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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.0.28] - 2026-07-09

Fixed

  • Silent Report Failures: kreview report now exits with code 1 when any dashboards fail to render, enabling Nextflow to retry with higher memory allocations. Previously exited 0 even when multiple reports failed.
  • ValueError in Feature Plotting: Added a fallback in report_template.qmd when the univariate top_col (selected by eval_stats) is filtered out during feature selection or matrix fusion. Falls back to the first available numeric column instead of crashing Plotly.
  • CUDA OOM on Shared GPU Nodes: Added PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to the KREVIEW_MULTIMODAL_SINGLE_GPU Nextflow process, reducing PyTorch memory fragmentation on shared Slurm nodes. Prevents TabICL_ft failures from CUDA allocation errors.
  • IPython Home Path on HPC: Exported IPYTHONDIR="$PWD/.ipython" in report.nf, report_multimodal.nf, and multimodal_single.nf to prevent errors when /home is read-only on HPC Singularity nodes.
  • Healthy Normal Specificity = 0.0 in Multimodal Stacking: The stacking matrix parquet files did not carry the original 4-tier label text (e.g. "Healthy Normal", "Possible ctDNA-"), so evaluate_model() could not compute sensitivity at 100% healthy-normal specificity. Fixed by plumbing oof_sample_labels through the full pipeline: evaluator JSONs → baselines loader → stacking matrix alignment → parquet persistence → evaluate_model() in all paths (decomposed Nextflow + monolithic kreview run).

Added

  • _sample_label Column: The stacking_matrix.parquet and raw_features_matrix.parquet outputs from multimodal_prep() now include a _sample_label column carrying the 4-tier text labels. This enables downstream consumers (multimodal_single, dashboard) to compute per-label-tier specificity without joining back to labels.parquet.

[0.0.27] - 2026-06-25

Fixed

  • GPU Stacking random_state Duplicate: Resolved a TypeError: got multiple values for argument 'random_state' occurring during fine-tuning of TabPFN and TabICL GPU multimodal models in multimodal_eval() and multimodal_single(). This was caused by random_state being passed as both a positional argument and duplicated via unpacked gpu_kwargs.
  • Baseline Best Metric Recovery: Fixed a bug in _load_per_evaluator_baselines() where best_model and best_auc keys were resolved as None from the source model JSONs. They are now dynamically computed from the model's actual OOF AUC values.
  • Nextflow GPU Singularity Path: Added symlinks for python3 and python to /usr/local/bin inside the Dockerfile, and replaced container python sub-processes with native grep extraction in multimodal_single.nf's script blocks. This prevents python3 command not found (exit code 127) errors triggered by Singularity path-stripping behavior on HPC nodes.
  • GPU Pipeline Deadlock Avoidance: Configured KREVIEW_MULTIMODAL_SINGLE_GPU to catch non-zero exit codes during evaluation failures and output an error metadata JSON instead of terminating the pipeline immediately. This prevents Nextflow's .collect() channel from deadlocking when a GPU node experiences a runtime error.
  • Resource Limits Hardening: Bumped KREVIEW_ABLATE_CPU_SINGLE memory allocation from 32 GB to 64 GB to prevent OOM errors on large transcription factor annotation matrices, and bumped KREVIEW_EXTRACT walltime to 1.5h.

Added

  • Dashboard Readiness Column: Appended the unique _sample_id column to both the multimodal stacking_matrix.parquet and raw_features_matrix.parquet. This enables joining multimodal predictions back to labels.parquet for per-label-tier (e.g. Healthy Normal) and per-cancer-type performance comparisons in the kreview dashboard.

[0.0.26] - 2026-06-23

Fixed

  • BorutaShap Import: The pip package BorutaShapPlus provides an importable module named BorutaShap, not BorutaShapPlus. Corrected from BorutaShapPlus import BorutaShap → from BorutaShap import BorutaShap in eval_engine.py. This was the root cause of multimodal pipeline failures in v0.0.24 and v0.0.25 — previously misdiagnosed as a container build issue.
  • Test Skip Guard: Fixed test_boruta_shap_passes_through which used import BorutaShapPlus as its skip guard. Since the module name is BorutaShap, the test was always being silently skipped even when the package was correctly installed.
  • Report Memory (OOM): Bumped KREVIEW_REPORT and KREVIEW_REPORT_MULTIMODAL memory from 32 GB → 64 GB base (128 GB on retry). In v0.0.25, 8/26 reports OOM'd at SHAP summary plot computation (cell 31/53) and 3/26 OOM'd at pandoc --embed-resources HTML self-containment. Evaluators with 270+ features at --shap-samples 5000 require more memory than the 32 GB allocation.

[0.0.25] - 2026-06-22

Fixed

  • Container Dependencies: Moved papermill>=2.5.0 from [jupyter] optional extra to core dependencies. Quarto's --execute-params (-P) flag requires papermill at runtime, and the CPU Docker container did not include it — causing all 26 evaluator reports to fail on HPC with "The papermill package is required for processing --execute-params".
  • BorutaShapPlus Container Availability: Ensured BorutaShapPlus>=0.1.3 (already in core deps since v0.0.24) is present in rebuilt containers. The v0.0.24 container was built from a stale state, causing multimodal prep to fail with "No module named 'BorutaShapPlus'" and cascading failure of the entire multimodal pipeline chain.

[0.0.24] - 2026-06-18

Added

  • ARFS Feature Selection: Added Leshy (Boruta+LightGBM/SHAP) and GrootCV (cross-validated SHAP importance) as --multimodal-selection options. Install via pip install kreview[arfs].
  • Strategy Validation: _select_multimodal_features() now raises ValueError on unknown strategy names instead of silently falling through to MI ranking. Typos like --multimodal-selection borta_shap are caught immediately.
  • MI-Reduction Helper: Extracted _mi_reduce_confirmed() to deduplicate the >500-feature MI reduction logic across Boruta-SHAP, Leshy, and GrootCV strategies.
  • Tests: Added test_invalid_strategy_raises, test_valid_strategies_constant, test_leshy_passes_through, and test_grootcv_passes_through to test_selection.py.

Changed

  • BorutaShap → BorutaShapPlus: Migrated from BorutaShap>=1.0.17 to BorutaShapPlus>=0.1.3 with scipy binom_test and NumPy np.NaN compatibility shims.

