tests/testthat/_snaps/use-templates.md

In Yu-Group/simChef: Intensive Computational Experiments Made Easy

use_*_template() works

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_pred_cols <- stop('(Optional) Add name of column in `fit_results` with prediction result columns to be unnested.')
true_pred_col <- stop('Add name of column in `fit_results` with true responses here.')
est_pred_col <- stop('Add name of column in `fit_results` with the predicted responses here.')


pred_err <- create_evaluator(
  .eval_fun = summarize_pred_err,
  .name = 'Prediction Accuracy',
  nested_cols = nested_pred_cols,
  truth_col = true_pred_col,
  estimate_col = est_pred_col
)

pred_err_plot <- create_visualizer(
  .viz_fun = plot_pred_err,
  .name = 'Prediction Accuracy Plot',
  eval_name = 'Prediction Accuracy'
)

experiment <- create_experiment(name = 'Prediction Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(pred_err) %>% 
  add_visualizer(pred_err_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_pred_cols <- stop('(Optional) Add name of column in `fit_results` with prediction result columns to be unnested.')
true_pred_col <- stop('Add name of column in `fit_results` with true responses here.')
est_pred_col <- stop('Add name of column in `fit_results` with the predicted responses here.')
prob_pred_cols <- stop('Add name of column(s) in `fit_results` with the predicted probabilities here.')


pred_err <- create_evaluator(
  .eval_fun = summarize_pred_err,
  .name = 'Prediction Accuracy',
  nested_cols = nested_pred_cols,
  truth_col = true_pred_col,
  estimate_col = est_pred_col,
  prob_cols = prob_pred_cols
)

pred_err_plot <- create_visualizer(
  .viz_fun = plot_pred_err,
  .name = 'Prediction Accuracy Plot',
  eval_name = 'Prediction Accuracy'
)

roc_plot <- create_visualizer(
  .viz_fun = plot_pred_curve,
  .name = 'ROC Plot',
  curve = 'ROC',
  eval_fun_options = list(
    nested_cols = nested_pred_cols,
    truth_col = true_pred_col,
    prob_cols = prob_pred_cols
  )
)

pr_plot <- create_visualizer(
  .viz_fun = plot_pred_curve,
  .name = 'PR Plot',
  curve = 'PR',
  eval_fun_options = list(
    nested_cols = nested_pred_cols,
    truth_col = true_pred_col,
    prob_cols = prob_pred_cols
  )
)

experiment <- create_experiment(name = 'Prediction Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(pred_err) %>% 
  add_visualizer(pred_err_plot) %>% 
  add_visualizer(roc_plot) %>% 
  add_visualizer(pr_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_pred_cols <- stop('(Optional) Add name of column in `fit_results` with prediction result columns to be unnested.')
true_pred_col <- stop('Add name of column in `fit_results` with true responses here.')
est_pred_col <- stop('Add name of column in `fit_results` with the predicted responses here.')

nested_feature_cols <- stop('(Optional) Add name of column in `fit_results` with feature importance columns to be unnested here.')
feature_col <- stop('Add name of column in `fit_results` containing the feature names here.')
true_feature_col <- stop('Add name of column in `fit_results` containing the true feature support here.')
feature_imp_col <- stop('Add name of column in `fit_results` containing the feature importances here.')
feature_sel_col <- stop('(Optional) Add name of column in `fit_results` containing the (estimated) selected features here.')

pred_err <- create_evaluator(
  .eval_fun = summarize_pred_err,
  .name = 'Prediction Accuracy',
  nested_cols = nested_pred_cols,
  truth_col = true_pred_col,
  estimate_col = est_pred_col
)

fi <- create_evaluator(
  .eval_fun = summarize_feature_importance,
  .name = 'Feature Importances',
  nested_cols = nested_feature_cols,
  feature_col = feature_col,
  imp_col = feature_imp_col
)

feature_sel <- create_evaluator(
  .eval_fun = summarize_feature_selection_err,
  .name = 'Feature Selection Error',
  nested_cols = nested_feature_cols,
  truth_col = true_feature_col,
  estimate_col = feature_sel_col,
  imp_col = feature_imp_col
)

pred_err_plot <- create_visualizer(
  .viz_fun = plot_pred_err,
  .name = 'Prediction Accuracy Plot',
  eval_name = 'Prediction Accuracy'
)

fi_plot <- create_visualizer(
  .viz_fun = plot_feature_importance,
  .name = 'Feature Importances Plot',
  eval_name = 'Feature Importances',
  feature_col = feature_col
)

