diff --git a/.planning/STATE.md b/.planning/STATE.md index d148788..e010056 100644 --- a/.planning/STATE.md +++ b/.planning/STATE.md @@ -10,18 +10,18 @@ See: .planning/PROJECT.md (updated 2026-02-11) ## Current Position Phase: 5 of 6 (Output & CLI) -Plan: 1 of 3 in current phase (plan 05-01 complete) -Status: Phase 5 in progress — 05-01 complete -Last activity: 2026-02-11 — Plan 05-01 executed and verified +Plan: 2 of 3 in current phase (plans 05-01, 05-02 complete) +Status: Phase 5 in progress — 05-02 complete +Last activity: 2026-02-11 — Plan 05-02 executed and verified -Progress: [████████░░] 80.0% (16/20 plans complete across all phases) +Progress: [█████████░] 85.0% (17/20 plans complete across all phases) ## Performance Metrics **Velocity:** -- Total plans completed: 16 +- Total plans completed: 17 - Average duration: 5.0 min -- Total execution time: 1.4 hours +- Total execution time: 1.5 hours **By Phase:** @@ -31,17 +31,17 @@ Progress: [████████░░] 80.0% (16/20 plans complete across al | 02 - Prototype Evidence Layer | 2/2 | 8 min | 4.0 min/plan | | 03 - Core Evidence Layers | 6/6 | 52 min | 8.7 min/plan | | 04 - Scoring Integration | 3/3 | 10 min | 3.3 min/plan | -| 05 - Output & CLI | 1/3 | 4 min | 4.0 min/plan | +| 05 - Output & CLI | 2/3 | 9 min | 4.5 min/plan | **Recent Plan Details:** | Plan | Duration | Tasks | Files | |------|----------|-------|-------| -| Phase 03 P05 | 10 min | 2 tasks | 8 files | | Phase 03 P06 | 13 min | 2 tasks | 10 files | | Phase 04 P01 | 4 min | 2 tasks | 4 files | | Phase 04 P02 | 3 min | 2 tasks | 4 files | | Phase 04 P03 | 3 min | 2 tasks | 4 files | | Phase 05 P01 | 4 min | 2 tasks | 5 files | +| Phase 05 P02 | 5 min | 2 tasks | 6 files | ## Accumulated Context @@ -118,6 +118,12 @@ Recent decisions affecting current work: - [05-01]: Dual-format TSV+Parquet with identical data for downstream tool compatibility - [05-01]: YAML provenance sidecar includes statistics (tier counts) and column metadata - [05-01]: Fixed deprecated pl.count() -> pl.len() usage for polars 0.20.5+ compatibility +- [05-02]: matplotlib Agg backend for headless/CLI safety (non-interactive visualization) +- [05-02]: 300 DPI for publication-quality plots +- [05-02]: Tier color scheme: GREEN/ORANGE/RED for HIGH/MEDIUM/LOW (consistent across all plots) +- [05-02]: Graceful degradation (individual plot failures don't block batch generation) +- [05-02]: Dual-format reproducibility reports (JSON machine-readable + Markdown human-readable) +- [05-02]: Optional validation metrics in reproducibility reports (report generates whether or not validation provided) ### Pending Todos @@ -130,5 +136,5 @@ None yet. ## Session Continuity Last session: 2026-02-11 - Phase 5 execution -Stopped at: Plan 05-01 complete — tiering, evidence summary, and dual-format writer implemented with tests -Resume file: .planning/phases/05-output-cli/05-01-SUMMARY.md +Stopped at: Plan 05-02 complete — visualization and reproducibility report modules implemented with tests +Resume file: .planning/phases/05-output-cli/05-02-SUMMARY.md diff --git a/.planning/phases/05-output-cli/05-02-SUMMARY.md b/.planning/phases/05-output-cli/05-02-SUMMARY.md new file mode 100644 index 0000000..3142d99 --- /dev/null +++ b/.planning/phases/05-output-cli/05-02-SUMMARY.md @@ -0,0 +1,248 @@ +--- +phase: 05-output-cli +plan: 02 +subsystem: output +tags: [visualization, reproducibility, reporting, matplotlib, seaborn] +completed: 2026-02-11 +duration_minutes: 5 + +dependencies: + requires: + - config.schema (PipelineConfig, ScoringWeights, DataSourceVersions) + - persistence.provenance (ProvenanceTracker) + provides: + - visualizations.generate_all_plots + - reproducibility.generate_reproducibility_report + affects: + - output.__init__ (exports visualization and reproducibility functions) + +tech_stack: + added: + - matplotlib>=3.8.0 (visualization library with Agg backend) + - seaborn>=0.13.0 (statistical visualization on top of matplotlib) + patterns: + - Non-interactive backend (Agg) for headless/CLI safety + - Proper figure cleanup with plt.close() to prevent memory leaks + - Graceful degradation (individual plot failures don't block others) + - Dataclass-based report structure with dual format output (JSON + Markdown) + +key_files: + created: + - src/usher_pipeline/output/visualizations.py (plot generation functions) + - src/usher_pipeline/output/reproducibility.py (report generation) + - tests/test_visualizations.py (6 tests for plot creation) + - tests/test_reproducibility.py (7 tests for report content) + modified: + - pyproject.toml (added matplotlib and seaborn dependencies) + - src/usher_pipeline/output/__init__.py (exported new functions) + +decisions: + - matplotlib_backend: "Use Agg (non-interactive) backend for headless/CLI safety" + - plot_dpi: "300 DPI for publication-quality output" + - tier_colors: "GREEN/ORANGE/RED for HIGH/MEDIUM/LOW (consistent across plots)" + - error_handling: "Wrap each plot in try/except so failures don't block batch generation" + - report_formats: "JSON for machine-readable + Markdown for human-readable" + - validation_optional: "Validation metrics are optional in reproducibility report" + +metrics: + tasks: 2 + commits: 2 + tests: 13 + files_created: 4 + files_modified: 2 +--- + +# Phase 05 Plan 02: Visualization and Reproducibility Reports Summary + +**One-liner:** Matplotlib/seaborn visualizations (score distributions, layer contributions, tier breakdowns) and dual-format reproducibility reports (JSON + Markdown) with parameters, data versions, filtering steps, and validation metrics. + +## Execution Flow + +### Task 1: Visualization module with matplotlib/seaborn plots (commit: 150417f) + +**Created visualization module with 3 plot types + orchestrator:** + +1. **plot_score_distribution**: Histogram of composite scores colored by confidence tier (HIGH=green, MEDIUM=orange, LOW=red), 30 bins, stacked display +2. **plot_layer_contributions**: Bar chart showing non-null count per evidence layer (gnomAD, expression, annotation, localization, animal model, literature) +3. **plot_tier_breakdown**: Pie chart with percentage labels for tier distribution +4. **generate_all_plots**: Orchestrator that creates all 3 plots with error handling (one failure doesn't block others) + +**Key technical decisions:** +- Use matplotlib Agg backend (non-interactive, headless-safe) +- Save all plots at 300 DPI for publication quality +- Always call `plt.close(fig)` after savefig to prevent memory leaks (critical pitfall from research) +- Convert polars DataFrame to pandas via `to_pandas()` for seaborn compatibility (acceptable overhead for small result sets) + +**Dependencies added:** +- matplotlib>=3.8.0 +- seaborn>=0.13.0 + +**Tests created (6 tests):** +- test_plot_score_distribution_creates_file +- test_plot_layer_contributions_creates_file +- test_plot_tier_breakdown_creates_file +- test_generate_all_plots_creates_all_files +- test_generate_all_plots_returns_paths +- test_plots_handle_empty_dataframe (edge case) + +All tests pass with only expected warnings for empty DataFrame edge cases. + +### Task 2: Reproducibility report module (commit: 5af63ea) + +**Created reproducibility report generation with dual formats:** + +1. **FilteringStep dataclass**: Captures step_name, input_count, output_count, criteria +2. **ReproducibilityReport dataclass**: Contains run_id (UUID4), timestamp, pipeline_version, parameters (scoring weights), data_versions (Ensembl/gnomAD/GTEx/HPA), software_environment (Python/polars/duckdb versions), filtering_steps, validation_metrics (optional), tier_statistics +3. **generate_reproducibility_report**: Extracts all metadata from config, provenance, and tiered DataFrame +4. **Report output methods:** + - `to_json()`: Indented JSON for machine parsing + - `to_markdown()`: Tables and headers for human reading + - `to_dict()`: Dictionary for programmatic access + +**Key design patterns:** +- NULL-preserving tier counting with polars `group_by().agg(pl.len())` +- Optional validation metrics (report generates whether or not validation results are provided) +- Filtering steps extracted from ProvenanceTracker.get_steps() +- Software versions captured at report generation time (sys.version, pl.__version__, duckdb.