Fixed

  • Quarto EROFS on Read-Only Singularity: Quarto 1.9+ creates a .quarto/ project directory next to the QMD source. Inside read-only Singularity containers, this failed with EROFS (error 30). Templates are now staged to a writable .render_{feat_name}/ directory before quarto render.
  • Report publishDir Double-Nesting: Fixed publishDir "${params.outdir}/reports" → "${params.outdir}" in report.nf and report_multimodal.nf to prevent results/reports/reports/ nesting.
  • GrootCV Constructor: Fixed invalid parameters (estimator, n_iterations, cv) → correct ARFS API (objective="binary", n_folds=5, n_iter=50, silent=True).
  • Leshy Constructor: Fixed n_iter (not a valid param) → max_iter=50; added n_estimators=1000, verbose=0.
  • Test Import: Updated test_selection.py skip guard from import BorutaShap to import BorutaShapPlus.

[0.0.23] - 2026-06-16

Fixed

  • Scoreboard Nextflow Quote-Escaping: Replaced python3 -c "..." with bash heredoc (python3 << 'EOF') in scoreboard.nf to prevent Nextflow shell interpolation from stripping escaped quotes, which caused NameError: name 'evaluator' is not defined and KREVIEW_SCOREBOARD failing all retry attempts.
  • scipy.stats Version Attribute: Fixed AttributeError in the binom_test compatibility shim by importing scipy (top-level) instead of accessing __version__ on scipy.stats (which has no such attribute). This caused KREVIEW_MULTIMODAL_PREP to crash when --multimodal-selection boruta_shap was used (scipy ≥1.12).
  • Quarto HPC Cache Permissions: Export XDG_CACHE_HOME and XDG_DATA_HOME to writable local paths in report.nf and report_multimodal.nf, preventing Quarto from failing on HPC compute nodes with read-only ~/.cache directories.

[0.0.22] - 2026-06-11

Fixed

  • WPSGenome MAD Memory Optimization: Replaced the nested Python loop that materialized ~446M rows in memory with a DuckDB-native window function query, reducing peak memory usage to under 100MB and runtime to ~7s.
  • GPU Ablation TypeError: Fixed a bug where _build_ablation_model_factories called GPUModelCVAdapter directly with invalid model_name argument instead of using _build_gpu_model builder, causing GPU ablation to silently fail and default to ALL.

[0.0.21] - 2026-06-10

Performance

  • WPSGenome SQL pushdown: Uses DuckDB native list_avg(), list_max(), list_min() to aggregate 1.9B rows inside DuckDB instead of materializing in pandas. Reduces peak memory from 96+ GB to ~17 GB.
  • DuckDB resource tuning: Default threads 4→8, memory 4GB→32GB, exposed via --duckdb-threads / --duckdb-memory CLI options.
  • Nextflow KREVIEW_EXTRACT: cpus 4→8, memory 32GB→64GB per attempt, maxRetries 7→3 (SQL pushdown eliminates the memory ladder).

Added

  • configure_duckdb() API for pre-connection resource configuration.
  • --duckdb-threads and --duckdb-memory CLI options on extract and run commands.
  • sql_pivot_column evaluator property for multi-row SQL result pivoting.
  • Exact MAD hybrid computation via second lightweight DuckDB query.
  • Nextflow params: duckdb_threads, duckdb_memory.

Fixed

  • Nextflow resilience: Added explicit errorStrategy + maxRetries to 14 processes that relied on global defaults. One failed evaluator no longer terminates the entire pipeline — it is ignored after 3 retries.
  • KREVIEW_FUSE uses terminate (not ignore) since downstream processes depend on the super-matrix.
  • Added ifEmpty channel guards on EXTRACT→SELECT and GPU ablation .combine() to prevent pipeline deadlocks when upstream processes are ignored.
  • Added missing withName config blocks for KREVIEW_MULTIMODAL_SINGLE_CPU, KREVIEW_MULTIMODAL_SINGLE_GPU, and KREVIEW_MULTIMODAL_MERGE.

Changed

  • get_duckdb_conn() defaults: 8 threads, 32GB memory (was 4 threads, 4GB).

[0.0.20] - 2026-06-09

Added

  • Feature Group Ablation: kreview eval ablate {cpu,gpu,merge} CLI commands for nested CV feature group selection. Identifies suffix-based feature groups, evaluates all subsets via inner CV, and picks the optimal subset per model per fold using sensitivity_at_100spec_healthy.
  • Nested CV Pipeline: Per-model per-fold feature subsets flow from ablation → merge → eval via --best-subset. Supports both CPU (LR/RF/XGB) and GPU (TabPFN/TabICL) models.
  • Nextflow Ablation Stages: 3 new modules (ablate_cpu_single.nf, ablate_gpu_single.nf, merge_ablation.nf) wired into kreview_eval.nf behind params.run_ablation flag. All stages run in parallel per evaluator.
  • Scoreboard Columns: nested_cv flag and best_sens_100spec_healthy (best sensitivity across ALL models) added to build_scoreboard().
  • Bootstrap CIs: _compute_oof_metrics() produces 95% bootstrap CIs for AUC, sensitivity@100%spec, and all clinical metrics.
  • GPU Subgroup Stats: gpu_models() now reports assay_stats and cancer_type_stats for subgroup analysis.
  • Test Suite: tests/test_ablation.py with 17 tests covering feature grouping, subset generation, OOF metrics, backward compatibility, and scoreboard.

Changed

  • eval cpu and eval gpu accept optional --best-subset for nested CV with per-fold feature subsets.
  • eval_cpu_single.nf and eval_gpu_single.nf accept optional best_subset input channel (backward compatible via sentinel file).
  • nextflow.config: new params run_ablation (default: false), ablation_inner_folds (default: 3).

[0.0.19] - 2026-06-08

Fixed

  • WPSGenome Extraction Failure: Removed broken SQL pushdown (extract_sql()) that used trim(col, '[]') on native list<float> arrays, silently producing NULL results. Replaced with pure-Python extract() using _to_array() helper that handles numpy arrays, Python lists, and legacy string-encoded arrays. Added _to_array() as a public utility.
  • WPSGenome Last-Row-Wins Bug: Rewrote extract() to aggregate per-region-type (TSS, CTCF) statistics across all regions instead of overwriting with the last row. Now computes mean, peak_valley, std, and MAD of per-region array means.
  • WPSPanel Last-Row-Wins Bug: Rewrote extract() to aggregate per-region-type (TSS, CTCF) statistics across all panel regions (~59-85 rows per type) instead of overwriting with the last row. local_depth now averaged across panel regions instead of using last value.
  • GPUModelCVAdapter Serialization: Added __getstate__/__setstate__ to exclude unpicklable model_factory lambda from pickle. Deserialized adapters work for inference (predict_proba) but raise TypeError on fit() — preventing silent misuse.
  • Joblib Corrupt File Detection: _save_fitted_models() now validates post-write file size (>100 bytes) and deletes truncated files. Error-level logging replaces silent log.warning. Partial files from failed joblib.dump() are cleaned up.
  • parse_array() Numpy Passthrough: Now handles numpy arrays and Python lists as input (passthrough), not just string-encoded arrays. Previously returned [] for native parquet list<float> data, causing silent data loss.

