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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.

[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.