__version__) + +**Tests created (7 tests):** +- test_generate_report_has_all_fields +- test_report_to_json_parseable +- test_report_to_markdown_has_headers +- test_report_tier_statistics_match +- test_report_includes_validation_when_provided +- test_report_without_validation +- test_report_software_versions + +All tests pass. + +## Deviations from Plan + +**Auto-fixed Issues:** + +**1. [Rule 1 - Bug] Fixed deprecated polars API** +- **Found during:** Task 2 testing +- **Issue:** `pl.count()` is deprecated in polars 0.20.5+, replaced with `pl.len()` +- **Fix:** Updated `pl.count().alias("count")` to `pl.len().alias("count")` in both visualizations.py and reproducibility.py +- **Files modified:** src/usher_pipeline/output/visualizations.py, src/usher_pipeline/output/reproducibility.py +- **Commit:** Included in 5af63ea + +**2. [Rule 1 - Bug] Fixed matplotlib/seaborn deprecation warnings** +- **Found during:** Task 1 testing +- **Issue:** seaborn barplot warning about passing `palette` without `hue`, and `set_xticklabels()` warning about fixed ticks +- **Fix:** Added `hue=labels` and `legend=False` to barplot call, changed `ax.set_xticklabels()` to `plt.setp()` +- **Files modified:** src/usher_pipeline/output/visualizations.py +- **Commit:** Included in 150417f (amended during testing) + +**3. [Rule 3 - Blocking] __init__.py already updated** +- **Found during:** Task 2 commit preparation +- **Issue:** Discovered that src/usher_pipeline/output/__init__.py was already updated with reproducibility and visualization exports by a parallel process +- **Resolution:** No action needed - integration work already completed by plan 05-01 +- **Impact:** Positive - reduces risk of merge conflicts and ensures consistency + +## Verification Results + +**Import verification:** +``` +✓ from usher_pipeline.output.visualizations import generate_all_plots - OK +✓ from usher_pipeline.output.reproducibility import generate_reproducibility_report, ReproducibilityReport - OK +``` + +**Test results:** +``` +✓ 6/6 visualization tests pass +✓ 7/7 reproducibility tests pass +✓ Total: 13/13 tests pass +``` + +**Success criteria met:** +- [x] Visualization module produces 3 PNG plots at 300 DPI +- [x] Score distribution plot with tier color coding (GREEN/ORANGE/RED) +- [x] Layer contributions bar chart showing evidence coverage +- [x] Tier breakdown pie chart with percentages +- [x] Reproducibility report generates in both JSON and Markdown formats +- [x] Report contains parameters, data versions, filtering steps, tier statistics +- [x] Optional validation metrics handled gracefully +- [x] matplotlib Agg backend used (no display required) +- [x] All tests pass +- [x] Proper figure cleanup (plt.close) implemented + +## Self-Check: PASSED + +**Created files exist:** +``` +✓ FOUND: src/usher_pipeline/output/visualizations.py +✓ FOUND: src/usher_pipeline/output/reproducibility.py +✓ FOUND: tests/test_visualizations.py +✓ FOUND: tests/test_reproducibility.py +``` + +**Commits exist:** +``` +✓ FOUND: 150417f (Task 1 - visualization module) +✓ FOUND: 5af63ea (Task 2 - reproducibility module) +``` + +**Tests pass:** +``` +✓ 13/13 tests pass (6 visualization + 7 reproducibility) +``` + +**Dependencies installed:** +``` +✓ matplotlib>=3.8.0 installed +✓ seaborn>=0.13.0 installed +``` + +All verification checks passed successfully. + +## Integration Notes + +**For downstream consumers:** + +1. **Visualization usage:** + ```python + from usher_pipeline.output.visualizations import generate_all_plots + + plots = generate_all_plots(tiered_df, output_dir) + # Returns: {"score_distribution": Path, "layer_contributions": Path, "tier_breakdown": Path} + ``` + +2. **Reproducibility report usage:** + ```python + from usher_pipeline.output.reproducibility import generate_reproducibility_report + + report = generate_reproducibility_report( + config=config, + tiered_df=tiered_df, + provenance=provenance, + validation_result=validation_dict # Optional + ) + + report.to_json(output_dir / "reproducibility.json") + report.to_markdown(output_dir / "reproducibility.md") + ``` + +3. **Expected columns in tiered_df:** + - composite_score, confidence_tier + - gnomad_score, expression_score, annotation_score, localization_score, animal_model_score, literature_score (can be NULL) + +4. **Plot output:** + - All plots saved as PNG at 300 DPI + - Figures properly closed (no memory leaks) + - Empty DataFrames handled gracefully + +5. **Report content:** + - JSON: Machine-readable, parseable with standard json library + - Markdown: Human-readable with tables, headers, formatted statistics + - Both contain identical information, just different presentations + +## Next Steps + +Plan 05-03 will integrate these modules into the CLI with an `output` command that: +- Loads tiered results from DuckDB +- Generates all plots to output directory +- Creates reproducibility reports in both formats +- Provides summary statistics to console + +This plan completes the reporting infrastructure. All visualization and documentation generation logic is now available as reusable modules.