Changed

  • WPSBackground Cleanup: Removed bare try/except for consistency with WPSGenome/WPSPanel. Errors now propagate to CLI handler. Added structured logging for empty/success cases with feature counts and chromosome counts. Metrics list moved to class constant _metrics.

Removed

  • WPSGenome SQL Pushdown: Removed extract_sql(), sql_pivot_column, and CLI pivot code. See v0.0.17 entry — the SQL path was fundamentally incompatible with native array storage.
  • WPSGenome FFT Features: Dropped spectral_max_power and spectral_dominant_freq. Empirical analysis (r=0.91–1.00 with basic stats) proved FFT is redundant with mean/std/peak_valley. Nucleosome periodicity is captured by WPSBackgroundEvaluator.
  • WPSPanel spectral_max_power: Removed — real-data analysis (4 patient samples) showed r=0.90–1.00 with std after per-region-type aggregation. spectral_dominant_freq retained (38.5% CV in wps_tf across samples — potentially informative for curated TSS/CTCF loci).

[0.0.18] - 2026-06-05

Added

  • Fine-tuned GPU Model Variants: tabpfn_ft and tabicl_ft via FinetunedTabPFNClassifier and FinetunedTabICLClassifier with configurable --finetune-epochs (default: 50) and --finetune-lr (default: 1e-5). Total GPU model count: 4 (was 2).
  • GPUModelCVAdapter: sklearn-compatible wrapper in eval_engine.py that resolves AttributeError: 'FinetunedTabPFNClassifier' has no attribute 'classes_' during cross_val_predict. Exposes classes_ as a plain attribute set during fit().
  • kreview eval multimodal nested subgroup: 5 subcommands — run (monolithic backward-compat), prep, single, ablation, merge.
  • Decomposed multimodal Nextflow pipeline: 4 new NF modules (multimodal_prep.nf, multimodal_single.nf, multimodal_ablation.nf, multimodal_merge.nf) enabling per-model parallel execution. Legacy eval_multimodal.nf kept for standalone testing via kreview eval multimodal run.
  • GPU foundation model expansion: 4 GPU model variants (tabpfn, tabpfn_ft, tabicl, tabicl_ft) evaluated per-evaluator in parallel via KREVIEW_EVAL_GPU_SINGLE. Each process runs all requested GPU models and outputs a single {evaluator}_gpu_model_results.json.
  • Intentional 7-color palette: Vanilla/FT shade families (#00e5ff/#00b0ff for TabPFN, #76ff03/#64dd17 for TabICL) in report templates.
  • Stacking feature selection: MI or Boruta-SHAP on stacking matrix via --stacking-selection.
  • Decomposed NF resource configs: Per-process withName blocks for KREVIEW_MULTIMODAL_PREP, KREVIEW_MULTIMODAL_SINGLE_GPU, and KREVIEW_MULTIMODAL_ABLATION.

Changed

  • Default --gpu-models → tabpfn,tabpfn_ft,tabicl,tabicl_ft (4 models, was 2).
  • kreview eval multimodal uses nested Typer sub-commands (was flat hyphenated commands).
  • NF config: decomposed process resource configs replace monolithic withName: 'KREVIEW_EVAL_MULTIMODAL'.
  • Report _name_map includes TabPFN-FT and TabICL-FT display names.
  • multimodal_cpu_only NF param marked as legacy (decomposed pipeline routes CPU/GPU per-process).

Removed

  • --no-finetune CLI flag (use --gpu-models tabpfn,tabicl for zero-shot only).
  • Dead KREVIEW_EVAL_MULTIMODAL import from kreview_eval.nf.

Fixed

  • TabPFN AttributeError: 'FinetunedTabPFNClassifier' has no attribute 'classes_' via GPUModelCVAdapter wrapper.
  • Stale multimodal-prep (hyphen) references in CLI help strings → multimodal prep (space).
  • Stale DAG comments in eval_cpu_single.nf, eval_gpu_single.nf, report_multimodal.nf referencing removed KREVIEW_EVAL_MULTIMODAL.

[0.0.17] - 2026-06-04

Added

  • GPU Model Pre-flight Validation: _validate_gpu_models() in cli_eval.py verifies GPU model packages are installed and TABPFN_TOKEN is set before any work begins. Shared across eval gpu, eval multimodal, and monolithic run commands. Exits with code 1 and actionable instructions on failure.

Fixed

  • OOF Sample IDs Alignment (P0): oof_sample_ids scoped to train-split only in CPU, GPU, and monolithic paths to match oof_probs length from cross_val_predict. Added oof_sample_ids and oof_labels to merge skip set preventing GPU from overwriting CPU alignment keys. This was the root cause of multimodal stacking failure ("25 had IDs but no probs").
  • BorutaShap scipy Compatibility (P1): Monkey-patched scipy.stats.binom_test with binomtest().pvalue wrapper for BorutaShap compatibility on scipy ≥1.12 (removed in 1.12). Eliminates ImportError: cannot import name 'binom_test' in multimodal raw feature selection.
  • Scoreboard Crash Handling (P2): Added try/except with traceback to scoreboard.nf inline Python. Moved file writes inside try block. Added KREVIEW_SCOREBOARD withName config block (retry 3×, then ignore). Added per-evaluator try/except in build_scoreboard() so a malformed evaluator result doesn't kill the scoreboard for all evaluators.
  • WPSGenome Timeout (P3): Implemented DuckDB SQL pushdown (extract_sql()) for WPSGenomeEvaluator — parses string-encoded arrays via list_avg/list_max/list_min in C++. Added sql_pivot_column for tall-to-wide pivot in CLI. Used pivot_table(aggfunc='first') for duplicate safety with null guard on pivot failure fallback. Added time/queue escalation in nextflow.config (retries 3+ → cmobic_cpu 8h). Note: This SQL pushdown was later found to be broken — krewlyzer stores arrays as native FLOAT[], not string-encoded. Removed in [Unreleased].
  • GPU Silent Failure Logging (P4): Record per-model error keys ({model}_error) in GPU results dict. Detect all-models-failed state and set results["error"]. Added visible ⚠ WARNING stdout output in both cli_eval.py and monolithic cli.py.
  • REPORT Blocked by SCOREBOARD (P5): Guarded REPORT channel with .ifEmpty(file('NO_SCOREBOARD')) so reports render even when scoreboard fails. Templates already check scoreboard_path.exists().
  • REPORT_MULTIMODAL SLURM Rejection: Added withName: 'KREVIEW_REPORT_MULTIMODAL' config block with explicit queue = params.partition ?: 'cmobic_short'. Previously fell through to process_medium label which lacked a queue directive, causing iris institutional config to inject the inaccessible cpu partition into sbatch.
  • Nextflow Channel Deadlocks: Added .ifEmpty([]) guards on JSON collect, joblib collect, and GPU collect channels to prevent pipeline deadlock when tasks fail with errorStrategy = 'ignore'.
  • Pivot Crash on Duplicate Rows: Switched from pivot() to pivot_table(aggfunc='first') in CLI wide-pivot for WPSGenome tall-to-wide conversion, preventing ValueError: cannot reshape on duplicate region_type rows.
  • Null Guard on Pivot Failure: Added null check on feat_matrix after pivot failure fallback, preventing AttributeError: NoneType has no attribute 'columns'.
  • Queue Directive Safety Net: Added queue = params.partition ?: 'cmobic_short' to process_medium and process_high label defaults. Prevents any future process from failing SLURM submission due to missing partition.

