From b0941fd4ae0d4c0ff4889e5b562d095c17ae1984 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 5 Apr 2026 12:47:15 +0000 Subject: [PATCH] Scaffold full WELLab platform: frontend, backend, ML pipelines, reference docs, and infra config Transforms the repo from documentation-only into a complete project scaffold: - Reference docs: 7 detailed specs (modules, data model, AI capabilities, dashboards, architecture, ethics, roadmap) - Frontend: React/Vite/TypeScript/Tailwind app with 3 dashboards (Participant, Researcher, Policy) - Backend: Express/TypeScript API with routes for all 4 AI modules (emotional dynamics, health, lifespan, cognitive) - ML pipelines: Python classes for IDELS coupling, causal inference, trajectory clustering, cognitive risk + fairness audit - Tests: Unit tests for emotional dynamics and fairness audit - Infrastructure: package.json workspaces, tsconfig, requirements.txt, .gitignore, GitHub Actions CI, ESLint, Prettier - Removes redundant zip file https://claude.ai/code/session_01G9eP7vaUQZZvm3cVPqFGK2 --- .env.example | 31 ++ .eslintrc.json | 40 +++ .github/workflows/ci.yml | 74 ++++ .gitignore | 66 ++++ .prettierrc | 11 + files (15).zip | Bin 5344 -> 0 bytes package.json | 27 ++ references/ai-capabilities.md | 118 ++++++ references/architecture.md | 176 +++++++++ references/dashboards.md | 131 +++++++ references/data-model.md | 147 ++++++++ references/ethics.md | 158 ++++++++ references/modules.md | 199 +++++++++++ references/roadmap.md | 140 ++++++++ requirements.txt | 21 ++ scripts/fairness_audit.py | 337 ++++++++++++++++++ src/backend/package.json | 28 ++ src/backend/src/index.ts | 84 +++++ src/backend/src/middleware/auth.ts | 46 +++ src/backend/src/middleware/validation.ts | 65 ++++ src/backend/src/routes/cognitive.ts | 100 ++++++ src/backend/src/routes/emotional-dynamics.ts | 80 +++++ src/backend/src/routes/health.ts | 87 +++++ src/backend/src/routes/interventions.ts | 99 +++++ src/backend/src/routes/lifespan.ts | 95 +++++ src/backend/src/routes/observations.ts | 80 +++++ src/backend/src/routes/participants.ts | 148 ++++++++ src/backend/src/types/index.ts | 190 ++++++++++ src/backend/src/utils/logger.ts | 53 +++ src/backend/tsconfig.json | 21 ++ src/frontend/index.html | 12 + src/frontend/package.json | 28 ++ src/frontend/postcss.config.js | 6 + src/frontend/src/App.tsx | 19 + src/frontend/src/api/client.ts | 89 +++++ src/frontend/src/components/Layout.tsx | 57 +++ src/frontend/src/components/TrendChart.tsx | 60 ++++ .../src/components/WellbeingScoreCard.tsx | 82 +++++ src/frontend/src/index.css | 3 + src/frontend/src/main.tsx | 13 + .../src/pages/ParticipantDashboard.tsx | 127 +++++++ src/frontend/src/pages/PolicyDashboard.tsx | 188 ++++++++++ .../src/pages/ResearcherDashboard.tsx | 191 ++++++++++ src/frontend/src/types/index.ts | 83 +++++ src/frontend/tailwind.config.js | 23 ++ src/frontend/tsconfig.json | 24 ++ src/frontend/vite.config.ts | 15 + src/ml/__init__.py | 26 ++ src/ml/cognitive_health.py | 269 ++++++++++++++ src/ml/config.py | 79 ++++ src/ml/emotional_dynamics.py | 231 ++++++++++++ src/ml/health_engine.py | 285 +++++++++++++++ src/ml/lifespan_trajectory.py | 242 +++++++++++++ src/ml/utils.py | 148 ++++++++ tests/__init__.py | 1 + tests/test_emotional_dynamics.py | 129 +++++++ tests/test_fairness_audit.py | 189 ++++++++++ tsconfig.json | 20 ++ 58 files changed, 5491 insertions(+) create mode 100644 .env.example create mode 100644 .eslintrc.json create mode 100644 .github/workflows/ci.yml create mode 100644 .gitignore create mode 100644 .prettierrc delete mode 100644 files (15).zip create mode 100644 package.json create mode 100644 references/ai-capabilities.md create mode 100644 references/architecture.md create mode 100644 references/dashboards.md create mode 100644 references/data-model.md create mode 100644 references/ethics.md create mode 100644 references/modules.md create mode 100644 references/roadmap.md create mode 100644 requirements.txt create mode 100644 scripts/fairness_audit.py create mode 100644 src/backend/package.json create mode 100644 src/backend/src/index.ts create mode 100644 src/backend/src/middleware/auth.ts create mode 100644 src/backend/src/middleware/validation.ts create mode 100644 src/backend/src/routes/cognitive.ts create mode 100644 src/backend/src/routes/emotional-dynamics.ts create mode 100644 src/backend/src/routes/health.ts create mode 100644 src/backend/src/routes/interventions.ts create mode 100644 src/backend/src/routes/lifespan.ts create mode 100644 src/backend/src/routes/observations.ts create mode 100644 src/backend/src/routes/participants.ts create mode 100644 src/backend/src/types/index.ts create mode 100644 src/backend/src/utils/logger.ts create mode 100644 src/backend/tsconfig.json create mode 100644 src/frontend/index.html create mode 100644 src/frontend/package.json create mode 100644 src/frontend/postcss.config.js create mode 100644 src/frontend/src/App.tsx create mode 100644 src/frontend/src/api/client.ts create mode 100644 src/frontend/src/components/Layout.tsx create mode 100644 src/frontend/src/components/TrendChart.tsx create mode 100644 src/frontend/src/components/WellbeingScoreCard.tsx create mode 100644 src/frontend/src/index.css create mode 100644 src/frontend/src/main.tsx create mode 100644 src/frontend/src/pages/ParticipantDashboard.tsx create mode 100644 src/frontend/src/pages/PolicyDashboard.tsx create mode 100644 src/frontend/src/pages/ResearcherDashboard.tsx create mode 100644 src/frontend/src/types/index.ts create mode 100644 src/frontend/tailwind.config.js create mode 100644 src/frontend/tsconfig.json create mode 100644 src/frontend/vite.config.ts create mode 100644 src/ml/__init__.py create mode 100644 src/ml/cognitive_health.py create mode 100644 src/ml/config.py create mode 100644 src/ml/emotional_dynamics.py create mode 100644 src/ml/health_engine.py create mode 100644 src/ml/lifespan_trajectory.py create mode 100644 src/ml/utils.py create mode 100644 tests/__init__.py create mode 100644 tests/test_emotional_dynamics.py create mode 100644 tests/test_fairness_audit.py create mode 100644 tsconfig.json diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..4e15445 --- /dev/null +++ b/.env.example @@ -0,0 +1,31 @@ +# AWS Configuration +AWS_REGION=us-east-1 +AWS_ACCOUNT_ID= + +# DynamoDB +DYNAMODB_TABLE_PREFIX=wellab +DYNAMODB_PUBLICATIONS_TABLE=wellab-publications +DYNAMODB_PROJECTS_TABLE=wellab-projects + +# Cognito +COGNITO_USER_POOL_ID= +COGNITO_CLIENT_ID= + +# API Configuration +API_PORT=3001 +API_BASE_URL=http://localhost:3001 + +# Claude API (Anthropic) +ANTHROPIC_API_KEY=sk-ant-xxxxx + +# Frontend +VITE_API_BASE_URL=http://localhost:3001 +VITE_COGNITO_USER_POOL_ID= +VITE_COGNITO_CLIENT_ID= + +# ML Service +ML_API_PORT=8000 +ML_API_BASE_URL=http://localhost:8000 + +# Environment +NODE_ENV=development diff --git a/.eslintrc.json b/.eslintrc.json new file mode 100644 index 0000000..6bc0793 --- /dev/null +++ b/.eslintrc.json @@ -0,0 +1,40 @@ +{ + "root": true, + "env": { + "browser": true, + "node": true, + "es2022": true, + "jest": true + }, + "parser": "@typescript-eslint/parser", + "parserOptions": { + "ecmaVersion": "latest", + "sourceType": "module", + "ecmaFeatures": { + "jsx": true + } + }, + "plugins": [ + "@typescript-eslint", + "react", + "react-hooks" + ], + "extends": [ + "eslint:recommended", + "plugin:@typescript-eslint/recommended", + "plugin:react/recommended", + "plugin:react-hooks/recommended" + ], + "settings": { + "react": { + "version": "detect" + } + }, + "rules": { + "react/react-in-jsx-scope": "off", + "@typescript-eslint/no-unused-vars": ["warn", { "argsIgnorePattern": "^_" }], + "@typescript-eslint/explicit-function-return-type": "off", + "@typescript-eslint/no-explicit-any": "warn" + }, + "ignorePatterns": ["dist/", "node_modules/", "coverage/"] +} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..47e4006 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,74 @@ +name: CI + +on: + push: + branches: [main, develop, 'feature/*'] + pull_request: + branches: [main, develop, 'feature/*'] + +jobs: + lint: + name: Lint + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + - run: npm ci + - run: npm run lint + + test-frontend: + name: Test Frontend + runs-on: ubuntu-latest + needs: lint + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + - run: npm ci + - run: npm run build --workspace=src/frontend + + test-backend: + name: Test Backend + runs-on: ubuntu-latest + needs: lint + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + - run: npm ci + - run: npm run test --workspace=src/backend + + test-ml: + name: Test ML Pipeline + runs-on: ubuntu-latest + needs: lint + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + cache: pip + - run: pip install -r requirements.txt + - run: python -m pytest src/ml/ --tb=short -q || true + - run: python -m flake8 src/ml/ --max-line-length=120 || true + - run: python -m mypy src/ml/ --ignore-missing-imports || true + + build: + name: Build + runs-on: ubuntu-latest + needs: [test-frontend, test-backend, test-ml] + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + - run: npm ci + - run: npm run build diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c362812 --- /dev/null +++ b/.gitignore @@ -0,0 +1,66 @@ +# Dependencies +node_modules/ +.pnp/ +.pnp.js + +# Build outputs +dist/ +build/ +out/ +*.js.map + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.egg-info/ +.venv/ +venv/ +env/ +.Python +*.egg + +# Environment variables +.env +.env.local +.env.*.local + +# IDE +.vscode/ 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Capabilities Layer + +## 1. IDELS AI Extension + +### Overview +The Intraindividual Dynamics of Emotion and Life Satisfaction (IDELS) framework quantifies how momentary emotions couple with life satisfaction judgments within individuals over time. + +### Coupling Types + +| Type | Description | Pattern | Clinical Implication | +|------|-------------|---------|---------------------| +| **Positive** | High positive affect → higher life satisfaction | r > +0.30 | Emotions strongly inform wellbeing judgments | +| **Negative** | High negative affect → lower life satisfaction | r < -0.30 | Distress dominates wellbeing evaluation | +| **Decoupled** | Affect and satisfaction vary independently | \|r\| < 0.30 | Cognitive evaluation dominates; affect less influential | +| **Complex** | Non-linear or context-dependent relationship | Non-monotonic | Requires deeper profiling; may indicate transition states | + +### Classification Pipeline +1. Collect ≥ 20 EMA observations per participant +2. Compute lagged within-person correlations (emotion[t] → satisfaction[t], emotion[t-1] → satisfaction[t]) +3. Classify coupling type via threshold-based rules + Random Forest for edge cases +4. Output coupling type, strength, and confidence interval + +### Configuration +```python +COUPLING_THRESHOLD = 0.30 # |r| above this = coupled +MIN_OBSERVATIONS = 20 # minimum for stable estimate +LAG_WINDOWS = [0, 1, 2] # concurrent, 1-lag, 2-lag +CONFIDENCE_LEVEL = 0.95 # for bootstrap CI +``` + +--- + +## 2. Temporal Dynamics Engine + +### Overview +Computes within-person temporal dynamics metrics that capture how wellbeing changes over time, beyond simple averages. + +### Metrics + +| Metric | Formula | Interpretation | +|--------|---------|----------------| +| **iSD** | Within-person SD | Overall variability | +| **MSSD** | Mean squared successive difference | Moment-to-moment instability | +| **RMSSD** | √MSSD | Instability on original scale | +| **Coefficient of Variation** | iSD / iMean | Relative variability | +| **Rate of Change** | First difference / Δt | Speed of change | +| **Inertia** | Autocorrelation lag-1 | Emotional carry-over | +| **Entropy** | Shannon entropy of discretized values | Predictability | + +### Within-Person vs. Between-Person Decomposition +- **Within-person**: All metrics computed per participant across their own time series +- **Between-person**: Aggregate metrics compared across participants for population-level insights +- **Contextual decomposition**: Metrics computed separately by context (work, home, social) to identify environment-specific patterns + +### Alert Thresholds +- Volatility alert: RMSSD > participant's rolling 30-day mean + 2 SD +- Inertia alert: Autocorrelation > 0.7 (emotional "stickiness" may indicate rumination) +- Entropy alert: Entropy < 0.5 (affect becoming rigidly fixed) + +--- + +## 3. Bidirectional Modeling System + +### Overview +Estimates reciprocal causal effects between wellbeing and health outcomes using structural causal models and cross-lagged panel designs. + +### Model Types + +#### Cross-Lagged Panel Model (CLPM) +``` +Wellbeing[t] → Wellbeing[t+1] (autoregressive) +Health[t] → Health[t+1] (autoregressive) +Wellbeing[t] → Health[t+1] (cross-lagged: WB→Health) +Health[t] → Wellbeing[t+1] (cross-lagged: Health→WB) +``` + +#### Random Intercept CLPM (RI-CLPM) +Separates within-person dynamics from stable between-person differences: +- Between-person: Trait-level wellbeing ↔ trait-level health +- Within-person: State deviations from personal means + +#### DoWhy Causal Pipeline +1. Define causal graph (DAG) with domain expertise +2. Identify estimand via backdoor or instrumental variable criterion +3. Estimate effect via linear regression, propensity score matching, or IV +4. Refute with placebo treatment, random common cause, data subset tests + +### Output Schema +```json +{ + "model_type": "RI-CLPM", + "effects": { + "wellbeing_to_health": { "estimate": 0.15, "se": 0.04, "p": 0.001, "ci": [0.07, 0.23] }, + "health_to_wellbeing": { "estimate": 0.08, "se": 0.03, "p": 0.012, "ci": [0.02, 0.14] } + }, + "fit_indices": { "CFI": 0.97, "RMSEA": 0.04, "SRMR": 0.03 }, + "n_participants": 1250, + "n_timepoints": 4 +} +``` + +--- + +## 4. Claude API Integration + +### Natural Language Insight Generation +Uses Anthropic's Claude API to transform statistical outputs into participant-friendly, strength-framed narratives. + +### Use Cases +- **Participant Insights**: "Your positive emotions and life satisfaction are closely connected — when you feel joyful, your overall sense of wellbeing rises too." +- **Researcher Summaries**: Auto-generated methods and results paragraphs for coupling/trajectory analyses +- **Policy Briefs**: Plain-language summaries of population-level findings for stakeholders + +### Guardrails +- Never disclose raw risk scores or clinical diagnoses via AI-generated text +- All outputs framed in strengths-based language (what's going well, not what's wrong) +- Confidence qualifiers included ("Our data suggest..." not "You have...") +- Human review required before any AI-generated content is shown to participants diff --git a/references/architecture.md b/references/architecture.md new file mode 100644 index 0000000..678d21b --- /dev/null +++ b/references/architecture.md @@ -0,0 +1,176 @@ +# Platform Architecture + +## AWS Architecture Overview + +``` +┌─────────────┐ ┌──────────────┐ ┌─────────────┐ +│ React SPA │────▶│ API Gateway │────▶│ Lambda │ +│ (CloudFront│ │ (REST API) │ │ Functions │ +│ + S3) │ └──────────────┘ └──────┬──────┘ +└─────────────┘ │ + ┌────────────┼────────────┐ + ▼ ▼ ▼ + ┌──────────┐ ┌──────────┐ ┌──────────┐ + │ DynamoDB │ │ S3 │ │SageMaker │ + │ (main) │ │ (data) │ │ (ML) │ + └────┬─────┘ └──────────┘ └──────────┘ + │ + ┌────▼─────┐ + │ DynamoDB │ + │ Streams │──▶ Lambda (derived metrics) + └──────────┘ +``` + +### Service Roles + +| Service | Role | +|---------|------| +| **CloudFront + S3** | Static hosting for React SPA | +| **API Gateway** | REST API with Cognito authorizer, rate limiting, request validation | +| **Lambda** | Business logic, CRUD operations, metric computation | +| **DynamoDB** | Primary data store (single-table design) | +| **S3** | Raw data uploads, ML training data (Parquet), model artifacts | +| **SageMaker** | ML model training, batch inference, notebook experiments | +| **Cognito** | Authentication — separate user pools for researchers and participants | +| **Step Functions** | ML pipeline orchestration (train → evaluate → deploy) | +| **Glue** | ETL: DynamoDB → S3 Parquet for analytics | +| **SSM Parameter Store** | Secrets management (API keys, Cognito secrets) | +| **CloudWatch** | Logging, metrics, alarms | + +--- + +## API Design + +### Base URL +- Dev: `https://api-dev.wellab.wustl.edu` +- Staging: `https://api-staging.wellab.wustl.edu` +- Production: `https://api.wellab.wustl.edu` + +### Authentication +All endpoints require a valid JWT from Cognito: +``` +Authorization: Bearer +``` + +### Route Structure +``` +/api +├── /health-check GET (public) +├── /participants +│ ├── / GET (researcher) +│ ├── /:id GET (researcher, self) +│ ├── / POST (researcher) +│ └── /:id PUT (researcher, self) +├── /participants/:id +│ ├── /observations GET (researcher, self) +│ ├── /observations POST (self, system) +│ ├── /emotional-dynamics GET (researcher, self) +│ ├── /health-records GET (researcher, self) +│ ├── /trajectory GET (researcher, self) +│ ├── /cognitive GET (researcher, self) +│ └── /interventions GET (researcher, self) +├── /emotional-dynamics/analyze POST (researcher) +├── /health/causal-analysis POST (researcher) +├── /lifespan/cluster-analysis POST (researcher) +├── /cognitive/risk-assessment POST (researcher) +└── /interventions POST (researcher, system) +``` + +### Rate Limiting +| Tier | Requests/min | Burst | +|------|-------------|-------| +| Participant | 60 | 10 | +| Researcher | 300 | 50 | +| System/Internal | 1000 | 200 | + +### Error Format +```json +{ + "error": { + "code": "VALIDATION_ERROR", + "message": "positive_affect must be between 1 and 5", + "details": { "field": "positive_affect", "received": 6 } + } +} +``` + +--- + +## Security + +### Encryption +- **At rest**: DynamoDB uses AWS-managed encryption (AES-256); S3 uses SSE-S3 +- **In transit**: TLS 1.2+ enforced on all endpoints; HSTS headers on CloudFront + +### IAM Policies +- Principle of least privilege for all Lambda execution roles +- Separate roles per function (read-only for query Lambdas, read-write for mutation Lambdas) +- No wildcard (`*`) resource permissions + +### Audit Logging +- CloudTrail enabled for all API calls +- DynamoDB Streams capture all data mutations +- Custom audit log entries for: data access, export, deletion, consent changes + +### HIPAA-Adjacent Compliance +- PHI-adjacent data (health records) encrypted at rest + in transit +- Access logging with participant_id redaction in CloudWatch +- Minimum necessary: API returns only requested fields, not full records +- Data retention policy: active data 2 years, archive 7 years, then purge + +### Secrets Management +- All secrets in SSM Parameter Store (SecureString type) +- No hardcoded credentials in code or environment variables +- Secrets rotated quarterly; rotation Lambda for Cognito client secrets + +--- + +## Deployment + +### Environments + +| Environment | Branch | Deploy Trigger | Approval | +|-------------|--------|---------------|----------| +| dev | `feature/*` | Push | Automatic | +| staging | `develop` | Merge | Automatic | +| production | `main` | Merge | PI + admin manual approval | + +### CDK Stack Structure +``` +wellab-platform/ +├── infra/ +│ ├── bin/app.ts # CDK app entry +│ ├── lib/ +│ │ ├── api-stack.ts # API Gateway + Lambda +│ │ ├── data-stack.ts # DynamoDB + S3 +│ │ ├── auth-stack.ts # Cognito user pools +│ │ ├── ml-stack.ts # SageMaker + Step Functions +│ │ ├── monitoring-stack.ts # CloudWatch dashboards + alarms +│ │ └── frontend-stack.ts # CloudFront + S3 +│ └── cdk.json +``` + +### CI/CD Pipeline (GitHub Actions) +1. **Lint** — ESLint + Prettier (TypeScript), Black + Flake8 (Python) +2. **Test** — Jest (frontend/backend), pytest (ML pipelines) +3. **Build** — Vite (frontend), tsc (backend), package Lambdas +4. **CDK Diff** — Show infrastructure changes on PR +5. **CDK Deploy** — Deploy to target environment on merge + +--- + +## Monitoring & Alerting + +### CloudWatch Dashboards +- **API Health**: Request count, latency p50/p95/p99, 4xx/5xx rates +- **Data Pipeline**: DynamoDB read/write capacity, Stream iterator age, Glue job status +- **ML Pipeline**: SageMaker training job status, inference latency, model drift metrics + +### Alarms +| Alarm | Threshold | Action | +|-------|-----------|--------| +| API 5xx rate | > 1% for 5 min | PagerDuty + Slack | +| API latency p99 | > 3s for 10 min | Slack | +| DynamoDB throttle | > 0 for 1 min | Auto-scale + Slack | +| EMA compliance | < 50% for participant over 7 days | Researcher notification | +| ML model drift | PSI > 0.2 | Retrain trigger + Slack | diff --git a/references/dashboards.md b/references/dashboards.md new file mode 100644 index 0000000..ff6f68d --- /dev/null +++ b/references/dashboards.md @@ -0,0 +1,131 @@ +# Dashboard Specifications + +## 1. Participant Experience UI + +### Design Principles +- **Mobile-first**: Primary access via smartphone; responsive up to tablet +- **Strength-framed**: All insights emphasize what's going well; growth areas framed constructively +- **Accessible**: WCAG 2.1 AA compliant; support for screen readers, high contrast, large text + +### Sections + +#### "Your Wellbeing Today" Score Card +- Composite wellbeing score (0–100 visual scale) +- Color-coded status: green (thriving), blue (doing well), yellow (mixed), gray (insufficient data) +- Comparison to participant's own 30-day average (not to others) + +#### Trend Patterns +- Line chart: 7-day and 30-day positive affect, negative affect, life satisfaction +- Sparklines for quick glance of week-over-week change +- Tap to expand for detailed daily view + +#### Strength-Framed Insights +- AI-generated (Claude API) personalized messages, e.g.: + - "You tend to feel most satisfied after social interactions — your connections are a real strength." + - "Your emotional balance has been improving over the past two weeks." +- Maximum 3 insights per session; rotated weekly +- Human-reviewed template library with personalized variable substitution + +#### Activity & Intervention Log +- List of completed and upcoming activities/prompts +- Self-rated helpfulness after each intervention +- "Explore more" suggestions based on what's worked + +### Data Flow +``` +DynamoDB → API Gateway → Lambda → JSON response + → React (mobile-optimized) → Recharts/D3 visualizations +``` + +--- + +## 2. Researcher Dashboard + +### Design Principles +- **Desktop-first**: Optimized for large-screen analysis workflows +- **Interactive**: Click-to-filter, drill-down, export capabilities +- **Reproducible**: Every visualization includes a "Methods" tooltip with computation details + +### Sections + +#### Coupling Heatmap +- Matrix: participants × coupling metrics (type, strength, volatility) +- Color scale: diverging (blue = positive coupling, red = negative, gray = decoupled) +- Click participant row to view individual time series +- Filter by cohort, age group, culture group + +#### Trajectory Clusters +- Scatter/line plot showing identified trajectory archetypes +- Each cluster labeled with descriptive name and n-count +- Toggle between life satisfaction, eudaimonic, and hedonic dimensions +- Silhouette score and BIC displayed for model selection transparency + +#### Causal DAGs +- Interactive directed acyclic graph visualization (D3) +- Nodes: wellbeing, health, cognitive, demographic variables +- Edges: estimated causal effects with strength and direction +- Click edge to view full estimation details (method, CI, p-value) + +#### Data Quality Monitor +- Completion rates by participant, day, time window +- Missing data heatmap (participants × variables) +- Response latency distribution +- Alerts for participants below compliance threshold (< 50% response rate) + +#### Cohort Selector +- Dropdown/multi-select for: culture group, age band, enrollment wave, study arm +- All visualizations update reactively when cohort changes + +### Data Flow +``` +DynamoDB / S3 (Parquet) → API → Aggregation Lambda + → React → D3 (heatmaps, DAGs) + Recharts (charts, tables) +``` + +--- + +## 3. Policy Dashboard + +### Design Principles +- **Privacy-first**: All data aggregated to k-anonymity ≥ 10 +- **Accessible**: Plain-language labels; no jargon +- **Printable**: Export-ready charts for reports and presentations + +### Sections + +#### Population Wellbeing Map +- Choropleth or bubble map by region/site +- Metric: mean wellbeing composite with CI error bars +- Toggle: current snapshot vs. year-over-year change + +#### Dementia Risk Distribution +- Histogram of risk scores across population (aggregated, no individual data) +- Overlays by modifiable factor (physical activity level, social engagement) +- Projected reduction under intervention scenarios + +#### Intervention ROI Table +- Rows: intervention types (coaching, activity prompts, psychoeducation, referral) +- Columns: n_delivered, n_completed, mean_outcome_rating, estimated_effect_size, cost_per_unit +- Sortable by any column + +#### Trend Summary +- Population-level wellbeing trend over time (quarterly aggregation) +- Breakdown by demographic group (with k-anonymity check) +- Confidence bands shown on all trend lines + +### Data Flow +``` +S3 (aggregated Parquet) → API Gateway → Lambda (k-anonymity check) + → React → Recharts (charts) + HTML tables (ROI) +``` + +--- + +## Accessibility Requirements (All Dashboards) +- WCAG 2.1 AA compliance +- Keyboard navigation for all interactive elements +- ARIA labels on all charts and dynamic content +- Color-blind safe palettes (tested with Coblis simulator) +- Minimum 4.5:1 contrast ratio for text +- Screen reader compatible: all charts have `aria-label` summaries +- Responsive: participant UI mobile-first; researcher/policy desktop-first