_hexbin_interaction.png"), format = "file"),
tar_target(fig_missingness_path, save_report_figure(fig_missingness, "fig_missingness.png"), format = "file"),
tar_target(fig_num_cor_path, save_report_figure(fig_num_cor, "fig_num_cor.png"), format = "file"),
tar_target(fig_strategy_showdown_path, save_report_figure(fig_strategy_showdown, "fig_strategy_showdown.png", out_dir = "reports/figures"), format = "file"),
tar_target(fig_final_conf_mat_path, save_report_figure(fig_final_conf_mat, "fig_final_conf_mat.png"), format = "file"),
tar_target(fig_final_curves_path, save_report_figure(fig_final_curves, "fig_final_curves.png"), format = "file"),
# ---- Saved Table Paths ----
tar_target(tbl_fraud_by_month_path, save_report_table(tbl_fraud_by_month_gt, "tbl_fraud_by_month.rds"), format = "file"),
tar_target(tbl_strategy_showdown_path, save_report_table(tbl_strategy_showdown_gt, "tbl_strategy_showdown.rds", out_dir = "reports/tables"), format = "file"),
tar_target(tbl_final_conf_mat_path, save_report_table(final_conf_mat, "tbl_final_conf_mat.rds", out_dir = "reports/tables"), format = "file"),
# ---- Report Assembly ----
tar_target(
report_assets,
c(
fig_fraud_by_month_path,
tbl_fraud_by_month_path,
fig_strategy_showdown_path,
fig_strategy_showdown_path,
tbl_strategy_showdown_path,
fig_var_imp_path,
fig_hexbin_interaction_path,
@@ -290,33 +254,9 @@ list(
),
format = "file"
),
tar_quarto(
report_slides,
path = "index.qmd"
),
# production model deployment
tar_target(
data_full,
connect_baf(baf_feature_prefix, use_duckdb = TRUE) |>
collect()
),
tar_target(
production_recipe_blueprint,
build_baf_recipe(data_full)
),
tar_target(
winning_params,
tune_lgbm(imbalance_windows)
),
tar_target(
production_model_uri,
train_production_model(
data = data_full,
recipe = production_recipe_blueprint, # <--- Pass the untrained blueprint!
best_params = winning_params,
model_filename = "baf_lgbm_prod_v1.txt"
),
format = "rds"
)
)
)