usher-exploring/.planning/phases/06-validation/06-03-PLAN.md

phase, plan, type, wave, depends_on, files_modified, autonomous, must_haves

phase	plan	type	wave	depends_on	files_modified	autonomous	must_haves
06-validation	03	execute	2	06-01 06-02	src/usher_pipeline/scoring/validation_report.py src/usher_pipeline/cli/validate_cmd.py src/usher_pipeline/cli/main.py tests/test_validation.py	true	truths artifacts key_links CLI validate command runs positive controls, negative controls, and sensitivity analysis in sequence Comprehensive validation report documents all three validation prongs with pass/fail verdicts Weight tuning recommendations are generated based on validation results with documented rationale Tests verify negative control logic, recall@k computation, weight perturbation, and report generation path provides exports src/usher_pipeline/scoring/validation_report.py Comprehensive validation report combining all three validation prongs generate_comprehensive_validation_report recommend_weight_tuning path provides exports src/usher_pipeline/cli/validate_cmd.py CLI validate command orchestrating full validation pipeline validate path provides tests/test_validation.py Unit tests for negative controls, recall@k, sensitivity, and validation report from to via pattern src/usher_pipeline/cli/validate_cmd.py src/usher_pipeline/scoring/negative_controls.py validate_negative_controls import from usher_pipeline.scoring import validate_negative_controls from to via pattern src/usher_pipeline/cli/validate_cmd.py src/usher_pipeline/scoring/sensitivity.py run_sensitivity_analysis import from usher_pipeline.scoring import run_sensitivity_analysis from to via pattern src/usher_pipeline/cli/validate_cmd.py src/usher_pipeline/scoring/validation.py validate_positive_controls_extended import from usher_pipeline.scoring import validate_positive_controls_extended from to via pattern src/usher_pipeline/cli/main.py src/usher_pipeline/cli/validate_cmd.py Click group add_command cli.add_command.*validate

Create comprehensive validation report generator, CLI validate command, and unit tests for all Phase 6 validation modules.

Purpose: This plan wires together the positive control, negative control, and sensitivity analysis modules (from Plans 01 and 02) into a single CLI command and comprehensive report. Tests ensure correctness with synthetic data. This completes Phase 6 by providing the user-facing validation workflow.

Output: validation_report.py, validate_cmd.py (CLI), updated main.py, and test_validation.py.

<execution_context> @/Users/gbanyan/.claude/get-shit-done/workflows/execute-plan.md @/Users/gbanyan/.claude/get-shit-done/templates/summary.md </execution_context>

@.planning/PROJECT.md @.planning/ROADMAP.md @.planning/STATE.md @.planning/phases/06-validation/06-RESEARCH.md

Need SUMMARYs from Plans 01 and 02 for what was actually built

@.planning/phases/06-validation/06-01-SUMMARY.md @.planning/phases/06-validation/06-02-SUMMARY.md

@src/usher_pipeline/scoring/init.py @src/usher_pipeline/scoring/negative_controls.py @src/usher_pipeline/scoring/sensitivity.py @src/usher_pipeline/scoring/validation.py @src/usher_pipeline/cli/score_cmd.py @src/usher_pipeline/cli/main.py @tests/test_scoring.py

Task 1: Create comprehensive validation report and CLI validate command src/usher_pipeline/scoring/validation_report.py, src/usher_pipeline/cli/validate_cmd.py, src/usher_pipeline/cli/main.py **Create `src/usher_pipeline/scoring/validation_report.py`:**

1. generate_comprehensive_validation_report(positive_metrics: dict, negative_metrics: dict, sensitivity_result: dict, sensitivity_summary: dict) -> str function:

   • Generate a multi-section Markdown report combining all three validation prongs
   • Section 1: "Positive Control Validation" -- median percentile, recall@k table, per-source breakdown, pass/fail
   • Section 2: "Negative Control Validation" -- median percentile, top quartile count, in-HIGH-tier count, pass/fail
   • Section 3: "Sensitivity Analysis" -- Spearman rho table (layer x delta), overall stability verdict, most/least sensitive layers
   • Section 4: "Overall Validation Summary" -- all-pass/partial-fail/fail verdict
   • Section 5: "Weight Tuning Recommendations" -- call recommend_weight_tuning()
   • Return the full Markdown string

2. recommend_weight_tuning(positive_metrics: dict, negative_metrics: dict, sensitivity_summary: dict) -> str function:

   • Analyze validation results and suggest weight adjustments
   • If positive controls pass AND negative controls pass AND sensitivity stable: "Current weights are validated. No tuning recommended."
   • If positive controls fail: suggest increasing weights for layers where known genes score highly
   • If negative controls fail (housekeeping genes ranking too high): suggest examining which layers boost housekeeping genes
   • If sensitivity unstable: identify most sensitive layer and suggest reducing its weight
   • Document rationale for each recommendation
   • CRITICAL: Note that any tuning is "post-validation" and flag circular validation risk per research pitfall
   • Return formatted recommendation text

3. save_validation_report(report_text: str, output_path: Path) -> None: Write report to file

Create src/usher_pipeline/cli/validate_cmd.py:

Follow the established CLI pattern from score_cmd.py (config load, store init, checkpoint, steps, summary, cleanup):

1. Click command validate with options:

   • --force: Re-run even if validation checkpoint exists
   • --skip-sensitivity: Skip sensitivity analysis (faster iteration)
   • --output-dir: Output directory for validation report (default: {data_dir}/validation)
   • --top-n: Top N genes for sensitivity analysis (default: 100)

