docs(04-03): complete CLI score command and tests plan
- SUMMARY.md: CLI orchestration with checkpoint-restart + 10 comprehensive tests - STATE.md: Updated position (Phase 4 complete), progress (75%), velocity, decisions - Duration: 3 minutes, 2 tasks, 4 files (3 created, 1 modified)
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---
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phase: 04-scoring-integration
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plan: 03
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subsystem: CLI and Testing
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tags:
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- cli
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- scoring
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- testing
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- validation
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- checkpoint-restart
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dependency_graph:
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requires:
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- 04-01 (scoring integration module)
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- 04-02 (QC and validation modules)
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provides:
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- CLI score command with full pipeline orchestration
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- Comprehensive unit and integration test coverage
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affects:
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- main CLI entry point (command registration)
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- test suite coverage metrics
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tech_stack:
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added:
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- click command with --force, --skip-qc, --skip-validation options
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- pytest fixtures for synthetic DuckDB stores
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patterns:
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- Checkpoint-restart pattern (skip if scored_genes exists)
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- Synthetic data testing (no external dependencies)
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- NULL preservation validation in tests
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key_files:
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created:
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- src/usher_pipeline/cli/score_cmd.py (343 lines)
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- tests/test_scoring.py (269 lines)
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- tests/test_scoring_integration.py (305 lines)
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modified:
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- src/usher_pipeline/cli/main.py (added score command registration)
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decisions:
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- Score command follows evidence_cmd.py pattern for consistency
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- Separate --skip-qc and --skip-validation flags for flexible iteration
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- Tests use tmp_path fixtures for isolated DuckDB instances
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- Synthetic test data designed to ensure known genes rank highly (0.8-0.95 scores across all layers)
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metrics:
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duration_minutes: 3
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tasks_completed: 2
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files_created: 3
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files_modified: 1
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test_coverage: 10 tests (7 unit + 3 integration)
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completed_date: 2026-02-11
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---
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# Phase 04 Plan 03: CLI Score Command and Tests Summary
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**One-liner:** CLI command orchestrating full scoring pipeline (known genes → composite scores → QC → validation) with comprehensive test coverage for NULL preservation and validation logic
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## Tasks Completed
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### Task 1: CLI Score Command with Checkpoint-Restart
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**Commit:** d57a5f2
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Created `src/usher_pipeline/cli/score_cmd.py` following the established pattern from `evidence_cmd.py`:
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**Implementation:**
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- Single `score` command (not a group) with options: `--force`, `--skip-qc`, `--skip-validation`
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- Uses `@click.pass_context` to access config_path from CLI context
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- 5-step pipeline flow:
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1. Load known genes (OMIM Usher + SYSCILIA SCGS) to DuckDB
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2. Compute composite scores with NULL-preserving weighted average
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3. Persist scored_genes table with per-layer contributions
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4. Run QC checks (unless --skip-qc) with warnings/errors display
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5. Validate known gene rankings (unless --skip-validation) with report generation
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**Checkpoint-restart:**
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- Checks `store.has_checkpoint('scored_genes')` before processing
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- If exists and not --force, displays summary and returns early
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- Allows fast iteration during development
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**CLI integration:**
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- Updated `src/usher_pipeline/cli/main.py` to import and register score command
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- Command appears in `usher-pipeline --help` alongside setup and evidence
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**Output:**
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- Displays comprehensive summary: total genes, mean score, quality flag distribution
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- Shows QC pass/fail status and validation pass/fail status
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- Saves provenance sidecar to `data_dir/scoring/scoring.provenance.json`
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**Verification:**
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```bash
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# CLI help works
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usher-pipeline score --help # Shows --force, --skip-qc, --skip-validation options
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# Command registered
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usher-pipeline --help # Lists score command
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```
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### Task 2: Unit and Integration Tests for Scoring Module
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**Commit:** a6ad6c6
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Created comprehensive test coverage for scoring module with 10 tests using synthetic data.
