fastml version 0.7.9

New features

• n_cores and engine threads are now separate: n_cores previously sized both the parallel worker pool and the thread count passed to engines that accept one, so a request for k cores could demand up to k^2 threads and contend with itself on engines that thread aggressively. n_cores now controls the worker pool only, and a new engine_threads argument (default 1) controls the per-engine thread count for num.threads (ranger), num_threads (LightGBM), and nthread (xgboost). Total CPU demand is therefore the product the user specifies. Behaviour at the defaults (n_cores = 1, engine_threads = 1) is unchanged; a previous n_cores = k run is now written n_cores = k, engine_threads = k if the old thread allocation is wanted. Determinism warnings for multithreaded engines are keyed to engine_threads rather than n_cores.

Bug fixes

• Prediction no longer falls back to unpreprocessed data: in process_model(), a failure to apply a native survival model’s stored recipe to the evaluation data silently returned the raw, unbaked data, so the model predicted on features it was not trained on and reported plausible but meaningless metrics. The failure is now fatal, with an error naming the model and the underlying cause.
• Engine defaults are no longer discarded when engine arguments are applied: parsnip::set_engine() replaces the engine arguments already attached to a specification rather than adding to them, so the second application in train_models() silently dropped every default the spec builder had set — LightGBM’s counts, bagging_freq and verbose, an xgboost early-stopping configuration (early_stop, validation), and sparsediscrim’s regularization_method. Arguments are now merged through the new internal fastml_merge_engine_args(), with pipeline values winning only on the keys they define. Case Study B’s reported metrics are unchanged, since the dropped LightGBM arguments were inert for that configuration; the defect is live wherever mtry or sample_size are tuned for LightGBM, xgboost early stopping is requested, or sparsediscrim is used.
• Parallel execution restored: fastml_setup_parallel() called doFuture::registerDoFuture(flavor = "%dofuture%"), but registerDoFuture() accepts no arguments, so any call with n_cores > 1 failed with “unused argument”. The argument has been dropped. Parallel execution was unusable in 0.7.8 with current doFuture.
• Parallel cleanup no longer corrupts foreach: when no foreach backend was registered before a fastml() call, the previous backend’s data/info hooks are NULL, and passing them back to foreach::setDoPar() on exit left foreach::getDoParName() erroring for the rest of the session. The sequential default is now registered instead.
• Silent preprocessing fallbacks removed package-wide: the same “fall back to unprocessed data” pattern appeared in eight further places. On prediction paths (predict_risk() and predict_survival() for native survival fits) it is now fatal via the shared helper fastml_bake_or_abort(). Where aborting would discard an otherwise complete result — assembling processed_test_data in fastml(), and the explanation utilities (explain_ale(), explain_stability(), interaction_strength(), plot_ice(), surrogate_tree()) — the fallback remains but now warns and states that the output describes raw rather than preprocessed features.
• Risk-prediction errors are no longer discarded: the error handler around the survival risk-prediction block dropped the original condition, leaving users with NA risk scores and no diagnostic. The original message is now surfaced as a warning; if the fallback prediction path also fails, both messages are reported.
• Recipe scanner now detects embedded external data: fastml_detect_leaky_recipe_steps() tests for data.frame/tbl_df components before the generic list branch. Data frames are themselves lists, so the previous ordering recursed into their columns and the check was unreachable; a custom recipe step carrying an external lookup table was silently accepted. Such steps are now flagged and training is aborted.
• Pretrained recipes are now actually rejected: fastml_validate_user_recipe() determined trained state from recipe$trained, a field prepped recipes do not carry, so the check never fired. Trained state is now read via recipes::fully_trained(), with the tr_info training-set record as a fallback for prepped recipes without steps.