Changed

  • Version Bump: __init__.py, settings.ini, nextflow.config → 0.0.17.
  • Version-agnostic run_hpc.sh: Auto-detects version from nextflow.config at runtime. Dynamic GPU_MODELS based on TABPFN_TOKEN availability. No manual edits needed between releases.

[0.0.16] - 2026-06-03

Added

  • 80/20 Stratified Holdout Validation: _assign_train_test_split() in CtDNALabeler.label_all() assigns a split column (train/test/exclude) to labels.parquet. Split is stratified by 4-tier label with random_state=42 for reproducibility. evaluate_holdout() in eval_engine.py provides unbiased AUC estimates on the held-out 20%.
  • Sensitivity at Fixed Specificity: evaluate_model() now computes sensitivity at 100%, 99%, and 95% specificity, plus a healthy-normal-only variant using sample_labels. 12 new JSON fields per model.
  • Intelligent GPU Feature Capping: gpu_models() accepts --max-gpu-features (default: 150) and caps features using score-based priority (MI > AUC > variance). Returns gpu_feature_cap_indices for holdout dimension consistency.
  • CH-Only → Undetermined Label: Samples with n_non_ch_variants == 0 and no SV/CNA/IMPACT match are now labeled Undetermined (was: Possible ctDNA−). Undetermined is excluded from binary classification via build_binary_target().
  • WPSGenome Streaming: extract_columns and max_chunk_rows class attributes on FeatureEvaluator enable DuckDB column projection and chunked reads. WPSGenome sets max_chunk_rows = 5_000_000 and projects to 4 columns, reducing peak memory ~80%.
  • Scoreboard Enhancements: build_scoreboard() surfaces sens_at_100spec, holdout_auc, holdout_sens_100spec, holdout_n_train/n_test, and auc_drop (CV overfit diagnostic).
  • LABEL_UNDETERMINED Constant: CtDNALabeler.LABEL_UNDETERMINED = "Undetermined" added alongside existing 5-tier constants.
  • Report Value Boxes: Both report_template.qmd and report_multimodal_template.qmd now display "Sens @ 100% Spec" (with detection count) and "Holdout AUC" (with dynamic color: green/yellow/red by AUC drop severity) in the Executive Summary.
  • Report Clinical Metrics: Model summary tables auto-discover and display Sens@100%Spec, Sens@95%Spec, and Holdout AUC per model.
  • Report Scoreboard Expansion: Scoreboard display expanded from 7 to 13 columns (best_model, holdout_auc, auc_drop, sens_at_100spec, sens_at_95spec, holdout_n_train, holdout_n_test).

Changed

  • Feature Selection Train-Only: score_features() and select_features() (mRMR + hybrid paths) now filter to split == "train" before scoring, preventing test set leakage into feature rankings.
  • LABEL_META_COLS: Added split to the 22-entry meta column set in core.py to prevent it from leaking into feature matrices.
  • Nextflow GPU Module: eval_gpu_single.nf now stages eval_stats parquet and passes --eval-stats-dir + --max-gpu-features to the CLI.
  • Nextflow Workflow: kreview_eval.nf wires SELECT.out.eval_stats → EVAL_GPU_SINGLE.
  • Version Bump: __init__.py, settings.ini, nextflow.config → 0.0.16.
  • Report meta_cols Dynamic Import: Both templates now import LABEL_META_COLS from kreview.core instead of maintaining a stale hardcoded copy. Prevents label metadata columns from leaking into feature sets.
  • Report Glossary: Added Undetermined label definition and train/test split documentation to both template glossary sidebars.
  • MODEL_LABELS / POSITIVE_LABELS Public API: Promoted from private _MODEL_LABELS / _POSITIVE_LABELS in selection.py to public exports. Backward-compat aliases maintained.

Fixed

  • GPU Holdout Dimension Mismatch: cli.py and cli_eval.py now apply gpu_feature_cap_indices to X_test before calling evaluate_holdout(), matching the capped training feature space.
  • Data Leakage in mRMR/Hybrid Selection: Both select_features() code paths now restrict MI scoring to train-only rows.
  • Unused Import: Removed pandas import from report.py (F401).
  • Boolean Comparisons: Replaced == False with ~ operator in labels.py (E712).
  • Ambiguous Variable: Renamed l → lbl in list comprehensions in eval_engine.py (E741).
  • Nested If Statements: Merged nested if blocks in eval_engine.py and registry.py (SIM102).
  • Ternary Simplifications: Converted if/else blocks to ternary in eval_engine.py (SIM108).
  • Test Lint: Removed 5 unused imports from test files and fixed 7 E712/SIM108 in test assertions.

[0.0.15] - 2026-06-01

Added

  • CPU+GPU JSON Merge Helpers: load_model_results(directory, evaluator_name) and load_all_model_results(directory) in eval_engine.py transparently discover and merge *_model_results.json (CPU) and *_gpu_model_results.json (GPU) files. GPU model keys (AUC, OOF probs, SHAP) are merged into the CPU dict. Used by report templates, scoreboard, and multimodal engine.
  • KREVIEW_SCOREBOARD: New Nextflow process (scoreboard.nf) generates scoreboard_combined__all.parquet after all CPU/GPU eval jobs complete. Uses build_scoreboard() which now calls load_all_model_results() for unified GPU+CPU scoring.
  • GPU Exit Code: kreview eval gpu now exits with code 1 if NO models produced valid AUC results (was silent success). Prevents Nextflow from treating empty GPU results as success.
  • Unit Tests: 11 new tests in test_merge_helpers.py covering CPU-only, GPU-only, CPU+GPU merge, malformed JSON, and directory scan scenarios.

