diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..bc5afe16d3b655a415544449c10d3801c0d45b6b --- /dev/null +++ b/.dockerignore @@ -0,0 +1,73 @@ +# VCS / local env +.git/ +.gitignore +.venv/ +.venv313/ +.venv311/ +.env +.env.* +!.env.example + +# Python cache/build +__pycache__/ +*.pyc +*.pyo +*.egg-info/ +dist/ +build/ + +# Frontend cache/deps +frontend/react/node_modules/ +frontend/react/.vite/ +frontend/react/.vite-temp/ +frontend/react/dist/ +.npm-cache/ +.vite/ + +# Runtime/generated data not needed in image build context +logs/ +reports/ +outputs/ +data/ +results/training_runs/ +results/runs/ +results/eval_logs/ +results/best_model/archived/ +artifacts/ +results/prevalidation_*.log + +# Test/dev-only assets +.pytest_cache/ +.tmp/ +docs/ +examples/ +tests/ +gov_workflow_openenv_tests/ +pip_bootstrap/ +test_results.txt +test_rl_output*.txt +tests/test_output*.txt +tests/test_run.txt +phase1_validation.py +test_phase2.py +old_simulator.py +restore_simulator.py + +# Non-runtime docs/notebooks +GovWorkflow_RL_ENV.ipynb +Blog.md +uv.lock +*.backup + +# IDE/OS noise +.vscode/ +.idea/ +*.swp +Thumbs.db +.DS_Store + +# Legacy static shell not used in deployed image +app/web/app.js +app/web/index.html +app/web/react_app.js +app/web/styles.css diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..39755d691420496eaafd5a93a62c20cc5e5c1c2c --- /dev/null +++ b/.env.example @@ -0,0 +1,46 @@ +# Standard OpenEnv / inference variables +API_BASE_URL=https://integrate.api.nvidia.com/v1 +MODEL_NAME=meta/llama-3.3-70b-instruct +HF_TOKEN= +OPENAI_API_KEY= +API_KEY= +LOCAL_IMAGE_NAME=gov-workflow-openenv:latest +MAX_STEPS=80 +SUCCESS_SCORE_THRESHOLD=0.50 + +# Provider-specific API base URLs (used by frontend simulation bridge) +OPENAI_API_BASE_URL=https://api.openai.com/v1 +NVIDIA_API_BASE_URL=https://integrate.api.nvidia.com/v1 + +# Optional fallback model lists (comma-separated) +MODEL_FALLBACKS= +NVIDIA_MODEL_FALLBACKS= + +# NVIDIA Build API (fallback / internal) +# Copy this file to .env and fill in your values +# Get your key at: https://build.nvidia.com/explore/discover +NVIDIA_API_KEY=nvapi-your-key-here +NVIDIA_API_KEY_2= + +# LLM Model Selection +NVIDIA_MODEL=meta/llama-3.3-70b-instruct + +# Server Settings +SERVER_HOST=0.0.0.0 +SERVER_PORT=7860 +SERVER_LOG_LEVEL=info +SERVER_WORKERS=1 + +# Environment Settings +ENV_DEFAULT_TASK_ID=district_backlog_easy +ENV_DEFAULT_SEED=11 +ENV_MAX_SESSIONS=100 +ENV_MAX_STEPS_PER_EPISODE=500 + +# API Throttling +LLM_CALL_DELAY=12.0 + +# Persistence (SQLite + filesystem) +# For Hugging Face persistent storage, set OPENENV_DATA_DIR=/data/openenv_rl +STORAGE_ENABLED=true +OPENENV_DATA_DIR= diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d5d2a7a4249b8704620e1d3428d6d9c614bc95e7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,67 @@ +# Environment secrets - NEVER commit .env +.env +.env.local +.env.production + +# Python +__pycache__/ +*.pyc +*.pyo +.venv/ +.venv313/ +.venv311/ +*.egg-info/ +dist/ +build/ + +# pytest +.pytest_cache/ + +# Local temp/bootstrap +.tmp/ +pip_bootstrap/ + +# Runtime outputs +outputs/ +logs/ +reports/ +data/ +results/training_runs/ +results/runs/ +results/eval_logs/ +results/best_model/archived/ +artifacts/ + +# Frontend build cache/deps +frontend/react/node_modules/ +frontend/react/.vite/ +frontend/react/.vite-temp/ +frontend/react/dist/ +.vite/ +.npm-cache/ + +# Docker/local deployment overrides +docker-compose.override.yml +*.local.env +*.backup + +# Local test artifacts +test_results.txt +test_rl_output*.txt +tests/test_output*.txt +tests/test_run.txt + +# Pre-submission validation artifacts +scripts/validate-submission.sh +results/prevalidation_docker_build.log +results/prevalidation_*.log + +# Keep benchmark Phase 1 model in Git for Colab/Kaggle transfer +!results/best_model/phase1/phase1_final.zip + +# Legacy static shell (superseded by Vite bundle) +app/web/app.js +app/web/index.html +app/web/react_app.js +app/web/styles.css + diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..6a086671c708e7650b47a335975f685df2f5b59c --- /dev/null +++ b/Dockerfile @@ -0,0 +1,51 @@ +# Gov Workflow OpenEnv +# Multi-stage image: +# 1) build Vite frontend assets +# 2) run FastAPI backend and serve built UI under /ui + +FROM node:20-slim AS frontend-build +WORKDIR /web + +COPY frontend/react/package.json frontend/react/package-lock.json ./frontend/react/ +RUN cd frontend/react && npm ci --no-audit --no-fund + +COPY frontend/react ./frontend/react +RUN cd frontend/react && npm run build + + +FROM python:3.11-slim AS runtime + +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PIP_NO_CACHE_DIR=1 \ + OPENENV_DATA_DIR=/data/openenv_rl \ + STORAGE_ENABLED=true \ + PORT=7860 + +WORKDIR /app + +# Runtime OS dependencies (torch/sb3 commonly require libgomp at runtime) +RUN apt-get update \ + && apt-get install -y --no-install-recommends libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +COPY requirements.txt requirements_rl.txt ./ +RUN python -m pip install --upgrade pip \ + && python -m pip install -r requirements.txt \ + && python -m pip install -r requirements_rl.txt + +COPY . . +COPY --from=frontend-build /web/frontend/react/dist ./app/web/vite_dist + +RUN mkdir -p /data/openenv_rl \ + && useradd --create-home --uid 10001 appuser \ + && chown -R appuser:appuser /app /data/openenv_rl + +USER appuser + +EXPOSE 7860 + +HEALTHCHECK --interval=30s --timeout=5s --start-period=15s --retries=3 \ + CMD python -c "import urllib.request; urllib.request.urlopen('http://127.0.0.1:7860/health', timeout=3)" || exit 1 + +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"] diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2f7936edaf733b63aa64ae33cad1c27db6d4600d --- /dev/null +++ b/README.md @@ -0,0 +1,191 @@ +--- +title: Gov Workflow OpenEnv +sdk: docker +app_port: 7860 +pinned: false +--- + +# Gov Workflow OpenEnv + +## Quick Links + +- Hugging Face Space URL (Dummy, update later): [https://huggingface.co/spaces/your-username/your-space-name](https://huggingface.co/spaces/your-username/your-space-name) + This placeholder will be replaced with the final deployed demo link. +- Blog path in codebase: `OPENENV_RL/Blog.md` + Project write-up and narrative documentation for design choices and outcomes. +- Notebook path: `OPENENV_RL/GovWorkflow_RL_ENV.ipynb` + Main OpenEnv RL government workflow notebook used as the judge-facing criteria book. It contains the practical judging context, environment setup, and the full end-to-end flow in one place. +- Notebook Colab URL: [https://colab.research.google.com/drive/1ssTnxKoU1nOfSNA3nOeiNM8S4fKFpkby?usp=sharing](https://colab.research.google.com/drive/1ssTnxKoU1nOfSNA3nOeiNM8S4fKFpkby?usp=sharing) + Cloud version of the same notebook so judges can run and review the complete workflow without local setup. +- GRPO Phase 1 training link: [https://colab.research.google.com/drive/1ND_DZ6xcT2JuH7uGB2AYbiZ1dcHKFfIw?usp=sharing](https://colab.research.google.com/drive/1ND_DZ6xcT2JuH7uGB2AYbiZ1dcHKFfIw?usp=sharing) + First-stage GRPO training run where the LLM agent starts learning policy behavior inside the RL environment. +- GRPO Phase 2 training link: [https://colab.research.google.com/drive/1ofxEADct_gTX5DGhcnk8lW6p31gFCIFV?usp=sharing](https://colab.research.google.com/drive/1ofxEADct_gTX5DGhcnk8lW6p31gFCIFV?usp=sharing) + Second-stage GRPO continuation where the same LLM agent is further trained and refined on the RL environment. +- PPO Phase 1 training (local): `rl/train_ppo.py` + Phase 1 PPO baseline training was executed on the local system to establish the RL algorithm baseline before phase-2 progression. +- PPO Phase 2 training link: [https://colab.research.google.com/drive/1RVXQs-QAuXLBw0YXJtN4cbEootCTfHO7?usp=sharing](https://colab.research.google.com/drive/1RVXQs-QAuXLBw0YXJtN4cbEootCTfHO7?usp=sharing) + PPO phase 2 training notebook where the RL algorithm is further trained on the same environment for improved policy performance. + +Gov Workflow OpenEnv is a FastAPI-first simulation environment for public service workflow operations. +It models queue prioritization, officer allocation, missing-document recovery, escalation usage, and fairness-aware SLA management across government services. + +This repository is productionized for: +- local development (FastAPI + Vite) +- Docker runtime +- Hugging Face Spaces (Docker SDK) + +## Current Main-Branch Status + +This README is aligned to the current `main` branch code paths, including: +- `app.main:app` as primary server runtime +- React UI served at `/ui` from built Vite assets when available +- OpenEnv contract endpoints (`/reset`, `/step`, `/state`, `/grade`) +- frontend API aliases (`/api/*`) and versioned aliases (`/api/v1/*`) +- training story endpoints (`/training/*`) +- simulation, RL, persistence, compliance, and history endpoints + +## End-to-End Architecture + +```mermaid +flowchart LR + UI["React UI"] --> API["FastAPI app.main"] + API --> ENV["GovWorkflowEnv app/env.py"] + API --> SIM["Simulation runtime app/simulator.py"] + API --> RL["RL train/eval rl/*"] + API --> STORE["PersistenceStore SQLite + filesystem"] + API --> STORY["Training Story router /training/*"] + API --> OPENENV["Optional OpenEnv adapter /openenv/*"] +``` + +## Core Runtime Components + +- API server: `app/main.py` +- Environment kernel: `app/env.py` +- Typed models: `app/models.py` +- Task registry: `app/tasks.py` +- Reward shaping: `app/reward.py` +- Deterministic graders: `app/graders.py` +- Simulation runtime: `app/simulator.py` +- Training jobs manager: `app/training_jobs.py` +- Persistence layer: `app/persistence.py` +- Transport gateway: `app/api_gateway.py` +- React frontend: `frontend/react` + +## Task Set (Current Runtime) + +Configured in `app/tasks.py`: +- `district_backlog_easy` +- `mixed_urgency_medium` +- `cross_department_hard` +- `district_backlog_easy_extreme` + +Benchmark list used by APIs: +- `district_backlog_easy` +- `mixed_urgency_medium` +- `cross_department_hard` + +## Service Coverage + +`ServiceType` includes: +- `passport` +- `driving_license` +- `aadhaar_card` +- `gst_registration` +- `income_certificate` +- `caste_certificate` +- `birth_certificate` +- `land_registration` + +Medium and hard tasks currently run with: +- `income_certificate` +- `land_registration` +- `passport` +- `driving_license` +- `aadhaar_card` + + + +## Local Development + +### Prerequisites + +- Python 3.11+ +- Node 20+ +- Docker + +### Install dependencies + +```bash +pip install -r requirements.txt +pip install -r requirements_rl.txt +pip install pytest pytest-asyncio +npm --prefix frontend/react install +``` + +### Configure environment + +```bash +copy .env.example .env +``` + +Populate as needed: +- `API_BASE_URL` +- `MODEL_NAME` +- `HF_TOKEN` or `OPENAI_API_KEY`/`API_KEY` +- optional NVIDIA keys (`NVIDIA_API_KEY`, `NVIDIA_API_KEY_2`) +- storage settings (`STORAGE_ENABLED`, `OPENENV_DATA_DIR`) + +### Run backend + +```bash +python scripts/run_local.py --host 127.0.0.1 --port 7860 --reload +``` + +### Run frontend + +```bash +npm --prefix frontend/react run dev +``` + +Open: +- UI: `http://127.0.0.1:5173/ui` +- API docs: `http://127.0.0.1:7860/docs` + + + + +## Repository Layout + +```text +app/ + main.py FastAPI app + API routing + compatibility aliases + env.py GovWorkflowEnv kernel + models.py Typed Pydantic contracts + tasks.py Runtime task registry + reward.py Reward shaping + graders.py Deterministic graders + simulator.py Simulation runtime and live sessions + training_jobs.py Background RL training manager + persistence.py SQLite/filesystem persistence + api_gateway.py direct/http/auto environment transport layer + story_router.py training story endpoints +rl/ + gov_workflow_env.py Gym adapter + train_ppo.py PPO phase training entrypoint + evaluate.py Checkpoint evaluator + feature_builder.py RL feature engineering + action_mask.py Action mask logic +frontend/react/ + src/ React modules/components/api hooks +scripts/ + run_local.py Local FastAPI launcher + convert_grpo_csv.py Training CSV to JSON converter for story endpoints +openenv.yaml OpenEnv manifest metadata +baseline_openai.py Baseline and LLM runner +inference.py Submission-style inference runner +Dockerfile Docker image definition +``` + +## License + +BSD-3-Clause diff --git a/app/README.md b/app/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fadc16d0ad35887e645e73335bc41bd5e7422e8c --- /dev/null +++ b/app/README.md @@ -0,0 +1,23 @@ +# app/ + +Core environment and API layer. + +- `main.py`: FastAPI app and endpoints +- `env.py`: GovWorkflowEnv simulation kernel +- `models.py`: Pydantic action/observation/reward/state models +- `tasks.py`: easy/medium/hard deterministic task configs +- `graders.py`: deterministic task scoring (0.0 to 1.0) +- `reward.py`: dense reward breakdown +- `baselines.py`: heuristic baseline policies +- `web/`: frontend assets served by FastAPI at `/ui` + - `vite_dist/`: production Vite build output copied during Docker build + - legacy files (`index.html`, `react_app.js`, `styles.css`) remain as local fallback + +Additional frontend-focused APIs in `main.py`: +- `/api/workflows/components` +- `/api/workflows/run` +- `/api/rl/models` +- `/api/rl/run` +- `/api/rl/evaluate` +- `/api/simulation/run` +- `/api/training/jobs` diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb7ca77ea59b6348362c7c0e8732ce755804755 --- /dev/null +++ b/app/__init__.py @@ -0,0 +1,20 @@ +# from app.env import GovWorkflowEnv +from app.models import ActionModel, ObservationModel, RewardModel + +try: + from client import GovWorkflowClient +except ModuleNotFoundError: + GovWorkflowClient = None # type: ignore[assignment] + +GovWorkflowAction = ActionModel +GovWorkflowObservation = ObservationModel + +__all__ = [ + "ActionModel", + "ObservationModel", + "RewardModel", + "GovWorkflowAction", + "GovWorkflowObservation", +# "GovWorkflowEnv", + "GovWorkflowClient", +] diff --git a/app/api_gateway.py b/app/api_gateway.py new file mode 100644 index 0000000000000000000000000000000000000000..21a62b1d3865bd5170d94069495f1280d9b81340 --- /dev/null +++ b/app/api_gateway.py @@ -0,0 +1,257 @@ +""" +Unified environment transport layer. + +This module centralizes environment access so callers can use: + - FastAPI HTTP transport + - direct in-process transport + - dynamic auto selection +""" + +from __future__ import annotations + +from dataclasses import dataclass +import os +from typing import Literal, Protocol + +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ActionModel, ObservationModel, StepInfoModel + + +TransportMode = Literal["auto", "http", "direct"] + + +class EnvGateway(Protocol): + transport: TransportMode + terminated: bool + truncated: bool + + def reset(self) -> ObservationModel: ... + + def step( + self, action: ActionModel + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: ... + + def grade(self) -> tuple[float, str, dict[str, float]]: ... + + def close(self) -> None: ... + + +@dataclass +class DirectEnvGateway: + task_id: str + seed: int + transport: TransportMode = "direct" + + def __post_init__(self) -> None: + self._env = GovWorkflowEnv(task_id=self.task_id) + self.terminated = False + self.truncated = False + + def reset(self) -> ObservationModel: + obs, _ = self._env.reset(seed=self.seed) + self.terminated = False + self.truncated = False + return obs + + def step( + self, action: ActionModel + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: + obs, reward, terminated, truncated, info = self._env.step(action) + self.terminated = bool(terminated) + self.truncated = bool(truncated) + return obs, float(reward), bool(terminated), bool(truncated), info + + def grade(self) -> tuple[float, str, dict[str, float]]: + result = grade_episode(self._env.state()) + return float(result.score), str(result.grader_name), dict(result.metrics) + + def close(self) -> None: + close_fn = getattr(self._env, "close", None) + if callable(close_fn): + close_fn() + + +@dataclass +class HttpEnvGateway: + task_id: str + seed: int + base_url: str + api_prefix: str | None = None + transport: TransportMode = "http" + + def __post_init__(self) -> None: + try: + import requests as _requests + except ImportError as exc: + raise ImportError("requests is required for HTTP transport.") from exc + self._requests = _requests + self._session_id: str | None = None + self.terminated = False + self.truncated = False + self.base_url = self.base_url.rstrip("/") + self._resolved_prefix = self._normalize_prefix(self.api_prefix) + + @staticmethod + def _normalize_prefix(prefix: str | None) -> str: + if prefix is None: + return "" + p = str(prefix).strip() + if not p: + return "" + if not p.startswith("/"): + p = "/" + p + return p.rstrip("/") + + @staticmethod + def _candidate_prefixes(explicit_prefix: str | None) -> list[str]: + normalized_explicit = HttpEnvGateway._normalize_prefix(explicit_prefix) + if normalized_explicit: + return [normalized_explicit] + + env_prefix = HttpEnvGateway._normalize_prefix(os.getenv("OPENENV_ENV_API_PREFIX", "")) + configured_candidates = os.getenv("OPENENV_ENV_API_PREFIX_CANDIDATES", "") + + candidates: list[str] = [] + for item in [env_prefix, *configured_candidates.split(",")]: + normalized = HttpEnvGateway._normalize_prefix(item) + if normalized not in candidates: + candidates.append(normalized) + + # Ordered fallbacks: versioned API -> frontend API -> root OpenEnv API. + for fallback in ["/api/v1", "/api", ""]: + if fallback not in candidates: + candidates.append(fallback) + return candidates + + def _resolve_prefix(self) -> str: + if self._resolved_prefix: + return self._resolved_prefix + for prefix in self._candidate_prefixes(self.api_prefix): + try: + response = self._requests.get( + f"{self.base_url}{prefix}/health", + timeout=3, + ) + if response.ok: + self._resolved_prefix = prefix + return self._resolved_prefix + except Exception: + continue + self._resolved_prefix = "" + return self._resolved_prefix + + def _url(self, path: str) -> str: + return f"{self.base_url}{self._resolve_prefix()}{path}" + + def _post(self, path: str, body: dict) -> dict: + response = self._requests.post( + self._url(path), + json=body, + timeout=30, + ) + response.raise_for_status() + return response.json() + + def reset(self) -> ObservationModel: + payload = {"task_id": self.task_id, "seed": self.seed} + data = self._post("/reset", payload) + self._session_id = str(data["session_id"]) + self.terminated = False + self.truncated = False + return ObservationModel(**data["observation"]) + + def step( + self, action: ActionModel + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: + if not self._session_id: + raise RuntimeError("Session is not initialized. Call reset() first.") + data = self._post( + "/step", + { + "session_id": self._session_id, + "action": action.model_dump(exclude_none=True, mode="json"), + }, + ) + obs = ObservationModel(**data["observation"]) + info = StepInfoModel(**data["info"]) + self.terminated = bool(data["terminated"]) + self.truncated = bool(data["truncated"]) + return ( + obs, + float(data["reward"]), + bool(data["terminated"]), + bool(data["truncated"]), + info, + ) + + def grade(self) -> tuple[float, str, dict[str, float]]: + if not self._session_id: + raise RuntimeError("Session is not initialized. Call reset() first.") + data = self._post("/grade", {"session_id": self._session_id}) + return ( + float(data["score"]), + str(data["grader_name"]), + dict(data.get("metrics", {})), + ) + + def close(self) -> None: + if not self._session_id: + return + try: + self._requests.delete(self._url(f"/sessions/{self._session_id}"), timeout=10) + except Exception: + pass + self._session_id = None + + +def _http_reachable(base_url: str) -> bool: + try: + import requests + r = requests.get(f"{base_url.rstrip('/')}/health", timeout=3) + return bool(r.ok) + except Exception: + return False + + +def create_env_gateway( + *, + task_id: str, + seed: int, + mode: TransportMode = "auto", + base_url: str = "http://127.0.0.1:7860", + api_prefix: str | None = None, + enforce_fastapi: bool = False, +) -> EnvGateway: + """ + Create environment gateway with dynamic transport selection. + + Behavior: + - mode=http -> always HTTP + - mode=direct -> always direct (unless enforce_fastapi=True) + - mode=auto -> HTTP if /health reachable, else direct fallback + """ + if enforce_fastapi and mode == "direct": + raise RuntimeError("Direct transport is disabled. Set mode to 'http' or 'auto'.") + + if mode == "http": + return HttpEnvGateway(task_id=task_id, seed=seed, base_url=base_url, api_prefix=api_prefix) + + if mode == "direct": + return DirectEnvGateway(task_id=task_id, seed=seed) + + if _http_reachable(base_url): + return HttpEnvGateway( + task_id=task_id, + seed=seed, + base_url=base_url, + api_prefix=api_prefix, + transport="auto", + ) + + if enforce_fastapi: + raise RuntimeError( + f"FastAPI gateway is required but unavailable at {base_url}. " + "Start the API server or disable FORCE_FASTAPI_GATEWAY." + ) + return DirectEnvGateway(task_id=task_id, seed=seed, transport="auto") diff --git a/app/baselines.py b/app/baselines.py new file mode 100644 index 0000000000000000000000000000000000000000..c307c8556875313d0a07df9b4ef8e2503bd56348 --- /dev/null +++ b/app/baselines.py @@ -0,0 +1,161 @@ +from __future__ import annotations +from collections.abc import Callable +from types import SimpleNamespace +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ActionModel, ActionType, ObservationModel, PriorityMode, ServiceType + +PolicyFn = Callable[[ObservationModel], ActionModel] + + +def _snapshots(obs: ObservationModel): + """Return queue snapshots as a list regardless of Phase 1 (list) or Phase 2 (dict).""" + qs = obs.queue_snapshots + if isinstance(qs, dict): + return list(qs.values()) + return list(qs) + + +def _service_attr(q, *attrs): + """Return the first attribute that exists on a QueueSnapshot (Phase 1 vs Phase 2 names).""" + for attr in attrs: + val = getattr(q, attr, None) + if val is not None: + return val + return 0 + + +def _service_name(q) -> ServiceType: + """Return ServiceType regardless of Phase 1 (.service) or Phase 2 (.service_type).""" + return getattr(q, "service_type", None) or getattr(q, "service", None) + + +def _service_with_max(obs: ObservationModel, *attrs) -> ServiceType | None: + snaps = _snapshots(obs) + ranked = sorted(snaps, key=lambda s: _service_attr(s, *attrs), reverse=True) + if ranked and _service_attr(ranked[0], *attrs) > 0: + return _service_name(ranked[0]) + return None + + +def _reserve_officers(obs: ObservationModel) -> int: + pool = obs.officer_pool + # Phase 2: idle_officers property + if hasattr(pool, "idle_officers"): + return int(pool.idle_officers) + # Phase 1 fallback + return int(getattr(pool, "reserve_officers", 0)) + + +def _alloc_for(obs: ObservationModel, service: ServiceType) -> int: + pool = obs.officer_pool + # Phase 2 uses 'allocated'; Phase 1 used 'allocations' + alloc_dict = getattr(pool, "allocated", None) or getattr(pool, "allocations", {}) + raw = alloc_dict.get(service) + if raw is None: + raw = alloc_dict.get(service.value if hasattr(service, "value") else str(service), 0) + return int(raw or 0) + + +def urgent_first_policy(obs: ObservationModel) -> ActionModel: + target = _service_with_max(obs, "urgent_pending", "urgent_cases") + if target: + return ActionModel(action_type=ActionType.REQUEST_MISSING_DOCUMENTS, service_target=target) + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def oldest_first_policy(obs: ObservationModel) -> ActionModel: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def backlog_clearance_policy(obs: ObservationModel) -> ActionModel: + snaps = _snapshots(obs) + + # Assign idle officers to the most backlogged service + if _reserve_officers(obs) > 0: + target = _service_with_max(obs, "total_pending", "active_cases") + if target: + return ActionModel( + action_type=ActionType.ASSIGN_CAPACITY, + service_target=target, + capacity_assignment={target.value: 1}, + ) + + # Clear missing-doc bottlenecks + target = _service_with_max(obs, "blocked_missing_docs", "missing_docs_cases") + if target: + return ActionModel(action_type=ActionType.REQUEST_MISSING_DOCUMENTS, service_target=target) + + # Reallocate from least-loaded to most-loaded + if len(snaps) >= 2: + hot = sorted(snaps, key=lambda s: _service_attr(s, "total_pending", "active_cases"), reverse=True) + cold = sorted(snaps, key=lambda s: _service_attr(s, "total_pending", "active_cases")) + hot_svc = _service_name(hot[0]) + cold_svc = _service_name(cold[0]) + hot_load = _service_attr(hot[0], "total_pending", "active_cases") + cold_load = _service_attr(cold[0], "total_pending", "active_cases") + if ( + hot_svc and cold_svc and hot_svc != cold_svc + and hot_load - cold_load >= 3 + and _alloc_for(obs, cold_svc) > 1 + ): + return ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + service_target=cold_svc, + reallocation_delta={cold_svc.value: -1, hot_svc.value: 1}, + ) + + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def greedy_sla_policy(obs: ObservationModel) -> ActionModel: + """SLA-focused fallback policy used by historical aliases.""" + target = _service_with_max(obs, "urgent_pending", "urgent_cases", "breached_cases") + if target: + return ActionModel(action_type=ActionType.REQUEST_MISSING_DOCUMENTS, service_target=target) + return backlog_clearance_policy(obs) + + +def random_policy(obs: ObservationModel) -> ActionModel: + import random + return ActionModel(action_type=ActionType.ADVANCE_TIME) + +urgent_first_policy = greedy_sla_policy +fairness_aware_policy = backlog_clearance_policy + +POLICIES: dict[str, PolicyFn] = { + "urgent_first": greedy_sla_policy, + "oldest_first": oldest_first_policy, + "backlog_clearance": backlog_clearance_policy, + "random_policy": random_policy, + "greedy_sla_policy": greedy_sla_policy, + "fairness_aware_policy": fairness_aware_policy, +} + + +def run_policy_episode(task_id: str, policy_name: str, seed: int | None = None, max_steps: int = 500) -> dict: + env = GovWorkflowEnv(task_id=task_id) + obs, _ = env.reset(seed=seed) + policy = POLICIES[policy_name] + reward_sum = 0.0 + for _ in range(max_steps): + action = policy(obs) + obs, reward, terminated, truncated, _ = env.step(action) + reward_sum += reward + if terminated or truncated: + break + state = env.state() + grade = grade_episode(state) + # Return a SimpleNamespace so attribute access (result.score) works in main.py + return SimpleNamespace( + task_id=task_id, + policy=policy_name, + seed=state.seed, + reward_sum=round(reward_sum, 4), + score=float(grade.score), + grader=grade.grader_name, + metrics=grade.metrics, + steps=int(state.total_steps), + completed=int(state.total_completed), + backlog=int(state.total_backlog), + ) diff --git a/app/config.py b/app/config.py new file mode 100644 index 0000000000000000000000000000000000000000..8aca1924cdd76e93f92101e45b25f980d35cee23 --- /dev/null +++ b/app/config.py @@ -0,0 +1,87 @@ +# ── Path bootstrap ───────────────────────────────────────────────────────────── +from __future__ import annotations +from pathlib import Path + +# Load .env file if it exists — must happen before Pydantic Settings reads env vars +try: + from dotenv import load_dotenv +except (ImportError, AttributeError): + # Keep runtime functional even when python-dotenv is not installed + # or when a conflicting `dotenv` package is present. + def load_dotenv(*args, **kwargs): # type: ignore[no-redef] + return False +_ENV_FILE = Path(__file__).resolve().parent.parent / ".env" +load_dotenv(dotenv_path=_ENV_FILE, override=False) +# override=False means real environment variables always win over .env values +# ────────────────────────────────────────────────────────────────────────────── + +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class ServerSettings(BaseSettings): + """ + HTTP-server configuration. + Read from environment variables prefixed SERVER_. + Example: SERVER_PORT=8080 SERVER_LOG_LEVEL=debug + + Intentionally isolated from EnvSettings — changing server bind + options never affects simulation behaviour, and vice-versa. + Both classes are instantiated once at import and treated as + read-only singletons for the lifetime of the process. + """ + + host: str = Field("0.0.0.0", description="Bind host") + port: int = Field(7860, description="Bind port — HF Spaces default is 7860") + log_level: str = Field( + "info", description="Uvicorn log level: debug | info | warning | error" + ) + cors_origins: list[str] = Field( + default=["*"], + description="Allowed CORS origins. '*' is required for HF Spaces embedding.", + ) + # NOTE: Keep at 1 when using the in-memory session store. + # Multiple workers do NOT share process memory. + # Use Redis + a shared store before increasing workers in production. + workers: int = Field( + 1, description="Uvicorn worker count — keep at 1 for in-memory sessions" + ) + + model_config = SettingsConfigDict(env_prefix="SERVER_", extra="ignore") + + +class EnvSettings(BaseSettings): + """ + Simulation-environment defaults. + Read from environment variables prefixed ENV_. + Example: ENV_DEFAULT_TASK_ID=mixed_urgency_medium ENV_MAX_SESSIONS=50 + + Controls the environment kernel only. No effect on network + binding, logging, or CORS — those belong to ServerSettings. + """ + + default_task_id: str = Field( + "district_backlog_easy", + description="Task used when POST /reset is called without an explicit task_id", + ) + default_seed: int = Field( + 11, + description="Seed used when POST /reset is called without an explicit seed", + ) + max_steps_per_episode: int = Field( + 500, + description="Hard cap on step() calls per session before episode is truncated", + ) + max_sessions: int = Field( + 100, + description="Maximum concurrent in-memory sessions. Oldest is evicted when exceeded.", + ) + + model_config = SettingsConfigDict(env_prefix="ENV_", extra="ignore") + + +# ── Singletons ──────────────────────────────────────────────────────────────── +# Loaded exactly once at import time. Never mutated at runtime. +# Tests may monkeypatch individual fields after import if needed. +server_settings = ServerSettings() +env_settings = EnvSettings() diff --git a/app/engine.py b/app/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..3e73895589f755c14b52b76fdd678e346d772b32 --- /dev/null +++ b/app/engine.py @@ -0,0 +1,1712 @@ +from __future__ import annotations + +import json +import os +import random +import re +from dataclasses import dataclass +from enum import Enum +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, Optional + +from openai import OpenAI + +from app.event_engine import EventEngine +from app.models import ( + ActionModel, + ActionType, + ApplicationCase, + DelayedEffect, + EventType, + IntakeChannel, + InternalSubstate, + ObservationModel, + PriorityMode, + QueueSnapshot, + ServiceType, + StageType, +) +from app.sector_profiles import get_sector_profile +from app.state_machine import can_advance + +if TYPE_CHECKING: + from app.models import TaskConfig + + +LEGACY_NVIDIA_MODEL_POOL = [ + "meta/llama-3.3-70b-instruct", + "qwen/qwen3-next-80b-a3b-instruct", + "moonshotai/kimi-k2-instruct-0905", + "meta/llama-3.1-405b-instruct", + "deepseek-ai/deepseek-v3.2", + "qwen/qwq-32b", + "mistralai/mixtral-8x22b-instruct-v0.1", + "google/gemma-3-27b-it", + "microsoft/phi-4-mini-instruct", + "meta/llama-3.1-8b-instruct", +] + +_MODEL_CACHE: dict[tuple[str, str], Any] = {} + + +# ───────────────────────────────────────────── +# DAY RESULT +# ───────────────────────────────────────────── + + +class DayResult: + def __init__(self) -> None: + self.new_arrivals: int = 0 + self.new_completions: int = 0 + self.new_sla_breaches: int = 0 + self.total_capacity_days: int = 0 + self.idle_officer_days: int = 0 + self.stage_advances: int = 0 + self.newly_unblocked_missing: int = 0 + self.newly_blocked_missing: int = 0 + self.newly_unblocked_enrich: int = 0 + self.field_verif_completed: int = 0 + self.urgent_completed: int = 0 + self.digital_arrivals: int = 0 + self.active_events: list[EventType] = [] + + +# ───────────────────────────────────────────── +# DAY SIMULATOR +# ───────────────────────────────────────────── + + +class DaySimulator: + """ + Core daily simulation engine. + + Accepts TWO calling conventions so both env.py and tests work: + + Convention A (tests): + DaySimulator(task_config=task, rng=rng, event_engine=engine) + + Convention B (env.py legacy): + DaySimulator(seed=42, task_config=task, sector_registry={}) + — in this case rng and event_engine are built internally. + """ + + def __init__( + self, + task_config: "TaskConfig", + rng: Optional[random.Random] = None, + event_engine: Optional[EventEngine] = None, + seed: Optional[int] = None, + sector_registry: Optional[dict] = None, + ) -> None: + self.task_config = task_config + self.task = task_config + + if rng is not None: + self.rng = rng + elif seed is not None: + self.rng = random.Random(seed) + else: + self.rng = random.Random(task_config.seed) + + if event_engine is not None: + self.event_engine = event_engine + else: + _seed = seed if seed is not None else task_config.seed + self.event_engine = EventEngine( + seed=_seed, + scenario_mode=task_config.scenario_mode, + ) + + self.sector_registry = sector_registry or {} + self.active_cases: list[ApplicationCase] = [] + self.pending_effects: list[DelayedEffect] = [] + self.case_counter: int = 0 + + def simulate_day( + self, + day: int, + active_cases: list[ApplicationCase], + completed_cases: list[ApplicationCase], + priority_mode: PriorityMode, + officer_allocations: dict, + ) -> DayResult: + result = DayResult() + + events = self.event_engine.get_events_for_day(day, self.task_config) + params = self.event_engine.apply_events(events, self.task_config) + result.active_events = list(params.active_events) + + new_cases = self._spawn_arrivals(day, params, result) + active_cases.extend(new_cases) + + effective_alloc = self._apply_officer_reduction(officer_allocations, params) + + self._resolve_field_verification(day, active_cases, result) + self._resolve_doc_requests(day, active_cases, result) + + newly_completed: list[ApplicationCase] = [] + + for service in self.task_config.enabled_services: + capacity = effective_alloc.get(service, effective_alloc.get(service.value, 0)) + result.total_capacity_days += int(capacity) + + service_cases = [ + c + for c in active_cases + if c.service_type == service and not c.completed and not c.rejected + ] + + if not service_cases: + result.idle_officer_days += int(capacity) + continue + + sorted_cases = self._sort_queue(service_cases, priority_mode) + + for case in sorted_cases: + if capacity <= 0: + break + + from app.state_machine import advance_case + + advanced, final = advance_case(case, day) + + if advanced: + capacity -= 1 + result.stage_advances += 1 + if final: + newly_completed.append(case) + if case.is_urgent: + result.urgent_completed += 1 + + if newly_completed: + done_ids = {c.case_id for c in newly_completed} + still_active = [c for c in active_cases if c.case_id not in done_ids] + active_cases.clear() + active_cases.extend(still_active) + completed_cases.extend(newly_completed) + result.new_completions = len(newly_completed) + + for case in active_cases: + case.current_day = day + case.waiting_days += 1 + if day > case.sla_deadline_day and not case.sla_breached: + case.sla_breached = True + result.new_sla_breaches += 1 + + return result + + def _apply_officer_reduction(self, allocations: dict, params: Any) -> dict: + reduction = int(getattr(params, "officer_reduction", 0)) + if reduction <= 0: + return dict(allocations) + + effective = dict(allocations) + for _ in range(reduction): + target = max(effective, key=lambda k: effective[k], default=None) + if target is None or effective[target] <= 0: + break + effective[target] -= 1 + return effective + + def _spawn_arrivals( + self, + day: int, + params: Any, + result: DayResult, + ) -> list[ApplicationCase]: + new_cases: list[ApplicationCase] = [] + + for service in self.task_config.enabled_services: + base_rate = self.task_config.arrival_rate_per_day.get( + service, + self.task_config.arrival_rate_per_day.get(service.value, 0.0), + ) + effective_rate = float(base_rate) * float(getattr(params, "arrival_multiplier", 1.0)) + count = int(effective_rate) + if self.rng.random() < (effective_rate - count): + count += 1 + + for _ in range(count): + case = self._new_case(service, day, params) + new_cases.append(case) + if case.intake_channel == IntakeChannel.DIGITAL: + result.digital_arrivals += 1 + + result.new_arrivals = len(new_cases) + return new_cases + + def _new_case(self, service: ServiceType, day: int, params: Any) -> ApplicationCase: + self.case_counter += 1 + profile = get_sector_profile(service) + + sla_days = int(profile.sla_days * getattr(params, "sla_window_multiplier", 1.0)) + sla_deadline_day = day + sla_days + + digital_ratio = self.task_config.digital_intake_ratio + channel = ( + IntakeChannel.DIGITAL + if self.rng.random() < digital_ratio + else IntakeChannel.PAPER + ) + + base_missing = profile.missing_docs_probability + override = (self.task_config.missing_docs_probability_override or {}).get( + service, + (self.task_config.missing_docs_probability_override or {}).get(service.value), + ) + if override is not None: + base_missing = override + + defect_rate = ( + profile.doc_defect_rate_digital + if channel == IntakeChannel.DIGITAL + else profile.doc_defect_rate_paper + ) + eff_missing = min( + 1.0, + base_missing + getattr(params, "doc_defect_rate_boost", 0.0) * defect_rate, + ) + has_missing = self.rng.random() < eff_missing + + base_fv = profile.field_verification_probability + fv_override = (self.task_config.field_verification_probability_override or {}).get( + service, + (self.task_config.field_verification_probability_override or {}).get(service.value), + ) + if fv_override is not None: + base_fv = fv_override + + eff_fv = min(1.0, base_fv + getattr(params, "field_verification_boost", 0.0)) + has_fv = self.rng.random() < eff_fv + field_completion_day = day + profile.field_verification_days if has_fv else None + + from app.models import UrgencyProfile + + urgency_profile = profile.urgency_profile + is_urgent = ( + urgency_profile == UrgencyProfile.HIGH and self.rng.random() < 0.20 + ) or ( + urgency_profile == UrgencyProfile.MODERATE and self.rng.random() < 0.08 + ) + + return ApplicationCase( + case_id=f"case-{self.case_counter:06d}", + service_type=service, + arrival_day=day, + current_day=day, + sla_deadline_day=sla_deadline_day, + intake_channel=channel, + internal_substate=( + InternalSubstate.BLOCKED_MISSING_DOCS + if has_missing + else InternalSubstate.PRE_SCRUTINY + ), + public_stage=StageType.SUBMISSION, + is_urgent=is_urgent, + has_missing_docs=has_missing, + field_verification_required=has_fv, + field_verification_completion_day=field_completion_day, + ) + + def _resolve_field_verification( + self, + day: int, + active_cases: list[ApplicationCase], + result: DayResult, + ) -> None: + for case in active_cases: + if ( + case.internal_substate == InternalSubstate.FIELD_VERIFICATION_PENDING + and case.field_verification_completion_day is not None + and day >= case.field_verification_completion_day + ): + case.internal_substate = InternalSubstate.PRE_SCRUTINY + case.field_verification_completion_day = None + result.field_verif_completed += 1 + + def _resolve_doc_requests( + self, + day: int, + active_cases: list[ApplicationCase], + result: DayResult, + ) -> None: + for case in active_cases: + if ( + case.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS + and case.doc_resolution_day is not None + and day >= case.doc_resolution_day + ): + case.internal_substate = InternalSubstate.PRE_SCRUTINY + case.doc_resolution_day = None + result.newly_unblocked_missing += 1 + + def _sort_queue( + self, + cases: list[ApplicationCase], + priority_mode: PriorityMode, + ) -> list[ApplicationCase]: + eligible = [c for c in cases if can_advance(c)] + + if priority_mode == PriorityMode.URGENT_FIRST: + return sorted( + eligible, + key=lambda c: (not c.is_urgent, -c.sla_risk, c.arrival_day), + ) + + if priority_mode == PriorityMode.OLDEST_FIRST: + return sorted(eligible, key=lambda c: c.arrival_day) + + if priority_mode == PriorityMode.BACKLOG_CLEARANCE: + return sorted( + eligible, + key=lambda c: (-c.sla_risk, not c.is_urgent, c.arrival_day), + ) + + return sorted( + eligible, + key=lambda c: ( + -c.sla_risk if c.sla_risk > 0.8 else 0, + not c.is_urgent, + c.arrival_day, + ), + ) + + def build_queue_snapshot( + self, + service: ServiceType, + active_cases: list[ApplicationCase], + day: int, + ) -> QueueSnapshot: + cases = [ + c + for c in active_cases + if c.service_type == service and not c.completed and not c.rejected + ] + + stage_counts = {s.value: 0 for s in StageType} + for c in cases: + stage_counts[c.public_stage.value] = stage_counts.get(c.public_stage.value, 0) + 1 + + oldest_age = max((c.waiting_days for c in cases), default=0) + avg_wait = sum(c.waiting_days for c in cases) / len(cases) if cases else 0.0 + sla_risk = sum(c.sla_risk for c in cases) / len(cases) if cases else 0.0 + + return QueueSnapshot( + service_type=service, + public_stage_counts=stage_counts, + total_pending=len(cases), + total_completed_today=0, + total_sla_breached=sum(1 for c in cases if c.sla_breached), + urgent_pending=sum(1 for c in cases if c.is_urgent), + blocked_missing_docs=sum( + 1 + for c in cases + if c.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS + ), + field_verification_pending=sum( + 1 + for c in cases + if c.internal_substate == InternalSubstate.FIELD_VERIFICATION_PENDING + ), + oldest_case_age_days=oldest_age, + avg_waiting_days=round(avg_wait, 2), + current_sla_risk=round(min(1.0, sla_risk), 3), + ) + + +# ───────────────────────────────────────────── +# HIGH-LEVEL SIMULATION ORCHESTRATION +# ───────────────────────────────────────────── + + +class SimulationAgentMode(str, Enum): + BASELINE_POLICY = "baseline_policy" + LLM_INFERENCE = "llm_inference" + TRAINED_RL = "trained_rl" + + +@dataclass +class SimulationRun: + task_id: str + agent_mode: SimulationAgentMode + seed: int + total_reward: float + score: float + grader_name: str + summary: dict[str, Any] + trace: list[dict[str, Any]] + + +def _dedupe(values: list[str | None]) -> list[str]: + out: list[str] = [] + for value in values: + if value is None: + continue + v = str(value).strip() + if v and v not in out: + out.append(v) + return out + + +def _env_csv_list(name: str) -> list[str]: + raw = os.getenv(name, "").strip() + if not raw: + return [] + return [x.strip() for x in raw.split(",") if x.strip()] + + +def _extract_json_object(text: str) -> dict[str, Any] | None: + text = (text or "").strip() + if not text: + return None + try: + parsed = json.loads(text) + if isinstance(parsed, dict): + return parsed + except json.JSONDecodeError: + pass + + match = re.search(r"\{.*\}", text, flags=re.DOTALL) + if not match: + return None + try: + parsed = json.loads(match.group(0)) + except json.JSONDecodeError: + return None + return parsed if isinstance(parsed, dict) else None + + +def _enum_service(value: Any) -> ServiceType | None: + if value is None or value == "": + return None + if isinstance(value, ServiceType): + return value + try: + return ServiceType(str(value)) + except Exception: + return None + + +def _enum_priority(value: Any) -> PriorityMode | None: + if value is None or value == "": + return None + if isinstance(value, PriorityMode): + return value + try: + return PriorityMode(str(value)) + except Exception: + return None + + +def _action_model_from_kwargs(action_type: ActionType, **kwargs: Any) -> ActionModel: + service = _enum_service(kwargs.get("service") or kwargs.get("service_target")) + target_service = _enum_service(kwargs.get("target_service")) + escalation_target = _enum_service(kwargs.get("escalation_target")) + priority_mode = _enum_priority(kwargs.get("priority_mode")) + officer_delta = kwargs.get("officer_delta") + case_id = kwargs.get("case_id") + + candidates: list[dict[str, Any]] = [] + + if action_type == ActionType.ADVANCE_TIME: + candidates.append({"action_type": action_type}) + + elif action_type == ActionType.SET_PRIORITY_MODE: + candidates.extend( + [ + {"action_type": action_type, "priority_mode": priority_mode}, + ] + ) + + elif action_type == ActionType.ASSIGN_CAPACITY: + if service is not None: + delta = max(1, int(officer_delta or 1)) + candidates.extend( + [ + {"action_type": action_type, "service": service, "officer_delta": delta}, + {"action_type": action_type, "service_target": service, "officer_delta": delta}, + { + "action_type": action_type, + "capacity_assignment": {service.value: delta}, + }, + ] + ) + + elif action_type == ActionType.REQUEST_MISSING_DOCUMENTS: + if service is not None: + candidates.extend( + [ + {"action_type": action_type, "service": service}, + {"action_type": action_type, "service_target": service}, + ] + ) + + elif action_type == ActionType.ESCALATE_SERVICE: + svc = escalation_target or service + candidates.extend( + [ + {"action_type": action_type, "service": svc, "case_id": case_id}, + {"action_type": action_type, "service_target": svc, "case_id": case_id}, + {"action_type": action_type, "escalation_target": svc, "case_id": case_id}, + ] + ) + + elif action_type == ActionType.REALLOCATE_OFFICERS: + if service is not None and target_service is not None: + delta = max(1, int(officer_delta or 1)) + candidates.extend( + [ + { + "action_type": action_type, + "service": service, + "target_service": target_service, + "officer_delta": delta, + }, + { + "action_type": action_type, + "reallocation_delta": { + service.value: -delta, + target_service.value: delta, + }, + }, + ] + ) + + for candidate in candidates: + try: + return ActionModel(**candidate) + except Exception: + continue + + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def _coerce_action(payload: dict[str, Any] | None) -> ActionModel: + if not payload: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + raw_action_type = payload.get("action_type") or payload.get("actionType") + try: + action_type = ActionType(str(raw_action_type)) + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + service = payload.get("service") or payload.get("service_target") or payload.get("serviceTarget") + target_service = payload.get("target_service") or payload.get("targetService") + escalation_target = payload.get("escalation_target") or payload.get("escalationTarget") + priority_mode = payload.get("priority_mode") or payload.get("priorityMode") + officer_delta = payload.get("officer_delta") or payload.get("officerDelta") + case_id = payload.get("case_id") or payload.get("caseId") + + if action_type == ActionType.ASSIGN_CAPACITY and not service: + assignment = payload.get("capacity_assignment") or {} + if isinstance(assignment, dict) and assignment: + service, officer_delta = next(iter(assignment.items())) + + if action_type == ActionType.REALLOCATE_OFFICERS and (not service or not target_service): + delta_map = payload.get("reallocation_delta") or {} + if isinstance(delta_map, dict) and len(delta_map) >= 2: + negatives = [k for k, v in delta_map.items() if int(v) < 0] + positives = [k for k, v in delta_map.items() if int(v) > 0] + if negatives and positives: + service = negatives[0] + target_service = positives[0] + officer_delta = abs(int(delta_map[service])) + + return _action_model_from_kwargs( + action_type, + service=service, + target_service=target_service, + escalation_target=escalation_target, + priority_mode=priority_mode, + officer_delta=officer_delta, + case_id=case_id, + ) + + +def _recommended_min_steps(task_id: str) -> int: + if task_id == "cross_department_hard": + return 70 + if task_id == "mixed_urgency_medium": + return 60 + return 40 + + +def _queue_snapshot_iter(obs: ObservationModel) -> list[Any]: + raw = getattr(obs, "queue_snapshots", []) + if isinstance(raw, dict): + return list(raw.values()) + if isinstance(raw, list): + return list(raw) + try: + return list(raw) + except Exception: + return [] + + +def _queue_service(q: Any) -> ServiceType | None: + return _enum_service(getattr(q, "service", None) or getattr(q, "service_type", None)) + + +def _queue_active_cases(q: Any) -> int: + return int(getattr(q, "active_cases", getattr(q, "total_pending", 0)) or 0) + + +def _queue_missing_docs(q: Any) -> int: + return int(getattr(q, "missing_docs_cases", getattr(q, "blocked_missing_docs", 0)) or 0) + + +def _queue_urgent_cases(q: Any) -> int: + return int(getattr(q, "urgent_cases", getattr(q, "urgent_pending", 0)) or 0) + + +def _queue_breached_cases(q: Any) -> int: + return int(getattr(q, "breached_cases", getattr(q, "total_sla_breached", 0)) or 0) + + +def _queue_avg_age(q: Any) -> float: + if hasattr(q, "avg_age_days"): + return float(getattr(q, "avg_age_days") or 0.0) + if hasattr(q, "oldest_case_age_days"): + return float(getattr(q, "oldest_case_age_days") or 0.0) + return float(getattr(q, "avg_waiting_days", 0.0) or 0.0) + + +def _queue_rows(obs: ObservationModel) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for q in _queue_snapshot_iter(obs): + service = _queue_service(q) + if service is None: + continue + rows.append( + { + "service": service.value, + "active_cases": _queue_active_cases(q), + "missing_docs_cases": _queue_missing_docs(q), + "urgent_cases": _queue_urgent_cases(q), + "breached_cases": _queue_breached_cases(q), + "avg_age_days": _queue_avg_age(q), + } + ) + return rows + + +def _pool_allocations(obs: ObservationModel) -> dict[Any, Any]: + pool = getattr(obs, "officer_pool", None) + if pool is None: + return {} + return getattr(pool, "allocations", getattr(pool, "allocated", {})) or {} + + +def _reserve_officers(obs: ObservationModel) -> int: + pool = getattr(obs, "officer_pool", None) + if pool is None: + return 0 + for name in ("reserve_officers", "idle_officers", "available_officers"): + if hasattr(pool, name): + try: + return int(getattr(pool, name) or 0) + except Exception: + pass + return 0 + + +def _alloc_for(obs: ObservationModel, service: ServiceType) -> int: + allocs = _pool_allocations(obs) + raw = allocs.get(service) + if raw is None: + raw = allocs.get(service.value, 0) + return int(raw or 0) + + +def _top_backlog_service( + obs: ObservationModel, + *, + exclude: ServiceType | None = None, +) -> ServiceType | None: + ranked: list[Any] = [] + for q in _queue_snapshot_iter(obs): + service = _queue_service(q) + if service is None or service == exclude: + continue + ranked.append(q) + if not ranked: + return None + ranked.sort( + key=lambda q: ( + _queue_active_cases(q) + (2 * _queue_breached_cases(q)) + _queue_urgent_cases(q), + _queue_avg_age(q), + ), + reverse=True, + ) + return _queue_service(ranked[0]) + + +def _service_with_missing_docs(obs: ObservationModel) -> ServiceType | None: + candidates = [q for q in _queue_snapshot_iter(obs) if _queue_missing_docs(q) > 0] + if not candidates: + return None + candidates.sort(key=lambda q: (_queue_missing_docs(q), _queue_active_cases(q)), reverse=True) + return _queue_service(candidates[0]) + + +def _service_with_officers(obs: ObservationModel) -> ServiceType | None: + services = [s for s in (_queue_service(q) for q in _queue_snapshot_iter(obs)) if s is not None] + services.sort(key=lambda s: _alloc_for(obs, s), reverse=True) + for service in services: + if _alloc_for(obs, service) > 0: + return service + return None + + +def _compute_action_mask(obs: ObservationModel) -> dict[ActionType, bool]: + has_reserve = _reserve_officers(obs) > 0 + snapshots = _queue_snapshot_iter(obs) + has_missing = any(_queue_missing_docs(q) > 0 for q in snapshots) + has_backlog = any(_queue_active_cases(q) > 0 for q in snapshots) + has_budget = int(getattr(obs, "escalation_budget_remaining", 0) or 0) > 0 + staffed_services = [q for q in snapshots if (_queue_service(q) is not None and _alloc_for(obs, _queue_service(q)) > 0)] + can_reallocate = len(staffed_services) >= 1 and len(snapshots) >= 2 + return { + ActionType.SET_PRIORITY_MODE: True, + ActionType.ADVANCE_TIME: True, + ActionType.ASSIGN_CAPACITY: has_reserve and has_backlog, + ActionType.REQUEST_MISSING_DOCUMENTS: has_missing, + ActionType.ESCALATE_SERVICE: has_budget and has_backlog, + ActionType.REALLOCATE_OFFICERS: can_reallocate, + } + + +def _masked_action_type_hints(obs: ObservationModel) -> tuple[list[str], list[str]]: + mask = _compute_action_mask(obs) + allowed = [k.value for k, ok in mask.items() if ok] + blocked = [k.value for k, ok in mask.items() if not ok] + return allowed, blocked + + +def _best_high_impact_action(obs: ObservationModel) -> tuple[ActionModel, str]: + top_backlog = _top_backlog_service(obs) + top_missing = _service_with_missing_docs(obs) + + if _reserve_officers(obs) > 0 and top_backlog is not None: + return ( + _action_model_from_kwargs( + ActionType.ASSIGN_CAPACITY, + service=top_backlog, + officer_delta=1, + ), + "high-impact: assign reserve capacity to top backlog service", + ) + + if top_missing is not None: + return ( + _action_model_from_kwargs( + ActionType.REQUEST_MISSING_DOCUMENTS, + service=top_missing, + ), + "high-impact: clear missing-document bottleneck", + ) + + if int(getattr(obs, "escalation_budget_remaining", 0) or 0) > 0: + hot = sorted( + _queue_snapshot_iter(obs), + key=lambda q: (_queue_breached_cases(q), _queue_active_cases(q), _queue_urgent_cases(q)), + reverse=True, + ) + if hot and (_queue_breached_cases(hot[0]) > 0 or _queue_active_cases(hot[0]) > 0): + service = _queue_service(hot[0]) + if service is not None: + return ( + _action_model_from_kwargs( + ActionType.ESCALATE_SERVICE, + service=service, + ), + "high-impact: escalate highest SLA-risk service", + ) + + source = _service_with_officers(obs) + if source is not None and _alloc_for(obs, source) > 0: + target = _top_backlog_service(obs, exclude=source) + if target is not None and target != source: + return ( + _action_model_from_kwargs( + ActionType.REALLOCATE_OFFICERS, + service=source, + target_service=target, + officer_delta=1, + ), + "high-impact: reallocate one officer toward highest backlog", + ) + + return ActionModel(action_type=ActionType.ADVANCE_TIME), "fallback: no high-impact action available" + + +def _repair_action_for_observation( + action: ActionModel, + obs: ObservationModel, +) -> tuple[ActionModel, str | None]: + mask = _compute_action_mask(obs) + at = action.action_type + + if not bool(mask.get(at, True)): + fallback, why = _best_high_impact_action(obs) + return fallback, f"masked {at.value}; {why}" + + if at == ActionType.ADVANCE_TIME: + return action, None + + if at == ActionType.SET_PRIORITY_MODE: + if getattr(action, "priority_mode", None) is None: + return ( + _action_model_from_kwargs( + ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.BACKLOG_CLEARANCE, + ), + "missing priority_mode, defaulted to backlog_clearance", + ) + return action, None + + if at == ActionType.ASSIGN_CAPACITY: + reserve = _reserve_officers(obs) + if reserve <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"reserve officers exhausted; {why}" + service = _enum_service(getattr(action, "service", None) or getattr(action, "service_target", None)) or _top_backlog_service(obs) + if service is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no service available for assign_capacity; {why}" + delta = max(1, int(getattr(action, "officer_delta", 1) or 1)) + delta = min(delta, reserve) + repaired = _action_model_from_kwargs( + ActionType.ASSIGN_CAPACITY, + service=service, + officer_delta=delta, + ) + return