Improvements

• Parallel path is now tested outside CRAN gating: the two parallel defects above shipped precisely because the only test touching that path was CRAN-gated. A new tests/testthat/test-parallel-setup.R covers fastml_setup_parallel() directly — backend registration, plan and future.seed restoration, recovery of a caller-registered backend, and the n_cores/engine_threads separation — and runs in an ordinary CRAN check, costing roughly four seconds because it exercises the helper rather than fitting models. Reintroducing each defect was confirmed to make these tests fail. The end-to-end multicore run stays behind skip_on_cran(), since it spends about twenty seconds on multisession worker startup to reach the same defects.
• Survival execution path is announced: under verbose = TRUE, train_models() now reports whether each algorithm is fitted through a parsnip workflow (the guarded resampling path) or through its native engine, so users can tell which path a given method took rather than inferring it.
• Tuning finalization extracted: the block in process_model() that selected the best configuration from a tune_results object (with metric fallback) and refit the finalized workflow is now the internal helper finalize_tuned_model(). Behaviour is unchanged, including the NULL-with-warning return when no configuration can be selected.
• Process model test coverage: added tests/testthat/test-process-model.R covering the recipe-application abort contract, its diagnostic message, finalize_tuned_model()’s graceful NULL return, and a clean end-to-end native survival evaluation.
• Recipe guard test coverage: added tests/testthat/test-security-guards.R with behavioural tests for the recipe scanner, covering safe-listed and non-safe-listed standard steps (false-positive checks), embedded data frames and tibbles, global-environment and parent-frame references, abort-on-rejection behaviour, and pretrained/non-recipe input.

fastml version 0.7.8

New features

• Validation Split Resampling: Added resampling_method = "validation_split" to fastml() and train_models(). The holdout proportion is derived from folds as 1 - 1 / folds, with stratification support where applicable.
• Explicit Save Helper: Added exported save_fastml() as the primary helper for persisting fitted fastml objects.

Improvements

• Nested CV Parameter Tracking: Improved nested cross-validation selection so the chosen outer split better follows the final hyperparameter configuration selected from inner results.
• Survival Holdout Plumbing: Holdout evaluation now forwards survival-specific column metadata (start_col, time_col, status_col) through the evaluation path.
• Prediction Model Validation: predict.fastml() now treats native survival and Royston-Parmar model objects as valid prediction targets when flattening and selecting fitted models.
• Safer Task Detection: Survival auto-detection now ignores missing status values when checking for two-level event coding, and numeric auto-detection only upgrades clearly binary numeric outcomes to classification.
• RNG State Restoration: fastml(), train_models(), and bootstrap confidence interval computations now restore the caller’s .Random.seed after execution.
• Documentation Updates: Expanded documentation for folds, flatten_and_rename_models(), and get_best_model_idx() for clearer usage and cleaner package checks.

Bug fixes

• Fixed event_class validation in both fastml() and train_models() so invalid values are rejected consistently.
• Fixed multiclass handling so logistic_reg is converted to multinom_reg before the training loop, avoiding per-iteration mutation and preserving engine parameter transfer.
• Fixed discriminant model specification helpers to use parsnip::discrim_linear() and parsnip::discrim_quad(), resolving dependency warnings caused by referencing unexported discrim objects.
• Fixed default engine resolution by removing duplicate switch entries for survival algorithms such as survreg and royston_parmar.
• Removed package-owned restoration of deleted objects into .GlobalEnv inside sandboxed preprocessing guards, resolving the corresponding R CMD check NOTE about global environment assignments.
• Deprecated save.fastml() in favour of save_fastml() to avoid confusion with a non-generic S3-style naming pattern.
• Removed dead internal statements in model evaluation and selection paths, including unused holdout label handling and stray performance-value expressions.
• Added missing Rd argument documentation for flatten_and_rename_models() and get_best_model_idx(), resolving R CMD check \usage warnings.
• Added regression tests covering event class validation, engine lookup, nested CV parameter selection, multiclass algorithm swapping, and sandbox global-environment protections.

fastml version 0.7.7

New features

• Feature Importance Stability Analysis: Added explain_stability() function to analyze feature importance stability across cross-validation folds. This helps identify features that are consistently important vs. those whose importance varies across different data subsets.
• Store Fold Models: Added store_fold_models parameter to fastml() to optionally store models trained on each CV fold, enabling stability analysis with explain_stability().
• S3 Methods for Stability Objects: Added print.fastml_stability() and plot.fastml_stability() methods for convenient display of stability analysis results.