feature_sel_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_err,
  .name = 'Feature Selection Error Plot',
  eval_name = 'Feature Selection Error'
)

feature_roc_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_curve,
  .name = 'Feature Selection ROC Plot',
  curve = 'ROC',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    imp_col = feature_imp_col
  )
)

feature_pr_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_curve,
  .name = 'Feature Selection PR Plot',
  curve = 'PR',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    imp_col = feature_imp_col
  )
)

experiment <- create_experiment(name = 'Prediction Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(pred_err) %>% 
  add_evaluator(fi) %>% 
  add_evaluator(feature_sel) %>% 
  add_visualizer(pred_err_plot) %>% 
  add_visualizer(fi_plot) %>% 
  add_visualizer(feature_sel_plot) %>% 
  add_visualizer(feature_roc_plot) %>% 
  add_visualizer(feature_pr_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_pred_cols <- stop('(Optional) Add name of column in `fit_results` with prediction result columns to be unnested.')
true_pred_col <- stop('Add name of column in `fit_results` with true responses here.')
est_pred_col <- stop('Add name of column in `fit_results` with the predicted responses here.')
prob_pred_cols <- stop('Add name of column(s) in `fit_results` with the predicted probabilities here.')

nested_feature_cols <- stop('(Optional) Add name of column in `fit_results` with feature importance columns to be unnested here.')
feature_col <- stop('Add name of column in `fit_results` containing the feature names here.')
true_feature_col <- stop('Add name of column in `fit_results` containing the true feature support here.')
feature_imp_col <- stop('Add name of column in `fit_results` containing the feature importances here.')
feature_sel_col <- stop('(Optional) Add name of column in `fit_results` containing the (estimated) selected features here.')

pred_err <- create_evaluator(
  .eval_fun = summarize_pred_err,
  .name = 'Prediction Accuracy',
  nested_cols = nested_pred_cols,
  truth_col = true_pred_col,
  estimate_col = est_pred_col,
  prob_cols = prob_pred_cols
)

fi <- create_evaluator(
  .eval_fun = summarize_feature_importance,
  .name = 'Feature Importances',
  nested_cols = nested_feature_cols,
  feature_col = feature_col,
  imp_col = feature_imp_col
)

feature_sel <- create_evaluator(
  .eval_fun = summarize_feature_selection_err,
  .name = 'Feature Selection Error',
  nested_cols = nested_feature_cols,
  truth_col = true_feature_col,
  estimate_col = feature_sel_col,
  imp_col = feature_imp_col
)

pred_err_plot <- create_visualizer(
  .viz_fun = plot_pred_err,
  .name = 'Prediction Accuracy Plot',
  eval_name = 'Prediction Accuracy'
)

roc_plot <- create_visualizer(
  .viz_fun = plot_pred_curve,
  .name = 'ROC Plot',
  curve = 'ROC',
  eval_fun_options = list(
    nested_cols = nested_pred_cols,
    truth_col = true_pred_col,
    prob_cols = prob_pred_cols
  )
)

pr_plot <- create_visualizer(
  .viz_fun = plot_pred_curve,
  .name = 'PR Plot',
  curve = 'PR',
  eval_fun_options = list(
    nested_cols = nested_pred_cols,
    truth_col = true_pred_col,
    prob_cols = prob_pred_cols
  )
)

fi_plot <- create_visualizer(
  .viz_fun = plot_feature_importance,
  .name = 'Feature Importances Plot',
  eval_name = 'Feature Importances',
  feature_col = feature_col
)

feature_sel_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_err,
  .name = 'Feature Selection Error Plot',
  eval_name = 'Feature Selection Error'
)

feature_roc_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_curve,
  .name = 'Feature Selection ROC Plot',
  curve = 'ROC',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    imp_col = feature_imp_col
  )
)

feature_pr_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_curve,
  .name = 'Feature Selection PR Plot',
  curve = 'PR',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    imp_col = feature_imp_col
  )
)

experiment <- create_experiment(name = 'Prediction Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(pred_err) %>% 
  add_evaluator(fi) %>% 
  add_evaluator(feature_sel) %>% 
  add_visualizer(pred_err_plot) %>% 
  add_visualizer(roc_plot) %>% 
  add_visualizer(pr_plot) %>% 
  add_visualizer(fi_plot) %>% 
  add_visualizer(feature_sel_plot) %>% 
  add_visualizer(feature_roc_plot) %>% 
  add_visualizer(feature_pr_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_feature_cols <- stop('(Optional) Add name of column in `fit_results` with feature importance columns to be unnested here.')
feature_col <- stop('Add name of column in `fit_results` containing the feature names here.')
true_feature_col <- stop('Add name of column in `fit_results` containing the true feature support here.')
feature_imp_col <- stop('Add name of column in `fit_results` containing the feature importances here.')
feature_sel_col <- stop('(Optional) Add name of column in `fit_results` containing the (estimated) selected features here.')