2. Pipeline steps:

   • Step 1: Load configuration and initialize store
   • Step 2: Check scored_genes checkpoint exists (error if not -- must run score first)
   • Step 3: Run positive control validation (validate_positive_controls_extended)
   • Step 4: Run negative control validation (validate_negative_controls)
   • Step 5: Run sensitivity analysis (unless --skip-sensitivity) -- run_sensitivity_analysis + summarize_sensitivity
   • Step 6: Generate comprehensive validation report (generate_comprehensive_validation_report)
   • Step 7: Save report to output_dir/validation_report.md and provenance sidecar

3. Use click.echo with styled output matching score_cmd.py patterns (green for success, yellow for warnings, red for errors, bold for step headers)

4. Provenance tracking: record_step for each validation phase with metrics

5. Final summary: display overall pass/fail, recall@top-10%, housekeeping median percentile, sensitivity stability

Update src/usher_pipeline/cli/main.py:

• Import validate from validate_cmd
• Add: cli.add_command(validate)
• Follow the existing pattern used for score and report commands Run: cd /Users/gbanyan/Project/usher-exploring && python -c "from usher_pipeline.cli.validate_cmd import validate; print(f'Command name: {validate.name}'); print('OK')" exits 0 AND cd /Users/gbanyan/Project/usher-exploring && python -c "from usher_pipeline.cli.main import cli; commands = list(cli.commands.keys()); print(f'CLI commands: {commands}'); assert 'validate' in commands; print('OK')" exits 0 validation_report.py generates comprehensive multi-section Markdown report with weight tuning recommendations. validate_cmd.py provides CLI command running all three validation prongs. main.py registers validate as a CLI subcommand. All follow established patterns from score_cmd.py.

Task 2: Create unit tests for all validation modules tests/test_validation.py Create `tests/test_validation.py` with comprehensive tests using synthetic DuckDB data. Follow the test pattern from tests/test_scoring.py (use tmp_path fixtures, create in-memory DuckDB with synthetic data).

Test helper: create_synthetic_scored_db(tmp_path) function:

• Create DuckDB with gene_universe (20 genes: GENE001-GENE020)
• Create scored_genes table with composite_score and all 6 layer scores
• Design scores so that:
  • MYO7A, IFT88, BBS1 (known cilia genes) get high scores (0.8-0.95)
  • GAPDH, ACTB, RPL13A (housekeeping genes) get low scores (0.1-0.3)
  • Other genes get mid-range scores (0.3-0.6)
• This ensures positive controls rank high and negative controls rank low in tests

Tests to include:

1. test_compile_housekeeping_genes_structure: Verify compile_housekeeping_genes() returns DataFrame with 13 genes, correct columns (gene_symbol, source, confidence), all confidence=HIGH, all source=literature_validated

2. test_compile_housekeeping_genes_known_genes_present: Assert GAPDH, ACTB, RPL13A, TBP are in the gene_symbol column

3. test_validate_negative_controls_with_synthetic_data: Use synthetic DB where housekeeping genes score low. Assert validation_passed=True, median_percentile < 0.5

4. test_validate_negative_controls_inverted_logic: Create a DB where housekeeping genes score HIGH (artificial scenario). Assert validation_passed=False

5. test_compute_recall_at_k: Use synthetic DB. Assert recall@k returns dict with recalls_absolute and recalls_percentage keys. With 3 known genes in top 5 of 20, recall@5 should be high (>0.5)

6. test_perturb_weight_renormalizes: Perturb gnomad by +0.10, assert weights still sum to 1.0. Perturb by -0.25 (more than weight value), assert weight >= 0.0 and sum = 1.0

7. test_perturb_weight_invalid_layer: perturb_weight with layer="nonexistent" should raise ValueError

8. test_generate_comprehensive_validation_report_format: Pass mock metrics dicts, assert report contains expected sections ("Positive Control", "Negative Control", "Sensitivity Analysis", "Weight Tuning")

9. test_recommend_weight_tuning_all_pass: Pass metrics indicating all validations pass. Assert response contains "No tuning recommended" or similar

All tests should use tmp_path for DuckDB isolation. Import from usher_pipeline.scoring (not internal modules directly where possible). Use PipelineStore with direct conn assignment pattern from test_scoring.py. Run: cd /Users/gbanyan/Project/usher-exploring && python -m pytest tests/test_validation.py -v --tb=short -- all tests pass test_validation.py contains 9+ tests covering negative controls, recall@k, weight perturbation, sensitivity analysis, and report generation. All tests pass using synthetic DuckDB data with designed score patterns ensuring known genes rank high and housekeeping genes rank low.

- `python -m pytest tests/test_validation.py -v` -- all validation tests pass - `python -c "from usher_pipeline.cli.main import cli; assert 'validate' in cli.commands"` -- CLI command registered - `python -c "from usher_pipeline.scoring.validation_report import generate_comprehensive_validation_report, recommend_weight_tuning"` -- report functions importable - `usher-pipeline validate --help` displays usage information with all options

<success_criteria>

• CLI validate command runs positive + negative + sensitivity validations and generates comprehensive report
• Validation report includes all three prongs with pass/fail verdicts and weight tuning recommendations
• Unit tests cover negative controls, recall@k, perturbation, and report generation
• All tests pass with synthetic data
• validate command registered in main CLI </success_criteria>

After completion, create `.planning/phases/06-validation/06-03-SUMMARY.md`