Improvements

• Unified Explainer Infrastructure: Added fastml_prepare_explainer_inputs() helper function providing consistent data preparation across all explainer methods (explain_dalex(), explain_ale(), plot_ice(), interaction_strength(), surrogate_tree()).
• Positive Class Resolution: Added resolve_positive_class() helper for consistent positive class handling across explainer functions, respecting event_class settings.
• Enhanced explain_dalex(): Major refactoring with robust preprocessing (“baking”) helper that handles three scenarios: no preprocessor, successful baking, and fallback validation for already-processed data.
• Enhanced plot_ice(): Added target_class parameter for classification, improved feature validation with informative error messages, and added warnings for multiclass problems.
• Improved Resampling Metrics Aggregation: Resampling results now properly compute CV statistics (mean and SD across folds) instead of pooled metrics. Fixed grouping attributes that could carry over from fold processing.
• Better Model Validation in Predictions: Added valid_model() helper to properly validate workflow and native survival model types during prediction.

Bug fixes

• Fixed algorithm name matching in predict.fastml() to correctly resolve base algorithm names to their full “algorithm (engine)” format.
• Fixed fold metrics aggregation in guarded resampling to properly ungroup and convert results to plain tibbles.
• Fixed various edge cases in explainer functions when preprocessing pipelines are absent or data is already processed.
• Fixed unit tests across multiple test files for improved reliability and stability.
• Prevented Rplots.pdf files from being created during test execution by adding graphics device suppression to plotting tests.
• Added Rplots.pdf to .gitignore to prevent accidental tracking.

fastml version 0.7.5

Breaking changes

• Removed incomplete or unstable survival backends where correct, leakage-safe behavior could not be guaranteed.

New features

• Full Survival Analysis Support: Added training, resampling, prediction, metric computation, and model summarization for time-to-event outcomes.
• Guarded Survival Resampling: Introduced a workflow enforcing leakage-safe preprocessing, imputation, and model fitting within each resampling split.
• Integrated Brier Score (IBS): Added IBS and expanded survival metric support with flexible time handling and user-configurable summary outputs.
• New Survival Engines: Added support for parametric and semi-parametric models, including Cox, penalized Cox, Royston–Parmar, and flexible parametric survival models.
• Advanced Resampling Strategies: Implemented grouped, blocked, rolling, stratified, and unbiased nested cross-validation.
• Fold-wise Imputation: Added support for advanced imputation during resampling while preventing outcome leakage.
• Engine Parameters: Introduced an engine_params argument to allow passing engine-specific options in a consistent way.
• S3 Methods: Added explicit S3 method annotations for fastml generics.

Improvements

• Multiclass ROC AUC now defaults to macro averaging (tidymodels) and can be configured via multiclass_auc to use macro_weighted class-prevalence weighting.
• Improved robustness of survival predictions, including risk scores, survival probabilities, quantiles, medians, and time estimates.
• Enhanced survival summary outputs with clearer metric alignment and better handling of stratified and time-varying Cox models.
• Improved extraction of predictions and summaries for parametric survival engines.
• Strengthened recipe validation and sandboxing to harden preprocessing isolation and reduce user-induced leakage.
• Improved handling of novel and missing categorical levels during prediction.
• Integrated resampling metadata more tightly into training workflows and summaries.
• Added survival_metric_convention to align survival evaluation defaults with tidymodels conventions when desired.
• Parallel tuning now uses explicit RNG seeding to keep results stable across core counts.

Bug fixes

• Fixed multiple issues in survival label validation, prediction post-processing, and metric computation.
• Corrected survival risk and probability calculations for several engines and model types.
• Fixed log-rank calculation for time-varying Cox models.
• Fixed summary formatting when confidence intervals are unavailable.
• Removed inappropriate confusion matrix warnings for non-classification tasks.
• Fixed edge cases leading to NA survival predictions and early exits during survival time computation.
• Addressed naming collisions and alignment issues in tuning grids and metric selection.