Changed

  • GPU JSON Output Naming: GPU eval module now produces *_gpu_model_results.json (was *_model_results.json), preventing filename collision when CPU and GPU outputs are collected into the same Nextflow channel.
  • Report Input Signature: KREVIEW_REPORT now accepts 6 inputs (matrices, model_results, eval_stats, selection_qc, joblib_files, scoreboard) for complete dashboard rendering.
  • Workflow Wiring: kreview_eval.nf creates unified ch_all_jsons and ch_all_joblib channels that mix CPU+GPU outputs, feeds them to SCOREBOARD → REPORT.
  • Scoreboard Loading: build_scoreboard() now uses load_all_model_results() instead of manual glob+load loop.
  • Multimodal Baselines Loading: _load_per_evaluator_baselines() now uses load_all_model_results() instead of manual glob+load loop.
  • FSD Density Calculation: Added select_dtypes(include="number") filter before density computation in fsd.py and fsd_genomewide.py to prevent TypeError on non-numeric metadata columns.
  • Multimodal NF Module: Removed unnecessary staging loop — --results-dir . uses Nextflow work dir symlinks directly.
  • Dockerfile Optimization: Selective builder COPY (only pyproject.toml, kreview/, LICENSE), single pip install "${WHL}[gpu]" pass to ensure local wheel resolution, dropped python3.12-dev and wget from GPU runtime, merged OCI labels into single LABEL instruction.
  • CI Parallelism: test.yml split into parallel test (Python) and docker (matrix [cpu, gpu]) jobs with fail-fast: false. GPU build gets jlumbroso/free-disk-space cleanup (~20-30 GB freed).
  • .dockerignore: Expanded to exclude docs/, nextflow/, scripts/, tests/, .github/, .agents/ — reduces build context transfer.

Fixed

  • OOF Label Key Search: "oof_labels".endswith("_oof_labels") is False — fixed to check both exact match and suffix match. Applied to both report_template.qmd and report_multimodal_template.qmd.
  • FSD TypeError: Non-numeric columns (e.g., sample_id, filename) caused TypeError: ufunc 'divide' not supported in FSD density calculation. Now filtered to numeric columns only.
  • Multimodal Validation Logging: Now shows CPU vs GPU JSON counts separately for better debugging.
  • Docker GPU Build CI Failure: GPU image build exceeded runner disk space (~14 GB free). Fixed by adding jlumbroso/free-disk-space action and optimizing Dockerfile layers.

Removed

  • tabpfn-extensions[interpretability] from [gpu] extras — unused since v0.0.13 (SHAP computation replaced by shapiq). Eliminates transformers, wandb, huggingface-hub, tokenizers transitive dependencies (~500 MB).
  • Deprecated HPC scripts: run_hpc_0.8.3.sh, run_hpc_v0.0.10.sh, run_hpc_v0.0.11.sh, runner.sh, utils/symlinker.py — replaced by unified scripts/run_hpc.sh.

Breaking Changes

  • GPU JSON output renamed: {eval}_model_results.json → {eval}_gpu_model_results.json
  • KREVIEW_REPORT input signature expanded from 2 to 6 inputs

[0.0.14] - 2026-05-25

Added

  • Reproducibility Seed: --seed CLI flag on run, eval cpu, eval gpu, eval multimodal, and select commands (default: 42).
  • Deterministic Mode: --deterministic / --no-deterministic flag for PyTorch cudnn (default: True) on all eval commands.
  • kreview/reproducibility.py: seed_everything() utility for Python, PyTorch, and cuDNN seeding.
  • Nextflow Params: seed and deterministic params in nextflow.config, forwarded through all 8 pipeline modules.
  • Reproducibility Docs: New section in models-and-metrics.md documenting seed propagation strategy.

Fixed

  • 3 hardcoded random_state=42 in _select_multimodal_features() now accept caller seed.
  • 7 internal forwarding gaps where random_state was not passed to child functions.
  • GPU models (TabPFN, TabICL) now receive random_state parameter via _build_gpu_model().
  • shapiq.TabularExplainer and shap.PermutationExplainer now seeded for reproducible SHAP values.
  • BorutaShap.fit() now receives random_state for deterministic feature selection.
  • SLURM Partition Routing: All withName process blocks now pin queue explicitly to bypass the nf-core iris institutional config's dynamic queue closure that injects the inaccessible cpu partition into sbatch -p.
  • KREVIEW_LABEL Error Strategy: Changed from inherited 'ignore' (from iris config) to 'terminate' — pipeline aborts immediately if labeling fails instead of deadlocking.

[0.0.13] - 2026-05-24

Added

  • GPU Foundation Models: TabPFN v8.0.3 and TabICL v2.1 support via _build_gpu_model() with updated import paths (tabpfn.TabPFNClassifier, tabicl.TabICLClassifier).
  • shapiq Integration: SHAP values for GPU models now computed via shapiq.TabularExplainer (model-agnostic kernel-based Shapley values), replacing the deprecated tabpfn-extensions interpreter.
  • Unified Model Persistence: _save_fitted_models() helper in cli_eval.py provides consistent joblib saves for both CPU and GPU models. --skip-gpu-joblib flag opts out of large GPU model files.
  • Multimodal GPU Models: kreview eval multimodal now accepts --gpu-models tabpfn,tabicl for GPU-accelerated stacking and raw feature evaluation.
  • Pre-computed SHAP Fallback: Report templates render mean |SHAP| bar charts from JSON when joblib files are unavailable (e.g., --skip-gpu-joblib).
  • KREVIEW_REPORT_MULTIMODAL: New Nextflow process for rendering multimodal stacking dashboards in multistage mode.
  • HPC Script v0.0.13: Updated SLURM launch script with GPU multimodal params, Boruta-SHAP selection, and top_percentile.

Changed

  • Feature Selection: --top-percentile replaces --top-k for MI-based feature selection. Percentage-based selection adapts to varying feature set sizes across evaluators.
  • Boruta-SHAP MI Reducer: When Boruta-SHAP confirms >500 features, an MI-based reducer narrows the set to top_percentile (default 10%).
  • SLURM Hardening: KREVIEW_LABEL and KREVIEW_EVAL_GPU_SINGLE processes now set cache = 'lenient' and scratch = false to prevent institutional queue/cache interference on IRIS.
  • Dynamic Report Rendering: ROC CI reverse-map, DCA loop, and AUC deltas now dynamically discover models from DYNAMIC_MODELS instead of hardcoding LR/RF/XGB.

Fixed

  • ROC CI KeyError: Hardcoded {"Logistic Regression": "lr", ...} reverse-map in report_template.qmd replaced with dynamic lookup from DYNAMIC_MODELS, preventing crashes when GPU models are present.
  • DCA Loop: DCA now renders curves for all trained models (was hardcoded to RF/XGB only).
  • AUC Deltas: Pairwise AUC delta display now discovers all auc_delta_* keys dynamically.
  • Monolithic GPU Fitted Capture: gpu_res, gpu_fitted = gpu_models(...) now captures fitted models (was _ = ...).