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#### test_scoring.py (7 unit tests):
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1. **test_compile_known_genes_returns_expected_structure**
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- Verifies DataFrame structure (gene_symbol, source, confidence columns)
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- Asserts >= 38 genes (10 OMIM Usher + 28 SYSCILIA SCGS v2)
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- Confirms MYO7A and IFT88 present
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- Validates all confidence = HIGH
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- Checks both sources present (omim_usher, syscilia_scgs_v2)
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2. **test_compile_known_genes_no_duplicates_within_source**
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- Verifies no duplicate gene_symbol within same source
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- Allows genes to appear in both sources (separate rows)
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3. **test_scoring_weights_validate_sum_defaults**
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- ScoringWeights() with defaults passes validate_sum()
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4. **test_scoring_weights_validate_sum_custom_valid**
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- Custom weights summing to 1.0 pass validation
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5. **test_scoring_weights_validate_sum_invalid**
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- Weights summing to 1.35 raise ValueError
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6. **test_scoring_weights_validate_sum_close_to_one**
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- Weights within 1e-6 of 1.0 pass (0.999999)
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- Weights outside tolerance fail (0.99)
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7. **test_null_preservation_in_composite**
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- Creates in-memory DuckDB with 3 genes
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- GENE1: 2 evidence layers (gnomad + annotation) → non-NULL score, moderate_evidence
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- GENE2: 1 evidence layer (gnomad only) → non-NULL score, sparse_evidence
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- GENE3: 0 evidence layers → **NULL score, no_evidence**
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- Verifies NULL preservation (not zero) for genes without evidence
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#### test_scoring_integration.py (3 integration tests):
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1. **test_scoring_pipeline_end_to_end**
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- Creates synthetic store with 20 genes (17 generic + 3 known: MYO7A, IFT88, CDH23)
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- 6 evidence tables with varying NULL rates:
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- gnomad: 15/20 (75%)
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- expression: 12/20 (60%)
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- annotation: 18/20 (90%)
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- localization: 10/20 (50%)
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- animal_models: 8/20 (40%)
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- literature: 14/20 (70%)
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- Known genes receive high scores (0.8-0.95) across all 6 layers
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- Verifies:
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- All 20 genes present in results
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- Genes with evidence have non-NULL composite_score
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- Genes without evidence have NULL composite_score
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- evidence_count values correct (0-6)
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- quality_flag matches evidence_count thresholds
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- Known genes rank in top 5 (at least 2 of 3)
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2. **test_qc_detects_missing_data**
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- Creates synthetic store with 100 genes
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- gnomad: 5% coverage (95% NULL) → should trigger ERROR (>80% threshold)
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- expression: 40% coverage (60% NULL) → should trigger WARNING (>50% threshold)
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- Other layers: >50% coverage (no warnings)
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- Verifies QC detects and reports errors for high missing data rates
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3. **test_validation_passes_with_known_genes_ranked_highly**
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- Uses synthetic_store with known genes scoring highly
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- Loads known genes, computes scores, persists, runs validation
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- Verifies:
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- validation_passed = True
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- median_percentile >= 0.75 (top quartile threshold)
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- Known genes rank highly as expected
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**Test Execution:**
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```bash
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pytest tests/test_scoring.py tests/test_scoring_integration.py -v
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# 10 passed, 7 warnings in 0.68s
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```
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## Deviations from Plan
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None - plan executed exactly as written.
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## Self-Check: PASSED
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### Files Created:
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```bash
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[ -f "/Users/gbanyan/Project/usher-exploring/src/usher_pipeline/cli/score_cmd.py" ] && echo "FOUND: score_cmd.py" || echo "MISSING: score_cmd.py"
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# FOUND: score_cmd.py
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[ -f "/Users/gbanyan/Project/usher-exploring/tests/test_scoring.py" ] && echo "FOUND: test_scoring.py" || echo "MISSING: test_scoring.py"
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# FOUND: test_scoring.py
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[ -f "/Users/gbanyan/Project/usher-exploring/tests/test_scoring_integration.py" ] && echo "FOUND: test_scoring_integration.py" || echo "MISSING: test_scoring_integration.py"
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# FOUND: test_scoring_integration.py
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```
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### Commits Exist:
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```bash
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git log --oneline --all | grep -q "d57a5f2" && echo "FOUND: d57a5f2" || echo "MISSING: d57a5f2"
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# FOUND: d57a5f2
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git log --oneline --all | grep -q "a6ad6c6" && echo "FOUND: a6ad6c6" || echo "MISSING: a6ad6c6"
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# FOUND: a6ad6c6
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```
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### CLI Command Works:
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```bash
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usher-pipeline score --help
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# Shows --force, --skip-qc, --skip-validation options
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usher-pipeline --help | grep score
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# score Compute multi-evidence composite scores for all genes.
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```
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### Tests Pass:
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```bash
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pytest tests/test_scoring.py tests/test_scoring_integration.py -v
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# 10 passed, 7 warnings in 0.68s
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```
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## Verification
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All success criteria met:
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- ✅ `usher-pipeline score --help` shows available options
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- ✅ Score command registered in main CLI
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- ✅ Unit tests pass: known genes, weight validation, NULL handling
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- ✅ Integration tests pass: end-to-end scoring with synthetic data, QC detection, validation
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- ✅ All tests runnable with `pytest tests/test_scoring*.py`
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## Notes
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- Tests use synthetic data exclusively (no external API calls, fast, reproducible)
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- NULL preservation pattern validated: genes with no evidence get NULL composite_score, not zero
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- Known genes designed to rank highly in synthetic data (0.8-0.95 scores across all layers)
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- QC thresholds: 50% missing = warning, 80% missing = error
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- Validation threshold: median percentile >= 0.75 (top quartile)
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