fi <- create_evaluator(
  .eval_fun = summarize_feature_importance,
  .name = 'Feature Importances',
  nested_cols = nested_feature_cols,
  feature_col = feature_col,
  imp_col = feature_imp_col
)

feature_sel <- create_evaluator(
  .eval_fun = summarize_feature_selection_err,
  .name = 'Feature Selection Error',
  nested_cols = nested_feature_cols,
  truth_col = true_feature_col,
  estimate_col = feature_sel_col,
  imp_col = feature_imp_col
)

fi_plot <- create_visualizer(
  .viz_fun = plot_feature_importance,
  .name = 'Feature Importances Plot',
  eval_name = 'Feature Importances',
  feature_col = feature_col
)

feature_sel_plot <- create_visualizer(
  .viz_fun = plot_feature_selection_err,
  .name = 'Feature Selection Error Plot',
  eval_name = 'Feature Selection Error'
)

experiment <- create_experiment(name = 'Feature Selection Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(fi) %>% 
  add_evaluator(feature_sel) %>% 
  add_visualizer(fi_plot) %>% 
  add_visualizer(feature_sel_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

dgp <- create_dgp(
  .dgp_fun = stop('Add DGP function here.'),
  .name = stop('Add name of DGP here.'),
  stop('Add additional arguments (if necessary) to pass to DGP here.')
)

method <- create_method(
  .method_fun = stop('Add Method function here.'),
  .name = stop('Add name of Method here.'),
  stop('Add additional arguments (if necessary) to pass to Method here.')
)

nested_feature_cols <- stop('(Optional) Add name of column in `fit_results` with feature importance columns to be unnested here.')
feature_col <- stop('Add name of column in `fit_results` containing the feature names here.')
true_feature_col <- stop('Add name of column in `fit_results` containing the true feature support here.')
pval_col <- stop('Add name of column in `fit_results` containing the p-values here.')

inf_err <- create_evaluator(
  .eval_fun = summarize_testing_err,
  .name = 'Hypothesis Testing Error',
  nested_cols = nested_feature_cols,
  truth_col = true_feature_col,
  pval_col = pval_col
)

fi_pval <- create_evaluator(
  .eval_fun = summarize_feature_importance,
  .name = 'P-value Summary Statistics',
  eval_id = 'pval',
  nested_cols = nested_feature_cols,
  feature_col = feature_col,
  imp_col = pval_col
)

inf_err_plot <- create_visualizer(
  .viz_fun = plot_testing_err,
  .name = 'Hypothesis Testing Error Plot',
  eval_name = 'Hypothesis Testing Error'
)

inf_roc_plot <- create_visualizer(
  .viz_fun = plot_testing_curve,
  .name = 'Feature ROC Plot',
  curve = 'ROC',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    pval_col = pval_col
  )
)

inf_pr_plot <- create_visualizer(
  .viz_fun = plot_testing_curve,
  .name = 'Feature Selection PR Plot',
  curve = 'PR',
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    truth_col = true_feature_col,
    pval_col = pval_col
  )
)

reject_prob_plot <- create_visualizer(
  .viz_fun = plot_reject_prob,
  .name = 'Rejection Probability Curve',
  feature_col = feature_col,
  eval_fun_options = list(
    nested_cols = nested_feature_cols,
    pval_col = pval_col
  )
)

experiment <- create_experiment(name = 'Inference Experiment') %>% 
  add_dgp(dgp) %>% 
  add_method(method) %>% 
  add_evaluator(inf_err) %>% 
  add_evaluator(fi_pval) %>% 
  add_visualizer(inf_err_plot) %>% 
  add_visualizer(inf_roc_plot) %>% 
  add_visualizer(inf_pr_plot) %>% 
  add_visualizer(reject_prob_plot)

init_docs(experiment)  #> fill out documentation before proceeding!

results <- run_experiment(
  experiment = experiment,
  n_reps = stop('Add number of replicates here.'),
  save = TRUE
)

render_docs(experiment)

Yu-Group/simChef documentation built on March 25, 2024, 3:22 a.m.