Dependencies

  • tabpfn >= 8.0.3 (was >= 2.0)
  • tabicl >= 2.1 (was >= 0.0.4)
  • Added shapiq for GPU model SHAP computation.

[0.0.12] - 2026-05-24

Added

  • Nextflow publishDir: All 8 multistage modules now publish outputs to params.outdir with mode: copy. Output structure: labels/, matrices/raw/, matrices/selected/, models/cpu/, models/gpu/, matrices/fused/, models/multimodal/, reports/.
  • Label-first DAG: KREVIEW_LABEL runs once as the first step in multistage mode, producing labels.parquet shared across all extract jobs. Eliminates 26× redundant re-labeling (~13 min saved).
  • --labels CLI flag: kreview extract now accepts --labels /path/to/labels.parquet to skip internal labeling and use pre-computed labels.
  • Boruta-SHAP in HPC script: scripts/run_hpc_v0.0.11.sh now passes --multimodal_selection boruta_shap for rigorous multimodal feature selection.

Changed

  • Report DAG dependency: Report process now receives both matrices AND *_model_results.json from CPU/GPU eval, enabling complete dashboard rendering (ROC, SHAP, metrics). Report runs in parallel with FUSE + MULTIMODAL.
  • Extract module: extract.nf now accepts labels.parquet as input from upstream KREVIEW_LABEL.

Documentation

  • README: Comprehensive overhaul — added mRMR/Boruta-SHAP, GPU models, multimodal stacking, Nextflow HPC, pipeline architecture Mermaid diagram, fixed stale --workers flag.
  • Statistical Tests: Detailed mRMR documentation (optimization objective, F-statistic/Pearson explanation, algorithm steps), Boruta-SHAP documentation (shadow features, SHAP importance, flowchart, configuration table, when-to-choose guide), fixed stale Selection QC keys.
  • Nextflow Operations: Updated module list to match actual filenames, added iris profile docs, added publishDir output structure tree, added parq-cli tip.
  • Pipeline CLI: Fixed stale --workers 4, added --labels to extract example, added --ch-hotspot-maf, added label-first modular pipeline flow, added parq-cli tip.
  • Pipeline Architecture: Updated DAG (Label-first, Report parallel with Multimodal), fixed process table with actual _single.nf module names, added publishDir column.

[0.0.11] - 2026-05-24

Changed

  • Feature Selection (Default): Default single-evaluator strategy changed from hybrid_union to mrmr (Minimum Redundancy Maximum Relevance). mRMR selects features maximizing target correlation while minimizing inter-feature redundancy, preventing multi-collinearity.
  • Multimodal Selection: --multimodal-selection now supports boruta_shap (interaction-aware, SHAP-based) in addition to mi (mutual information, default).
  • CLI Log Output: kreview select and kreview run now display method-aware selection summaries (mRMR shows variance-dropped count; hybrid_union shows AUC∩MI overlap breakdown).

Added

  • mrmr-selection dependency: Added mrmr-selection>=0.2.8 to pyproject.toml.
  • BorutaShap dependency: Added BorutaShap>=1.0.17 to pyproject.toml.
  • Structured startup logging: kreview select and kreview eval multimodal now emit log.info("select_start", ...) and log.info("eval_multimodal_start", ...) at startup for machine-parseable audit trails.
  • Multimodal selection method persistence: multimodal_results.json now includes raw_features_selection_method ("mi" or "boruta_shap").
  • Selection Strategy value box: Multimodal dashboard executive summary displays the cross-evaluator selection strategy.
  • Scoreboard selection_method column: Now visible in both single-evaluator and multimodal dashboard scoreboard tables.
  • mRMR scatter disclaimer: When strategy="mrmr", the Feature Selection scatter plot displays a callout explaining that AUC/MI axes are observational (mRMR uses F-statistic + Pearson correlation internally).
  • mRMR scatter coloring: Selected features labeled "Selected (mRMR)" with cyan color, distinct from hybrid_union's 4-way AUC/MI overlap coloring.

Fixed

  • KeyError crash in cli_select.py: Fixed crash when --strategy mrmr was used — the log output tried to access hybrid-union-only QC keys (n_overlap_both, n_auc_only, n_mi_only).
  • Misleading cli.py log output: kreview run --strategy mrmr now shows mRMR-specific summary instead of zeroed-out hybrid_union counts.
  • Stale docstrings: Updated module docstrings in selection.py, cli_select.py, cli_eval.py, and eval_engine.py to reflect mRMR as default.

[0.0.10] - 2026-05-22

Added

  • Modular CLI: Extracted run into atomic sub-commands (label, extract, fuse, eval, select, report).
  • Nextflow DAG: Complete rebuild of monolithic pipeline into a multi-stage, per-evaluator parallelized workflow.
  • GPU Evaluation: Native support for PyTorch-based GPU models (TabPFN, TabICL) via the kreview eval --gpu-models target.
  • Multimodal Models: Cross-evaluator stacking capability allowing evaluation of multiple fragmentomics assays simultaneously.
  • Testing Optimizaton: Dropped unit testing execution time from >6min to <30s via module-scoped caching.
  • Docker Containers: Stabilized multi-target release action generating discrete CPU and GPU containers cleanly.

[0.0.9] - 2026-05-07

Changed

  • Feature Selection: Replaced Cohen's D --top-n selection with hybrid union: top X% by Univariate AUC ∪ top X% by Mutual Information. This captures both linear and non-linear predictors.
  • CLI: --top-n deprecated (prints warning, ignored). Use --top-percentile (default: 10%) instead. --compute-univariate-auc now defaults to True.
  • Volcano Plot: X-axis changed from Cohen's D to Univariate AUC for better alignment with model-based selection.
  • Top-20 Bar Chart: Ranked by Univariate AUC instead of Cohen's D.
  • Statistical Ledger: Sort priority changed to univariate_auc > mutual_info > cohens_d > kw_statistic.
  • Feature Cards: Display AUC + MI scores instead of Cohen's D.