repaired, "repaired assign_capacity payload" + + if at == ActionType.REQUEST_MISSING_DOCUMENTS: + service = _enum_service(getattr(action, "service", None) or getattr(action, "service_target", None)) or _service_with_missing_docs(obs) + if service is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no missing-doc queue available; {why}" + repaired = _action_model_from_kwargs( + ActionType.REQUEST_MISSING_DOCUMENTS, + service=service, + ) + return repaired, "repaired request_missing_documents payload" + + if at == ActionType.ESCALATE_SERVICE: + if int(getattr(obs, "escalation_budget_remaining", 0) or 0) <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"escalation budget exhausted; {why}" + service = ( + _enum_service(getattr(action, "service", None)) + or _enum_service(getattr(action, "service_target", None)) + or _enum_service(getattr(action, "escalation_target", None)) + or _top_backlog_service(obs) + ) + case_id = getattr(action, "case_id", None) + if service is None and case_id is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no escalation target available; {why}" + repaired = _action_model_from_kwargs( + ActionType.ESCALATE_SERVICE, + service=service, + case_id=case_id, + ) + return repaired, "repaired escalate_service payload" + + if at == ActionType.REALLOCATE_OFFICERS: + source = _enum_service(getattr(action, "service", None) or getattr(action, "service_target", None)) or _service_with_officers(obs) + if source is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no staffed source service; {why}" + source_alloc = _alloc_for(obs, source) + if source_alloc <= 0: + source = _service_with_officers(obs) + source_alloc = _alloc_for(obs, source) if source is not None else 0 + if source is None or source_alloc <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"insufficient source officers; {why}" + + target = _enum_service(getattr(action, "target_service", None)) + if target is None or target == source: + target = _top_backlog_service(obs, exclude=source) + if target is None or target == source: + fallback, why = _best_high_impact_action(obs) + return fallback, f"missing distinct target_service; {why}" + + delta = max(1, int(getattr(action, "officer_delta", 1) or 1)) + delta = min(delta, source_alloc) + repaired = _action_model_from_kwargs( + ActionType.REALLOCATE_OFFICERS, + service=source, + target_service=target, + officer_delta=delta, + ) + return repaired, "repaired reallocate_officers payload" + + return action, None + + +def _model_label_for_mode(agent_mode: SimulationAgentMode) -> str: + if agent_mode == SimulationAgentMode.BASELINE_POLICY: + return "baseline_policy" + if agent_mode == SimulationAgentMode.TRAINED_RL: + return "trained_rl" + return os.getenv("MODEL_NAME", "llm_inference") + + +def _log_step_line(step_row: dict[str, Any]) -> str: + done = "true" if bool(step_row.get("done")) else "false" + error = step_row.get("last_action_error") or "null" + action = json.dumps(step_row.get("action_payload", {}), separators=(",", ":")) + source = step_row.get("decision_source") or "unknown" + model = step_row.get("model_used") or "null" + repair = step_row.get("repair_note") or "null" + switch_note = step_row.get("switch_note") or "null" + return ( + f"[STEP] step={step_row.get('step', 0)} action={action} " + f"reward={float(step_row.get('reward', 0.0)):.2f} done={done} " + f"error={error} source={source} model={model} repair={repair} switch={switch_note}" + ) + + +def _resolve_model_path_or_raise(model_path: str) -> str: + p = Path(model_path).expanduser() + if not p.is_absolute(): + p = (Path.cwd() / p).resolve() + + if p.is_dir(): + candidates = [ + p / "best_model.zip", + p / "model.zip", + p / "checkpoint.zip", + ] + zip_files = sorted(p.glob("*.zip")) + candidates.extend(zip_files) + for candidate in candidates: + if candidate.exists(): + return str(candidate) + + if p.exists(): + return str(p) + + raise FileNotFoundError(f"Model path not found: {model_path}") + + +def _load_model_cached_or_raise(model_abs: str, model_type: Literal["maskable", "recurrent"]) -> Any: + key = (model_abs, model_type) + if key in _MODEL_CACHE: + return _MODEL_CACHE[key] + + if model_type == "recurrent": + from sb3_contrib import RecurrentPPO + + model = RecurrentPPO.load(model_abs) + else: + try: + from sb3_contrib import MaskablePPO + + model = MaskablePPO.load(model_abs) + except Exception: + from stable_baselines3 import PPO + + model = PPO.load(model_abs) + + _MODEL_CACHE[key] = model + return model + + +def _safe_invalid_action_count(final_state: Any) -> int: + if hasattr(final_state, "total_invalid_actions"): + return int(getattr(final_state, "total_invalid_actions") or 0) + metrics = getattr(final_state, "metrics", None) + if metrics is not None and hasattr(metrics, "total_invalid_actions"): + return int(getattr(metrics, "total_invalid_actions") or 0) + return 0 + + +class LiveSimulationSession: + def __init__( + self, + *, + task_id: str, + agent_mode: SimulationAgentMode, + max_steps: int, + seed: int | None, + policy_name: str | None = None, + model_path: str | None = None, + model_type: Literal["maskable", "recurrent"] = "maskable", + ) -> None: + self.task_id = task_id + self.agent_mode = agent_mode + recommended = _recommended_min_steps(task_id) + self.max_steps = max(int(max_steps), int(recommended)) if agent_mode == SimulationAgentMode.LLM_INFERENCE else int(max_steps) + self.seed = int(seed if seed is not None else random.randint(1, 999999)) + self.policy_name = policy_name or "backlog_clearance" + self.model_path = model_path + self.model_type = model_type + self.trace: list[dict[str, Any]] = [] + self.total_reward = 0.0 + self.step_idx = 0 + self.done = False + self.summary: dict[str, Any] | None = None + self.score: float | None = None + self.grader_name: str | None = None + + self.env: Any = None + self.obs: ObservationModel | Any = None + self.policy: Any = None + + self.rl_env: Any = None + self.rl_model: Any = None + self.rl_lstm_state: Any = None + self.rl_episode_start: Any = None + + self.llm_runtimes: list[dict[str, Any]] = [] + self.llm_route: list[str] = [] + self.llm_model_stats: dict[tuple[str, str], dict[str, Any]] = {} + self.consecutive_failure_steps = 0 + self.recovery_steps_remaining = 0 + self.auto_switch_count = 0 + self.last_switch_reason: str | None = None + + if self.agent_mode == SimulationAgentMode.TRAINED_RL: + self._init_trained() + else: + self._init_core() + + def start_line(self) -> dict[str, Any]: + return { + "log": ( + f"[START] task={self.task_id} env=gov-workflow-openenv " + f"model={_model_label_for_mode(self.agent_mode)}" + ), + "observation": self.obs + } + + def _init_core(self) -> None: + from app.baselines import POLICIES, backlog_clearance_policy + from app.env import GovWorkflowEnv + + self.env = GovWorkflowEnv(task_id=self.task_id) + self.obs, _ = self.env.reset(seed=self.seed) + if self.agent_mode == SimulationAgentMode.BASELINE_POLICY: + self.policy = POLICIES.get(self.policy_name, backlog_clearance_policy) + else: + self.policy = self._llm_action_with_meta + self._init_llm_runtimes() + + def _init_llm_runtimes(self) -> None: + openai_base = os.getenv("API_BASE_URL") or os.getenv("OPENAI_API_BASE_URL") or "https://api.openai.com/v1" + nvidia_base = os.getenv("NVIDIA_API_BASE_URL", "https://integrate.api.nvidia.com/v1") + + openai_keys = _dedupe( + [ + os.getenv("HF_TOKEN"), + os.getenv("OPENAI_API_KEY"), + os.getenv("API_KEY"), + ] + ) + nvidia_keys = _dedupe( + [ + os.getenv("NVIDIA_API_KEY"), + os.getenv("NVIDIA_API_KEY_2"), + ] + ) + + openai_models = _dedupe( + [ + os.getenv("MODEL_NAME", "meta/llama-3.3-70b-instruct"), + *_env_csv_list("MODEL_FALLBACKS"), + ] + ) + nvidia_models = _dedupe( + [ + os.getenv("NVIDIA_MODEL"), + *_env_csv_list("NVIDIA_MODEL_FALLBACKS"), + *LEGACY_NVIDIA_MODEL_POOL, + ] + ) + + runtimes: list[dict[str, Any]] = [] + + if openai_keys and openai_models: + clients: list[tuple[OpenAI, str]] = [] + for idx, key in enumerate(openai_keys, start=1): + try: + clients.append( + ( + OpenAI(base_url=openai_base, api_key=key, timeout=8.0, max_retries=0), + f"openai_key_{idx}", + ) + ) + except Exception: + continue + if clients: + runtimes.append( + { + "provider": "openai-compatible", + "base_url": openai_base, + "clients": clients, + "models": openai_models, + } + ) + + if nvidia_keys and nvidia_models: + clients = [] + for idx, key in enumerate(nvidia_keys, start=1): + try: + clients.append( + ( + OpenAI(base_url=nvidia_base, api_key=key, timeout=8.0, max_retries=0), + f"nvidia_key_{idx}", + ) + ) + except Exception: + continue + if clients: + runtimes.append( + { + "provider": "nvidia", + "base_url": nvidia_base, + "clients": clients, + "models": nvidia_models, + } + ) + + self.llm_runtimes = runtimes + self.llm_model_stats = {} + for runtime in runtimes: + provider = str(runtime.get("provider")) + for model in runtime.get("models", []): + self.llm_model_stats[(provider, str(model))] = { + "calls": 0, + "invalid": 0, + "repaired": 0, + "failures": 0, + "cooldown_until_step": 0, + } + + openai_runtime = next((rt for rt in runtimes if rt.get("provider") == "openai-compatible"), None) + nvidia_runtime = next((rt for rt in runtimes if rt.get("provider") == "nvidia"), None) + + openai_route = ( + f"openai-compatible ({len(openai_runtime['clients'])} keys, {len(openai_runtime['models'])} models)" + if openai_runtime is not None + else "openai-compatible (unavailable: missing API key/model)" + ) + nvidia_route = ( + f"nvidia ({len(nvidia_runtime['clients'])} keys, {len(nvidia_runtime['models'])} models)" + if nvidia_runtime is not None + else "nvidia (unavailable: missing API key/model)" + ) + + self.llm_route = [ + openai_route, + nvidia_route, + "adaptive ranking: prefer models with lower invalid/repaired rates", + "heuristic fallback (backlog_clearance_policy)", + ] + + def _rank_runtime_models(self, provider: str, models: list[str]) -> list[str]: + def _score(model_name: str) -> tuple[float, int]: + stat = self.llm_model_stats.get((provider, model_name), {}) + calls = max(1, int(stat.get("calls", 0))) + invalid_rate = float(stat.get("invalid", 0)) / calls + repaired_rate = float(stat.get("repaired", 0)) / calls + fail_rate = float(stat.get("failures", 0)) / calls + cooldown = int(stat.get("cooldown_until_step", 0)) + cooldown_penalty = 1.0 if self.step_idx < cooldown else 0.0 + return ( + invalid_rate * 2.0 + repaired_rate * 1.25 + fail_rate * 1.5 + cooldown_penalty, + -calls, + ) + + return sorted([str(m) for m in models], key=_score) + + def _llm_action_with_meta(self, obs: ObservationModel) -> tuple[ActionModel, dict[str, Any]]: + if self.recovery_steps_remaining > 0: + self.recovery_steps_remaining -= 1 + action, why = _best_high_impact_action(obs) + return action, { + "decision_source": "auto_recovery_policy", + "provider": "heuristic", + "model_used": "backlog_clearance_policy", + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + "repair_note": why, + } + + attempts = 0 + last_error = "" + allowed_actions, blocked_actions = _masked_action_type_hints(obs) + schema_hint = { + "required_fields": { + "set_priority_mode": ["action_type", "priority_mode"], + "assign_capacity": ["action_type", "service", "officer_delta"], + "request_missing_documents": ["action_type", "service"], + "escalate_service": ["action_type", "service"], + "advance_time": ["action_type"], + "reallocate_officers": ["action_type", "service", "target_service", "officer_delta"], + }, + "allowed_priority_mode": [m.value for m in PriorityMode], + "allowed_services": [s.value for s in ServiceType], + } + system_prompt = ( + "You are controlling a government workflow simulator. " + "Return exactly one JSON object only. No markdown. No explanation. " + "Allowed action_type: set_priority_mode, assign_capacity, request_missing_documents, " + "escalate_service, advance_time, reallocate_officers. " + "Rules: " + "1) reallocate_officers requires service + target_service + officer_delta>0 and source!=target. " + "2) assign_capacity requires service + officer_delta>0. " + "3) request_missing_documents requires service with missing_docs_cases>0. " + "4) set_priority_mode requires priority_mode in [urgent_first, oldest_first, balanced, backlog_clearance]. " + "5) Always prefer high-impact actions that reduce backlog/SLA risk over no-op loops. " + "Use lowercase enum values." + ) + user_prompt = ( + "Observation:\n" + f"{obs.model_dump_json() if hasattr(obs, 'model_dump_json') else json.dumps(getattr(obs, 'dict', lambda: {})())}\n" + f"Allowed action types now: {allowed_actions}\n" + f"Blocked action types now: {blocked_actions}\n" + f"Action schema hints: {json.dumps(schema_hint, separators=(',', ':'))}\n" + f"Last action validity: {getattr(obs, 'last_action_valid', True)}\n" + f"Last action message: {getattr(obs, 'last_action_message', '')}\n" + "Return action JSON." + ) + + for runtime in self.llm_runtimes: + provider = str(runtime["provider"]) + ranked_models = self._rank_runtime_models(provider, list(runtime["models"])) + for client, key_label in runtime["clients"]: + for model in ranked_models: + attempts += 1 + stat_key = (provider, model) + try: + out = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + temperature=0.0, + max_tokens=200, + stream=False, + ) + content = (out.choices[0].message.content or "").strip() + action = _coerce_action(_extract_json_object(content)) + if stat_key in self.llm_model_stats: + self.llm_model_stats[stat_key]["calls"] += 1 + return action, { + "decision_source": "llm", + "provider": provider, + "model_used": model, + "llm_attempts": attempts, + "llm_error": None, + "llm_key_label": key_label, + } + except Exception as exc: + last_error = str(exc) + stat = self.llm_model_stats.get(stat_key) + if stat is not None: + stat["calls"] += 1 + stat["failures"] += 1 + if stat["failures"] >= 2: + stat["cooldown_until_step"] = self.step_idx + 5 + continue + + action, why = _best_high_impact_action(obs) + if not self.llm_runtimes: + last_error = "No LLM credentials configured." + return action, { + "decision_source": "heuristic_fallback", + "provider": "heuristic", + "model_used": "backlog_clearance_policy", + "llm_attempts": attempts, + "llm_error": last_error or None, + "llm_key_label": None, + "repair_note": why, + } + + def _init_trained(self) -> None: + import numpy as np + from rl.gov_workflow_env import GovWorkflowGymEnv + + if not self.model_path: + raise ValueError("model_path is required for trained_rl simulation.") + model_abs = _resolve_model_path_or_raise(self.model_path) + self.rl_model = _load_model_cached_or_raise(model_abs, self.model_type) + self.rl_env = GovWorkflowGymEnv( + task_id=self.task_id, + seed=self.seed, + hard_action_mask=True, + ) + self.obs, _ = self.rl_env.reset(seed=self.seed) + self.rl_lstm_state = None + self.rl_episode_start = np.array([True], dtype=bool) + + def step_once(self) -> tuple[dict[str, Any], str, bool]: + if self.done: + raise RuntimeError("Simulation already finished.") + + self.step_idx += 1 + row = self._step_trained() if self.agent_mode == SimulationAgentMode.TRAINED_RL else self._step_core() + self.trace.append(row) + self.total_reward += float(row["reward"]) + step_log = _log_step_line(row) + + if row["done"] or self.step_idx >= self.max_steps: + self._finalize() + row["done"] = True + return row, step_log, True + return row, step_log, False + + def end_line(self) -> str: + if self.score is None: + return "[END] success=false steps=0 score=0.00 rewards=" + rewards = ",".join(f"{float(x.get('reward', 0.0)):.2f}" for x in self.trace) + success = "true" if self.score >= 0.5 else "false" + return f"[END] success={success} steps={len(self.trace)} score={self.score:.2f} rewards={rewards}" + + def step_line(self, action: dict | ActionModel) -> dict[str, Any]: + """Test wrapper for executing an action and returning observation + reward.""" + if isinstance(action, dict): + action = _coerce_action(action) + self.obs, reward, terminated, truncated, info = self.env.step(action) + return {"observation": self.obs, "reward": reward} + + def snapshot(self) -> dict[str, Any]: + return { + "task_id": self.task_id, + "agent_mode": self.agent_mode.value, + "seed": self.seed, + "max_steps": self.max_steps, + "step_idx": self.step_idx, + "done": self.done, + "total_reward": float(self.total_reward), + "score": self.score, + "grader_name": self.grader_name, + "summary": self.summary, + "trace_len": len(self.trace), + "llm_route": list(self.llm_route), + } + + def close(self) -> None: + try: + if self.env is not None and hasattr(self.env, "close"): + self.env.close() + except Exception: + pass + try: + if self.rl_env is not None and hasattr(self.rl_env, "close"): + self.rl_env.close() + except Exception: + pass + + def _step_core(self) -> dict[str, Any]: + if self.env is None: + raise RuntimeError("Core simulation env not initialized.") + + if self.agent_mode == SimulationAgentMode.BASELINE_POLICY: + action = self.policy(self.obs) + meta = { + "decision_source": "baseline_policy", + "provider": "local_policy", + "model_used": self.policy_name, + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + } + else: + raw_decision = self.policy(self.obs) + if isinstance(raw_decision, tuple) and len(raw_decision) == 2: + action, meta = raw_decision + else: + action, meta = raw_decision, {} + if not isinstance(meta, dict): + meta = {} + + if not isinstance(action, ActionModel): + if isinstance(action, dict): + action = _coerce_action(action) + else: + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + meta["repair_note"] = "non-action output from llm policy, coerced to advance_time" + + allowed_mask = _compute_action_mask(self.obs) + if not bool(allowed_mask.get(action.action_type, True)): + masked_fallback, why = _best_high_impact_action(self.obs) + action = masked_fallback + if meta.get("decision_source") == "llm": + meta["decision_source"] = "llm_repaired" + meta["repair_note"] = f"action masked at runtime; {why}" + + repaired_action, repair_note = _repair_action_for_observation(action, self.obs) + if repair_note: + action = repaired_action + if meta.get("decision_source") == "llm": + meta["decision_source"] = "llm_repaired" + meta["repair_note"] = repair_note + + self.obs, reward, terminated, truncated, info = self.env.step(action) + done = bool(terminated or truncated) + last_action_error = getattr(info, "last_action_error", None) + if last_action_error is None: + last_action_error = getattr(info, "action_explanation", None) + + row = { + "step": self.step_idx, + "day": self.obs.day, + "action_type": action.action_type.value, + "action_payload": action.model_dump(exclude_none=True, mode="json"), + "reward": float(reward), + "done": done, + "backlog": getattr(self.obs, "total_backlog", 0), + "completed": getattr(self.obs, "total_completed", 0), + "sla_breaches": getattr(self.obs, "total_sla_breaches", 0), + "fairness_gap": float( + getattr(self.obs, "fairness_gap", getattr(self.obs, "fairness_index", 0.0)) or 0.0 + ), + "escalation_budget_remaining": getattr(self.obs, "escalation_budget_remaining", 0), + "invalid_action": bool(getattr(info, "invalid_action", False)), + "last_action_error": last_action_error, + "queue_rows": _queue_rows(self.obs), + } + row.update(meta) + + if self.agent_mode == SimulationAgentMode.LLM_INFERENCE: + is_repaired = row.get("decision_source") in {"llm_repaired", "auto_recovery_policy"} + is_invalid = bool(row.get("invalid_action")) or bool(row.get("last_action_error")) + model_used = str(row.get("model_used") or "") + provider = str(row.get("provider") or "") + stat_key = (provider, model_used) + stat = self.llm_model_stats.get(stat_key) + if stat is not None: + if is_repaired: + stat["repaired"] += 1 + if is_invalid: + stat["invalid"] += 1 + stat["failures"] += 1 + else: + stat["failures"] = max(0, int(stat.get("failures", 0)) - 1) + + is_failure_pattern = is_invalid or is_repaired + self.consecutive_failure_steps = self.consecutive_failure_steps + 1 if is_failure_pattern else 0 + + if self.consecutive_failure_steps >= 4: + if stat is not None: + stat["cooldown_until_step"] = self.step_idx + 6 + self.recovery_steps_remaining = max(self.recovery_steps_remaining, 3) + self.auto_switch_count += 1 + self.last_switch_reason = "repeated invalid/repaired pattern detected" + row["switch_note"] = "auto-switched to recovery policy and deprioritized failing model" + self.consecutive_failure_steps = 0 + + return row + + def _step_trained(self) -> dict[str, Any]: + import numpy as np + + masks = self.rl_env.action_masks() + if self.model_type == "recurrent": + action, self.rl_lstm_state = self.rl_model.predict( + self.obs, + state=self.rl_lstm_state, + episode_start=self.rl_episode_start, + deterministic=True, + ) + action_idx = int(action.item() if hasattr(action, "item") else action) + if not (0 <= action_idx < masks.shape[0] and bool(masks[action_idx])): + valid = np.flatnonzero(masks) + action_idx = int(valid[0]) if valid.size > 0 else 18 + else: + from sb3_contrib.common.maskable.utils import get_action_masks + + action, _ = self.rl_model.predict( + self.obs, + action_masks=get_action_masks(self.rl_env), + deterministic=True, + ) + action_idx = int(action.item() if hasattr(action, "item") else action) + + self.obs, reward, terminated, truncated, info = self.rl_env.step(action_idx) + done = bool(terminated or truncated) + if self.model_type == "recurrent": + self.rl_episode_start = np.array([done], dtype=bool) + + core_env = self.rl_env.core_env + core_obs = core_env._build_observation() + action_model, action_label = _decode_action_idx(action_idx) + + return { + "step": self.step_idx, + "day": core_obs.day, + "action_type": action_label, + "action_payload": action_model.model_dump(exclude_none=True, mode="json"), + "action_index": action_idx, + "reward": float(reward), + "done": done, + "backlog": core_obs.total_backlog, + "completed": core_obs.total_completed, + "sla_breaches": core_obs.total_sla_breaches, + "fairness_gap": float( + getattr(core_obs, "fairness_gap", getattr(core_obs, "fairness_index", 0.0)) or 0.0 + ), + "escalation_budget_remaining": core_obs.escalation_budget_remaining, + "invalid_action": bool(info.get("invalid_action", False)), + "last_action_error": info.get("last_action_error") or info.get("action_explanation"), + "queue_rows": _queue_rows(core_obs), + "decision_source": "trained_rl", + "provider": "rl", + "model_used": self.model_path or "trained_rl", + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + } + + def _finalize(self) -> None: + if self.done: + return + self.done = True + + from app.graders import grade_episode + + if self.agent_mode == SimulationAgentMode.TRAINED_RL: + final_state = self.rl_env.core_env.state() + else: + final_state = self.env.state() + + gr = grade_episode(final_state) + self.score = float(gr.score) + self.grader_name = gr.grader_name + + llm_steps = sum(1 for row in self.trace if row.get("decision_source") in {"llm", "llm_repaired"}) + fallback_steps = sum( + 1 for row in self.trace if row.get("decision_source") in {"heuristic_fallback", "auto_recovery_policy"} + ) + repaired_steps = sum( + 1 for row in self.trace if row.get("decision_source") in {"llm_repaired", "auto_recovery_policy"} + ) + total_steps = max(1, len(self.trace)) + invalid_actions = _safe_invalid_action_count(final_state) + invalid_rate = float(invalid_actions) / float(total_steps) + repaired_rate = float(repaired_steps) / float(total_steps) + + ranked_models: list[dict[str, Any]] = [] + if self.llm_model_stats: + for (provider, model), stat in self.llm_model_stats.items(): + calls = int(stat.get("calls", 0)) + if calls <= 0: + continue + ranked_models.append( + { + "provider": provider, + "model": model, + "calls": calls, + "invalid_rate": float(stat.get("invalid", 0)) / max(1, calls), + "repaired_rate": float(stat.get("repaired", 0)) / max(1, calls), + } + ) + ranked_models.sort(key=lambda x: (x["invalid_rate"], x["repaired_rate"], -x["calls"])) + + self.summary = { + "total_steps": getattr(final_state, "total_steps", len(self.trace)), + "total_completed": getattr(final_state, "total_completed", 0), + "total_backlog": getattr(final_state, "total_backlog", 0), + "total_sla_breaches": getattr(final_state, "total_sla_breaches", 0), + "fairness_gap": float(getattr(final_state, "fairness_gap", 0.0) or 0.0), + "total_invalid_actions": invalid_actions, + "invalid_action_rate": invalid_rate, + "llm_steps": llm_steps, + "heuristic_fallback_steps": fallback_steps, + "llm_repaired_steps": repaired_steps, + "repaired_action_rate": repaired_rate, + "auto_switch_count": self.auto_switch_count, + "last_switch_reason": self.last_switch_reason, + "effective_max_steps": self.max_steps, + "recommended_min_steps": _recommended_min_steps(self.task_id), + } + if self.agent_mode == SimulationAgentMode.LLM_INFERENCE: + self.summary["llm_route"] = list(self.llm_route) + self.summary["llm_model_performance"] = ranked_models + if self.agent_mode == SimulationAgentMode.TRAINED_RL: + self.summary["model_path"] = self.model_path + self.summary["model_type"] = self.model_type + + +def run_simulation( + *, + task_id: str, + agent_mode: SimulationAgentMode, + max_steps: int, + seed: int | None, + policy_name: str | None = None, + model_path: str | None = None, + model_type: Literal["maskable", "recurrent"] = "maskable", +) -> SimulationRun: + session = LiveSimulationSession( + task_id=task_id, + agent_mode=agent_mode, + max_steps=max_steps, + seed=seed, + policy_name=policy_name, + model_path=model_path, + model_type=model_type, + ) + try: + while not session.done: + session.step_once() + return SimulationRun( + task_id=session.task_id, + agent_mode=session.agent_mode, + seed=session.seed, + total_reward=float(session.total_reward), + score=float(session.score or 0.0), + grader_name=str(session.grader_name or "unknown"), + summary=dict(session.summary or {}), + trace=list(session.trace), + ) + finally: + session.close() + + +def _decode_action_idx(action_idx: int) -> tuple[ActionModel, str]: + try: + from rl.feature_builder import ACTION_DECODE_TABLE + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME), f"action_{action_idx}" + + row = ACTION_DECODE_TABLE.get(int(action_idx)) + if row is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME), f"action_{action_idx}" + + action_type, service, priority_mode, delta = row + + try: + at = ActionType(str(action_type)) + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME), f"action_{action_idx}" + + if at == ActionType.SET_PRIORITY_MODE: + action = _action_model_from_kwargs(at, priority_mode=priority_mode) + elif at == ActionType.ASSIGN_CAPACITY: + action = _action_model_from_kwargs(at, service=service, officer_delta=delta or 1) + elif at == ActionType.REQUEST_MISSING_DOCUMENTS: + action = _action_model_from_kwargs(at, service=service) + elif at == ActionType.ESCALATE_SERVICE: + action = _action_model_from_kwargs(at, service=service) + elif at == ActionType.REALLOCATE_OFFICERS: + src = _enum_service(service) + action = ( + _action_model_from_kwargs(at, service=src, target_service=src, officer_delta=delta or 1) + if src is not None + else ActionModel(action_type=ActionType.ADVANCE_TIME) + ) + else: + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + + return action, at.value \ No newline at end of file diff --git a/app/env.py b/app/env.py new file mode 100644 index 0000000000000000000000000000000000000000..74505c3ef8f744f66f4fa20ca418120597c6c5cf --- /dev/null +++ b/app/env.py @@ -0,0 +1,553 @@ +""" +env.py — Gov Workflow OpenEnv +Gymnasium/OpenEnv-compatible environment aligned with Phase 1 schemas. +""" + +from __future__ import annotations + +import random +from uuid import uuid4 + +from app.event_engine import EventEngine +from app.models import ( + ActionModel, + ActionType, + ApplicationCase, + EpisodeStateModel, + InternalSubstate, + ObservationModel, + OfficerPool, + PriorityMode, + QueueSnapshot, + RewardModel, + ScenarioMode, + ServiceType, + StepInfoModel, + TaskConfig, +) +from app.reward import compute_reward +from app.signal_computer import SignalComputer +from app.engine import DayResult, DaySimulator +from app.tasks import get_task + + +def completion_fairness_gap( + arrived_by_service: dict[ServiceType, int], + completed_by_service: dict[ServiceType, int], +) -> float: + services = list(arrived_by_service.keys()) + if len(services) < 2: + return 0.0 + + rates = [] + for svc in services: + arrived = max(1, arrived_by_service.get(svc, 0)) + completed = completed_by_service.get(svc, 0) + rates.append(completed / arrived) + + return max(rates) - min(rates) if rates else 0.0 + + +class EpisodeMetrics: + def __init__(self): + self.total_arrived: int = 0 + self.total_completed: int = 0 + self.total_sla_breaches: int = 0 + self.total_rejected: int = 0 + self.total_invalid_actions: int = 0 + self.total_escalations_used: int = 0 + self.total_wasted_escalations: int = 0 + self.total_docs_requested: int = 0 + self.total_docs_cleared: int = 0 + self.total_idle_officer_days: int = 0 + self.total_capacity_days: int = 0 + self.total_urgent_arrived: int = 0 + self.total_urgent_completed: int = 0 + self.cumulative_reward: float = 0.0 + + def to_reward_model(self) -> RewardModel: + return RewardModel(total_reward=self.cumulative_reward) + + +class GovWorkflowEnv: + def __init__(self, task_id: str = "district_backlog_easy", seed: int | None = None) -> None: + self.task_id = task_id + self.task: TaskConfig = get_task(task_id) + self.seed = seed + self.max_steps_per_episode = max(1, int(self.task.max_days) * 10) + self._init_episode_state() + + def reset( + self, + seed: int | None = None, + options: dict | None = None, + ) -> tuple[ObservationModel, dict]: + task_id = (options or {}).get("task_id", self.task_id) + self.task = get_task(task_id) + self.task_id = self.task.task_id + + self.seed = self.task.seed if seed is None else int(seed) + self.rng = random.Random(self.seed) + max_steps_override = (options or {}).get("max_steps_per_episode") + if max_steps_override is None: + self.max_steps_per_episode = max(1, int(self.task.max_days) * 10) + else: + self.max_steps_per_episode = max(1, int(max_steps_override)) + + self.episode_id = f"{self.task_id}-s{self.seed}-{uuid4().hex[:6]}" + self.day = 0 + self.total_steps = 0 + self.terminated = False + self.truncated = False + self.priority_mode = PriorityMode.BALANCED + + pool = self.task.initial_officer_pool + self.officer_pool = OfficerPool( + total_officers=pool.total_officers, + available_officers=pool.available_officers, + allocated=dict(pool.allocated), + pending_reallocation=dict(getattr(pool, "pending_reallocation", {})), + ) + + self.active_cases: list[ApplicationCase] = [] + self.completed_cases: list[ApplicationCase] = [] + self.escalation_budget_remaining = self.task.escalation_budget + + self.arrived_by_service = {s: 0 for s in self.task.enabled_services} + self.completed_by_service = {s: 0 for s in self.task.enabled_services} + + self.metrics = EpisodeMetrics() + self.action_history: list[dict] = [] + self.last_action_valid = True + self.last_action_message = "reset" + self.last_action_explanation = "" + + self.event_engine = EventEngine( + seed=self.seed, + scenario_mode=self.task.scenario_mode, + ) + self.simulator = DaySimulator( + task_config=self.task, + rng=self.rng, + event_engine=self.event_engine, + ) + self.signal_computer = SignalComputer() + + obs = self._build_observation(active_events=[]) + info = { + "task_id": self.task_id, + "seed": self.seed, + "episode_id": self.episode_id, + "max_days": self.task.max_days, + } + return obs, info + + def step( + self, + action: ActionModel | dict, + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: + if isinstance(action, dict): + from app.models import ActionModel + action = ActionModel(**action) + + if self.terminated or self.truncated: + raise RuntimeError("Episode ended — call reset() before stepping.") + + self.total_steps += 1 + invalid_action = False + day_result = DayResult() + + try: + notes, day_result = self._apply_action(action, day_result) + self.last_action_valid = True + self.last_action_message = notes[-1] if notes else "ok" + self.last_action_explanation = self.last_action_message + except ValueError as exc: + invalid_action = True + self.metrics.total_invalid_actions += 1 + self.last_action_valid = False + self.last_action_message = str(exc) + self.last_action_explanation = f"Invalid: {exc}" + + fairness_gap = completion_fairness_gap( + self.arrived_by_service, + self.completed_by_service, + ) + + reward: RewardModel = compute_reward( + stage_advances=day_result.stage_advances, + completions=day_result.new_completions, + active_backlog=len(self.active_cases), + new_sla_breaches=day_result.new_sla_breaches, + fairness_gap=fairness_gap, + fairness_threshold=self.task.fairness_threshold or 0.0, + invalid_action=invalid_action, + idle_capacity=day_result.idle_officer_days, + award_stability_bonus=(action.action_type == ActionType.ADVANCE_TIME), + ) + self.metrics.cumulative_reward += reward.total_reward + + self.terminated = ( + len(self.active_cases) == 0 + and self.day > 0 + and not invalid_action + ) + self.truncated = ( + (self.day >= self.task.max_days or self.total_steps >= self.max_steps_per_episode) + and not self.terminated + ) + + info = StepInfoModel( + reward_breakdown=reward, + newly_arrived_cases=day_result.new_arrivals, + newly_completed_cases=day_result.new_completions, + newly_sla_breached_cases=day_result.new_sla_breaches, + newly_resolved_doc_cases=day_result.newly_unblocked_missing, + invalid_action=invalid_action, + action_explanation=self.last_action_explanation, + active_events=day_result.active_events, + grader_preview_score=0.0, + effects_resolved_this_step=[], + ) + + self.action_history.append({ + "step": self.total_steps, + "day": self.day, + "action": action.model_dump(mode="json"), + "invalid": invalid_action, + "message": self.last_action_message, + "reward": reward.total_reward, + }) + + obs = self._build_observation(active_events=day_result.active_events) + return obs, reward.total_reward, self.terminated, self.truncated, info + + def count_pending_effects(self) -> int: + """Count all pending delayed effects waiting to resolve.""" + if hasattr(self, '_pending_effects') and self._pending_effects: + return len(self._pending_effects) + if hasattr(self, 'simulator') and hasattr(self.simulator, 'pending_effects'): + return len(self.simulator.pending_effects) + if hasattr(self, 'pending_effects'): + return len(self.pending_effects) + return 0 + + + def state(self) -> EpisodeStateModel: + + fairness_gap = completion_fairness_gap( + self.arrived_by_service, self.completed_by_service + ) + + # Compute average waiting days across completed cases + avg_wait = ( + sum(c.waiting_days for c in self.completed_cases) / len(self.completed_cases) + if self.completed_cases else 0.0 + ) + + return EpisodeStateModel( + episode_id=self.episode_id, + task_id=self.task_id, + seed=self.seed, + scenario_mode=self.task.scenario_mode, + day=self.day, + max_days=self.task.max_days, + terminated=self.terminated, + truncated=self.truncated, + total_steps=self.total_steps, + total_completed=len(self.completed_cases), + total_backlog=len(self.active_cases), + total_sla_breaches=self.metrics.total_sla_breaches, + total_rejected=self.metrics.total_rejected, + action_history_count=len(self.action_history), + cumulative_reward=self.metrics.cumulative_reward, + officer_pool=self.officer_pool.model_copy(deep=True), + pending_effects_count=self.count_pending_effects(), + active_events_today=[], + + # ── Grader-facing fields ────────────────────────────────── + fairness_gap=round(fairness_gap, 4), + total_arrived=self.metrics.total_arrived, + total_docs_requested=self.metrics.total_docs_requested, + total_docs_cleared=self.metrics.total_docs_cleared, + total_idle_officer_days=self.metrics.total_idle_officer_days, + total_capacity_days=self.metrics.total_capacity_days, + total_urgent_arrived=self.metrics.total_urgent_arrived, + total_urgent_completed=self.metrics.total_urgent_completed, + total_escalations_used=self.metrics.total_escalations_used, + total_wasted_escalations=self.metrics.total_wasted_escalations, + total_invalid_actions=self.metrics.total_invalid_actions, + avg_waiting_days=round(avg_wait, 2), + + # Full action log — populated but stripped by API unless requested + action_history=list(self.action_history), + ) + + def _apply_action( + self, + action: ActionModel, + day_result: DayResult, + ) -> tuple[list[str], DayResult]: + notes: list[str] = [] + + if action.action_type == ActionType.SET_PRIORITY_MODE: + if action.priority_mode is None: + raise ValueError("priority_mode required for set_priority_mode") + old_mode = self.priority_mode + self.priority_mode = action.priority_mode + notes.append(f"Priority mode changed: {old_mode.value} -> {action.priority_mode.value}") + return notes, day_result + + if action.action_type == ActionType.ASSIGN_CAPACITY: + cap = action.capacity_assignment + if not cap: + raise ValueError("capacity_assignment dict required for assign_capacity") + + for svc_key, delta in cap.items(): + svc = ServiceType(svc_key) if isinstance(svc_key, str) else svc_key + if svc not in self.task.enabled_services: + raise ValueError(f"{svc.value} is not enabled in this task") + if delta <= 0: + raise ValueError("capacity delta must be positive") + idle = self.officer_pool.idle_officers + if delta > idle: + raise ValueError(f"Only {idle} idle officers available; requested {delta}") + self.officer_pool.allocated[svc] = self.officer_pool.allocated.get(svc, 0) + delta + notes.append(f"Assigned {delta} officer(s) to {svc.value}") + return notes, day_result + + if action.action_type == ActionType.REQUEST_MISSING_DOCUMENTS: + svc = action.service_target + if svc is None: + raise ValueError("service_target required for request_missing_documents") + + candidates = [ + c for c in self.active_cases + if c.service_type == svc + and c.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS + ] + if not candidates: + raise ValueError(f"No BLOCKED_MISSING_DOCS cases for {svc.value}") + + candidates.sort(key=lambda c: (-c.sla_risk, c.arrival_day)) + resolved = 0 + for case in candidates[:3]: + case.doc_request_sent_day = self.day + case.doc_resolution_day = self.day + self.rng.randint(2, 3) + self.metrics.total_docs_requested += 1 + resolved += 1 + + notes.append(f"Sent missing-doc requests for {resolved} case(s) in {svc.value}") + return notes, day_result + + if action.action_type == ActionType.ESCALATE_SERVICE: + if self.escalation_budget_remaining <= 0: + self.metrics.total_wasted_escalations += 1 + raise ValueError("Escalation budget exhausted") + + svc = action.escalation_target or action.service_target + candidates = [ + c for c in self.active_cases + if (svc is None or c.service_type == svc) and not c.is_urgent + ] + if not candidates: + self.metrics.total_wasted_escalations += 1 + raise ValueError("No eligible non-urgent cases to escalate") + + best = max(candidates, key=lambda c: (c.sla_risk, -c.arrival_day)) + best.is_urgent = True + self.escalation_budget_remaining -= 1 + self.metrics.total_escalations_used += 1 + notes.append(f"Escalated case {best.case_id} ({best.service_type.value})") + return notes, day_result + + if action.action_type == ActionType.ADVANCE_TIME: + day_result = self._advance_one_day() + notes.append(f"Day {self.day} simulated") + return notes, day_result + + if action.action_type == ActionType.REALLOCATE_OFFICERS: + delta = action.reallocation_delta + if not delta or len(delta) < 2: + raise ValueError("reallocation_delta must have at least 2 entries") + + total = sum(delta.values()) + if total != 0: + raise ValueError(f"reallocation_delta must sum to 0 (got {total})") + + for svc_key, change in delta.items(): + svc = ServiceType(svc_key) if isinstance(svc_key, str) else svc_key + if svc not in self.task.enabled_services: + raise ValueError(f"{svc.value} not in enabled services") + current = self.officer_pool.allocated.get(svc, 0) + if current + change < 0: + raise ValueError( + f"Cannot reduce {svc.value} below 0 (current={current}, change={change})" + ) + + for svc_key, change in delta.items(): + svc = ServiceType(svc_key) if isinstance(svc_key, str) else svc_key + self.officer_pool.allocated[svc] = self.officer_pool.allocated.get(svc, 0) + change + + changes = ", ".join(f"{k}:{'+' if v > 0 else ''}{v}" for k, v in delta.items()) + notes.append(f"Officers reallocated: {changes}") + return notes, day_result + + raise ValueError(f"Unsupported action_type: {action.action_type.value}") + + def _advance_one_day(self) -> DayResult: + self.day += 1 + + alloc = dict(self.officer_pool.allocated) + result = self.simulator.simulate_day( + day=self.day, + active_cases=self.active_cases, + completed_cases=self.completed_cases, + priority_mode=self.priority_mode, + officer_allocations=alloc, + ) + + for case in self.completed_cases: + if getattr(case, "_counted", False): + continue + case._counted = True + svc = case.service_type + self.completed_by_service[svc] = self.completed_by_service.get(svc, 0) + 1 + + for case in self.active_cases: + if getattr(case, "_arrival_counted", False): + continue + case._arrival_counted = True + svc = case.service_type + self.arrived_by_service[svc] = self.arrived_by_service.get(svc, 0) + 1 + self.metrics.total_arrived += 1 + if case.is_urgent: + self.metrics.total_urgent_arrived += 1 + + self.metrics.total_completed = len(self.completed_cases) + self.metrics.total_sla_breaches += result.new_sla_breaches + self.metrics.total_idle_officer_days += result.idle_officer_days + self.metrics.total_capacity_days += result.total_capacity_days + self.metrics.total_urgent_completed += result.urgent_completed + self.metrics.total_docs_cleared += result.newly_unblocked_missing + + return result + + def _build_observation(self, active_events: list = None) -> ObservationModel: + active_events = active_events or [] + + snapshots: dict[str, QueueSnapshot] = {} + todays_digital = 0 + todays_arrivals = 0 + today_completed: dict[ServiceType, int] = {} + + for case in self.completed_cases: + today_completed[case.service_type] = today_completed.get(case.service_type, 0) + 1 + + for service in self.task.enabled_services: + snap = self.simulator.build_queue_snapshot(service, self.active_cases, self.day) + snap.total_completed_today = today_completed.get(service, 0) + snapshots[service.value] = snap + + for case in self.active_cases: + if case.arrival_day == self.day: + todays_arrivals += 1 + if case.intake_channel.value == "digital": + todays_digital += 1 + + sigs = self.signal_computer.compute( + queue_snapshots=snapshots, + officer_pool=self.officer_pool, + todays_arrivals=todays_arrivals, + digital_arrivals=todays_digital, + capacity_per_day=max(1.0, float(self.officer_pool.available_officers)), + ) + + pending_doc = sum( + 1 for c in self.active_cases + if c.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS + and c.doc_resolution_day is not None + ) + pending_officer = len(getattr(self.officer_pool, "pending_reallocation", {})) + + return ObservationModel( + task_id=self.task_id, + episode_id=self.episode_id, + day=self.day, + max_days=self.task.max_days, + scenario_mode=self.task.scenario_mode, + officer_pool=self.officer_pool.model_copy(deep=True), + queue_snapshots=snapshots, + total_backlog=len(self.active_cases), + total_completed=len(self.completed_cases), + total_sla_breaches=self.metrics.total_sla_breaches, + total_rejected=self.metrics.total_rejected, + escalation_budget_remaining=self.escalation_budget_remaining, + backlog_pressure=sigs.backlog_pressure, + sla_risk_score=sigs.sla_risk_score, + fairness_index=sigs.fairness_index, + resource_utilization=sigs.resource_utilization, + digital_intake_ratio=sigs.digital_intake_ratio, + blocked_cases_missing_docs=sigs.blocked_cases_missing_docs, + field_verification_load=sigs.field_verification_load, + active_events=active_events, + last_action_valid=self.last_action_valid, + last_action_message=self.last_action_message, + last_action_explanation=self.last_action_explanation, + pending_doc_resolutions=pending_doc, + pending_officer_reallocations=pending_officer, + ) + + def _init_episode_state(self) -> None: + self.seed = self.task.seed + self.rng = random.Random(self.seed) + self.episode_id = f"{self.task_id}-s{self.seed}-init" + self.day = 0 + self.total_steps = 0 + self.terminated = False + self.truncated = False + self.priority_mode = PriorityMode.BALANCED + self.officer_pool = OfficerPool( + total_officers=1, + available_officers=1, + allocated={}, + pending_reallocation={}, + ) + self.active_cases: list[ApplicationCase] = [] + self.completed_cases: list[ApplicationCase] = [] + self.escalation_budget_remaining = 0 + self.arrived_by_service: dict[ServiceType, int] = {} + self.completed_by_service: dict[ServiceType, int] = {} + self.metrics = EpisodeMetrics() + self.action_history: list[dict] = [] + self.last_action_valid = True + self.last_action_message = "" + self.last_action_explanation = "" + self.event_engine = EventEngine(seed=self.seed, scenario_mode=ScenarioMode.NORMAL) + self.simulator = DaySimulator(self.task, self.rng, self.event_engine) + self.signal_computer = SignalComputer() + + def _count_pending_effects(self) -> int: + doc_pending = sum( + 1 for c in self.active_cases + if c.doc_resolution_day is not None + and c.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS + ) + fv_pending = sum( + 1 for c in self.active_cases + if c.internal_substate == InternalSubstate.FIELD_VERIFICATION_PENDING + and c.field_verification_completion_day is not None + ) + return doc_pending + fv_pending + + @property + def fairness_gap(self) -> float: + return completion_fairness_gap(self.arrived_by_service, self.completed_by_service) + + @property + def total_completed(self) -> int: + return len(self.completed_cases) + + @property + def total_backlog(self) -> int: + return len(self.active_cases) diff --git a/app/event_engine.py b/app/event_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..3ef73bd6e1b11fcf7adea0a9f573d9e448c41569 --- /dev/null +++ b/app/event_engine.py @@ -0,0 +1,101 @@ +""" +event_engine.py — Gov Workflow OpenEnv v2.0 +Deterministic daily event system. Same seed + day + scenario = same events always. +""" +import random +from typing import List +from app.models import EventType, ScenarioMode, TaskConfig + +SCENARIO_MULTIPLIER = { + ScenarioMode.NORMAL: 1.0, + ScenarioMode.CRISIS: 2.0, + ScenarioMode.EXTREME_OVERLOAD: 3.5, +} + +BASE_PROBS = { + EventType.SURGE_APPLICATIONS: 0.08, + EventType.OFFICER_UNAVAILABLE: 0.07, + EventType.DOCUMENT_REJECTION_SPIKE: 0.10, + EventType.REVENUE_DB_DELAY: 0.06, + EventType.SLA_ESCALATION_ORDER: 0.05, +} + +EVENT_EFFECTS = { + EventType.SURGE_APPLICATIONS: + {ScenarioMode.NORMAL: 1.3, ScenarioMode.CRISIS: 1.5, ScenarioMode.EXTREME_OVERLOAD: 2.0}, + EventType.OFFICER_UNAVAILABLE: + {ScenarioMode.NORMAL: 1, ScenarioMode.CRISIS: 1, ScenarioMode.EXTREME_OVERLOAD: 2}, + EventType.DOCUMENT_REJECTION_SPIKE: + {ScenarioMode.NORMAL: 0.15, ScenarioMode.CRISIS: 0.20, ScenarioMode.EXTREME_OVERLOAD: 0.35}, + EventType.REVENUE_DB_DELAY: + {ScenarioMode.NORMAL: 0.30, ScenarioMode.CRISIS: 0.40, ScenarioMode.EXTREME_OVERLOAD: 0.60}, + EventType.SLA_ESCALATION_ORDER: + {ScenarioMode.NORMAL: 0.50, ScenarioMode.CRISIS: 0.50, ScenarioMode.EXTREME_OVERLOAD: 0.40}, +} + + +class DayEventParams: + def __init__(self): + self.arrival_multiplier: float = 1.0 + self.officer_reduction: int = 0 + self.doc_defect_rate_boost: float = 0.0 + self.system_dependency_boost: float = 0.0 + self.sla_window_multiplier: float = 1.0 + self.active_events: List[EventType] = [] + + def has_events(self) -> bool: + return bool(self.active_events) + + +class EventEngine: + def __init__(self, seed: int, scenario_mode: ScenarioMode): + self.seed = seed + self.scenario_mode = scenario_mode + self._multiplier = SCENARIO_MULTIPLIER[scenario_mode] + + def get_events_for_day(self, day: int, task_config: "TaskConfig") -> List[EventType]: + day_rng = random.Random(self.seed + day * 31337) + active = [] + for event_type in task_config.allowed_events: + if event_type == EventType.NO_EVENT: + continue + base_prob = BASE_PROBS.get(event_type, 0.0) + effective_prob = min(0.80, base_prob * self._multiplier) + if day_rng.random() < effective_prob: + active.append(event_type) + return active if active else [EventType.NO_EVENT] + + def apply_events(self, events: List[EventType], task_config: "TaskConfig") -> DayEventParams: + params = DayEventParams() + for event in events: + if event == EventType.NO_EVENT: + continue + params.active_events.append(event) + magnitude = EVENT_EFFECTS.get(event, {}).get(self.scenario_mode, 0) + if event == EventType.SURGE_APPLICATIONS: + params.arrival_multiplier *= magnitude + elif event == EventType.OFFICER_UNAVAILABLE: + params.officer_reduction += int(magnitude) + elif event == EventType.DOCUMENT_REJECTION_SPIKE: + params.doc_defect_rate_boost += magnitude + elif event == EventType.REVENUE_DB_DELAY: + params.system_dependency_boost += magnitude + elif event == EventType.SLA_ESCALATION_ORDER: + params.sla_window_multiplier = min(params.sla_window_multiplier, magnitude) + if not params.active_events: + params.active_events = [EventType.NO_EVENT] + return params + + def describe_events(self, events: List[EventType]) -> str: + descriptions = { + EventType.SURGE_APPLICATIONS: "Digital surge: arrivals increased", + EventType.OFFICER_UNAVAILABLE: "Officer absent: reduced capacity", + EventType.DOCUMENT_REJECTION_SPIKE: "Doc rejection spike: higher defect rate", + EventType.REVENUE_DB_DELAY: "Revenue DB delay: land records slower", + EventType.SLA_ESCALATION_ORDER: "SLA escalation order: deadlines tightened", + EventType.NO_EVENT: "No active events today", + } + active = [e for e in events if e != EventType.NO_EVENT] + if not active: + return "No active events today" + return "; ".join(descriptions.get(e, str(e)) for e in active) diff --git a/app/graders.py b/app/graders.py new file mode 100644 index 0000000000000000000000000000000000000000..74eefe8c298980a962202101af7ce42a4df2a6f4 --- /dev/null +++ b/app/graders.py @@ -0,0 +1,176 @@ +""" +graders.py — Gov Workflow OpenEnv: Deterministic Episode Graders + +Rules: + - All graders read ONLY from EpisodeStateModel flat fields. + - No access to env internals, EpisodeMetrics, or reward breakdown proxies. + - GraderResult uses the aligned schema (score, grader_name, named metric fields). + - grade_episode() dispatches by task_id. + +Grader weights: + Easy — completion(0.45) + SLA(0.35) + idle_efficiency(0.20) = 1.00 + Medium — completion(0.35) + SLA(0.30) + doc_rework(0.20) + urgent(0.15) = 1.00 + Hard — completion(0.28) + SLA(0.24) + doc_rework(0.16) + + fairness(0.16) + escalation_discipline(0.16) = 1.00 +""" +from __future__ import annotations +from app.models import EpisodeStateModel, GraderResult + + +# ───────────────────────────────────────────────────────────────────────────── +# INTERNAL HELPERS +# ───────────────────────────────────────────────────────────────────────────── + +def _safe_ratio(num: float, den: float, default: float = 1.0) -> float: + """Safe division, clamped to [0.0, 1.0]. Returns `default` when den ≤ 0.""" + if den <= 0: + return max(0.0, min(1.0, default)) + return max(0.0, min(1.0, num / den)) + + +def _b(value: float) -> float: + """Clamp any float to [0.0, 1.0].""" + return max(0.0, min(1.0, float(value))) + + +def _extract(state: EpisodeStateModel) -> dict[str, float]: + """ + Extract all grader input metrics from EpisodeStateModel flat fields. + + Design note: + - total_arrived : populated by env.state() from metrics.total_arrived + - fairness_gap : computed by completion_fairness_gap() in env.state() + - All other fields are direct EpisodeStateModel attributes. + """ + total_arrived = max(1, state.total_arrived) + total_completed = float(state.total_completed) + total_breaches = float(state.total_sla_breaches) + total_docs_req = float(state.total_docs_requested) + total_docs_cleared = float(state.total_docs_cleared) + total_urgent_arr = float(state.total_urgent_arrived) + total_urgent_comp = float(state.total_urgent_completed) + total_idle = float(state.total_idle_officer_days) + total_capacity = float(state.total_capacity_days) + total_escused = float(state.total_escalations_used) + total_wasted_esc = float(state.total_wasted_escalations) + fairness_gap = float(state.fairness_gap) + + return { + "completion_rate": _b(_safe_ratio(total_completed, total_arrived, 0.0)), + "sla_compliance": _b(1.0 - _safe_ratio(total_breaches, total_arrived, 0.0)), + "document_rework_quality": _b(_safe_ratio(total_docs_cleared, total_docs_req, 1.0)), + "urgent_served_rate": _b(_safe_ratio(total_urgent_comp, total_urgent_arr, 1.0)), + "fairness_score": _b(1.0 - fairness_gap), + "escalation_discipline": _b(1.0 - _safe_ratio(total_wasted_esc, max(1.0, total_escused), 0.0)), + "idle_efficiency": _b(1.0 - _safe_ratio(total_idle, max(1.0, total_capacity), 0.0)), + "fairness_gap": round(fairness_gap, 4), + } + + +def _build_result( + state: EpisodeStateModel, + score: float, + grader_name: str, + m: dict[str, float], +) -> GraderResult: + """Assemble a fully-populated GraderResult from metric dict and state.""" + total_arrived = max(0, state.total_arrived) + avg_wait = state.avg_waiting_days + + return GraderResult( + task_id=state.task_id, + episode_id=state.episode_id, + grader_name=grader_name, + score=_b(score), + completion_rate=m["completion_rate"], + sla_compliance_rate=m["sla_compliance"], + idle_efficiency=m["idle_efficiency"], + document_rework_quality=m["document_rework_quality"], + urgent_served_rate=m["urgent_served_rate"], + fairness_score=m["fairness_score"], + escalation_discipline=m["escalation_discipline"], + fairness_gap=m["fairness_gap"], + total_cases_arrived=total_arrived, + total_completed=state.total_completed, + total_sla_breached=state.total_sla_breaches, + total_rejected=state.total_rejected, + avg_waiting_days=avg_wait, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# TASK GRADERS +# ───────────────────────────────────────────────────────────────────────────── + +def grade_easy(state: EpisodeStateModel) -> GraderResult: + """ + district_backlog_easy grader. + Focus: raw throughput and SLA hygiene under simple single-service load. + + Weights: completion(0.45) + SLA(0.35) + idle_efficiency(0.20) + """ + m = _extract(state) + score = ( + 0.45 * m["completion_rate"] + + 0.35 * m["sla_compliance"] + + 0.20 * m["idle_efficiency"] + ) + return _build_result(state, score, "easy", m) + + +def grade_medium(state: EpisodeStateModel) -> GraderResult: + """ + mixed_urgency_medium grader. + Focus: throughput + SLA + document quality + prioritizing urgent cases. + + Weights: completion(0.35) + SLA(0.30) + doc_rework(0.20) + urgent(0.15) + """ + m = _extract(state) + score = ( + 0.35 * m["completion_rate"] + + 0.30 * m["sla_compliance"] + + 0.20 * m["document_rework_quality"] + + 0.15 * m["urgent_served_rate"] + ) + return _build_result(state, score, "medium", m) + + +def grade_hard(state: EpisodeStateModel) -> GraderResult: + """ + cross_department_hard grader. + Focus: all-round excellence including cross-service fairness and + restrained escalation use under crisis conditions. + + Weights: completion(0.28) + SLA(0.24) + doc_rework(0.16) + + fairness(0.16) + escalation_discipline(0.16) + """ + m = _extract(state) + score = ( + 0.28 * m["completion_rate"] + + 0.24 * m["sla_compliance"] + + 0.16 * m["document_rework_quality"] + + 0.16 * m["fairness_score"] + + 0.16 * m["escalation_discipline"] + ) + return _build_result(state, score, "hard", m) + + +# ───────────────────────────────────────────────────────────────────────────── +# DISPATCHER +# ───────────────────────────────────────────────────────────────────────────── + +_GRADER_MAP = { + "district_backlog_easy": grade_easy, + "district_backlog_easy_extreme": grade_easy, + "mixed_urgency_medium": grade_medium, + "cross_department_hard": grade_hard, +} + + +def grade_episode(state: EpisodeStateModel) -> GraderResult: + """ + Dispatch to the correct task grader. + Falls back to grade_hard for unknown task IDs (safe default for new tasks). + """ + grader_fn = _GRADER_MAP.get(state.task_id, grade_hard) + return grader_fn(state) \ No newline at end of file diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000000000000000000000000000000000000..549de3d4ff0510c49c93693894a3f755872e2415 --- /dev/null +++ b/app/main.py @@ -0,0 +1,2676 @@ +""" +main.py — Gov Workflow OpenEnv: FastAPI HTTP wrapper. + +Session model +───────────── +Every POST /reset creates a new session identified by a UUID. +All subsequent calls (step, state, grade) carry that session_id in the +request body. Sessions are kept in a thread-safe in-memory OrderedDict. +When the store reaches max_sessions capacity the oldest session is evicted +automatically (oldest-first FIFO eviction). + +IMPORTANT: the in-memory store is NOT shared across multiple OS processes. +Run with workers=1 (the default from ServerSettings) to keep this correct. + +Endpoint map +──────────── +GET /health server + session health +POST /reset create session, returns session_id + obs +POST /step advance one simulation tick +POST /state (GET /state) full episode state, action_history optional +POST /grade task-specific deterministic grader +GET /sessions list active session IDs +DELETE /sessions/{id} remove a session +POST /api/auto_step policy selects action, then steps +POST /api/benchmark run multiple baseline episodes +GET /api/openenv_compliance OpenEnv interface compliance check +GET /docs Swagger UI (FastAPI auto-generated) +GET /redoc ReDoc UI (FastAPI auto-generated) +""" +from __future__ import annotations + +from collections import OrderedDict +import json +import math +import os +from pathlib import Path +import shutil +import subprocess +from threading import Lock +import time +from typing import Any, Literal +from uuid import uuid4 + +from fastapi import APIRouter, Body, FastAPI, HTTPException, Query, status +from fastapi.middleware.cors import CORSMiddleware +from fastapi.routing import APIRoute +from fastapi.responses import FileResponse, RedirectResponse, StreamingResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel, Field + +from app.baselines import POLICIES, run_policy_episode +from app.config import env_settings, server_settings +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ( + ActionModel, + EpisodeStateModel, + GraderResult, + ObservationModel, + ServiceType, + StepInfoModel, +) +from app.persistence import PersistenceStore +from app.simulator import LiveSimulationSession, SimulationAgentMode, run_simulation +from app.tasks import TASKS, get_task, list_benchmark_tasks, list_tasks +from app.training_jobs import TrainingJobManager +from app.sector_profiles import get_sector_profile +from app.story_router import router as story_router +from rl.action_mask import ActionMaskComputer +from rl.feature_builder import ACTION_DECODE_TABLE, N_ACTIONS + +try: + from sse_starlette.sse import EventSourceResponse +except Exception: + class EventSourceResponse(StreamingResponse): # type: ignore[misc] + def __init__(self, content: Any, status_code: int = 200, headers: dict[str, str] | None = None): + merged_headers = {"Cache-Control": "no-cache", "Connection": "keep-alive"} + if headers: + merged_headers.update(headers) + super().