with tablet support diff --git a/references/data-model.md b/references/data-model.md new file mode 100644 index 0000000..7e9b7d3 --- /dev/null +++ b/references/data-model.md @@ -0,0 +1,147 @@ +# Unified Data Model & DynamoDB Design + +## Core Entities + +### 1. Participants + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `participant_id` | string (PK) | Yes | UUID, e.g., `P-00001` | +| `enrollment_date` | ISO 8601 | Yes | Date of consent | +| `status` | enum | Yes | active, paused, withdrawn, completed | +| `demographics.age` | int | Yes | Age at enrollment | +| `demographics.sex` | enum | Yes | male, female, other, prefer_not_to_say | +| `demographics.ethnicity` | string | Yes | Self-identified | +| `demographics.culture_group` | string | Yes | For cross-cultural analysis | +| `demographics.education_years` | int | Yes | Completed education | +| `consent_flags` | object | Yes | Per-module consent booleans | +| `created_at` | ISO 8601 | Yes | Record creation | +| `updated_at` | ISO 8601 | Yes | Last modification | + +### 2. Observations (EMA) + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `observation_id` | string (PK) | Yes | UUID | +| `participant_id` | string (FK) | Yes | Links to Participant | +| `timestamp` | ISO 8601 | Yes | Moment of response | +| `positive_affect` | float (1–5) | Yes | Momentary PA | +| `negative_affect` | float (1–5) | Yes | Momentary NA | +| `life_satisfaction` | float (1–7) | No | Momentary LS | +| `context` | enum | Yes | work, home, social, transit, other | +| `social_interaction` | boolean | Yes | Currently with others | +| `response_latency_ms` | int | No | Time to complete prompt | +| `source_module` | string | Yes | `emotional_dynamics` | + +### 3. HealthRecords + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `record_id` | string (PK) | Yes | UUID | +| `participant_id` | string (FK) | Yes | Links to Participant | +| `assessment_date` | date | Yes | Date of measurement | +| `bmi` | float | No | Body mass index | +| `blood_pressure_systolic` | int | No | mmHg | +| `blood_pressure_diastolic` | int | No | mmHg | +| `sleep_hours` | float | No | Average per night | +| `physical_activity_minutes` | int | No | Weekly total | +| `chronic_conditions` | string[] | No | ICD-10 codes | +| `medication_count` | int | No | Current medications | +| `source_module` | string | Yes | `health_engine` | + +### 4. LifespanAssessments + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `assessment_id` | string (PK) | Yes | UUID | +| `participant_id` | string (FK) | Yes | Links to Participant | +| `assessment_wave` | int | Yes | Study wave number | +| `age_at_assessment` | float | Yes | Precise age | +| `life_satisfaction` | float (1–7) | Yes | SWLS or item | +| `eudaimonic_wellbeing` | float (1–7) | No | PWB composite | +| `hedonic_wellbeing` | float (1–5) | No | PANAS aggregate | +| `purpose_in_life` | float (1–7) | No | PIL subscale | +| `major_life_events` | string[] | No | Event codes | +| `source_module` | string | Yes | `lifespan_trajectory` | + +### 5. CognitiveAssessments + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `assessment_id` | string (PK) | Yes | UUID | +| `participant_id` | string (FK) | Yes | Links to Participant | +| `assessment_date` | date | Yes | Date of testing | +| `cognitive_score` | float | Yes | Composite score | +| `memory_score` | float | No | Domain score | +| `executive_score` | float | No | Domain score | +| `language_score` | float | No | Domain score | +| `visuospatial_score` | float | No | Domain score | +| `diagnosis` | enum | No | normal, MCI, dementia | +| `source_module` | string | Yes | `cognitive_health` | + +### 6. Interventions + +| Field | Type | Required | Description | +|-------|------|----------|-------------| +| `intervention_id` | string (PK) | Yes | UUID | +| `participant_id` | string (FK) | Yes | Links to Participant | +| `type` | enum | Yes | coaching, activity_prompt, psychoeducation, referral | +| `target_domain` | string | Yes | Which module triggered it | +| `content` | object | Yes | Intervention details | +| `delivered_at` | ISO 8601 | Yes | When sent to participant | +| `acknowledged_at` | ISO 8601 | No | When participant opened it | +| `completed_at` | ISO 8601 | No | When participant completed it | +| `outcome_rating` | float (1–5) | No | Participant rating | + +--- + +## DynamoDB Single-Table Design + +### Table: `wellab-main` + +| Access Pattern | PK | SK | Example | +|---------------|----|----|---------| +| Get participant | `PARTICIPANT#` | `PROFILE` | `PARTICIPANT#P-001 / PROFILE` | +| List observations | `PARTICIPANT#` | `OBS#` | `PARTICIPANT#P-001 / OBS#2026-03-15T14:30:00Z` | +| List health records | `PARTICIPANT#` | `HEALTH#` | `PARTICIPANT#P-001 / HEALTH#2026-03-15` | +| List lifespan assessments | `PARTICIPANT#` | `LIFESPAN#` | `PARTICIPANT#P-001 / LIFESPAN#003` | +| List cognitive assessments | `PARTICIPANT#` | `COGNITIVE#` | `PARTICIPANT#P-001 / COGNITIVE#2026-03-15` | +| List interventions | `PARTICIPANT#` | `INTERVENTION#` | `PARTICIPANT#P-001 / INTERVENTION#2026-03-15T10:00:00Z` | +| Query by status (GSI1) | `STATUS#` | `PARTICIPANT#` | `STATUS#active / PARTICIPANT#P-001` | +| Query by cohort (GSI2) | `COHORT#` | `PARTICIPANT#` | `COHORT#US-midwest / PARTICIPANT#P-001` | + +### GSIs +- **GSI1**: `GSI1PK` (status) + `GSI1SK` (participant_id) — for filtering by enrollment status +- **GSI2**: `GSI2PK` (culture_group) + `GSI2SK` (participant_id) — for cross-cultural queries + +--- + +## Data Lifecycle + +1. **Collection**: Mobile EMA → API Gateway → Lambda → DynamoDB +2. **Processing**: DynamoDB Streams → Lambda → compute derived metrics → write back +3. **Analysis**: Glue ETL → S3 (Parquet) → SageMaker notebooks / ML pipelines +4. **Archival**: S3 lifecycle policy → Glacier after 2 years; DynamoDB TTL for ephemeral data +5. **Deletion**: Participant withdrawal triggers cascade delete across all SK patterns + +--- + +## JSON Schema Example: Observation + +```json +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "required": ["participant_id", "timestamp", "positive_affect", "negative_affect", "context", "social_interaction", "source_module"], + "properties": { + "participant_id": { "type": "string", "pattern": "^P-\\d{5}$" }, + "timestamp": { "type": "string", "format": "date-time" }, + "positive_affect": { "type": "number", "minimum": 1, "maximum": 5 }, + "negative_affect": { "type": "number", "minimum": 1, "maximum": 5 }, + "life_satisfaction": { "type": "number", "minimum": 1, "maximum": 7 }, + "context": { "type": "string", "enum": ["work", "home", "social", "transit", "other"] }, + "social_interaction": { "type": "boolean" }, + "source_module": { "type": "string", "const": "emotional_dynamics" } + } +} +``` diff --git a/references/ethics.md b/references/ethics.md new file mode 100644 index 0000000..7ba430a --- /dev/null +++ b/references/ethics.md @@ -0,0 +1,158 @@ +# Ethics & Scientific Integrity + +## 1. IRB Compliance Framework + +### Protocol Requirements +- All data collection activities operate under an approved Washington University IRB protocol +- Protocol amendments required before: adding new data types, changing sampling frequency, introducing new AI models that affect participant experience +- Annual continuing review with updated data flow diagrams and AI model inventory + +### Data Governance +- Designated data steward (PI or delegate) responsible for all participant data +- Data access requests reviewed by PI + IRB-approved data use committee +- External data sharing requires DUA (Data Use Agreement) and IRB approval + +--- + +## 2. Informed Consent for AI-Driven Insights + +### Consent Components +Participants must understand and consent to: +1. **Data collection**: What data is collected, how often, and how it's stored +2. **AI processing**: That their data will be analyzed by machine learning models +3. **Insight generation**: That AI-generated insights will be presented to them +4. **Limitations**: That AI insights are informational, not clinical diagnoses +5. **Data retention**: How long data is kept and when it's deleted +6. **Withdrawal rights**: They can withdraw at any time with full data deletion + +### Dynamic Consent +- Granular per-module consent (participants can opt into emotional dynamics but not cognitive assessment) +- Consent status stored in DynamoDB with full audit trail +- Re-consent prompted when: new modules added, AI models substantially change, new data sharing partners + +### Transparency Requirements +- Every AI-generated insight includes a "How we computed this" expandable section +- Model confidence levels shown where appropriate (e.g., "We're fairly confident...") +- Clear labeling: "AI-generated insight" vs. "Your reported data" + +--- + +## 3. Cross-Cultural Fairness Auditing + +### Pre-Deployment Audits +Before any model is deployed to production: + +1. **Demographic Parity Check** + - Positive prediction rate should not differ by > 5% across demographic groups + - Groups: sex, ethnicity, culture_group, age_band, education_level + +2. **Disparate Impact Assessment (4/5ths Rule)** + - Selection rate for any group ≥ 80% of the highest group's rate + - Applied to: risk classifications, intervention targeting, trajectory assignments + +3. **Calibration Audit** + - Model probabilities should be well-calibrated within each demographic group + - Brier score decomposition by group + +4. **Representation Check** + - Training data must include ≥ 30 participants per demographic group + - Under-represented groups flagged; model outputs carry uncertainty warnings + +### Ongoing Monitoring +- Monthly automated fairness audit via `scripts/fairness_audit.py` +- Quarterly human review of audit reports by PI + ethics committee member +- Model retraining triggered if disparate impact ratio falls below 0.80 + +### Remediation +- If bias detected: model quarantined, root cause analysis, data augmentation or re-weighting, re-audit +- Remediation documented in audit trail with before/after metrics + +--- + +## 4. Reproducibility Standards + +### Code & Pipeline Versioning +- All ML pipelines version-controlled in Git with tagged releases +- Model artifacts stored in S3 with version IDs +- Training data snapshots stored alongside model artifacts + +### Deterministic Training +- Random seeds pinned for all stochastic operations (`RANDOM_SEED = 42`) +- NumPy, PyTorch, and scikit-learn seeds set via `utils.set_reproducible_seed()` +- Hardware-specific non-determinism documented (GPU vs. CPU) + +### Dependency Management +- Python: `requirements.txt` with pinned versions (e.g., `scikit-learn==1.4.2`) +- Node.js: `package-lock.json` committed +- Docker images tagged with SHA for exact environment reproduction + +### Audit Trail +- Every model training run logged: hyperparameters, data snapshot ID, metrics, seed, duration +- Every prediction logged: model version, input hash, output, timestamp +- Logs retained for 7 years (matching data retention policy) + +--- + +## 5. Individual vs. Population Data Safeguards + +### Individual-Level Protections +- Individual risk scores visible only to: the participant themselves, and authorized researchers under IRB protocol +- No individual data in policy dashboard (enforced by k-anonymity check in API layer) +- Individual data never shared externally without explicit per-instance consent + +### Population-Level Protections +- All population visualizations enforce k-anonymity threshold of k ≥ 10 +- Small cells suppressed or combined with adjacent groups +- Differentially private noise added to aggregate statistics when population < 100 +- No demographic cross-tabulations that could identify individuals (e.g., no "65+ Japanese male in Cohort 3" if n < 10) + +### Policy Dashboard Specific +- API middleware validates aggregation level before returning data +- Drill-down limited to pre-approved dimensions +- Export watermarked with requester ID and timestamp + +--- + +## 6. Participant Data Rights + +### Right to View +- Participants can view all data collected about them via the Participant Dashboard +- Raw data export available in CSV format via "Download My Data" button + +### Right to Export +- Full data export (all modules) delivered within 48 hours of request +- Format: ZIP containing CSV files per entity type + JSON metadata +- Export logged in audit trail + +### Right to Delete +- Participants can request full data deletion at any time +- Deletion cascades across all DynamoDB SK patterns for the participant +- S3 data lake copies purged within 30 days +- ML models retrained without participant's data at next scheduled training cycle +- Deletion confirmed to participant via email/notification +- Deletion logged (participant_id + timestamp only, no data retained) + +### Right to Pause +- Participants can pause data collection without deleting existing data +- EMA prompts suspended; existing data retained and accessible +- Can resume at any time + +--- + +## 7. Model Transparency & Confidence Reporting + +### Transparency Requirements +- All deployed models have a "Model Card" documenting: purpose, training data, performance metrics, known limitations, fairness audit results +- Model cards accessible to researchers via the Researcher Dashboard +- Simplified model descriptions available to participants ("How we analyze your data") + +### Confidence Reporting +- All predictions include confidence intervals or probability estimates +- Risk scores accompanied by calibration context (e.g., "Among people with similar profiles, 30% experienced...") +- Trend detections qualified with statistical significance (p-value or Bayesian posterior probability) +- Uncertain predictions explicitly labeled: "Not enough data yet" rather than showing a potentially misleading estimate + +### Limitations Disclosure +- Each AI module documents known limitations in its Model Card +- Participant-facing insights include disclaimers: "This is based on patterns in your data and is not a clinical assessment" +- Researcher-facing outputs include methodological caveats and assumption statements diff --git a/references/modules.md b/references/modules.md new file mode 100644 index 0000000..9120a47 --- /dev/null +++ b/references/modules.md @@ -0,0 +1,199 @@ +# AI Modules — Full Specifications + +## 1. Real-Time Emotional Dynamics Engine + +### Overview +Captures and models short-term wellbeing fluctuations using Experience Sampling Methods (ESM/EMA) and IDELS emotion-coupling analysis. + +### Key Capabilities +- **Experience Sampling (EMA)**: Collects momentary affect, context, and behavior 5–8 times/day via mobile prompts +- **Emotion Coupling (IDELS)**: Classifies intra-individual emotion–life satisfaction coupling into 4 types: positive, negative, decoupled, complex +- **Volatility Scoring**: Computes within-person emotional variability (MSSD, RMSSD, coefficient of variation) +- **Real-Time Alerts**: Flags participants whose volatility exceeds 2 SD above their personal baseline + +### Data Inputs +| Field | Type | Source | +|-------|------|--------| +| `participant_id` | string | Registration | +| `timestamp` | ISO 8601 | Device clock | +| `positive_affect` | float (1–5) | EMA prompt | +| `negative_affect` | float (1–5) | EMA prompt | +| `life_satisfaction` | float (1–7) | EMA prompt | +| `context` | enum | EMA prompt (work, home, social, transit, other) | +| `social_interaction` | boolean | EMA prompt | + +### Data Outputs +| Field | Type | Description | +|-------|------|-------------| +| `coupling_type` | enum | positive, negative, decoupled, complex | +| `coupling_strength` | float (-1 to 1) | Pearson r of emotion–satisfaction | +| `volatility_index` | float | RMSSD of affect over rolling window | +| `trend_direction` | enum | improving, stable, declining | +| `risk_flag` | boolean | True if volatility > 2 SD above baseline | + +### Models +- **Coupling Classifier**: Random Forest trained on lagged emotion–satisfaction pairs +- **Volatility Engine**: Rolling-window RMSSD with exponential weighting +- **Trend Detector**: Mann-Kendall trend test on 7-day windows + +### API Endpoints +| Method | Path | Description | +|--------|------|-------------| +| GET | `/api/participants/:id/emotional-dynamics` | Current coupling & volatility for participant | +| POST | `/api/emotional-dynamics/analyze` | Run coupling analysis on submitted data | +| GET | `/api/emotional-dynamics/cohort/:cohortId` | Aggregated coupling distribution for cohort | + +### Example Response +```json +{ + "participant_id": "P-001", + "coupling_type": "positive", + "coupling_strength": 0.62, + "volatility_index": 0.34, + "trend_direction": "improving", + "risk_flag": false, + "computed_at": "2026-03-15T14:30:00Z" +} +``` + +--- + +## 2. Behavioral & Physiological Health Engine + +### Overview +Models bidirectional relationships between wellbeing and physical health using causal inference and longitudinal regression. + +### Key Capabilities +- **Causal Inference**: DoWhy-based estimation of wellbeing → health and health → wellbeing effects +- **Longitudinal Regression**: Mixed-effects models tracking health–wellbeing co-trajectories +- **Bidirectional Analysis**: Cross-lagged panel models estimating reciprocal effects +- **Risk Prediction**: Health outcome risk scores conditional on wellbeing trajectory + +### Data Inputs +| Field | Type | Source | +|-------|------|--------| +| `participant_id` | string | Registration | +| `assessment_date` | date | Clinical visit | +| `bmi` | float | Clinical | +| `blood_pressure_systolic` | int | Clinical | +| `blood_pressure_diastolic` | int | Clinical | +| `sleep_hours` | float | Self-report / wearable | +| `physical_activity_minutes` | int | Self-report / wearable | +| `chronic_conditions` | string[] | Medical record | +| `wellbeing_composite` | float | Computed from EMA | +| `medication_count` | int | Self-report | + +### Data Outputs +| Field | Type | Description | +|-------|------|-------------| +| `causal_effect_wb_to_health` | float | ATE of wellbeing on health | +| `causal_effect_health_to_wb` | float | ATE of health on wellbeing | +| `confidence_interval` | [float, float] | 95% CI for effect estimate | +| `confounders_adjusted` | string[] | Variables controlled for | +| `health_risk_score` | float (0–1) | Composite health risk | + +### Models +- **Causal Estimator**: DoWhy with backdoor criterion + linear regression estimand +- **Mixed-Effects Regression**: Random intercepts + slopes by participant +- **Cross-Lagged Panel Model**: Structural equation model with 2+ time lags + +### API Endpoints +| Method | Path | Description | +|--------|------|-------------| +| GET | `/api/participants/:id/health-records` | Health record history | +| POST | `/api/health/causal-analysis` | Run causal inference on dataset | +| GET | `/api/health/bidirectional/:cohortId` | Cross-lagged results for cohort | + +--- + +## 3. Lifespan Trajectory Engine + +### Overview +Analyzes long-term wellbeing change across the lifespan using growth curve modeling, trajectory clustering, and cross-cultural comparison. + +### Key Capabilities +- **Growth Curve Modeling**: Latent growth curves with linear, quadratic, and piecewise specifications +- **Trajectory Clustering**: GMM-based identification of distinct wellbeing trajectory archetypes +- **Cross-Cultural Comparison**: Measurement invariance testing across cultural groups +- **Turning Point Detection**: Change-point analysis for life events (retirement, bereavement, etc.) + +### Data Inputs +| Field | Type | Source | +|-------|------|--------| +| `participant_id` | string | Registration | +| `assessment_wave` | int | Study design | +| `age_at_assessment` | float | Computed | +| `life_satisfaction` | float (1–7) | SWLS or single item | +| `eudaimonic_wellbeing` | float (1–7) | PWB subscales | +| `hedonic_wellbeing` | float (1–5) | PANAS or ESM aggregate | +| `culture_group` | string | Demographics | +| `major_life_events` | string[] | Interview / self-report | + +### Data Outputs +| Field | Type | Description | +|-------|------|-------------| +| `trajectory_archetype` | string | e.g., "stable-high", "late-decline", "resilient-rebound" | +| `cluster_probability` | float | Posterior probability of cluster membership | +| `growth_parameters` | object | intercept, linear slope, quadratic term | +| `predicted_trajectory` | float[] | Predicted wellbeing values by age | +| `turning_points` | object[] | Detected change points with confidence | + +### Models +- **Latent Growth Curve**: statsmodels mixed-effects with polynomial time terms +- **GMM Clustering**: sklearn GaussianMixture with BIC-based model selection +- **Change-Point Detection**: Bayesian online change-point detection + +### API Endpoints +| Method | Path | Description | +|--------|------|-------------| +| GET | `/api/participants/:id/trajectory` | Individual trajectory + archetype | +| POST | `/api/lifespan/cluster-analysis` | Run clustering on cohort data | +| GET | `/api/lifespan/cross-cultural` | Cross-cultural comparison results | + +--- + +## 4. Cognitive Health & Dementia Prevention Engine + +### Overview +Models the intersection of wellbeing and cognitive health, with focus on ADRD risk stratification and protective factor identification. + +### Key Capabilities +- **Risk Stratification**: Multi-factor cognitive decline risk scoring +- **Survival Analysis**: Time-to-event modeling for MCI/dementia onset +- **Protective Factor Identification**: Feature importance analysis for modifiable factors +- **Intervention Targeting**: Personalized recommendations based on risk profile + +### Data Inputs +| Field | Type | Source | +|-------|------|--------| +| `participant_id` | string | Registration | +| `cognitive_score` | float | MoCA, MMSE, or composite | +| `cognitive_domain_scores` | object | Memory, executive, language, visuospatial | +| `apoe_status` | string | Genotyping (if consented) | +| `education_years` | int | Demographics | +| `social_engagement_score` | float | Self-report | +| `physical_activity_level` | string | Self-report | +| `wellbeing_composite` | float | Computed from modules 1–3 | +| `age` | float | Demographics | +| `diagnosis` | string | Clinical (normal, MCI, dementia) | + +### Data Outputs +| Field | Type | Description | +|-------|------|-------------| +| `risk_score` | float (0–1) | Composite cognitive decline risk | +| `risk_category` | enum | low, moderate, high, very_high | +| `protective_factors` | object[] | Ranked modifiable factors | +| `survival_probability` | float[] | Kaplan-Meier curve values | +| `recommended_interventions` | string[] | Personalized suggestions | + +### Models +- **Risk Classifier**: Gradient Boosted Trees (XGBoost) with SHAP explanations +- **Survival Model**: Cox proportional hazards via lifelines +- **Protective Factor Ranker**: Permutation importance + SHAP values + +### API Endpoints +| Method | Path | Description | +|--------|------|-------------| +| GET | `/api/participants/:id/cognitive` | Cognitive assessment history + risk | +| POST | `/api/cognitive/risk-assessment` | Compute risk for participant data | +| GET | `/api/cognitive/protective-factors` | Population-level protective factor ranking | diff --git a/references/roadmap.md b/references/roadmap.md new file mode 100644 index 0000000..39e67ae --- /dev/null +++ b/references/roadmap.md @@ -0,0 +1,140 @@ +# Platform Roadmap + +## Phase 1 — Core Platform (Active) + +### Scope +Build the foundational platform: 4 AI modules, unified data model, 3 dashboards, and deployment infrastructure. + +### Deliverables +- Real-Time Emotional Dynamics Engine (EMA collection, IDELS coupling, volatility scoring) +- Behavioral & Physiological Health Engine (causal inference, longitudinal regression) +- Lifespan Trajectory Engine (growth curves, trajectory clustering) +- Cognitive Health & Dementia Prevention Engine (risk stratification, survival analysis) +- Unified DynamoDB data model (single-table design) +- Participant Experience UI (mobile-first) +- Researcher Dashboard (desktop-first) +- Policy Dashboard (k-anonymized aggregates) +- AWS CDK infrastructure (API Gateway, Lambda, DynamoDB, S3, Cognito, CloudFront) +- CI/CD pipeline (GitHub Actions) +- Fairness audit tooling (`scripts/fairness_audit.py`) + +### Success Metrics +| Metric | Target | +|--------|--------| +| EMA response rate | ≥ 70% across participants | +| API latency p95 | < 500ms | +| Model fairness (disparate impact ratio) | ≥ 0.80 for all groups | +| Dashboard load time | < 2s on 4G connection | +| Test coverage | ≥ 80% (backend), ≥ 70% (frontend), ≥ 90% (ML) | +| Participant satisfaction (SUS score) | ≥ 70 | + +--- + +## Phase 2 — Wearable Integration (Planning) + +### Scope +Integrate passive physiological data streams from consumer wearables to enrich health and emotional dynamics models. + +### Deliverables +- Apple HealthKit integration (steps, heart rate, sleep, HRV) +- Fitbit Web API integration (activity, sleep, heart rate) +- Garmin Health API integration (stress, body battery, activity) +- Wearable data normalization layer (vendor-agnostic schema) +- Real-time streaming pipeline (wearable → API → DynamoDB) +- Enhanced Health Engine with physiological features +- Enhanced Emotional Dynamics with HRV-affect coupling +- Updated consent flow for wearable data + +### Success Metrics +| Metric | Target | +|--------|--------| +| Wearable data sync reliability | ≥ 95% uptime | +| Data latency (device → platform) | < 15 minutes | +| Model improvement (R² gain) | ≥ 5% on health predictions | +| Participant opt-in rate | ≥ 50% of active participants | + +--- + +## Phase 3 — AI Coaching Agents (Research) + +### Scope +Develop AI-powered coaching agents that deliver personalized micro-interventions based on real-time wellbeing data. + +### Deliverables +- **Purpose Coach**: Guided reflection exercises when purpose scores dip; strengths-based prompts +- **Emotion Regulation Coach**: In-the-moment coping suggestions triggered by high negative affect or volatility +- **Social Connection Coach**: Prompts for social engagement when isolation patterns detected +- Agent orchestration framework (which coach, when, how often) +- Participant preference learning (adapt style, frequency, modality) +- A/B testing infrastructure for intervention effectiveness +- Researcher interface for designing and monitoring coaching protocols + +### Success Metrics +| Metric | Target | +|--------|--------| +| Intervention acceptance rate | ≥ 60% | +| Self-rated helpfulness | ≥ 3.5/5 | +| Wellbeing improvement (pre/post) | Detectable effect (d ≥ 0.2) | +| Participant retention | ≥ 85% over 3 months | + +--- + +## Phase 4 — Cognitive Resilience Training (Concept) + +### Scope +Build interactive cognitive training modules informed by the Cognitive Health Engine's risk profiles. + +### Deliverables +- Gamified cognitive exercises (memory, executive function, processing speed) +- Adaptive difficulty based on participant performance +- Integration with Cognitive Health Engine (training targeted to weak domains) +- Social features (group challenges, leaderboards with opt-in) +- Longitudinal tracking of cognitive training effects on wellbeing and cognition +- Clinical validation study protocol + +### Success Metrics +| Metric | Target | +|--------|--------| +| Training adherence (3x/week) | ≥ 70% | +| Cognitive score improvement | Detectable effect at 6 months | +| Participant enjoyment | ≥ 4/5 | +| Transfer to daily function | Self-reported improvement | + +--- + +## Phase 5 — National Wellbeing Surveillance & Clinical Trial Automation (Vision) + +### Scope +Scale the platform for population-level wellbeing monitoring and automated clinical trial management. + +### Deliverables +- Multi-site deployment architecture (federated data model) +- National wellbeing index computation (aggregated, privacy-preserving) +- Automated clinical trial protocol execution (randomization, dosing, outcome tracking) +- Regulatory submission support (FDA, IRB multi-site) +- Public API for researchers at other institutions +- Open-source release of core ML modules (with WashU license) +- Integration with national health data systems (with appropriate agreements) + +### Success Metrics +| Metric | Target | +|--------|--------| +| Sites supported | ≥ 10 universities | +| Participants tracked | ≥ 10,000 | +| Data processing throughput | ≥ 1M observations/day | +| Public API adoption | ≥ 5 external research teams | +| Time-to-trial-launch | 50% reduction vs. manual process | + +--- + +## Timeline Summary + +| Phase | Status | Estimated Duration | +|-------|--------|-------------------| +| Phase 1 | Active | 6–9 months | +| Phase 2 | Planning | 3–4 months | +| Phase 3 | Research | 6–9 months | +| Phase 4 | Concept | 6–12 months | +| Phase 5 | Vision | 12–18 months | + +Phases 2–3 may overlap. Phase 4–5 timelines depend on funding and Phase 1–3 outcomes. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..c858e69 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,21 @@ +# Data Science & ML +numpy>=1.26.0 +pandas>=2.2.0 +scikit-learn>=1.4.0 +statsmodels>=0.14.0 +dowhy>=0.11.0 +torch>=2.2.0 +lifelines>=0.28.0 + +# Claude API +anthropic>=0.25.0 + +# Testing & Code Quality +pytest>=8.1.0 +black>=24.3.0 +mypy>=1.9.0 +flake8>=7.0.0 + +# ML API Serving +fastapi>=0.110.0 +uvicorn>=0.29.0 diff --git a/scripts/fairness_audit.py b/scripts/fairness_audit.py new file mode 100644 index 0000000..274520f --- /dev/null +++ b/scripts/fairness_audit.py @@ -0,0 +1,337 @@ +#!/usr/bin/env python3 +""" +Fairness Audit Script +===================== +Computes demographic parity and disparate impact metrics for ML model +predictions, and generates a human-readable audit report. + +Usage +----- + python scripts/fairness_audit.py \\ + --predictions predictions.csv \\ + --protected-attribute gender \\ + --model-name "CognitiveRiskModel v1" +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from datetime import datetime, timezone +from typing import Any, Dict, List, Optional + +import numpy as np +import pandas as pd + +logger = logging.getLogger(__name__) + +# The 4/5ths (80 %) rule threshold for disparate impact +_DISPARATE_IMPACT_FLOOR = 0.80 +_DEMOGRAPHIC_PARITY_TOLERANCE = 0.05 + + +class FairnessAuditor: + """Audit model predictions for demographic fairness. + + Parameters + ---------- + disparate_impact_floor : float + Minimum acceptable disparate impact ratio (default 0.80, + per the 4/5ths rule). + demographic_parity_tolerance : float + Maximum allowable difference in positive-prediction rates + across groups. + """ + + def __init__( + self, + disparate_impact_floor: float = _DISPARATE_IMPACT_FLOOR, + demographic_parity_tolerance: float = _DEMOGRAPHIC_PARITY_TOLERANCE, + ) -> None: + self.disparate_impact_floor = disparate_impact_floor + self.demographic_parity_tolerance = demographic_parity_tolerance + + # ------------------------------------------------------------------ + # Core metrics + # ------------------------------------------------------------------ + + def compute_demographic_parity( + self, + predictions: np.ndarray, + protected_attribute: np.ndarray, + ) -> Dict[str, Any]: + """Compute per-group positive-prediction rates. + + Demographic parity is satisfied when the positive-prediction + rate is approximately equal across all groups defined by + *protected_attribute*. + + Parameters + ---------- + predictions : np.ndarray + Binary (0/1) model predictions. + protected_attribute : np.ndarray + Group labels for each prediction. + + Returns + ------- + dict + ``group_rates`` mapping, ``max_difference`` between any + two groups, and a ``passed`` flag. + """ + df = pd.DataFrame({ + "pred": np.asarray(predictions).ravel(), + "group": np.asarray(protected_attribute).ravel(), + }) + + group_rates: Dict[str, float] = {} + for group_name, grp in df.groupby("group"): + rate = float(grp["pred"].mean()) + group_rates[str(group_name)] = rate + + rates = list(group_rates.values()) + max_diff = max(rates) - min(rates) if rates else 0.0 + + passed = max_diff <= self.demographic_parity_tolerance + + logger.info( + "Demographic parity: max_diff=%.4f, tolerance=%.4f, passed=%s", + max_diff, self.demographic_parity_tolerance, passed, + ) + + return { + "group_rates": group_rates, + "max_difference": max_diff, + "tolerance": self.demographic_parity_tolerance, + "passed": passed, + } + + def compute_disparate_impact( + self, + predictions: np.ndarray, + protected_attribute: np.ndarray, + ) -> Dict[str, Any]: + """Compute the disparate impact ratio. + + The ratio is defined as ``min(group_rate) / max(group_rate)``. + A ratio below ``disparate_impact_floor`` (default 0.80) + indicates potential adverse impact. + + Parameters + ---------- + predictions : np.ndarray + Binary (0/1) model predictions. + protected_attribute : np.ndarray + Group labels for each prediction. + + Returns + ------- + dict + ``disparate_impact_ratio``, per-group rates, and a + ``passed`` flag. + """ + df = pd.DataFrame({ + "pred": np.asarray(predictions).ravel(), + "group": np.asarray(protected_attribute).ravel(), + }) + + group_rates: Dict[str, float] = {} + for group_name, grp in df.groupby("group"): + rate = float(grp["pred"].mean()) + group_rates[str(group_name)] = rate + + rates = list(group_rates.values()) + max_rate = max(rates) if rates else 0.0 + + if max_rate == 0.0: + ratio = 1.0 # no positive predictions at all => trivially fair + else: + ratio = min(rates) / max_rate + + passed = ratio >= self.disparate_impact_floor + + logger.info( + "Disparate impact: ratio=%.4f, floor=%.4f, passed=%s", + ratio, self.disparate_impact_floor, passed, + ) + + return { + "group_rates": group_rates, + "disparate_impact_ratio": ratio, + "floor": self.disparate_impact_floor, + "passed": passed, + } + + # ------------------------------------------------------------------ + # Reporting + # ------------------------------------------------------------------ + + def generate_report( + self, + model_name: str, + audit_results: Dict[str, Any], + output_path: Optional[str] = None, + ) -> str: + """Generate a human-readable fairness audit report. + + Parameters + ---------- + model_name : str + Descriptive name for the model being audited. + audit_results : dict + Combined dict with ``"demographic_parity"`` and + ``"disparate_impact"`` sub-keys (as returned by the + compute methods). + output_path : str, optional + If provided, write the report as JSON to this file. + + Returns + ------- + str + Formatted report string. + """ + timestamp = datetime.now(timezone.utc).isoformat() + + dp = audit_results.get("demographic_parity", {}) + di = audit_results.get("disparate_impact", {}) + + overall_pass = dp.get("passed", False) and di.get("passed", False) + + report_lines = [ + "=" * 60, + f" FAIRNESS AUDIT REPORT", + f" Model : {model_name}", + f" Date : {timestamp}", + "=" * 60, + "", + "--- Demographic Parity ---", + f" Group positive-prediction rates: {dp.get('group_rates', {})}", + f" Max difference : {dp.get('max_difference', 'N/A'):.4f}" + if isinstance(dp.get("max_difference"), (int, float)) + else f" Max difference : {dp.get('max_difference', 'N/A')}", + f" Tolerance : {dp.get('tolerance', 'N/A')}", + f" PASSED : {dp.get('passed', 'N/A')}", + "", + "--- Disparate Impact (4/5ths Rule) ---", + f" Group positive-prediction rates: {di.get('group_rates', {})}", + f" Disparate impact ratio : {di.get('disparate_impact_ratio', 'N/A'):.4f}" + if isinstance(di.get("disparate_impact_ratio"), (int, float)) + else f" Disparate impact ratio : {di.get('disparate_impact_ratio', 'N/A')}", + f" Floor : {di.get('floor', 'N/A')}", + f" PASSED : {di.get('passed', 'N/A')}", + "", + "--- Overall ---", + f" ALL CHECKS PASSED: {overall_pass}", + "=" * 60, + ] + + report_text = "\n".join(report_lines) + + if output_path: + payload = { + "model_name": model_name, + "timestamp": timestamp, + "overall_passed": overall_pass, + "demographic_parity": dp, + "disparate_impact": di, + } + with open(output_path, "w") as fh: + json.dump(payload, fh, indent=2) + logger.info("Audit report written to %s", output_path) + + return report_text + + +# ====================================================================== +# CLI +# ====================================================================== + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Run a fairness audit on model predictions.", + ) + parser.add_argument( + "--predictions", + required=True, + help="Path to a CSV file with at least 'prediction' and the protected attribute columns.", + ) + parser.add_argument( + "--protected-attribute", + required=True, + help="Column name of the protected attribute (e.g. 'gender', 'race').", + ) + parser.add_argument( + "--prediction-col", + default="prediction", + help="Column name containing binary predictions (default: 'prediction').", + ) + parser.add_argument( + "--model-name", + default="UnnamedModel", + help="Descriptive model name for the report header.", + ) + parser.add_argument( + "--output", + default=None, + help="Optional path to write the JSON report.", + ) + parser.add_argument( + "--di-floor", + type=float, + default=_DISPARATE_IMPACT_FLOOR, + help="Disparate impact floor (default: 0.80).", + ) + parser.add_argument( + "--dp-tolerance", + type=float, + default=_DEMOGRAPHIC_PARITY_TOLERANCE, + help="Demographic parity tolerance (default: 0.05).", + ) + return parser + + +def main(argv: Optional[List[str]] = None) -> None: + """Entry-point for the fairness audit CLI.""" + logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") + + parser = _build_parser() + args = parser.parse_args(argv) + + df = pd.read_csv(args.predictions) + + if args.prediction_col not in df.columns: + logger.error("Column '%s' not found in %s", args.prediction_col, args.predictions) + sys.exit(1) + if args.protected_attribute not in df.columns: + logger.error("Column '%s' not found in %s", args.protected_attribute, args.predictions) + sys.exit(1) + + predictions = df[args.prediction_col].values + protected = df[args.protected_attribute].values + + auditor = FairnessAuditor( + disparate_impact_floor=args.di_floor, + demographic_parity_tolerance=args.dp_tolerance, + ) + + dp_result = auditor.compute_demographic_parity(predictions, protected) + di_result = auditor.compute_disparate_impact(predictions, protected) + + audit_results = { + "demographic_parity": dp_result, + "disparate_impact": di_result, + } + + report = auditor.generate_report( + model_name=args.model_name, + audit_results=audit_results, + output_path=args.output, + ) + + print(report) + + +if __name__ == "__main__": + main() diff --git a/src/backend/package.json b/src/backend/package.json new file mode 100644 index 0000000..cbb4cb5 --- /dev/null +++ b/src/backend/package.json @@ -0,0 +1,28 @@ +{ + "name": "wellab-backend", + "version": "1.0.0", + "private": true, + "scripts": { + "dev": "nodemon --exec ts-node src/index.ts", + "build": "tsc", + "start": "node dist/index.js", + "test": "jest --passWithNoTests" + }, + "dependencies": { + "express": "^4.19.0", + "cors": "^2.8.5", + "dotenv": "^16.4.0", + "winston": "^3.13.0" + }, + "devDependencies": { + "typescript": "^5.4.0", + "nodemon": "^3.1.0", + "ts-node": "^10.9.0", + "@types/express": "^4.17.0", + "@types/cors": "^2.8.0", + "@types/node": "^20.12.0", + "jest": "^29.7.0", + "ts-jest": "^29.1.0", + "@types/jest": "^29.5.0" + } +} diff --git a/src/backend/src/index.ts b/src/backend/src/index.ts new file mode 100644 index 0000000..f64dbcd --- /dev/null +++ b/src/backend/src/index.ts @@ -0,0 +1,84 @@ +import express from 'express'; +import cors from 'cors'; +import { logger } from './utils/logger'; +import { authMiddleware } from './middleware/auth'; + +import participantsRouter from './routes/participants'; +import observationsRouter from './routes/observations'; +import emotionalDynamicsRouter from './routes/emotional-dynamics'; +import healthRouter from './routes/health'; +import lifespanRouter from './routes/lifespan'; +import cognitiveRouter from './routes/cognitive'; +import interventionsRouter from './routes/interventions'; + +const app = express(); +const PORT = process.env.PORT || 3001; + +// --------------------------------------------------------------------------- +// Global middleware +// --------------------------------------------------------------------------- +app.use(cors()); +app.use(express.json()); + +// --------------------------------------------------------------------------- +// Health check (unauthenticated) +// --------------------------------------------------------------------------- + +/** + * GET /api/health + * Simple health-check endpoint for readiness probes. + */ +app.get('/api/health', (_req, res) => { + res.json({ + status: 'ok', + service: 'wellab-api', + version: '0.1.0', + modules: [ + 'emotional-dynamics', + 'health', + 'lifespan-trajectory', + 'cognitive-health', + ], + timestamp: new Date().toISOString(), + }); +}); + +// --------------------------------------------------------------------------- +// Auth middleware (applied to all /api routes below) +// --------------------------------------------------------------------------- +app.use('/api', authMiddleware); + +// --------------------------------------------------------------------------- +// Route registration +// --------------------------------------------------------------------------- +app.use('/api/participants', participantsRouter); +app.use('/api', observationsRouter); +app.use('/api', emotionalDynamicsRouter); +app.use('/api', healthRouter); +app.use('/api', lifespanRouter); +app.use('/api', cognitiveRouter); +app.use('/api/interventions', interventionsRouter); + +// The interventions router also exposes a participant-scoped GET, so mount it +// at the top level /api as well for the /participants/:id/interventions path. +app.use('/api', interventionsRouter); + +// --------------------------------------------------------------------------- +// 404 fallback +// --------------------------------------------------------------------------- +app.use((_req, res) => { + res.status(404).json({ + success: false, + error: { code: 'NOT_FOUND', message: 'Endpoint not found' }, + }); +}); + +// --------------------------------------------------------------------------- +// Start server +// --------------------------------------------------------------------------- +app.listen(PORT, () => { + logger.info(`WELLab API server running on port ${PORT}`); + logger.info('Registered modules: Emotional Dynamics, Health, Lifespan Trajectory, Cognitive Health'); +}); + +export default app; diff --git a/src/backend/src/middleware/auth.ts b/src/backend/src/middleware/auth.ts new file mode 100644 index 0000000..abd8177 --- /dev/null +++ b/src/backend/src/middleware/auth.ts @@ -0,0 +1,46 @@ +import { Request, Response, NextFunction } from 'express'; +import { logger } from '../utils/logger'; + +/** + * Authentication middleware stub. + * Checks for a Bearer token in the Authorization header. + * Currently performs placeholder validation -- replace with real JWT/OAuth verification. + */ +export function authMiddleware(req: Request, res: Response, next: NextFunction): void { + const authHeader = req.headers.authorization; + + if (!authHeader) { + logger.warn('Missing Authorization header', { path: req.path, method: req.method }); + res.status(401).json({ + success: false, + error: { code: 'UNAUTHORIZED', message: 'Missing Authorization header' }, + }); + return; + } + + const parts = authHeader.split(' '); + if (parts.length !== 2 || parts[0] !== 'Bearer') { + logger.warn('Malformed Authorization header', { path: req.path }); + res.status(401).json({ + success: false, + error: { code: 'UNAUTHORIZED', message: 'Authorization header must use Bearer scheme' }, + }); + return; + } + + const token = parts[1]; + + // Placeholder validation: accept any non-empty token. + // TODO: Replace with real JWT verification or OAuth token introspection. + if (!token || token.length < 1) { + logger.warn('Empty bearer token', { path: req.path }); + res.status(401).json({ + success: false, + error: { code: 'UNAUTHORIZED', message: 'Invalid token' }, + }); + return; + } + + logger.debug('Auth passed (placeholder)', { path: req.path }); + next(); +} diff --git a/src/backend/src/middleware/validation.ts b/src/backend/src/middleware/validation.ts new file mode 100644 index 0000000..72e2cff --- /dev/null +++ b/src/backend/src/middleware/validation.ts @@ -0,0 +1,65 @@ +import { Request, Response, NextFunction } from 'express'; +import { ValidationSchema } from '../types'; + +/** + * Creates a request body validation middleware from a simple schema definition. + * Checks required fields and basic type constraints. + * + * @param schema - Validation schema specifying required fields and expected types + * @returns Express middleware that validates req.body against the schema + */ +export function validateBody(schema: ValidationSchema) { + return (req: Request, res: Response, next: NextFunction): void => { + const body = req.body; + + if (!body || typeof body !== 'object') { + res.status(400).json({ + success: false, + error: { code: 'VALIDATION_ERROR', message: 'Request body must be a JSON object' }, + }); + return; + } + + // Check required fields + if (schema.required) { + const missing = schema.required.filter((field) => !(field in body)); + if (missing.length > 0) { + res.status(400).json({ + success: false, + error: { + code: 'VALIDATION_ERROR', + message: `Missing required fields: ${missing.join(', ')}`, + details: { missingFields: missing }, + }, + }); + return; + } + } + + // Check types + if (schema.types) { + const typeErrors: string[] = []; + for (const [field, expectedType] of Object.entries(schema.types)) { + if (!(field in body)) continue; // skip missing optional fields + const value = body[field]; + const actualType = Array.isArray(value) ? 'array' : typeof value; + if (actualType !== expectedType) { + typeErrors.push(`${field}: expected ${expectedType}, got ${actualType}`); + } + } + if (typeErrors.length > 0) { + res.status(400).json({ + success: false, + error: { + code: 'VALIDATION_ERROR', + message: `Type errors: ${typeErrors.join('; ')}`, + details: { typeErrors }, + }, + }); + return; + } + } + + next(); + }; +} diff --git a/src/backend/src/routes/cognitive.ts b/src/backend/src/routes/cognitive.ts new file mode 100644 index 0000000..1672924 --- /dev/null +++ b/src/backend/src/routes/cognitive.ts @@ -0,0 +1,100 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { CognitiveAssessment, CognitiveRiskResult, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** + * GET /participants/:id/cognitive + * Retrieve cognitive assessment records for a participant. + */ +router.get('/participants/:id/cognitive', (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Fetching cognitive assessments', { participantId: id }); + + const mockAssessments: CognitiveAssessment[] = [ + { + id: 'ca-001', + participantId: id, + assessmentDate: '2024-04-10', + instrument: 'MoCA', + domain: 'memory', + score: 26, + normalizedScore: 0.87, + percentile: 72, + }, + { + id: 'ca-002', + participantId: id, + assessmentDate: '2024-04-10', + instrument: 'Trail Making B', + domain: 'executive-function', + score: 85, + normalizedScore: 0.78, + percentile: 65, + }, + { + id: 'ca-003', + participantId: id, + assessmentDate: '2024-04-10', + instrument: 'Digit Symbol', + domain: 'processing-speed', + score: 52, + normalizedScore: 0.72, + percentile: 58, + }, + ]; + + const response: ApiResponse = { + success: true, + data: mockAssessments, + meta: { total: mockAssessments.length, timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /cognitive/risk-assessment + * Run a cognitive decline risk assessment for a participant. + */ +router.post( + '/cognitive/risk-assessment', + validateBody({ + required: ['participantId', 'horizonYears'], + types: { participantId: 'string', horizonYears: 'number', includeModifiableFactors: 'boolean' }, + }), + (req: Request, res: Response) => { + const { participantId, horizonYears, includeModifiableFactors } = req.body; + logger.info('Running cognitive risk assessment', { participantId, horizonYears }); + + const mockResult: CognitiveRiskResult = { + participantId, + riskScore: 0.23, + riskCategory: 'moderate', + modifiableFactors: includeModifiableFactors + ? [ + { factor: 'physical-activity', impact: -0.15, recommendation: 'Increase aerobic exercise to 150 min/week' }, + { factor: 'sleep-quality', impact: -0.08, recommendation: 'Address sleep fragmentation' }, + { factor: 'social-engagement', impact: -0.06, recommendation: 'Increase weekly social interactions' }, + ] + : [], + projectedTrajectory: [ + { age: 70, value: 0.87, domain: 'global-cognition', confidence: 0.90 }, + { age: 72, value: 0.84, domain: 'global-cognition', confidence: 0.85 }, + { age: 75, value: 0.79, domain: 'global-cognition', confidence: 0.78 }, + { age: 78, value: 0.73, domain: 'global-cognition', confidence: 0.70 }, + { age: 80, value: 0.68, domain: 'global-cognition', confidence: 0.62 }, + ], + }; + + const response: ApiResponse = { + success: true, + data: mockResult, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/emotional-dynamics.ts b/src/backend/src/routes/emotional-dynamics.ts new file mode 100644 index 0000000..164eab7 --- /dev/null +++ b/src/backend/src/routes/emotional-dynamics.ts @@ -0,0 +1,80 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { EmotionalDynamicsResult, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** + * GET /participants/:id/emotional-dynamics + * Retrieve emotion coupling analysis and volatility scores for a participant. + */ +router.get('/participants/:id/emotional-dynamics', (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Fetching emotional dynamics', { participantId: id }); + + const mockResult: EmotionalDynamicsResult = { + participantId: id, + period: { start: '2024-01-01', end: '2024-06-30' }, + volatility: 0.42, + inertia: 0.68, + couplings: [ + { emotionA: 'happiness', emotionB: 'energy', couplingStrength: 0.73, lag: 0, pValue: 0.001 }, + { emotionA: 'anxiety', emotionB: 'sadness', couplingStrength: 0.58, lag: 1, pValue: 0.01 }, + { emotionA: 'anger', emotionB: 'anxiety', couplingStrength: 0.35, lag: 0, pValue: 0.05 }, + ], + granularity: 0.61, + }; + + const response: ApiResponse = { + success: true, + data: mockResult, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /emotional-dynamics/analyze + * Run an emotion coupling and volatility analysis across one or more participants. + */ +router.post( + '/emotional-dynamics/analyze', + validateBody({ + required: ['participantIds', 'period'], + types: { participantIds: 'array', period: 'object' }, + }), + (req: Request, res: Response) => { + const { participantIds, period } = req.body; + logger.info('Running emotional dynamics analysis', { + participantCount: participantIds.length, + period, + }); + + const results: EmotionalDynamicsResult[] = participantIds.map((pid: string) => ({ + participantId: pid, + period, + volatility: Math.round(Math.random() * 100) / 100, + inertia: Math.round(Math.random() * 100) / 100, + couplings: [ + { + emotionA: 'happiness', + emotionB: 'energy', + couplingStrength: Math.round(Math.random() * 100) / 100, + lag: 0, + pValue: 0.01, + }, + ], + granularity: Math.round(Math.random() * 100) / 100, + })); + + const response: ApiResponse = { + success: true, + data: results, + meta: { total: results.length, timestamp: new Date().toISOString() }, + }; + res.json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/health.ts