Added

  • mutual_info_score(): New function in eval_engine.py using sklearn.feature_selection.mutual_info_classif(k=3) for non-linear feature relevance scoring.
  • selection_qc metadata: Saved in model_results.json with method, overlap stats, and feature counts for audit trail.
  • Feature Selection QC Scatter: New dashboard plot showing AUC vs MI with 4-color category coding (Both / AUC-only / MI-only / Dropped).
  • Scoreboard columns: selection_method, n_selected_features, selection_overlap_pct.
  • Structured logging: feature_scoring_complete, feature_selection_complete, univariate_auc_disabled, variance_guard_dropped, model_skip_insufficient_data.
  • Resume checkpoint: Warns on legacy JSONs missing selection_qc (pre-v0.0.9 Cohen's D runs).
  • Tests: 13 new tests for univariate_auc (6) and mutual_info_score (7) covering bounds, NaN handling, constant features, signal detection, and reproducibility.
  • univariate_auc logging: log.warning("univariate_auc_failed") replaces bare silent except.

Fixed

  • Duplicate label mask (GAP-1): Removed duplicate 4-tier label mask construction; reuse scoring target for model target.
  • Redundant import (GAP-3): Removed import structlog as _slog inside evaluator loop; use module-level log.
  • Silent degradation (GAP-4): Explicit warning when --no-compute-univariate-auc degrades to MI-only selection.
  • Double imputation (GAP-6): Cached _impute() result in variance guard to avoid re-computation.
  • Silent model skip (GAP-7): Added echo + structlog warning when model eligibility fails (< 20 samples or single class).

[0.0.8] - 2026-05-06

Fixed

  • AUC Consistency (D-01): ROC plots and KDE density now use out-of-fold predictions matching the official AUC value boxes. Eliminates the misleading AUC discrepancy (e.g., 0.845 vs 0.72) caused by mixing training/subsample models.
  • Dashboard Crashes (D-02/D-03): All cohens_d_true_vs_healthy references guarded with column existence checks. Statistical Ledger falls back to kw_statistic → univariate_auc when Cohen's D is unavailable.
  • Classification Labels (C-02): Confusion matrix and classification report now correctly show "ctDNA Negative" instead of "Healthy Normal".
  • Fold Variability (C-01): Now displays all three models (LR, RF, XGBoost) with per-model std in title instead of hardcoded RF-only.
  • Duplicate Imports (C-03): Removed redundant plotly import in report template.
  • Volcano Hover (GAP-1): NaN-safe hover text shows "N/A" instead of "nan" for zero-variance features.
  • JSON Bloat (GAP-6): OOF probability arrays rounded to 6 decimal places (~40% size reduction per evaluator).
  • HPC Memory (OOM): Base memory bumped 256→512GB for genome-wide evaluators (FSD, WPS).

Added

  • Structured Logging: structlog-based logging in CLI report() with per-evaluator timing, success/fail counts, and summary statistics.
  • Smart Resume (GAP-4): Resume checkpoint validates JSON freshness — warns if OOF keys are missing from pre-hardening runs.
  • SHAP Waterfall Guards (S-04): Validates array lengths before plotting to prevent IndexError.
  • SHAP Docstrings: All three SHAP helper functions now have full docstrings.
  • Variable Initialization: oof_y, oof_preds, and kde_labels initialized at template top level to prevent NameError when models fail.

Removed

  • plot_threshold_sensitivity() — dead code replaced by pre-computed threshold sweep in v0.0.7.
  • Redundant cross_val_score import and second independent CV computation.
  • Stale plot_threshold_sensitivity entry from _modidx.py.

[0.0.7] - 2026-04-13

Added

  • Dashboard Redesign: Restructured from single-page to 6-page progressive disclosure hierarchy (Executive Summary, Model Validation, Feature Explanation, Biomarker Yield, Cohort & QC, Data Explorer).
  • XGBoost Integration: Full XGBoost model evaluation alongside LR and RF in single_feature_model().
  • Bootstrap AUC CIs: 95% confidence intervals for all model AUCs using scipy.stats.bootstrap.
  • Decision Curve Analysis (DCA): Standalone decision_curve_analysis() function computing net clinical benefit across thresholds for RF and XGBoost models.
  • Precision-Recall Curves: PR curves and average precision for all three models (LR, RF, XGBoost).
  • Fold-Level AUC Tracking: Per-fold AUC tracking via cross_val_score for all three models with *_auc_std stability metric.
  • Threshold Sensitivity Sweep: 50-point sweep of sensitivity, specificity, and PPV across thresholds (0.01–0.99) for RF.
  • Feature Stability: CV cross-fold feature importance consistency scoring (0.0–1.0 scale).
  • QC Metrics: Per-feature n_missing, pct_missing, and is_zero_variance computed in evaluate_feature().
  • Feature Cards: Auto-generated metadata cards from evaluator registry with tier, category, and derived feature type detection.
  • SHAP Tabbed Interface: Unified SHAP tabs for RF and XGBoost with consistent layout.
  • Verdict Value Box: AUC-threshold verdict (Strong ≥0.80, Moderate ≥0.70, Weak <0.70) in Executive Summary.
  • Label × Cancer Type Sunburst: Interactive sunburst visualization replacing static bar chart.
  • Per-Sample Coverage Histogram: Histogram of non-null feature percentage per sample.
  • Per-Chromosome Feature Chart: Conditional chromosome-level feature bar chart.
  • Feature Importances Tab: Top-20 RF Gini importance bar chart in Model Validation.
  • AUC Deltas: RF–LR and XGB–RF AUC deltas surfaced in Performance Metrics.
  • Training Time: Model training time displayed in Performance Metrics tab.
  • VAF Scatter: Re-added tumor burden independence scatter (top feature vs max_vaf, LOWESS trendline).
  • great_tables Integration: Column-grouped data explorer using great_tables with auto-detected feature family spanners.
  • Quarto Auto-Discovery: _find_quarto() probes PATH then 7 well-known install locations (Positron, Homebrew, system, conda).
  • Methods Link: Sidebar link to API documentation site.
  • Documentation: New dashboard guide, DCA methodology doc, feature cards API reference, comprehensive CLI flag docs.
  • Tests: 24 new tests (53 total) covering DCA, PR curves, fold AUC, threshold sweep, feature stability, QC metrics, training time, AUC deltas, and feature cards.

Changed

  • JSON schema expanded from 24 to 44 fields per feature set.
  • Dashboard template: 1,555 lines, 52 balanced code blocks.
  • Language discipline applied across docs and template (removed promotional/overclaiming language).
  • custom.scss: bumped font size to 0.95rem, added QC warning class, added @media print rules.

Fixed

  • Silent except: pass in calibration code replaced with log.warning().
  • Division by len(df) without zero-guard in class balance text.
  • Scoreboard NaN values: sensitivity, specificity, n_samples, n_positive now correctly extracted from rf_classification_report dict instead of non-existent top-level keys.
  • QC tab Figure code dump: unsuppressed fig.update_traces() return displayed as raw Figure({...}) text.
  • Dashboard debug: true leaked Python output into rendered HTML; replaced with warning: false.
  • QC row heights summed to 135%; rebalanced to 100% (10+40+25+25).
  • Undefined _to_float_array in wps_panel.py and wps_genomewide.py replaced with correctly imported parse_array.
  • SHAP waterfall rendering stabilized for edge cases with zero feature contributions.
  • Typo fallsbacks → fallbacks in pyproject.toml.
  • great_tables>=0.15.0 added to pyproject.toml dependencies.