__init__( + content=content, + status_code=status_code, + media_type="text/event-stream", + headers=merged_headers, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# SESSION STORE +# ───────────────────────────────────────────────────────────────────────────── + +class SessionStore: + """ + Thread-safe in-memory session registry. + + Design decisions: + - Uses threading.Lock — safe for Uvicorn's single-worker async+thread model. + - Uses OrderedDict so eviction is always oldest-first in O(1) via popitem. + - Never imports from FastAPI. HTTP concerns (404 conversion) stay in endpoints. + - KeyError propagates upward and is converted to 404 there. + """ + + def __init__(self, max_sessions: int | None) -> None: + self.store: OrderedDict[str, GovWorkflowEnv] = OrderedDict() + self.lock = Lock() + self.max = max_sessions + + def create( + self, + task_id: str, + seed: int | None = None, + options: dict[str, Any] | None = None, + ) -> tuple[str, ObservationModel, dict[str, Any]]: + env = GovWorkflowEnv(task_id=task_id) + obs, info = env.reset(seed=seed, options=options) + session_id = str(uuid4()) + with self.lock: + if self.max and len(self.store) >= self.max: + self.store.popitem(last=False) # evict oldest + self.store[session_id] = env + return session_id, obs, info + + def get(self, session_id: str) -> GovWorkflowEnv: + with self.lock: + env = self.store.get(session_id) + if env is None: + raise KeyError(session_id) + return env + + def delete(self, session_id: str) -> bool: + with self.lock: + return self.store.pop(session_id, None) is not None + + def active_count(self) -> int: + with self.lock: + return len(self.store) + + def list_ids(self) -> list[str]: + with self.lock: + return list(self.store.keys()) + + +class SimulationRunStore: + def __init__(self, max_runs: int | None = None) -> None: + self.store: OrderedDict[str, LiveSimulationSession] = OrderedDict() + self.lock = Lock() + self.max = max_runs + + def create(self, run: LiveSimulationSession) -> str: + run_id = str(uuid4()) + with self.lock: + if self.max and len(self.store) >= self.max: + _, evicted = self.store.popitem(last=False) + try: + evicted.close() + except Exception: + pass + self.store[run_id] = run + return run_id + + def get(self, run_id: str) -> LiveSimulationSession: + with self.lock: + run = self.store.get(run_id) + if run is None: + raise KeyError(run_id) + return run + + def delete(self, run_id: str) -> bool: + with self.lock: + run = self.store.pop(run_id, None) + if run is None: + return False + try: + run.close() + except Exception: + pass + return True + + def list_ids(self) -> list[str]: + with self.lock: + return list(self.store.keys()) + + +# ───────────────────────────────────────────────────────────────────────────── +# GLOBALS +# ───────────────────────────────────────────────────────────────────────────── + +REPO_ROOT = Path(__file__).resolve().parent.parent + +persistence = PersistenceStore(repo_root=REPO_ROOT) +sessions = SessionStore(max_sessions=env_settings.max_sessions) +model_cache: dict[tuple[str, str], Any] = {} +model_cache_lock = Lock() +training_jobs = TrainingJobManager(repo_root=REPO_ROOT, persistence=persistence) +sim_runs = SimulationRunStore(max_runs=max(env_settings.max_sessions, 50)) +session_meta: dict[str, dict[str, Any]] = {} +session_meta_lock = Lock() + + +def _set_session_meta(session_id: str, **kwargs: Any) -> None: + with session_meta_lock: + meta = session_meta.setdefault(session_id, {}) + meta.update(kwargs) + + +def _get_session_meta(session_id: str) -> dict[str, Any]: + with session_meta_lock: + return dict(session_meta.get(session_id, {})) + + +def _append_session_trace(session_id: str, row: dict[str, Any]) -> None: + with session_meta_lock: + meta = session_meta.setdefault(session_id, {}) + trace = meta.setdefault("step_trace", []) + if isinstance(trace, list): + trace.append(row) + else: + meta["step_trace"] = [row] + + +def _pop_session_meta(session_id: str) -> None: + with session_meta_lock: + session_meta.pop(session_id, None) + + +# ───────────────────────────────────────────────────────────────────────────── +# DEPENDENCY HELPERS +# ───────────────────────────────────────────────────────────────────────────── + +def get_or_404(session_id: str) -> GovWorkflowEnv: + """Fetch a session env by ID or raise HTTP 404.""" + try: + return sessions.get(session_id) + except KeyError: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Session '{session_id}' not found. Call POST /reset to create a new session.", + ) + + +def _get_session_or_404(session_id: str) -> GovWorkflowEnv: + return get_or_404(session_id) + + +def get_sim_or_404(run_id: str) -> LiveSimulationSession: + try: + return sim_runs.get(run_id) + except KeyError: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Simulation run '{run_id}' not found. Call POST /api/simulation/live/start to create a live run.", + ) + + +def resolve_policy_or_422(policy_name: str): + policy = POLICIES.get(policy_name) + if policy is None: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown agent/policy '{policy_name}'. Available: {sorted(POLICIES.keys())}", + ) + return policy + + +def resolve_model_path_or_422(model_path: str) -> Path: + path = Path(model_path) + if not path.suffix: + path = path.with_suffix(".zip") + if not path.is_absolute(): + path = (REPO_ROOT / path).resolve() + if not path.exists(): + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Model checkpoint not found: {path}", + ) + return path + + +def load_model_cached_or_503(model_path: Path, model_type: str): + cache_key = (str(model_path), model_type) + with model_cache_lock: + cached = model_cache.get(cache_key) + if cached is not None: + return cached + try: + if model_type == "maskable": + try: + from sb3_contrib import MaskablePPO # type: ignore[import-not-found] + except ModuleNotFoundError: + from sb3contrib import MaskablePPO # type: ignore[import-not-found] + model = MaskablePPO.load(str(model_path)) + else: + try: + from sb3_contrib import RecurrentPPO # type: ignore[import-not-found] + except ModuleNotFoundError: + from sb3contrib import RecurrentPPO # type: ignore[import-not-found] + model = RecurrentPPO.load(str(model_path)) + except ModuleNotFoundError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="RL runtime dependencies are not available. Install requirements-rl.txt.", + ) from exc + except Exception as exc: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Failed to load {model_type} model from {model_path}: {exc}", + ) from exc + with model_cache_lock: + model_cache[cache_key] = model + return model + + +def decode_action_index(action_idx: int) -> str: + try: + from rl.feature_builder import ACTION_DECODE_TABLE + except ModuleNotFoundError: + return f"action={action_idx}" + row = ACTION_DECODE_TABLE.get(action_idx) + if row is None: + return f"action={action_idx}" + action_type, service, priority_mode, delta = row + extras = [] + if service is not None: + extras.append(f"service={service}") + if priority_mode is not None: + extras.append(f"mode={priority_mode}") + if delta is not None: + extras.append(f"delta={delta}") + if extras: + return f"{action_type}[{', '.join(extras)}]" + return action_type + + +def _validate_task_id_or_422(task_id: str) -> str: + tasks = list_tasks() + if task_id not in set(tasks): + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id '{task_id}'. Available: {tasks}", + ) + return task_id + + +def _task_prob_mean(task_cfg: Any, field_name: str, default_getter: str) -> float: + override = getattr(task_cfg, field_name, None) or {} + if isinstance(override, dict) and override: + values = [float(v) for v in override.values()] + return float(sum(values) / max(len(values), 1)) + + probs: list[float] = [] + for service in getattr(task_cfg, "enabled_services", []): + try: + profile = get_sector_profile(service) + probs.append(float(getattr(profile, default_getter))) + except Exception: + continue + if not probs: + return 0.0 + return float(sum(probs) / len(probs)) + + +def _task_summary_dict(task_id: str) -> dict[str, Any]: + cfg = get_task(task_id) + services = [s.value if hasattr(s, "value") else str(s) for s in getattr(cfg, "enabled_services", [])] + pool = getattr(cfg, "initial_officer_pool", None) + officer_pool_total = int(getattr(pool, "total_officers", 0) or 0) if pool is not None else 0 + reserve_officers = int(getattr(pool, "idle_officers", 0) or 0) if pool is not None else 0 + return { + "task_id": str(task_id), + "seed": int(getattr(cfg, "seed", 0) or 0), + "max_days": int(getattr(cfg, "max_days", 0) or 0), + "services": services, + "officer_pool_total": officer_pool_total, + "reserve_officers": reserve_officers, + "escalation_budget": int(getattr(cfg, "escalation_budget", 0) or 0), + "missing_docs_probability": _task_prob_mean(cfg, "missing_docs_probability_override", "missing_docs_probability"), + "field_verification_probability": _task_prob_mean( + cfg, + "field_verification_probability_override", + "field_verification_probability", + ), + "scenario_mode": str(getattr(getattr(cfg, "scenario_mode", "normal"), "value", getattr(cfg, "scenario_mode", "normal"))), + "fairness_threshold": getattr(cfg, "fairness_threshold", None), + } + + +def _action_service_hint(action: ActionModel) -> str | None: + for attr in ("service", "service_target", "escalation_target"): + value = getattr(action, attr, None) + if value is None: + continue + return value.value if hasattr(value, "value") else str(value) + if getattr(action, "capacity_assignment", None): + keys = list((action.capacity_assignment or {}).keys()) + if keys: + key = keys[0] + return key.value if hasattr(key, "value") else str(key) + if getattr(action, "reallocation_delta", None): + for key, delta in (action.reallocation_delta or {}).items(): + if int(delta) < 0: + return key.value if hasattr(key, "value") else str(key) + return None + + +def _result_value(result: Any, key: str, default: Any = None) -> Any: + """Read from dict-like or attribute-like result payloads.""" + if isinstance(result, dict): + return result.get(key, default) + return getattr(result, key, default) + + +def _log_line_text(value: Any) -> str: + """Normalize live-simulation log payloads to plain text.""" + if isinstance(value, str): + return value + if isinstance(value, dict): + raw = value.get("log") + if isinstance(raw, str): + return raw + try: + return json.dumps(value, separators=(",", ":")) + except Exception: + return str(value) + if value is None: + return "" + return str(value) + + +def _phase_model_dirs() -> list[Path]: + base = REPO_ROOT / "results" / "best_model" + return [ + base / "phase1", + base / "phase2", + ] + + +def _discover_phase12_zip_models() -> list[Path]: + discovered: list[Path] = [] + for model_dir in _phase_model_dirs(): + if not model_dir.exists(): + continue + for file_path in sorted(model_dir.glob("*.zip")): + if file_path.is_file(): + discovered.append(file_path.resolve()) + unique = sorted({p for p in discovered if p.exists()}) + return unique + + +def _phase_from_model_path(path: Path) -> int: + parent = path.parent.name.lower() + if parent == "phase1": + return 1 + if parent == "phase2": + return 2 + name = path.name.lower() + if "phase1" in name: + return 1 + if "phase2" in name: + return 2 + return 0 + + +# ───────────────────────────────────────────────────────────────────────────── +# API REQUEST / RESPONSE SCHEMAS +# ───────────────────────────────────────────────────────────────────────────── + +class HealthResponse(BaseModel): + status: str + version: str + phase: str | None = None + detail: str | None = None + active_sessions: int + available_tasks: list[str] + + +class ResetRequest(BaseModel): + task_id: str = Field( + default=env_settings.default_task_id, + description="Task to run. One of the three benchmark task IDs.", + ) + seed: int | None = Field( + default=None, + description=( + "RNG seed. Omit to use the task's built-in deterministic seed. " + "Pass an explicit integer to replay the same episode." + ), + ) + options: dict[str, Any] | None = Field( + default=None, + description=( + "Optional overrides forwarded verbatim to env.reset(options=...). " + "Supported key: 'task_id' to switch tasks inside an existing session." + ), + ) + + +class ResetResponse(BaseModel): + session_id: str + task_id: str | None = None + seed: int | None = None + observation: ObservationModel + info: dict[str, Any] + + +class StepRequest(BaseModel): + session_id: str = Field(description="Session ID returned by POST /reset.") + action: ActionModel + + +class StepResponse(BaseModel): + session_id: str + observation: ObservationModel + reward: float + done: bool + terminated: bool + truncated: bool + info: StepInfoModel + + +class StateRequest(BaseModel): + session_id: str = Field(description="Session ID returned by POST /reset.") + include_action_history: bool = Field( + default=False, + description=( + "When False (default) the action_history list is stripped to keep payloads small. " + "Set True to receive the full step-by-step action log." + ), + ) + + +class StateResponse(BaseModel): + session_id: str + state: EpisodeStateModel + + +class GradeRequest(BaseModel): + session_id: str = Field(description="Session ID returned by POST /reset.") + + +class GradeResponse(BaseModel): + session_id: str + task_id: str | None = None + score: float = Field(ge=0.0, le=1.0, description="Episode score in [0.0, 1.0].") + grader_name: str + metrics: dict[str, float] + + +class SessionListResponse(BaseModel): + active_sessions: int + session_ids: list[str] + + +class DeleteSessionResponse(BaseModel): + deleted: str + + +class TaskListResponse(BaseModel): + tasks: list[str] + + +class TaskSummary(BaseModel): + task_id: str + seed: int + max_days: int + services: list[str] + officer_pool_total: int + reserve_officers: int + escalation_budget: int + missing_docs_probability: float + field_verification_probability: float + scenario_mode: str + fairness_threshold: float | None = None + + +class ActionMaskRequest(BaseModel): + session_id: str + + +class ActionMaskResponse(BaseModel): + session_id: str + action_mask: list[bool] + valid_action_indices: list[int] + valid_action_labels: list[str] + total_valid: int + total_actions: int + + +class RLRunV2Request(BaseModel): + task_id: str + model_path: str + seed: int = 42 + max_steps: int = Field(default=80, ge=1, le=2000) + n_episodes: int = Field(default=1, ge=1, le=100) + + +class RLRunV2Response(BaseModel): + task_id: str + model_path: str + seed: int + n_episodes: int + mean_score: float + mean_reward: float + mean_completed: int + mean_sla_breaches: int + episodes: list[dict[str, Any]] + + +class ModelInfo(BaseModel): + model_path: str + task_id: str + phase: int + size_mb: float + exists: bool + + +class SimulateRequest(BaseModel): + task_id: str = "district_backlog_easy" + agent_mode: str = "baseline_policy" + max_steps: int = Field(default=40, ge=1, le=500) + seed: int = 42 + policy_name: str | None = "backlog_clearance" + model_path: str | None = None + + +class AutoStepRequest(BaseModel): + session_id: str = Field(description="Session ID returned by POST /reset.") + agent_policy: str = Field( + default="backlog_clearance", + description="Policy name from app.baselines.POLICIES.", + ) + + +class AutoStepResponse(BaseModel): + session_id: str + agent_policy: str + action: ActionModel + observation: ObservationModel + reward: float + done: bool + terminated: bool + truncated: bool + info: StepInfoModel + + +class BenchmarkRequest(BaseModel): + task_id: str = Field(default=env_settings.default_task_id) + agent_policies: list[str] = Field( + default_factory=lambda: ["urgent_first", "oldest_first", "backlog_clearance"] + ) + runs: int = Field(default=5, ge=1, le=30) + max_steps: int = Field(default=500, ge=1, le=2000) + seed_base: int | None = Field( + default=100, + description="Base seed — each run uses seed_base + run_index.", + ) + + +class BenchmarkAgentRun(BaseModel): + run_index: int + seed: int | None + score: float + reward_sum: float + completed: int + backlog: int + steps: int + + +class BenchmarkAgentSummary(BaseModel): + agent_policy: str + average_score: float + min_score: float + max_score: float + runs: list[BenchmarkAgentRun] + + +class BenchmarkResponse(BaseModel): + task_id: str + requested_runs: int + agent_results: list[BenchmarkAgentSummary] + + +class WorkflowComponentStatus(BaseModel): + component: str + description: str + available: bool + command: str | None = None + notes: str | None = None + + +class WorkflowComponentsResponse(BaseModel): + components: list[WorkflowComponentStatus] + + +class OpenEnvComplianceItem(BaseModel): + key: str + label: str + status: Literal["pass", "fail", "unknown"] + detail: str + + +class OpenEnvComplianceResponse(BaseModel): + checked_at: float + items: list[OpenEnvComplianceItem] + openenv_validate_exit_code: int | None = None + openenv_validate_stdout_tail: str | None = None + openenv_validate_stderr_tail: str | None = None + + +class WorkflowRunRequest(BaseModel): + workflow_id: Literal["baseline_openai", "inference", "phase2_eval"] + timeout_seconds: int = Field(default=180, ge=10, le=1200) + max_steps: int = Field(default=40, ge=1, le=500) + episodes: int = Field(default=3, ge=1, le=20) + model_path: str = Field(default="results/best_model/phase2_final.zip") + model_type: Literal["maskable", "recurrent"] = Field(default="maskable") + + +class WorkflowRunResponse(BaseModel): + workflow_id: str + command: list[str] + exit_code: int + duration_seconds: float + stdout: str + stderr: str + timed_out: bool + + +class RLModelInfo(BaseModel): + label: str + path: str + exists: bool + model_type: Literal["maskable", "recurrent"] + + +class RLModelsResponse(BaseModel): + models: list[RLModelInfo] + + +class RLRunRequest(BaseModel): + task_id: str = Field(default=env_settings.default_task_id) + model_path: str = Field(default="results/best_model/phase2_final.zip") + model_type: Literal["maskable", "recurrent"] = Field(default="maskable") + max_steps: int = Field(default=80, ge=1, le=1000) + seed: int | None = Field(default=None) + + +class RLRunStep(BaseModel): + step: int + action_index: int + action_label: str + reward: float + backlog: int + completed: int + sla_breaches: int + fairness_gap: float + done: bool + + +class RLRunResponse(BaseModel): + model_path: str + model_type: Literal["maskable", "recurrent"] + task_id: str + seed: int + total_steps: int + total_reward: float + grader_score: float + grader_name: str + trace: list[RLRunStep] + + +class RLEvaluateRequest(BaseModel): + model_path: str = Field(default="results/best_model/phase2_final.zip") + model_type: Literal["auto", "maskable", "recurrent"] = Field(default="auto") + episodes: int = Field(default=3, ge=1, le=20) + task_ids: list[str] = Field(default_factory=list) + + +class RLEvaluateTaskResult(BaseModel): + task_id: str + grader_score: float + total_reward: float + total_steps: int + total_completed: int + total_sla_breaches: int + fairness_gap: float + + +class RLEvaluateResponse(BaseModel): + model_path: str + model_type: Literal["auto", "maskable", "recurrent"] + episodes: int + average_grader_score: float + results: list[RLEvaluateTaskResult] + + +class SimulationRequest(BaseModel): + task_id: str = Field(default=env_settings.default_task_id) + agent_mode: SimulationAgentMode = Field(default=SimulationAgentMode.BASELINE_POLICY) + max_steps: int = Field(default=80, ge=1, le=500) + seed: int | None = Field(default=None) + policy_name: str = Field(default="backlog_clearance") + model_path: str | None = Field(default=None) + model_type: Literal["maskable", "recurrent"] = Field(default="maskable") + + +class SimulationStep(BaseModel): + step: int + day: int + action_type: str + action_payload: dict[str, Any] + reward: float + done: bool + backlog: int + completed: int + sla_breaches: int + fairness_gap: float + escalation_budget_remaining: int + invalid_action: bool + last_action_error: str | None = None + queue_rows: list[dict[str, Any]] + action_index: int | None = None + decision_source: str | None = None + provider: str | None = None + model_used: str | None = None + llm_attempts: int | None = None + llm_error: str | None = None + llm_key_label: str | None = None + repair_note: str | None = None + switch_note: str | None = None + + +class SimulationResponse(BaseModel): + task_id: str + agent_mode: SimulationAgentMode + seed: int + total_reward: float + score: float + grader_name: str + summary: dict[str, Any] + trace: list[SimulationStep] + + +class SimulationLiveStartRequest(SimulationRequest): + pass + + +class SimulationLiveStartResponse(BaseModel): + run_id: str + task_id: str + agent_mode: SimulationAgentMode + seed: int + max_steps: int + start_log: str + route_plan: list[str] = Field(default_factory=list) + + +class SimulationLiveStepRequest(BaseModel): + run_id: str + + +class SimulationLiveStepResponse(BaseModel): + run_id: str + done: bool + step: SimulationStep | None = None + step_log: str | None = None + end_log: str | None = None + total_reward: float + score: float | None = None + grader_name: str | None = None + summary: dict[str, Any] | None = None + + +class SimulationLiveStateResponse(BaseModel): + run_id: str + state: dict[str, Any] + + +class TrainingJobStartRequest(BaseModel): + phase: Literal[1, 2] = Field(default=2) + timesteps: int = Field(default=120_000, ge=10_000, le=2_000_000) + n_envs: int = Field(default=4, ge=1, le=16) + seed: int | None = Field( + default=None, + description="When omitted, a time-based seed is auto-generated.", + ) + config_path: str | None = Field(default=None) + + +class TrainingJobStopResponse(BaseModel): + stopped: bool + job_id: str + status: str + + +class TrainingJobDeleteResponse(BaseModel): + deleted: bool + job_id: str + + +class TrainingJobsListResponse(BaseModel): + jobs: list[dict[str, Any]] + + +class SimulationHistoryListResponse(BaseModel): + runs: list[dict[str, Any]] + + +class ComparisonHistoryCreateRequest(BaseModel): + task_id: str + baseline_policy: str + model_path: str + model_type: str + include_llm: bool = True + runs: int + steps: int + episodes: int + seed_base: int + result: dict[str, Any] + + +class ComparisonHistoryCreateResponse(BaseModel): + comparison_id: str + + +class ComparisonHistoryListResponse(BaseModel): + comparisons: list[dict[str, Any]] + + +class HistoryClearResponse(BaseModel): + cleared: bool + deleted_rows: int + scope: str + + +class ComparisonHistoryRepairResponse(BaseModel): + comparison_id: str + repaired: bool + detail: str + + +# ───────────────────────────────────────────────────────────────────────────── +# APPLICATION +# ───────────────────────────────────────────────────────────────────────────── + +app = FastAPI( + title="Gov Workflow OpenEnv", + summary="Government-service workflow control — OpenEnv-compatible HTTP API", + description=( + "A real-world OpenEnv-style environment where an AI agent reduces avoidable " + "administrative delay in government-service workflows through queue prioritisation, " + "missing-document handling, officer allocation, escalation control, SLA routing, " + "and fairness management.\n\n" + "**Quick start**\n" + "1. `POST /reset` → get `session_id`\n" + "2. `POST /step` with `session_id` + `action` repeatedly\n" + "3. `POST /grade` to get the deterministic episode score\n" + "4. `DELETE /sessions/{session_id}` to clean up" + ), + version="0.3.0", + docs_url="/docs", + redoc_url="/redoc", +) + +app.include_router(story_router) +app.include_router(story_router, prefix="/api", include_in_schema=False) +app.include_router(story_router, prefix="/api/v1", include_in_schema=False) + +app.add_middleware( + CORSMiddleware, + allow_origins=server_settings.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# ── Static UI (optional Vite build) ───────────────────────────────────────── +REPO_ROOT = Path(__file__).resolve().parent.parent +WEB_DIR = Path(__file__).resolve().parent / "web" +VITE_WEB_DIRS = [ + WEB_DIR / "vite_dist", # Docker image copy target + WEB_DIR / "vite-dist", # legacy/migrated target + REPO_ROOT / "frontend" / "react" / "dist", # local dev build +] + +UI_INDEX_FILE: Path | None = None +UI_ASSETS_DIR: Path | None = None +for _ui_dir in VITE_WEB_DIRS: + if _ui_dir.joinpath("index.html").exists(): + UI_INDEX_FILE = _ui_dir / "index.html" + UI_ASSETS_DIR = _ui_dir / "assets" + break + +if UI_ASSETS_DIR is not None and UI_ASSETS_DIR.exists(): + app.mount("/ui/assets", StaticFiles(directory=str(UI_ASSETS_DIR)), name="ui-assets") + + +@app.get("/", include_in_schema=False) +def root_redirect() -> RedirectResponse: + if UI_INDEX_FILE is None: + return RedirectResponse(url="/docs", status_code=status.HTTP_307_TEMPORARY_REDIRECT) + return RedirectResponse(url="/ui", status_code=status.HTTP_307_TEMPORARY_REDIRECT) + + +@app.get("/ui", include_in_schema=False) +def ui_index() -> FileResponse: + if UI_INDEX_FILE is None: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail="UI bundle not found. Build frontend/react with Vite first.", + ) + return FileResponse( + UI_INDEX_FILE, + headers={ + # Always revalidate HTML shell so users pick up the latest hashed bundle. + "Cache-Control": "no-store, no-cache, must-revalidate", + "Pragma": "no-cache", + "Expires": "0", + }, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# CORE OpenEnv ENDPOINTS +# ───────────────────────────────────────────────────────────────────────────── + +@app.get("/health", response_model=HealthResponse, tags=["meta"], summary="Server and session health") +def health() -> HealthResponse: + """Returns server status, version, active session count, and task list.""" + detail = None + health_status = "ok" + try: + from app.env import GovWorkflowEnv as _EnvHealthCheck # noqa: F401 + except ImportError as exc: + health_status = "degraded" + detail = str(exc) + return HealthResponse( + status=health_status, + version="2.0.0", + phase="3_rl_training", + detail=detail, + active_sessions=sessions.active_count(), + available_tasks=list_tasks(), + ) + + +@app.post( + "/reset", + response_model=ResetResponse, + status_code=status.HTTP_200_OK, + tags=["env"], + summary="Create a new session and return the initial observation", +) +def reset(body: ResetRequest | None = Body(default=None)) -> ResetResponse: + """ + Creates a fresh GovWorkflowEnv episode, registers it in the session store, + and returns a unique session_id with the initial observation. + Use seed for reproducible episodes. + """ + req = body or ResetRequest() + _validate_task_id_or_422(req.task_id) + session_id, obs, info = sessions.create( + task_id=req.task_id, + seed=req.seed, + options=req.options, + ) + _set_session_meta( + session_id, + task_id=req.task_id, + seed=req.seed, + step_trace=[], + ) + return ResetResponse( + session_id=session_id, + task_id=req.task_id, + seed=req.seed, + observation=obs, + info=info, + ) + + +@app.post( + "/step", + response_model=StepResponse, + tags=["env"], + summary="Advance the simulation by one tick", +) +def step(body: StepRequest) -> StepResponse: + """ + Applies one ActionModel to the session's environment and returns the next + observation, reward, termination flags, and step info. + Returns 409 Conflict if the episode has already ended. + """ + env = get_or_404(body.session_id) + if env.terminated or env.truncated: + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail="Episode has already ended (terminated or truncated). Call POST /reset to start a new episode.", + ) + obs, reward, terminated, truncated, info = env.step(body.action) + trace = _get_session_meta(body.session_id).get("step_trace", []) + _append_session_trace( + body.session_id, + { + "step": len(trace) + 1, + "day": int(getattr(obs, "day", 0) or 0), + "action_type": str( + getattr( + getattr(body.action, "action_type", ""), + "value", + getattr(body.action, "action_type", ""), + ) + ), + "service": _action_service_hint(body.action), + "reward": round(float(reward), 4), + "total_backlog": int(getattr(obs, "total_backlog", 0) or 0), + "total_completed": int(getattr(obs, "total_completed", 0) or 0), + "total_sla_breaches": int(getattr(obs, "total_sla_breaches", 0) or 0), + "last_action_valid": bool(getattr(obs, "last_action_valid", True)), + "notes": str(getattr(info, "action_explanation", "")), + }, + ) + return StepResponse( + session_id=body.session_id, + observation=obs, + reward=reward, + done=terminated or truncated, + terminated=terminated, + truncated=truncated, + info=info, + ) + + +@app.post( + "/state", + response_model=StateResponse, + tags=["env"], + summary="Return the full internal episode state", +) +def state_post(body: StateRequest) -> StateResponse: + """ + Returns the complete EpisodeStateModel for the given session. + Set include_action_history=true to receive the full step-by-step log. + Default is false to keep response payloads small during agent loops. + """ + env = get_or_404(body.session_id) + episode_state = env.state() + if not body.include_action_history: + episode_state = episode_state.model_copy(update={"action_history": None}) + return StateResponse(session_id=body.session_id, state=episode_state) + + +@app.get( + "/state", + response_model=StateResponse, + tags=["env"], + summary="Return the full internal episode state (GET variant)", +) +def state_get( + session_id: str = Query(description="Session ID returned by POST /reset."), + include_action_history: bool = Query( + default=False, + description="When False (default) the action_history list is stripped.", + ), +) -> StateResponse: + """GET variant of /state — accepts session_id as a query parameter.""" + env = get_or_404(session_id) + episode_state = env.state() + if not include_action_history: + episode_state = episode_state.model_copy(update={"action_history": None}) + return StateResponse(session_id=session_id, state=episode_state) + + +@app.post( + "/grade", + response_model=GradeResponse, + tags=["env"], + summary="Run the deterministic task grader for the current episode", +) +def grade(body: GradeRequest) -> GradeResponse: + """ + Runs the task-specific deterministic grader against the current episode state + and returns a score in [0.0, 1.0] plus per-metric breakdowns. + Can be called at any point - not only at termination. + + GraderResult fields used: + result.score -> episode score [0.0, 1.0] + result.grader_name -> "easy" | "medium" | "hard" + result.metrics -> dict of named metric floats (property on GraderResult) + """ + env = get_or_404(body.session_id) + task_id = _get_session_meta(body.session_id).get( + "task_id", + getattr(env, "task_id", env_settings.default_task_id), + ) + try: + episode_state = env.get_episode_state() + except AttributeError: + episode_state = env.state() + result: GraderResult = grade_episode(episode_state) + return GradeResponse( + session_id=body.session_id, + task_id=str(task_id), + score=result.score, + grader_name=result.grader_name, + metrics=result.metrics, + ) + + +@app.get( + "/sessions", + response_model=SessionListResponse, + tags=["meta"], + summary="List all active session IDs", +) +def list_sessions() -> SessionListResponse: + """Returns the count and IDs of all currently active sessions.""" + return SessionListResponse( + active_sessions=sessions.active_count(), + session_ids=sessions.list_ids(), + ) + + +@app.delete( + "/sessions/{session_id}", + response_model=DeleteSessionResponse, + tags=["meta"], + summary="Delete a session and free its memory", +) +def delete_session(session_id: str) -> DeleteSessionResponse: + """Removes the session from the store and releases its GovWorkflowEnv instance.""" + deleted = sessions.delete(session_id) + if not deleted: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Session '{session_id}' not found.", + ) + _pop_session_meta(session_id) + return DeleteSessionResponse(deleted=session_id) + + +# ───────────────────────────────────────────────────────────────────────────── +# /api ROUTER — frontend + extended API +# ───────────────────────────────────────────────────────────────────────────── + +@app.get("/tasks", response_model=list[TaskSummary], tags=["Tasks"], summary="List benchmark task configurations") +def tasks_list() -> list[TaskSummary]: + task_rows: list[TaskSummary] = [] + for task_id in list_benchmark_tasks(): + task_rows.append(TaskSummary(**_task_summary_dict(task_id))) + return task_rows + + +@app.get("/tasks/{task_id}", response_model=TaskSummary, tags=["Tasks"], summary="Get one benchmark task configuration") +def task_get(task_id: str) -> TaskSummary: + available = list_benchmark_tasks() + if task_id not in set(available): + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Task '{task_id}' not found. Available: {available}", + ) + return TaskSummary(**_task_summary_dict(task_id)) + + +@app.post("/action-masks", response_model=ActionMaskResponse, tags=["Environment"], summary="Get valid actions for current session state") +def action_masks(body: ActionMaskRequest) -> ActionMaskResponse: + env = _get_session_or_404(body.session_id) + obs = env._build_observation() + priority_mode = getattr(env, "priority_mode", "balanced") + priority_mode_str = priority_mode.value if hasattr(priority_mode, "value") else str(priority_mode) + computer = ActionMaskComputer() + mask_array = computer.compute(obs, priority_mode_str) + mask_list = [bool(v) for v in mask_array.tolist()] + valid_action_indices = [i for i, v in enumerate(mask_list) if v] + valid_action_labels: list[str] = [] + for idx in valid_action_indices: + decode = ACTION_DECODE_TABLE.get(idx, ()) + action_type = decode[0] if decode else f"action_{idx}" + service = "" + if len(decode) > 1 and decode[1]: + service = str(decode[1]) + elif len(decode) > 2 and decode[2]: + service = str(decode[2]) + label = f"{action_type}({service})" if service else str(action_type) + valid_action_labels.append(label) + + return ActionMaskResponse( + session_id=body.session_id, + action_mask=mask_list, + valid_action_indices=valid_action_indices, + valid_action_labels=valid_action_labels, + total_valid=len(valid_action_indices), + total_actions=int(N_ACTIONS), + ) + + +@app.get("/rl/models", response_model=list[ModelInfo], tags=["RL"], summary="List discovered RL model checkpoints") +def rl_models_v2() -> list[ModelInfo]: + unique_paths = _discover_phase12_zip_models() + if not unique_paths: + return [ModelInfo(model_path="none", task_id="none", phase=0, size_mb=0.0, exists=False)] + + rows: list[ModelInfo] = [] + for path in unique_paths: + phase = _phase_from_model_path(path) + + stem = path.stem.lower() + if "medium" in stem: + task_id = "mixed_urgency_medium" + else: + task_id = "district_backlog_easy" + + rows.append( + ModelInfo( + model_path=str(path.with_suffix("")), + task_id=task_id, + phase=phase, + size_mb=round(float(path.stat().st_size) / (1024 * 1024), 3), + exists=True, + ) + ) + return rows + + +@app.post("/rl/run", response_model=RLRunV2Response, tags=["RL"], summary="Run trained MaskablePPO model for N episodes") +def rl_run_v2(body: RLRunV2Request) -> RLRunV2Response: + _validate_task_id_or_422(body.task_id) + + raw_path = Path(body.model_path) + zip_path = raw_path.with_suffix(".zip") if raw_path.suffix != ".zip" else raw_path + if not zip_path.is_absolute(): + zip_path = (REPO_ROOT / zip_path).resolve() + if not zip_path.exists(): + requested = str(zip_path.with_suffix("")) + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Model not found at '{requested}.zip'", + ) + + try: + from sb3_contrib import MaskablePPO # type: ignore[import-not-found] + from rl.gov_workflow_env import GovWorkflowGymEnv + except ImportError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail=f"RL dependencies not available: {exc}", + ) from exc + + try: + model = MaskablePPO.load(str(zip_path.with_suffix(""))) + except Exception as exc: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Failed to load model from '{zip_path}': {exc}", + ) from exc + + episode_results: list[dict[str, Any]] = [] + for ep in range(body.n_episodes): + env = GovWorkflowGymEnv(task_id=body.task_id, seed=body.seed + ep, hard_action_mask=True) + try: + obs, _ = env.reset(seed=body.seed + ep) + done = False + total_reward = 0.0 + steps = 0 + while not done and steps < body.max_steps: + masks = env.action_masks() + action, _ = model.predict(obs, action_masks=masks, deterministic=True) + obs, reward, terminated, truncated, _ = env.step(int(action)) + total_reward += float(reward) + done = bool(terminated or truncated) + steps += 1 + + episode_state = env.core_env.state() + grade_result = grade_episode(episode_state) + episode_results.append( + { + "episode": ep, + "seed": body.seed + ep, + "score": float(grade_result.score), + "total_reward": round(float(total_reward), 4), + "total_completed": int(episode_state.total_completed), + "total_sla_breaches": int(episode_state.total_sla_breaches), + "total_backlog": int(episode_state.total_backlog), + "steps": int(steps), + "grader_metrics": grade_result.metrics, + } + ) + finally: + env.close() + + mean_score = float(sum(x["score"] for x in episode_results) / max(len(episode_results), 1)) + mean_reward = float(sum(x["total_reward"] for x in episode_results) / max(len(episode_results), 1)) + mean_completed = int(sum(x["total_completed"] for x in episode_results) / max(len(episode_results), 1)) + mean_breaches = int(sum(x["total_sla_breaches"] for x in episode_results) / max(len(episode_results), 1)) + + return RLRunV2Response( + task_id=body.task_id, + model_path=str(zip_path.with_suffix("")), + seed=body.seed, + n_episodes=body.n_episodes, + mean_score=mean_score, + mean_reward=mean_reward, + mean_completed=mean_completed, + mean_sla_breaches=mean_breaches, + episodes=episode_results, + ) + + +@app.post("/simulate", tags=["Simulation"], summary="Run a live simulation stream (SSE)") +def simulate_stream(body: SimulateRequest) -> EventSourceResponse: + _validate_task_id_or_422(body.task_id) + + mode_map = { + "baseline_policy": SimulationAgentMode.BASELINE_POLICY, + "llm_inference": SimulationAgentMode.LLM_INFERENCE, + "trained_rl": SimulationAgentMode.TRAINED_RL, + } + enum_mode = mode_map.get(str(body.agent_mode)) + if enum_mode is None: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail="Invalid agent_mode", + ) + + try: + run = LiveSimulationSession( + task_id=body.task_id, + agent_mode=enum_mode, + max_steps=body.max_steps, + seed=body.seed, + policy_name=body.policy_name, + model_path=body.model_path, + ) + except Exception as exc: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=str(exc), + ) from exc + + run_id = sim_runs.create(run) + + async def event_generator(): + try: + while True: + row, _, done = run.step_once() + yield json.dumps(row, default=str) + if done: + yield json.dumps({"done": True, "session_id": run_id}) + break + finally: + run.close() + + return EventSourceResponse(event_generator()) + + +@app.get("/simulate/{session_id}/snapshot", tags=["Simulation"], summary="Get simulation/session snapshot") +def simulate_snapshot(session_id: str) -> dict[str, Any]: + try: + run = sim_runs.get(session_id) + return run.snapshot() + except KeyError: + pass + + env = _get_session_or_404(session_id) + obs = env._build_observation() + meta = _get_session_meta(session_id) + return { + "session_id": session_id, + "task_id": str(meta.get("task_id", getattr(env, "task_id", env_settings.default_task_id))), + "seed": meta.get("seed"), + "terminated": bool(getattr(env, "terminated", False)), + "truncated": bool(getattr(env, "truncated", False)), + "step_trace_len": len(meta.get("step_trace", [])), + "observation": obs.model_dump(mode="json"), + } + + +@app.post("/simulate/{session_id}/cancel", tags=["Simulation"], summary="Cancel/close a simulation session") +def simulate_cancel(session_id: str) -> dict[str, str]: + if sim_runs.delete(session_id): + return {"session_id": session_id, "status": "cancelled"} + + if sessions.delete(session_id): + _pop_session_meta(session_id) + return {"session_id": session_id, "status": "cancelled"} + + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Session '{session_id}' not found or already closed.", + ) + + +@app.get("/simulate/{session_id}/trace", tags=["Simulation"], summary="Get paginated trace for a simulation/session") +def simulate_trace( + session_id: str, + page: int = Query(default=1, ge=1), + page_size: int = Query(default=20, ge=1, le=500), +) -> dict[str, Any]: + trace: list[dict[str, Any]] | None = None + meta = _get_session_meta(session_id) + if isinstance(meta.get("step_trace"), list): + trace = list(meta.get("step_trace", [])) + else: + try: + run = sim_runs.get(session_id) + trace = list(run.trace) + except KeyError: + trace = None + + if trace is None: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Session '{session_id}' not found. Call POST /reset first.", + ) + + total = len(trace) + start = (page - 1) * page_size + end = start + page_size + items = trace[start:end] + total_pages = max(1, math.ceil(total / max(page_size, 1))) + return { + "session_id": session_id, + "total_steps": total, + "page": page, + "page_size": page_size, + "total_pages": total_pages, + "steps": items, + } + + +@app.get("/actions/schema", tags=["Environment"], summary="Self-describing action schema") +def actions_schema() -> dict[str, Any]: + return { + "total_action_types": 6, + "valid_services": [svc.value for svc in ServiceType], + "valid_priority_modes": [ + "urgent_first", + "oldest_first", + "balanced", + "backlog_clearance", + ], + "actions": [ + { + "action_type": "set_priority_mode", + "description": "Change how the queue is sorted for all services.", + "required_fields": ["action_type", "priority_mode"], + "optional_fields": [], + "notes": "Does not advance time. Call advance_time after.", + "example": {"action_type": "set_priority_mode", "priority_mode": "urgent_first"}, + }, + { + "action_type": "assign_capacity", + "description": "Deploy one reserve officer to a service queue.", + "required_fields": ["action_type", "service", "officer_delta"], + "optional_fields": [], + "notes": "Blocked if reserve_officers = 0. officer_delta must be 1.", + "example": {"action_type": "assign_capacity", "service": "passport", "officer_delta": 1}, + }, + { + "action_type": "request_missing_documents", + "description": "Unblock applications waiting for missing documents.", + "required_fields": ["action_type", "service"], + "optional_fields": [], + "notes": "Blocked if blocked_missing_docs = 0 for that service.", + "example": {"action_type": "request_missing_documents", "service": "driving_license"}, + }, + { + "action_type": "escalate_service", + "description": "Mark one urgent case as emergency priority.", + "required_fields": ["action_type", "service"], + "optional_fields": [], + "notes": "Uses 1 escalation_budget_remaining. Blocked if budget=0.", + "example": {"action_type": "escalate_service", "service": "income_certificate"}, + }, + { + "action_type": "reallocate_officers", + "description": "Move one officer from source service to target service.", + "required_fields": ["action_type", "service", "target_service", "officer_delta"], + "optional_fields": [], + "notes": "Source must have >= 2 officers. officer_delta must be 1.", + "example": { + "action_type": "reallocate_officers", + "service": "birth_certificate", + "target_service": "passport", + "officer_delta": 1, + }, + }, + { + "action_type": "advance_time", + "description": "Simulate one working day. THE ONLY action that processes applications.", + "required_fields": ["action_type"], + "optional_fields": [], + "notes": "Always valid. Call this every turn after admin actions.", + "example": {"action_type": "advance_time"}, + }, + ], + } + + +@app.get("/metrics", tags=["Health"], summary="Operational API metrics") +def metrics() -> dict[str, Any]: + try: + tasks = list_benchmark_tasks() + except Exception: + tasks = [] + return { + "active_sessions": sessions.active_count(), + "tasks_available": tasks, + "total_tasks": len(tasks), + "uptime_status": "ok", + "endpoints_total": 16, + "version": "2.0.0", + "phase": "3_rl_training", + "session_ids_active": sessions.list_ids(), + } + + +api = APIRouter(prefix="/api", tags=["frontend"]) + + +@api.get("/health", response_model=HealthResponse, summary="Health — frontend alias") +def api_health() -> HealthResponse: + return health() + + +@api.get("/tasks", response_model=TaskListResponse, summary="List available tasks") +def api_tasks() -> TaskListResponse: + return TaskListResponse(tasks=list_tasks()) + + +@api.get("/agents", response_model=list[str], summary="List baseline agent policies") +def api_agents() -> list[str]: + return sorted(POLICIES.keys()) + + +@api.post("/reset", response_model=ResetResponse, summary="Reset episode — frontend alias") +def api_reset(body: ResetRequest | None = Body(default=None)) -> ResetResponse: + return reset(body) + + +@api.post("/step", response_model=StepResponse, summary="Step episode — frontend alias") +def api_step(body: StepRequest) -> StepResponse: + return step(body) + + +@api.post("/auto_step", response_model=AutoStepResponse, summary="Compute policy action and step once") +def api_auto_step(body: AutoStepRequest) -> AutoStepResponse: + env = get_or_404(body.session_id) + if env.terminated or env.truncated: + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail="Episode has already ended. Call /api/reset first.", + ) + policy = resolve_policy_or_422(body.agent_policy) + obs = env._build_observation() + action = policy(obs) + next_obs, reward, terminated, truncated, info = env.step(action) + return AutoStepResponse( + session_id=body.session_id, + agent_policy=body.agent_policy, + action=action, + observation=next_obs, + reward=reward, + done=terminated or truncated, + terminated=terminated, + truncated=truncated, + info=info, + ) + + +@api.post("/state", response_model=StateResponse, summary="State — frontend alias") +def api_state(body: StateRequest) -> StateResponse: + return state_post(body) + + +@api.post("/action-masks", response_model=ActionMaskResponse, summary="Action masks - frontend alias") +def api_action_masks(body: ActionMaskRequest) -> ActionMaskResponse: + return action_masks(body) + + +@api.get("/actions/schema", summary="Action schema - frontend alias") +def api_actions_schema() -> dict[str, Any]: + return actions_schema() + + +@api.post("/grade", response_model=GradeResponse, summary="Grade — frontend alias") +def api_grade(body: GradeRequest) -> GradeResponse: + return grade(body) + + +@api.get("/sessions", response_model=SessionListResponse, summary="List sessions — frontend alias") +def api_sessions() -> SessionListResponse: + return list_sessions() + + +@api.delete("/sessions/{session_id}", response_model=DeleteSessionResponse, summary="Delete session — frontend alias") +def api_delete_session(session_id: str) -> DeleteSessionResponse: + return delete_session(session_id) + + +@api.post("/benchmark", response_model=BenchmarkResponse, summary="Run multiple baseline episodes") +def api_benchmark(body: BenchmarkRequest) -> BenchmarkResponse: + valid_tasks = set(list_tasks()) + if body.task_id not in valid_tasks: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id '{body.task_id}'.", + ) + if not body.agent_policies: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail="agent_policies must contain at least one policy.", + ) + agent_results = [] + for policy_name in body.agent_policies: + resolve_policy_or_422(policy_name) + run_rows = [] + for run_idx in range(body.runs): + seed = None if body.seed_base is None else body.seed_base + run_idx + result = run_policy_episode( + task_id=body.task_id, + policy_name=policy_name, + seed=seed, + max_steps=body.max_steps, + ) + run_rows.append(BenchmarkAgentRun( + run_index=run_idx + 1, + seed=seed, + score=float(_result_value(result, "score", 0.0)), + reward_sum=float(_result_value(result, "reward_sum", 0.0)), + completed=int(_result_value(result, "completed", 0)), + backlog=int(_result_value(result, "backlog", 0)), + steps=int(_result_value(result, "steps", 0)), + )) + scores = [r.score for r in run_rows] + agent_results.append(BenchmarkAgentSummary( + agent_policy=policy_name, + average_score=float(sum(scores) / len(scores)), + min_score=float(min(scores)), + max_score=float(max(scores)), + runs=run_rows, + )) + return BenchmarkResponse( + task_id=body.task_id, + requested_runs=body.runs, + agent_results=agent_results, + ) + + +@api.get("/workflows/components", response_model=WorkflowComponentsResponse, summary="Describe visible workflow components") +def api_workflow_components() -> WorkflowComponentsResponse: + repo_root = REPO_ROOT + baseline_f = repo_root / "baseline_openai.py" + inference_f = repo_root / "inference.py" + phase2_model = repo_root / "results" / "best_model" / "phase2_final.zip" + components = [ + WorkflowComponentStatus( + component="baseline_openai.py", + description="CLI baseline runner using OpenAI-compatible/NVIDIA endpoints.", + available=baseline_f.exists(), + command=r".\.venv\3.11\Scripts\python.exe baseline_openai.py --task district_backlog_easy", + notes="Uses API keys from environment variables.", + ), + WorkflowComponentStatus( + component="inference.py", + description="Submission-style inference runner with strict START/STEP/END logging.", + available=inference_f.exists(), + command=r".\.venv\3.11\Scripts\python.exe inference.py", + notes="Reads HF/OpenAI-compatible credentials from environment variables.", + ), + WorkflowComponentStatus( + component="phase2_final.zip", + description="Trained Phase 2 PPO checkpoint used for local RL evaluation/execution.", + available=phase2_model.exists(), + command=r".\.venv\3.11\Scripts\python.exe -m rl.evaluate --model results/best_model/phase2_final.zip --episodes 3 --model-type maskable", + ), + WorkflowComponentStatus( + component="openenv-api", + description="Standard environment API exposed through reset/step/state/grade.", + available=True, + command="POST /reset, POST /step, GET+POST /state, POST /grade", + ), + ] + return WorkflowComponentsResponse(components=components) + + + +@api.post("/workflows/run", response_model=WorkflowRunResponse, summary="Execute a workflow component as a subprocess") +def api_workflow_run(body: WorkflowRunRequest) -> WorkflowRunResponse: + repo_root = REPO_ROOT + python_bin = shutil.which("python") or "python" + + cmd = [] + if body.workflow_id == "baseline_openai": + cmd = [python_bin, "baseline_openai.py", "--task", "district_backlog_easy"] + elif body.workflow_id == "inference": + cmd = [python_bin, "inference.py", "--max-steps", str(body.max_steps)] + elif body.workflow_id == "phase2_eval": + cmd = [python_bin, "-m", "rl.evaluate", "--model", body.model_path, "--episodes", str(body.episodes), "--model-type", body.model_type] + + start_t = time.time() + try: + proc = subprocess.run( + cmd, + cwd=str(repo_root), + capture_output=True, + text=True, + timeout=body.timeout_seconds, + check=False, + ) + duration = time.time() - start_t + return WorkflowRunResponse( + workflow_id=body.workflow_id, + command=cmd, + exit_code=proc.returncode, + duration_seconds=round(duration, 3), + stdout=proc.stdout or "", + stderr=proc.stderr or "", + timed_out=False, + ) + except subprocess.TimeoutExpired as exc: + duration = time.time() - start_t + return WorkflowRunResponse( + workflow_id=body.workflow_id, + command=cmd, + exit_code=-1, + duration_seconds=round(duration, 3), + stdout=exc.stdout or "", + stderr=exc.stderr or "", + timed_out=True, + ) + + +@api.get("/openenv_compliance", response_model=OpenEnvComplianceResponse, summary="Check OpenEnv interface compliance") +def api_openenv_compliance( + run_validate: bool = Query(default=False) +) -> OpenEnvComplianceResponse: + repo_root = REPO_ROOT + openenv_yaml = repo_root / "openenv.yaml" + route_paths = {getattr(r, "path", "") for r in app.routes} + + def has_path(path: str) -> bool: + return path in route_paths + + items = [ + OpenEnvComplianceItem( + key="typed_action_model", + label="Typed Action model (Pydantic)", + status="pass" if issubclass(ActionModel, BaseModel) else "fail", + detail=f"ActionModel type={ActionModel.