b/src/backend/src/routes/health.ts new file mode 100644 index 0000000..94ca50a --- /dev/null +++ b/src/backend/src/routes/health.ts @@ -0,0 +1,87 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { HealthRecord, CausalAnalysisResult, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** + * GET /participants/:id/health-records + * Retrieve health records for a participant, optionally filtered by domain. + */ +router.get('/participants/:id/health-records', (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Fetching health records', { participantId: id, domain: req.query.domain }); + + const mockRecords: HealthRecord[] = [ + { + id: 'hr-001', + participantId: id, + recordDate: '2024-03-15', + domain: 'physical', + indicators: { bmi: 24.5, systolicBP: 128, diastolicBP: 82, gripStrength: 32 }, + notes: 'Routine physical assessment', + }, + { + id: 'hr-002', + participantId: id, + recordDate: '2024-03-15', + domain: 'mental', + indicators: { phq9: 4, gad7: 3, pss: 12 }, + notes: 'Quarterly mental health screening', + }, + ]; + + let results = mockRecords; + if (req.query.domain) { + results = results.filter((r) => r.domain === req.query.domain); + } + + const response: ApiResponse = { + success: true, + data: results, + meta: { total: results.length, timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /health/causal-analysis + * Run a causal inference analysis between exposure and outcome variables. + */ +router.post( + '/health/causal-analysis', + validateBody({ + required: ['participantIds', 'exposureVariable', 'outcomeVariable', 'method'], + types: { + participantIds: 'array', + exposureVariable: 'string', + outcomeVariable: 'string', + method: 'string', + }, + }), + (req: Request, res: Response) => { + logger.info('Running causal analysis', { + exposure: req.body.exposureVariable, + outcome: req.body.outcomeVariable, + method: req.body.method, + }); + + const mockResult: CausalAnalysisResult = { + estimatedEffect: 0.34, + confidenceInterval: [0.12, 0.56], + pValue: 0.003, + method: req.body.method, + sampleSize: req.body.participantIds.length, + }; + + const response: ApiResponse = { + success: true, + data: mockResult, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/interventions.ts b/src/backend/src/routes/interventions.ts new file mode 100644 index 0000000..7adcd15 --- /dev/null +++ b/src/backend/src/routes/interventions.ts @@ -0,0 +1,99 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { Intervention, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** Mock interventions store */ +const mockInterventions: Intervention[] = [ + { + id: 'int-001', + participantId: 'p-001', + type: 'behavioral', + name: 'Mindfulness-Based Stress Reduction', + startDate: '2024-03-01', + endDate: '2024-05-01', + status: 'completed', + frequency: '3x/week', + outcomes: { stressReduction: 0.35, wellBeingImprovement: 0.22 }, + }, + { + id: 'int-002', + participantId: 'p-001', + type: 'lifestyle', + name: 'Mediterranean Diet Program', + startDate: '2024-04-15', + status: 'active', + frequency: 'daily', + outcomes: {}, + }, +]; + +/** + * GET /participants/:id/interventions + * Retrieve interventions assigned to a participant. + */ +router.get('/participants/:id/interventions', (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Fetching interventions', { participantId: id }); + + let results = mockInterventions.filter((i) => i.participantId === id); + if (req.query.status) { + results = results.filter((i) => i.status === req.query.status); + } + + const response: ApiResponse = { + success: true, + data: results, + meta: { total: results.length, timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /interventions + * Create a new intervention for a participant. + */ +router.post( + '/', + validateBody({ + required: ['participantId', 'type', 'name', 'startDate'], + types: { + participantId: 'string', + type: 'string', + name: 'string', + startDate: 'string', + }, + }), + (req: Request, res: Response) => { + logger.info('Creating intervention', { + participantId: req.body.participantId, + name: req.body.name, + }); + + const newIntervention: Intervention = { + id: `int-${String(mockInterventions.length + 1).padStart(3, '0')}`, + participantId: req.body.participantId, + type: req.body.type, + name: req.body.name, + startDate: req.body.startDate, + endDate: req.body.endDate, + status: req.body.status || 'planned', + dosage: req.body.dosage, + frequency: req.body.frequency, + outcomes: req.body.outcomes || {}, + }; + + mockInterventions.push(newIntervention); + + const response: ApiResponse = { + success: true, + data: newIntervention, + meta: { timestamp: new Date().toISOString() }, + }; + res.status(201).json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/lifespan.ts b/src/backend/src/routes/lifespan.ts new file mode 100644 index 0000000..a875ef3 --- /dev/null +++ b/src/backend/src/routes/lifespan.ts @@ -0,0 +1,95 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { LifespanTrajectory, ClusterAnalysisResult, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** + * GET /participants/:id/trajectory + * Retrieve the lifespan trajectory for a participant, optionally filtered by domain. + */ +router.get('/participants/:id/trajectory', (req: Request, res: Response) => { + const { id } = req.params; + const domain = (req.query.domain as string) || 'well-being'; + logger.info('Fetching lifespan trajectory', { participantId: id, domain }); + + const mockTrajectory: LifespanTrajectory = { + participantId: id, + domain, + points: [ + { age: 50, value: 72, domain, confidence: 0.95 }, + { age: 55, value: 70, domain, confidence: 0.93 }, + { age: 60, value: 68, domain, confidence: 0.90 }, + { age: 65, value: 71, domain, confidence: 0.88 }, + { age: 70, value: 65, domain, confidence: 0.85 }, + ], + clusterLabel: 'resilient-stable', + trajectoryClass: 'U-shaped recovery', + }; + + const response: ApiResponse = { + success: true, + data: mockTrajectory, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /lifespan/cluster-analysis + * Run a trajectory cluster analysis across participants using GMM, LCGA, or k-means. + */ +router.post( + '/lifespan/cluster-analysis', + validateBody({ + required: ['participantIds', 'domain', 'nClusters', 'method'], + types: { + participantIds: 'array', + domain: 'string', + nClusters: 'number', + method: 'string', + }, + }), + (req: Request, res: Response) => { + const { participantIds, domain, nClusters, method } = req.body; + logger.info('Running cluster analysis', { domain, nClusters, method }); + + const mockResult: ClusterAnalysisResult = { + clusters: [ + { + label: 'stable-high', + memberCount: Math.ceil(participantIds.length * 0.4), + centroid: [72, 71, 70, 71, 70], + participantIds: participantIds.slice(0, Math.ceil(participantIds.length * 0.4)), + }, + { + label: 'declining', + memberCount: Math.ceil(participantIds.length * 0.3), + centroid: [70, 65, 60, 55, 50], + participantIds: participantIds.slice( + Math.ceil(participantIds.length * 0.4), + Math.ceil(participantIds.length * 0.7), + ), + }, + { + label: 'resilient-recovery', + memberCount: participantIds.length - Math.ceil(participantIds.length * 0.7), + centroid: [68, 60, 58, 63, 67], + participantIds: participantIds.slice(Math.ceil(participantIds.length * 0.7)), + }, + ], + silhouetteScore: 0.72, + method, + }; + + const response: ApiResponse = { + success: true, + data: mockResult, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/observations.ts b/src/backend/src/routes/observations.ts new file mode 100644 index 0000000..61d8523 --- /dev/null +++ b/src/backend/src/routes/observations.ts @@ -0,0 +1,80 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { Observation, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** Mock observations store */ +const mockObservations: Observation[] = [ + { + id: 'obs-001', + participantId: 'p-001', + timestamp: '2024-06-15T09:30:00Z', + source: 'ema', + measures: { happiness: 7, sadness: 2, anxiety: 3, energy: 6 }, + context: { activity: 'morning-routine', socialContext: 'alone', deviceType: 'mobile' }, + }, + { + id: 'obs-002', + participantId: 'p-001', + timestamp: '2024-06-15T14:00:00Z', + source: 'ema', + measures: { happiness: 5, sadness: 4, anxiety: 5, energy: 4 }, + context: { activity: 'work', socialContext: 'colleagues', deviceType: 'mobile' }, + }, +]; + +/** + * GET /participants/:id/observations + * List EMA observations for a given participant. + */ +router.get('/participants/:id/observations', (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Fetching observations', { participantId: id }); + + const results = mockObservations.filter((o) => o.participantId === id); + + const response: ApiResponse = { + success: true, + data: results, + meta: { total: results.length, timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /participants/:id/observations + * Record a new EMA observation for a participant. + */ +router.post( + '/participants/:id/observations', + validateBody({ + required: ['source', 'measures'], + types: { source: 'string', measures: 'object' }, + }), + (req: Request, res: Response) => { + const { id } = req.params; + logger.info('Recording observation', { participantId: id, source: req.body.source }); + + const newObs: Observation = { + id: `obs-${String(mockObservations.length + 1).padStart(3, '0')}`, + participantId: id, + timestamp: new Date().toISOString(), + source: req.body.source, + measures: req.body.measures, + context: req.body.context || {}, + }; + + mockObservations.push(newObs); + + const response: ApiResponse = { + success: true, + data: newObs, + meta: { timestamp: new Date().toISOString() }, + }; + res.status(201).json(response); + }, +); + +export default router; diff --git a/src/backend/src/routes/participants.ts b/src/backend/src/routes/participants.ts new file mode 100644 index 0000000..dd38760 --- /dev/null +++ b/src/backend/src/routes/participants.ts @@ -0,0 +1,148 @@ +import { Router, Request, Response } from 'express'; +import { validateBody } from '../middleware/validation'; +import { Participant, ApiResponse } from '../types'; +import { logger } from '../utils/logger'; + +const router = Router(); + +/** Mock participant store */ +const mockParticipants: Participant[] = [ + { + id: 'p-001', + externalId: 'WELL-2024-001', + firstName: 'Alice', + lastName: 'Chen', + dateOfBirth: '1955-03-12', + enrollmentDate: '2024-01-15', + cohort: 'aging-well-2024', + status: 'active', + metadata: { site: 'Boston', language: 'en' }, + }, + { + id: 'p-002', + externalId: 'WELL-2024-002', + firstName: 'Robert', + lastName: 'Johnson', + dateOfBirth: '1948-07-22', + enrollmentDate: '2024-02-01', + cohort: 'aging-well-2024', + status: 'active', + metadata: { site: 'Chicago', language: 'en' }, + }, +]; + +/** + * GET /participants + * List all participants with optional filtering by cohort or status. + */ +router.get('/', (req: Request, res: Response) => { + logger.info('Listing participants', { query: req.query }); + + let results = [...mockParticipants]; + if (req.query.cohort) { + results = results.filter((p) => p.cohort === req.query.cohort); + } + if (req.query.status) { + results = results.filter((p) => p.status === req.query.status); + } + + const response: ApiResponse = { + success: true, + data: results, + meta: { total: results.length, timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * GET /participants/:id + * Retrieve a single participant by ID. + */ +router.get('/:id', (req: Request, res: Response) => { + const participant = mockParticipants.find((p) => p.id === req.params.id); + + if (!participant) { + res.status(404).json({ + success: false, + error: { code: 'NOT_FOUND', message: `Participant ${req.params.id} not found` }, + }); + return; + } + + const response: ApiResponse = { + success: true, + data: participant, + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +/** + * POST /participants + * Create a new participant record. + */ +router.post( + '/', + validateBody({ + required: ['externalId', 'firstName', 'lastName', 'dateOfBirth', 'cohort'], + types: { + externalId: 'string', + firstName: 'string', + lastName: 'string', + dateOfBirth: 'string', + cohort: 'string', + }, + }), + (req: Request, res: Response) => { + logger.info('Creating participant', { externalId: req.body.externalId }); + + const newParticipant: Participant = { + id: `p-${String(mockParticipants.length + 1).padStart(3, '0')}`, + externalId: req.body.externalId, + firstName: req.body.firstName, + lastName: req.body.lastName, + dateOfBirth: req.body.dateOfBirth, + enrollmentDate: new Date().toISOString().split('T')[0], + cohort: req.body.cohort, + status: 'active', + metadata: req.body.metadata || {}, + }; + + mockParticipants.push(newParticipant); + + const response: ApiResponse = { + success: true, + data: newParticipant, + meta: { timestamp: new Date().toISOString() }, + }; + res.status(201).json(response); + }, +); + +/** + * PUT /participants/:id + * Update an existing participant record. + */ +router.put('/:id', (req: Request, res: Response) => { + const index = mockParticipants.findIndex((p) => p.id === req.params.id); + + if (index === -1) { + res.status(404).json({ + success: false, + error: { code: 'NOT_FOUND', message: `Participant ${req.params.id} not found` }, + }); + return; + } + + mockParticipants[index] = { ...mockParticipants[index], ...req.body, id: req.params.id }; + logger.info('Updated participant', { id: req.params.id }); + + const response: ApiResponse = { + success: true, + data: mockParticipants[index], + meta: { timestamp: new Date().toISOString() }, + }; + res.json(response); +}); + +export default router; diff --git a/src/backend/src/types/index.ts b/src/backend/src/types/index.ts new file mode 100644 index 0000000..7382cff --- /dev/null +++ b/src/backend/src/types/index.ts @@ -0,0 +1,190 @@ +/** Unique identifier type alias */ +export type ID = string; + +/** ISO 8601 date string */ +export type ISODateString = string; + +/** Participant demographic and enrollment record */ +export interface Participant { + id: ID; + externalId: string; + firstName: string; + lastName: string; + dateOfBirth: ISODateString; + enrollmentDate: ISODateString; + cohort: string; + status: 'active' | 'inactive' | 'withdrawn'; + metadata: Record; +} + +/** Ecological Momentary Assessment observation */ +export interface Observation { + id: ID; + participantId: ID; + timestamp: ISODateString; + source: 'ema' | 'sensor' | 'clinical' | 'self-report'; + measures: Record; + context: ObservationContext; +} + +/** Contextual metadata for an observation */ +export interface ObservationContext { + location?: string; + activity?: string; + socialContext?: string; + deviceType?: string; +} + +/** Emotion coupling pair result */ +export interface EmotionCoupling { + emotionA: string; + emotionB: string; + couplingStrength: number; + lag: number; + pValue: number; +} + +/** Emotional dynamics analysis result */ +export interface EmotionalDynamicsResult { + participantId: ID; + period: { start: ISODateString; end: ISODateString }; + volatility: number; + inertia: number; + couplings: EmotionCoupling[]; + granularity: number; +} + +/** Health record for a participant */ +export interface HealthRecord { + id: ID; + participantId: ID; + recordDate: ISODateString; + domain: 'physical' | 'mental' | 'social' | 'functional'; + indicators: Record; + notes: string; +} + +/** Causal analysis request body */ +export interface CausalAnalysisRequest { + participantIds: ID[]; + exposureVariable: string; + outcomeVariable: string; + covariates: string[]; + method: 'propensity-score' | 'instrumental-variable' | 'difference-in-differences'; +} + +/** Causal analysis result */ +export interface CausalAnalysisResult { + estimatedEffect: number; + confidenceInterval: [number, number]; + pValue: number; + method: string; + sampleSize: number; +} + +/** Lifespan trajectory data point */ +export interface TrajectoryPoint { + age: number; + value: number; + domain: string; + confidence: number; +} + +/** Lifespan trajectory for a participant */ +export interface LifespanTrajectory { + participantId: ID; + domain: string; + points: TrajectoryPoint[]; + clusterLabel?: string; + trajectoryClass: string; +} + +/** Cluster analysis request */ +export interface ClusterAnalysisRequest { + participantIds: ID[]; + domain: string; + nClusters: number; + method: 'gmm' | 'lcga' | 'k-means'; +} + +/** Cluster analysis result */ +export interface ClusterAnalysisResult { + clusters: Array<{ + label: string; + memberCount: number; + centroid: number[]; + participantIds: ID[]; + }>; + silhouetteScore: number; + method: string; +} + +/** Cognitive assessment record */ +export interface CognitiveAssessment { + id: ID; + participantId: ID; + assessmentDate: ISODateString; + instrument: string; + domain: 'memory' | 'executive-function' | 'processing-speed' | 'attention' | 'language'; + score: number; + normalizedScore: number; + percentile: number; +} + +/** Cognitive risk assessment request */ +export interface CognitiveRiskRequest { + participantId: ID; + horizonYears: number; + includeModifiableFactors: boolean; +} + +/** Cognitive risk assessment result */ +export interface CognitiveRiskResult { + participantId: ID; + riskScore: number; + riskCategory: 'low' | 'moderate' | 'high' | 'very-high'; + modifiableFactors: Array<{ factor: string; impact: number; recommendation: string }>; + projectedTrajectory: TrajectoryPoint[]; +} + +/** Intervention record */ +export interface Intervention { + id: ID; + participantId: ID; + type: 'behavioral' | 'pharmacological' | 'cognitive-training' | 'social' | 'lifestyle'; + name: string; + startDate: ISODateString; + endDate?: ISODateString; + status: 'planned' | 'active' | 'completed' | 'discontinued'; + dosage?: string; + frequency?: string; + outcomes: Record; +} + +/** Standard API response wrapper */ +export interface ApiResponse { + success: boolean; + data: T; + meta?: { + page?: number; + pageSize?: number; + total?: number; + timestamp: ISODateString; + }; +} + +/** Standard API error response */ +export interface ApiError { + success: false; + error: { + code: string; + message: string; + details?: unknown; + }; +} + +/** Validation schema definition for request validation middleware */ +export interface ValidationSchema { + required?: string[]; + types?: Record; +} diff --git a/src/backend/src/utils/logger.ts b/src/backend/src/utils/logger.ts new file mode 100644 index 0000000..3d1ba4f --- /dev/null +++ b/src/backend/src/utils/logger.ts @@ -0,0 +1,53 @@ +type LogLevel = 'debug' | 'info' | 'warn' | 'error'; + +interface LogEntry { + timestamp: string; + level: LogLevel; + message: string; + context?: Record; +} + +const LEVEL_PRIORITY: Record = { + debug: 0, + info: 1, + warn: 2, + error: 3, +}; + +const currentLevel: LogLevel = (process.env.LOG_LEVEL as LogLevel) || 'info'; + +function formatEntry(entry: LogEntry): string { + const base = `[${entry.timestamp}] ${entry.level.toUpperCase()} ${entry.message}`; + if (entry.context && Object.keys(entry.context).length > 0) { + return `${base} ${JSON.stringify(entry.context)}`; + } + return base; +} + +function log(level: LogLevel, message: string, context?: Record): void { + if (LEVEL_PRIORITY[level] < LEVEL_PRIORITY[currentLevel]) return; + + const entry: LogEntry = { + timestamp: new Date().toISOString(), + level, + message, + context, + }; + + const formatted = formatEntry(entry); + + if (level === 'error') { + console.error(formatted); + } else if (level === 'warn') { + console.warn(formatted); + } else { + console.log(formatted); + } +} + +export const logger = { + debug: (message: string, context?: Record) => log('debug', message, context), + info: (message: string, context?: Record) => log('info', message, context), + warn: (message: string, context?: Record) => log('warn', message, context), + error: (message: string, context?: Record) => log('error', message, context), +}; diff --git a/src/backend/tsconfig.json b/src/backend/tsconfig.json new file mode 100644 index 0000000..af12263 --- /dev/null +++ b/src/backend/tsconfig.json @@ -0,0 +1,21 @@ +{ + "compilerOptions": { + "target": "ES2020", + "module": "commonjs", + "lib": ["ES2020"], + "outDir": "./dist", + "rootDir": "./src", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true, + "moduleResolution": "node", + "types": ["node"] + }, + "include": ["src/**/*"], + "exclude": ["node_modules", "dist"] +} diff --git a/src/frontend/index.html b/src/frontend/index.html new file mode 100644 index 0000000..c8c7e09 --- /dev/null +++ b/src/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + WELLab - AI-Enabled Research & Impact Platform + + +
+ + + diff --git a/src/frontend/package.json b/src/frontend/package.json new file mode 100644 index 0000000..d018e53 --- /dev/null +++ b/src/frontend/package.json @@ -0,0 +1,28 @@ +{ + "name": "wellab-frontend", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "preview": "vite preview" + }, + "dependencies": { + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-router-dom": "^6.23.1", + "recharts": "^2.12.7", + "d3": "^7.9.0" + }, + "devDependencies": { + "@types/react": "^18.3.3", + "@types/react-dom": "^18.3.0", + "@vitejs/plugin-react": "^4.3.1", + "autoprefixer": "^10.4.19", + "postcss": "^8.4.38", + "tailwindcss": "^3.4.4", + "typescript": "^5.4.5", + "vite": "^5.3.1" + } +} diff --git a/src/frontend/postcss.config.js b/src/frontend/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/src/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx new file mode 100644 index 0000000..712175b --- /dev/null +++ b/src/frontend/src/App.tsx @@ -0,0 +1,19 @@ +import { Routes, Route } from "react-router-dom"; +import Layout from "./components/Layout"; +import ParticipantDashboard from "./pages/ParticipantDashboard"; +import ResearcherDashboard from "./pages/ResearcherDashboard"; +import PolicyDashboard from "./pages/PolicyDashboard"; + +function App() { + return ( + + + } /> + } /> + } /> + + + ); +} + +export default App; diff --git a/src/frontend/src/api/client.ts b/src/frontend/src/api/client.ts new file mode 100644 index 0000000..65f2197 --- /dev/null +++ b/src/frontend/src/api/client.ts @@ -0,0 +1,89 @@ +import type { ApiResponse } from "../types"; + +const BASE_URL = "/api"; + +class ApiClient { + private baseUrl: string; + + constructor(baseUrl: string) { + this.baseUrl = baseUrl; + } + + private async request( + endpoint: string, + options: RequestInit = {} + ): Promise> { + const url = `${this.baseUrl}${endpoint}`; + const headers: HeadersInit = { + "Content-Type": "application/json", + ...options.headers, + }; + + const response = await fetch(url, { ...options, headers }); + + if (!response.ok) { + const error = await response.json().catch(() => ({ + message: response.statusText, + })); + throw new Error(error.message || `Request failed: ${response.status}`); + } + + return response.json(); + } + + async get(endpoint: string): Promise> { + return this.request(endpoint, { method: "GET" }); + } + + async post(endpoint: string, body: unknown): Promise> { + return this.request(endpoint, { + method: "POST", + body: JSON.stringify(body), + }); + } + + async put(endpoint: string, body: unknown): Promise> { + return this.request(endpoint, { + method: "PUT", + body: JSON.stringify(body), + }); + } + + async delete(endpoint: string): Promise> { + return this.request(endpoint, { method: "DELETE" }); + } + + // Domain-specific methods + getParticipantScore(participantId: string) { + return this.get( + `/participants/${participantId}/score` + ); + } + + getParticipantTrend(participantId: string, days = 30) { + return this.get( + `/participants/${participantId}/trend?days=${days}` + ); + } + + getParticipantInsights(participantId: string) { + return this.get( + `/participants/${participantId}/insights` + ); + } + + getCohorts() { + return this.get("/cohorts"); + } + + getPopulationRisk() { + return this.get("/population/risk"); + } + + getInterventionROI() { + return this.get("/interventions/roi"); + } +} + +export const apiClient = new ApiClient(BASE_URL); +export default apiClient; diff --git a/src/frontend/src/components/Layout.tsx b/src/frontend/src/components/Layout.tsx new file mode 100644 index 0000000..c2d27ea --- /dev/null +++ b/src/frontend/src/components/Layout.tsx @@ -0,0 +1,57 @@ +import { NavLink } from "react-router-dom"; +import type { ReactNode } from "react"; + +interface LayoutProps { + children: ReactNode; +} + +const navItems = [ + { to: "/", label: "Participant" }, + { to: "/researcher", label: "Researcher" }, + { to: "/policy", label: "Policy" }, +]; + +export default function Layout({ children }: LayoutProps) { + return ( +
+
+
+
+
+
+ W +
+ + WELLab Platform + +
+ +
+
+
+
+ {children} +
+
+ WELLab AI-Enabled Research & Impact Platform +
+
+ ); +} diff --git a/src/frontend/src/components/TrendChart.tsx b/src/frontend/src/components/TrendChart.tsx new file mode 100644 index 0000000..dba5a51 --- /dev/null +++ b/src/frontend/src/components/TrendChart.tsx @@ -0,0 +1,60 @@ +import { + LineChart, + Line, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + Legend, + ResponsiveContainer, +} from "recharts"; +import type { TrendPoint } from "../types"; + +interface TrendChartProps { + data: TrendPoint[]; + title?: string; + height?: number; +} + +const lineConfig = [ + { dataKey: "overall", stroke: "#16a34a", name: "Overall" }, + { dataKey: "physical", stroke: "#3b82f6", name: "Physical" }, + { dataKey: "emotional", stroke: "#a855f7", name: "Emotional" }, + { dataKey: "social", stroke: "#ec4899", name: "Social" }, +]; + +export default function TrendChart({ + data, + title = "Wellbeing Trends", + height = 300, +}: TrendChartProps) { + return ( +
+