[0.0.6] - 2026-04-10

Fixed

  • Bin-Level Extractor Coverage Filtering: Fixed critical data bug where FSC and FSR on-target extractors computed median() across all 28,823 genomic bins, including the 98% with zero coverage. The zero-coverage sentinel values (-29.897 for log2, 0.0 for ratios) dominated the median, producing constant features and AUC=0.500 for all models. Now filters to total > 0 bins before aggregation.
  • SHAP Feature Shape Mismatch: Fixed TreeExplainer crashes caused by passing a feature-subset matrix instead of the full model-trained feature set. SHAP now computes on all 50 model features; --shap-features only limits visualization display count.
  • SHAP Binary Output Handling: Added robust handling for all TreeExplainer return shapes (list, 3D array, 2D array) and safe expected_value extraction for binary classifiers.
  • Error Visibility: Changed Quarto stderr capture from first 500 chars to last 1500 chars, surfacing actual Python errors instead of kernel boot messages.

Added

  • Nextflow HPC Parity: Wired all new CLI flags (--top-n, --shap-samples, --shap-features, --resume, --skip-report, --cvd-safe) into Nextflow nextflow.config and run.nf with HPC-optimized SLURM defaults (10-fold CV, 5000 SHAP samples, 64GB RAM).
  • CLI Configuration Logging: All three commands (label, run, report) now log their full parameter state at startup for reproducibility.
  • Resume Support: --resume flag skips evaluators with existing model results, enabling incremental re-runs.
  • Variance Guard: CLI automatically drops zero-variance features before model training with a logged warning, preventing wasted compute on constant columns.
  • Coverage Monitoring: All bin-level extractors now emit n_covered_bins and n_total_bins in the feature matrix for downstream QC.

Changed

  • Memory Management: Aggressive dashboard optimization — subsampled KDE plots (2000 samples), gc.collect() between SHAP runs, explicit dataframe deletion after filtering.
  • SHAP Explainer: Switched from generic shap.Explainer to shap.TreeExplainer to prevent OOM-causing KernelExplainer fallback.
  • HPC Resource Allocation: process_high memory increased from 32GB to 64GB for SHAP-heavy report generation.

[0.0.5] - 2026-04-09

Fixed

  • PyPI Release Wheel Artifact Synchorization: Enforced a permanent "Triple Bump" protocol standardizing manual version matching globally across settings.ini, nextflow.config, and kreview/__init__.py natively. This resolves a decoupling bug that structurally blocked Github Actions python -m build commands from publishing Wheels accurately tracking downstream package footprints correctly.

[0.0.4] - 2026-04-09

Fixed

  • Documentation Sync: Hardcoded the mike version provider default target alias gracefully to latest intrinsically allowing GitHub Action GH-Pages orchestration arrays to map tags seamlessly.
  • Git Flow Constraints: Implemented official mandatory 8-step PR-driven workflow structures strictly bypassing raw fast-forward terminal merges blocking CI.

[0.0.3] - 2026-04-09

Added

  • Global Config Parity: Synchronized dynamic execution limits tracking across native Python modules (v0.0.3) and HPC workflow blocks (nextflow.config 0.0.3).
  • Core CLI Flags: Engineered @app.callback() directly into kreview Typer infrastructure enabling standalone --version invocation blocks natively supporting Nextflow software_versions.yaml registry capture logging.

Fixed

  • Systemic Module Sync: Repaired native __init__.py decoupling bugs where sequential tool paths caused legacy 0.0.1 footprints to bypass metadata injections.
  • Git Asset Purge: Permanently bound dynamic CLI Quarto runtimes (html, static_plots, nohup) directly inside .gitignore blocking blob registry accumulation remotely.
  • CI Formatting: Established uniform black . compliance thresholds natively locking both nbs/ JSON source notebooks and /kreview Python exports securely for Github Actions lint workflows.

[0.0.2] - 2026-04-09

Added

  • Diagnostic Upgrades: Implemented explicit Sensitivity and Specificity clinical evaluations automatically calculating Youden's J static cutoffs and logging metrics securely to the results JSON matrix.
  • Model Expansion: Added scalable XGBoost modeling alongside native Random Forest and Logistic Regression classifiers inline.
  • Theme Selection: Introduced set_theme() support parsing --cvd-safe toggle parameters. Allows zero-friction toggling between Okabe-Ito (colorblind secure) and global Muted Neon visualization workflows.
  • Cluster Deployment Optimization: Injected [all] pip extra support resolving seamless multi-tool HPC dependencies smoothly (pip install -e ".[all]").

Fixed

  • Dashboard Standalone Render: Resolved format: dashboard clipping anomalies resulting from undocumented Quarto _quarto.yml project context bleeding. Hardcoded formatting constraints dynamically allowing pipeline usage deeply isolated in site-packages perfectly.
  • Path Resolution Hacks: Removed namespace package AST resolution assumptions and substituted secure __file__ contextual path checks, completely resolving editable pip install -e . template NoneType bugs natively.
  • CLI Options: Restored destroyed --verbose option parsing.

[0.0.1] - 2026-04-09

Added

  • Formally released the production-grade kreview Evaluation Framework for fragmentomics features.
  • Five-tier classification algorithm implemented for accurate clinical ctDNA labeling: Possible ctDNA−, True ctDNA+, Possible ctDNA+, Healthy Normal, Malignancy (Heme).
  • Scikit-learn Pipeline injection to eliminate cross-fold scaling leakage during standard ML evaluate_feature.
  • DuckDB I/O optimizations strictly mapping internal batch chunks dynamically configured for Desktop (--chunk-size 50) and HPC SLURM networks (--chunk-size 500).
  • Integrated dynamic kaleido plotting backends for clinical PDF Quarto workflows.
  • Dedicated Nextflow NF-Core DSL2 environment scaling wrapper specifically designed to ingest manifest.txt parameters recursively without crushing symmetric work/ limits.
  • GitHub Container Registry release pipeline (ghcr.io/msk-access/kreview) fully established utilizing OCI registry labels.

Fixed

  • Out-of-fold probability caching logic strictly refactored away from simplistic aggregation bias.
  • Hardcoded papermill exceptions mapped into modular pipeline architecture.
  • Replaced ambiguous test metrics with explicitly verified Benjamini-Hochberg (FDR) corrections over Mann-Whitney metrics.