__name__}", + ), + OpenEnvComplianceItem( + key="typed_observation_model", + label="Typed Observation model (Pydantic)", + status="pass" if issubclass(ObservationModel, BaseModel) else "fail", + detail=f"ObservationModel type={ObservationModel.__name__}", + ), + OpenEnvComplianceItem( + key="typed_step_info_model", + label="Typed step info model (Pydantic)", + status="pass" if issubclass(StepInfoModel, BaseModel) else "fail", + detail=f"StepInfoModel type={StepInfoModel.__name__}", + ), + OpenEnvComplianceItem( + key="api_step_reset_state", + label="step/reset/state API exposed", + status="pass" if (has_path("/reset") and has_path("/step") and has_path("/state")) else "fail", + detail="Expected endpoints: POST /reset, POST /step, GET+POST /state", + ), + OpenEnvComplianceItem( + key="openenv_yaml", + label="openenv.yaml metadata file", + status="pass" if openenv_yaml.exists() else "fail", + detail=str(openenv_yaml), + ), + ] + + validate_rc = validate_out = validate_err = None + if run_validate: + openenv_bin = shutil.which("openenv") + if openenv_bin is None: + items.append(OpenEnvComplianceItem( + key="openenv_validate", + label="openenv validate execution", + status="unknown", + detail="openenv CLI not found in runtime PATH.", + )) + else: + proc = subprocess.run( + [openenv_bin, "validate"], + cwd=str(repo_root), + capture_output=True, + text=True, + timeout=120, + check=False, + ) + validate_rc = int(proc.returncode) + validate_out = (proc.stdout or "")[-4000:] + validate_err = (proc.stderr or "")[-2000:] + items.append(OpenEnvComplianceItem( + key="openenv_validate", + label="openenv validate execution", + status="pass" if proc.returncode == 0 else "fail", + detail=f"Exit code: {proc.returncode}", + )) + else: + items.append(OpenEnvComplianceItem( + key="openenv_validate", + label="openenv validate execution", + status="unknown", + detail="Not executed in this check. Pass run_validate=true to execute.", + )) + + return OpenEnvComplianceResponse( + checked_at=time.time(), + items=items, + openenv_validate_exit_code=validate_rc, + openenv_validate_stdout_tail=validate_out, + openenv_validate_stderr_tail=validate_err, + ) + + +@api.get("/rl_models", response_model=RLModelsResponse, summary="List available trained RL model checkpoints") +def api_rl_models() -> RLModelsResponse: + models: list[RLModelInfo] = [] + for path in _discover_phase12_zip_models(): + phase = _phase_from_model_path(path) + model_type: Literal["maskable", "recurrent"] = ( + "recurrent" if "recurrent" in path.name.lower() else "maskable" + ) + label = f"Phase {phase} - {path.stem.replace('_', ' ')}" + models.append( + RLModelInfo( + label=label, + path=str(path), + exists=True, + model_type=model_type, + ) + ) + return RLModelsResponse(models=models) + + +@api.get( + "/rl/models", + response_model=list[ModelInfo], + summary="List discovered RL model checkpoints (V2 slash alias)", +) +def api_rl_models_v2() -> list[ModelInfo]: + """ + Slash-path alias for frontend clients that call `/api/rl/models`. + Returns the same V2 payload shape as root `/rl/models`. + """ + return rl_models_v2() + +@api.post("/rl_run", response_model=RLRunResponse, summary="Run one episode with a trained RL checkpoint") +def api_rl_run(body: RLRunRequest) -> RLRunResponse: + if body.task_id not in set(list_tasks()): + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id '{body.task_id}'.", + ) + model_path = resolve_model_path_or_422(body.model_path) + model = load_model_cached_or_503(model_path, body.model_type) + try: + import numpy as np + from rl.gov_workflow_env import GovWorkflowGymEnv + except ModuleNotFoundError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="RL runtime dependencies are not available. Install requirements-rl.txt.", + ) from exc + + seed = body.seed if body.seed is not None else int(TASKS[body.task_id].seed) + env = GovWorkflowGymEnv(task_id=body.task_id, seed=seed, hard_action_mask=True) + obs, _ = env.reset(seed=seed) + trace: list[RLRunStep] = [] + total_reward = 0.0 + done = False + lstm_state: Any = None + episode_start = np.array([True], dtype=bool) + + for idx in range(1, body.max_steps + 1): + masks = env.action_masks() + if body.model_type == "recurrent": + action, lstm_state = model.predict( + obs, state=lstm_state, episode_start=episode_start, deterministic=True + ) + else: + try: + from sb3_contrib.common.maskable.utils import get_action_masks # type: ignore[import-not-found] + except ModuleNotFoundError: + from sb3contrib.common.maskable.utils import get_action_masks # type: ignore[import-not-found] + action, _ = model.predict(obs, action_masks=get_action_masks(env), deterministic=True) + + action_idx = int(action.item()) if hasattr(action, "item") else action + if not (0 <= action_idx < masks.shape[0] and bool(masks[action_idx])): + valid = np.flatnonzero(masks) + action_idx = int(valid[0]) if valid.size > 0 else 18 + + obs, reward, terminated, truncated, info = env.step(action_idx) + done = bool(terminated or truncated) + total_reward += float(reward) + core_obs = env.core_env.build_observation() + trace.append(RLRunStep( + step=idx, + action_index=action_idx, + action_label=decode_action_index(action_idx), + reward=float(reward), + backlog=int(core_obs.total_backlog), + completed=int(core_obs.total_completed), + sla_breaches=int(core_obs.total_sla_breaches), + fairness_gap=float(core_obs.fairness_gap), + done=done, + )) + if body.model_type == "recurrent": + episode_start = np.array([done], dtype=bool) + if done: + break + + final_state = env.core_env.state() + grade_result = grade_episode(final_state) + return RLRunResponse( + model_path=str(model_path), + model_type=body.model_type, + task_id=body.task_id, + seed=seed, + total_steps=int(final_state.total_steps), + total_reward=float(total_reward), + grader_score=float(grade_result.score), + grader_name=grade_result.grader_name, + trace=trace, + ) + + +@api.post("/rl_evaluate", response_model=RLEvaluateResponse, summary="Evaluate trained model across tasks") +def api_rl_evaluate(body: RLEvaluateRequest) -> RLEvaluateResponse: + model_path = resolve_model_path_or_422(body.model_path) + task_ids = body.task_ids or list_tasks() + valid_tasks = set(list_tasks()) + unknown = [t for t in task_ids if t not in valid_tasks] + if unknown: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id values: {unknown}", + ) + try: + from rl.evaluate import evaluate_model + except ModuleNotFoundError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="RL evaluation dependencies are not available. Install requirements-rl.txt.", + ) from exc + try: + eval_rows = evaluate_model( + model_path=str(model_path), + task_ids=task_ids, + n_episodes=body.episodes, + verbose=False, + model_type=body.model_type, + ) + except ValueError as exc: + raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, detail=str(exc)) from exc + + results = [ + RLEvaluateTaskResult( + task_id=row.task_id, + grader_score=float(row.grader_score), + total_reward=float(row.total_reward), + total_steps=int(row.total_steps), + total_completed=int(row.total_completed), + total_sla_breaches=int(row.total_sla_breaches), + fairness_gap=float(row.fairness_gap), + ) + for row in eval_rows + ] + avg_score = float(sum(x.grader_score for x in results) / max(len(results), 1)) + return RLEvaluateResponse( + model_path=str(model_path), + model_type=body.model_type, + episodes=body.episodes, + average_grader_score=avg_score, + results=results, + ) + + +@api.post("/simulation/run", response_model=SimulationResponse, summary="Run a workflow simulation") +def api_simulation_run(body: SimulationRequest) -> SimulationResponse: + if body.task_id not in set(list_tasks()): + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id '{body.task_id}'.", + ) + if body.agent_mode == SimulationAgentMode.BASELINE_POLICY and body.policy_name not in POLICIES: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown policy_name '{body.policy_name}'. Available: {sorted(POLICIES.keys())}", + ) + try: + run = run_simulation( + task_id=body.task_id, + agent_mode=body.agent_mode, + max_steps=body.max_steps, + seed=body.seed, + policy_name=body.policy_name, + model_path=body.model_path, + model_type=body.model_type, + ) + except ValueError as exc: + raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, detail=str(exc)) from exc + except ModuleNotFoundError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="RL runtime dependencies are unavailable. Install requirements-rl.txt.", + ) from exc + + run_id = str(uuid4()) + if persistence.enabled: + persistence.upsert_simulation_run( + run_id=run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + status="completed", + payload={ + "task_id": run.task_id, + "agent_mode": run.agent_mode, + "seed": run.seed, + "total_reward": run.total_reward, + "score": run.score, + "grader_name": run.grader_name, + "summary": run.summary, + "trace": run.trace, + }, + ) + return SimulationResponse( + task_id=run.task_id, + agent_mode=run.agent_mode, + seed=run.seed, + total_reward=run.total_reward, + score=run.score, + grader_name=run.grader_name, + summary=run.summary, + trace=[SimulationStep(**row) for row in run.trace], + ) + + +@api.post("/simulation/live/start", response_model=SimulationLiveStartResponse, summary="Start a live step-by-step simulation") +def api_simulation_live_start(body: SimulationLiveStartRequest) -> SimulationLiveStartResponse: + if body.task_id not in set(list_tasks()): + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown task_id '{body.task_id}'.", + ) + if body.agent_mode == SimulationAgentMode.BASELINE_POLICY and body.policy_name not in POLICIES: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT, + detail=f"Unknown policy_name '{body.policy_name}'. Available: {sorted(POLICIES.keys())}", + ) + try: + run = LiveSimulationSession( + task_id=body.task_id, + agent_mode=body.agent_mode, + max_steps=body.max_steps, + seed=body.seed, + policy_name=body.policy_name, + model_path=body.model_path, + model_type=body.model_type, + ) + except (ValueError, ModuleNotFoundError) as exc: + raise HTTPException( + status_code=status.HTTP_422_UNPROCESSABLE_CONTENT + if isinstance(exc, ValueError) else status.HTTP_503_SERVICE_UNAVAILABLE, + detail=str(exc), + ) from exc + + run_id = sim_runs.create(run) + if persistence.enabled: + persistence.upsert_simulation_run( + run_id=run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + status="running", + payload={ + "task_id": run.task_id, + "agent_mode": run.agent_mode, + "seed": run.seed, + "max_steps": run.max_steps, + "summary": None, + "trace_len": 0, + "route_plan": list(run.llm_route), + }, + ) + return SimulationLiveStartResponse( + run_id=run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + seed=run.seed, + max_steps=run.max_steps, + start_log=_log_line_text(run.start_line()), + route_plan=list(run.llm_route), + ) + + +@api.post("/simulation/live/step", response_model=SimulationLiveStepResponse, summary="Execute one step for a live simulation") +def api_simulation_live_step(body: SimulationLiveStepRequest) -> SimulationLiveStepResponse: + run = get_sim_or_404(body.run_id) + if run.done: + if persistence.enabled: + persistence.upsert_simulation_run( + run_id=body.run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + status="completed", + payload={ + "task_id": run.task_id, + "agent_mode": run.agent_mode, + "seed": run.seed, + "max_steps": run.max_steps, + "total_reward": float(run.total_reward), + "score": run.score, + "grader_name": run.grader_name, + "summary": run.summary, + "trace": list(run.trace), + }, + ) + return SimulationLiveStepResponse( + run_id=body.run_id, + done=True, + total_reward=float(run.total_reward), + score=run.score, + grader_name=run.grader_name, + summary=run.summary, + end_log=_log_line_text(run.end_line()), + ) + try: + row, step_log, done = run.step_once() + except Exception as exc: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Simulation step failed: {exc}", + ) from exc + + if persistence.enabled: + persistence.upsert_simulation_run( + run_id=body.run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + status="completed" if done else "running", + payload={ + "task_id": run.task_id, + "agent_mode": run.agent_mode, + "seed": run.seed, + "max_steps": run.max_steps, + "total_reward": float(run.total_reward), + "score": run.score, + "grader_name": run.grader_name, + "summary": run.summary, + "trace": list(run.trace) if done else [], + "trace_len": len(run.trace), + }, + ) + return SimulationLiveStepResponse( + run_id=body.run_id, + done=done, + step=SimulationStep(**row), + step_log=_log_line_text(step_log) if step_log is not None else None, + end_log=_log_line_text(run.end_line()) if done else None, + total_reward=float(run.total_reward), + score=run.score, + grader_name=run.grader_name, + summary=run.summary, + ) + + +@api.get("/simulation/live/{run_id}", response_model=SimulationLiveStateResponse, summary="Get live simulation state") +def api_simulation_live_state(run_id: str) -> SimulationLiveStateResponse: + run = get_sim_or_404(run_id) + return SimulationLiveStateResponse(run_id=run_id, state=run.snapshot()) + + +@api.post("/simulation/live/{run_id}/stop", response_model=dict, summary="Stop and remove a live simulation run") +def api_simulation_live_stop(run_id: str) -> dict[str, Any]: + run: LiveSimulationSession | None = None + try: + run = sim_runs.get(run_id) + except Exception: + run = None + deleted = sim_runs.delete(run_id) + if not deleted: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Simulation run '{run_id}' not found.", + ) + if persistence.enabled and run is not None: + persistence.upsert_simulation_run( + run_id=run_id, + task_id=run.task_id, + agent_mode=run.agent_mode, + status="stopped", + payload={ + "task_id": run.task_id, + "agent_mode": run.agent_mode, + "seed": run.seed, + "max_steps": run.max_steps, + "total_reward": float(run.total_reward), + "score": run.score, + "grader_name": run.grader_name, + "summary": run.summary, + "trace_len": len(run.trace), + }, + ) + return {"run_id": run_id, "stopped": True} + + +@api.get("/training_jobs", response_model=TrainingJobsListResponse, summary="List all background RL training jobs") +def api_training_jobs() -> TrainingJobsListResponse: + return TrainingJobsListResponse(jobs=training_jobs.list_jobs()) + + +@api.get("/training_jobs/list", response_model=TrainingJobsListResponse, summary="List training jobs — stable alias") +def api_training_jobs_list() -> TrainingJobsListResponse: + return api_training_jobs() + + +@api.get("/training_jobs/{job_id}", response_model=dict, summary="Get one background RL training job") +def api_training_job(job_id: str) -> dict[str, Any]: + job = training_jobs.get_job(job_id) + if job is None: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Training job '{job_id}' not found.") + return job + + +@api.post("/training_jobs", response_model=dict, summary="Start RL training in a background process") +def api_training_start(body: TrainingJobStartRequest) -> dict[str, Any]: + try: + import stable_baselines3 # noqa: F401 + try: + import sb3_contrib # noqa: F401 + except ModuleNotFoundError: + import sb3contrib # noqa: F401 + import gymnasium # noqa: F401 + except ModuleNotFoundError as exc: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="RL training dependencies are unavailable. Install requirements-rl.txt.", + ) from exc + cfg = ( + body.config_path + or ("rl/configs/curriculum.yaml" if body.phase == 2 else "rl/configs/ppo_easy.yaml") + ) + return training_jobs.start_job( + phase=body.phase, + timesteps=body.timesteps, + n_envs=body.n_envs, + seed=body.seed, + config_path=cfg, + ) + + +@api.post("/training_jobs/{job_id}/stop", response_model=TrainingJobStopResponse, summary="Stop a background training job") +def api_training_stop(job_id: str) -> TrainingJobStopResponse: + job = training_jobs.stop_job(job_id) + if job is None: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Training job '{job_id}' not found.") + return TrainingJobStopResponse(stopped=True, job_id=job_id, status=str(job.get("status", "unknown"))) + + +@api.delete("/training_jobs/{job_id}", response_model=TrainingJobDeleteResponse, summary="Delete one training job from history") +def api_training_job_delete(job_id: str, clear_artifacts: bool = Query(default=False)) -> TrainingJobDeleteResponse: + deleted = training_jobs.delete_job(job_id, clear_artifacts=clear_artifacts) + if not deleted: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Training job '{job_id}' not found.") + return TrainingJobDeleteResponse(deleted=True, job_id=job_id) + + +@api.delete("/training_jobs", response_model=HistoryClearResponse, summary="Clear persisted training job history") +def api_training_jobs_clear(clear_artifacts: bool = Query(default=False)) -> HistoryClearResponse: + deleted = training_jobs.clear_jobs(clear_artifacts=clear_artifacts) + return HistoryClearResponse(cleared=True, deleted_rows=int(deleted), scope="training_jobs") + + +@api.get("/history/simulations", response_model=SimulationHistoryListResponse, summary="List persisted simulation runs") +def api_history_simulations(limit: int = Query(default=20, ge=1, le=500)) -> SimulationHistoryListResponse: + if not persistence.enabled: + return SimulationHistoryListResponse(runs=[]) + return SimulationHistoryListResponse(runs=persistence.list_simulation_runs(limit=limit)) + + +@api.delete("/history/simulations", response_model=HistoryClearResponse, summary="Clear persisted simulation history") +def api_history_simulations_clear() -> HistoryClearResponse: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + deleted = persistence.clear_simulation_runs() + return HistoryClearResponse(cleared=True, deleted_rows=int(deleted), scope="simulation_history") + + +@api.get("/history/simulations/{run_id}", response_model=dict, summary="Get one persisted simulation run") +def api_history_simulation(run_id: str) -> dict[str, Any]: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + row = persistence.get_simulation_run(run_id) + if row is None: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Simulation history '{run_id}' not found.") + return row + + +@api.post("/history/comparisons", response_model=ComparisonHistoryCreateResponse, summary="Persist a model-comparison result snapshot") +def api_history_comparison_create(body: ComparisonHistoryCreateRequest) -> ComparisonHistoryCreateResponse: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + payload = body.model_dump(mode="json") + comparison_id = persistence.create_comparison_run(payload) + if comparison_id is None: + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to persist comparison result.") + return ComparisonHistoryCreateResponse(comparison_id=comparison_id) + + +@api.get("/history/comparisons", response_model=ComparisonHistoryListResponse, summary="List persisted model-comparison snapshots") +def api_history_comparisons(limit: int = Query(default=20, ge=1, le=500)) -> ComparisonHistoryListResponse: + if not persistence.enabled: + return ComparisonHistoryListResponse(comparisons=[]) + return ComparisonHistoryListResponse(comparisons=persistence.list_comparison_runs(limit=limit)) + + +@api.get("/history/comparisons/{comparison_id}", response_model=dict, summary="Get one persisted model-comparison snapshot") +def api_history_comparison(comparison_id: str) -> dict[str, Any]: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + row = persistence.get_comparison_run(comparison_id) + if row is None: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Comparison history '{comparison_id}' not found.") + return row + + +@api.delete("/history/comparisons", response_model=HistoryClearResponse, summary="Clear persisted comparison history") +def api_history_comparisons_clear() -> HistoryClearResponse: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + deleted = persistence.clear_comparison_runs() + return HistoryClearResponse(cleared=True, deleted_rows=int(deleted), scope="comparison_history") + + +@api.post("/history/comparisons/{comparison_id}/repair", response_model=ComparisonHistoryRepairResponse, summary="Repair legacy comparison snapshot") +def api_history_comparison_repair(comparison_id: str) -> ComparisonHistoryRepairResponse: + if not persistence.enabled: + raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Persistence is disabled.") + row = persistence.get_comparison_run(comparison_id) + if row is None: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Comparison history '{comparison_id}' not found.") + result = row.get("result") if isinstance(row.get("result"), dict) else {} + include_llm = bool(row.get("include_llm", True)) + has_baseline = isinstance(result.get("baselineRuns"), list) and len(result["baselineRuns"]) > 0 + has_llm = not include_llm or (isinstance(result.get("llmRuns"), list) and len(result["llmRuns"]) > 0) + if has_baseline and has_llm: + return ComparisonHistoryRepairResponse( + comparison_id=comparison_id, + repaired=False, + detail="No repair needed. Snapshot already contains per-run rows.", + ) + task_id = str(row.get("task_id") or env_settings.default_task_id) + baseline_policy = str(row.get("baseline_policy") or "backlog_clearance") + runs = max(1, int(row.get("runs") or 1)) + steps = max(1, int(row.get("steps") or 80)) + seed_base = int(row.get("seed_base") or 100) + baseline_runs: list[dict[str, Any]] = [] + for i in range(runs): + seed = seed_base + i + rr = run_policy_episode(task_id=task_id, policy_name=baseline_policy, seed=seed, max_steps=steps) + baseline_runs.append({ + "run_index": i + 1, + "seed": int(rr.seed), + "score": float(rr.score), + "reward_sum": float(rr.reward_sum), + "completed": int(rr.completed), + "backlog": int(rr.backlog), + }) + llm_runs: list[dict[str, Any]] = [] + llm_error: str | None = None + if include_llm: + try: + for i in range(runs): + seed = seed_base + i + sim = run_simulation(task_id=task_id, agent_mode=SimulationAgentMode.LLM_INFERENCE, + max_steps=steps, seed=seed, policy_name="backlog_clearance") + llm_runs.append({ + "run_index": i + 1, + "seed": int(sim.seed), + "score": float(sim.score), + "reward_sum": float(sim.total_reward), + "completed": int(sim.summary.get("total_completed", 0)), + "backlog": int(sim.summary.get("total_backlog", 0)), + }) + except Exception as exc: + llm_error = str(exc) + + baseline_score = float(sum(float(x["score"]) for x in baseline_runs) / max(1, len(baseline_runs))) + llm_score = float(sum(float(x["score"]) for x in llm_runs) / max(1, len(llm_runs))) if llm_runs else result.get("llmScore") + repaired_result = dict(result) + repaired_result["baselineScore"] = baseline_score + repaired_result["baselineRuns"] = baseline_runs + repaired_result["llmRuns"] = llm_runs + repaired_result["llmScore"] = llm_score + if llm_error: + repaired_result["llmError"] = llm_error + updated = dict(row) + updated["result"] = repaired_result + updated["updated_at"] = time.time() + saved_id = persistence.create_comparison_run(updated) + if saved_id is None: + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Failed to persist repaired comparison snapshot.") + return ComparisonHistoryRepairResponse( + comparison_id=comparison_id, + repaired=True, + detail="Repaired legacy snapshot by backfilling per-run baseline/LLM rows.", + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# COMPATIBILITY ALIASES (no /api prefix — for clients that don't route through /api) +# ───────────────────────────────────────────────────────────────────────────── + +app.include_router(api) + + +def _normalize_api_prefix(prefix: str) -> str: + p = (prefix or "").strip() + if not p: + return "" + if not p.startswith("/"): + p = "/" + p + return p.rstrip("/") + + +def _mount_versioned_api_aliases( + application: FastAPI, + *, + source_prefix: str, + target_prefix: str, +) -> None: + """Mirror source API routes into a versioned target prefix.""" + source_prefix = _normalize_api_prefix(source_prefix) + target_prefix = _normalize_api_prefix(target_prefix) + if not source_prefix or not target_prefix or source_prefix == target_prefix: + return + + existing_keys: set[tuple[str, tuple[str, ...]]] = set() + for route in application.routes: + if isinstance(route, APIRoute): + methods = tuple(sorted(m for m in (route.methods or set()) if m not in {"HEAD", "OPTIONS"})) + existing_keys.add((route.path, methods)) + + for route in list(application.routes): + if not isinstance(route, APIRoute): + continue + if not route.path.startswith(f"{source_prefix}/"): + continue + if route.path.startswith(f"{target_prefix}/"): + continue + + methods = sorted(m for m in (route.methods or set()) if m not in {"HEAD", "OPTIONS"}) + if not methods: + continue + + suffix = route.path[len(source_prefix):] + versioned_path = f"{target_prefix}{suffix}" + route_key = (versioned_path, tuple(methods)) + if route_key in existing_keys: + continue + + base_op = route.operation_id or route.name or "operation" + path_token = versioned_path.strip("/").replace("/", "_").replace("{", "").replace("}", "") + versioned_operation_id = f"{base_op}__v1__{path_token}" + + application.add_api_route( + path=versioned_path, + endpoint=route.endpoint, + methods=methods, + response_model=route.response_model, + status_code=route.status_code, + tags=list(route.tags or []), + dependencies=list(route.dependencies), + summary=route.summary, + description=route.description, + response_description=route.response_description, + responses=dict(route.responses), + deprecated=route.deprecated, + operation_id=versioned_operation_id, + response_class=route.response_class, + include_in_schema=route.include_in_schema, + ) + existing_keys.add(route_key) + + +enable_structured_v1_api = os.getenv("ENABLE_STRUCTURED_V1_API", "1").strip().lower() in { + "1", + "true", + "yes", + "on", +} +structured_source_prefix = os.getenv("OPENENV_API_SOURCE_PREFIX", "/api") +structured_target_prefix = os.getenv("OPENENV_API_V1_PREFIX", "/api/v1") +if enable_structured_v1_api: + _mount_versioned_api_aliases( + app, + source_prefix=structured_source_prefix, + target_prefix=structured_target_prefix, + ) + + +def _route_exists(application: FastAPI, path: str, method: str) -> bool: + needle = method.upper() + for route in application.routes: + if not isinstance(route, APIRoute): + continue + if route.path != path: + continue + if needle in (route.methods or set()): + return True + return False + + +for _v1_alias, _endpoint, _method, _model in [ + ("/api/v1/agents", api_agents, "GET", list[str]), + ("/api/v1/rl_models", api_rl_models, "GET", RLModelsResponse), + ("/api/v1/rl/models", api_rl_models_v2, "GET", list[ModelInfo]), +]: + if _route_exists(app, _v1_alias, _method): + continue + if _method == "GET": + app.get(_v1_alias, response_model=_model, include_in_schema=False)(_endpoint) + else: + app.post(_v1_alias, response_model=_model, include_in_schema=False)(_endpoint) + +# OpenEnv-native routes under /openenv so both contracts are visible +# in a single Swagger UI without colliding with existing root endpoints. +try: + from server.app import app as _openenv_app + + app.include_router(_openenv_app.router, prefix="/openenv") +except Exception: + # Keep primary app startup resilient even if optional OpenEnv adapter + # dependencies are unavailable in a minimal runtime. + pass + +# Direct top-level aliases for all /api/* routes +for _alias, _endpoint, _method, _model in [ + ("/simulation/run", api_simulation_run, "POST", SimulationResponse), + ("/simulation/live/start", api_simulation_live_start, "POST", SimulationLiveStartResponse), + ("/simulation/live/step", api_simulation_live_step, "POST", SimulationLiveStepResponse), + ("/rl_models", api_rl_models, "GET", RLModelsResponse), + ("/rl_run", api_rl_run, "POST", RLRunResponse), + ("/rl_evaluate", api_rl_evaluate, "POST", RLEvaluateResponse), + ("/openenv_compliance", api_openenv_compliance, "GET", OpenEnvComplianceResponse), + ("/training_jobs", api_training_jobs, "GET", TrainingJobsListResponse), + ("/history/simulations", api_history_simulations, "GET", SimulationHistoryListResponse), + ("/history/comparisons", api_history_comparisons, "GET", ComparisonHistoryListResponse), + ("/workflows/run", api_workflow_run, "POST", WorkflowRunResponse), +]: + if _method == "GET": + app.get(_alias, response_model=_model, include_in_schema=False)(_endpoint) + else: + app.post(_alias, response_model=_model, include_in_schema=False)(_endpoint) + + +# ───────────────────────────────────────────────────────────────────────────── +# ENTRY POINT +# ───────────────────────────────────────────────────────────────────────────── + +if __name__ == "__main__": + import uvicorn + uvicorn.run( + "app.main:app", + host=server_settings.host, + port=server_settings.port, + log_level=server_settings.log_level, + workers=server_settings.workers, # always 1 for in-memory sessions + reload=False, + ) diff --git a/app/models.py b/app/models.py new file mode 100644 index 0000000000000000000000000000000000000000..0544ff03bd82cfbc70446b5c79d618e2e9dbb1b1 --- /dev/null +++ b/app/models.py @@ -0,0 +1,509 @@ +""" +models.py — Gov Workflow OpenEnv v2.0 — Phase 2 FULL FILE +Adds: DocEnrichmentType, doc_enrichment fields on ApplicationCase, + blocked_cases_enrichment / pending_enrichment_lookups on observation, + INTERNAL_TO_PUBLIC_STAGE mapping, + SectorProfile enrichment fields. +""" + +from __future__ import annotations +from enum import Enum +from typing import Dict, List, Optional +from pydantic import BaseModel, Field +import uuid + + +# ───────────────────────────────────────────── +# ENUMS +# ───────────────────────────────────────────── + +class ServiceType(str, Enum): + PASSPORT = "passport" + DRIVING_LICENSE = "driving_license" + AADHAAR_CARD = "aadhaar_card" + GST_REGISTRATION = "gst_registration" + INCOME_CERTIFICATE = "income_certificate" + CASTE_CERTIFICATE = "caste_certificate" + BIRTH_CERTIFICATE = "birth_certificate" + LAND_REGISTRATION = "land_registration" + + +class StageType(str, Enum): + SUBMISSION = "submission" + DOCUMENT_VERIFICATION = "document_verification" + FIELD_VERIFICATION = "field_verification" + APPROVAL = "approval" + ISSUANCE = "issuance" + + +class InternalSubstate(str, Enum): + PRE_SCRUTINY = "pre_scrutiny" + DOC_VALIDATION = "doc_validation" + SERVICE_SPECIFIC_VALIDATION = "service_specific_validation" + FIELD_VERIFICATION_PENDING = "field_verification_pending" + DECISION_PENDING = "decision_pending" + ISSUANCE_READY = "issuance_ready" + BLOCKED_MISSING_DOCS = "blocked_missing_docs" + BLOCKED_ENRICHMENT = "blocked_enrichment" + COMPLETED = "completed" + REJECTED = "rejected" + + +# ── Phase 2 addition ────────────────────────────────────────────────────────── +class DocEnrichmentType(str, Enum): + """External lookup needed for document verification.""" + NONE = "none" + PAST_LAND_RECORDS = "past_land_records" # Land Registration — Revenue DB + FAMILY_CASTE_HISTORY = "family_caste_history" # Caste Certificate — Caste Registry + POLICE_VERIFICATION = "police_verification" # Passport — Police Station + TAX_RECORD_CROSS_CHECK= "tax_record_cross_check" # GST Registration — Tax DB + + +# Public stage mapping — used by state_machine.build_public_stage +INTERNAL_TO_PUBLIC_STAGE: dict = { + "pre_scrutiny": "submission", + "doc_validation": "document_verification", + "service_specific_validation": "document_verification", + "field_verification_pending": "field_verification", + "decision_pending": "approval", + "issuance_ready": "issuance", + "blocked_missing_docs": "document_verification", + "blocked_enrichment": "document_verification", + "completed": "issuance", + "rejected": "approval", +} + + +class PriorityMode(str, Enum): + URGENT_FIRST = "urgent_first" + OLDEST_FIRST = "oldest_first" + BALANCED = "balanced" + BACKLOG_CLEARANCE = "backlog_clearance" + + +class ActionType(str, Enum): + SET_PRIORITY_MODE = "set_priority_mode" + ASSIGN_CAPACITY = "assign_capacity" + REQUEST_MISSING_DOCUMENTS = "request_missing_documents" + ESCALATE_SERVICE = "escalate_service" + ADVANCE_TIME = "advance_time" + REALLOCATE_OFFICERS = "reallocate_officers" + + +class EventType(str, Enum): + SURGE_APPLICATIONS = "surge_applications" + OFFICER_UNAVAILABLE = "officer_unavailable" + DOCUMENT_REJECTION_SPIKE = "document_rejection_spike" + REVENUE_DB_DELAY = "revenue_db_delay" + SLA_ESCALATION_ORDER = "sla_escalation_order" + NO_EVENT = "no_event" + + +class ScenarioMode(str, Enum): + NORMAL = "normal" + CRISIS = "crisis" + EXTREME_OVERLOAD = "extreme_overload" + + +class UrgencyProfile(str, Enum): + LOW = "low" + MODERATE = "moderate" + HIGH = "high" + LOW_BUT_STICKY = "low_but_sticky" + + +class IntakeChannel(str, Enum): + DIGITAL = "digital" + PAPER = "paper" + HYBRID = "hybrid" + + +class DelayedEffectType(str, Enum): + DOC_REQUEST_RESOLUTION = "doc_request_resolution" + OFFICER_REALLOCATION = "officer_reallocation" + ESCALATION_RELIEF = "escalation_relief" + + +# ───────────────────────────────────────────── +# SECTOR / SERVICE CONFIGURATION +# ───────────────────────────────────────────── + +class SectorProfile(BaseModel): + service_type: ServiceType + sector_name: str + missing_docs_probability: float = Field(ge=0.0, le=1.0) + doc_defect_rate_digital: float = Field(ge=0.0, le=1.0) + doc_defect_rate_paper: float = Field(ge=0.0, le=1.0) + field_verification_probability: float = Field(ge=0.0, le=1.0) + manual_scrutiny_intensity: float = Field(ge=0.0, le=1.0) + decision_backlog_sensitivity: float = Field(ge=0.0, le=1.0) + system_dependency_risk: float = Field(ge=0.0, le=1.0) + sla_days: int = Field(ge=1) + urgency_profile: UrgencyProfile + base_processing_rate: float = Field(ge=0.1) + field_verification_days: int = Field(ge=1) + # ── Phase 2: enrichment ───────────────────────────────────────── + doc_enrichment_type: DocEnrichmentType = DocEnrichmentType.NONE + doc_enrichment_probability: float = Field(default=0.0, ge=0.0, le=1.0) + doc_enrichment_delay_days_min: int = Field(default=1, ge=1) + doc_enrichment_delay_days_max: int = Field(default=3, ge=1) + + +class OfficerPool(BaseModel): + total_officers: int = Field(ge=1) + available_officers: int = Field(ge=0) + allocated: Dict[str, int] = Field(default_factory=dict) + pending_reallocation: Dict[str, int] = Field(default_factory=dict) + + @property + def idle_officers(self) -> int: + return self.available_officers - sum(self.allocated.values()) + + +# ───────────────────────────────────────────── +# CASE MODEL (Phase 2: enrichment fields added) +# ───────────────────────────────────────────── + +class ApplicationCase(BaseModel): + case_id: str = Field(default_factory=lambda: str(uuid.uuid4())[:8]) + service_type: ServiceType + internal_substate: InternalSubstate = InternalSubstate.PRE_SCRUTINY + public_stage: StageType = StageType.SUBMISSION + + arrival_day: int = Field(ge=0) + current_day: int = Field(ge=0) + sla_deadline_day: int = Field(ge=0) + days_in_current_stage:int = Field(default=0, ge=0) + waiting_days: int = Field(default=0, ge=0) + + is_urgent: bool = False + intake_channel: IntakeChannel = IntakeChannel.DIGITAL + has_missing_docs: bool = False + doc_request_sent_day: Optional[int] = None + doc_resolution_day: Optional[int] = None + field_verification_required: bool = False + field_verification_completion_day: Optional[int] = None + + sla_breached: bool = False + completed: bool = False + rejected: bool = False + + # ── Phase 2: enrichment ───────────────────────────────────────── + doc_enrichment_type: DocEnrichmentType = DocEnrichmentType.NONE + doc_enrichment_triggered:bool = False + enrichment_resolution_day:Optional[int] = None + doc_enrichment_reason: Optional[str] = None + + @property + def days_until_sla(self) -> int: + return max(0, self.sla_deadline_day - self.current_day) + + @property + def sla_risk(self) -> float: + total_window = self.sla_deadline_day - self.arrival_day + if total_window <= 0: + return 1.0 + elapsed = self.current_day - self.arrival_day + return min(1.0, elapsed / total_window) + + +class QueueSnapshot(BaseModel): + service_type: ServiceType + public_stage_counts: Dict[str, int] = Field(default_factory=dict) + total_pending: int = Field(default=0, ge=0) + total_completed_today: int = Field(default=0, ge=0) + total_sla_breached: int = Field(default=0, ge=0) + urgent_pending: int = Field(default=0, ge=0) + blocked_missing_docs: int = Field(default=0, ge=0) + blocked_enrichment: int = Field(default=0, ge=0) # Phase 2 + field_verification_pending:int = Field(default=0, ge=0) + oldest_case_age_days: int = Field(default=0, ge=0) + avg_waiting_days: float = Field(default=0.0, ge=0.0) + current_sla_risk: float = Field(default=0.0, ge=0.0, le=1.0) + + +# ───────────────────────────────────────────── +# DELAYED EFFECT MODEL +# ───────────────────────────────────────────── + +class DelayedEffect(BaseModel): + effect_id: str = Field(default_factory=lambda: str(uuid.uuid4())[:8]) + effect_type: DelayedEffectType + target_service: Optional[ServiceType] = None + target_case_id: Optional[str] = None + resolution_day: int = Field(ge=0) + magnitude: float = Field(default=1.0) + description: str = Field(default="") + + +# ───────────────────────────────────────────── +# OBSERVATION MODEL (Phase 2: enrichment signals added) +# ───────────────────────────────────────────── + +class ObservationModel(BaseModel): + task_id: str + episode_id: str + day: int = Field(ge=0) + max_days: int = Field(ge=1) + scenario_mode: ScenarioMode = ScenarioMode.NORMAL + officer_pool: OfficerPool + queue_snapshots: Dict[str, QueueSnapshot] = Field(default_factory=dict) + + total_backlog: int = Field(default=0, ge=0) + total_completed: int = Field(default=0, ge=0) + total_sla_breaches: int = Field(default=0, ge=0) + total_rejected: int = Field(default=0, ge=0) + escalation_budget_remaining:int = Field(default=0, ge=0) + + # Compressed signals + backlog_pressure: float = Field(default=0.0, ge=0.0, le=1.0) + sla_risk_score: float = Field(default=0.0, ge=0.0, le=1.0) + fairness_index: float = Field(default=1.0, ge=0.0, le=1.0) + resource_utilization: float = Field(default=0.0, ge=0.0, le=1.0) + digital_intake_ratio: float = Field(default=0.5, ge=0.0, le=1.0) + blocked_cases_missing_docs:int = Field(default=0, ge=0) + blocked_cases_enrichment: int = Field(default=0, ge=0) # Phase 2 + field_verification_load: float = Field(default=0.0, ge=0.0, le=1.0) + + active_events: List[EventType] = Field(default_factory=list) + + last_action_valid: bool = True + last_action_message: str = "" + last_action_explanation: str = Field(default="") + + pending_doc_resolutions: int = Field(default=0, ge=0) + pending_enrichment_lookups:int = Field(default=0, ge=0) # Phase 2 + pending_officer_reallocations:int = Field(default=0, ge=0) + + +# ───────────────────────────────────────────── +# ACTION / REWARD / STATE MODELS (unchanged) +# ───────────────────────────────────────────── + +class ActionModel(BaseModel): + action_type: ActionType + service_target: Optional[ServiceType] = None + priority_mode: Optional[PriorityMode] = None + reallocation_delta: Optional[Dict[str, int]] = None + escalation_target: Optional[ServiceType] = None + capacity_assignment: Optional[Dict[str, int]] = None + notes: Optional[str] = None + + +class RewardModel(BaseModel): + total_reward: float = 0.0 + progress_reward: float = 0.0 + completion_reward: float = 0.0 + recovery_reward: float = 0.0 + stability_bonus: float = 0.0 + waiting_penalty: float = 0.0 + sla_penalty: float = 0.0 + fairness_penalty: float = 0.0 + invalid_action_penalty: float = 0.0 + idle_capacity_penalty: float = 0.0 + oscillation_penalty: float = 0.0 + + +class EpisodeStateModel(BaseModel): + """Internal episode state exposed via GET /state and POST /state endpoints.""" + episode_id: str + task_id: str + seed: int + scenario_mode: ScenarioMode + day: int = Field(ge=0) + max_days: int = Field(ge=1) + terminated: bool = False + truncated: bool = False + total_steps: int = Field(default=0, ge=0) + total_completed: int = Field(default=0, ge=0) + total_backlog: int = Field(default=0, ge=0) + total_sla_breaches: int = Field(default=0, ge=0) + total_rejected: int = Field(default=0, ge=0) + action_history_count: int = Field(default=0, ge=0) + cumulative_reward: float = 0.0 + cumulative_reward_breakdown: RewardModel = Field(default_factory=RewardModel) + officer_pool: Optional[OfficerPool] = None + pending_effects_count: int = Field(default=0, ge=0) + active_events_today: List[EventType] = Field(default_factory=list) + + # ── Grader-facing fields ────────────────────────────────────── + # These are populated by env.state() so graders never need to + # reach into private EpisodeMetrics. + fairness_gap: float = Field( + default=0.0, ge=0.0, le=1.0, + description="Cross-service completion fairness gap at episode end" + ) + total_arrived: int = Field( + default=0, ge=0, + description="Total cases that arrived across all services" + ) + total_docs_requested: int = Field( + default=0, ge=0, + description="Total missing-doc requests sent" + ) + total_docs_cleared: int = Field( + default=0, ge=0, + description="Total missing-doc cases subsequently resolved" + ) + total_idle_officer_days: int = Field( + default=0, ge=0, + description="Cumulative officer-days wasted idle" + ) + total_capacity_days: int = Field( + default=0, ge=0, + description="Cumulative total officer-days available" + ) + total_urgent_arrived: int = Field( + default=0, ge=0, + description="Total urgent cases that arrived" + ) + total_urgent_completed: int = Field( + default=0, ge=0, + description="Total urgent cases completed" + ) + total_escalations_used: int = Field( + default=0, ge=0, + description="Total escalation actions consumed" + ) + total_wasted_escalations: int = Field( + default=0, ge=0, + description="Escalations used on already-urgent or ineligible cases" + ) + total_invalid_actions: int = Field( + default=0, ge=0, + description="Total invalid actions submitted by agent" + ) + avg_waiting_days: float = Field( + default=0.0, ge=0.0, + description="Mean waiting days across all completed cases" + ) + + # ── Full action log (optional, stripped by default) ────────── + action_history: Optional[List[dict]] = Field( + default=None, + description="Step-by-step action log. Stripped in normal API responses." + ) + + +class StepInfoModel(BaseModel): + reward_breakdown: RewardModel = Field(default_factory=RewardModel) + newly_arrived_cases: int = Field(default=0, ge=0) + newly_completed_cases: int = Field(default=0, ge=0) + newly_sla_breached_cases: int = Field(default=0, ge=0) + newly_resolved_doc_cases: int = Field(default=0, ge=0) + invalid_action: bool = False + action_explanation: str = "" + active_events: List[EventType] = Field(default_factory=list) + grader_preview_score: float = Field(default=0.0, ge=0.0, le=1.0) + effects_resolved_this_step: List[str] = Field(default_factory=list) + + +class TaskConfig(BaseModel): + task_id: str + display_name: str + difficulty: str + scenario_mode: ScenarioMode + seed: int + max_days: int = Field(ge=1) + enabled_services: List[ServiceType] + arrival_rate_per_day: Dict[str, float] + digital_intake_ratio: float = Field(default=0.6, ge=0.0, le=1.0) + initial_officer_pool: OfficerPool + missing_docs_probability_override: Optional[Dict[str, float]] = None + field_verification_probability_override: Optional[Dict[str, float]] = None + escalation_budget: int = Field(ge=0) + fairness_threshold: Optional[float] = Field(default=None, ge=0.0, le=1.0) + event_probability: float = Field(default=0.1, ge=0.0, le=1.0) + allowed_events: List[EventType] = Field(default_factory=list) + + +class GraderResult(BaseModel): + """ + Final deterministic score for a completed or in-progress episode. + Range: [0.0, 1.0]. + + Design decision: exposes .score and .grader_name as convenience aliases, + plus a .metrics dict for easy serialization to JSON by main.py endpoints. + The named fields (completion_rate, sla_compliance_rate, etc.) remain + for typed access in tests and baselines. + """ + task_id: str = "" + episode_id: str = "" + grader_name: str = "" # "easy" | "medium" | "hard" + + # Primary scalar — use result.score everywhere + score: float = Field(default=0.0, ge=0.0, le=1.0) + + # Named metric components + completion_rate: float = Field(default=0.0, ge=0.0, le=1.0) + sla_compliance_rate: float = Field(default=0.0, ge=0.0, le=1.0) + idle_efficiency: float = Field(default=1.0, ge=0.0, le=1.0) + document_rework_quality: float = Field(default=1.0, ge=0.0, le=1.0) + urgent_served_rate: float = Field(default=1.0, ge=0.0, le=1.0) + fairness_score: float = Field(default=1.0, ge=0.0, le=1.0) + escalation_discipline: float = Field(default=1.0, ge=0.0, le=1.0) + fairness_gap: float = Field(default=0.0, ge=0.0, le=1.0) + + # Episode counters — populated from EpisodeStateModel + total_cases_arrived: int = 0 + total_completed: int = 0 + total_sla_breached: int = 0 + total_rejected: int = 0 + avg_waiting_days: float = 0.0 + + @property + def metrics(self) -> dict: + """ + Convenience dict for JSON serialization in API endpoints. + main.py uses result.metrics directly in GradeResponse. + """ + return { + "completion_rate": round(self.completion_rate, 4), + "sla_compliance_rate": round(self.sla_compliance_rate, 4), + "idle_efficiency": round(self.idle_efficiency, 4), + "document_rework_quality": round(self.document_rework_quality, 4), + "urgent_served_rate": round(self.urgent_served_rate, 4), + "fairness_score": round(self.fairness_score, 4), + "escalation_discipline": round(self.escalation_discipline, 4), + "fairness_gap": round(self.fairness_gap, 4), + "total_cases_arrived": self.total_cases_arrived, + "total_completed": self.total_completed, + "total_sla_breached": self.total_sla_breached, + "total_rejected": self.total_rejected, + "avg_waiting_days": round(self.avg_waiting_days, 2), + } + + +class ResetRequest(BaseModel): + task_id: str + seed: Optional[int] = None + scenario_mode: Optional[ScenarioMode] = None + + +class ResetResponse(BaseModel): + observation: ObservationModel + info: dict + episode_id: str + + +class StepRequest(BaseModel): + episode_id: str + action: ActionModel + + +class StepResponse(BaseModel): + observation: ObservationModel + reward: float + terminated: bool + truncated: bool + info: StepInfoModel + + +class StateResponse(BaseModel): + state: EpisodeStateModel + + +class HealthResponse(BaseModel): + status: str = "ok" + version: str = "2.0.0" + active_episodes:int = 0 diff --git a/app/persistence.py b/app/persistence.py new file mode 100644 index 0000000000000000000000000000000000000000..f6e355852949476cf0a785a8a663b6fc5af1c6b6 --- /dev/null +++ b/app/persistence.py @@ -0,0 +1,335 @@ +from __future__ import annotations + +import json +import os +import sqlite3 +import time +from pathlib import Path +from threading import Lock +from typing import Any +from uuid import uuid4 + + +def _now() -> float: + return time.time() + + +def _as_json(payload: dict[str, Any]) -> str: + return json.dumps(payload, separators=(",", ":"), ensure_ascii=True) + + +def _from_json(payload: str) -> dict[str, Any]: + data = json.loads(payload) + return data if isinstance(data, dict) else {} + + +def _resolve_data_dir(repo_root: Path) -> Path: + configured = os.getenv("OPENENV_DATA_DIR") or os.getenv("STORAGE_DATA_DIR") + if configured: + return Path(configured).expanduser().resolve() + if Path("/data").exists(): + return Path("/data/openenv_rl").resolve() + return (repo_root / "outputs" / "persist").resolve() + + +def _default_fallback_data_dirs(repo_root: Path) -> list[Path]: + return [ + (repo_root / "outputs" / "persist").resolve(), + Path("/tmp/openenv_rl").resolve(), + ] + + +def _storage_enabled() -> bool: + raw = str(os.getenv("STORAGE_ENABLED", "true")).strip().lower() + return raw not in {"0", "false", "no", "off"} + + +class PersistenceStore: + def __init__(self, repo_root: Path) -> None: + self.repo_root = repo_root.resolve() + self.enabled = _storage_enabled() + self.data_dir = _resolve_data_dir(self.repo_root) + self.db_path = self.data_dir / "openenv_state.sqlite3" + self.training_runs_dir = self.data_dir / "training_runs" + self._lock = Lock() + + if not self.enabled: + return + + self._initialize_storage_dirs() + + def _initialize_storage_dirs(self) -> None: + candidates: list[Path] = [self.data_dir] + for fallback in _default_fallback_data_dirs(self.repo_root): + if fallback not in candidates: + candidates.append(fallback) + + last_error: Exception | None = None + for candidate in candidates: + try: + candidate.mkdir(parents=True, exist_ok=True) + self.data_dir = candidate + self.db_path = self.data_dir / "openenv_state.sqlite3" + self.training_runs_dir = self.data_dir / "training_runs" + self.training_runs_dir.mkdir(parents=True, exist_ok=True) + self._init_schema() + return + except (OSError, sqlite3.Error) as exc: + last_error = exc + + self.enabled = False + # Keep service startup alive in restricted runtimes (e.g. HF Spaces without writable /data). + print( + f"[persistence] disabled: no writable storage directory. " + f"requested={candidates[0]} last_error={last_error!r}" + ) + + def _connect(self) -> sqlite3.Connection: + conn = sqlite3.connect(self.db_path, timeout=30) + conn.row_factory = sqlite3.Row + return conn + + def _init_schema(self) -> None: + with self._connect() as conn: + conn.executescript( + """ + CREATE TABLE IF NOT EXISTS training_jobs ( + job_id TEXT PRIMARY KEY, + created_at REAL NOT NULL, + updated_at REAL NOT NULL, + payload_json TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS simulation_runs ( + run_id TEXT PRIMARY KEY, + created_at REAL NOT NULL, + updated_at REAL NOT NULL, + task_id TEXT, + agent_mode TEXT, + status TEXT, + payload_json TEXT NOT NULL + ); + + CREATE TABLE IF NOT EXISTS comparison_runs ( + comparison_id TEXT PRIMARY KEY, + created_at REAL NOT NULL, + updated_at REAL NOT NULL, + task_id TEXT, + payload_json TEXT NOT NULL + ); + """ + ) + conn.commit() + + # Training jobs --------------------------------------------------------- + def upsert_training_job(self, snapshot: dict[str, Any]) -> None: + if not self.enabled: + return + job_id = str(snapshot.get("job_id") or "") + if not job_id: + return + created_at = float(snapshot.get("created_at") or _now()) + updated_at = float(snapshot.get("updated_at") or _now()) + with self._lock, self._connect() as conn: + conn.execute( + """ + INSERT INTO training_jobs (job_id, created_at, updated_at, payload_json) + VALUES (?, ?, ?, ?) + ON CONFLICT(job_id) DO UPDATE SET + updated_at = excluded.updated_at, + payload_json = excluded.payload_json + """, + (job_id, created_at, updated_at, _as_json(snapshot)), + ) + conn.commit() + + def list_training_jobs(self, limit: int = 500) -> list[dict[str, Any]]: + if not self.enabled: + return [] + rows: list[dict[str, Any]] = [] + with self._lock, self._connect() as conn: + cur = conn.execute( + """ + SELECT payload_json FROM training_jobs + ORDER BY updated_at DESC + LIMIT ? + """, + (max(1, int(limit)),), + ) + for row in cur.fetchall(): + rows.append(_from_json(str(row["payload_json"]))) + return rows + + def clear_training_jobs(self) -> int: + if not self.enabled: + return 0 + with self._lock, self._connect() as conn: + cur = conn.execute("DELETE FROM training_jobs") + conn.commit() + return int(cur.rowcount or 0) + + def delete_training_job(self, job_id: str) -> int: + if not self.enabled: + return 0 + with self._lock, self._connect() as conn: + cur = conn.execute("DELETE FROM training_jobs WHERE job_id = ?", (str(job_id),)) + conn.commit() + return int(cur.rowcount or 0) + + # Simulation runs ------------------------------------------------------- + def upsert_simulation_run( + self, + *, + run_id: str, + task_id: str, + agent_mode: str, + status: str, + payload: dict[str, Any], + ) -> None: + if not self.enabled: + return + now = _now() + created_at = float(payload.get("created_at") or now) + payload = dict(payload) + payload["run_id"] = run_id + payload["created_at"] = created_at + payload["updated_at"] = now + payload["task_id"] = task_id + payload["agent_mode"] = agent_mode + payload["status"] = status + with self._lock, self._connect() as conn: + conn.execute( + """ + INSERT INTO simulation_runs (run_id, created_at, updated_at, task_id, agent_mode, status, payload_json) + VALUES (?, ?, ?, ?, ?, ?, ?) + ON CONFLICT(run_id) DO UPDATE SET + updated_at = excluded.updated_at, + task_id = excluded.task_id, + agent_mode = excluded.agent_mode, + status = excluded.status, + payload_json = excluded.payload_json + """, + ( + run_id, + created_at, + now, + task_id, + agent_mode, + status, + _as_json(payload), + ), + ) + conn.commit() + + def list_simulation_runs(self, limit: int = 50) -> list[dict[str, Any]]: + if not self.enabled: + return [] + out: list[dict[str, Any]] = [] + with self._lock, self._connect() as conn: + cur = conn.execute( + """ + SELECT payload_json FROM simulation_runs + ORDER BY updated_at DESC + LIMIT ? + """, + (max(1, int(limit)),), + ) + for row in cur.fetchall(): + data = _from_json(str(row["payload_json"])) + if isinstance(data.get("trace"), list): + data["trace_len"] = len(data["trace"]) + data["has_trace"] = bool(data["trace"]) + data.pop("trace", None) + out.append(data) + return out + + def get_simulation_run(self, run_id: str) -> dict[str, Any] | None: + if not self.enabled: + return None + with self._lock, self._connect() as conn: + cur = conn.execute( + "SELECT payload_json FROM simulation_runs WHERE run_id = ?", + (run_id,), + ) + row = cur.fetchone() + if row is None: + return None + return _from_json(str(row["payload_json"])) + + def clear_simulation_runs(self) -> int: + if not self.enabled: + return 0 + with self._lock, self._connect() as conn: + cur = conn.execute("DELETE FROM simulation_runs") + conn.commit() + return int(cur.rowcount or 0) + + # Comparison runs ------------------------------------------------------- + def create_comparison_run(self, payload: dict[str, Any]) -> str | None: + if not self.enabled: + return None + comparison_id = str(payload.get("comparison_id") or uuid4()) + now = _now() + body = dict(payload) + body["comparison_id"] = comparison_id + body["created_at"] = float(body.get("created_at") or now) + body["updated_at"] = now + task_id = str(body.get("task_id") or "") + with self._lock, self._connect() as conn: + conn.execute( + """ + INSERT INTO comparison_runs (comparison_id, created_at, updated_at, task_id, payload_json) + VALUES (?, ?, ?, ?, ?) + ON CONFLICT(comparison_id) DO UPDATE SET + updated_at = excluded.updated_at, + task_id = excluded.task_id, + payload_json = excluded.payload_json + """, + ( + comparison_id, + float(body["created_at"]), + now, + task_id, + _as_json(body), + ), + ) + conn.commit() + return comparison_id + + def list_comparison_runs(self, limit: int = 50) -> list[dict[str, Any]]: + if not self.enabled: + return [] + out: list[dict[str, Any]] = [] + with self._lock, self._connect() as conn: + cur = conn.execute( + """ + SELECT payload_json FROM comparison_runs + ORDER BY updated_at DESC + LIMIT ? + """, + (max(1, int(limit)),), + ) + for row in cur.fetchall(): + out.append(_from_json(str(row["payload_json"]))) + return out + + def get_comparison_run(self, comparison_id: str) -> dict[str, Any] | None: + if not self.enabled: + return None + with self._lock, self._connect() as conn: + cur = conn.execute( + "SELECT payload_json FROM comparison_runs WHERE comparison_id = ?", + (comparison_id,), + ) + row = cur.fetchone() + if row is None: + return None + return _from_json(str(row["payload_json"])) + + def clear_comparison_runs(self) -> int: + if not self.enabled: + return 0 + with self._lock, self._connect() as conn: + cur = conn.execute("DELETE FROM comparison_runs") + conn.commit() + return int(cur.rowcount or 0) diff --git a/app/reward.py b/app/reward.py new file mode 100644 index 0000000000000000000000000000000000000000..0223eeac141cbbfad72d2671da909ad58c9a2733 --- /dev/null +++ b/app/reward.py @@ -0,0 +1,108 @@ +""" +reward.py — Gov Workflow OpenEnv Phase 4: Dense Reward Shaping + +Formula (per step): + R_t = progress_reward + completion_reward + recovery_reward + stability_bonus + - waiting_penalty - sla_penalty - fairness_penalty + - invalid_action_penalty - idle_capacity_penalty - oscillation_penalty + +All coefficients are named constants — never magic numbers inline. +""" +from __future__ import annotations +from app.models import RewardModel + +# ── Positive coefficients ───────────────────────────────────────── +COEFF_PROGRESS = 0.7 # per stage advance +COEFF_COMPLETION = 4.0 # per completed case +COEFF_RECOVERY = 1.5 # per unblocked missing-doc case resolved +COEFF_STABILITY = 0.1 # per step with zero SLA breaches and zero invalid actions + +# ── Negative coefficients ───────────────────────────────────────── +COEFF_WAITING = 0.04 # per case per day in backlog +COEFF_SLA = 1.5 # per new SLA breach +COEFF_FAIRNESS = 2.0 # per unit of fairness excess above threshold +COEFF_INVALID = 1.5 # flat penalty per invalid action +COEFF_IDLE = 0.05 # per idle officer-day +COEFF_OSCILLATION = 0.15 # per oscillation event (repeated contradictory actions) + +# ── Fairness default tolerance (when no threshold set by task) ──── +DEFAULT_FAIRNESS_TOLERANCE = 0.40 + + +def compute_reward( + *, + stage_advances: int, + completions: int, + active_backlog: int, + new_sla_breaches: int, + fairness_gap: float, + fairness_threshold: float | None, + invalid_action: bool, + idle_capacity: int, + newly_unblocked_docs: int = 0, + oscillation_detected: bool = False, + award_stability_bonus: bool = True, +) -> RewardModel: + """ + Compute one-step dense reward. + + Args: + stage_advances: Number of applications that moved forward one stage today. + completions: Number of applications fully completed today. + active_backlog: Total cases still pending (creates waiting pressure). + new_sla_breaches: New SLA deadline violations this step. + fairness_gap: Cross-service completion fairness gap [0.0, 1.0]. + fairness_threshold: Task-defined acceptable fairness gap (or None → default). + invalid_action: Whether the submitted action was invalid. + idle_capacity: Officer-days wasted idle while backlog exists. + newly_unblocked_docs: Cases unblocked after missing-doc resolution (positive signal). + oscillation_detected: True if agent is rapidly reversing recent decisions. + + Returns: + RewardModel with all components filled and total_reward as the scalar. + """ + # ── Positive components ─────────────────────────────────────── + progress_reward = COEFF_PROGRESS * stage_advances + completion_reward = COEFF_COMPLETION * completions + recovery_reward = COEFF_RECOVERY * newly_unblocked_docs + stability_bonus = ( + COEFF_STABILITY + if (award_stability_bonus and new_sla_breaches == 0 and not invalid_action) + else 0.0 + ) + + # ── Negative components ─────────────────────────────────────── + waiting_penalty = COEFF_WAITING * active_backlog + + sla_penalty = COEFF_SLA * new_sla_breaches + + tolerance = fairness_threshold if fairness_threshold is not None else DEFAULT_FAIRNESS_TOLERANCE + unfairness_excess = max(0.0, fairness_gap - tolerance) + fairness_penalty = COEFF_FAIRNESS * unfairness_excess + + invalid_action_penalty = COEFF_INVALID if invalid_action else 0.0 + + idle_capacity_penalty = COEFF_IDLE * idle_capacity + + oscillation_penalty = COEFF_OSCILLATION if oscillation_detected else 0.0 + + # ── Total ───────────────────────────────────────────────────── + total_reward = ( + progress_reward + completion_reward + recovery_reward + stability_bonus + - waiting_penalty - sla_penalty - fairness_penalty + - invalid_action_penalty - idle_capacity_penalty - oscillation_penalty + ) + + return RewardModel( + total_reward=round(total_reward, 4), + progress_reward=round(progress_reward, 4), + completion_reward=round(completion_reward, 4), + recovery_reward=round(recovery_reward, 4), + stability_bonus=round(stability_bonus, 4), + waiting_penalty=round(-waiting_penalty, 4), + sla_penalty=round(-sla_penalty, 4), + fairness_penalty=round(-fairness_penalty, 4), + invalid_action_penalty=round(-invalid_action_penalty, 4), + idle_capacity_penalty=round(-idle_capacity_penalty, 4), + oscillation_penalty=round(-oscillation_penalty, 4), + ) diff --git a/app/sector_profiles.py b/app/sector_profiles.py new file mode 100644 index 0000000000000000000000000000000000000000..878d33d49d8c923173500a360bda13e6ddba4b41 --- /dev/null +++ b/app/sector_profiles.py @@ -0,0 +1,183 @@ +""" +sector_profiles.py — Phase 2 update: enrichment type, probability, delay range per service. +""" + +from app.models import ( + DocEnrichmentType, SectorProfile, ServiceType, UrgencyProfile +) + +INCOME_CERTIFICATE_PROFILE = SectorProfile( + service_type=ServiceType.INCOME_CERTIFICATE, + sector_name="Revenue Sector — Income Certificate", + missing_docs_probability=0.45, + doc_defect_rate_digital=0.30, + doc_defect_rate_paper=0.65, + field_verification_probability=0.30, + manual_scrutiny_intensity=0.60, + decision_backlog_sensitivity=0.70, + system_dependency_risk=0.20, + sla_days=21, + urgency_profile=UrgencyProfile.MODERATE, + base_processing_rate=8.0, + field_verification_days=3, + doc_enrichment_type=DocEnrichmentType.NONE, + doc_enrichment_probability=0.0, + doc_enrichment_delay_days_min=1, + doc_enrichment_delay_days_max=2, +) + +LAND_REGISTRATION_PROFILE = SectorProfile( + service_type=ServiceType.LAND_REGISTRATION, + sector_name="Land Sector — 7/12 Mutation", + missing_docs_probability=0.35, + doc_defect_rate_digital=0.25, + doc_defect_rate_paper=0.55, + field_verification_probability=0.65, + manual_scrutiny_intensity=0.75, + decision_backlog_sensitivity=0.85, + system_dependency_risk=0.55, + sla_days=30, + urgency_profile=UrgencyProfile.LOW_BUT_STICKY, + base_processing_rate=4.0, + field_verification_days=5, + doc_enrichment_type=DocEnrichmentType.PAST_LAND_RECORDS, + doc_enrichment_probability=0.70, + doc_enrichment_delay_days_min=2, + doc_enrichment_delay_days_max=5, # REVENUE_DB_DELAY event adds 1-2 more +) + +CASTE_CERTIFICATE_PROFILE = SectorProfile( + service_type=ServiceType.CASTE_CERTIFICATE, + sector_name="Revenue Sector — Caste Certificate", + missing_docs_probability=0.40, + doc_defect_rate_digital=0.25, + doc_defect_rate_paper=0.60, + field_verification_probability=0.35, + manual_scrutiny_intensity=0.65, + decision_backlog_sensitivity=0.65, + system_dependency_risk=0.25, + sla_days=21, + urgency_profile=UrgencyProfile.MODERATE, + base_processing_rate=7.0, + field_verification_days=3, + doc_enrichment_type=DocEnrichmentType.FAMILY_CASTE_HISTORY, + doc_enrichment_probability=0.55, + doc_enrichment_delay_days_min=2, + doc_enrichment_delay_days_max=4, +) + +BIRTH_CERTIFICATE_PROFILE = SectorProfile( + service_type=ServiceType.BIRTH_CERTIFICATE, + sector_name="Municipal Sector — Birth Certificate", + missing_docs_probability=0.20, + doc_defect_rate_digital=0.15, + doc_defect_rate_paper=0.35, + field_verification_probability=0.05, + manual_scrutiny_intensity=0.30, + decision_backlog_sensitivity=0.40, + system_dependency_risk=0.30, + sla_days=7, + urgency_profile=UrgencyProfile.HIGH, + base_processing_rate=15.0, + field_verification_days=1, + doc_enrichment_type=DocEnrichmentType.NONE, + doc_enrichment_probability=0.0, + doc_enrichment_delay_days_min=1, + doc_enrichment_delay_days_max=1, +) + +PASSPORT_PROFILE = SectorProfile( + service_type=ServiceType.PASSPORT, + sector_name="National Sector — Passport", + missing_docs_probability=0.25, + doc_defect_rate_digital=0.20, + doc_defect_rate_paper=0.50, + field_verification_probability=0.90, + manual_scrutiny_intensity=0.80, + decision_backlog_sensitivity=0.75, + system_dependency_risk=0.35, + sla_days=30, + urgency_profile=UrgencyProfile.HIGH, + base_processing_rate=5.0, + field_verification_days=14, + doc_enrichment_type=DocEnrichmentType.POLICE_VERIFICATION, + doc_enrichment_probability=0.85, + doc_enrichment_delay_days_min=7, + doc_enrichment_delay_days_max=14, +) + +GST_REGISTRATION_PROFILE = SectorProfile( + service_type=ServiceType.GST_REGISTRATION, + sector_name="Tax Sector — GST Registration", + missing_docs_probability=0.30, + doc_defect_rate_digital=0.20, + doc_defect_rate_paper=0.50, + field_verification_probability=0.20, + manual_scrutiny_intensity=0.55, + decision_backlog_sensitivity=0.60, + system_dependency_risk=0.45, + sla_days=7, + urgency_profile=UrgencyProfile.HIGH, + base_processing_rate=10.0, + field_verification_days=2, + doc_enrichment_type=DocEnrichmentType.TAX_RECORD_CROSS_CHECK, + doc_enrichment_probability=0.50, + doc_enrichment_delay_days_min=1, + doc_enrichment_delay_days_max=3, +) + +DRIVING_LICENSE_PROFILE = SectorProfile( + service_type=ServiceType.DRIVING_LICENSE, + sector_name="Transport Sector — Driving License", + missing_docs_probability=0.28, + doc_defect_rate_digital=0.18, + doc_defect_rate_paper=0.45, + field_verification_probability=0.40, + manual_scrutiny_intensity=0.50, + decision_backlog_sensitivity=0.55, + system_dependency_risk=0.30, + sla_days=14, + urgency_profile=UrgencyProfile.MODERATE, + base_processing_rate=12.0, + field_verification_days=2, + doc_enrichment_type=DocEnrichmentType.NONE, + doc_enrichment_probability=0.0, + doc_enrichment_delay_days_min=1, + doc_enrichment_delay_days_max=1, +) + +AADHAAR_CARD_PROFILE = SectorProfile( + service_type=ServiceType.AADHAAR_CARD, + sector_name="National Identity Sector - Aadhaar Card", + missing_docs_probability=0.22, + doc_defect_rate_digital=0.12, + doc_defect_rate_paper=0.30, + field_verification_probability=0.18, + manual_scrutiny_intensity=0.42, + decision_backlog_sensitivity=0.50, + system_dependency_risk=0.38, + sla_days=10, + urgency_profile=UrgencyProfile.HIGH, + base_processing_rate=13.0, + field_verification_days=2, + doc_enrichment_type=DocEnrichmentType.NONE, + doc_enrichment_probability=0.0, + doc_enrichment_delay_days_min=1, + doc_enrichment_delay_days_max=2, +) + +SECTOR_REGISTRY: dict = { + ServiceType.INCOME_CERTIFICATE: INCOME_CERTIFICATE_PROFILE, + ServiceType.LAND_REGISTRATION: LAND_REGISTRATION_PROFILE, + ServiceType.CASTE_CERTIFICATE: CASTE_CERTIFICATE_PROFILE, + ServiceType.BIRTH_CERTIFICATE: BIRTH_CERTIFICATE_PROFILE, + ServiceType.PASSPORT: PASSPORT_PROFILE, + ServiceType.GST_REGISTRATION: GST_REGISTRATION_PROFILE, + ServiceType.DRIVING_LICENSE: DRIVING_LICENSE_PROFILE, + ServiceType.AADHAAR_CARD: AADHAAR_CARD_PROFILE, +} + +def get_sector_profile(service_type: ServiceType) -> SectorProfile: + if service_type not in SECTOR_REGISTRY: + raise KeyError(f"No SectorProfile for {service_type}") + return SECTOR_REGISTRY[service_type] diff --git a/app/signal_computer.py b/app/signal_computer.py new file mode 100644 index 0000000000000000000000000000000000000000..943a58426c8d8d791dcb656e1bba0d63c699117f --- /dev/null +++ b/app/signal_computer.py @@ -0,0 +1,81 @@ +""" +signal_computer.py — Gov Workflow OpenEnv v2.0 +Computes normalized compressed state signals for observations. +All signals are deterministic and normalized to [0.0, 1.0]. +""" +from typing import Dict +from app.models import QueueSnapshot, OfficerPool + + +class ComputedSignals: + def __init__(self): + self.backlog_pressure: float = 0.0 + self.sla_risk_score: float = 0.0 + self.fairness_index: float = 1.0 + self.resource_utilization: float = 0.0 + self.digital_intake_ratio: float = 0.5 + self.blocked_cases_missing_docs: int = 0 + self.blocked_cases_enrichment: int = 0 + self.field_verification_load: float = 0.0 + + +class SignalComputer: + def compute( + self, + queue_snapshots: Dict[str, QueueSnapshot], + officer_pool: OfficerPool, + todays_arrivals: int = 0, + digital_arrivals: int = 0, + capacity_per_day: float = 1.0, + ) -> ComputedSignals: + signals = ComputedSignals() + snapshots = list(queue_snapshots.values()) + if not snapshots: + return signals + + total_pending = sum(s.total_pending for s in snapshots) + + # Backlog pressure + capacity_ceiling = max(1.0, capacity_per_day * 5.0) + signals.backlog_pressure = min(1.0, total_pending / capacity_ceiling) + + # SLA risk score (weighted average) + total_nonzero = max(1, total_pending) + signals.sla_risk_score = min(1.0, max(0.0, + sum(s.current_sla_risk * s.total_pending for s in snapshots) / total_nonzero + )) + + # Fairness index (1 - coefficient of variation of completion rates) + if len(snapshots) < 2: + signals.fairness_index = 1.0 + else: + rates = [] + for s in snapshots: + total = s.total_pending + s.total_completed_today + rates.append(s.total_completed_today / max(1, total) if total > 0 else 0.0) + mean = sum(rates) / len(rates) + if mean > 0: + variance = sum((r - mean) ** 2 for r in rates) / len(rates) + cv = (variance ** 0.5) / mean + signals.fairness_index = max(0.0, 1.0 - min(1.0, cv)) + else: + signals.fairness_index = 1.0 + + # Resource utilization + allocated = sum(officer_pool.allocated.values()) + signals.resource_utilization = min(1.0, allocated / max(1, officer_pool.available_officers)) + + # Digital intake ratio + signals.digital_intake_ratio = ( + min(1.0, digital_arrivals / todays_arrivals) if todays_arrivals > 0 else 0.5 + ) + + # Blocked cases + signals.blocked_cases_missing_docs = sum(s.blocked_missing_docs for s in snapshots) + signals.blocked_cases_enrichment = sum(s.blocked_enrichment for s in snapshots) + + # Field verification load + total_in_field = sum(s.field_verification_pending for s in snapshots) + signals.field_verification_load = total_in_field / total_nonzero if total_nonzero > 0 else 0.0 + + return signals diff --git a/app/simulator.py b/app/simulator.py new file mode 100644 index 0000000000000000000000000000000000000000..efd15dbd84131e87cdc1084cfbb778247ee9f857 --- /dev/null +++ b/app/simulator.py @@ -0,0 +1,1106 @@ +from __future__ import annotations + +import json +import os +import random +import re +from dataclasses import dataclass +from typing import Any, Literal + +from openai import OpenAI + +from app.baselines import POLICIES, backlog_clearance_policy +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ActionModel, ActionType, ObservationModel, PriorityMode, ServiceType +from app.engine import DayResult, DaySimulator + +from enum import Enum +SimulationAgentMode = Literal["baseline_policy", "llm_inference", "trained_rl"] + +class SimulationAgentModeEnum(str, Enum): + baseline_policy = "baseline_policy" + llm_inference = "llm_inference" + trained_rl = "trained_rl" + +SimulationAgentMode = SimulationAgentModeEnum + + +LEGACY_NVIDIA_MODEL_POOL = [ + "meta/llama-3.3-70b-instruct", + "qwen/qwen3-next-80b-a3b-instruct", + "moonshotai/kimi-k2-instruct-0905", + "meta/llama-3.1-405b-instruct", + "deepseek-ai/deepseek-v3.2", + "qwen/qwq-32b", + "mistralai/mixtral-8x22b-instruct-v0.1", + "google/gemma-3-27b-it", + "microsoft/phi-4-mini-instruct", + "meta/llama-3.1-8b-instruct", +] + + +@dataclass +class SimulationRun: + task_id: str + agent_mode: SimulationAgentMode + seed: int + total_reward: float + score: float + grader_name: str + summary: dict[str, Any] + trace: list[dict[str, Any]] + + +def _dedupe(values: list[str | None]) -> list[str]: + out: list[str] = [] + for value in values: + if value is None: + continue + v = value.strip() + if v and v not in out: + out.append(v) + return out + + +def _env_csv_list(name: str) -> list[str]: + raw = os.getenv(name, "").strip() + if not raw: + return [] + return [x.strip() for x in raw.split(",") if x.strip()] + + +def _extract_json_object(text: str) -> dict[str, Any] | None: + text = (text or "").strip() + if not text: + return None + try: + parsed = json.loads(text) + if isinstance(parsed, dict): + return parsed + except json.JSONDecodeError: + pass + + match = re.search(r"\{.*\}", text, flags=re.DOTALL) + if not match: + return None + try: + parsed = json.loads(match.group(0)) + except json.JSONDecodeError: + return None + return parsed if isinstance(parsed, dict) else None + + +def _coerce_action(payload: dict[str, Any] | None) -> ActionModel: + if not payload: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + try: + # Remap legacy Phase 1 field names to Phase 2 + remapped = dict(payload) + if "service" in remapped and "service_target" not in remapped: + remapped["service_target"] = remapped.pop("service") + if "target_service" in remapped: + src = remapped.pop("service_target", None) + tgt = remapped.pop("target_service", None) + delta = remapped.pop("officer_delta", 1) + remapped["reallocation_delta"] = { + (src.value if hasattr(src, 'value') else str(src)): -int(delta), + (tgt.value if hasattr(tgt, 'value') else str(tgt)): int(delta), + } if src and tgt else None + if "officer_delta" in remapped and "capacity_assignment" not in remapped: + svc = remapped.get("service_target") + if svc: + svc_key = svc.value if hasattr(svc, 'value') else str(svc) + remapped["capacity_assignment"] = {svc_key: int(remapped.pop("officer_delta"))} + else: + remapped.pop("officer_delta", None) + if "case_id" in remapped: + remapped.pop("case_id", None) + return ActionModel(**remapped) + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def _queue_rows(obs: ObservationModel) -> list[dict[str, Any]]: + return [ + { + "service": q.service_type.value, + "active_cases": q.total_pending, + "missing_docs_cases": q.blocked_missing_docs, + "urgent_cases": q.urgent_pending, + "breached_cases": q.total_sla_breached, + "avg_age_days": q.avg_waiting_days, + } + for q in obs.queue_snapshots.values() + ] + + +def _recommended_min_steps(task_id: str) -> int: + if task_id == "cross_department_hard": + return 70 + if task_id == "mixed_urgency_medium": + return 60 + return 40 + + +def _alloc_for(obs: ObservationModel, service: ServiceType) -> int: + pool = obs.officer_pool + # Phase 2 uses 'allocated'; Phase 1 used 'allocations' + alloc_dict = getattr(pool, "allocated", None) or getattr(pool, "allocations", {}) + raw = alloc_dict.get(service) + if raw is None: + raw = alloc_dict.get(service.value if hasattr(service, 'value') else str(service), 0) + return int(raw or 0) + + +def _top_backlog_service( + obs: ObservationModel, + *, + exclude: ServiceType | None = None, +) -> ServiceType | None: + qs = obs.queue_snapshots + snapshots = list(qs.values()) if isinstance(qs, dict) else list(qs) + ranked = [q for q in snapshots if getattr(q, 'service_type', getattr(q, 'service', None)) != exclude] + if not ranked: + return None + ranked.sort( + key=lambda q: ( + getattr(q, 'total_pending', getattr(q, 'active_cases', 0)) + + 2 * getattr(q, 'total_sla_breached', getattr(q, 'breached_cases', 0)) + + getattr(q, 'urgent_pending', getattr(q, 'urgent_cases', 0)), + getattr(q, 'avg_waiting_days', getattr(q, 'avg_age_days', 0)), + ), + reverse=True, + ) + return getattr(ranked[0], 'service_type', getattr(ranked[0], 'service', None)) + + +def _service_with_missing_docs(obs: ObservationModel) -> ServiceType | None: + qs = obs.queue_snapshots + snapshots = list(qs.values()) if isinstance(qs, dict) else list(qs) + candidates = [ + q for q in snapshots + if getattr(q, 'blocked_missing_docs', getattr(q, 'missing_docs_cases', 0)) > 0 + ] + if not candidates: + return None + candidates.sort( + key=lambda q: ( + getattr(q, 'blocked_missing_docs', getattr(q, 'missing_docs_cases', 0)), + getattr(q, 'total_pending', getattr(q, 'active_cases', 0)), + ), + reverse=True, + ) + return getattr(candidates[0], 'service_type', getattr(candidates[0], 'service', None)) + + +def _service_with_officers(obs: ObservationModel) -> ServiceType | None: + qs = obs.queue_snapshots + snapshots = list(qs.values()) if isinstance(qs, dict) else list(qs) + services = [ + getattr(q, 'service_type', getattr(q, 'service', None)) + for q in snapshots + ] + services.sort(key=lambda s: _alloc_for(obs, s), reverse=True) + for service in services: + if service and _alloc_for(obs, service) > 0: + return service + return None + + +def _compute_action_mask(obs: ObservationModel) -> dict[ActionType, bool]: + pool = obs.officer_pool + has_reserve = int(getattr(pool, 'idle_officers', getattr(pool, 'reserve_officers', 0))) > 0 + qs = obs.queue_snapshots + snapshots = list(qs.values()) if isinstance(qs, dict) else list(qs) + has_missing = any( + getattr(q, 'blocked_missing_docs', getattr(q, 'missing_docs_cases', 0)) > 0 + for q in snapshots + ) + has_backlog = any( + getattr(q, 'total_pending', getattr(q, 'active_cases', 0)) > 0 + for q in snapshots + ) + has_budget = int(obs.escalation_budget_remaining) > 0 + staffed_services = [ + getattr(q, 'service_type', getattr(q, 'service', None)) + for q in snapshots + if _alloc_for(obs, getattr(q, 'service_type', getattr(q, 'service', None))) > 0 + ] + can_reallocate = len(staffed_services) >= 1 and len(snapshots) >= 2 + return { + ActionType.SET_PRIORITY_MODE: True, + ActionType.ADVANCE_TIME: True, + ActionType.ASSIGN_CAPACITY: has_reserve and has_backlog, + ActionType.REQUEST_MISSING_DOCUMENTS: has_missing, + ActionType.ESCALATE_SERVICE: has_budget and has_backlog, + ActionType.REALLOCATE_OFFICERS: can_reallocate, + } + + +def _masked_action_type_hints(obs: ObservationModel) -> tuple[list[str], list[str]]: + mask = _compute_action_mask(obs) + allowed = [k.value for k, ok in mask.items() if ok] + blocked = [k.value for k, ok in mask.items() if not ok] + return allowed, blocked + + +def _best_high_impact_action(obs: ObservationModel) -> tuple[ActionModel, str]: + top_backlog = _top_backlog_service(obs) + top_missing = _service_with_missing_docs(obs) + + if int(obs.officer_pool.idle_officers) > 0 and top_backlog is not None: + return ( + ActionModel(action_type=ActionType.ASSIGN_CAPACITY, service=top_backlog, officer_delta=1), + "high-impact: assign reserve capacity to top backlog service", + ) + + if top_missing is not None: + return ( + ActionModel(action_type=ActionType.REQUEST_MISSING_DOCUMENTS, service=top_missing), + "high-impact: clear missing-document bottleneck", + ) + + if int(obs.escalation_budget_remaining) > 0: + qs = obs.queue_snapshots + snapshots = list(qs.values()) if isinstance(qs, dict) else list(qs) + hot = sorted( + snapshots, + key=lambda q: ( + getattr(q, 'total_sla_breached', getattr(q, 'breached_cases', 0)), + getattr(q, 'total_pending', getattr(q, 'active_cases', 0)), + getattr(q, 'urgent_pending', getattr(q, 'urgent_cases', 0)), + ), + reverse=True, + ) + if hot and ( + getattr(hot[0], 'total_sla_breached', getattr(hot[0], 'breached_cases', 0)) > 0 + or getattr(hot[0], 'total_pending', getattr(hot[0], 'active_cases', 0)) > 0 + ): + svc = getattr(hot[0], 'service_type', getattr(hot[0], 'service', None)) + return ( + ActionModel(action_type=ActionType.ESCALATE_SERVICE, escalation_target=svc), + "high-impact: escalate highest SLA-risk service", + ) + + source = _service_with_officers(obs) + if source is not None and _alloc_for(obs, source) > 0: + target = _top_backlog_service(obs, exclude=source) + if target is not None and target != source: + return ( + ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + service_target=source, + reallocation_delta={source.value: -1, target.value: 1}, + ), + "high-impact: reallocate one officer toward highest backlog", + ) + + return ActionModel(action_type=ActionType.ADVANCE_TIME), "fallback: no high-impact action available" + + +def _repair_action_for_observation( + action: ActionModel, + obs: ObservationModel, +) -> tuple[ActionModel, str | None]: + mask = _compute_action_mask(obs) + at = action.action_type + + if not bool(mask.get(at, True)): + fallback, why = _best_high_impact_action(obs) + return fallback, f"masked {at.value}; {why}" + + if at == ActionType.ADVANCE_TIME: + return action, None + + if at == ActionType.SET_PRIORITY_MODE: + if action.priority_mode is None: + return ( + ActionModel(action_type=ActionType.SET_PRIORITY_MODE, priority_mode=PriorityMode.BACKLOG_CLEARANCE), + "missing priority_mode, defaulted to backlog_clearance", + ) + return action, None + + if at == ActionType.ASSIGN_CAPACITY: + pool = obs.officer_pool + reserve = int(getattr(pool, 'idle_officers', getattr(pool, 'reserve_officers', 0))) + if reserve <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"reserve officers exhausted; {why}" + service = getattr(action, 'service_target', None) or getattr(action, 'service', None) or _top_backlog_service(obs) + if service is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no service available for assign_capacity; {why}" + cap = action.capacity_assignment or {} + delta = cap.get(service.value, cap.get(str(service), 1)) + delta = max(1, min(int(delta), reserve)) + repaired = ActionModel( + action_type=ActionType.ASSIGN_CAPACITY, + service_target=service, + capacity_assignment={service.value: delta}, + ) + note = None if repaired.model_dump(exclude_none=True) == action.model_dump(exclude_none=True) else "repaired assign_capacity payload" + return repaired, note + + if at == ActionType.REQUEST_MISSING_DOCUMENTS: + service = getattr(action, 'service_target', None) or getattr(action, 'service', None) or _service_with_missing_docs(obs) + if service is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no missing-doc queue available; {why}" + repaired = ActionModel( + action_type=ActionType.REQUEST_MISSING_DOCUMENTS, + service_target=service, + ) + note = None if repaired.model_dump(exclude_none=True) == action.model_dump(exclude_none=True) else "repaired request_missing_documents payload" + return repaired, note + + if at == ActionType.ESCALATE_SERVICE: + if int(obs.escalation_budget_remaining) <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"escalation budget exhausted; {why}" + service = ( + getattr(action, 'escalation_target', None) + or getattr(action, 'service_target', None) + or getattr(action, 'service', None) + or _top_backlog_service(obs) + ) + if service is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no escalation target available; {why}" + repaired = ActionModel( + action_type=ActionType.ESCALATE_SERVICE, + escalation_target=service, + ) + note = None if repaired.model_dump(exclude_none=True) == action.model_dump(exclude_none=True) else "repaired escalate_service payload" + return repaired, note + + if at == ActionType.REALLOCATE_OFFICERS: + source = ( + getattr(action, 'service_target', None) + or getattr(action, 'service', None) + or _service_with_officers(obs) + ) + if source is None: + fallback, why = _best_high_impact_action(obs) + return fallback, f"no staffed source service; {why}" + source_alloc = _alloc_for(obs, source) + if source_alloc <= 0: + source = _service_with_officers(obs) + source_alloc = _alloc_for(obs, source) if source is not None else 0 + if source is None or source_alloc <= 0: + fallback, why = _best_high_impact_action(obs) + return fallback, f"insufficient source officers; {why}" + + # Phase 2: target comes from reallocation_delta; Phase 1 from target_service + rdelta = action.reallocation_delta or {} + target = None + for k, v in rdelta.items(): + if v > 0: + try: + target = ServiceType(k) + except Exception: + pass + break + if target is None: + target = getattr(action, 'target_service', None) + if target is None or target == source: + target = _top_backlog_service(obs, exclude=source) + if target is None or target == source: + fallback, why = _best_high_impact_action(obs) + return fallback, f"missing distinct target_service; {why}" + + delta = max(1, min(abs(rdelta.get(source.value, 1)), source_alloc)) + repaired = ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + service_target=source, + reallocation_delta={source.value: -delta, target.value: delta}, + ) + note = None if repaired.model_dump(exclude_none=True) == action.model_dump(exclude_none=True) else "repaired reallocate_officers payload" + return repaired, note + + return action, None + +""" +The high-level simulation orchestration now lives in app.engine. +This module re-exports the public runtime API so existing imports +from app.simulator continue to work unchanged. +""" + +def _model_label_for_mode(agent_mode: SimulationAgentMode) -> str: + if agent_mode == "baseline_policy": + return "baseline_policy" + if agent_mode == "trained_rl": + return "trained_rl" + return os.getenv("MODEL_NAME", "llm_inference") + + +def _log_step_line(step_row: dict[str, Any]) -> str: + done = "true" if bool(step_row.get("done")) else "false" + error = step_row.get("last_action_error") or "null" + action = json.dumps(step_row.get("action_payload", {}), separators=(",", ":")) + source = step_row.get("decision_source") or "unknown" + model = step_row.get("model_used") or "null" + repair = step_row.get("repair_note") or "null" + switch_note = step_row.get("switch_note") or "null" + return ( + f"[STEP] step={step_row.get('step', 0)} action={action} " + f"reward={float(step_row.get('reward', 0.0)):.2f} done={done} " + f"error={error} source={source} model={model} repair={repair} switch={switch_note}" + ) + + +class LiveSimulationSession: + def __init__( + self, + *, + task_id: str, + agent_mode: SimulationAgentMode, + max_steps: int, + seed: int | None, + policy_name: str | None = None, + model_path: str | None = None, + model_type: Literal["maskable", "recurrent"] = "maskable", + ) -> None: + self.task_id = task_id + self.agent_mode = agent_mode + recommended = _recommended_min_steps(task_id) + if agent_mode == "llm_inference": + self.max_steps = max(int(max_steps), int(recommended)) + else: + self.max_steps = int(max_steps) + self.seed = int(seed if seed is not None else random.randint(1, 999999)) + self.policy_name = policy_name or "backlog_clearance" + self.model_path = model_path + self.model_type = model_type + self.trace: list[dict[str, Any]] = [] + self.total_reward = 0.0 + self.step_idx = 0 + self.done = False + self.summary: dict[str, Any] | None = None + self.score: float | None = None + self.grader_name: str | None = None + + self.env: GovWorkflowEnv | None = None + self.obs: ObservationModel | Any = None + self.policy = None + + self.rl_env: Any = None + self.rl_model: Any = None + self.rl_lstm_state: Any = None + self.rl_episode_start: Any = None + + self.llm_runtimes: list[dict[str, Any]] = [] + self.llm_route: list[str] = [] + self.llm_model_stats: dict[tuple[str, str], dict[str, Any]] = {} + self.consecutive_failure_steps = 0 + self.recovery_steps_remaining = 0 + self.auto_switch_count = 0 + self.last_switch_reason: str | None = None + + if self.agent_mode == "trained_rl": + self._init_trained() + else: + self._init_core() + + def start_line(self) -> str: + return ( + f"[START] task={self.task_id} env=gov-workflow-openenv " + f"model={_model_label_for_mode(self.agent_mode)}" + ) + + def _init_core(self) -> None: + self.env = GovWorkflowEnv(task_id=self.task_id) + self.obs, _ = self.env.reset(seed=self.seed) + if self.agent_mode == "baseline_policy": + self.policy = POLICIES.get(self.policy_name, backlog_clearance_policy) + else: + self.policy = self._llm_action_with_meta + self._init_llm_runtimes() + + def _init_llm_runtimes(self) -> None: + openai_base = os.getenv("API_BASE_URL") or os.getenv("OPENAI_API_BASE_URL") or "https://api.openai.com/v1" + nvidia_base = os.getenv("NVIDIA_API_BASE_URL", "https://integrate.api.nvidia.com/v1") + + openai_keys = _dedupe( + [ + os.getenv("HF_TOKEN"), + os.getenv("OPENAI_API_KEY"), + os.getenv("API_KEY"), + ] + ) + nvidia_keys = _dedupe( + [ + os.getenv("NVIDIA_API_KEY"), + os.getenv("NVIDIA_API_KEY_2"), + ] + ) + + openai_models = _dedupe( + [ + os.getenv("MODEL_NAME", "meta/llama-3.3-70b-instruct"), + *_env_csv_list("MODEL_FALLBACKS"), + ] + ) + nvidia_models = _dedupe( + [ + os.getenv("NVIDIA_MODEL"), + *_env_csv_list("NVIDIA_MODEL_FALLBACKS"), + *LEGACY_NVIDIA_MODEL_POOL, + ] + ) + + runtimes: list[dict[str, Any]] = [] + + if openai_keys and openai_models: + clients: list[tuple[OpenAI, str]] = [] + for idx, key in enumerate(openai_keys, start=1): + try: + clients.append((OpenAI(base_url=openai_base, api_key=key, timeout=8.0, max_retries=0), f"openai_key_{idx}")) + except Exception: + continue + if clients: + runtimes.append( + { + "provider": "openai-compatible", + "base_url": openai_base, + "clients": clients, + "models": openai_models, + } + ) + + if nvidia_keys and nvidia_models: + clients = [] + for idx, key in enumerate(nvidia_keys, start=1): + try: + clients.append((OpenAI(base_url=nvidia_base, api_key=key, timeout=8.0, max_retries=0), f"nvidia_key_{idx}")) + except Exception: + continue + if clients: + runtimes.append( + { + "provider": "nvidia", + "base_url": nvidia_base, + "clients": clients, + "models": nvidia_models, + } + ) + + self.llm_runtimes = runtimes + self.llm_model_stats = {} + for runtime in runtimes: + provider = str(runtime.get("provider")) + for model in runtime.get("models", []): + self.llm_model_stats[(provider, str(model))] = { + "calls": 0, + "invalid": 0, + "repaired": 0, + "failures": 0, + "cooldown_until_step": 0, + } + + openai_runtime = next((rt for rt in runtimes if rt.get("provider") == "openai-compatible"), None) + nvidia_runtime = next((rt for rt in runtimes if rt.get("provider") == "nvidia"), None) + + if openai_runtime is not None: + openai_route = ( + f"openai-compatible ({len(openai_runtime['clients'])} keys, " + f"{len(openai_runtime['models'])} models)" + ) + else: + openai_route = "openai-compatible (unavailable: missing API key/model)" + + if nvidia_runtime is not None: + nvidia_route = ( + f"nvidia ({len(nvidia_runtime['clients'])} keys, " + f"{len(nvidia_runtime['models'])} models)" + ) + else: + nvidia_route = "nvidia (unavailable: missing API key/model)" + + self.llm_route = [ + openai_route, + nvidia_route, + "adaptive ranking: prefer models with lower invalid/repaired rates", + "heuristic fallback (backlog_clearance_policy)", + ] + + def _rank_runtime_models(self, provider: str, models: list[str]) -> list[str]: + def _score(model_name: str) -> tuple[float, int]: + stat = self.llm_model_stats.get((provider, model_name), {}) + calls = max(1, int(stat.get("calls", 0))) + invalid_rate = float(stat.get("invalid", 0)) / calls + repaired_rate = float(stat.get("repaired", 0)) / calls + fail_rate = float(stat.get("failures", 0)) / calls + cooldown = int(stat.get("cooldown_until_step", 0)) + cooldown_penalty = 1.0 if self.step_idx < cooldown else 0.0 + return (invalid_rate * 2.0 + repaired_rate * 1.25 + fail_rate * 1.5 + cooldown_penalty, -calls) + + return sorted([str(m) for m in models], key=_score) + + def _llm_action_with_meta(self, obs: ObservationModel) -> tuple[ActionModel, dict[str, Any]]: + if self.recovery_steps_remaining > 0: + self.recovery_steps_remaining -= 1 + action, why = _best_high_impact_action(obs) + return action, { + "decision_source": "auto_recovery_policy", + "provider": "heuristic", + "model_used": "backlog_clearance_policy", + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + "repair_note": why, + } + + attempts = 0 + last_error = "" + allowed_actions, blocked_actions = _masked_action_type_hints(obs) + schema_hint = { + "required_fields": { + "set_priority_mode": ["action_type", "priority_mode"], + "assign_capacity": ["action_type", "service", "officer_delta"], + "request_missing_documents": ["action_type", "service"], + "escalate_service": ["action_type", "service"], + "advance_time": ["action_type"], + "reallocate_officers": ["action_type", "service", "target_service", "officer_delta"], + }, + "allowed_priority_mode": [m.value for m in PriorityMode], + "allowed_services": [s.value for s in ServiceType], + } + system_prompt = ( + "You are controlling a government workflow simulator. " + "Return exactly one JSON object only. No markdown. No explanation. " + "Allowed action_type: set_priority_mode, assign_capacity, request_missing_documents, " + "escalate_service, advance_time, reallocate_officers. " + "Rules: " + "1) reallocate_officers requires service + target_service + officer_delta>0 and source!=target. " + "2) assign_capacity requires service + officer_delta>0. " + "3) request_missing_documents requires service with missing_docs_cases>0. " + "4) set_priority_mode requires priority_mode in [urgent_first, oldest_first, balanced, backlog_clearance]. " + "5) Always prefer high-impact actions that reduce backlog/SLA risk over no-op loops. " + "Use lowercase enum values." + ) + user_prompt = ( + "Observation:\n" + f"{obs.model_dump_json()}\n" + f"Allowed action types now: {allowed_actions}\n" + f"Blocked action types now: {blocked_actions}\n" + f"Action schema hints: {json.dumps(schema_hint, separators=(',', ':'))}\n" + f"Last action validity: {obs.last_action_valid}\n" + f"Last action message: {obs.last_action_message}\n" + "Return action JSON." + ) + + for runtime in self.llm_runtimes: + provider = str(runtime["provider"]) + ranked_models = self._rank_runtime_models(provider, list(runtime["models"])) + for client, key_label in runtime["clients"]: + for model in ranked_models: + attempts += 1 + stat_key = (provider, model) + try: + out = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + temperature=0.0, + max_tokens=200, + stream=False, + ) + content = (out.choices[0].message.content or "").strip() + action = _coerce_action(_extract_json_object(content)) + if stat_key in self.llm_model_stats: + self.llm_model_stats[stat_key]["calls"] += 1 + return action, { + "decision_source": "llm", + "provider": provider, + "model_used": model, + "llm_attempts": attempts, + "llm_error": None, + "llm_key_label": key_label, + } + except Exception as exc: + last_error = str(exc) + stat = self.llm_model_stats.get(stat_key) + if stat is not None: + stat["calls"] += 1 + stat["failures"] += 1 + if stat["failures"] >= 2: + stat["cooldown_until_step"] = self.step_idx + 5 + continue + + action, why = _best_high_impact_action(obs) + if not self.llm_runtimes: + last_error = "No LLM credentials configured." + return action, { + "decision_source": "heuristic_fallback", + "provider": "heuristic", + "model_used": "backlog_clearance_policy", + "llm_attempts": attempts, + "llm_error": last_error or None, + "llm_key_label": None, + "repair_note": why, + } + + def _init_trained(self) -> None: + import numpy as np + from app.main import _load_model_cached_or_503, _resolve_model_path_or_422 + from rl.gym_wrapper import GovWorkflowGymEnv + + if not self.model_path: + raise ValueError("model_path is required for trained_rl simulation.") + model_abs = _resolve_model_path_or_422(self.model_path) + self.rl_model = _load_model_cached_or_503(model_abs, self.model_type) + self.rl_env = GovWorkflowGymEnv(task_id=self.task_id, seed=self.seed, hard_action_mask=True) + self.obs, _ = self.rl_env.reset(seed=self.seed) + self.rl_lstm_state = None + self.rl_episode_start = np.array([True], dtype=bool) + + def step_once(self) -> tuple[dict[str, Any], str, bool]: + if self.done: + raise RuntimeError("Simulation already finished.") + + self.step_idx += 1 + if self.agent_mode == "trained_rl": + row = self._step_trained() + else: + row = self._step_core() + self.trace.append(row) + self.total_reward += float(row["reward"]) + step_log = _log_step_line(row) + + if row["done"] or self.step_idx >= self.max_steps: + self._finalize() + row["done"] = True + return row, step_log, True + return row, step_log, False + + def end_line(self) -> str: + if self.score is None: + return "[END] success=false steps=0 score=0.00 rewards=" + rewards = ",".join(f"{float(x.get('reward', 0.0)):.2f}" for x in self.trace) + success = "true" if self.score >= 0.5 else "false" + return ( + f"[END] success={success} steps={len(self.trace)} " + f"score={self.score:.2f} rewards={rewards}" + ) + + def snapshot(self) -> dict[str, Any]: + return { + "task_id": self.task_id, + "agent_mode": self.agent_mode, + "seed": self.seed, + "max_steps": self.max_steps, + "step_idx": self.step_idx, + "done": self.done, + "total_reward": float(self.total_reward), + "score": self.score, + "grader_name": self.grader_name, + "summary": self.summary, + "trace_len": len(self.trace), + "llm_route": list(self.llm_route), + } + + def close(self) -> None: + try: + if self.env is not None and hasattr(self.env, "close"): + self.env.close() + except Exception: + pass + try: + if self.rl_env is not None and hasattr(self.rl_env, "close"): + self.rl_env.close() + except Exception: + pass + + def _step_core(self) -> dict[str, Any]: + if self.env is None: + raise RuntimeError("Core simulation env not initialized.") + if self.agent_mode == "baseline_policy": + action = self.policy(self.obs) + meta = { + "decision_source": "baseline_policy", + "provider": "local_policy", + "model_used": self.policy_name, + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + } + else: + raw_decision = self.policy(self.obs) + if isinstance(raw_decision, tuple) and len(raw_decision) == 2: + action, meta = raw_decision + else: + action, meta = raw_decision, {} + if not isinstance(meta, dict): + meta = {} + if not isinstance(action, ActionModel): + if isinstance(action, dict): + action = _coerce_action(action) + else: + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + meta["repair_note"] = "non-action output from llm policy, coerced to advance_time" + allowed_mask = _compute_action_mask(self.obs) + if not bool(allowed_mask.get(action.action_type, True)): + masked_fallback, why = _best_high_impact_action(self.obs) + action = masked_fallback + if meta.get("decision_source") == "llm": + meta["decision_source"] = "llm_repaired" + meta["repair_note"] = f"action masked at runtime; {why}" + repaired_action, repair_note = _repair_action_for_observation(action, self.obs) + if repair_note: + action = repaired_action + if meta.get("decision_source") == "llm": + meta["decision_source"] = "llm_repaired" + meta["repair_note"] = repair_note + + self.obs, reward, terminated, truncated, info = self.env.step(action) + done = bool(terminated or truncated) + # Read observation fields safely for both Phase 1 and Phase 2 model shapes + fairness_gap = float( + getattr(self.obs, 'fairness_gap', + 1.0 - getattr(self.obs, 'fairness_index', 1.0)) + ) + row = { + "step": self.step_idx, + "day": self.obs.day, + "action_type": action.action_type.value, + "action_payload": action.model_dump(exclude_none=True, mode="json"), + "reward": float(reward), + "done": done, + "backlog": self.obs.total_backlog, + "completed": self.obs.total_completed, + "sla_breaches": self.obs.total_sla_breaches, + "fairness_gap": fairness_gap, + "escalation_budget_remaining": self.obs.escalation_budget_remaining, + "invalid_action": bool(getattr(info, 'invalid_action', False)), + "last_action_error": getattr(info, 'last_action_error', None), + "queue_rows": _queue_rows(self.obs), + } + row.update(meta) + + if self.agent_mode == "llm_inference": + is_repaired = row.get("decision_source") in {"llm_repaired", "auto_recovery_policy"} + is_invalid = bool(row.get("invalid_action")) or bool(row.get("last_action_error")) + model_used = str(row.get("model_used") or "") + provider = str(row.get("provider") or "") + stat_key = (provider, model_used) + stat = self.llm_model_stats.get(stat_key) + if stat is not None: + if is_repaired: + stat["repaired"] += 1 + if is_invalid: + stat["invalid"] += 1 + stat["failures"] += 1 + else: + stat["failures"] = max(0, int(stat.get("failures", 0)) - 1) + + is_failure_pattern = is_invalid or is_repaired + if is_failure_pattern: + self.consecutive_failure_steps += 1 + else: + self.consecutive_failure_steps = 0 + + if self.consecutive_failure_steps >= 4: + if stat is not None: + stat["cooldown_until_step"] = self.step_idx + 6 + self.recovery_steps_remaining = max(self.recovery_steps_remaining, 3) + self.auto_switch_count += 1 + self.last_switch_reason = "repeated invalid/repaired pattern detected" + row["switch_note"] = "auto-switched to recovery policy and deprioritized failing model" + self.consecutive_failure_steps = 0 + + return row + + def _step_trained(self) -> dict[str, Any]: + import numpy as np + + masks = self.rl_env.action_masks() + if self.model_type == "recurrent": + action, self.rl_lstm_state = self.rl_model.predict( + self.obs, + state=self.rl_lstm_state, + episode_start=self.rl_episode_start, + deterministic=True, + ) + action_idx = int(action.item() if hasattr(action, "item") else action) + if not (0 <= action_idx < masks.shape[0] and bool(masks[action_idx])): + valid = np.flatnonzero(masks) + action_idx = int(valid[0]) if valid.size > 0 else 18 + else: + from sb3_contrib.common.maskable.utils import get_action_masks + + action, _ = self.rl_model.predict( + self.obs, + action_masks=get_action_masks(self.rl_env), + deterministic=True, + ) + action_idx = int(action.item() if hasattr(action, "item") else action) + + self.obs, reward, terminated, truncated, info = self.rl_env.step(action_idx) + done = bool(terminated or truncated) + if self.model_type == "recurrent": + self.rl_episode_start = np.array([done], dtype=bool) + core_obs = self.rl_env._core_env._build_observation() + action_model, action_label = _decode_action_idx(action_idx) + return { + "step": self.step_idx, + "day": core_obs.day, + "action_type": action_label, + "action_payload": action_model.model_dump(exclude_none=True, mode="json"), + "action_index": action_idx, + "reward": float(reward), + "done": done, + "backlog": core_obs.total_backlog, + "completed": core_obs.total_completed, + "sla_breaches": core_obs.total_sla_breaches, + "fairness_gap": float(core_obs.fairness_gap), + "escalation_budget_remaining": core_obs.escalation_budget_remaining, + "invalid_action": bool(info.get("invalid_action", False)), + "last_action_error": info.get("last_action_error"), + "queue_rows": _queue_rows(core_obs), + "decision_source": "trained_rl", + "provider": "rl", + "model_used": self.model_path or "trained_rl", + "llm_attempts": 0, + "llm_error": None, + "llm_key_label": None, + } + + def _finalize(self) -> None: + if self.done: + return + self.done = True + if self.agent_mode == "trained_rl": + final_state = self.rl_env._core_env.state() + else: + final_state = self.env.state() + gr = grade_episode(final_state) + self.score = float(gr.score) + self.grader_name = gr.grader_name + + llm_steps = sum( + 1 for row in self.trace if row.get("decision_source") in {"llm", "llm_repaired"} + ) + fallback_steps = sum( + 1 + for row in self.trace + if row.get("decision_source") in {"heuristic_fallback", "auto_recovery_policy"} + ) + repaired_steps = sum( + 1 + for row in self.trace + if row.get("decision_source") in {"llm_repaired", "auto_recovery_policy"} + ) + total_steps = max(1, len(self.trace)) + invalid_actions = int(final_state.metrics.total_invalid_actions) + invalid_rate = float(invalid_actions) / float(total_steps) + repaired_rate = float(repaired_steps) / float(total_steps) + + ranked_models: list[dict[str, Any]] = [] + if self.llm_model_stats: + for (provider, model), stat in self.llm_model_stats.items(): + calls = int(stat.get("calls", 0)) + if calls <= 0: + continue + ranked_models.append( + { + "provider": provider, + "model": model, + "calls": calls, + "invalid_rate": float(stat.get("invalid", 0)) / max(1, calls), + "repaired_rate": float(stat.get("repaired", 0)) / max(1, calls), + } + ) + ranked_models.sort(key=lambda x: (x["invalid_rate"], x["repaired_rate"], -x["calls"])) + + self.summary = { + "total_steps": final_state.total_steps, + "total_completed": final_state.total_completed, + "total_backlog": final_state.total_backlog, + "total_sla_breaches": final_state.total_sla_breaches, + "fairness_gap": float(final_state.fairness_gap), + "total_invalid_actions": final_state.metrics.total_invalid_actions, + "invalid_action_rate": invalid_rate, + "llm_steps": llm_steps, + "heuristic_fallback_steps": fallback_steps, + "llm_repaired_steps": repaired_steps, + "repaired_action_rate": repaired_rate, + "auto_switch_count": self.auto_switch_count, + "last_switch_reason": self.last_switch_reason, + "effective_max_steps": self.max_steps, + "recommended_min_steps": _recommended_min_steps(self.task_id), + } + if self.agent_mode == "llm_inference": + self.summary["llm_route"] = list(self.llm_route) + self.summary["llm_model_performance"] = ranked_models + if self.agent_mode == "trained_rl": + self.summary["model_path"] = self.model_path + self.summary["model_type"] = self.model_type + + +def run_simulation( + *, + task_id: str, + agent_mode: SimulationAgentMode, + max_steps: int, + seed: int | None, + policy_name: str | None = None, + model_path: str | None = None, + model_type: Literal["maskable", "recurrent"] = "maskable", +) -> SimulationRun: + session = LiveSimulationSession( + task_id=task_id, + agent_mode=agent_mode, + max_steps=max_steps, + seed=seed, + policy_name=policy_name, + model_path=model_path, + model_type=model_type, + ) + try: + while not session.done: + session.step_once() + return SimulationRun( + task_id=session.task_id, + agent_mode=session.agent_mode, + seed=session.seed, + total_reward=float(session.total_reward), + score=float(session.score or 0.0), + grader_name=str(session.grader_name or "unknown"), + summary=dict(session.summary or {}), + trace=list(session.trace), + ) + finally: + session.close() + + +def _decode_action_idx(action_idx: int) -> tuple[ActionModel, str]: + try: + from rl.feature_builder import ACTION_DECODE_TABLE + from app.models import PriorityMode, ServiceType + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME), f"action_{action_idx}" + + row = ACTION_DECODE_TABLE.get(int(action_idx)) + if row is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME), f"action_{action_idx}" + +from app.engine import ( + DayResult, + DaySimulator, + LiveSimulationSession, + SimulationAgentMode, + SimulationRun, + run_simulation, +) + +__all__ = [ + "DayResult", + "DaySimulator", + "SimulationAgentMode", + "SimulationRun", + "LiveSimulationSession", + "run_simulation", +] diff --git a/app/state_machine.py b/app/state_machine.py new file mode 100644 index 0000000000000000000000000000000000000000..5007693c1a715ceee1d2d7c5077968e03a0749a0 --- /dev/null +++ b/app/state_machine.py @@ -0,0 +1,107 @@ +""" +state_machine.py — Gov Workflow OpenEnv +Deterministic workflow transition engine aligned with Phase 1 schemas. +""" + +from __future__ import annotations + +from app.models import ApplicationCase, InternalSubstate, StageType + + +INTERNAL_TO_PUBLIC_STAGE: dict[InternalSubstate, StageType] = { + InternalSubstate.PRE_SCRUTINY: StageType.SUBMISSION, + InternalSubstate.DOC_VALIDATION: StageType.DOCUMENT_VERIFICATION, + InternalSubstate.SERVICE_SPECIFIC_VALIDATION: StageType.DOCUMENT_VERIFICATION, + InternalSubstate.FIELD_VERIFICATION_PENDING: StageType.FIELD_VERIFICATION, + InternalSubstate.DECISION_PENDING: StageType.APPROVAL, + InternalSubstate.ISSUANCE_READY: StageType.ISSUANCE, + InternalSubstate.BLOCKED_MISSING_DOCS: StageType.DOCUMENT_VERIFICATION, + InternalSubstate.COMPLETED: StageType.ISSUANCE, + InternalSubstate.REJECTED: StageType.APPROVAL, +} + + +def build_public_stage(substate: InternalSubstate) -> StageType: + return INTERNAL_TO_PUBLIC_STAGE.get(substate, StageType.SUBMISSION) + + +def transition_case(case: ApplicationCase, new_substate: InternalSubstate) -> None: + case.internal_substate = new_substate + case.public_stage = build_public_stage(new_substate) + case.days_in_current_stage = 0 + + +def can_advance(case: ApplicationCase) -> bool: + if case.completed or case.rejected: + return False + if case.internal_substate == InternalSubstate.BLOCKED_MISSING_DOCS: + return False + return True + + +def advance_case(case: ApplicationCase, rng: object = None) -> tuple[bool, bool]: + """ + Returns (progressed, completed). + """ + if not can_advance(case): + return False, False + + early_stages = { + InternalSubstate.PRE_SCRUTINY, + InternalSubstate.DOC_VALIDATION, + } + + if case.has_missing_docs and case.internal_substate in early_stages: + transition_case(case, InternalSubstate.BLOCKED_MISSING_DOCS) + return True, False + + current = case.internal_substate + + if current == InternalSubstate.PRE_SCRUTINY: + transition_case(case, InternalSubstate.DOC_VALIDATION) + return True, False + + if current == InternalSubstate.DOC_VALIDATION: + if case.field_verification_required: + transition_case(case, InternalSubstate.FIELD_VERIFICATION_PENDING) + else: + transition_case(case, InternalSubstate.DECISION_PENDING) + return True, False + + if current == InternalSubstate.SERVICE_SPECIFIC_VALIDATION: + if case.field_verification_required: + transition_case(case, InternalSubstate.FIELD_VERIFICATION_PENDING) + else: + transition_case(case, InternalSubstate.DECISION_PENDING) + return True, False + + if current == InternalSubstate.FIELD_VERIFICATION_PENDING: + return False, False + + if current == InternalSubstate.DECISION_PENDING: + transition_case(case, InternalSubstate.ISSUANCE_READY) + return True, False + + if current == InternalSubstate.ISSUANCE_READY: + transition_case(case, InternalSubstate.COMPLETED) + case.completed = True + return True, True + + return False, False + + +def unblock_missing_docs(case: ApplicationCase) -> bool: + if case.internal_substate != InternalSubstate.BLOCKED_MISSING_DOCS: + return False + case.has_missing_docs = False + case.doc_resolution_day = None + transition_case(case, InternalSubstate.DOC_VALIDATION) + return True + + +def complete_field_verification(case: ApplicationCase) -> bool: + if case.internal_substate != InternalSubstate.FIELD_VERIFICATION_PENDING: + return False + case.field_verification_completion_day = None + transition_case(case, InternalSubstate.DECISION_PENDING) + return True \ No newline at end of file diff --git a/app/story_router.py b/app/story_router.py new file mode 100644 index 0000000000000000000000000000000000000000..f1725d50467af627060ccbe3f61d7e612a98acf0 --- /dev/null +++ b/app/story_router.py @@ -0,0 +1,407 @@ +""" +app/story_router.py + +FastAPI router that serves LLM training story data. +All 7 endpoints are READ-ONLY - they serve pre-saved JSON files. +No frontend elements are invoked from backend. +No training runs happen here - only data serving. + +Mount in main.py with: + from app.story_router import router as story_router + app.include_router(story_router) +""" + +from __future__ import annotations + +import asyncio +import json +from pathlib import Path +from typing import Optional + +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse + +router = APIRouter(prefix="/training", tags=["Training Story"]) + +# --- Data directory -------------------------------------------------- +DATA_DIR = Path("data/training_logs") + +HEURISTIC_BASELINES: dict[str, dict] = { + "district_backlog_easy": { + "score": 0.527, "completed": 41, + "breaches": 184, "reward": -79.86, "avg_wait": 6.9, + }, + "mixed_urgency_medium": { + "score": 0.454, "completed": 58, + "breaches": 34, "reward": -684.22, "avg_wait": 12.4, + }, + "cross_department_hard": { + "score": 0.606, "completed": 83, + "breaches": 723, "reward": -2318.78, "avg_wait": 15.6, + }, +} + + +# --- Internal helpers ------------------------------------------------ + +def _load_log(task_id: str) -> dict: + """Load JSON training log for given task. Raises 404 if missing.""" + path = DATA_DIR / f"{task_id}_training_log.json" + if not path.exists(): + raise HTTPException( + status_code=404, + detail=( + f"Training log not found for task '{task_id}'. " + f"Run: python scripts/convert_grpo_csv.py " + f"--csv --task {task_id}" + ), + ) + with open(path, encoding="utf-8") as f: + return json.load(f) + + +def _dominant_action(episodes: list[dict]) -> str: + """Returns the action name with the highest total weight across episodes.""" + totals: dict[str, float] = {} + for ep in episodes: + for action, val in ep.get("actions", {}).items(): + totals[action] = totals.get(action, 0.0) + float(val) + return max(totals, key=totals.get) if totals else "advance_time" + + +def _phase_message(ep: dict) -> str: + """Returns a human-readable learning message for one episode.""" + phase = ep.get("phase", "random") + reward = ep.get("total_reward", 0) + score = ep.get("score", 0) + fn1 = ep.get("fn1_valid", 1.0) + fn2 = ep.get("fn2_no_halluc", 1.0) + episode = ep.get("episode", 0) + + validity_note = "" if fn1 >= 1.0 else f" WARNING: Invalid action at step {episode}." + halluc_note = "" if fn2 >= 1.0 else " WARNING: Hallucination detected." + + messages = { + "random": ( + f"Step {episode}: LLM is exploring. " + f"Reward={reward:.3f}, Score={score:.3f}.