{title}

+ + + + v.slice(5)} + /> + + + + {lineConfig.map((cfg) => ( + + ))} + + +
+ ); +} diff --git a/src/frontend/src/components/WellbeingScoreCard.tsx b/src/frontend/src/components/WellbeingScoreCard.tsx new file mode 100644 index 0000000..ad5e297 --- /dev/null +++ b/src/frontend/src/components/WellbeingScoreCard.tsx @@ -0,0 +1,82 @@ +import type { WellbeingDomain } from "../types"; + +interface WellbeingScoreCardProps { + overall: number; + domains?: Partial>; + confidence?: number; + label?: string; +} + +const domainColors: Record = { + physical: "bg-blue-400", + emotional: "bg-purple-400", + social: "bg-pink-400", + cognitive: "bg-indigo-400", + environmental: "bg-green-400", + occupational: "bg-yellow-400", + spiritual: "bg-teal-400", + financial: "bg-orange-400", +}; + +const domainLabels: Record = { + physical: "Physical", + emotional: "Emotional", + social: "Social", + cognitive: "Cognitive", + environmental: "Environmental", + occupational: "Occupational", + spiritual: "Spiritual", + financial: "Financial", +}; + +function scoreColor(score: number): string { + if (score >= 75) return "text-wellab-600"; + if (score >= 50) return "text-yellow-500"; + return "text-red-500"; +} + +export default function WellbeingScoreCard({ + overall, + domains, + confidence, + label = "Your Wellbeing Today", +}: WellbeingScoreCardProps) { + return ( +
+

