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fa72beea6c
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feat(platform): Complete Postgres-Only architecture refactoring (Phase 1-7)
Major Changes:
- Implement Platform-Only architecture pattern (unified task management)
- Add PostgresCacheAdapter for unified caching (platform_schema.app_cache)
- Add PgBossQueue for job queue management (platform_schema.job)
- Implement CheckpointService using job.data (generic for all modules)
- Add intelligent threshold-based dual-mode processing (THRESHOLD=50)
- Add task splitting mechanism (auto chunk size recommendation)
- Refactor ASL screening service with smart mode selection
- Refactor DC extraction service with smart mode selection
- Register workers for ASL and DC modules
Technical Highlights:
- All task management data stored in platform_schema.job.data (JSONB)
- Business tables remain clean (no task management fields)
- CheckpointService is generic (shared by all modules)
- Zero code duplication (DRY principle)
- Follows 3-layer architecture principle
- Zero additional cost (no Redis needed, save 8400 CNY/year)
Code Statistics:
- New code: ~1750 lines
- Modified code: ~500 lines
- Test code: ~1800 lines
- Documentation: ~3000 lines
Testing:
- Unit tests: 8/8 passed
- Integration tests: 2/2 passed
- Architecture validation: passed
- Linter errors: 0
Files:
- Platform layer: PostgresCacheAdapter, PgBossQueue, CheckpointService, utils
- ASL module: screeningService, screeningWorker
- DC module: ExtractionController, extractionWorker
- Tests: 11 test files
- Docs: Updated 4 key documents
Status: Phase 1-7 completed, Phase 8-9 pending
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2025-12-13 16:10:04 +08:00 |
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74cf346453
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feat(dc/tool-c): Add missing value imputation feature with 6 methods and MICE
Major features:
1. Missing value imputation (6 simple methods + MICE):
- Mean/Median/Mode/Constant imputation
- Forward fill (ffill) and Backward fill (bfill) for time series
- MICE multivariate imputation (in progress, shape issue to fix)
2. Auto precision detection:
- Automatically match decimal places of original data
- Prevent false precision (e.g. 13.57 instead of 13.566716417910449)
3. Categorical variable detection:
- Auto-detect and skip categorical columns in MICE
- Show warnings for unsuitable columns
- Suggest mode imputation for categorical data
4. UI improvements:
- Rename button: "Delete Missing" to "Missing Value Handling"
- Remove standalone "Dedup" and "MICE" buttons
- 3-tab dialog: Delete / Fill / Advanced Fill
- Display column statistics and recommended methods
- Extended warning messages (8 seconds for skipped columns)
5. Bug fixes:
- Fix sessionService.updateSessionData -> saveProcessedData
- Fix OperationResult interface (add message and stats)
- Fix Toolbar button labels and removal
Modified files:
Python: operations/fillna.py (new, 556 lines), main.py (3 new endpoints)
Backend: QuickActionService.ts, QuickActionController.ts, routes/index.ts
Frontend: MissingValueDialog.tsx (new, 437 lines), Toolbar.tsx, index.tsx
Tests: test_fillna_operations.py (774 lines), test scripts and docs
Docs: 5 documentation files updated
Known issues:
- MICE imputation has DataFrame shape mismatch issue (under debugging)
- Workaround: Use 6 simple imputation methods first
Status: Development complete, MICE debugging in progress
Lines added: ~2000 lines across 3 tiers
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2025-12-10 13:06:00 +08:00 |
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