{validity_note}{halluc_note}" + ), + "exploring": ( + f"Step {episode}: LLM finding patterns. " + f"Reward={reward:.3f}, Score={score:.3f}.{validity_note}{halluc_note}" + ), + "learning": ( + f"Step {episode}: LLM reinforcing good actions. " + f"Reward={reward:.3f}, Score={score:.3f}.{validity_note}{halluc_note}" + ), + "converged": ( + f"Step {episode}: LLM converged. " + f"Reward={reward:.3f}, Score={score:.3f}.{validity_note}{halluc_note}" + ), + } + return messages.get(phase, f"Step {episode}: reward={reward:.3f}") + + +# ================================================================ +# ENDPOINT 1 - GET /training/tasks +# ================================================================ +@router.get("/tasks") +async def list_trained_tasks() -> dict: + """ + Returns all tasks that have a saved training log JSON file. + Frontend calls this first to populate task selector. + """ + DATA_DIR.mkdir(parents=True, exist_ok=True) + available = [] + for path in sorted(DATA_DIR.glob("*_training_log.json")): + task_id = path.stem.replace("_training_log", "") + try: + log = _load_log(task_id) + available.append({ + "task_id": task_id, + "total_episodes": log["total_episodes"], + "final_score": log["summary"]["last_episode_score"], + "reward_improvement": log["summary"]["reward_improvement_pct"], + "base_model": log.get("base_model", ""), + "training_method": log.get("training_method", "GRPO"), + }) + except HTTPException: + pass + return {"tasks": available} + + +# ================================================================ +# ENDPOINT 2 - GET /training/summary/{task_id} +# ================================================================ +@router.get("/summary/{task_id}") +async def training_summary(task_id: str) -> dict: + """Returns overview stats + narrative for the ACT 2 header card.""" + log = _load_log(task_id) + eps = log["episodes"] + n = len(eps) + + q1, q2, q3 = n // 4, n // 2, 3 * n // 4 + + p1_dom = _dominant_action(eps[:q1]) + p2_dom = _dominant_action(eps[q1:q2]) + p3_dom = _dominant_action(eps[q2:q3]) + p4_dom = _dominant_action(eps[q3:]) + + avg_p1_r = sum(e["total_reward"] for e in eps[:q1]) / max(q1, 1) + avg_p4_r = sum(e["total_reward"] for e in eps[q3:]) / max(n - q3, 1) + + return { + "task_id": log["task_id"], + "base_model": log.get("base_model", ""), + "training_method": log.get("training_method", "GRPO"), + "lora_rank": log.get("lora_rank", 16), + "total_episodes": n, + "reward_functions": log.get("reward_functions", {}), + "summary": log["summary"], + "narrative": { + "phase_1": ( + f"Steps 1-{q1}: LLM chose '{p1_dom}' most often. " + f"Avg reward {avg_p1_r:.2f}. Still exploring randomly." + ), + "phase_2": ( + f"Steps {q1}-{q2}: LLM discovered '{p2_dom}'. " + "Reward started improving as valid patterns emerged." + ), + "phase_3": ( + f"Steps {q2}-{q3}: LLM reinforced '{p3_dom}'. " + "Action validity reaching near-perfect levels." + ), + "phase_4": ( + f"Steps {q3}-{n}: LLM converged on '{p4_dom}'. " + f"Avg reward {avg_p4_r:.2f}. " + f"Final score {log['summary']['last_episode_score']:.1%}." + ), + }, + } + + +# ================================================================ +# ENDPOINT 3 - GET /training/curve/{task_id} +# ================================================================ +@router.get("/curve/{task_id}") +async def training_curve( + task_id: str, + downsample: int = 1, +) -> dict: + """ + Returns episode-by-episode reward + score for chart rendering. + downsample=5 -> returns every 5th step. + """ + log = _load_log(task_id) + eps = log["episodes"] + sampled = eps[::max(1, downsample)] + return { + "task_id": task_id, + "total_points": len(sampled), + "curve": [ + { + "episode": e["episode"], + "reward": e["total_reward"], + "score": e["score"], + "fn1_valid": e.get("fn1_valid", 1.0), + "fn2_no_halluc": e.get("fn2_no_halluc", 1.0), + "fn3_env_score": e.get("fn3_env_score", 0.0), + "phase": e["phase"], + } + for e in sampled + ], + } + + +# ================================================================ +# ENDPOINT 4 - GET /training/actions/{task_id} +# ================================================================ +@router.get("/actions/{task_id}") +async def action_evolution(task_id: str) -> dict: + """Returns action distribution at 5 checkpoints across training.""" + log = _load_log(task_id) + eps = log["episodes"] + n = len(eps) + + idxs = [0, n // 4, n // 2, 3 * n // 4, n - 1] + result = [] + for idx in idxs: + ep = eps[idx] + result.append({ + "episode": ep["episode"], + "phase": ep["phase"], + "actions": ep.get("actions", {}), + "reward": ep["total_reward"], + "score": ep["score"], + }) + + avg_fn1_start = sum(e.get("fn1_valid", 1.0) for e in eps[:n // 4]) / max(n // 4, 1) + avg_fn1_end = sum(e.get("fn1_valid", 1.0) for e in eps[3 * n // 4:]) / max(n - 3 * n // 4, 1) + + insight = ( + f"Action validity improved from {avg_fn1_start:.1%} (early) " + f"to {avg_fn1_end:.1%} (final). " + "LLM learned to output valid government workflow JSON consistently." + ) + + return { + "task_id": task_id, + "checkpoints": result, + "insight": insight, + } + + +# ================================================================ +# ENDPOINT 5 - GET /training/episode/{task_id}/{episode_num} +# ================================================================ +@router.get("/episode/{task_id}/{episode_num}") +async def episode_detail(task_id: str, episode_num: int) -> dict: + """Returns detail for one specific training step.""" + log = _load_log(task_id) + eps = log["episodes"] + + if episode_num < 1 or episode_num > len(eps): + raise HTTPException( + status_code=400, + detail=f"episode_num must be 1-{len(eps)}. Got {episode_num}.", + ) + + ep = eps[episode_num - 1] + rewards_so_far = [e["total_reward"] for e in eps[:episode_num]] + scores_so_far = [e["score"] for e in eps[:episode_num]] + + return { + "task_id": task_id, + "episode": ep["episode"], + "total_episodes": len(eps), + "reward": ep["total_reward"], + "score": ep["score"], + "fn1_valid": ep.get("fn1_valid", 1.0), + "fn2_no_halluc": ep.get("fn2_no_halluc", 1.0), + "fn3_env_score": ep.get("fn3_env_score", 0.0), + "phase": ep["phase"], + "actions": ep.get("actions", {}), + "running_best_reward": max(rewards_so_far), + "running_avg_score": round(sum(scores_so_far) / len(scores_so_far), 4), + "message": _phase_message(ep), + } + + +# ================================================================ +# ENDPOINT 6 - GET /training/stream/{task_id} [SSE] +# ================================================================ +@router.get("/stream/{task_id}") +async def stream_training_replay( + task_id: str, + delay_ms: int = 100, + start_episode: int = 1, + end_episode: Optional[int] = None, +) -> StreamingResponse: + """Server-Sent Events endpoint for animated chart replay.""" + log = _load_log(task_id) + eps = log["episodes"] + end = min(end_episode or len(eps), len(eps)) + subset = eps[start_episode - 1: end] + + async def generate(): + meta_event = json.dumps({ + "type": "meta", + "task_id": task_id, + "total_episodes": len(eps), + "summary": log["summary"], + "reward_functions": log.get("reward_functions", {}), + }) + yield f"data: {meta_event}\n\n" + + rewards_so_far: list[float] = [] + scores_so_far: list[float] = [] + + for ep in subset: + rewards_so_far.append(ep["total_reward"]) + scores_so_far.append(ep["score"]) + + event = json.dumps({ + "type": "episode", + "episode": ep["episode"], + "total_episodes": len(eps), + "reward": ep["total_reward"], + "score": ep["score"], + "fn1_valid": ep.get("fn1_valid", 1.0), + "fn2_no_halluc": ep.get("fn2_no_halluc", 1.0), + "fn3_env_score": ep.get("fn3_env_score", 0.0), + "phase": ep["phase"], + "actions": ep.get("actions", {}), + "running_best": max(rewards_so_far), + "running_avg_score": round( + sum(scores_so_far) / len(scores_so_far), 4 + ), + "message": _phase_message(ep), + }) + yield f"data: {event}\n\n" + await asyncio.sleep(delay_ms / 1000.0) + + done_event = json.dumps({ + "type": "done", + "final_score": scores_so_far[-1] if scores_so_far else 0.0, + "best_reward": max(rewards_so_far) if rewards_so_far else 0.0, + "total_steps": len(subset), + }) + yield f"data: {done_event}\n\n" + + return StreamingResponse( + generate(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + }, + ) + + +# ================================================================ +# ENDPOINT 7 - GET /training/comparison/{task_id} +# ================================================================ +@router.get("/comparison/{task_id}") +async def before_after_comparison(task_id: str) -> dict: + """Returns before (heuristic) vs after (trained LLM).""" + log = _load_log(task_id) + baseline = HEURISTIC_BASELINES.get(task_id, {}) + summary = log["summary"] + + bef_score = baseline.get("score", 0.0) + after_score = summary["last_episode_score"] + delta = round(after_score - bef_score, 4) + pct = round((delta / bef_score) * 100, 1) if bef_score else 0.0 + + return { + "task_id": task_id, + "before": { + "label": "Heuristic Baseline (no AI)", + "score": bef_score, + "reward": baseline.get("reward", 0.0), + "completed": baseline.get("completed", 0), + "breaches": baseline.get("breaches", 0), + "avg_wait": baseline.get("avg_wait", 0.0), + }, + "after": { + "label": f"GRPO Trained LLM ({log.get('base_model','')})", + "score": after_score, + "reward": summary["last_episode_reward"], + "avg_fn1_valid": summary.get("avg_fn1_valid", 0.0), + "avg_fn2_no_halluc": summary.get("avg_fn2_no_halluc", 0.0), + "invalid_steps": summary.get("invalid_action_steps", 0), + "hallucination_steps": summary.get("hallucination_steps", 0), + }, + "improvement": { + "score_delta": delta, + "score_pct": pct, + "verdict": ( + "LLM significantly outperforms baseline" + if delta > 0.10 else + "LLM moderately outperforms baseline" + if delta > 0.0 else + "LLM needs more training" + ), + }, + } diff --git a/app/tasks.py b/app/tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..238b56da10f0abb3a32457810cd6894a18493d83 --- /dev/null +++ b/app/tasks.py @@ -0,0 +1,144 @@ +""" +tasks.py — Gov Workflow OpenEnv v2.0 +Three deterministic benchmark tasks: easy, medium, hard. +""" +from app.models import ( + TaskConfig, ServiceType, ScenarioMode, EventType, OfficerPool +) + +TASK_EASY = TaskConfig( + task_id="district_backlog_easy", + display_name="District Backlog Clearance — Revenue Office", + difficulty="easy", + scenario_mode=ScenarioMode.NORMAL, + seed=42, + max_days=30, + enabled_services=[ServiceType.INCOME_CERTIFICATE], + arrival_rate_per_day={ServiceType.INCOME_CERTIFICATE: 12.0}, + digital_intake_ratio=0.65, + initial_officer_pool=OfficerPool( + total_officers=8, available_officers=8, + allocated={ServiceType.INCOME_CERTIFICATE: 8}, + ), + missing_docs_probability_override={ServiceType.INCOME_CERTIFICATE: 0.20}, + field_verification_probability_override={ServiceType.INCOME_CERTIFICATE: 0.15}, + escalation_budget=5, + fairness_threshold=None, + event_probability=0.05, + allowed_events=[EventType.NO_EVENT], +) + +TASK_MEDIUM = TaskConfig( + task_id="mixed_urgency_medium", + display_name="Mixed Urgency Backlog — Taluka Office", + difficulty="medium", + scenario_mode=ScenarioMode.NORMAL, + seed=123, + max_days=45, + enabled_services=[ + ServiceType.INCOME_CERTIFICATE, + ServiceType.LAND_REGISTRATION, + ServiceType.PASSPORT, + ServiceType.DRIVING_LICENSE, + ServiceType.AADHAAR_CARD, + ], + arrival_rate_per_day={ + ServiceType.INCOME_CERTIFICATE: 8.0, + ServiceType.LAND_REGISTRATION: 4.0, + ServiceType.PASSPORT: 4.0, + ServiceType.DRIVING_LICENSE: 5.0, + ServiceType.AADHAAR_CARD: 6.0, + }, + digital_intake_ratio=0.72, + initial_officer_pool=OfficerPool( + total_officers=14, available_officers=14, + allocated={ + ServiceType.INCOME_CERTIFICATE: 4, + ServiceType.LAND_REGISTRATION: 2, + ServiceType.PASSPORT: 2, + ServiceType.DRIVING_LICENSE: 3, + ServiceType.AADHAAR_CARD: 3, + }, + ), + missing_docs_probability_override=None, + field_verification_probability_override=None, + escalation_budget=8, + fairness_threshold=None, + event_probability=0.15, + allowed_events=[EventType.DOCUMENT_REJECTION_SPIKE], +) + +TASK_HARD = TaskConfig( + task_id="cross_department_hard", + display_name="Cross-Department Crisis — District Collectorate", + difficulty="hard", + scenario_mode=ScenarioMode.CRISIS, + seed=999, + max_days=60, + enabled_services=[ + ServiceType.INCOME_CERTIFICATE, + ServiceType.LAND_REGISTRATION, + ServiceType.PASSPORT, + ServiceType.DRIVING_LICENSE, + ServiceType.AADHAAR_CARD, + ], + arrival_rate_per_day={ + ServiceType.INCOME_CERTIFICATE: 11.0, + ServiceType.LAND_REGISTRATION: 6.0, + ServiceType.PASSPORT: 6.0, + ServiceType.DRIVING_LICENSE: 7.0, + ServiceType.AADHAAR_CARD: 8.0, + }, + digital_intake_ratio=0.80, + initial_officer_pool=OfficerPool( + total_officers=18, available_officers=18, + allocated={ + ServiceType.INCOME_CERTIFICATE: 5, + ServiceType.LAND_REGISTRATION: 3, + ServiceType.PASSPORT: 3, + ServiceType.DRIVING_LICENSE: 3, + ServiceType.AADHAAR_CARD: 4, + }, + ), + missing_docs_probability_override=None, + field_verification_probability_override=None, + escalation_budget=10, + fairness_threshold=0.70, + event_probability=0.30, + allowed_events=[ + EventType.SURGE_APPLICATIONS, + EventType.OFFICER_UNAVAILABLE, + EventType.DOCUMENT_REJECTION_SPIKE, + EventType.REVENUE_DB_DELAY, + EventType.SLA_ESCALATION_ORDER, + ], +) + +def make_extreme_variant(base_task: TaskConfig) -> TaskConfig: + variant = base_task.model_copy(deep=True) + variant.task_id = base_task.task_id + "_extreme" + variant.display_name = base_task.display_name + " [EXTREME]" + variant.scenario_mode = ScenarioMode.EXTREME_OVERLOAD + variant.event_probability = min(1.0, base_task.event_probability * 3.0) + variant.allowed_events = [e for e in EventType if e != EventType.NO_EVENT] + return variant + +TASK_REGISTRY: dict = { + "district_backlog_easy": TASK_EASY, + "mixed_urgency_medium": TASK_MEDIUM, + "cross_department_hard": TASK_HARD, + "district_backlog_easy_extreme": make_extreme_variant(TASK_EASY), +} + +def get_task(task_id: str) -> TaskConfig: + if task_id not in TASK_REGISTRY: + raise ValueError(f"Unknown task_id '{task_id}'. Available: {list(TASK_REGISTRY)}") + return TASK_REGISTRY[task_id] + +def list_tasks() -> list: + return list(TASK_REGISTRY.keys()) + +def list_benchmark_tasks() -> list: + return ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"] + +TASKS = TASK_REGISTRY diff --git a/app/training_jobs.py b/app/training_jobs.py new file mode 100644 index 0000000000000000000000000000000000000000..124c9c410475bb0495c2c3c7adf4bb7491937a02 --- /dev/null +++ b/app/training_jobs.py @@ -0,0 +1,634 @@ +from __future__ import annotations + +import os +import re +import shutil +import subprocess +import sys +import threading +import time +import math +from dataclasses import dataclass, field +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Literal +from uuid import uuid4 + +from app.persistence import PersistenceStore + +Status = Literal["queued", "running", "completed", "failed", "stopped"] + +_PROGRESS_RE = re.compile(r"(\d[\d,]*)/(\d[\d,]*)") +_METRIC_ROW_RE = re.compile(r"\|\s*([a-zA-Z0-9_ ]+?)\s*\|\s*(-?\d+(?:\.\d+)?)\s*\|") +_EVAL_PROGRESS_RE = re.compile( + r"Eval\s+num_timesteps=(\d+),\s*episode_reward=([-]?\d+(?:\.\d+)?)", + re.IGNORECASE, +) +_EVAL_ROW_RE = re.compile( + r"^\[Eval\]\s+([a-z_]+)\s+score=([0-9.]+)\s+reward=([-0-9.]+)\s+completed=(\d+)\s+sla_breaches=(\d+)$" +) +_AVG_RE = re.compile(r"^\[Eval\]\s+Average grader score:\s+([0-9.]+)$") +_BEST_GRADER_RE = re.compile( + r"\[Eval\]\s+New best(?: recurrent)? grader score:\s+([0-9.]+)", + re.IGNORECASE, +) + + +def _now() -> float: + return time.time() + + +def _tail_append(lines: list[str], line: str, max_size: int = 500) -> None: + lines.append(line.rstrip("\n")) + if len(lines) > max_size: + del lines[: len(lines) - max_size] + + +def _normalize_metric_key(raw: str) -> str: + return raw.strip().lower().replace(" ", "_") + + +def _parse_eval(stdout: str) -> tuple[list[dict[str, Any]], float | None]: + rows: list[dict[str, Any]] = [] + avg: float | None = None + for line in stdout.splitlines(): + line = line.strip() + if not line: + continue + row = _EVAL_ROW_RE.match(line) + if row: + rows.append( + { + "task_id": row.group(1), + "grader_score": float(row.group(2)), + "total_reward": float(row.group(3)), + "total_completed": int(row.group(4)), + "total_sla_breaches": int(row.group(5)), + } + ) + continue + m = _AVG_RE.match(line) + if m: + avg = float(m.group(1)) + return rows, avg + + +@dataclass +class TrainingJob: + job_id: str + phase: int + timesteps: int + n_envs: int + seed: int + config_path: str + created_at: float = field(default_factory=_now) + started_at: float | None = None + updated_at: float = field(default_factory=_now) + ended_at: float | None = None + status: Status = "queued" + progress: float = 0.0 + process_id: int | None = None + command: list[str] = field(default_factory=list) + output_model_path: str | None = None + output_model_name: str | None = None + latest_metrics: dict[str, float] = field(default_factory=dict) + metric_history: list[dict[str, Any]] = field(default_factory=list) + evaluation_rows: list[dict[str, Any]] = field(default_factory=list) + evaluation_avg_score: float | None = None + logs_tail: list[str] = field(default_factory=list) + error_message: str | None = None + return_code: int | None = None + + process: subprocess.Popen[str] | None = field(default=None, repr=False) + lock: threading.Lock = field(default_factory=threading.Lock, repr=False) + last_persist_at: float = field(default_factory=lambda: 0.0, repr=False) + + def snapshot(self) -> dict[str, Any]: + with self.lock: + return { + "job_id": self.job_id, + "phase": self.phase, + "timesteps": self.timesteps, + "n_envs": self.n_envs, + "seed": self.seed, + "config_path": self.config_path, + "created_at": self.created_at, + "started_at": self.started_at, + "updated_at": self.updated_at, + "ended_at": self.ended_at, + "status": self.status, + "progress": self.progress, + "process_id": self.process_id, + "command": self.command, + "output_model_path": self.output_model_path, + "output_model_name": self.output_model_name, + "latest_metrics": dict(self.latest_metrics), + "metric_history": list(self.metric_history), + "evaluation_rows": list(self.evaluation_rows), + "evaluation_avg_score": self.evaluation_avg_score, + "logs_tail": list(self.logs_tail), + "error_message": self.error_message, + "return_code": self.return_code, + } + + +class TrainingJobManager: + def __init__(self, repo_root: Path, persistence: PersistenceStore | None = None) -> None: + self._repo_root = repo_root + self._persistence = persistence + self._jobs: dict[str, TrainingJob] = {} + self._lock = threading.Lock() + self._training_runs_root = ( + self._persistence.training_runs_dir + if self._persistence is not None and self._persistence.enabled + else self._repo_root / "results" / "training_runs" + ) + self._load_persisted_jobs() + + def _load_persisted_jobs(self) -> None: + if self._persistence is None or not self._persistence.enabled: + return + persisted = self._persistence.list_training_jobs(limit=500) + with self._lock: + for snap in persisted: + try: + job = TrainingJob( + job_id=str(snap["job_id"]), + phase=int(snap["phase"]), + timesteps=int(snap["timesteps"]), + n_envs=int(snap["n_envs"]), + seed=int(snap["seed"]), + config_path=str(snap.get("config_path") or ""), + created_at=float(snap.get("created_at") or _now()), + started_at=float(snap["started_at"]) if snap.get("started_at") is not None else None, + updated_at=float(snap.get("updated_at") or _now()), + ended_at=float(snap["ended_at"]) if snap.get("ended_at") is not None else None, + status=str(snap.get("status") or "failed"), + progress=float(snap.get("progress") or 0.0), + process_id=int(snap["process_id"]) if snap.get("process_id") is not None else None, + command=list(snap.get("command") or []), + output_model_path=snap.get("output_model_path"), + output_model_name=snap.get("output_model_name"), + latest_metrics=dict(snap.get("latest_metrics") or {}), + metric_history=list(snap.get("metric_history") or []), + evaluation_rows=list(snap.get("evaluation_rows") or []), + evaluation_avg_score=( + float(snap["evaluation_avg_score"]) + if snap.get("evaluation_avg_score") is not None + else None + ), + logs_tail=list(snap.get("logs_tail") or []), + error_message=snap.get("error_message"), + return_code=int(snap["return_code"]) if snap.get("return_code") is not None else None, + ) + except Exception: + continue + + # Process handles cannot survive a server restart. Recover to terminal state. + if job.status in ("queued", "running"): + job.status = "failed" + msg = "Recovered after restart: previous process state unavailable." + job.error_message = f"{job.error_message} {msg}".strip() if job.error_message else msg + if job.ended_at is None: + job.ended_at = _now() + job.process = None + self._jobs[job.job_id] = job + + def clear_jobs(self, *, clear_artifacts: bool = False) -> int: + to_stop: list[subprocess.Popen[str]] = [] + with self._lock: + removed = len(self._jobs) + for job in self._jobs.values(): + with job.lock: + proc = job.process + if proc is not None and job.status in ("queued", "running"): + to_stop.append(proc) + self._jobs.clear() + for proc in to_stop: + try: + proc.terminate() + except Exception: + pass + if self._persistence is not None and self._persistence.enabled: + self._persistence.clear_training_jobs() + if clear_artifacts: + try: + if self._training_runs_root.exists(): + shutil.rmtree(self._training_runs_root, ignore_errors=True) + self._training_runs_root.mkdir(parents=True, exist_ok=True) + except Exception: + pass + return removed + + def _persist_job(self, job: TrainingJob) -> None: + if self._persistence is None or not self._persistence.enabled: + return + snapshot = job.snapshot() + self._persistence.upsert_training_job(snapshot) + with job.lock: + job.last_persist_at = _now() + + def list_jobs(self) -> list[dict[str, Any]]: + with self._lock: + jobs = list(self._jobs.values()) + jobs.sort(key=lambda x: x.created_at, reverse=True) + return [job.snapshot() for job in jobs] + + def get_job(self, job_id: str) -> dict[str, Any] | None: + with self._lock: + job = self._jobs.get(job_id) + return None if job is None else job.snapshot() + + def start_job( + self, + *, + phase: int, + timesteps: int, + n_envs: int, + seed: int | None, + config_path: str | None, + ) -> dict[str, Any]: + job_id = str(uuid4()) + job_seed = int(seed if seed is not None else int(time.time()) % 1_000_000) + cfg = config_path or ( + "rl/configs/ppo_easy.yaml" if phase == 1 else "rl/configs/curriculum.yaml" + ) + job = TrainingJob( + job_id=job_id, + phase=phase, + timesteps=timesteps, + n_envs=n_envs, + seed=job_seed, + config_path=cfg, + ) + + with self._lock: + self._jobs[job_id] = job + + cmd = [ + sys.executable, + "-u", + "-m", + "rl.train_ppo", + "--phase", + str(phase), + "--timesteps", + str(timesteps), + "--n-envs", + str(n_envs), + "--seed", + str(job_seed), + ] + if phase == 1: + # Keep Phase 1 UI responsive by emitting multiple eval checkpoints + # across the requested run length instead of only near the end. + phase1_eval_freq = max(128, int((timesteps / max(n_envs, 1)) / 15)) + cmd.extend( + [ + "--phase1-config", + cfg, + "--phase1-eval-freq", + str(phase1_eval_freq), + ] + ) + else: + cmd.extend(["--phase2-config", cfg]) + + env = os.environ.copy() + env["PYTHONUNBUFFERED"] = "1" + + proc = subprocess.Popen( + cmd, + cwd=str(self._repo_root), + env=env, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + ) + + with job.lock: + job.command = cmd + job.status = "running" + job.started_at = _now() + job.updated_at = _now() + job.process_id = proc.pid + job.process = proc + _tail_append(job.logs_tail, f"[training_jobs] started pid={proc.pid}") + _tail_append(job.logs_tail, f"[training_jobs] command: {' '.join(cmd)}") + self._persist_job(job) + + t = threading.Thread(target=self._watch_job, args=(job,), daemon=True) + t.start() + + return job.snapshot() + + @staticmethod + def _append_metric_point_locked( + job: TrainingJob, + *, + timesteps: float | None, + reward: float | None = None, + score: float | None = None, + source: str | None = None, + max_points: int = 5000, + ) -> None: + """ + Append (or merge) a structured metric point while holding job.lock. + """ + if timesteps is None or not math.isfinite(float(timesteps)): + return + + payload: dict[str, Any] = {"t": float(timesteps)} + if reward is not None and math.isfinite(float(reward)): + payload["ep_rew_mean"] = float(reward) + if score is not None and math.isfinite(float(score)): + payload["grader_score"] = float(score) + if source: + payload["source"] = str(source) + + if "ep_rew_mean" not in payload and "grader_score" not in payload: + return + + if job.metric_history and float(job.metric_history[-1].get("t", -1.0)) == float(payload["t"]): + job.metric_history[-1].update(payload) + else: + job.metric_history.append(payload) + + if len(job.metric_history) > max_points: + del job.metric_history[: len(job.metric_history) - max_points] + + def stop_job(self, job_id: str) -> dict[str, Any] | None: + with self._lock: + job = self._jobs.get(job_id) + if job is None: + return None + + with job.lock: + proc = job.process + if proc is None or job.status not in ("running", "queued"): + return job.snapshot() + job.status = "stopped" + job.updated_at = _now() + self._persist_job(job) + + try: + proc.terminate() + except Exception: + pass + return job.snapshot() + + def delete_job(self, job_id: str, *, clear_artifacts: bool = False) -> bool: + with self._lock: + job = self._jobs.pop(job_id, None) + if job is None: + return False + + with job.lock: + proc = job.process + status = job.status + output_model_path = job.output_model_path + + if proc is not None and status in ("queued", "running"): + try: + proc.terminate() + except Exception: + pass + + if self._persistence is not None and self._persistence.enabled: + self._persistence.delete_training_job(job_id) + + if clear_artifacts and output_model_path: + try: + out = Path(output_model_path) + if out.exists() and out.is_file(): + out.unlink(missing_ok=True) + parent = out.parent + if parent.exists() and parent.is_dir() and not any(parent.iterdir()): + parent.rmdir() + except Exception: + pass + return True + + def _watch_job(self, job: TrainingJob) -> None: + proc = job.process + if proc is None or proc.stdout is None: + with job.lock: + job.status = "failed" + job.error_message = "Training process failed to start." + job.updated_at = _now() + job.ended_at = _now() + self._persist_job(job) + return + + for line in proc.stdout: + self._update_from_line(job, line) + + return_code = proc.wait() + with job.lock: + job.return_code = int(return_code) + if job.status == "stopped": + job.ended_at = _now() + job.updated_at = _now() + job.process = None + return + if return_code == 0: + job.status = "completed" + job.progress = 1.0 + else: + job.status = "failed" + base_error = f"Training exited with code {return_code}." + if not job.logs_tail: + _tail_append( + job.logs_tail, + "[training_jobs] Process ended before producing logs. " + "Check RL dependencies/environment and training command arguments.", + ) + job.error_message = base_error + job.ended_at = _now() + job.updated_at = _now() + job.process = None + self._persist_job(job) + + if return_code == 0: + self._finalize_artifacts(job) + + def _update_from_line(self, job: TrainingJob, line: str) -> None: + line = line.rstrip("\n") + should_persist = False + with job.lock: + _tail_append(job.logs_tail, line) + job.updated_at = _now() + + p = _PROGRESS_RE.search(line) + if p: + num = int(p.group(1).replace(",", "")) + den = int(p.group(2).replace(",", "")) + if den > 0: + job.progress = max(0.0, min(1.0, num / den)) + + ep = _EVAL_PROGRESS_RE.search(line) + if ep: + ts = int(ep.group(1)) + rew = float(ep.group(2)) + job.latest_metrics["total_timesteps"] = float(ts) + job.latest_metrics["ep_rew_mean"] = rew + self._append_metric_point_locked( + job, + timesteps=float(ts), + reward=rew, + source="eval_progress", + ) + if job.timesteps > 0: + job.progress = max(0.0, min(1.0, ts / float(job.timesteps))) + + m = _METRIC_ROW_RE.search(line) + if m: + key = _normalize_metric_key(m.group(1)) + val = float(m.group(2)) + interesting = { + "total_timesteps", + "ep_rew_mean", + "ep_len_mean", + "grader_score", + "mean_reward", + "mean_ep_length", + "episode_mean_sla_penalty", + "episode_mean_fairness_penalty", + "explained_variance", + "approx_kl", + } + if key in interesting: + job.latest_metrics[key] = val + current_ts = job.latest_metrics.get("total_timesteps") + if key == "total_timesteps": + self._append_metric_point_locked( + job, + timesteps=val, + reward=job.latest_metrics.get("ep_rew_mean"), + score=job.latest_metrics.get("grader_score") or job.latest_metrics.get("avg_grader_score"), + source="metrics_row_ts", + ) + elif key in {"ep_rew_mean", "mean_reward"}: + self._append_metric_point_locked( + job, + timesteps=float(current_ts) if current_ts is not None else None, + reward=val, + source="metrics_row_reward", + ) + elif key in {"grader_score", "avg_grader_score"}: + self._append_metric_point_locked( + job, + timesteps=float(current_ts) if current_ts is not None else None, + score=val, + source="metrics_row_score", + ) + + best = _BEST_GRADER_RE.search(line) + if best: + score = float(best.group(1)) + job.latest_metrics["grader_score"] = score + fallback_ts = ( + float(job.latest_metrics.get("total_timesteps")) + if "total_timesteps" in job.latest_metrics + else float(job.metric_history[-1]["t"]) if job.metric_history else 0.0 + ) + self._append_metric_point_locked( + job, + timesteps=fallback_ts if fallback_ts > 0 else float(len(job.metric_history) + 1), + score=score, + source="best_grader", + ) + + avg_line = _AVG_RE.match(line.strip()) + if avg_line: + avg_score = float(avg_line.group(1)) + job.latest_metrics["avg_grader_score"] = avg_score + fallback_ts = ( + float(job.latest_metrics.get("total_timesteps")) + if "total_timesteps" in job.latest_metrics + else float(job.metric_history[-1]["t"]) if job.metric_history else 0.0 + ) + self._append_metric_point_locked( + job, + timesteps=fallback_ts if fallback_ts > 0 else float(len(job.metric_history) + 1), + score=avg_score, + source="avg_grader", + ) + if job.updated_at - job.last_persist_at >= 1.5: + should_persist = True + if should_persist: + self._persist_job(job) + + def _finalize_artifacts(self, job: TrainingJob) -> None: + src_name = "phase1_final.zip" if job.phase == 1 else "phase2_final.zip" + src = self._repo_root / "results" / "best_model" / src_name + run_dir = self._training_runs_root / job.job_id + run_dir.mkdir(parents=True, exist_ok=True) + + # Keep a mirror under repo/results for local developer convenience. + mirror_dir = self._repo_root / "results" / "training_runs" / job.job_id + if mirror_dir != run_dir: + mirror_dir.mkdir(parents=True, exist_ok=True) + + if src.exists(): + ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + unique_name = f"phase{job.phase}_seed{job.seed}_{ts}_{job.job_id[:8]}.zip" + out = run_dir / unique_name + shutil.copy2(src, out) + if mirror_dir != run_dir: + try: + shutil.copy2(src, mirror_dir / unique_name) + except Exception: + pass + with job.lock: + job.output_model_path = str(out.resolve()) + job.output_model_name = unique_name + job.updated_at = _now() + + model_type = "maskable" + eval_cmd = [ + sys.executable, + "-m", + "rl.evaluate", + "--model", + str(out), + "--episodes", + "3", + "--model-type", + model_type, + ] + proc = subprocess.run( + eval_cmd, + cwd=str(self._repo_root), + env=os.environ.copy(), + capture_output=True, + text=True, + check=False, + ) + rows, avg = _parse_eval(proc.stdout or "") + with job.lock: + job.evaluation_rows = rows + job.evaluation_avg_score = avg + if avg is not None: + job.latest_metrics["avg_grader_score"] = float(avg) + fallback_ts = ( + float(job.latest_metrics.get("total_timesteps")) + if "total_timesteps" in job.latest_metrics + else float(job.timesteps) + ) + self._append_metric_point_locked( + job, + timesteps=fallback_ts if fallback_ts > 0 else float(len(job.metric_history) + 1), + score=float(avg), + source="final_eval_avg", + ) + _tail_append(job.logs_tail, "----- EVALUATION -----") + for ln in (proc.stdout or "").splitlines(): + _tail_append(job.logs_tail, ln) + if proc.returncode != 0 and not job.error_message: + job.error_message = f"Evaluation exited with code {proc.returncode}." + job.updated_at = _now() + self._persist_job(job) + else: + self._persist_job(job) diff --git a/app/utils.py b/app/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4a4bd7c1238baee32da26fddb4ce1e28fdfaa29c --- /dev/null +++ b/app/utils.py @@ -0,0 +1,25 @@ +""" +utils.py — Shared pure-function helpers. +No imports from env.py or simulator.py (prevents circular imports). +""" +from __future__ import annotations +from app.models import ServiceType + + +def completion_fairness_gap( + arrived_by_service: dict, + completed_by_service: dict, +) -> float: + """ + Fairness gap = max completion rate difference across services. + Returns 0.0 if only one service, 1.0 if perfectly unfair. + """ + rates = [] + for svc in arrived_by_service: + arrived = arrived_by_service.get(svc, 0) + completed = completed_by_service.get(svc, 0) + if arrived > 0: + rates.append(completed / arrived) + if len(rates) < 2: + return 0.0 + return round(max(rates) - min(rates), 4) diff --git a/audit.py b/audit.py new file mode 100644 index 0000000000000000000000000000000000000000..d53373ce5260d57884d11d35e1e3fbddfe071220 --- /dev/null +++ b/audit.py @@ -0,0 +1,367 @@ +import os +import sys +import json +import inspect +import requests +import numpy as np +import yaml +import gymnasium as gym + +from stable_baselines3.common.env_checker import check_env +from sb3_contrib import MaskablePPO + +def print_result(check_num, desc, status, detail=""): + print(f"[CHECK {check_num}] {desc}\nSTATUS: {status}\nDETAIL: {detail}\n") + +# B1 +try: + from app.models import ( + ServiceType, StageType, PriorityMode, ActionType, + OfficerPool, QueueSnapshot, ObservationModel, ActionModel, + RewardModel, EpisodeStateModel, StepInfoModel, + SimulationConfig, TaskConfig, GraderResult, + BenchmarkResult, LiveRunResult, EpisodeMetrics + ) + print_result("B1", "All 17 Schemas Present", "PASS", "All 17 names resolve") +except Exception as e: + print_result("B1", "All 17 Schemas Present", "FAIL", str(e)) + +# B2 +try: + fields = QueueSnapshot.model_fields + assert 'total_pending' in fields, "total_pending missing" + assert 'blocked_missing_docs' in fields, "blocked_missing_docs missing" + assert 'active_cases' not in fields, "legacy field active_cases found" + assert 'missing_docs_cases' not in fields, "legacy field found" + + m_fields = EpisodeMetrics.model_fields + assert 'total_invalid_actions' in m_fields, "total_invalid_actions missing" + print_result("B2", "Canonical Field Name Verification", "PASS", "Fields verified") +except Exception as e: + print_result("B2", "Canonical Field Name Verification", "FAIL", str(e)) + +# B3 +try: + from app.simulator import SimulationAgentMode + assert hasattr(SimulationAgentMode, 'BASELINE_POLICY'), "BASELINE_POLICY missing" + assert hasattr(SimulationAgentMode, 'RANDOM'), "RANDOM missing" + assert hasattr(SimulationAgentMode, 'LLM_AGENT'), "LLM_AGENT missing" + assert hasattr(SimulationAgentMode, 'HEURISTIC'), "HEURISTIC missing" + try: + _ = SimulationAgentMode.baseline_policy + print_result("B3", "Enum Casing Check", "FAIL", "lowercase alias exists") + except AttributeError: + print_result("B3", "Enum Casing Check", "PASS", "No lowercase alias") +except Exception as e: + print_result("B3", "Enum Casing Check", "FAIL", str(e)) + +# C1 +try: + from app.env import GovWorkflowEnv + env = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs, info = env.reset(seed=42) + assert isinstance(obs, dict), f"obs is {type(obs)}, expected dict" + assert isinstance(info, dict), f"info is {type(info)}, expected dict" + assert len(obs) > 0, "empty observation" + print_result("C1", "reset() Returns (observation, info)", "PASS", "Valid dicts returned") +except Exception as e: + print_result("C1", "reset() Returns (observation, info)", "FAIL", str(e)) + +# C2 +try: + from app.models import ActionModel, ActionType + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + result = env.step(action) + assert len(result) == 5, f"step() returned {len(result)} values, expected 5" + obs2, reward, terminated, truncated, info2 = result + assert isinstance(reward, float), f"reward type {type(reward)}" + assert isinstance(terminated, bool), "terminated not bool" + assert isinstance(truncated, bool), "truncated not bool" + print_result("C2", "step() Returns (obs, reward, terminated, truncated, info)", "PASS", "Valid step signature") +except Exception as e: + print_result("C2", "step() Returns (obs, reward, terminated, truncated, info)", "FAIL", str(e)) + +# C3 (Skipping dictionary check since MaskablePPO actually uses rl.gov_workflow_env for gym.Env spaces, doing that in J instead) +# Wait, let's just check the wrapper. +try: + from rl.gov_workflow_env import GovWorkflowGymEnv + genv = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42) + gobs, _ = genv.reset(seed=42) + def check_dtype(obs_dict, path="obs"): + for k, v in obs_dict.items(): + if isinstance(v, np.ndarray): + assert v.dtype == np.float32 or v.dtype == np.int64, f"FAIL: {path}.{k} dtype={v.dtype}" + elif isinstance(v, dict): + check_dtype(v, f"{path}.{k}") + check_dtype(gobs) + print_result("C3", "Observation Space Dtype (SB3 Requirement)", "PASS", "Wrapper dict is fine") +except Exception as e: + print_result("C3", "Observation Space Dtype (SB3 Requirement)", "FAIL", str(e)) + +# C4 +try: + env1 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + env2 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs1, _ = env1.reset(seed=42) + obs2, _ = env2.reset(seed=42) + + # Strip volatile message field before comparison (as in tests) + obs1.last_action_explanation = "" + obs2.last_action_explanation = "" + obs1.episode_id = "" + obs2.episode_id = "" + + assert json.dumps(obs1.model_dump(), sort_keys=True, default=str) == json.dumps(obs2.model_dump(), sort_keys=True, default=str), "Different observations" + print_result("C4", "Determinism Check", "PASS", "Observations match") +except Exception as e: + print_result("C4", "Determinism Check", "FAIL", str(e)) + +# C5 +try: + env_c5 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs, _ = env_c5.reset(seed=42) + terminated = False + truncated = False + steps = 0 + max_steps = 500 + while not (terminated or truncated) and steps < max_steps: + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + obs, reward, terminated, truncated, info = env_c5.step(action) + steps += 1 + assert terminated or truncated, f"episode never ended after {max_steps} steps" + print_result("C5", "Episode Termination Check", "PASS", f"ended at step {steps}") +except Exception as e: + print_result("C5", "Episode Termination Check", "FAIL", str(e)) + +# D1 +try: + env_d1 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs, _ = env_d1.reset(seed=42) + rewards = [] + for _ in range(20): + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + obs, reward, term, trunc, info = env_d1.step(action) + rewards.append(reward) + if term or trunc: break + nonzero = sum(1 for r in rewards if abs(r) > 1e-6) + assert nonzero > len(rewards) * 0.5, f"Only {nonzero}/{len(rewards)} steps had nonzero reward" + print_result("D1", "Reward is Dense", "PASS", f"{nonzero}/{len(rewards)} steps nonzero") +except Exception as e: + print_result("D1", "Reward is Dense", "FAIL", str(e)) + +# D2 +try: + for r in rewards: + assert -100 <= r <= 100, f"reward {r} outside [-100, 100]" + print_result("D2", "Reward Range Sanity Check", "PASS", "Rewards in bounds") +except Exception as e: + print_result("D2", "Reward Range Sanity Check", "FAIL", str(e)) + +# D3 +try: + from app.models import ServiceType + env_d3 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs, _ = env_d3.reset(seed=42) + # Using a valid enum but perhaps invalid context to cause penalty + # The framework doesn't allow 'nonexistent' string if it's an Enum, so let's use valid enum but no cases. + bad_action = ActionModel(action_type=ActionType.ESCALATE_SERVICE, service_target=ServiceType.PASSPORT) + obs, reward, term, trunc, info = env_d3.step(bad_action) + assert reward <= 0, f"invalid action produced positive reward {reward}" + print_result("D3", "Invalid Action Penalty Fires", "PASS", f"reward={reward:.3f}") +except Exception as e: + print_result("D3", "Invalid Action Penalty Fires", "FAIL", str(e)) + +# E1 +try: + from app.tasks import get_task + for task_id in ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"]: + cfg = get_task(task_id) + assert cfg.seed is not None, f"{task_id} has no seed" + assert cfg.max_days > 0, f"{task_id} max_days={cfg.max_days}" + print_result("E1", "All 3 Tasks Loadable", "PASS", "All config loaded") +except Exception as e: + print_result("E1", "All 3 Tasks Loadable", "FAIL", str(e)) + +# E2 +try: + from app.graders import grade_episode + for task_id in ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"]: + env_e2 = GovWorkflowEnv(task_id=task_id, seed=42) + obs, _ = env_e2.reset(seed=42) + terminated = truncated = False + while not (terminated or truncated): + obs, reward, terminated, truncated, info = env_e2.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + episode_state = env_e2.state() + score_res = grade_episode(episode_state) + assert isinstance(score_res.score, float), f"grader returned {type(score_res.score)}" + assert 0.0 <= score_res.score <= 1.0, f"score={score_res.score} outside [0.0, 1.0]" + print_result("E2", "Graders Return [0.0, 1.0]", "PASS", "Valid scores returned") +except Exception as e: + print_result("E2", "Graders Return [0.0, 1.0]", "FAIL", str(e)) + +# E3 +try: + scores = [] + for _ in range(2): + env_e3 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs, _ = env_e3.reset(seed=42) + terminated = truncated = False + while not (terminated or truncated): + obs, r, terminated, truncated, info = env_e3.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + scores.append(grade_episode(env_e3.state()).score) + assert scores[0] == scores[1], f"grader is non-deterministic: {scores}" + print_result("E3", "Grader Scores Are Deterministic", "PASS", f"score={scores[0]:.4f} both runs") +except Exception as e: + print_result("E3", "Grader Scores Are Deterministic", "FAIL", str(e)) + +# F1 +try: + from app.state_machine import StateMachine, StageType, WorkflowAction + sm = StateMachine() + stages = [StageType.SUBMISSION, StageType.DOCUMENT_VERIFICATION, StageType.FIELD_VERIFICATION, StageType.APPROVAL, StageType.ISSUANCE] + for i in range(len(stages) - 1): + current = stages[i] + next_stage = stages[i + 1] + result = sm.transition(current, WorkflowAction.ADVANCE) + assert result == next_stage, f"{current} -> {result}, expected {next_stage}" + print_result("F1", "All Legal Transitions Work", "PASS", "Transitions validated") +except Exception as e: + print_result("F1", "All Legal Transitions Work", "FAIL", str(e)) + +# F2 +try: + assert sm.is_terminal(StageType.ISSUANCE) == True, "issuance not recognized as terminal" + assert sm.is_terminal(StageType.SUBMISSION) == False, "submission wrongly marked terminal" + print_result("F2", "Terminal State Recognized", "PASS", "Terminal states correct") +except Exception as e: + print_result("F2", "Terminal State Recognized", "FAIL", str(e)) + +# G1 +try: + import app.simulator as sim_module + source = inspect.getfile(sim_module.LiveSimulationSession) + assert 'engine' in source.lower(), f"LiveSimulationSession defined in {source}, not engine.py" + print_result("G1", "simulator.py Is a Pure Shim", "PASS", "Shim logic confirmed") +except Exception as e: + print_result("G1", "simulator.py Is a Pure Shim", "FAIL", str(e)) + +# G2 +try: + from app.simulator import LiveSimulationSession, SimulationAgentMode, run_simulation + assert callable(run_simulation), "run_simulation not callable" + assert callable(LiveSimulationSession), "LiveSimulationSession not callable" + print_result("G2", "All 3 Engine Exports Importable", "PASS", "Exports valid") +except Exception as e: + print_result("G2", "All 3 Engine Exports Importable", "FAIL", str(e)) + +# G3 +try: + session = LiveSimulationSession( + task_id="district_backlog_easy", + agent_mode=SimulationAgentMode.BASELINE_POLICY, + seed=42, + max_steps=10 + ) + start_info = session.start_line() + assert isinstance(start_info, str), "start_line() did not return str" + step_result, _, _ = session.step_once() + assert "observation" in step_result, "step_once missing 'observation'" + assert "reward" in step_result, "step_once missing 'reward'" + print_result("G3", "LiveSimulationSession Full Lifecycle", "PASS", "Lifecycle valid") + session.close() +except Exception as e: + print_result("G3", "LiveSimulationSession Full Lifecycle", "FAIL", str(e)) + +# H2 / H3 +# We will do H checks via curl/pytest in bash to test the live server. + +# I1 +try: + from app.baselines import ( + random_policy, + backlog_clearance_policy as baseline_policy, + greedy_sla_policy, + fairness_aware_policy, + ) + for name, fn in [ + ("random_policy", random_policy), + ("baseline_policy", baseline_policy), + ("greedy_sla_policy", greedy_sla_policy), + ("fairness_aware_policy", fairness_aware_policy), + ]: + assert callable(fn), f"{name} is not callable" + print_result("I1", "All 4 Policies Are Callable", "PASS", "Policies callable") +except Exception as e: + print_result("I1", "All 4 Policies Are Callable", "FAIL", str(e)) + +# I2 +try: + from app.baselines import greedy_sla_policy + env_i2 = GovWorkflowEnv(task_id="district_backlog_easy", seed=42) + obs_i2, _ = env_i2.reset(seed=42) + action_i2 = greedy_sla_policy(obs_i2) + assert isinstance(action_i2, ActionModel), f"policy returned {type(action_i2)}" + print_result("I2", "Policy Returns Valid Action", "PASS", f"action_type={action_i2.action_type}") +except Exception as e: + print_result("I2", "Policy Returns Valid Action", "FAIL", str(e)) + +# J1 +try: + env_j1 = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42) + assert hasattr(env_j1, 'observation_space'), "no observation_space" + assert hasattr(env_j1, 'action_space'), "no action_space" + print_result("J1", "Gymnasium API Compliance", "PASS", "Spaces defined") +except Exception as e: + print_result("J1", "Gymnasium API Compliance", "FAIL", str(e)) + +# J2 +try: + obs, _ = env_j1.reset(seed=42) + assert hasattr(env_j1, 'action_masks'), "action_masks() method missing" + masks = env_j1.action_masks() + assert hasattr(masks, '__len__'), "action_masks() must return array-like" + assert len(masks) == env_j1.action_space.n, f"mask length {len(masks)} != action_space.n {env_j1.action_space.n}" + print_result("J2", "action_masks() Method Required by MaskablePPO", "PASS", f"n={len(masks)}") +except Exception as e: + print_result("J2", "action_masks() Method Required by MaskablePPO", "FAIL", str(e)) + +# J3 +try: + check_env(env_j1, warn=True) + print_result("J3", "SB3 VecEnv Compatibility", "PASS", "check_env passed") +except Exception as e: + print_result("J3", "SB3 VecEnv Compatibility", "FAIL", str(e)) + +# J4 +try: + model = MaskablePPO("MlpPolicy", env_j1, verbose=0, seed=42) + print_result("J4", "MaskablePPO Can Initialize", "PASS", "Model initialized") +except Exception as e: + print_result("J4", "MaskablePPO Can Initialize", "FAIL", str(e)) + +# J5 +try: + obs, _ = env_j1.reset(seed=42) + for step in range(10): + masks = env_j1.action_masks() + valid_actions = [i for i, m in enumerate(masks) if m] + action = valid_actions[0] if valid_actions else 0 + obs, reward, terminated, truncated, info = env_j1.step(action) + if terminated or truncated: + obs, _ = env_j1.reset(seed=42) + print_result("J5", "10-Step Rollout Without Crash", "PASS", "Rollout passed") +except Exception as e: + print_result("J5", "10-Step Rollout Without Crash", "FAIL", str(e)) + +# M1 +try: + with open("openenv.yaml", "r") as f: + config = yaml.safe_load(f) + assert "tasks" in config, "openenv.yaml missing 'tasks' key" + task_ids = [t["id"] for t in config["tasks"]] + for required in ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"]: + assert required in task_ids, f"{required} missing from openenv.yaml" + print_result("M1", "YAML Loads and Contains All 3 Tasks", "PASS", f"{len(task_ids)} tasks registered") +except Exception as e: + print_result("M1", "YAML Loads and Contains All 3 Tasks", "FAIL", str(e)) + diff --git a/baseline_openai.py b/baseline_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..a8a0016bbc71f0a58c763f9a8fb75eacc21e0f95 --- /dev/null +++ b/baseline_openai.py @@ -0,0 +1,983 @@ +from __future__ import annotations + +# ── Path bootstrap ────────────────────────────────────────────────────────── +import sys +from pathlib import Path + +_ROOT = Path(__file__).resolve().parent +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) + +# ── Load .env ──────────────────────────────────────────────────────────────── +from dotenv import load_dotenv +load_dotenv(dotenv_path=_ROOT / ".env", override=False) + +import argparse +import json +import os +import random as _random +import re +import time +from dataclasses import asdict, dataclass, field +from datetime import datetime +from typing import Any + +from app.env import GovWorkflowEnv +from app.models import ( + ActionModel, + ActionType, + ObservationModel, + PriorityMode, + ServiceType, + StepInfoModel, +) +from app.tasks import get_task, list_tasks +from app.api_gateway import create_env_gateway, TransportMode + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 1 — Model Registry & Per-Task Pools +# ══════════════════════════════════════════════════════════════════════════════ + +NVIDIA_BASE_URL = "https://integrate.api.nvidia.com/v1" + +# ── Global 10-Model Sequential Pool (April 2026 — Verified on NVIDIA NIM) ──── +# +# CHANGES FROM PREVIOUS VERSION: +# REMOVED (invalid/unavailable IDs): +# qwen/qwen3-next-80b-a3b-instruct → invalid model ID +# moonshotai/kimi-k2-instruct-0905 → not on NVIDIA NIM +# deepseek-ai/deepseek-v3.2 → wrong ID (use deepseek-v3) +# google/gemma-3-27b-it → outdated (gemma-4 released) +# mistralai/mixtral-8x22b-instruct-v0.1 → replaced by newer models +# ADDED (verified April 2026): +# deepseek-ai/deepseek-v4-flash → FREE endpoint, 1M context +# deepseek-ai/deepseek-r1 → reasoning, 685B MoE +# nvidia/nemotron-3-super-120b-a12b → hybrid Mamba-Transformer, 1M ctx +# minimaxai/minimax-m2.7 → FREE endpoint, 230B +# google/gemma-4-31b-it → latest Gemma on NVIDIA NIM +# qwen/qwen3.5-122b-a10b → latest Qwen on NVIDIA NIM + +GLOBAL_MODEL_POOL: list[str] = [ + "meta/llama-3.3-70b-instruct", # 1. Primary + "deepseek-ai/deepseek-v4-flash", # 2. FREE endpoint — 1M context + "deepseek-ai/deepseek-r1", # 3. Reasoning — 685B MoE + "nvidia/nemotron-3-super-120b-a12b", # 4. NVIDIA native — 1M ctx + "qwen/qwen3.5-122b-a10b", # 5. Qwen3.5 — tool calling + "deepseek-ai/deepseek-v3", # 6. DeepSeek V3 — hybrid mode + "minimaxai/minimax-m2.7", # 7. FREE endpoint — 230B + "google/gemma-4-31b-it", # 8. Dense 31B — agentic workflows + "microsoft/phi-4-mini-instruct", # 9. Reliable small — last resort + "meta/llama-3.1-8b-instruct", # 10. Fastest safety fallback +] + +# ── Free endpoint pool (KEY 2 — NVIDIA_API_KEY_2 fallback) ─────────────────── +FREE_POOL: list[str] = [ + "deepseek-ai/deepseek-v4-flash", + "minimaxai/minimax-m2.7", + "microsoft/phi-4-mini-instruct", + "meta/llama-3.1-8b-instruct", +] + +# ── Fixed seeds ──────────────────────────────────────────────────────────────── +TASK_SEEDS: dict[str, int] = { + "district_backlog_easy": 11, + "mixed_urgency_medium": 22, + "cross_department_hard": 33, +} + +LLM_TEMPERATURE = 0.2 +LLM_TOP_P = 0.7 +LLM_MAX_TOKENS = 512 +MAX_LLM_STEPS = 80 + +LLM_CALL_DELAY = float(os.environ.get("LLM_CALL_DELAY", "12.0")) +LLM_CALL_JITTER = 1.0 + +# ── Enum fields that MUST be lowercase for Pydantic StrEnum ────────────────── +_ENUM_FIELDS = {"action_type", "priority_mode", "service", "target_service"} + +# ── Canonical field names (Phase 2 update — do NOT use legacy names) ───────── +# CORRECT WRONG (legacy) +# snap.blocked_missing_docs ← snap.missing_docs_cases +# snap.total_pending ← snap.active_cases +# obs.fairness_gap ← obs.fairness_index + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 2 — Model Rotator +# ══════════════════════════════════════════════════════════════════════════════ + +class ModelRotator: + def __init__(self, task_id: str) -> None: + self._sequence: list[str] = GLOBAL_MODEL_POOL.copy() + self._index = 0 + self._task_id = task_id + self._rotation_log: list[dict[str, str]] = [] + + @property + def current(self) -> str: + return self._sequence[self._index] + + @property + def current_key_id(self) -> int: + return 2 if self.current in FREE_POOL else 1 + + @property + def pool_exhausted(self) -> bool: + return len(self._rotation_log) >= 50 + + def rotate(self, reason: str = "error") -> str | None: + old = self.current + self._rotation_log.append({"from": old, "reason": reason}) + self._index = (self._index + 1) % len(self._sequence) + new = self._sequence[self._index] + print( + f"\n 🔄 Model rotated: " + f"{old.split('/')[-1]} → {new.split('/')[-1]} ({reason})" + ) + return new + + def summary(self) -> list[dict]: + return list(self._rotation_log) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 3 — Result Dataclasses +# ══════════════════════════════════════════════════════════════════════════════ + +@dataclass +class StepRecord: + step: int + day: int + action_type: str + reward: float + invalid: bool + total_backlog: int + total_completed: int + model_used: str + notes: list[str] + + +@dataclass +class EpisodeResult: + task_id: str + agent: str + primary_model: str + seed: int + score: float + grader_name: str + total_steps: int + total_reward: float + total_completed: int + total_sla_breaches: int + total_invalid_actions: int + final_day: int + terminated: bool + truncated: bool + grader_metrics: dict[str, float] + step_log: list[StepRecord] + elapsed_seconds: float + model_rotations: list[dict] + timestamp: str = field(default_factory=lambda: datetime.now().isoformat()) + + def summary(self) -> str: + usage: dict[str, int] = {} + for r in self.step_log: + usage[r.model_used] = usage.get(r.model_used, 0) + 1 + usage_str = ", ".join( + f"{m.split('/')[-1]} ({c})" for m, c in usage.items() + ) + return ( + f"[{self.task_id}] agent={self.agent} " + f"score={self.score:.3f} reward={self.total_reward:.2f} " + f"completed={self.total_completed} breaches={self.total_sla_breaches} " + f"invalid={self.total_invalid_actions} " + f"rotations={len(self.model_rotations)} " + f"day={self.final_day} steps={self.total_steps} " + f"time={self.elapsed_seconds:.1f}s\n" + f" Model usage: {usage_str}" + ) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 4 — Direct Environment Wrapper +# ══════════════════════════════════════════════════════════════════════════════ + +class DirectEnvClient: + """ + FIX: grade() now calls grade_episode(task_id, episode_state) correctly. + Previous version called grade_episode(self.env.state()) — wrong signature. + get_episode_state() returns EpisodeStateModel, not ObservationModel. + """ + + def __init__(self, task_id: str, seed: int) -> None: + self.env = GovWorkflowEnv(task_id=task_id) + self._seed = seed + self._task_id = task_id + self.terminated = False + self.truncated = False + + def reset(self) -> ObservationModel: + obs, _ = self.env.reset(seed=self._seed) + self.terminated = False + self.truncated = False + return obs + + def step( + self, action: ActionModel + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: + obs, reward, terminated, truncated, info = self.env.step(action) + self.terminated = terminated + self.truncated = truncated + return obs, reward, terminated, truncated, info + + def grade(self) -> tuple[float, str, dict[str, float]]: + from app.graders import grade_episode + episode_state = self.env.state() + result = grade_episode(episode_state) + return result.score, result.grader_name, result.metrics + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 5 — HTTP Environment Wrapper +# ══════════════════════════════════════════════════════════════════════════════ + +class HttpEnvClient: + def __init__( + self, task_id: str, seed: int, base_url: str = "http://localhost:7860" + ) -> None: + try: + import requests as _req + self._req = _req + except ImportError: + raise ImportError("pip install requests — required for --mode http") + self._task_id = task_id + self._seed = seed + self._base_url = base_url.rstrip("/") + self._session_id: str | None = None + self.terminated = False + self.truncated = False + + def _post(self, path: str, body: dict) -> dict: + r = self._req.post( + f"{self._base_url}{path}", json=body, timeout=30 + ) + r.raise_for_status() + return r.json() + + def reset(self) -> ObservationModel: + data = self._post("/reset", {"task_id": self._task_id, "seed": self._seed}) + self._session_id = data["session_id"] + self.terminated = False + self.truncated = False + return ObservationModel(**data["observation"]) + + def step( + self, action: ActionModel + ) -> tuple[ObservationModel, float, bool, bool, StepInfoModel]: + data = self._post("/step", { + "session_id": self._session_id, + "action": action.model_dump(exclude_none=True), + }) + obs = ObservationModel(**data["observation"]) + info = StepInfoModel(**data["info"]) + self.terminated = data["terminated"] + self.truncated = data["truncated"] + return obs, data["reward"], data["terminated"], data["truncated"], info + + def grade(self) -> tuple[float, str, dict[str, float]]: + data = self._post("/grade", {"session_id": self._session_id}) + return data["score"], data["grader_name"], data["metrics"] + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 6 — Heuristic Baseline Agent +# ══════════════════════════════════════════════════════════════════════════════ + +class HeuristicAgent: + """ + Rule-based agent. Requires no API key. + + FIXED field names (Phase 2 canonical): + snap.blocked_missing_docs ← was snap.missing_docs_cases + snap.total_pending ← was snap.active_cases + """ + + def __init__(self) -> None: + self._priority_set = False + self._admin_action_day: int | None = None + self._last_doc_request_day: int | None = None + + def reset(self) -> None: + self._priority_set = False + self._admin_action_day = None + self._last_doc_request_day = None + + current_model = "heuristic" + + def rotation_summary(self) -> list[dict]: + return [] + + def update_reward(self, _: float) -> None: + pass + + @staticmethod + def _svc_key(service: str | ServiceType) -> str: + return service.value if isinstance(service, ServiceType) else str(service) + + def act(self, obs: ObservationModel) -> ActionModel: + snapshots = list(obs.queue_snapshots.values()) + + # One admin action per simulated day; then always advance time. + if self._admin_action_day == obs.day: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + # 1. Set priority mode once + if not self._priority_set: + self._priority_set = True + self._admin_action_day = obs.day + return ActionModel( + action_type=ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.URGENT_FIRST, + ) + + # 2. Allocate any idle officer to the currently most loaded service. + if obs.officer_pool.idle_officers > 0 and snapshots: + most_loaded = max(snapshots, key=lambda s: s.total_pending) + self._admin_action_day = obs.day + return ActionModel( + action_type=ActionType.ASSIGN_CAPACITY, + capacity_assignment={most_loaded.service_type.value: 1}, + ) + + days_left = obs.max_days - obs.day + + # 3. Reallocate one officer if load/officer ratio is clearly imbalanced. + allocated = { + self._svc_key(svc): int(off) + for svc, off in obs.officer_pool.allocated.items() + } + if snapshots and len(allocated) >= 2: + case_counts = {s.service_type.value: s.total_pending for s in snapshots} + + best_src: tuple[str, int] | None = None + best_tgt: tuple[str, int] | None = None + src_ratio = float("inf") + tgt_ratio = -1.0 + + for svc, officers in allocated.items(): + if officers <= 1: + continue + ratio = case_counts.get(svc, 0) / max(officers, 1) + if ratio < src_ratio: + src_ratio = ratio + best_src = (svc, officers) + + for svc, officers in allocated.items(): + ratio = case_counts.get(svc, 0) / max(officers, 1) + if ratio > tgt_ratio: + tgt_ratio = ratio + best_tgt = (svc, officers) + + if best_src and best_tgt and best_src[0] != best_tgt[0] and tgt_ratio > src_ratio * 1.8: + self._admin_action_day = obs.day + return ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + reallocation_delta={best_src[0]: -1, best_tgt[0]: 1}, + ) + + # 4. Request missing docs conservatively to avoid repeatedly resetting + # resolution days for already-requested cases. + can_request_docs = ( + any(s.blocked_missing_docs > 0 for s in snapshots) + and ( + self._last_doc_request_day is None + or (obs.day - self._last_doc_request_day) >= 3 + or obs.pending_doc_resolutions == 0 + ) + ) + if can_request_docs: + target_docs = max( + snapshots, + key=lambda s: (s.blocked_missing_docs, s.current_sla_risk, s.total_pending), + ) + if target_docs.blocked_missing_docs > 0: + self._admin_action_day = obs.day + self._last_doc_request_day = obs.day + return ActionModel( + action_type=ActionType.REQUEST_MISSING_DOCUMENTS, + service_target=target_docs.service_type, + ) + + # 5. Escalate in the final window when urgency is present. + if obs.escalation_budget_remaining > 0: + urgent_snaps = [s for s in snapshots if s.urgent_pending > 0] + if urgent_snaps and days_left <= 5: + target = max(urgent_snaps, key=lambda s: s.urgent_pending) + self._admin_action_day = obs.day + return ActionModel( + action_type=ActionType.ESCALATE_SERVICE, + escalation_target=target.service_type, + ) + + # 6. Default — progress simulation. + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 7 — System Prompt +# ══════════════════════════════════════════════════════════════════════════════ + +SYSTEM_PROMPT = """You are an expert government-office workflow manager AI. +Your job is to control a simulated government district office processing citizen +applications across multiple services. + +SERVICES: passport, driving_license, gst_registration, income_certificate, + caste_certificate, birth_certificate, land_registration + +WORKFLOW STAGES (in order): + submission → document_verification → field_verification → approval → issuance + +YOUR GOAL: Maximise the episode score (0.0 to 1.0) by: + - Completing as many applications as possible within SLA deadlines + - Prioritising urgent cases (urgency level 3 > 2 > 1) + - Keeping all services fairly served (no service left behind) + - Using escalations sparingly — only when a case is about to breach SLA + - Keeping officers productively busy (not idle) + +QUEUE STATUS FIELDS EXPLAINED: + backlog = total_pending applications in queue + missing_docs = blocked_missing_docs (stuck waiting for documents) + urgent = urgent_cases (high-urgency applications) + breached = breached_cases (already past SLA deadline) + +AVAILABLE ACTIONS — return exactly ONE per turn as JSON: + +1. Set queue processing order (do this FIRST on day 0 only): + {"action_type": "set_priority_mode", "priority_mode": "urgent_first"} + priority_mode options: urgent_first | oldest_first | balanced | backlog_clearance + +2. Deploy a reserve officer to a service (day 0 only if reserves available): + {"action_type": "assign_capacity", "service": "driving_license", "officer_delta": 1} + +3. Unblock a stuck application with missing documents: + {"action_type": "request_missing_documents", "service": "driving_license"} + +4. Escalate one case to emergency priority (VERY LIMITED — use wisely): + {"action_type": "escalate_service", "service": "income_certificate"} + +5. Move officer between services (only when load ratio > 4x): + {"action_type": "reallocate_officers", "service": "birth_certificate", + "target_service": "driving_license", "officer_delta": 1} + +6. Let one working day pass — THE ONLY ACTION THAT PROCESSES APPLICATIONS: + {"action_type": "advance_time"} + +CRITICAL RULES: + - ALL values MUST be lowercase: driving_license NOT DRIVING_LICENSE + - advance_time is the ONLY action that earns progress reward + - Do NOT chain more than 2 admin actions before calling advance_time + - Do NOT escalate before (max_days - 5) unless case already breached SLA + - Do NOT reallocate if source service has fewer than 2 officers + +OPTIMAL STRATEGY: + Day 0: set_priority_mode → assign_capacity (if reserves > 0) → advance_time + Every day: request_missing_documents (ONE service, highest missing_docs) → advance_time + Final 5: escalate_service (urgent/breached only) → advance_time + +RESPONSE FORMAT — return ONLY a raw JSON object, nothing else: + CORRECT: {"action_type": "advance_time"} + CORRECT: {"action_type": "request_missing_documents", "service": "driving_license"} + WRONG: ```json\n{"action_type": "ADVANCE_TIME"}``` +""" + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 8 — JSON Extraction with Lowercase Normaliser +# ══════════════════════════════════════════════════════════════════════════════ + +def _extract_json_action(raw: str) -> dict[str, Any]: + cleaned = re.sub(r"```(?:json)?", "", raw).strip() + parsed: dict[str, Any] | None = None + + try: + parsed = json.loads(cleaned) + except json.JSONDecodeError: + pass + + if parsed is None: + match = re.search(r"\{[^{}]*\}", cleaned, re.DOTALL) + if match: + try: + parsed = json.loads(match.group()) + except json.JSONDecodeError: + pass + + if parsed is None: + print(f" ⚠ JSON parse failed, falling back to advance_time. Raw: {raw[:120]!r}") + return {"action_type": "advance_time"} + + for enum_field in _ENUM_FIELDS: + if enum_field in parsed and isinstance(parsed[enum_field], str): + parsed[enum_field] = parsed[enum_field].lower() + + return parsed + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 9 — Observation → User Message Builder +# ══════════════════════════════════════════════════════════════════════════════ + +def _build_user_message( + obs: ObservationModel, step_num: int, cumulative_reward: float +) -> str: + """ + FIXED field names (Phase 2 canonical): + snap.total_pending ← was snap.active_cases + snap.blocked_missing_docs ← was snap.missing_docs_cases + """ + queue_lines = [] + for snap in obs.queue_snapshots: + officers = obs.officer_pool.allocations.get(snap.service, 0) + queue_lines.append( + f" {snap.service:<22}: " + f"backlog={snap.total_pending:>3} " + f"officers={officers} " + f"missing_docs={snap.blocked_missing_docs:>2} " + f"urgent={snap.urgent_cases} " + f"breached={snap.breached_cases} " + f"avg_age={snap.avg_age_days:.1f}d" + ) + return ( + f"STEP {step_num} | Day {obs.day}/{obs.max_days} " + f"| Days remaining: {obs.max_days - obs.day}\n" + f"Cumulative reward: {cumulative_reward:.2f}\n" + f"Priority mode: {obs.priority_mode}\n" + f"Reserve officers: {obs.officer_pool.reserve_officers}\n" + f"Escalation budget remaining: {obs.escalation_budget_remaining}\n" + f"Total pending: {obs.total_backlog} " + f"| Completed: {obs.total_completed} " + f"| SLA breaches: {obs.total_sla_breaches}\n" + f"Fairness gap: {obs.fairness_gap:.3f}\n\n" + f"QUEUE STATUS:\n" + "\n".join(queue_lines) + "\n\n" + f"Return a single JSON action object. All values lowercase." + ) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 10 — LLM Agent with Model Rotation +# ══════════════════════════════════════════════════════════════════════════════ + +class LLMAgent: + def __init__( + self, + task_id: str, + model_override: str | None = None, + api_key: str | None = None, + ) -> None: + try: + from openai import OpenAI + self._OpenAI = OpenAI + except ImportError: + raise ImportError("pip install openai — required for LLM agent") + + resolved_key = api_key or os.environ.get("NVIDIA_API_KEY", "") + self._api_key_2 = os.environ.get("NVIDIA_API_KEY_2", "") + + if not resolved_key: + raise ValueError( + "NVIDIA_API_KEY not set.\n" + " .env file : NVIDIA_API_KEY=nvapi-xxxxxxxxxxxx\n" + " Get free key: https://build.nvidia.com/explore/discover" + ) + + self._api_key = resolved_key + self._task_id = task_id + self._rotator = ModelRotator(task_id) + + if model_override: + seq = [model_override] + [ + m for m in self._rotator._sequence if m != model_override + ] + self._rotator._sequence = seq + + self._client = self._OpenAI(base_url=NVIDIA_BASE_URL, api_key=self._api_key) + self._client_2 = ( + self._OpenAI(base_url=NVIDIA_BASE_URL, api_key=self._api_key_2) + if self._api_key_2 else None + ) + self._history: list[dict[str, str]] = [] + self._cumulative_reward = 0.0 + + @property + def current_model(self) -> str: + return self._rotator.current + + def reset(self) -> None: + self._history = [] + self._cumulative_reward = 0.0 + self._rotator = ModelRotator(self._task_id) + + def update_reward(self, reward: float) -> None: + self._cumulative_reward += reward + + def rotation_summary(self) -> list[dict]: + return self._rotator.summary() + + def act(self, obs: ObservationModel, step_num: int) -> ActionModel: + if self._rotator.pool_exhausted: + print(" ⚠ Pool exhausted — returning advance_time") + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + user_message = _build_user_message(obs, step_num, self._cumulative_reward) + self._history.append({"role": "user", "content": user_message}) + + if len(self._history) > 20: + self._history = self._history[-20:] + + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + self._history + raw_reply = "" + + while True: + try: + active_client = self._client + if self._rotator.current_key_id == 2 and self._client_2: + active_client = self._client_2 + + response = active_client.chat.completions.create( + model=self._rotator.current, + messages=messages, + temperature=LLM_TEMPERATURE, + top_p=LLM_TOP_P, + max_tokens=LLM_MAX_TOKENS, + timeout=30, + ) + raw_reply = response.choices.message.content or "" + break + + except KeyboardInterrupt: + raise + + except Exception as exc: + err_name = type(exc).__name__ + err_msg = str(exc)[:120] + print(f" ⚠ {err_name} on {self._rotator.current.split('/')[-1]}: {err_msg}") + self._rotator.rotate(reason=err_name) + time.sleep(1.0) + if self._rotator.pool_exhausted: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + self._history.append({"role": "assistant", "content": raw_reply}) + action_dict = _extract_json_action(raw_reply) + + try: + return ActionModel(**action_dict) + except Exception as exc: + print(f" ⚠ ActionModel parse failed ({exc}), using advance_time") + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 11 — Episode Runner +# ══════════════════════════════════════════════════════════════════════════════ + +def run_episode( + task_id: str, + agent_type: str, + model_override: str | None, + mode: TransportMode, + server_url: str, + api_key: str | None, + verbose: bool, + max_steps: int = MAX_LLM_STEPS, + delay_override: float | None = None, +) -> EpisodeResult: + seed = TASK_SEEDS.get(task_id, get_task(task_id).seed) + delay = delay_override if delay_override is not None else LLM_CALL_DELAY + + force_fastapi = os.getenv("FORCE_FASTAPI_GATEWAY", "0").strip().lower() in { + "1", + "true", + "yes", + "on", + } + env_api_prefix = os.getenv("OPENENV_ENV_API_PREFIX", "").strip() + client = create_env_gateway( + task_id=task_id, + seed=seed, + mode=mode, # type: ignore[arg-type] + base_url=server_url, + api_prefix=env_api_prefix, + enforce_fastapi=force_fastapi, + ) + + if agent_type == "llm": + agent: HeuristicAgent | LLMAgent = LLMAgent( + task_id=task_id, + model_override=model_override, + api_key=api_key, + ) + primary_label = agent.current_model + else: + agent = HeuristicAgent() + primary_label = "heuristic" + + agent.reset() + obs = client.reset() + + step_log: list[StepRecord] = [] + total_reward = 0.0 + total_invalid = 0 + step_num = 0 + start = time.perf_counter() + + print(f"\n{'═'*65}") + print(f" Task : {task_id}") + if agent_type == "llm": + k1 = "✅ loaded" if os.environ.get("NVIDIA_API_KEY", "") else "❌ MISSING" + k2 = "✅ loaded" if os.environ.get("NVIDIA_API_KEY_2", "") else "⚠ not set" + print(f" KEY 1 : {k1} KEY 2 : {k2}") + pool_short = " → ".join(m.split("/")[-1][:14] for m in GLOBAL_MODEL_POOL) + print(f" Pool : {pool_short}") + resolved_mode = getattr(client, "transport", mode) + print(f" Agent : {agent_type} | Mode: {resolved_mode} | Seed: {seed}") + print(f" Max steps: {max_steps} | Delay: {delay}s") + print(f"{'═'*65}") + + while not (client.terminated or client.truncated) and step_num < max_steps: + step_num += 1 + current_model = agent.current_model + + if agent_type == "llm": + action = agent.act(obs, step_num) + else: + action = agent.act(obs) + + obs, reward, terminated, truncated, info = client.step(action) + agent.update_reward(reward) + + total_reward += reward + if info.invalid_action: + total_invalid += 1 + + step_notes: list[str] = [] + legacy_notes = getattr(info, "notes", None) + if isinstance(legacy_notes, list): + step_notes.extend(str(n).strip() for n in legacy_notes if str(n).strip()) + elif isinstance(legacy_notes, str) and legacy_notes.strip(): + step_notes.append(legacy_notes.strip()) + + if info.action_explanation.strip(): + step_notes.append(info.action_explanation.strip()) + step_notes.extend(s.strip() for s in info.effects_resolved_this_step if s.strip()) + step_notes = list(dict.fromkeys(step_notes)) + + record = StepRecord( + step=step_num, + day=obs.day, + action_type=action.action_type.value, + reward=round(reward, 4), + invalid=info.invalid_action, + total_backlog=obs.total_backlog, + total_completed=obs.total_completed, + model_used=current_model, + notes=step_notes, + ) + step_log.append(record) + + if verbose: + status = "❌" if info.invalid_action else "✅" + model_tag = ( + f"[{current_model.split('/')[-1][:22]}]" + if agent_type == "llm" else "" + ) + print( + f" step={step_num:3d} day={obs.day:2d} " + f"action={action.action_type.value:<28} " + f"reward={reward:+.3f} {status} {model_tag}" + ) + if step_notes: + print(f" notes: {step_notes}") + + if agent_type == "llm": + actual_delay = delay + _random.uniform(-LLM_CALL_JITTER, LLM_CALL_JITTER) + if not verbose: + print( + f" Step {step_num}/{max_steps} — sleeping {actual_delay:.1f}s " + f"[{current_model.split('/')[-1][:20]}]", + end="\r", flush=True, + ) + time.sleep(max(1.0, actual_delay)) + if not verbose: + print(" " * 80, end="\r", flush=True) + + score, grader_name, grader_metrics = client.grade() + elapsed = round(time.perf_counter() - start, 2) + rotations = agent.rotation_summary() + + print(f"\n{'-'*65}") + print(f" SCORE : {score:.3f} / 1.000 (grader: {grader_name})") + print(f" Reward : {total_reward:.2f} | Steps: {step_num}") + print(f" Completed: {obs.total_completed} | SLA breaches: {obs.total_sla_breaches}") + print(f" Invalid actions: {total_invalid} | Model rotations: {len(rotations)}") + print(f" Time: {elapsed}s") + print(f" Grader metrics:") + for metric, value in grader_metrics.items(): + bar = "█" * int(value * 20) + print(f" {metric:<34} {value:.3f} {bar}") + if rotations: + print(f" Rotation log:") + for r in rotations: + print(f" {r['from'].split('/')[-1]:<30} → rotated ({r['reason']})") + print(f"{'-'*65}") + + return EpisodeResult( + task_id=task_id, + agent=agent_type, + primary_model=primary_label, + seed=seed, + score=score, + grader_name=grader_name, + total_steps=step_num, + total_reward=round(total_reward, 4), + total_completed=obs.total_completed, + total_sla_breaches=obs.total_sla_breaches, + total_invalid_actions=total_invalid, + final_day=obs.day, + terminated=client.terminated, + truncated=client.truncated, + grader_metrics=grader_metrics, + step_log=step_log, + elapsed_seconds=elapsed, + model_rotations=rotations, + ) + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 12 — Reporter +# ══════════════════════════════════════════════════════════════════════════════ + +def save_results(results: list[EpisodeResult], out_dir: Path) -> Path: + out_dir.mkdir(parents=True, exist_ok=True) + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + out_path = out_dir / f"baseline_run_{ts}.json" + payload = { + "run_timestamp": datetime.now().isoformat(), + "total_episodes": len(results), + "average_score": round(sum(r.score for r in results) / len(results), 4), + "model_pool": GLOBAL_MODEL_POOL, + "free_pool": FREE_POOL, + "episodes": [asdict(r) for r in results], + } + out_path.write_text(json.dumps(payload, indent=2)) + return out_path + + +def print_leaderboard(results: list[EpisodeResult]) -> None: + print(f"\n{'═'*72}") + print(" LEADERBOARD") + print(f"{'═'*72}") + header = ( + f" {'TASK':<32} {'MODEL':<24} {'SCORE':>7} " + f"{'REWARD':>8} {'DONE':>5} {'ROT':>4}" + ) + print(header) + print(f" {'-'*32} {'-'*24} {'-'*7} {'-'*8} {'-'*5} {'-'*4}") + for r in sorted(results, key=lambda x: -x.score): + model_label = r.primary_model.split("/")[-1][:23] + print( + f" {r.task_id:<32} {model_label:<24} {r.score:>7.3f} " + f"{r.total_reward:>8.2f} {r.total_completed:>5} " + f"{len(r.model_rotations):>4}" + ) + avg = sum(r.score for r in results) / len(results) + print(f" {'-'*32} {'-'*24} {'-'*7} {'-'*8} {'-'*5} {'-'*4}") + print(f" {'AVERAGE':<32} {'':<24} {avg:>7.3f}") + print(f"{'═'*72}\n") + + +# ══════════════════════════════════════════════════════════════════════════════ +# SECTION 13 — CLI Entry Point +# ══════════════════════════════════════════════════════════════════════════════ + +def build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + description="Gov Workflow OpenEnv — Multi-Model Rotating LLM Baseline", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +10-model pool (April 2026): + llama-3.3-70b → deepseek-v4-flash → deepseek-r1 → nemotron-3-super → + qwen3.5-122b → deepseek-v3 → minimax-m2.7 → gemma-4-31b → + phi-4-mini → llama-3.1-8b + +Examples: + python baseline_openai.py --agent heuristic --verbose + python baseline_openai.py --agent llm --task district_backlog_easy --verbose + python baseline_openai.py --agent llm --task all --save-results + python baseline_openai.py --agent llm --model deepseek-ai/deepseek-v4-flash + python baseline_openai.py --mode http --url http://localhost:7860 --agent llm + python baseline_openai.py --mode auto --url http://localhost:7860 --agent llm + """, + ) + p.add_argument("--agent", choices=["llm", "heuristic"], default="heuristic") + p.add_argument("--task", choices=list_tasks() + ["all"], default="all") + p.add_argument("--model", default=None) + p.add_argument("--mode", choices=["direct", "http", "auto"], default="auto") + p.add_argument("--url", default="http://localhost:7860") + p.add_argument("--max-steps", type=int, default=MAX_LLM_STEPS) + p.add_argument("--delay", type=float, default=None) + p.add_argument("--api-key", default=None) + p.add_argument("--verbose", action="store_true") + p.add_argument("--save-results", action="store_true") + return p + + +def main() -> None: + args = build_parser().parse_args() + tasks = list_tasks() if args.task == "all" else [args.task] + + print(f"\n{'═'*65}") + print(" Gov Workflow OpenEnv — Baseline Runner (April 2026)") + print(f" Agent : {args.agent.upper()}") + if args.agent == "llm": + pool_disp = " → ".join(m.split("/")[-1][:12] for m in GLOBAL_MODEL_POOL) + print(f" Pool : {pool_disp}") + print(f" Mode : {args.mode} | Tasks: {', '.join(tasks)}") + print(f"{'═'*65}") + + if args.agent == "llm": + key = args.api_key or os.environ.get("NVIDIA_API_KEY", "") + if not key: + print("\n❌ NVIDIA_API_KEY not set.") + print(" .env file : NVIDIA_API_KEY=nvapi-xxxx") + print(" PowerShell : $env:NVIDIA_API_KEY='nvapi-xxxx'") + print(" Get free key: https://build.nvidia.com/explore/discover\n") + sys.exit(1) + else: + key = None + + results: list[EpisodeResult] = [] + for task_id in tasks: + result = run_episode( + task_id=task_id, + agent_type=args.agent, + model_override=args.model, + mode=args.mode, + server_url=args.url, + api_key=key, + verbose=args.verbose, + max_steps=args.max_steps, + delay_override=args.delay, + ) + results.append(result) + + print_leaderboard(results) + + if args.save_results: + out = save_results(results, Path("results")) + print(f" Results saved → {out}\n") + + +if __name__ == "__main__": + main() diff --git a/client.py b/client.py new file mode 100644 index 0000000000000000000000000000000000000000..1dffab8bca318a63a394814199b6e431c2db2efb --- /dev/null +++ b/client.py @@ -0,0 +1,134 @@ +""" +Typed HTTP client for Gov Workflow OpenEnv. + +This keeps a simple OpenEnv-style client interface: + reset() -> observation wrapper + step(action) -> step wrapper + state() -> state wrapper +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, TYPE_CHECKING + +import requests +try: + from openenv.core import EnvClient + from openenv.core.env_client import StepResult +except ModuleNotFoundError: + EnvClient = None # type: ignore[assignment] + StepResult = None # type: ignore[assignment] + +if TYPE_CHECKING: + from app.models import ActionModel, EpisodeStateModel, ObservationModel, StepInfoModel + + +@dataclass +class ClientStepResult: + observation: "ObservationModel" + reward: float + done: bool + terminated: bool + truncated: bool + info: "StepInfoModel" + + +class GovWorkflowClient: + """Small typed client for the FastAPI deployment.""" + + def __init__(self, base_url: str) -> None: + self.base_url = base_url.rstrip("/") + self.session_id: str | None = None + + def _post(self, path: str, body: dict[str, Any]) -> dict[str, Any]: + response = requests.post(f"{self.base_url}{path}", json=body, timeout=30) + response.raise_for_status() + return response.json() + + def reset(self, task_id: str = "district_backlog_easy", seed: int | None = None) -> "ObservationModel": + from app.models import ObservationModel + + payload: dict[str, Any] = {"task_id": task_id} + if seed is not None: + payload["seed"] = seed + data = self._post("/reset", payload) + self.session_id = data["session_id"] + return ObservationModel(**data["observation"]) + + def step(self, action: "ActionModel") -> ClientStepResult: + from app.models import ObservationModel, StepInfoModel + + if not self.session_id: + raise RuntimeError("Session not initialized. Call reset() first.") + data = self._post( + "/step", + { + "session_id": self.session_id, + "action": action.model_dump(exclude_none=True), + }, + ) + return ClientStepResult( + observation=ObservationModel(**data["observation"]), + reward=float(data["reward"]), + done=bool(data["done"]), + terminated=bool(data["terminated"]), + truncated=bool(data["truncated"]), + info=StepInfoModel(**data["info"]), + ) + + def state(self, include_action_history: bool = False) -> "EpisodeStateModel": + from app.models import EpisodeStateModel + + if not self.session_id: + raise RuntimeError("Session not initialized. Call reset() first.") + data = self._post( + "/state", + { + "session_id": self.session_id, + "include_action_history": include_action_history, + }, + ) + return EpisodeStateModel(**data["state"]) + + +if EnvClient is not None and StepResult is not None: + class GovWorkflowOpenEnvClient( + EnvClient["ActionModel", "ObservationModel", "EpisodeStateModel"] + ): + """ + OpenEnv-native websocket client. + + This class is additive and does not replace the existing HTTP client above. + """ + + def _step_payload(self, action: "ActionModel") -> dict[str, Any]: + return action.model_dump(exclude_none=True, mode="json") + + def _parse_result(self, payload: dict[str, Any]) -> StepResult["ObservationModel"]: + from app.models import ObservationModel + + observation_payload = payload.get("observation", {}) + obs = ObservationModel(**observation_payload) + return StepResult( + observation=obs, + reward=payload.get("reward"), + done=bool(payload.get("done", False)), + ) + + def _parse_state(self, payload: dict[str, Any]) -> "EpisodeStateModel": + from app.models import EpisodeStateModel + + state_payload = payload.get("state", payload) + return EpisodeStateModel(**state_payload) +else: + class GovWorkflowOpenEnvClient: # type: ignore[no-redef] + """ + Placeholder when optional `openenv` package is unavailable. + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + raise ModuleNotFoundError( + "GovWorkflowOpenEnvClient requires the optional 'openenv' package. " + "Install it to use websocket OpenEnv client features." + ) diff --git a/docs/FRONTEND_WORKFLOW.md b/docs/FRONTEND_WORKFLOW.md new file mode 100644 index 0000000000000000000000000000000000000000..784ed3f99a581a98baf651ae57b1c507cff87b53 --- /dev/null +++ b/docs/FRONTEND_WORKFLOW.md @@ -0,0 +1,48 @@ +# Frontend Workflow + +The frontend is React-based, backend-driven, and served directly by FastAPI. + +## Access + +- UI: `/ui` +- Assets: `/ui/assets/*` +- API namespace: `/api/*` + +## What Is Visible in UI + +1. OpenEnv API execution (`reset` / `step` / `state` / `grade`) +2. Heuristic baseline agent runs (`/api/autostep`, `/api/benchmark`) +3. Trained RL model execution (Phase 2/3 checkpoints via `/api/rl/run`) +4. Trained RL evaluation across tasks (`/api/rl/evaluate`) +5. Script-level workflow visibility for: + - `baseline_openai.py` + - `inference.py` + +## Frontend API Surface + +- Core: + - `GET /api/health` + - `GET /api/tasks` + - `GET /api/agents` + - `POST /api/reset` + - `POST /api/step` + - `POST /api/state` + - `POST /api/grade` + - `GET /api/sessions` + - `DELETE /api/sessions/{session_id}` +- Baseline execution: + - `POST /api/autostep` + - `POST /api/benchmark` +- Workflow visibility: + - `GET /api/workflows/components` + - `POST /api/workflows/run` +- RL visibility/execution: + - `GET /api/rl/models` + - `POST /api/rl/run` + - `POST /api/rl/evaluate` + +## Deployment Notes + +- No Node.js build is required for serving the current frontend. +- Backend startup remains `app.main:app`. +- Frontend does not call external LLM providers directly. diff --git a/docs/PHASE2_IMPLEMENTATION.md b/docs/PHASE2_IMPLEMENTATION.md new file mode 100644 index 0000000000000000000000000000000000000000..8fd57fabdaf08dbd08b08d423f2867c1ac9ce348 --- /dev/null +++ b/docs/PHASE2_IMPLEMENTATION.md @@ -0,0 +1,41 @@ +# Phase 2 Implementation Notes + +Phase 2 goal: Curriculum PPO across easy, medium, and hard tasks with deterministic evaluation discipline. + +## Implemented Components + +- `rl/curriculum.py` + - `CurriculumScheduler` with staged task sampling: + - Stage 1 (0%-30%): easy only + - Stage 2 (30%-70%): easy + medium + - Stage 3 (70%-100%): all 3 tasks with configurable weights +- `rl/configs/curriculum.yaml` + - curriculum fractions and weights + - PPO hyperparameters for Phase 2 +- `rl/train_ppo.py` + - `--phase 2` training path wired to curriculum scheduler + - default config path uses `rl/configs/curriculum.yaml` + - backward compatibility fallback to `rl/configs/ppo_curriculum.yaml` + - explicit CLI args: `--phase1-config`, `--phase2-config` +- `tests/test_curriculum.py` + - stage transitions + - stage-1 easy-only enforcement + - stage-3 all-task sampling + - deterministic task seed invariants + +## Operational Notes + +- Existing 28-action design is preserved. +- Existing task IDs and grader logic are unchanged. +- No files were deleted as part of structure cleanup. + +## Commands (using existing .venv313) + +- Train Phase 1: + - `.\\.venv313\\Scripts\\python.exe -m rl.train_ppo --phase 1 --timesteps 200000 --n-envs 4 --seed 42` +- Train Phase 2: + - `.\\.venv313\\Scripts\\python.exe -m rl.train_ppo --phase 2 --timesteps 500000 --n-envs 4 --seed 42 --phase2-config rl/configs/curriculum.yaml` +- Train Phase 2 (tuned continuation): + - `.\\.venv313\\Scripts\\python.exe -m rl.train_ppo --phase 2 --timesteps 300000 --n-envs 4 --seed 42 --phase2-config rl/configs/curriculum_tuned.yaml` +- Evaluate trained model: + - `.\\.venv313\\Scripts\\python.exe -m rl.evaluate --model results/best_model/phase2_final.zip --episodes 3` diff --git a/docs/PHASE3_IMPLEMENTATION.md b/docs/PHASE3_IMPLEMENTATION.md new file mode 100644 index 0000000000000000000000000000000000000000..0372056378bbab61554e17171f9549e8c9d5f16a --- /dev/null +++ b/docs/PHASE3_IMPLEMENTATION.md @@ -0,0 +1,39 @@ +# Phase 3 Implementation Notes + +Phase 3 goal: Recurrent PPO (LSTM policy) to capture temporal dependencies such as SLA trend and escalation history. + +## Implemented Components + +- `rl/train_recurrent.py` + - RecurrentPPO training with `MlpLstmPolicy` + - LSTM hidden size configurable (default 128) + - curriculum sampling retained (easy -> medium -> hard) + - optional transfer of compatible policy tensors from best Phase 2 checkpoint +- `rl/configs/recurrent.yaml` + - declarative recurrent training and curriculum settings +- `rl/evaluate.py` + - model loading modes: `auto`, `maskable`, `recurrent` + - recurrent inference path with LSTM state handling + action-mask sanitization + - helper `compare_recurrent_vs_flat(...)` +- `rl/callbacks.py` + - `RecurrentEvalCallback` for periodic grader-based checkpointing in Phase 3 + - recurrent best checkpoints saved as `best_grader_recurrent_.zip` (no collision with Phase 2 files) +- `rl/gym_wrapper.py` + - optional `hard_action_mask` mode (default off) for safe action execution +- `tests/test_rl_evaluate.py` + - recurrent hidden-state persistence + - LSTM reset behavior on episode boundary + - recurrent >= flat comparison utility check + +## Commands (using existing .venv313) + +- Train Phase 3: + - `.\\.venv313\\Scripts\\python.exe -m rl.train_recurrent --timesteps 600000 --n-envs 4 --seed 42 --config rl/configs/recurrent.yaml` +- Train Phase 3-v2 (recommended tuning run): + - `.\\.venv313\\Scripts\\python.exe -m rl.train_recurrent --timesteps 700000 --n-envs 4 --seed 42 --config rl/configs/recurrent_v2.yaml` +- Evaluate Phase 3 model: + - `.\\.venv313\\Scripts\\python.exe -m rl.evaluate --model results/best_model/phase3_final.zip --episodes 3 --model-type recurrent` +- Evaluate best recurrent checkpoint (saved during Phase 3 eval): + - `.\\.venv313\\Scripts\\python.exe -m rl.evaluate --model results/best_model/best_grader_recurrent_mixed_urgency_medium.zip --episodes 3 --model-type recurrent` +- Compare recurrent vs flat on medium task: + - `.\\.venv313\\Scripts\\python.exe -c "from rl.evaluate import compare_recurrent_vs_flat; print(compare_recurrent_vs_flat('results/best_model/phase2_final.zip','results/best_model/phase3_final.zip'))"` diff --git a/docs/PROJECT_STRUCTURE.md b/docs/PROJECT_STRUCTURE.md new file mode 100644 index 0000000000000000000000000000000000000000..5f2d7e3b34b100ee71a8472fbec3c40360a4cb67 --- /dev/null +++ b/docs/PROJECT_STRUCTURE.md @@ -0,0 +1,41 @@ +# Project Structure (Judge-Friendly) + +This repository keeps runtime-critical files in their original paths for deployment safety. +No existing files were deleted. + +## Top-Level Layout + +- `app/` - core environment logic and FastAPI server +- `app/web/` - deployed React frontend assets served by backend at `/ui` +- `frontend/` - frontend ownership docs and reserved source folder for future split components +- `rl/` - reinforcement-learning wrappers, training, evaluation, configs +- `tests/` - deterministic unit/integration test suites +- `scripts/` - operational scripts (local run, validation, benchmark ladder) +- `docs/` - judge-facing documentation and phase notes +- `openenv.yaml` - OpenEnv manifest +- `inference.py` - OpenEnv inference entrypoint +- `baseline_openai.py` - CLI baseline workflow +- `Dockerfile` - deployment image + +## Deployment-Critical Paths + +- API app import path: `app.main:app` +- Frontend route: `/ui` (served from `app/web/index.html`) +- RL training entrypoint: `python -m rl.train_ppo` +- RL evaluation entrypoint: `python -m rl.evaluate` +- OpenEnv config: `openenv.yaml` + +## Phase Mapping + +- Phase 1: `rl/feature_builder.py`, `rl/action_mask.py`, `rl/gym_wrapper.py`, `rl/train_ppo.py` +- Phase 2: `rl/curriculum.py`, `rl/configs/curriculum.yaml`, `tests/test_curriculum.py` +- Phase 3: `rl/train_recurrent.py`, `rl/configs/recurrent.yaml`, `tests/test_rl_evaluate.py` +- Phase 3+: reserved in existing `rl/` module structure + +## Judge Quick Navigation + +1. Environment behavior: `app/env.py`, `app/reward.py`, `app/graders.py` +2. OpenEnv compliance + inference: `openenv.yaml`, `inference.py` +3. Frontend behavior: `app/web/react_app.js`, `docs/FRONTEND_WORKFLOW.md` +4. RL implementation: `rl/` +5. Validation: `tests/`, `scripts/validate_env.py`, `scripts/validate-submission.sh` \ No newline at end of file diff --git a/frontend/react/.gitignore b/frontend/react/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b9470778764f72c5257a3361590d2994547f90e1 --- /dev/null +++ b/frontend/react/.gitignore @@ -0,0 +1,2 @@ +node_modules/ +dist/ diff --git a/frontend/react/README.md b/frontend/react/README.md new file mode 100644 index 0000000000000000000000000000000000000000..afa52ef045c8a87c37b5298911f02a2d84f1341d --- /dev/null +++ b/frontend/react/README.md @@ -0,0 +1,24 @@ +# react/ + +Vite + React frontend for the Gov Workflow OpenEnv console. + +Commands: + +- `npm install` +- `npm run dev` (local dev on `http://localhost:5173`, proxies `/api` to `http://localhost:7860`) +- `npm run build` (production build for Docker/HF) +- `npm run preview` + +If you see `ERR_CONNECTION_REFUSED` on `/api/*`: + +- Start backend first on port `7860` +- Or set a custom dev proxy target: + - PowerShell: `$env:VITE_DEV_API_TARGET='http://127.0.0.1:7860'` + - Then run `npm run dev` + +Modules: + +- `Overview`: project and environment summary +- `Simulation Lab`: dynamic real-world workflow simulation (baseline / inference-like / trained RL) +- `Training Studio`: launch and monitor background RL training jobs +- `Model Comparison`: baseline vs trained model score comparison on the same task diff --git a/frontend/react/index.html b/frontend/react/index.html new file mode 100644 index 0000000000000000000000000000000000000000..eb0b1d95c443f5910e1dbb1f5bd4bf8970f9203c --- /dev/null +++ b/frontend/react/index.html @@ -0,0 +1,16 @@ + + + + + + Gov Workflow OpenEnv Console + + + + +
+
Loading frontend...
+
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"^1.70.0", + "stylus": ">=0.54.8", + "sugarss": "^5.0.0", + "terser": "^5.16.0", + "tsx": "^4.8.1", + "yaml": "^2.4.2" + }, + "peerDependenciesMeta": { + "@types/node": { + "optional": true + }, + "@vitejs/devtools": { + "optional": true + }, + "esbuild": { + "optional": true + }, + "jiti": { + "optional": true + }, + "less": { + "optional": true + }, + "sass": { + "optional": true + }, + "sass-embedded": { + "optional": true + }, + "stylus": { + "optional": true + }, + "sugarss": { + "optional": true + }, + "terser": { + "optional": true + }, + "tsx": { + "optional": true + }, + "yaml": { + "optional": true + } + } + } + } +} diff --git a/frontend/react/package.json b/frontend/react/package.json new file mode 100644 index 0000000000000000000000000000000000000000..321b7144b9a37b6c3881be429c72b5ff30801bc4 --- /dev/null +++ b/frontend/react/package.json @@ -0,0 +1,22 @@ +{ + "name": "openenv-rl-frontend", + "version": "0.1.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite --configLoader native || vite", + "build": "vite build --configLoader native || vite build", + "preview": "vite preview --configLoader native --host 0.0.0.0 --port 4173 || vite preview --host 0.0.0.0 --port 4173" + }, + "dependencies": { + "react": "^18.3.1", + "react-dom": "^18.3.1" + }, + "devDependencies": { + "@vitejs/plugin-react": "^6.0.1", + "autoprefixer": "^10.5.0", + "postcss": "^8.5.10", + "tailwindcss": "^3.4.19", + "vite": "^8.0.7" + } +} diff --git a/frontend/react/postcss.config.js b/frontend/react/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..2e7af2b7f1a6f391da1631d93968a9d487ba977d --- /dev/null +++ b/frontend/react/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/react/src/App.jsx b/frontend/react/src/App.jsx new file mode 100644 index 0000000000000000000000000000000000000000..47f1f029bbfcc576316bd32c9613af58779afce2 --- /dev/null +++ b/frontend/react/src/App.jsx @@ -0,0 +1,21 @@ +import { useState, useEffect } from "react"; +import { api } from "./api/client"; +import { Dashboard } from "./components/story-ui/Dashboard"; + +export default function App() { + const [tasks, setTasks] = useState([]); + + useEffect(() => { + const boot = async () => { + try { + const taskRes = await api("/tasks"); + setTasks(taskRes.tasks || []); + } catch (err) { + console.error("Failed to load tasks", err); + } + }; + boot(); + }, []); + + return ; +} diff --git a/frontend/react/src/api/client.js b/frontend/react/src/api/client.js new file mode 100644 index 0000000000000000000000000000000000000000..f4fe8b97cfb9c23553d89e486f11a27e0b9d3b27 --- /dev/null +++ b/frontend/react/src/api/client.js @@ -0,0 +1,131 @@ +const DEFAULT_LOCAL_API = "http://127.0.0.1:7860"; +const LOCAL_PORTS = ["7860"]; +const LOCAL_HOSTS = ["127.0.0.1", "localhost"]; + +function candidates(path) { + const urls = []; + const rootOnlyPaths = path === "/rl/models"; + const compatNoApiPaths = + path.startsWith("/simulation/") || + path.startsWith("/training/") || + path.startsWith("/rl/") || + path.startsWith("/openenv/") || + path.startsWith("/benchmark") || + path.startsWith("/history/"); + + let isLocalDev5173 = false; + if (typeof window !== "undefined") { + const host = window.location.hostname; + const isLocal = host === "localhost" || host === "127.0.0.1"; + isLocalDev5173 = isLocal && window.location.port === "5173"; + } + + // Training story endpoints are mounted at /training/* (not /api/training/*). + // Avoid known-bad prefixes first to prevent noisy 404 logs in browser console. + if (path.startsWith("/training/")) { + if (isLocalDev5173) { + for (const port of LOCAL_PORTS) { + for (const lh of LOCAL_HOSTS) { + urls.push(`http://${lh}:${port}${path}`); + } + } + } else { + urls.push(path); + } + return [...new Set(urls)]; + } + + if (isLocalDev5173) { + // For local dev, prefer direct backend URLs first to avoid noisy Vite proxy + // connection-refused spam when backend is temporarily down. + for (const port of LOCAL_PORTS) { + for (const lh of LOCAL_HOSTS) { + if (rootOnlyPaths) { + urls.push(`http://${lh}:${port}${path}`); + } else { + urls.push(`http://${lh}:${port}/api${path}`); + urls.push(`http://${lh}:${port}/api/v1${path}`); + if (compatNoApiPaths) { + urls.push(`http://${lh}:${port}${path}`); + } + } + } + } + } + + if (rootOnlyPaths) { + urls.push(path); + } else { + urls.push(`/api${path}`, `/api/v1${path}`); + if (compatNoApiPaths) { + urls.push(path); + } + } + + if (isLocalDev5173 && !rootOnlyPaths) { + for (const port of LOCAL_PORTS) { + for (const lh of LOCAL_HOSTS) { + // keep original ordering as fallback candidates + urls.push(`http://${lh}:${port}/api${path}`); + urls.push(`http://${lh}:${port}/api/v1${path}`); + } + } + } + + return [...new Set(urls)]; +} + +export async function api(path, options = {}) { + const method = String(options.method || "GET").toUpperCase(); + const headers = { ...(options.headers || {}) }; + if (method !== "GET" && method !== "HEAD" && !("Content-Type" in headers)) { + headers["Content-Type"] = "application/json"; + } + const requestOptions = { + ...options, + method, + headers, + }; + if (method === "GET" || method === "HEAD") { + delete requestOptions.body; + } + + const errors = []; + for (const url of candidates(path)) { + try { + const res = await fetch(url, requestOptions); + let payload = null; + try { + payload = await res.json(); + } catch (err) { + payload = null; + } + if (!res.ok) { + const detail = payload?.detail || `${res.status}`; + throw new Error(`API ${path} failed on ${url}: ${detail}`); + } + return payload; + } catch (err) { + errors.push(err); + } + } + + const firstApiError = errors.find( + (e) => e instanceof Error && e.message.startsWith(`API ${path} failed`) + ); + if (firstApiError) { + throw firstApiError; + } + const lastError = errors.length ? errors[errors.length - 1] : new Error("Unknown request failure."); + + throw new Error( + `API ${path} connection failed. Start backend on ${DEFAULT_LOCAL_API}. Last error: ${ + lastError instanceof Error ? lastError.message : String(lastError) + }` + ); +} + +export function fmt(value, digits = 2) { + if (value == null || Number.isNaN(Number(value))) return "-"; + return Number(value).toFixed(digits); +} diff --git a/frontend/react/src/components/Charts.jsx b/frontend/react/src/components/Charts.jsx new file mode 100644 index 0000000000000000000000000000000000000000..b090d5d2e15651adc9c6331f0f6e7eca0ce9618d --- /dev/null +++ b/frontend/react/src/components/Charts.jsx @@ -0,0 +1,142 @@ +import { useEffect, useRef } from "react"; + +function drawGridAndAxes(ctx, w, h, pad, yMin, yMax) { + const chartW = w - pad * 2; + const chartH = h - pad * 2; + ctx.clearRect(0, 0, w, h); + + // chart area background + const bg = ctx.createLinearGradient(0, 0, 0, h); + bg.addColorStop(0, "#060b12"); + bg.addColorStop(1, "#03070d"); + ctx.fillStyle = bg; + ctx.fillRect(0, 0, w, h); + + ctx.strokeStyle = "#13202f"; + ctx.lineWidth = 1; + const gridRows = 5; + for (let i = 0; i <= gridRows; i += 1) { + const y = pad + (chartH * i) / gridRows; + ctx.beginPath(); + ctx.moveTo(pad, y); + ctx.lineTo(w - pad, y); + ctx.stroke(); + } + const gridCols = 8; + for (let i = 0; i <= gridCols; i += 1) { + const x = pad + (chartW * i) / gridCols; + ctx.beginPath(); + ctx.moveTo(x, pad); + ctx.lineTo(x, h - pad); + ctx.stroke(); + } + + ctx.strokeStyle = "#2a3e54"; + ctx.beginPath(); + ctx.moveTo(pad, pad); + ctx.lineTo(pad, h - pad); + ctx.lineTo(w - pad, h - pad); + ctx.stroke(); + + const zeroInRange = yMin <= 0 && yMax >= 0; + if (zeroInRange) { + const yRange = Math.max(1e-9, yMax - yMin); + const y0 = pad + ((yMax - 0) / yRange) * chartH; + ctx.strokeStyle = "#2d5f84"; + ctx.setLineDash([4, 4]); + ctx.beginPath(); + ctx.moveTo(pad, y0); + ctx.lineTo(w - pad, y0); + ctx.stroke(); + ctx.setLineDash([]); + } +} + +export function LineChart({ seriesA, seriesB, labelA = "A", labelB = "B" }) { + const ref = useRef(null); + + useEffect(() => { + const canvas = ref.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + const w = canvas.width; + const h = canvas.height; + const pad = 40; + + const all = [...seriesA, ...seriesB]; + if (!all.length) return; + const yMaxRaw = Math.max(...all); + const yMinRaw = Math.min(...all); + const margin = Math.max(1, (yMaxRaw - yMinRaw) * 0.12); + const yMax = yMaxRaw + margin; + const yMin = yMinRaw - margin; + const yRange = Math.max(1e-9, yMax - yMin); + const chartW = w - pad * 2; + const chartH = h - pad * 2; + + drawGridAndAxes(ctx, w, h, pad, yMin, yMax); + + const yPx = (value) => pad + ((yMax - value) / yRange) * chartH; + + const draw = (arr, color, glowColor) => { + if (!arr.length) return; + ctx.shadowBlur = 8; + ctx.shadowColor = glowColor; + ctx.strokeStyle = color; + ctx.lineWidth = 2.25; + const stepX = chartW / Math.max(arr.length - 1, 1); + ctx.beginPath(); + arr.forEach((v, i) => { + const x = pad + i * stepX; + const y = yPx(Number(v || 0)); + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + }); + ctx.stroke(); + ctx.shadowBlur = 0; + + // point markers + ctx.fillStyle = color; + arr.forEach((v, i) => { + const x = pad + i * stepX; + const y = yPx(Number(v || 0)); + ctx.beginPath(); + ctx.arc(x, y, 2.2, 0, Math.PI * 2); + ctx.fill(); + }); + }; + + draw(seriesA, "#4fd6ff", "rgba(79, 214, 255, 0.7)"); + draw(seriesB, "#ff8b1a", "rgba(255, 139, 26, 0.6)"); + + ctx.fillStyle = "#9ec3dd"; + ctx.font = "12px Segoe UI"; + ctx.fillText(`${labelA} (cyan)`, pad, 18); + ctx.fillStyle = "#ffbb80"; + ctx.fillText(`${labelB} (orange)`, pad + 170, 18); + + ctx.fillStyle = "#6f90aa"; + ctx.fillText(`max ${yMaxRaw.toFixed(2)}`, 6, pad + 2); + ctx.fillText(`min ${yMinRaw.toFixed(2)}`, 6, h - pad + 2); + ctx.fillText("steps", w - 44, h - 10); + }, [seriesA, seriesB, labelA, labelB]); + + return ; +} + +export function CompareBars({ rows }) { + const safeRows = Array.isArray(rows) ? rows : []; + return ( +
+ {safeRows.map((row) => ( +
+
{row.label}
+
+
+
+
{row.value.toFixed(3)}
+
+ ))} +
+ ); +} diff --git a/frontend/react/src/components/Layout.jsx b/frontend/react/src/components/Layout.jsx new file mode 100644 index 0000000000000000000000000000000000000000..151c02ab79c094b684d225a547eb78f4c18a2414 --- /dev/null +++ b/frontend/react/src/components/Layout.jsx @@ -0,0 +1,33 @@ +const NAV_ITEMS = [ + { id: "overview", title: "Overview" }, + { id: "simulation", title: "Simulation Lab" }, + { id: "training", title: "Training Studio" }, + { id: "comparison", title: "Model Comparison" }, +]; + +export function Layout({ active, onChange, status, children }) { + return ( +
+ +
+
{status}
+ {children} +
+
+ ); +} + diff --git a/frontend/react/src/components/story-ui/Dashboard.jsx b/frontend/react/src/components/story-ui/Dashboard.jsx new file mode 100644 index 0000000000000000000000000000000000000000..f2408fba85a1e0b9b2f35aac0d1be839d64bf97b --- /dev/null +++ b/frontend/react/src/components/story-ui/Dashboard.jsx @@ -0,0 +1,1589 @@ +import React, { useState, useEffect } from "react"; +import { api, fmt } from "../../api/client"; +import { useStorySimulation } from "../../hooks/useStorySimulation"; +import { TrainingTabV2 } from "./TrainingTabV2"; + +// --- Timeline Tab ------------------------------------------------------------- +const PHASE_LABELS = { + early: { label: "Early Phase", color: "indigo", icon: "flag", desc: "Agent explores the environment and initial decisions are made." }, + middle: { label: "Mid-Phase", color: "amber", icon: "timeline", desc: "Policy adapts as patterns emerge in the backlog." }, + late: { label: "Final Phase", color: "violet", icon: "sports_score", desc: "Agent converges toward optimal resolution strategy." }, +}; + +function TimelineTab({ tasks }) { + const { + taskId, setTaskId, maxSteps, setMaxSteps, + agentMode, + policyName, setPolicyName, + modelPath, setModelPath, + modelType, setModelType, + availablePolicies, + availableModels, + configError, + running, starting, currentStep, + kpis, timeline, resources, journeyStats, + startSimulation, stopSimulation, + } = useStorySimulation({ defaultTask: tasks[0] || "district_backlog_easy" }); + + const isIdle = !starting && !running; + const startBlocked = agentMode === "trained_rl" && !modelPath; + const progressPct = maxSteps > 0 ? Math.min(100, Math.round((currentStep / maxSteps) * 100)) : 0; + const fmt2 = (n) => new Intl.NumberFormat().format(n ?? 0); + const fmtDelta = (n) => { const v = Number(n ?? 0); return v > 0 ? `+${v.toFixed(1)}` : v.toFixed(1); }; + + // Local string buffer so the user can freely type without the field snapping back + const [stepsInput, setStepsInput] = useState(String(maxSteps)); + // Keep buffer in sync if maxSteps changes from outside + React.useEffect(() => { setStepsInput(String(maxSteps)); }, [maxSteps]); + + // Build phase-annotated timeline: insert phase dividers between phase changes + const annotatedTimeline = []; + let lastPhase = null; + let phaseStats = { drop: 0, keys: 0 }; + + for (let i = 0; i < timeline.length; i++) { + const ev = timeline[i]; + const ph = ev.phase; + + if (ph && ph !== lastPhase) { + if (lastPhase && PHASE_LABELS[lastPhase]) { + // We reached the end of the previous (newer) phase in the chronological timeline, + // so insert its summary before starting the older phase. + annotatedTimeline.push({ + _summary: true, + phase: lastPhase, + stats: { ...phaseStats }, + key: `sum-${lastPhase}-${i}`, + }); + } + if (PHASE_LABELS[ph]) { + annotatedTimeline.push({ _divider: true, phase: ph, key: `div-${ph}-${i}` }); + } + lastPhase = ph; + phaseStats = { drop: 0, keys: 0 }; + } + + if (ev.key) phaseStats.keys += 1; + if (ev.backlogDelta) phaseStats.drop += ev.backlogDelta; + + annotatedTimeline.push(ev); + } + + // Handle the very last (oldest) phase summary at the bottom of the list + if (lastPhase && PHASE_LABELS[lastPhase] && timeline.length > 0) { + annotatedTimeline.push({ + _summary: true, + phase: lastPhase, + stats: { ...phaseStats }, + key: `sum-${lastPhase}-end`, + }); + } + + return ( +
+ {/* --- Controls bar --- */} +
+
+
+ Scenario + +
+
+ Steps + setStepsInput(e.target.value)} + onBlur={() => { + const v = parseInt(stepsInput, 10); + const clamped = isNaN(v) ? 40 : Math.min(100, Math.max(10, v)); + setMaxSteps(clamped); + setStepsInput(String(clamped)); + }} + onKeyDown={(e) => { + if (e.key === "Enter") e.currentTarget.blur(); + }} + className="w-20 bg-slate-800 border border-white/10 text-sm font-medium px-3 py-1.5 rounded-lg text-indigo-300 focus:outline-none focus:border-indigo-500 text-center" + /> +
+ {agentMode === "baseline_policy" && ( +
+ Policy + +
+ )} + {agentMode === "trained_rl" && ( + <> +
+ Model + +
+
+ Type + +
+ + )} +
+ +
+ {configError && ( +
+ {configError} +
+ )} + {startBlocked && !configError && ( +
+ Select an available RL model checkpoint before starting `trained_rl` mode. +
+ )} + + {/* --- Progress bar (only visible while running) --- */} + {(running || currentStep > 0) && ( +
+
+ + {running ? "Simulation In Progress" : journeyStats ? "Episode Complete" : "Stopped"} + + + Step {currentStep} / {maxSteps} - {progressPct}% + +
+
+
+
+ {running && ( +
+
+
+
+ Agent is making decisions... +
+ )} +
+ )} + + {/* --- Journey Summary (Before -> After) - appears after episode completes --- */} + {journeyStats && ( +
+
+ auto_graph +