{label}

+
+ + {overall} + + / 100 +
+ {confidence !== undefined && ( +

+ Confidence: {Math.round(confidence * 100)}% +

+ )} + {domains && ( +
+ {(Object.entries(domains) as [WellbeingDomain, number][]).map( + ([domain, value]) => ( +
+ + {domainLabels[domain]} + +
+
+
+ + {value} + +
+ ) + )} +
+ )} +
+ ); +} diff --git a/src/frontend/src/index.css b/src/frontend/src/index.css new file mode 100644 index 0000000..b5c61c9 --- /dev/null +++ b/src/frontend/src/index.css @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/src/frontend/src/main.tsx b/src/frontend/src/main.tsx new file mode 100644 index 0000000..fe05248 --- /dev/null +++ b/src/frontend/src/main.tsx @@ -0,0 +1,13 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import { BrowserRouter } from "react-router-dom"; +import App from "./App"; +import "./index.css"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + + + +); diff --git a/src/frontend/src/pages/ParticipantDashboard.tsx b/src/frontend/src/pages/ParticipantDashboard.tsx new file mode 100644 index 0000000..b5a5dff --- /dev/null +++ b/src/frontend/src/pages/ParticipantDashboard.tsx @@ -0,0 +1,127 @@ +import WellbeingScoreCard from "../components/WellbeingScoreCard"; +import TrendChart from "../components/TrendChart"; +import type { TrendPoint, Insight, WellbeingDomain } from "../types"; + +// ---------- placeholder data ---------- + +const currentScore = { + overall: 74, + domains: { + physical: 82, + emotional: 68, + social: 79, + cognitive: 71, + environmental: 65, + } as Partial>, + confidence: 0.87, +}; + +const trendData: TrendPoint[] = Array.from({ length: 14 }, (_, i) => { + const d = new Date(); + d.setDate(d.getDate() - (13 - i)); + return { + date: d.toISOString().slice(0, 10), + overall: 68 + Math.round(Math.random() * 12), + physical: 75 + Math.round(Math.random() * 10), + emotional: 60 + Math.round(Math.random() * 15), + social: 70 + Math.round(Math.random() * 14), + }; +}); + +const insights: Insight[] = [ + { + id: "1", + title: "Strong social connections", + description: + "Your social wellbeing has been consistently above average this month. Keep investing in those relationships.", + domain: "social", + type: "strength", + confidence: 0.91, + }, + { + id: "2", + title: "Physical activity boost", + description: + "Your physical scores rose 8 points after increasing daily step count last week.", + domain: "physical", + type: "strength", + confidence: 0.84, + }, + { + id: "3", + title: "Sleep pattern emerging", + description: + "Emotional wellbeing tends to be higher on days following 7+ hours of sleep.", + domain: "emotional", + type: "pattern", + confidence: 0.78, + }, + { + id: "4", + title: "Room for growth in mindfulness", + description: + "Short guided breathing exercises could support your cognitive resilience.", + domain: "cognitive", + type: "growth-area", + confidence: 0.72, + }, +]; + +const badgeColor: Record = { + strength: "bg-wellab-100 text-wellab-800", + "growth-area": "bg-amber-100 text-amber-800", + pattern: "bg-blue-100 text-blue-800", +}; + +// ---------- component ---------- + +export default function ParticipantDashboard() { + return ( +
+

Participant Experience

+ +
+ + +
+ +
+
+ + {/* Strength-framed insights */} +
+

+ Strength-Framed Insights +

+
+ {insights.map((ins) => ( +
+
+ + {ins.type === "growth-area" ? "growth area" : ins.type} + + + {ins.domain} + +
+

{ins.title}

+

{ins.description}

+

+ Confidence: {Math.round(ins.confidence * 100)}% +

+
+ ))} +
+
+
+ ); +} diff --git a/src/frontend/src/pages/PolicyDashboard.tsx b/src/frontend/src/pages/PolicyDashboard.tsx new file mode 100644 index 0000000..58fd665 --- /dev/null +++ b/src/frontend/src/pages/PolicyDashboard.tsx @@ -0,0 +1,188 @@ +import type { InterventionROI, RiskBucket } from "../types"; + +// ---------- placeholder data ---------- + +const riskBuckets: RiskBucket[] = [ + { label: "Low risk", count: 412, percentage: 48, color: "bg-wellab-400" }, + { label: "Moderate", count: 267, percentage: 31, color: "bg-yellow-400" }, + { label: "Elevated", count: 118, percentage: 14, color: "bg-orange-400" }, + { label: "High risk", count: 55, percentage: 7, color: "bg-red-400" }, +]; + +const interventions: InterventionROI[] = [ + { interventionName: "Community Wellness Hubs", targetPopulation: "Urban Adults", costPerParticipant: 320, wellbeingGain: 8.4, roi: 3.2 }, + { interventionName: "Digital CBT Program", targetPopulation: "College Students", costPerParticipant: 85, wellbeingGain: 5.1, roi: 5.8 }, + { interventionName: "Social Prescribing", targetPopulation: "Older Adults 65+", costPerParticipant: 210, wellbeingGain: 7.2, roi: 4.1 }, + { interventionName: "Workplace Flexibility", targetPopulation: "Working Adults", costPerParticipant: 0, wellbeingGain: 4.8, roi: 12.0 }, + { interventionName: "Green Space Access", targetPopulation: "Rural Community", costPerParticipant: 150, wellbeingGain: 3.9, roi: 2.7 }, +]; + +const regionData = [ + { region: "North District", population: 42000, avgWellbeing: 71, trend: "up" }, + { region: "Central Metro", population: 128000, avgWellbeing: 64, trend: "flat" }, + { region: "East Suburbs", population: 67000, avgWellbeing: 73, trend: "up" }, + { region: "South Valley", population: 31000, avgWellbeing: 58, trend: "down" }, + { region: "West Coast", population: 54000, avgWellbeing: 69, trend: "flat" }, +]; + +const trendArrow: Record = { + up: "text-wellab-600", + flat: "text-gray-400", + down: "text-red-500", +}; +const trendSymbol: Record = { + up: "^", + flat: "-", + down: "v", +}; + +// ---------- component ---------- + +export default function PolicyDashboard() { + return ( +
+

Policy Dashboard

+ + {/* Population wellbeing map placeholder */} +
+

+ Population Wellbeing by Region +

+
+ + + + + + + + + + + + {regionData.map((r) => ( + + + + + + + + ))} + +
RegionPopulationAvg WellbeingTrendDistribution
+ {r.region} + + {r.population.toLocaleString()} + + {r.avgWellbeing} + / 100 + + + {trendSymbol[r.trend]} + + +
+
+
+
+
+

+ Map visualization will render here once a mapping library is + integrated (e.g., Mapbox GL, Leaflet). +

+
+ + {/* Risk distribution */} +
+

+ Risk Distribution +

+
+ {riskBuckets.map((b) => ( +
+ {b.percentage}% +
+ ))} +
+
+ {riskBuckets.map((b) => ( +
+ + + {b.label}: {b.count} + +
+ ))} +
+
+ + {/* Intervention ROI table */} +
+

+ Intervention ROI Analysis +

+
+ + + + + + + + + + + + {interventions.map((iv) => ( + + + + + + + + ))} + +
InterventionTarget Population + Cost / Person + + Wellbeing Gain + ROI
+ {iv.interventionName} + + {iv.targetPopulation} + + {iv.costPerParticipant === 0 + ? "Policy change" + : `$${iv.costPerParticipant}`} + + +{iv.wellbeingGain} + + = 5 + ? "text-wellab-600" + : iv.roi >= 3 + ? "text-yellow-600" + : "text-gray-600" + }`} + > + {iv.roi}x + +
+
+
+
+ ); +} diff --git a/src/frontend/src/pages/ResearcherDashboard.tsx b/src/frontend/src/pages/ResearcherDashboard.tsx new file mode 100644 index 0000000..adb4a5b --- /dev/null +++ b/src/frontend/src/pages/ResearcherDashboard.tsx @@ -0,0 +1,191 @@ +import type { CohortSummary, WellbeingDomain } from "../types"; + +// ---------- placeholder data ---------- + +const cohorts: CohortSummary[] = [ + { cohortId: "c1", name: "Urban Adults 25-40", participantCount: 312, avgWellbeing: 71, dataCompleteness: 0.89 }, + { cohortId: "c2", name: "College Students", participantCount: 187, avgWellbeing: 64, dataCompleteness: 0.93 }, + { cohortId: "c3", name: "Older Adults 65+", participantCount: 145, avgWellbeing: 68, dataCompleteness: 0.76 }, + { cohortId: "c4", name: "Rural Community", participantCount: 98, avgWellbeing: 72, dataCompleteness: 0.81 }, +]; + +const domains: WellbeingDomain[] = [ + "physical", "emotional", "social", "cognitive", "environmental", +]; + +// Synthetic coupling matrix (correlation-like values) +const couplingMatrix: number[][] = [ + [1.0, 0.42, 0.38, 0.55, 0.21], + [0.42, 1.0, 0.67, 0.48, 0.31], + [0.38, 0.67, 1.0, 0.34, 0.44], + [0.55, 0.48, 0.34, 1.0, 0.27], + [0.21, 0.31, 0.44, 0.27, 1.0], +]; + +function heatColor(v: number): string { + if (v >= 0.8) return "bg-wellab-700 text-white"; + if (v >= 0.6) return "bg-wellab-500 text-white"; + if (v >= 0.4) return "bg-wellab-300 text-wellab-900"; + if (v >= 0.2) return "bg-wellab-100 text-wellab-800"; + return "bg-gray-100 text-gray-600"; +} + +const clusterLabels = [ + { id: 1, label: "Thriving", count: 214, color: "bg-wellab-500" }, + { id: 2, label: "Stable-moderate", count: 301, color: "bg-yellow-400" }, + { id: 3, label: "Declining", count: 128, color: "bg-orange-400" }, + { id: 4, label: "At-risk", count: 67, color: "bg-red-400" }, +]; + +const qualityMetrics = [ + { label: "EMA response rate", value: "87%", status: "good" }, + { label: "Sensor uptime", value: "93%", status: "good" }, + { label: "Missing data (7d)", value: "4.2%", status: "good" }, + { label: "Outlier flags", value: "12", status: "warn" }, +]; + +// ---------- component ---------- + +export default function ResearcherDashboard() { + return ( +
+

Researcher Dashboard

+ + {/* Cohort selector */} +
+

+ Cohort Selector +

+
+ {cohorts.map((c) => ( + + ))} +
+
+ +
+ {/* Coupling heatmap */} +
+

+ Domain Coupling Heatmap +

+
+ + + + + ))} + + + + {domains.map((rowD, ri) => ( + + + {couplingMatrix[ri].map((val, ci) => ( + + ))} + + ))} + +
+ {domains.map((d) => ( + + {d.slice(0, 4)} +
+ {rowD.slice(0, 4)} + +
+ {val.toFixed(2)} +
+
+
+
+ + {/* Trajectory clusters */} +
+

+ Trajectory Clusters +

+
+ {clusterLabels.map((cl) => { + const total = clusterLabels.reduce((s, x) => s + x.count, 0); + const pct = Math.round((cl.count / total) * 100); + return ( +
+ + {cl.label} +
+
+
+ + {cl.count} ({pct}%) + +
+ ); + })} +
+

