trentuna/candle-annotator
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Author SHA1 Message Date
Marko Djordjevic
21f184aa8d feat(ui): implement disagreement detection, prediction summary, loading states, and update documentation 
- Add disagreement detection logic comparing human annotations vs predictions
- Display prediction summary in PredictionPanel (agreements/disagreements)
- Wire up 'Show only disagreements' filter toggle
- Add loading overlay during prediction fetching
- Update docker-compose.yml with healthchecks for all services
- Update DEPLOYMENT.md with comprehensive ML service setup instructions
- Update README.md with ML pipeline overview and architecture diagrams
- Update CLAUDE_DESCRIPTION.md with v3.0.0 ML integration details

Remaining tasks (11.2, 11.4, 11.5) deferred - core functionality complete
 2026-02-15 16:34:02 +01:00
Marko Djordjevic
952eb7413c feat(ui): add prediction chart rendering with histogram overlay, markers, filtering, and visibility toggle 
- Add histogram series to CandleChart for per-bar prediction colors (15% opacity)
- Add series markers showing label name and confidence % at prediction span starts
- Implement confidence threshold filtering for both histogram and markers
- Implement label type filtering from PredictionPanel checkboxes
- Implement prediction layer visibility toggle (show/hide)
- Add getVisibleCandles method to CandleChartHandle for on-demand prediction fetching
- Pass prediction state props from page.tsx to CandleChart

Tasks 10.1-10.5 complete.
 2026-02-15 16:26:17 +01:00
Marko Djordjevic
28ebe2c5d1 feat(ui): add prediction state management and PredictionPanel component 
- Create prediction type definitions in src/types/predictions.ts
- Add prediction state management to page.tsx with caching
- Implement PredictionPanel component with:
  - Master visibility toggle
  - Model info display (name, version, type, metrics)
  - Action buttons (Run on Visible, Predict All)
  - Confidence threshold slider
  - Label filter checkboxes with per-class metrics
  - Disagreement filter toggle
  - Prediction summary display
  - Model server offline banner
- Add on-demand and batch prediction fetching
- Implement prediction caching by chart and model version
- Add health polling for inference API (30s interval when offline)
- Ensure annotation tools work independently of prediction API

Tasks completed: 9.1-9.5, 12.1-12.3 (59/78 total)
 2026-02-15 16:20:07 +01:00
Marko Djordjevic
bb1b6d573f feat(api): add span annotation export and feedback loop support 
- Add GET /api/span-annotations/export endpoint for ML pipeline JSON/CSV export
- Add source and model_prediction fields to span_annotations schema
- Update POST endpoint to accept source (human/model/human_correction) and model_prediction metadata
- Support negative annotations (label 'O' for user corrections to model predictions)
- Create migration 0005 for new schema fields

Completes tasks 8.1-8.4 of candle-backend change
 2026-02-15 14:35:31 +01:00
Marko Djordjevic
205021e810 feat(api): add Next.js proxy routes for ML inference service 2026-02-15 14:30:09 +01:00
Marko Djordjevic
3a83fd38e9 feat(ml): implement FastAPI inference service with model loading, preprocessing, and prediction endpoints 2026-02-15 14:29:07 +01:00
Marko Djordjevic
f4c0f9a836 feat(ml): implement training stage with MLflow tracking and model wrappers 
- Create RandomForestModel and XGBoostModel wrappers with class weight support
- Implement temporal and random train/val/test splitting
- Add MLflow experiment tracking with full parameter and metric logging
- Create evaluation module for confusion matrix, feature importance, and classification reports
- Implement model training with sklearn/xgboost flavor logging and optional registry registration
- Store training run metadata in PostgreSQL
- Wire training stage into pipeline.py orchestrator
- Support both RandomForest and XGBoost models with configurable hyperparameters
 2026-02-15 14:22:19 +01:00
Marko Djordjevic
16763b967e feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns 2026-02-15 12:28:58 +01:00
Marko Djordjevic
fd29ab91e0 feat(ml): implement feature engineering pipeline 
- Create pipeline.py with CLI argument parsing for running stages
- Implement TA-Lib indicator computation with multi-output support
- Add candle feature extraction (body_size, wicks, ratios, etc.)
- Create custom feature loader with dynamic module import
- Wire all feature engineering stages with NaN handling
- Tasks completed: 2.2, 2.3, 3.1, 3.2, 3.3, 3.4, 3.5
 2026-02-15 12:22:59 +01:00
Marko Djordjevic
ea339a54a7 feat(ml): add database schema, config parser, and DVC setup 
- Initialize DVC with local storage backend (task 1.6)
- Create PostgreSQL schema for training_runs table (task 1.7)
- Add SQLAlchemy database connection setup (task 1.8)
- Create Pydantic config models for pipeline.yaml (task 2.1)
- Add migration runner for database setup
- Fix pyproject.toml package discovery config
 2026-02-15 12:08:53 +01:00
Marko Djordjevic
1a653c5866 feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup 2026-02-15 11:58:31 +01:00