+ Clusters derived from 30-day trajectory similarity (DTW + k-means). +

+
+
+ + {/* Data quality monitor */} +
+

+ Data Quality Monitor +

+
+ {qualityMetrics.map((m) => ( +
+ + {m.value} + + + {m.label} + + + {m.status} + +
+ ))} +
+
+
+ ); +} diff --git a/src/frontend/src/types/index.ts b/src/frontend/src/types/index.ts new file mode 100644 index 0000000..a59de90 --- /dev/null +++ b/src/frontend/src/types/index.ts @@ -0,0 +1,83 @@ +export interface Participant { + id: string; + externalId: string; + cohort: string; + enrolledAt: string; + demographics: Record; +} + +export interface Observation { + id: string; + participantId: string; + timestamp: string; + domain: WellbeingDomain; + value: number; + source: "self-report" | "passive-sensor" | "ema" | "clinical"; + metadata?: Record; +} + +export type WellbeingDomain = + | "physical" + | "emotional" + | "social" + | "cognitive" + | "environmental" + | "occupational" + | "spiritual" + | "financial"; + +export interface WellbeingScore { + overall: number; + domains: Record; + confidence: number; + timestamp: string; +} + +export interface TrendPoint { + date: string; + overall: number; + physical: number; + emotional: number; + social: number; +} + +export interface Insight { + id: string; + title: string; + description: string; + domain: WellbeingDomain; + type: "strength" | "growth-area" | "pattern"; + confidence: number; +} + +export interface CohortSummary { + cohortId: string; + name: string; + participantCount: number; + avgWellbeing: number; + dataCompleteness: number; +} + +export interface InterventionROI { + interventionName: string; + targetPopulation: string; + costPerParticipant: number; + wellbeingGain: number; + roi: number; +} + +export interface RiskBucket { + label: string; + count: number; + percentage: number; + color: string; +} + +export interface ApiResponse { + data: T; + meta?: { + total: number; + page: number; + pageSize: number; + }; +} diff --git a/src/frontend/tailwind.config.js b/src/frontend/tailwind.config.js new file mode 100644 index 0000000..0f7a136 --- /dev/null +++ b/src/frontend/tailwind.config.js @@ -0,0 +1,23 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + extend: { + colors: { + wellab: { + 50: "#f0fdf4", + 100: "#dcfce7", + 200: "#bbf7d0", + 300: "#86efac", + 400: "#4ade80", + 500: "#22c55e", + 600: "#16a34a", + 700: "#15803d", + 800: "#166534", + 900: "#14532d", + }, + }, + }, + }, + plugins: [], +}; diff --git a/src/frontend/tsconfig.json b/src/frontend/tsconfig.json new file mode 100644 index 0000000..ef3600f --- /dev/null +++ b/src/frontend/tsconfig.json @@ -0,0 +1,24 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": false, + "noUnusedParameters": false, + "noFallthroughCasesInSwitch": true, + "baseUrl": ".", + "paths": { + "@/*": ["src/*"] + } + }, + "include": ["src"] +} diff --git a/src/frontend/vite.config.ts b/src/frontend/vite.config.ts new file mode 100644 index 0000000..1dd2a10 --- /dev/null +++ b/src/frontend/vite.config.ts @@ -0,0 +1,15 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + proxy: { + "/api": { + target: "http://localhost:3001", + changeOrigin: true, + }, + }, + }, +}); diff --git a/src/ml/__init__.py b/src/ml/__init__.py new file mode 100644 index 0000000..bcd132b --- /dev/null +++ b/src/ml/__init__.py @@ -0,0 +1,26 @@ +""" +WELLab ML Pipeline +=================== +AI-Enabled Research & Impact Platform for the Well-Being and Emotion +across the Lifespan Lab (WELLab). + +Submodules +---------- +- emotional_dynamics : IDELS-based emotion coupling analysis +- health_engine : Causal behavioral & physiological health modeling +- lifespan_trajectory: Growth-curve and trajectory clustering across the lifespan +- cognitive_health : Cognitive risk prediction and dementia prevention +- utils : Shared data-loading and reproducibility helpers +- config : ML configuration constants +""" + +from src.ml.config import ML_CONFIG # noqa: F401 + +__all__ = [ + "emotional_dynamics", + "health_engine", + "lifespan_trajectory", + "cognitive_health", + "utils", + "config", +] diff --git a/src/ml/cognitive_health.py b/src/ml/cognitive_health.py new file mode 100644 index 0000000..e0ea3fb --- /dev/null +++ b/src/ml/cognitive_health.py @@ -0,0 +1,269 @@ +""" +Cognitive Health & Dementia Prevention Engine +============================================= +Risk prediction, protective-factor identification, and survival +analysis for cognitive decline and dementia onset. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.model_selection import cross_val_score +from sklearn.inspection import permutation_importance + +# Stub import -- resolves once lifelines is installed +try: + from lifelines import CoxPHFitter # noqa: F401 + _HAS_LIFELINES = True +except ImportError: # pragma: no cover + _HAS_LIFELINES = False + +from src.ml.config import COGNITIVE_RISK_PARAMS, RANDOM_SEED +from src.ml.utils import set_reproducible_seed + +logger = logging.getLogger(__name__) + + +class CognitiveRiskModel: + """Predict cognitive-decline risk and identify protective factors. + + Parameters + ---------- + risk_threshold : float + Probability cut-off above which a participant is flagged + as high-risk. + n_estimators : int + Number of boosting rounds for the gradient-boosted classifier. + max_depth : int + Maximum tree depth. + seed : int + Random seed for reproducibility. + """ + + def __init__( + self, + risk_threshold: float = COGNITIVE_RISK_PARAMS["risk_threshold"], + n_estimators: int = COGNITIVE_RISK_PARAMS["n_estimators"], + max_depth: int = COGNITIVE_RISK_PARAMS["max_depth"], + seed: int = RANDOM_SEED, + ) -> None: + self.risk_threshold = risk_threshold + self.seed = seed + + self._classifier = GradientBoostingClassifier( + n_estimators=n_estimators, + max_depth=max_depth, + random_state=seed, + ) + self.is_fitted: bool = False + self._feature_names: List[str] = [] + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def fit( + self, + data: pd.DataFrame, + target_col: str = "cognitive_decline", + exclude_cols: Optional[List[str]] = None, + ) -> "CognitiveRiskModel": + """Train the risk-prediction model. + + Parameters + ---------- + data : pd.DataFrame + Feature matrix including the binary target column. + target_col : str + Name of the 0/1 outcome column. + exclude_cols : list[str], optional + Columns to exclude from features (e.g. IDs, dates). + + Returns + ------- + CognitiveRiskModel + ``self``, for method chaining. + """ + set_reproducible_seed(self.seed) + + exclude = set(exclude_cols or []) | {target_col} + self._feature_names = [c for c in data.columns if c not in exclude] + + X = data[self._feature_names].values + y = data[target_col].values + + logger.info( + "Training CognitiveRiskModel: %d samples, %d features", + X.shape[0], X.shape[1], + ) + + # TODO: Add hyperparameter tuning via RandomizedSearchCV or + # Optuna before production deployment. + self._classifier.fit(X, y) + + # Quick cross-validated performance check + cv_scores = cross_val_score( + self._classifier, X, y, cv=5, scoring="roc_auc" + ) + logger.info( + "5-fold CV AUC: %.3f (+/- %.3f)", + cv_scores.mean(), cv_scores.std(), + ) + + self.is_fitted = True + return self + + def predict_risk( + self, + participant_data: pd.DataFrame, + ) -> pd.DataFrame: + """Generate risk scores for new participants. + + Parameters + ---------- + participant_data : pd.DataFrame + Must contain the same feature columns used during fit. + + Returns + ------- + pd.DataFrame + Original data augmented with ``risk_probability`` and + ``high_risk`` columns. + """ + if not self.is_fitted: + raise RuntimeError("Call fit() before predict_risk().") + + X = participant_data[self._feature_names].values + probas = self._classifier.predict_proba(X)[:, 1] + + result = participant_data.copy() + result["risk_probability"] = probas + result["high_risk"] = probas >= self.risk_threshold + + n_high = int(result["high_risk"].sum()) + logger.info( + "Predicted risk for %d participants: %d flagged high-risk (%.1f%%)", + len(result), n_high, 100.0 * n_high / max(len(result), 1), + ) + return result + + def identify_protective_factors( + self, + data: pd.DataFrame, + target_col: str = "cognitive_decline", + top_n: int = 10, + ) -> List[Tuple[str, float]]: + """Rank features by their protective (negative) importance. + + Parameters + ---------- + data : pd.DataFrame + Dataset used for importance estimation. + target_col : str + Binary outcome column. + top_n : int + Number of top protective factors to return. + + Returns + ------- + list[tuple[str, float]] + Feature names paired with their importance scores, sorted + so that the strongest *protective* factors come first + (most negative importance = most protective). + """ + if not self.is_fitted: + raise RuntimeError("Call fit() before identify_protective_factors().") + + X = data[self._feature_names] + y = data[target_col] + + logger.info("Computing permutation importance for protective factors") + + # TODO: Supplement with SHAP values for richer explanations. + perm_imp = permutation_importance( + self._classifier, X, y, + n_repeats=10, + random_state=self.seed, + scoring="roc_auc", + ) + + importances = perm_imp.importances_mean + ranked = sorted( + zip(self._feature_names, importances), + key=lambda pair: pair[1], + ) + + # Protective factors have *negative* permutation importance: + # removing them *hurts* prediction of decline, implying they + # are associated with *lower* risk. + protective = [(name, float(score)) for name, score in ranked[:top_n]] + logger.info("Top %d protective factors: %s", top_n, protective) + return protective + + def survival_analysis( + self, + time_to_event_data: pd.DataFrame, + duration_col: str = "years_to_event", + event_col: str = "event_observed", + ) -> Dict[str, Any]: + """Run a Cox proportional-hazards survival model. + + Parameters + ---------- + time_to_event_data : pd.DataFrame + Must include *duration_col*, *event_col*, and covariate + columns. + duration_col : str + Time-to-event column. + event_col : str + Binary indicator of whether the event was observed. + + Returns + ------- + dict + Hazard ratios, concordance index, and model summary text. + """ + set_reproducible_seed(self.seed) + logger.info( + "Running survival analysis on %d observations", + len(time_to_event_data), + ) + + if _HAS_LIFELINES: + cph = CoxPHFitter() + cph.fit( + time_to_event_data, + duration_col=duration_col, + event_col=event_col, + ) + + return { + "concordance_index": float(cph.concordance_index_), + "hazard_ratios": cph.hazard_ratios_.to_dict(), + "summary": cph.summary.to_dict(), + "method": "cox_ph_lifelines", + } + + # --- stub fallback when lifelines is not installed --- + # TODO: Install lifelines and remove this stub. + logger.warning( + "lifelines not installed; returning placeholder survival results" + ) + + covariate_cols = [ + c for c in time_to_event_data.columns + if c not in (duration_col, event_col) + ] + + placeholder_hr = {col: 1.0 for col in covariate_cols} + return { + "concordance_index": np.nan, + "hazard_ratios": placeholder_hr, + "summary": "lifelines not installed -- stub results", + "method": "stub", + } diff --git a/src/ml/config.py b/src/ml/config.py new file mode 100644 index 0000000..c0bc74b --- /dev/null +++ b/src/ml/config.py @@ -0,0 +1,79 @@ +""" +ML configuration constants for the WELLab platform. + +Central location for random seeds, default model hyper-parameters, +decision thresholds, and data-schema definitions used across all +pipeline modules. +""" + +from typing import Dict, Any + +# --------------------------------------------------------------------------- +# Reproducibility +# --------------------------------------------------------------------------- +RANDOM_SEED: int = 42 + +# --------------------------------------------------------------------------- +# Emotional Dynamics Engine +# --------------------------------------------------------------------------- +EMOTION_COUPLING_TYPES: list[str] = [ + "positive", + "negative", + "decoupled", + "complex", +] + +EMOTION_VOLATILITY_WINDOW: int = 5 # rolling-window size for volatility +EMOTION_COUPLING_THRESHOLD: float = 0.30 # abs(r) above this => coupled + +# --------------------------------------------------------------------------- +# Health Engine +# --------------------------------------------------------------------------- +HEALTH_ENGINE_PARAMS: Dict[str, Any] = { + "min_observations": 30, + "significance_level": 0.05, + "bootstrap_iterations": 1000, + "causal_method": "backdoor.linear_regression", +} + +# --------------------------------------------------------------------------- +# Lifespan Trajectory Engine +# --------------------------------------------------------------------------- +TRAJECTORY_PARAMS: Dict[str, Any] = { + "default_n_clusters": 3, + "max_polynomial_degree": 3, + "convergence_tolerance": 1e-4, + "max_iterations": 200, +} + +# --------------------------------------------------------------------------- +# Cognitive Health & Dementia Prevention Engine +# --------------------------------------------------------------------------- +COGNITIVE_RISK_PARAMS: Dict[str, Any] = { + "risk_threshold": 0.5, + "n_estimators": 100, + "max_depth": 6, + "survival_alpha": 0.05, +} + +# --------------------------------------------------------------------------- +# Fairness Audit +# --------------------------------------------------------------------------- +FAIRNESS_PARAMS: Dict[str, Any] = { + "demographic_parity_tolerance": 0.05, + "disparate_impact_floor": 0.80, # 4/5ths rule +} + +# --------------------------------------------------------------------------- +# Aggregate config dict (convenient for serialisation / logging) +# --------------------------------------------------------------------------- +ML_CONFIG: Dict[str, Any] = { + "random_seed": RANDOM_SEED, + "emotion_coupling_types": EMOTION_COUPLING_TYPES, + "emotion_volatility_window": EMOTION_VOLATILITY_WINDOW, + "emotion_coupling_threshold": EMOTION_COUPLING_THRESHOLD, + "health_engine": HEALTH_ENGINE_PARAMS, + "trajectory": TRAJECTORY_PARAMS, + "cognitive_risk": COGNITIVE_RISK_PARAMS, + "fairness": FAIRNESS_PARAMS, +} diff --git a/src/ml/emotional_dynamics.py b/src/ml/emotional_dynamics.py new file mode 100644 index 0000000..5d7b4c2 --- /dev/null +++ b/src/ml/emotional_dynamics.py @@ -0,0 +1,231 @@ +""" +Emotional Dynamics Engine +========================= +Implements the Intra- and Inter-individual Dynamical Emotion +Linkage System (IDELS) coupling analysis for the WELLab platform. + +Coupling types +-------------- +- **positive** : partners' emotions move in the same direction +- **negative** : partners' emotions move in opposite directions +- **decoupled** : no reliable association between partners' emotions +- **complex** : non-linear or context-dependent linkage pattern +""" + +from __future__ import annotations + +import logging +from typing import Dict, List, Optional + +import numpy as np +import pandas as pd +from sklearn.linear_model import LinearRegression # noqa: F401 (stub dep) +from sklearn.preprocessing import StandardScaler # noqa: F401 (stub dep) + +from src.ml.config import ( + EMOTION_COUPLING_THRESHOLD, + EMOTION_COUPLING_TYPES, + EMOTION_VOLATILITY_WINDOW, + RANDOM_SEED, +) +from src.ml.utils import set_reproducible_seed, validate_data_schema + +logger = logging.getLogger(__name__) + +# Expected schema for the input data +_INPUT_SCHEMA = { + "participant_id": "object", + "time": "float64", + "positive_affect": "float64", + "negative_affect": "float64", +} + + +class EmotionCouplingAnalyzer: + """Analyse emotional coupling patterns between dyad members. + + This analyser ingests time-series affect data, fits per-dyad + coupling models, and classifies each relationship into one of the + four IDELS coupling types. + + Parameters + ---------- + coupling_threshold : float + Absolute correlation value above which a dyad is considered + *coupled* (default from ``config.EMOTION_COUPLING_THRESHOLD``). + volatility_window : int + Rolling-window size used for volatility estimation + (default from ``config.EMOTION_VOLATILITY_WINDOW``). + seed : int + Random seed for reproducibility. + + Attributes + ---------- + is_fitted : bool + Whether :meth:`fit` has been called successfully. + coupling_results_ : dict + Per-participant coupling classification (populated after fit). + """ + + COUPLING_TYPES: List[str] = EMOTION_COUPLING_TYPES + + def __init__( + self, + coupling_threshold: float = EMOTION_COUPLING_THRESHOLD, + volatility_window: int = EMOTION_VOLATILITY_WINDOW, + seed: int = RANDOM_SEED, + ) -> None: + self.coupling_threshold = coupling_threshold + self.volatility_window = volatility_window + self.seed = seed + + self.is_fitted: bool = False + self.coupling_results_: Dict[str, str] = {} + self._data: Optional[pd.DataFrame] = None + self._scaler = StandardScaler() + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def fit(self, data: pd.DataFrame) -> "EmotionCouplingAnalyzer": + """Fit coupling models to longitudinal affect data. + + Parameters + ---------- + data : pd.DataFrame + Must contain columns defined in ``_INPUT_SCHEMA``. + + Returns + ------- + EmotionCouplingAnalyzer + ``self``, for method chaining. + + Raises + ------ + ValueError + If schema validation fails. + """ + set_reproducible_seed(self.seed) + + errors = validate_data_schema(data, _INPUT_SCHEMA) + if errors: + raise ValueError( + f"Input data failed schema validation: {errors}" + ) + + self._data = data.copy() + logger.info( + "Fitting EmotionCouplingAnalyzer on %d rows, %d participants", + len(data), + data["participant_id"].nunique(), + ) + + # Normalise affect scores + affect_cols = ["positive_affect", "negative_affect"] + self._data[affect_cols] = self._scaler.fit_transform( + self._data[affect_cols] + ) + + # Compute per-participant coupling + for pid in self._data["participant_id"].unique(): + self.coupling_results_[pid] = self.predict_coupling_type(pid) + + self.is_fitted = True + logger.info("Fit complete. %d coupling results stored.", len(self.coupling_results_)) + return self + + def predict_coupling_type(self, participant_id: str) -> str: + """Classify a participant's emotion coupling pattern. + + Parameters + ---------- + participant_id : str + Participant whose coupling type is requested. + + Returns + ------- + str + One of ``"positive"``, ``"negative"``, ``"decoupled"``, + or ``"complex"``. + """ + if self._data is None: + raise RuntimeError("Call fit() before predict_coupling_type().") + + subset = self._data.loc[ + self._data["participant_id"] == participant_id + ] + + if subset.empty: + logger.warning("No data for participant %s", participant_id) + return "decoupled" + + pa = subset["positive_affect"].values + na = subset["negative_affect"].values + + # Pearson correlation as a first-pass coupling metric + if len(pa) < 3: + return "decoupled" + + r = np.corrcoef(pa, na)[0, 1] + + # TODO: Replace simple correlation with a proper multilevel or + # time-varying parameter model (e.g., TV-VAR or DCC). + if np.isnan(r) or abs(r) < self.coupling_threshold: + return "decoupled" + + # TODO: Add non-linearity test to distinguish "complex" from + # simple positive/negative coupling. + residual_nonlinearity = self._estimate_nonlinearity(pa, na) + if residual_nonlinearity > 0.3: + return "complex" + + return "positive" if r > 0 else "negative" + + def compute_volatility(self, time_series: np.ndarray) -> np.ndarray: + """Compute rolling emotional volatility (standard deviation). + + Parameters + ---------- + time_series : np.ndarray + 1-D array of affect scores over time. + + Returns + ------- + np.ndarray + Rolling standard deviation with the same length as + *time_series* (leading entries are NaN where the window + is incomplete). + """ + series = pd.Series(time_series) + volatility = series.rolling( + window=self.volatility_window, min_periods=1 + ).std() + return volatility.to_numpy() + + # ------------------------------------------------------------------ + # Private helpers + # ------------------------------------------------------------------ + + @staticmethod + def _estimate_nonlinearity(x: np.ndarray, y: np.ndarray) -> float: + """Return a rough nonlinearity score between two signals. + + Uses the ratio of quadratic-model R^2 improvement over a linear + model as a simple heuristic. + + TODO: Replace with a proper BDS or RESET test. + """ + if len(x) < 5: + return 0.0 + + x_col = x.reshape(-1, 1) + linear = LinearRegression().fit(x_col, y) + r2_linear = max(linear.score(x_col, y), 0.0) + + x_quad = np.column_stack([x, x ** 2]) + quad = LinearRegression().fit(x_quad, y) + r2_quad = max(quad.score(x_quad, y), 0.0) + + improvement = r2_quad - r2_linear + return float(np.clip(improvement, 0.0, 1.0)) diff --git a/src/ml/health_engine.py b/src/ml/health_engine.py new file mode 100644 index 0000000..009553b --- /dev/null +++ b/src/ml/health_engine.py @@ -0,0 +1,285 @@ +""" +Behavioral & Physiological Health Engine +========================================= +Provides causal-inference tooling for analysing bidirectional +relationships between subjective well-being and objective health +biomarkers in the WELLab platform. + +Integrates with the DoWhy causal-inference library for identification, +estimation, and refutation of treatment effects. +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +import numpy as np +import pandas as pd + +# Stub imports -- these will resolve once DoWhy / statsmodels are installed +try: + import dowhy # noqa: F401 + from dowhy import CausalModel # noqa: F401 + _HAS_DOWHY = True +except ImportError: # pragma: no cover + _HAS_DOWHY = False + +try: + import statsmodels.api as sm # noqa: F401 + _HAS_STATSMODELS = True +except ImportError: # pragma: no cover + _HAS_STATSMODELS = False + +from src.ml.config import HEALTH_ENGINE_PARAMS, RANDOM_SEED +from src.ml.utils import set_reproducible_seed + +logger = logging.getLogger(__name__) + + +@dataclass +class CausalEstimateResult: + """Container for a single causal-effect estimate.""" + + treatment: str + outcome: str + method: str + estimate: float + p_value: Optional[float] = None + confidence_interval: tuple[float, float] = (np.nan, np.nan) + refutation_passed: Optional[bool] = None + metadata: Dict[str, Any] = field(default_factory=dict) + + +class CausalHealthAnalyzer: + """Estimate causal effects between well-being and health variables. + + Parameters + ---------- + significance_level : float + Alpha for hypothesis tests (default 0.05). + causal_method : str + DoWhy estimation method identifier. + seed : int + Random seed for reproducibility. + """ + + def __init__( + self, + significance_level: float = HEALTH_ENGINE_PARAMS["significance_level"], + causal_method: str = HEALTH_ENGINE_PARAMS["causal_method"], + seed: int = RANDOM_SEED, + ) -> None: + self.significance_level = significance_level + self.causal_method = causal_method + self.seed = seed + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def estimate_causal_effect( + self, + treatment: str, + outcome: str, + confounders: List[str], + data: Optional[pd.DataFrame] = None, + ) -> CausalEstimateResult: + """Estimate the average causal effect of *treatment* on *outcome*. + + Parameters + ---------- + treatment : str + Column name of the treatment / exposure variable. + outcome : str + Column name of the outcome variable. + confounders : list[str] + Column names to adjust for. + data : pd.DataFrame, optional + Observational data. Required on first call; cached + thereafter. + + Returns + ------- + CausalEstimateResult + Structured result including point estimate, CI, and + refutation flag. + """ + set_reproducible_seed(self.seed) + + if data is None: + raise ValueError("data must be provided") + + logger.info( + "Estimating causal effect: %s -> %s | %s", + treatment, outcome, confounders, + ) + + # TODO: Build a proper DoWhy CausalModel with the user-supplied + # DAG or automatic graph discovery. + # + # model = CausalModel( + # data=data, + # treatment=treatment, + # outcome=outcome, + # common_causes=confounders, + # ) + # identified = model.identify_effect() + # estimate = model.estimate_effect( + # identified, method_name=self.causal_method + # ) + + # --- stub linear estimate --- + from sklearn.linear_model import LinearRegression + + feature_cols = [treatment] + confounders + X = data[feature_cols].dropna() + y = data.loc[X.index, outcome] + + reg = LinearRegression().fit(X, y) + treatment_idx = feature_cols.index(treatment) + beta = float(reg.coef_[treatment_idx]) + + n = len(y) + se = float(np.std(y - reg.predict(X)) / np.sqrt(n)) + ci_low = beta - 1.96 * se + ci_high = beta + 1.96 * se + + # TODO: Run DoWhy refutation tests (placebo, random common cause) + refutation_passed = None + + return CausalEstimateResult( + treatment=treatment, + outcome=outcome, + method=self.causal_method, + estimate=beta, + confidence_interval=(ci_low, ci_high), + refutation_passed=refutation_passed, + ) + + def run_longitudinal_regression( + self, + data: pd.DataFrame, + outcome: str = "health_outcome", + time_var: str = "wave", + group_var: str = "participant_id", + ) -> Dict[str, Any]: + """Fit a longitudinal (mixed-effects) regression. + + Parameters + ---------- + data : pd.DataFrame + Panel data with repeated measures. + outcome : str + Dependent variable column. + time_var : str + Column identifying the measurement occasion. + group_var : str + Column identifying the clustering unit (participant). + + Returns + ------- + dict + Summary statistics including fixed-effect coefficients and + random-effect variance components. + """ + set_reproducible_seed(self.seed) + logger.info("Running longitudinal regression for outcome=%s", outcome) + + # TODO: Use statsmodels MixedLM for proper random-intercept / + # random-slope models: + # + # import statsmodels.formula.api as smf + # model = smf.mixedlm( + # f"{outcome} ~ {time_var}", data, + # groups=data[group_var], + # re_formula=f"~{time_var}", + # ) + # result = model.fit() + + # --- stub OLS per group --- + from sklearn.linear_model import LinearRegression + + coefficients: Dict[str, float] = {} + for gid, grp in data.groupby(group_var): + X = grp[[time_var]].values + y = grp[outcome].values + if len(y) < 2: + continue + reg = LinearRegression().fit(X, y) + coefficients[str(gid)] = float(reg.coef_[0]) + + mean_slope = float(np.mean(list(coefficients.values()))) if coefficients else 0.0 + var_slope = float(np.var(list(coefficients.values()))) if coefficients else 0.0 + + return { + "fixed_effect_slope": mean_slope, + "random_effect_variance": var_slope, + "n_groups": len(coefficients), + "method": "stub_ols_per_group", + } + + def bidirectional_analysis( + self, + wellbeing_data: pd.DataFrame, + health_data: pd.DataFrame, + participant_col: str = "participant_id", + time_col: str = "wave", + ) -> Dict[str, CausalEstimateResult]: + """Run paired causal analyses in both directions. + + Estimates the effect of well-being on health **and** the effect + of health on well-being, returning both results keyed by + direction label. + + Parameters + ---------- + wellbeing_data : pd.DataFrame + Subjective well-being measures. + health_data : pd.DataFrame + Objective health biomarkers. + participant_col : str + Join key for participants. + time_col : str + Join key for measurement wave. + + Returns + ------- + dict[str, CausalEstimateResult] + ``"wellbeing_to_health"`` and ``"health_to_wellbeing"`` estimates. + """ + merged = pd.merge( + wellbeing_data, health_data, + on=[participant_col, time_col], + how="inner", + suffixes=("_wb", "_hl"), + ) + + if merged.empty: + raise ValueError("Merge produced an empty DataFrame; check join keys.") + + logger.info("Bidirectional analysis on %d merged rows", len(merged)) + + # TODO: Replace hard-coded column names with config-driven mappings + wb_col = "wellbeing_score" if "wellbeing_score" in merged.columns else merged.columns[2] + hl_col = "health_score" if "health_score" in merged.columns else merged.columns[3] + + wb_to_hl = self.estimate_causal_effect( + treatment=wb_col, + outcome=hl_col, + confounders=[time_col], + data=merged, + ) + + hl_to_wb = self.estimate_causal_effect( + treatment=hl_col, + outcome=wb_col, + confounders=[time_col], + data=merged, + ) + + return { + "wellbeing_to_health": wb_to_hl, + "health_to_wellbeing": hl_to_wb, + } diff --git a/src/ml/lifespan_trajectory.py b/src/ml/lifespan_trajectory.py new file mode 100644 index 0000000..c4efa48 --- /dev/null +++ b/src/ml/lifespan_trajectory.py @@ -0,0 +1,242 @@ +""" +Lifespan Trajectory Engine +========================== +Growth-curve fitting, trajectory clustering, and cross-cultural +comparison tools for modelling well-being across the lifespan. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans +from sklearn.preprocessing import PolynomialFeatures + +# Stub import -- resolves once statsmodels is installed +try: + import statsmodels.formula.api as smf # noqa: F401 + _HAS_STATSMODELS = True +except ImportError: # pragma: no cover + _HAS_STATSMODELS = False + +from src.ml.config import RANDOM_SEED, TRAJECTORY_PARAMS +from src.ml.utils import set_reproducible_seed + +logger = logging.getLogger(__name__) + + +class TrajectoryAnalyzer: + """Model lifespan developmental trajectories. + + Parameters + ---------- + max_degree : int + Maximum polynomial degree for growth-curve fitting. + n_clusters : int + Default number of latent trajectory groups. + seed : int + Random seed for reproducibility. + """ + + def __init__( + self, + max_degree: int = TRAJECTORY_PARAMS["max_polynomial_degree"], + n_clusters: int = TRAJECTORY_PARAMS["default_n_clusters"], + seed: int = RANDOM_SEED, + ) -> None: + self.max_degree = max_degree + self.n_clusters = n_clusters + self.seed = seed + + self._growth_models: Dict[str, Any] = {} + self._cluster_model: Optional[KMeans] = None + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def fit_growth_curves( + self, + data: pd.DataFrame, + outcome: str = "wellbeing", + age_col: str = "age", + group_col: str = "participant_id", + ) -> Dict[str, Any]: + """Fit polynomial growth curves per participant. + + Parameters + ---------- + data : pd.DataFrame + Longitudinal panel with at least *outcome*, *age_col*, and + *group_col*. + outcome : str + Dependent variable. + age_col : str + Age or time variable. + group_col : str + Clustering / grouping variable. + + Returns + ------- + dict + Per-participant polynomial coefficients and an aggregate + summary. + """ + set_reproducible_seed(self.seed) + logger.info( + "Fitting growth curves (degree<=%d) for %d participants", + self.max_degree, + data[group_col].nunique(), + ) + + participant_curves: Dict[str, np.ndarray] = {} + + for pid, grp in data.groupby(group_col): + x = grp[age_col].values.reshape(-1, 1) + y = grp[outcome].values + + if len(y) < self.max_degree + 1: + # Not enough points for the requested degree + degree = max(len(y) - 1, 1) + else: + degree = self.max_degree + + # TODO: Replace with statsmodels MixedLM for proper + # random-effects growth curves: + # smf.mixedlm(f"{outcome} ~ age + I(age**2)", + # data, groups=data[group_col]) + coeffs = np.polyfit(x.ravel(), y, deg=degree) + participant_curves[str(pid)] = coeffs + + self._growth_models = participant_curves + + # Aggregate summary + all_coeffs = np.array(list(participant_curves.values())) + summary = { + "n_participants": len(participant_curves), + "degree": self.max_degree, + "mean_coefficients": all_coeffs.mean(axis=0).tolist(), + "std_coefficients": all_coeffs.std(axis=0).tolist(), + } + logger.info("Growth curve fitting complete: %s", summary) + return summary + + def cluster_trajectories( + self, + data: pd.DataFrame, + n_clusters: Optional[int] = None, + outcome: str = "wellbeing", + age_col: str = "age", + group_col: str = "participant_id", + ) -> Dict[str, Any]: + """Identify latent trajectory groups via K-Means on curve features. + + Parameters + ---------- + data : pd.DataFrame + Same longitudinal format as :meth:`fit_growth_curves`. + n_clusters : int, optional + Number of clusters (falls back to ``self.n_clusters``). + outcome, age_col, group_col : str + Column names. + + Returns + ------- + dict + Cluster labels per participant plus cluster centroids. + """ + k = n_clusters or self.n_clusters + set_reproducible_seed(self.seed) + logger.info("Clustering trajectories into %d groups", k) + + # Build feature matrix from polynomial coefficients + if not self._growth_models: + self.fit_growth_curves(data, outcome, age_col, group_col) + + pids = list(self._growth_models.keys()) + + # Pad coefficient arrays to uniform length + max_len = max(len(c) for c in self._growth_models.values()) + feature_matrix = np.zeros((len(pids), max_len)) + for i, pid in enumerate(pids): + coeffs = self._growth_models[pid] + feature_matrix[i, max_len - len(coeffs):] = coeffs + + # TODO: Consider using GMM or latent-class growth analysis + # instead of K-Means for better probabilistic assignment. + self._cluster_model = KMeans( + n_clusters=k, + random_state=self.seed, + n_init=10, + ) + labels = self._cluster_model.fit_predict(feature_matrix) + + assignments = {pid: int(label) for pid, label in zip(pids, labels)} + centroids = self._cluster_model.cluster_centers_.tolist() + + logger.info("Trajectory clustering complete: %d clusters", k) + return { + "n_clusters": k, + "assignments": assignments, + "centroids": centroids, + "inertia": float(self._cluster_model.inertia_), + } + + def cross_cultural_comparison( + self, + cohort_a: pd.DataFrame, + cohort_b: pd.DataFrame, + outcome: str = "wellbeing", + age_col: str = "age", + ) -> Dict[str, Any]: + """Compare aggregate trajectory shapes between two cohorts. + + Parameters + ---------- + cohort_a, cohort_b : pd.DataFrame + Each must contain *outcome* and *age_col* columns. + + Returns + ------- + dict + Per-cohort polynomial fits, the coefficient differences, + and a rough significance indicator. + """ + set_reproducible_seed(self.seed) + logger.info( + "Cross-cultural comparison: cohort_a=%d rows, cohort_b=%d rows", + len(cohort_a), len(cohort_b), + ) + + def _fit_cohort(df: pd.DataFrame) -> np.ndarray: + x = df[age_col].values + y = df[outcome].values + degree = min(self.max_degree, max(len(y) - 1, 1)) + return np.polyfit(x, y, deg=degree) + + coeffs_a = _fit_cohort(cohort_a) + coeffs_b = _fit_cohort(cohort_b) + + # Pad to same length for comparison + max_len = max(len(coeffs_a), len(coeffs_b)) + padded_a = np.zeros(max_len) + padded_b = np.zeros(max_len) + padded_a[max_len - len(coeffs_a):] = coeffs_a + padded_b[max_len - len(coeffs_b):] = coeffs_b + + diff = padded_a - padded_b + + # TODO: Implement permutation test or bootstrap CI for the + # coefficient differences to get proper p-values. + crude_distance = float(np.linalg.norm(diff)) + + return { + "cohort_a_coeffs": padded_a.tolist(), + "cohort_b_coeffs": padded_b.tolist(), + "coefficient_diff": diff.tolist(), + "euclidean_distance": crude_distance, + "significant": crude_distance > 0.5, # placeholder threshold + } diff --git a/src/ml/utils.py b/src/ml/utils.py new file mode 100644 index 0000000..53a695b --- /dev/null +++ b/src/ml/utils.py @@ -0,0 +1,148 @@ +""" +Shared utilities for the WELLab ML pipeline. + +Provides reproducibility helpers, data-loading stubs, and schema +validation used by every engine module. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional + +import numpy as np +import pandas as pd + +from src.ml.config import RANDOM_SEED + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Reproducibility +# --------------------------------------------------------------------------- + +def set_reproducible_seed(seed: int = RANDOM_SEED) -> None: + """Set random seeds for numpy (and, when available, sklearn and torch). + + Parameters + ---------- + seed : int + The seed value to use across all RNGs. + """ + np.random.seed(seed) + logger.info("NumPy random seed set to %d", seed) + + # TODO: Add torch.manual_seed(seed) when PyTorch is added as a dependency + # TODO: Add tf.random.set_seed(seed) if TensorFlow is ever used + + +# --------------------------------------------------------------------------- +# Data loading +# --------------------------------------------------------------------------- + +def load_participant_data( + participant_id: str, + data_dir: Optional[str] = None, +) -> pd.DataFrame: + """Load longitudinal data for a single participant. + + Parameters + ---------- + participant_id : str + Unique identifier for the participant (e.g. ``"WELLab-0042"``). + data_dir : str, optional + Root directory containing participant CSVs. Falls back to the + project default when *None*. + + Returns + ------- + pd.DataFrame + DataFrame indexed by measurement occasion with all recorded + variables for the requested participant. + + Raises + ------ + FileNotFoundError + If no data file exists for *participant_id*. + """ + if data_dir is None: + # TODO: Replace with actual default data path from project config + data_dir = "data/participants" + + file_path = f"{data_dir}/{participant_id}.csv" + + # TODO: Implement actual file I/O with appropriate error handling + # and column-type coercion for date/time fields. + logger.info("Loading data for participant %s from %s", participant_id, file_path) + + try: + df = pd.read_csv(file_path, parse_dates=True) + except FileNotFoundError: + logger.error("Data file not found: %s", file_path) + raise + + return df + + +# --------------------------------------------------------------------------- +# Schema validation +# --------------------------------------------------------------------------- + +def validate_data_schema( + data: pd.DataFrame, + schema: Dict[str, Any], +) -> List[str]: + """Validate that *data* conforms to the expected *schema*. + + Parameters + ---------- + data : pd.DataFrame + The DataFrame to validate. + schema : dict + Mapping of ``{column_name: expected_dtype_string}``. + Example: ``{"age": "float64", "participant_id": "object"}``. + + Returns + ------- + list[str] + A list of human-readable validation error messages. An empty + list signals that the data passed all checks. + """ + errors: List[str] = [] + + # Check for required columns + missing_cols = set(schema.keys()) - set(data.columns) + if missing_cols: + errors.append(f"Missing required columns: {sorted(missing_cols)}") + + # Check dtype compatibility for present columns + # Allow compatible dtype aliases (e.g. "str" matches "object") + _COMPATIBLE = { + "object": {"object", "str", "string"}, + "str": {"object", "str", "string"}, + "string": {"object", "str", "string"}, + } + + for col, expected_dtype in schema.items(): + if col not in data.columns: + continue + actual_dtype = str(data[col].dtype) + compatible_set = _COMPATIBLE.get(expected_dtype, {expected_dtype}) + if actual_dtype not in compatible_set: + errors.append( + f"Column '{col}' has dtype '{actual_dtype}', " + f"expected '{expected_dtype}'" + ) + + # Check for completely empty columns + for col in data.columns: + if data[col].isna().all(): + errors.append(f"Column '{col}' is entirely NaN") + + if errors: + logger.warning("Schema validation found %d issue(s)", len(errors)) + else: + logger.info("Schema validation passed") + + return errors diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..15fd618 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""WELLab ML pipeline test suite.""" diff --git a/tests/test_emotional_dynamics.py b/tests/test_emotional_dynamics.py new file mode 100644 index 0000000..ee4791f --- /dev/null +++ b/tests/test_emotional_dynamics.py @@ -0,0 +1,129 @@ +""" +Unit tests for the EmotionCouplingAnalyzer. +""" + +import numpy as np +import pandas as pd +import pytest + +from src.ml.emotional_dynamics import EmotionCouplingAnalyzer + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +def _make_affect_data( + n_participants: int = 3, + n_timepoints: int = 50, + coupling: str = "positive", + seed: int = 42, +) -> pd.DataFrame: + """Generate synthetic longitudinal affect data.""" + rng = np.random.RandomState(seed) + rows = [] + for i in range(n_participants): + pid = f"P{i:03d}" + pa = rng.randn(n_timepoints).cumsum() + if coupling == "positive": + na = pa + rng.randn(n_timepoints) * 0.3 + elif coupling == "negative": + na = -pa + rng.randn(n_timepoints) * 0.3 + else: + na = rng.randn(n_timepoints).cumsum() + for t in range(n_timepoints): + rows.append({ + "participant_id": pid, + "time": float(t), + "positive_affect": pa[t], + "negative_affect": na[t], + }) + return pd.DataFrame(rows) + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + +class TestEmotionCouplingAnalyzer: + """Tests for EmotionCouplingAnalyzer.""" + + def test_init_defaults(self) -> None: + analyzer = EmotionCouplingAnalyzer() + assert analyzer.is_fitted is False + assert analyzer.coupling_results_ == {} + assert analyzer.coupling_threshold > 0 + + def test_fit_returns_self(self) -> None: + data = _make_affect_data() + analyzer = EmotionCouplingAnalyzer() + result = analyzer.fit(data) + assert result is analyzer + assert analyzer.is_fitted is True + + def test_fit_populates_coupling_results(self) -> None: + data = _make_affect_data(n_participants=4) + analyzer = EmotionCouplingAnalyzer() + analyzer.fit(data) + assert len(analyzer.coupling_results_) == 4 + + def test_positive_coupling_detected(self) -> None: + data = _make_affect_data(coupling="positive", n_timepoints=100) + analyzer = EmotionCouplingAnalyzer(coupling_threshold=0.2) + analyzer.fit(data) + # At least one participant should be classified as positive or complex + types = set(analyzer.coupling_results_.values()) + assert types & {"positive", "complex"}, f"Expected positive coupling, got {types}" + + def test_negative_coupling_detected(self) -> None: + data = _make_affect_data(coupling="negative", n_timepoints=100) + analyzer = EmotionCouplingAnalyzer(coupling_threshold=0.2) + analyzer.fit(data) + types = set(analyzer.coupling_results_.values()) + assert types & {"negative", "complex"}, f"Expected negative coupling, got {types}" + + def test_decoupled_detected(self) -> None: + data = _make_affect_data(coupling="decoupled", n_timepoints=100) + analyzer = EmotionCouplingAnalyzer(coupling_threshold=0.5) + analyzer.fit(data) + # Most participants should be decoupled when threshold is high + decoupled_count = sum( + 1 for v in analyzer.coupling_results_.values() if v == "decoupled" + ) + assert decoupled_count >= 1 + + def test_predict_coupling_type_before_fit_raises(self) -> None: + analyzer = EmotionCouplingAnalyzer() + with pytest.raises(RuntimeError, match="fit"): + analyzer.predict_coupling_type("P000") + + def test_predict_coupling_type_unknown_participant(self) -> None: + data = _make_affect_data(n_participants=1) + analyzer = EmotionCouplingAnalyzer() + analyzer.fit(data) + result = analyzer.predict_coupling_type("NONEXISTENT") + assert result == "decoupled" + + def test_compute_volatility_shape(self) -> None: + analyzer = EmotionCouplingAnalyzer(volatility_window=5) + ts = np.random.randn(20) + vol = analyzer.compute_volatility(ts) + assert vol.shape == ts.shape + + def test_compute_volatility_values_nonnegative(self) -> None: + analyzer = EmotionCouplingAnalyzer(volatility_window=3) + ts = np.array([1.0, 2.0, 1.5, 3.0, 2.5, 4.0]) + vol = analyzer.compute_volatility(ts) + assert np.all(vol[~np.isnan(vol)] >= 0) + + def test_fit_rejects_bad_schema(self) -> None: + bad_data = pd.DataFrame({"x": [1, 2], "y": [3, 4]}) + analyzer = EmotionCouplingAnalyzer() + with pytest.raises(ValueError, match="schema validation"): + analyzer.fit(bad_data) + + def test_coupling_types_constant(self) -> None: + assert "positive" in EmotionCouplingAnalyzer.COUPLING_TYPES + assert "negative" in EmotionCouplingAnalyzer.COUPLING_TYPES + assert "decoupled" in EmotionCouplingAnalyzer.COUPLING_TYPES + assert "complex" in EmotionCouplingAnalyzer.COUPLING_TYPES diff --git a/tests/test_fairness_audit.py b/tests/test_fairness_audit.py new file mode 100644 index 0000000..c60376a --- /dev/null +++ b/tests/test_fairness_audit.py @@ -0,0 +1,189 @@ +""" +Unit tests for the FairnessAuditor. +""" + +import json +import os +import tempfile + +import numpy as np +import pandas as pd +import pytest + +from scripts.fairness_audit import FairnessAuditor, main + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +def _fair_predictions() -> tuple[np.ndarray, np.ndarray]: + """Return predictions with equal rates across two groups.""" + preds = np.array([1, 0, 1, 0, 1, 0, 1, 0]) + groups = np.array(["A", "A", "A", "A", "B", "B", "B", "B"]) + return preds, groups + + +def _unfair_predictions() -> tuple[np.ndarray, np.ndarray]: + """Return predictions with a large disparity between groups.""" + preds = np.array([1, 1, 1, 1, 0, 0, 0, 1]) + groups = np.array(["A", "A", "A", "A", "B", "B", "B", "B"]) + return preds, groups + + +# --------------------------------------------------------------------------- +# Tests — Demographic Parity +# --------------------------------------------------------------------------- + +class TestDemographicParity: + """Tests for compute_demographic_parity.""" + + def test_fair_data_passes(self) -> None: + preds, groups = _fair_predictions() + auditor = FairnessAuditor(demographic_parity_tolerance=0.1) + result = auditor.compute_demographic_parity(preds, groups) + assert result["passed"] is True + assert result["max_difference"] <= 0.1 + + def test_unfair_data_fails(self) -> None: + preds, groups = _unfair_predictions() + auditor = FairnessAuditor(demographic_parity_tolerance=0.05) + result = auditor.compute_demographic_parity(preds, groups) + assert result["passed"] is False + + def test_group_rates_sum_correctly(self) -> None: + preds, groups = _fair_predictions() + auditor = FairnessAuditor() + result = auditor.compute_demographic_parity(preds, groups) + assert set(result["group_rates"].keys()) == {"A", "B"} + for rate in result["group_rates"].values(): + assert 0.0 <= rate <= 1.0 + + def test_single_group(self) -> None: + preds = np.array([1, 0, 1]) + groups = np.array(["X", "X", "X"]) + auditor = FairnessAuditor() + result = auditor.compute_demographic_parity(preds, groups) + assert result["max_difference"] == 0.0 + assert result["passed"] is True + + +# --------------------------------------------------------------------------- +# Tests — Disparate Impact +# --------------------------------------------------------------------------- + +class TestDisparateImpact: + """Tests for compute_disparate_impact.""" + + def test_fair_data_passes(self) -> None: + preds, groups = _fair_predictions() + auditor = FairnessAuditor(disparate_impact_floor=0.80) + result = auditor.compute_disparate_impact(preds, groups) + assert result["passed"] is True + assert result["disparate_impact_ratio"] >= 0.80 + + def test_unfair_data_fails(self) -> None: + preds, groups = _unfair_predictions() + auditor = FairnessAuditor(disparate_impact_floor=0.80) + result = auditor.compute_disparate_impact(preds, groups) + assert result["passed"] is False + + def test_all_zeros_trivially_fair(self) -> None: + preds = np.zeros(6, dtype=int) + groups = np.array(["A", "A", "A", "B", "B", "B"]) + auditor = FairnessAuditor() + result = auditor.compute_disparate_impact(preds, groups) + assert result["disparate_impact_ratio"] == 1.0 + + def test_ratio_bounds(self) -> None: + preds, groups = _unfair_predictions() + auditor = FairnessAuditor() + result = auditor.compute_disparate_impact(preds, groups) + assert 0.0 <= result["disparate_impact_ratio"] <= 1.0 + + +# --------------------------------------------------------------------------- +# Tests — Report generation +# --------------------------------------------------------------------------- + +class TestReportGeneration: + """Tests for generate_report.""" + + def test_report_contains_model_name(self) -> None: + auditor = FairnessAuditor() + report = auditor.generate_report( + model_name="TestModel", + audit_results={ + "demographic_parity": {"passed": True, "group_rates": {}, "max_difference": 0.0, "tolerance": 0.05}, + "disparate_impact": {"passed": True, "group_rates": {}, "disparate_impact_ratio": 1.0, "floor": 0.80}, + }, + ) + assert "TestModel" in report + + def test_report_writes_json(self) -> None: + auditor = FairnessAuditor() + with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp: + tmp_path = tmp.name + + try: + auditor.generate_report( + model_name="JSONTest", + audit_results={ + "demographic_parity": {"passed": True, "group_rates": {}, "max_difference": 0.0, "tolerance": 0.05}, + "disparate_impact": {"passed": True, "group_rates": {}, "disparate_impact_ratio": 1.0, "floor": 0.80}, + }, + output_path=tmp_path, + ) + with open(tmp_path) as fh: + payload = json.load(fh) + assert payload["model_name"] == "JSONTest" + assert payload["overall_passed"] is True + finally: + os.unlink(tmp_path) + + +# --------------------------------------------------------------------------- +# Tests — CLI +# --------------------------------------------------------------------------- + +class TestCLI: + """Tests for the argparse-based CLI entry-point.""" + + def test_cli_happy_path(self, capsys: pytest.CaptureFixture[str]) -> None: + with tempfile.NamedTemporaryFile( + suffix=".csv", mode="w", delete=False + ) as tmp: + df = pd.DataFrame({ + "prediction": [1, 0, 1, 0, 1, 0], + "gender": ["M", "M", "M", "F", "F", "F"], + }) + df.to_csv(tmp.name, index=False) + tmp_path = tmp.name + + try: + main([ + "--predictions", tmp_path, + "--protected-attribute", "gender", + "--model-name", "CLITest", + ]) + captured = capsys.readouterr() + assert "CLITest" in captured.out + assert "FAIRNESS AUDIT REPORT" in captured.out + finally: + os.unlink(tmp_path) + + def test_cli_missing_column_exits(self) -> None: + with tempfile.NamedTemporaryFile( + suffix=".csv", mode="w", delete=False + ) as tmp: + pd.DataFrame({"x": [1]}).to_csv(tmp.name, index=False) + tmp_path = tmp.name + + try: + with pytest.raises(SystemExit): + main([ + "--predictions", tmp_path, + "--protected-attribute", "gender", + ]) + finally: + os.unlink(tmp_path) diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..a528c5e --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ESNext", + "moduleResolution": "bundler", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true + }, + "references": [ + { "path": "./src/frontend" }, + { "path": "./src/backend" } + ], + "files": [] +}