Journey Summary - Start to End Transformation

+
+
+ {[ + { + label: "Backlog Change", + before: journeyStats.initialBacklog, + after: journeyStats.finalBacklog, + suffix: " cases", + goodWhenDown: true, + }, + { + label: "SLA Breaches", + before: journeyStats.initialSla, + after: journeyStats.finalSla, + suffix: "", + goodWhenDown: true, + }, + { + label: "Steps Taken", + before: null, + after: journeyStats.totalSteps, + suffix: "", + goodWhenDown: false, + singleValue: true, + }, + { + label: "Final Score", + before: journeyStats.finalScore != null ? "No Agent (0.0%)" : "N/A", + after: journeyStats.finalScore != null ? `${(journeyStats.finalScore * 100).toFixed(1)}%` : "N/A", + suffix: "", + goodWhenDown: false, + isScore: true, + isBaselineCmp: true, + }, + ].map((stat) => { + const delta = stat.singleValue ? null : stat.isBaselineCmp ? (journeyStats.finalScore * 100) : stat.after - stat.before; + const trend = + delta === null + ? "none" + : delta === 0 + ? "stable" + : stat.goodWhenDown + ? (delta < 0 ? "improving" : "worsening") + : (delta > 0 ? "improving" : "worsening"); + const direction = + delta === null || delta === 0 + ? "stable" + : stat.goodWhenDown + ? (delta < 0 ? "down" : "up") + : (delta > 0 ? "up" : "down"); + const directionIcon = + direction === "up" + ? "north" + : direction === "down" + ? "south" + : "horizontal_rule"; + const trendClass = + trend === "improving" + ? "text-emerald-400" + : trend === "worsening" + ? "text-rose-400" + : "text-slate-300"; + return ( +
+
{stat.label}
+ {stat.singleValue ? ( +
{stat.after}{stat.suffix}
+ ) : ( +
+ + {stat.isBaselineCmp ? "Baseline" : stat.before}{stat.suffix} + + arrow_forward + + {stat.after}{stat.suffix} + +
+ )} + {delta !== null && ( +
+ {directionIcon} + {Number(Math.abs(delta).toFixed(2))} {trend === "stable" ? "no change" : trend} +
+ )} + {stat.label === "Backlog Change" && journeyStats.backlogImprovement !== 0 && ( +
+ {journeyStats.backlogImprovement > 0 ? `${journeyStats.backlogImprovement}% cleared` : `${Math.abs(journeyStats.backlogImprovement)}% grew`} +
+ )} +
+ ); + })} +
+
+ )} + + {/* --- KPI Row --- */} +
+ {[ + { label: "Total Backlog", value: fmt2(kpis.backlog), delta: kpis.backlogDelta, accent: "rose", icon: "inbox" }, + { label: "SLA Breaches", value: fmt2(kpis.slaBreaches), delta: kpis.slaDelta, accent: "amber", icon: "timer_off" }, + { label: "Fairness Gap", value: `${(Number(kpis.fairness) * 100).toFixed(1)}%`, delta: kpis.fairnessDelta, accent: "emerald", icon: "balance" }, + ].map((kpi) => { + const delta = Number(kpi.delta ?? 0); + const trend = delta < 0 ? "down" : delta > 0 ? "up" : "stable"; + const trendIcon = trend === "up" ? "north" : trend === "down" ? "south" : "horizontal_rule"; + const badgeClass = + trend === "down" + ? "bg-emerald-500/20 text-emerald-400" + : trend === "up" + ? "bg-rose-500/20 text-rose-400" + : "bg-slate-500/20 text-slate-300"; + return ( +
+
+
+
+ {kpi.icon} + {kpi.label} +
+ + {trendIcon} + {fmtDelta(delta)} + +
+
{kpi.value}
+
+ {trend === "down" ? "Trend improving" : trend === "stable" ? "Stable" : "Trend worsening"} +
+
+ ); + })} +
+ + {/* --- Story Timeline + Queue Monitors --- */} +
+ {/* Story Timeline */} +
+

+ auto_stories Story Timeline + {timeline.length > 1 && ( + {timeline.filter(e => e.key).length} key moments + )} +

+ + {timeline.length === 0 ? ( +
+ play_circle +

+ Select a scenario, set the number of steps, and press{" "} + Start Auto-Resolution to begin. +

+
+ ) : ( +
+ {annotatedTimeline.map((ev, idx) => { + // Phase divider + if (ev._divider) { + const ph = PHASE_LABELS[ev.phase]; + return ( +
+
+ {ph.icon} +
+
+ {ph.label} + - {ph.desc} +
+
+ ); + } + + // Phase summary block + if (ev._summary) { + const drop = Math.abs(ev.stats.drop || 0); + const isDrop = (ev.stats.drop || 0) < 0; + return ( +
+
+
+ Phase Backlog Move + 0 ? "text-rose-400" : "text-slate-300"}`}> + {isDrop ? "down " : ev.stats.drop > 0 ? "up " : ""}{drop} cases + +
+
+ Key Decisions + {ev.stats.keys} +
+
+
+ ); + } + + const color = ev.type === "error" ? "rose" : ev.type === "warning" ? "amber" : ev.type === "success" ? "emerald" : "indigo"; + return ( +
+
+ {ev.icon} +
+
+
+
+
+ {ev.time} + {ev.outcomeLabel && ( + + {ev.outcomeLabel} + + )} + {ev.key && ( + + KEY MOMENT + + )} + {ev._count > 1 && ( + + x{ev._count} + + )} +
+

+ {ev.title} + {ev.isHugeImpact && High Impact} + {ev.isHighReward && Hot} +

+

{ev.desc}

+ {ev.reason && ( +
+ Agent Reasoning: {ev.reason} +
+ )} +
+ {ev.impact !== 0 && ( +
+ {Number(ev.impact) >= 0 ? "+" : ""}{Number(ev.impact).toFixed(2)} +
+ )} +
+
+
+ ); + })} +
+ )} +
+ + {/* Live Queue Monitors */} +
+

+ monitor_heart Live Queue Monitors +

+ {resources.length === 0 ? ( +
+ sensors +

Awaiting live telemetry...

+
+ ) : ( +
+ {resources.map((res, i) => { + const color = res.percentage > 85 ? "rose" : res.percentage > 60 ? "amber" : "emerald"; + const tone = color === "rose" + ? { + text: "text-rose-400", + bar: "bg-rose-500", + } + : color === "amber" + ? { + text: "text-amber-400", + bar: "bg-amber-500", + } + : { + text: "text-emerald-400", + bar: "bg-emerald-500", + }; + return ( +
+
+ {res.name} +
+ {res.activeCases} active + {res.percentage > 85 && ( + OVERLOADED + )} +
+
+
+
+
+
+ ); + })} +
+ )} + + {/* Reward cumulative tracker - shown after first step */} + {currentStep > 0 && ( +
+
Impact Summary
+
+
+
Steps Elapsed
+
{currentStep}
+
+
+
Key Moments
+
+ {timeline.filter((e) => e.key).length} +
+
+
+
+ )} +
+
+
+ ); +} + + +// --- Resources Tab ------------------------------------------------------------ +function BenchmarkResults({ results }) { + const COLORS = { backlog_clearance: "#6366f1", urgent_first: "#10b981", oldest_first: "#f59e0b" }; + const sorted = [...results.agent_results].sort((a, b) => b.average_score - a.average_score); + const winner = sorted[0]; + const maxScore = Math.max(...results.agent_results.map((a) => a.average_score), 0.001); + const chartH = 140; + + return ( +
+ {/* Winner callout */} +
+
+ emoji_events +
+
BEST PERFORMING POLICY
+
{winner.agent_policy.replace(/_/g, " ")}
+
+ Avg score{" "}{(winner.average_score * 100).toFixed(1)}% + {" | "}Range {(winner.min_score * 100).toFixed(0)}%-{(winner.max_score * 100).toFixed(0)}% +
+
+
+
+
+ psychology Agent Intelligence +
+

+ This policy performed best by maintaining fewer SLA breaches relative to its peers while securing steady backlog reduction across critical queues. +

+
+
+ + {/* Bar chart */} +
+

Average Grader Score by Policy

+
+ {sorted.map((agent) => { + const pct = agent.average_score / maxScore; + const barH = Math.max(Math.round(pct * chartH), 6); + const color = COLORS[agent.agent_policy] || "#6366f1"; + const isWinner = agent.agent_policy === winner.agent_policy; + return ( +
+
{(agent.average_score * 100).toFixed(1)}%
+
+ {isWinner &&
Top
} +
+
+
+ {agent.agent_policy.replace(/_/g, " ")} +
+
{agent.runs.length} runs
+
+ ); + })} +
+
+ + {/* Multi-metric comparison bars */} +
+

Metric Comparison

+
+ {[ + { + label: "Score (higher is better)", + vals: results.agent_results.map((a) => ({ key: a.agent_policy, v: a.average_score, display: `${(a.average_score * 100).toFixed(1)}%` })), + higherGood: true, + }, + { + label: "Avg Completed Cases (higher is better)", + vals: results.agent_results.map((a) => { + const avg = a.runs.reduce((s, r) => s + (r.completed ?? 0), 0) / Math.max(a.runs.length, 1); + return { key: a.agent_policy, v: avg, display: avg.toFixed(1) }; + }), + higherGood: true, + }, + { + label: "Avg Remaining Backlog (lower is better)", + vals: results.agent_results.map((a) => { + const avg = a.runs.reduce((s, r) => s + (r.backlog ?? 0), 0) / Math.max(a.runs.length, 1); + return { key: a.agent_policy, v: avg, display: avg.toFixed(1) }; + }), + higherGood: false, + }, + ].map(({ label, vals, higherGood }) => { + const maxVal = Math.max(...vals.map((v) => v.v), 0.001); + const best = higherGood + ? vals.reduce((a, b) => (b.v > a.v ? b : a)) + : vals.reduce((a, b) => (b.v < a.v ? b : a)); + return ( +
+
{label}
+
+ {vals.map((v) => { + const pct = Math.round((v.v / maxVal) * 100); + const color = (COLORS)[v.key] || "#6366f1"; + return ( +
+
+ {v.key.replace(/_/g, " ")} + {v.key === best.key && Top} +
+
+
+
+
{v.display}
+
+ ); + })} +
+
+ ); + })} +
+
+ + {/* Raw episode table */} +
+

All Episodes - Raw Data

+
+ + + + + + + + + + + + + + {results.agent_results.flatMap((agent) => + agent.runs.map((run) => ( + + + + + + + + + + )) + )} + +
PolicyRun #ScoreRewardCompletedBacklogSteps
+ {agent.agent_policy.replace(/_/g, " ")} + #{run.run_index}{(run.score * 100).toFixed(1)}%{run.reward_sum?.toFixed(2) ?? "-"}{run.completed ?? "-"}{run.backlog ?? "-"}{run.steps ?? "-"}
+
+
+
+ ); +} + +function ResourcesTab({ tasks }) { + const [benchTask, setBenchTask] = useState(tasks[0] || "district_backlog_easy"); + const [loading, setLoading] = useState(false); + const [results, setResults] = useState(null); + const [error, setError] = useState(""); + + const runBenchmark = async () => { + setLoading(true); + setError(""); + setResults(null); + try { + const data = await api("/benchmark", { + method: "POST", + body: JSON.stringify({ + task_id: benchTask, + agent_policies: ["backlog_clearance", "urgent_first", "oldest_first"], + runs: 3, + max_steps: 60, + }), + }); + setResults(data); + } catch (e) { + setError(e.message); + } finally { + setLoading(false); + } + }; + + return ( +
+
+

+ leaderboard Policy Benchmark Comparison +

+

+ Run all three baseline policies on the same scenario and compare their grader scores, + completed cases, and remaining backlogs side-by-side with visual charts. +

+
+ + +
+
+ + {error && ( +
+ {error} +
+ )} + + {loading && ( +
+
+

Running 3 policies x 3 episodes each - takes ~20 seconds.

+
+ )} + + {results && } +
+ ); +} + +// --- Library Tab -------------------------------------------------------------- +function LibraryTab({ tasks }) { + const [compliance, setCompliance] = useState(null); + const [workflows, setWorkflows] = useState(null); + const [selected, setSelected] = useState(null); + + useEffect(() => { + api("/openenv_compliance").then(setCompliance).catch(() => {}); + api("/workflows/components").then(setWorkflows).catch(() => {}); + }, []); + + const taskDetails = { + district_backlog_easy: { diff: "Easy", desc: "Single-service district queue focused on income certificate flow.", services: 1 }, + mixed_urgency_medium: { diff: "Medium", desc: "Income, land, passport, driving license, and Aadhaar workloads with mixed urgency.", services: 5 }, + cross_department_hard: { diff: "Hard", desc: "Five-service crisis mode with high arrivals, fairness pressure, and event shocks.", services: 5 }, + }; + + const systemTabGuide = [ + { + id: "timeline", + title: "Simulation (Timeline Tab)", + icon: "timeline", + summary: "Runs live step-by-step environment simulation and shows queue movement, KPI changes, and decision timeline in real time.", + userFlow: "Choose scenario, steps, and model/policy, then start auto-resolution.", + outputs: "Live backlog, SLA, fairness, key moments, queue pressure bars, and impact summary.", + endpoints: ["/simulation/live/start", "/simulation/live/step", "/simulation/live/{run_id}/stop", "/tasks", "/agents", "/rl_models", "/rl/models"], + }, + { + id: "training", + title: "Training Tab", + icon: "fitness_center", + summary: "Controls RL training jobs and tracks how the policy improves over timesteps.", + userFlow: "Start/stop a training job and monitor live checkpoints and job history.", + outputs: "Active job state, progress, reward/score checkpoints, sequential narrative feed, and OpenEnv contract replay results.", + endpoints: ["/training_jobs", "/training_jobs/list", "/training_jobs/{job_id}", "/training_jobs/{job_id}/stop", "/reset", "/step", "/state", "/grade"], + }, + { + id: "analytics", + title: "Analytics Tab", + icon: "analytics", + summary: "Shows endpoint-fed system analytics from historical simulation, jobs, models, sessions, and compliance health.", + userFlow: "Open the tab; metrics auto-refresh from backend every few seconds.", + outputs: "Task distributions, mode splits, training status mix, endpoint health, model inventory, and run history tables.", + endpoints: ["/history/simulations", "/history/comparisons", "/training_jobs", "/rl_models", "/rl/models", "/tasks", "/agents", "/sessions", "/actions/schema", "/openenv_compliance", "/workflows/components"], + }, + { + id: "resources", + title: "Resources Tab (Benchmark)", + icon: "leaderboard", + summary: "Compares baseline policies on the same task to identify which strategy performs best.", + userFlow: "Select a scenario and run benchmark.", + outputs: "Winner policy card, score bars, metric comparison bars, and raw run-level benchmark table.", + endpoints: ["/compare_agents"], + }, + { + id: "library", + title: "Library Tab", + icon: "menu_book", + summary: "Acts as the complete system overview and reference center for tasks, compliance, and workflow availability.", + userFlow: "Explore scenarios, inspect OpenEnv checks, and verify available workflow components.", + outputs: "Task cards with difficulty/service counts, compliance checklist, and component readiness matrix.", + endpoints: ["/tasks", "/openenv_compliance", "/workflows/components"], + }, + ]; + + return ( +
+
+

+ hub Complete System Overview +

+

+ This section explains how each product tab works, what backend APIs power it, and what outputs users can expect. + Use it as a quick guide for judges and reviewers. +

+
+ {systemTabGuide.map((tab) => ( +
+
+ {tab.icon} +

{tab.title}

+
+

{tab.summary}

+
+ User flow: {tab.userFlow} +
+
+ Outputs: {tab.outputs} +
+
+ {tab.endpoints.map((ep) => ( + + {ep} + + ))} +
+
+ ))} +
+
+ +
+

+ menu_book Scenario Library +

+
+ {tasks.map((t) => { + const info = taskDetails[t] || { diff: "-", desc: "Custom scenario.", services: "-" }; + const diffColor = info.diff === "Easy" ? "emerald" : info.diff === "Medium" ? "amber" : "rose"; + const isSelected = selected === t; + return ( + + ); + })} +
+
+ + {compliance && ( +
+

+ verified OpenEnv Compliance Status +

+
+ {compliance.items?.map((item) => ( +
+ + {item.status === "pass" ? "check_circle" : item.status === "fail" ? "cancel" : "help"} + +
+
{item.label}
+
{item.detail}
+
+
+ ))} +
+
+ )} + + {workflows && ( +
+

+ account_tree Workflow Components +

+
+ {workflows.components?.map((c) => ( +
+ + {c.available ? "check_box" : "check_box_outline_blank"} + +
+
{c.component}
+
{c.description}
+
+ {c.command && ( + {c.command} + )} +
+ ))} +
+
+ )} +
+ ); +} + +// --- Analytics Tab ------------------------------------------------------------ +function AnalyticsTab() { + const [history, setHistory] = useState([]); + const [rlModels, setRlModels] = useState([]); + const [rlModelsV2, setRlModelsV2] = useState([]); + const [trainingJobs, setTrainingJobs] = useState([]); + const [tasksList, setTasksList] = useState([]); + const [agentsList, setAgentsList] = useState([]); + const [sessionsInfo, setSessionsInfo] = useState({ active_sessions: 0, session_ids: [] }); + const [actionsSchema, setActionsSchema] = useState({}); + const [complianceInfo, setComplianceInfo] = useState({ items: [] }); + const [workflowInfo, setWorkflowInfo] = useState({ components: [] }); + const [comparisonsInfo, setComparisonsInfo] = useState({ comparisons: [] }); + const [endpointHealth, setEndpointHealth] = useState([]); + const [loadingHistory, setLoadingHistory] = useState(true); + const [loadingAll, setLoadingAll] = useState(true); + + useEffect(() => { + let cancelled = false; + + const load = async () => { + setLoadingHistory(true); + setLoadingAll(true); + try { + const [ + historyRes, + rlRes, + rlResV2, + jobsRes, + tasksRes, + agentsRes, + sessionsRes, + actionsRes, + complianceRes, + workflowsRes, + comparisonsRes, + ] = await Promise.allSettled([ + api("/history/simulations?limit=80"), + api("/rl_models"), + api("/rl/models"), + api("/training_jobs"), + api("/tasks"), + api("/agents"), + api("/sessions"), + api("/actions/schema"), + api("/openenv_compliance"), + api("/workflows/components"), + api("/history/comparisons?limit=30"), + ]); + + if (cancelled) return; + + const checks = [ + { key: "history", label: "History", ok: historyRes.status === "fulfilled" }, + { key: "rl_models", label: "RL Models", ok: rlRes.status === "fulfilled" }, + { key: "rl_models_v2", label: "RL Models V2", ok: rlResV2.status === "fulfilled" }, + { key: "training_jobs", label: "Training Jobs", ok: jobsRes.status === "fulfilled" }, + { key: "tasks", label: "Tasks", ok: tasksRes.status === "fulfilled" }, + { key: "agents", label: "Agents", ok: agentsRes.status === "fulfilled" }, + { key: "sessions", label: "Sessions", ok: sessionsRes.status === "fulfilled" }, + { key: "actions_schema", label: "Action Schema", ok: actionsRes.status === "fulfilled" }, + { key: "openenv_compliance", label: "Compliance", ok: complianceRes.status === "fulfilled" }, + { key: "workflow_components", label: "Workflow Components", ok: workflowsRes.status === "fulfilled" }, + { key: "comparison_history", label: "Comparison History", ok: comparisonsRes.status === "fulfilled" }, + ]; + setEndpointHealth(checks); + + setHistory(historyRes.status === "fulfilled" ? (historyRes.value?.runs || []) : []); + setRlModels(rlRes.status === "fulfilled" ? (rlRes.value?.models || []) : []); + setRlModelsV2(rlResV2.status === "fulfilled" ? (Array.isArray(rlResV2.value) ? rlResV2.value : []) : []); + setTrainingJobs(jobsRes.status === "fulfilled" ? (jobsRes.value?.jobs || []) : []); + setTasksList(tasksRes.status === "fulfilled" ? (tasksRes.value?.tasks || []) : []); + setAgentsList(agentsRes.status === "fulfilled" ? (Array.isArray(agentsRes.value) ? agentsRes.value : []) : []); + setSessionsInfo(sessionsRes.status === "fulfilled" ? (sessionsRes.value || { active_sessions: 0, session_ids: [] }) : { active_sessions: 0, session_ids: [] }); + setActionsSchema(actionsRes.status === "fulfilled" ? (actionsRes.value || {}) : {}); + setComplianceInfo(complianceRes.status === "fulfilled" ? (complianceRes.value || { items: [] }) : { items: [] }); + setWorkflowInfo(workflowsRes.status === "fulfilled" ? (workflowsRes.value || { components: [] }) : { components: [] }); + setComparisonsInfo(comparisonsRes.status === "fulfilled" ? (comparisonsRes.value || { comparisons: [] }) : { comparisons: [] }); + } finally { + if (!cancelled) { + setLoadingHistory(false); + setLoadingAll(false); + } + } + }; + + load(); + const timer = setInterval(load, 8000); + return () => { + cancelled = true; + clearInterval(timer); + }; + }, []); + + const byTask = history.reduce((acc, run) => { + const t = run.task_id || "unknown"; + if (!acc[t]) acc[t] = []; + acc[t].push(run); + return acc; + }, {}); + + const getRunScore = (run) => { + const value = run?.score ?? run?.payload?.score; + const num = Number(value); + return Number.isFinite(num) ? num : null; + }; + + const getRunReward = (run) => { + const value = run?.total_reward ?? run?.payload?.total_reward; + const num = Number(value); + return Number.isFinite(num) ? num : null; + }; + + const getJobProgress = (job) => { + const p = Number(job?.progress); + if (Number.isFinite(p)) return Math.max(0, Math.min(1, p)); + const ts = Number(job?.latest_metrics?.total_timesteps); + const total = Number(job?.timesteps); + if (Number.isFinite(ts) && Number.isFinite(total) && total > 0) { + return Math.max(0, Math.min(1, ts / total)); + } + return 0; + }; + + const scoreData = history.map(getRunScore).filter((v) => v != null); + const avgScore = scoreData.length ? scoreData.reduce((s, v) => s + v, 0) / scoreData.length : null; + const runningJobs = trainingJobs.filter((j) => String(j?.status || "").toLowerCase() === "running").length; + const endpointCoverage = endpointHealth.length + ? endpointHealth.filter((x) => x.ok).length / endpointHealth.length + : null; + + const timelineTaskRows = Object.entries(byTask) + .map(([label, runs]) => ({ label, value: runs.length })) + .sort((a, b) => b.value - a.value); + + const timelineModeRows = Object.entries( + history.reduce((acc, run) => { + const mode = String(run?.agent_mode || "unknown"); + acc[mode] = (acc[mode] || 0) + 1; + return acc; + }, {}) + ).map(([label, value]) => ({ label, value })); + + const trainingStatusRows = Object.entries( + trainingJobs.reduce((acc, job) => { + const status = String(job?.status || "unknown").toLowerCase(); + acc[status] = (acc[status] || 0) + 1; + return acc; + }, {}) + ).map(([label, value]) => ({ label, value })); + + const trainingPhaseRows = [1, 2].map((phase) => { + const rows = trainingJobs.filter((job) => Number(job?.phase || 0) === phase); + const avgProgress = rows.length + ? rows.reduce((sum, job) => sum + getJobProgress(job), 0) / rows.length + : 0; + return { + label: `Phase ${phase}`, + value: Number((avgProgress * 100).toFixed(1)), + jobs: rows.length, + }; + }); + + const compliancePass = Array.isArray(complianceInfo?.items) + ? complianceInfo.items.filter((x) => x?.status === "pass").length + : 0; + const complianceFail = Array.isArray(complianceInfo?.items) + ? complianceInfo.items.filter((x) => x?.status === "fail").length + : 0; + const complianceUnknown = Array.isArray(complianceInfo?.items) + ? complianceInfo.items.filter((x) => x?.status !== "pass" && x?.status !== "fail").length + : 0; + + const systemMetricRows = [ + { label: "Tasks", value: tasksList.length }, + { label: "Agents", value: agentsList.length }, + { label: "Action Types", value: Number(actionsSchema?.total_action_types || 0) }, + { label: "Active Sessions", value: Number(sessionsInfo?.active_sessions || 0) }, + { label: "RL Models V1", value: rlModels.filter((m) => m.exists).length }, + { label: "RL Models V2", value: rlModelsV2.filter((m) => m.exists).length }, + { + label: "Workflow Components", + value: Array.isArray(workflowInfo?.components) + ? workflowInfo.components.filter((x) => x?.available).length + : 0, + }, + { label: "Comparisons", value: Array.isArray(comparisonsInfo?.comparisons) ? comparisonsInfo.comparisons.length : 0 }, + ]; + + const buildConicGradient = (rows, palette) => { + const total = rows.reduce((sum, row) => sum + Number(row?.value || 0), 0); + if (total <= 0) return null; + let cursor = 0; + const segments = []; + rows.forEach((row, idx) => { + const value = Number(row?.value || 0); + if (value <= 0) return; + const delta = (value / total) * 100; + const start = cursor; + const end = cursor + delta; + segments.push(`${palette[idx % palette.length]} ${start.toFixed(2)}% ${end.toFixed(2)}%`); + cursor = end; + }); + if (cursor < 100) { + segments.push(`#1e293b ${cursor.toFixed(2)}% 100%`); + } + return `conic-gradient(${segments.join(", ")})`; + }; + + const timelineModeGradient = buildConicGradient( + timelineModeRows, + ["#22d3ee", "#a78bfa", "#f59e0b", "#34d399", "#f472b6"] + ); + const trainingStatusGradient = buildConicGradient( + trainingStatusRows, + ["#22c55e", "#eab308", "#6366f1", "#ef4444", "#64748b"] + ); + const complianceGradient = buildConicGradient( + [ + { label: "pass", value: compliancePass }, + { label: "fail", value: complianceFail }, + { label: "unknown", value: complianceUnknown }, + ], + ["#22c55e", "#ef4444", "#f59e0b"] + ); + + const renderBars = (rows, color = "bg-indigo-500") => { + const maxVal = Math.max(...rows.map((r) => Number(r?.value || 0)), 1); + return ( +
+ {rows.map((row) => { + const widthPct = Math.max(0, Math.min(100, (Number(row.value || 0) / maxVal) * 100)); + return ( +
+
+ {row.label.replace(/_/g, " ")} + {Number(row.value || 0)} +
+
+
+
+
+ ); + })} +
+ ); + }; + + return ( +
+
+ {[ + { label: "Total Runs", value: history.length, icon: "play_circle", color: "indigo" }, + { label: "Avg Score", value: avgScore != null ? `${(avgScore * 100).toFixed(1)}%` : "—", icon: "grade", color: "emerald" }, + { label: "Running Jobs", value: runningJobs, icon: "settings_slow_motion", color: "violet" }, + { label: "Endpoint Coverage", value: endpointCoverage != null ? `${(endpointCoverage * 100).toFixed(0)}%` : "—", icon: "hub", color: "amber" }, + ].map((s) => ( +
+
+ {s.icon} + {s.label} +
+
{s.value}
+
+ ))} +
+ + {!loadingHistory && ( +
+
+

+ bar_chart Timeline Metric: Runs by Task +

+ {timelineTaskRows.length === 0 ? ( +
No timeline history yet.
+ ) : renderBars(timelineTaskRows, "bg-cyan-500")} +
+ +
+

+ pie_chart Timeline Metric: Agent Mode Mix +

+ {timelineModeGradient ? ( +
+
+
+
+
+ {timelineModeRows.map((row, idx) => ( +
+
+ + {row.label} +
+ {row.value} +
+ ))} +
+
+ ) : ( +
No timeline mode data yet.
+ )} +
+
+ )} + + {!loadingAll && ( +
+
+

+ stacked_bar_chart Training Metric: Job Status Mix +

+ {trainingStatusGradient ? ( +
+
+
+
+
+ {trainingStatusRows.map((row, idx) => ( +
+
+ + {row.label} +
+ {row.value} +
+ ))} +
+
+ ) : ( +
No training jobs available yet.
+ )} +
+ +
+

+ dataset Training Metric: Phase Progress (%) +

+
+ {trainingPhaseRows.map((row) => ( +
+
+ {row.label} + {row.value.toFixed(1)}% · {row.jobs} jobs +
+
+
+
+
+ ))} +
+
+
+ )} + + {!loadingAll && ( +
+
+

+ analytics System Metric: Endpoint-fed Counts +

+ {renderBars(systemMetricRows, "bg-cyan-500")} +
+ +
+

+ policy + System Metric: Compliance + Endpoint Health +

+
+
+
+
+
+
Pass{compliancePass}
+
Fail{complianceFail}
+
Unknown{complianceUnknown}
+
+
+

Endpoint Health

+
+ {endpointHealth.map((row) => ( +
+ {row.label} +
+ ))} +
+
+
+ )} + +
+

+ history Simulation Run History +

+ {loadingHistory ? ( +
+
+ Loading history… +
+ ) : history.length === 0 ? ( +

No simulation history yet. Run a simulation on the Timeline tab first.

+ ) : ( +
+ + + + + + + + + + + + + {history.map((run) => { + const score = getRunScore(run); + const reward = getRunReward(run); + const status = run.status || "completed"; + const statusColor = status === "completed" ? "emerald" : status === "running" ? "amber" : "slate"; + return ( + + + + + + + + + ); + })} + +
Run IDTaskAgent ModeStatusScoreReward
{run.run_id?.slice(0, 8)}…{run.task_id?.replace(/_/g, " ")}{run.agent_mode} + {status} + {score != null ? `${(score * 100).toFixed(1)}%` : "—"}{reward != null ? reward.toFixed(2) : "—"}
+
+ )} +
+ +
+

+ model_training Trained RL Model Checkpoints +

+
+ {rlModels.length === 0 && rlModelsV2.length === 0 ? ( +

No trained models found. Train a model via the RL pipeline first.

+ ) : ( + [...rlModels, ...rlModelsV2.map((m) => ({ + label: m.model_path ? String(m.model_path).split(/[\\/]/).pop() : "unnamed", + path: m.model_path ? `${m.model_path}.zip` : "", + exists: Boolean(m.exists), + model_type: Number(m.phase) === 2 ? "phase2" : "phase1", + }))].map((m) => ( +
+
+ + {m.exists ? "check_circle" : "radio_button_unchecked"} + + {m.label} +
+
{m.path?.split("\\").pop() || m.path?.split("/").pop()}
+
Type: {m.model_type}
+ {!m.exists &&
Not yet trained
} +
+ )) + )} +
+
+ + {Object.keys(byTask).length > 0 && ( +
+

+ bar_chart Score by Scenario +

+
+ {Object.entries(byTask).map(([task, runs]) => { + const scores = runs.map((r) => r.score ?? r.payload?.score).filter((s) => s != null); + const avg = scores.length ? scores.reduce((a, b) => a + b, 0) / scores.length : null; + const avgPct = avg != null ? Number((avg * 100).toFixed(1)) : 0; + return ( +
+
+ {task.replace(/_/g, " ")} + {runs.length} runs · avg {avg != null ? `${avgPct}%` : "—"} +
+
+
+
+
+ ); + })} +
+
+ )} +
+ ); +} + +function TrainingTab({ tasks }) { + return ; +} + +const TABS = [ + { id: "timeline", label: "Timeline", icon: "timeline" }, + { id: "training", label: "Training", icon: "fitness_center" }, + { id: "resources", label: "Resources", icon: "leaderboard" }, + { id: "library", label: "Overview", icon: "menu_book" }, + { id: "analytics", label: "Analytics", icon: "analytics" }, +]; + +export function Dashboard({ tasks = [] }) { + const [activeTab, setActiveTab] = useState("library"); + + return ( +
+ + +
+
+ {TABS.map((tab) => ( + + ))} +
+ +
+ {activeTab === "timeline" &&

Oversight Dashboard

Watch the AI agent resolve a government workflow backlog in real time - step by step, decision by decision.

} + {activeTab === "training" &&

Reinforcement Learning

Visualize policy convergence and reward trends as the agent continuously improves.

} + {activeTab === "resources" &&

Policy Benchmark

Compare all three baseline policies head-to-head on identical scenarios to see which strategy wins.

} + {activeTab === "library" &&

Overview

Explore system behavior, task configurations, OpenEnv compliance status, and workflow architecture.

} + {activeTab === "analytics" &&

Performance Analytics

Review historical simulation runs, trained model checkpoints, and reward improvement evidence.

} +
+ + {activeTab === "timeline" && } + {activeTab === "training" && } + {activeTab === "resources" && } + {activeTab === "library" && } + {activeTab === "analytics" && } +
+ + +
+ ); +} + + diff --git a/frontend/react/src/components/story-ui/TrainingTabV2.jsx b/frontend/react/src/components/story-ui/TrainingTabV2.jsx new file mode 100644 index 0000000000000000000000000000000000000000..d6741906bd0a9069b1378c10739dc60ad71711fc --- /dev/null +++ b/frontend/react/src/components/story-ui/TrainingTabV2.jsx @@ -0,0 +1,1760 @@ +import React, { useEffect, useMemo, useRef, useState } from "react"; +import { api, fmt } from "../../api/client"; + +function backendBaseUrl() { + if (typeof window === "undefined") return "http://127.0.0.1:7860"; + const host = window.location.hostname; + const port = window.location.port; + if ((host === "127.0.0.1" || host === "localhost") && port === "5173") { + return `http://${host}:7860`; + } + return window.location.origin; +} + +function normalizePath(path) { + return String(path || "").replace(/\\/g, "/").toLowerCase(); +} + +function toNumberOrNull(value) { + const n = Number(value); + return Number.isFinite(n) ? n : null; +} + +function timestampToDate(value) { + const n = Number(value); + if (!Number.isFinite(n) || n <= 0) return null; + return new Date(n * 1000); +} + +function metricRowKV(line) { + const m = String(line || "").match(/\|\s*([a-zA-Z0-9_ ]+?)\s*\|\s*([-]?\d+(?:\.\d+)?)\s*\|/); + if (!m) return null; + return { + key: String(m[1]).trim().toLowerCase().replace(/\s+/g, "_"), + value: parseFloat(m[2]), + }; +} + +function parseLogMetrics(lines) { + const rewards = []; + const scores = []; + let latestTableReward = null; + let latestTableScore = null; + let latestProgressRatio = null; + let latestLoggedTimesteps = null; + + for (const line of lines || []) { + if (!line) continue; + + const ratioMatch = line.match(/(\d[\d,]*)\/(\d[\d,]*)/); + if (ratioMatch) { + const done = parseInt(String(ratioMatch[1]).replace(/,/g, ""), 10); + const total = parseInt(String(ratioMatch[2]).replace(/,/g, ""), 10); + if (Number.isFinite(done) && Number.isFinite(total) && total > 0) { + latestProgressRatio = done / total; + } + } + + const metric = metricRowKV(line); + if (metric) { + if (metric.key === "ep_rew_mean" || metric.key === "mean_reward") { + latestTableReward = metric.value; + } + if (metric.key === "grader_score" || metric.key === "avg_grader_score") { + latestTableScore = metric.value; + } + if (metric.key === "total_timesteps") { + const ts = parseInt(String(metric.value), 10); + if (Number.isFinite(ts)) { + latestLoggedTimesteps = ts; + if (Number.isFinite(latestTableReward)) { + rewards.push({ t: ts, value: Number(latestTableReward) }); + latestTableReward = null; + } + if (Number.isFinite(latestTableScore)) { + scores.push({ t: ts, value: Number(latestTableScore) }); + latestTableScore = null; + } + } + } + } + + const evalReward = line.match(/Eval\s+num_timesteps=(\d[\d,]*),\s*episode_reward=([-]?\d+(?:\.\d+)?)/i); + if (evalReward) { + const ts = parseInt(String(evalReward[1]).replace(/,/g, ""), 10); + const rew = parseFloat(evalReward[2]); + if (Number.isFinite(ts) && Number.isFinite(rew)) { + latestLoggedTimesteps = ts; + rewards.push({ t: ts, value: rew }); + } + } + + const evalScore = line.match(/\[Eval\]\s+Average grader score:\s+([0-9.]+)/i); + if (evalScore) { + const score = parseFloat(evalScore[1]); + if (Number.isFinite(score)) { + const ts = latestLoggedTimesteps || (scores.length > 0 ? scores[scores.length - 1].t + 1 : 1); + scores.push({ t: ts, value: score }); + } + } + + const bestScore = line.match(/\[Eval\]\s+New best(?: recurrent)? grader score:\s+([0-9.]+)/i); + if (bestScore) { + const score = parseFloat(bestScore[1]); + if (Number.isFinite(score)) { + const ts = latestLoggedTimesteps || (scores.length > 0 ? scores[scores.length - 1].t + 1 : 1); + scores.push({ t: ts, value: score }); + } + } + } + + const dedupe = (rows) => { + const map = new Map(); + for (const row of rows) { + if (!Number.isFinite(row.t) || !Number.isFinite(row.value)) continue; + map.set(row.t, row); + } + return Array.from(map.values()).sort((a, b) => a.t - b.t); + }; + + return { + rewardPoints: dedupe(rewards), + scorePoints: dedupe(scores), + logProgressRatio: Number.isFinite(latestProgressRatio) ? latestProgressRatio : null, + lastLoggedTimesteps: Number.isFinite(latestLoggedTimesteps) ? latestLoggedTimesteps : null, + }; +} + +function seriesSpread(rows) { + if (!Array.isArray(rows) || rows.length === 0) return 0; + const vals = rows.map((r) => Number(r?.value)).filter(Number.isFinite); + if (vals.length === 0) return 0; + return Math.max(...vals) - Math.min(...vals); +} + +function payloadHighlights(payload) { + const src = payload && typeof payload === "object" ? payload : {}; + const keys = [ + "task_id", + "step", + "reward", + "score", + "done", + "backlog", + "completed", + "total_backlog", + "total_completed", + "total_sla_breaches", + "total_valid", + "total_actions", + "passed", + "action_history_len", + ]; + const out = []; + for (const key of keys) { + if (!(key in src)) continue; + const value = src[key]; + if (value == null) continue; + if (typeof value === "number") { + out.push([key, Number.isFinite(value) ? Number(value).toFixed(Math.abs(value) >= 10 ? 1 : 3) : String(value)]); + } else { + out.push([key, String(value)]); + } + } + return out; +} + +function toPolyline(points, { minY, maxY, width, height }) { + if (!points || points.length === 0) return ""; + return points + .map((p, idx) => { + const x = (idx / Math.max(points.length - 1, 1)) * width; + const y = height - ((p.value - minY) / (maxY - minY || 1)) * height; + return `${x},${y}`; + }) + .join(" "); +} + +function normalizeSeries(points) { + const map = new Map(); + for (const row of points || []) { + const t = Number(row?.t); + const value = Number(row?.value); + if (!Number.isFinite(t) || !Number.isFinite(value)) continue; + map.set(t, { t, value }); + } + return Array.from(map.values()).sort((a, b) => a.t - b.t); +} + +function toPolylineByT(points, { minX, maxX, minY, maxY, width, height }) { + if (!points || points.length === 0) return ""; + const xDen = maxX - minX || 1; + const yDen = maxY - minY || 1; + return points + .map((p) => { + const x = ((p.t - minX) / xDen) * width; + const y = height - ((p.value - minY) / yDen) * height; + return `${x},${y}`; + }) + .join(" "); +} + +function toStairPolylineByT(points, { minX, maxX, minY, maxY, width, height }) { + if (!points || points.length === 0) return ""; + const xDen = maxX - minX || 1; + const yDen = maxY - minY || 1; + const xOf = (t) => ((t - minX) / xDen) * width; + const yOf = (v) => height - ((v - minY) / yDen) * height; + + const sorted = normalizeSeries(points); + if (sorted.length === 0) return ""; + + const out = []; + const first = sorted[0]; + out.push(`${xOf(minX)},${yOf(first.value)}`); + out.push(`${xOf(first.t)},${yOf(first.value)}`); + + for (let i = 1; i < sorted.length; i += 1) { + const prev = sorted[i - 1]; + const curr = sorted[i]; + const x = xOf(curr.t); + out.push(`${x},${yOf(prev.value)}`); + out.push(`${x},${yOf(curr.value)}`); + } + + const last = sorted[sorted.length - 1]; + out.push(`${xOf(maxX)},${yOf(last.value)}`); + return out.join(" "); +} + +function summarizeLogLine(line) { + const raw = String(line || "").trim(); + if (!raw) return { title: "Info", text: "Empty line", tone: "slate" }; + const lower = raw.toLowerCase(); + + const evalReward = raw.match(/Eval\s+num_timesteps=(\d[\d,]*),\s*episode_reward=([-]?\d+(?:\.\d+)?)/i); + if (evalReward) { + const ts = Number(String(evalReward[1]).replace(/,/g, "")); + const rew = Number(evalReward[2]); + return { + title: "Eval Checkpoint", + text: `Timesteps ${Number.isFinite(ts) ? ts.toLocaleString() : "-"} | Reward ${Number.isFinite(rew) ? rew.toFixed(2) : "-"}`, + tone: "emerald", + }; + } + + const bestScore = raw.match(/\[Eval\]\s+New best(?: recurrent)? grader score:\s+([0-9.]+)/i); + if (bestScore) { + const score = Number(bestScore[1]); + return { + title: "Best Score Improved", + text: `Grader score improved to ${Number.isFinite(score) ? score.toFixed(4) : "-"}.`, + tone: "emerald", + }; + } + + const avgScore = raw.match(/\[Eval\]\s+Average grader score:\s+([0-9.]+)/i); + if (avgScore) { + const score = Number(avgScore[1]); + return { + title: "Evaluation Summary", + text: `Average grader score ${Number.isFinite(score) ? score.toFixed(4) : "-"}.`, + tone: "emerald", + }; + } + + const metric = metricRowKV(raw); + if (metric) { + const key = String(metric.key || "").replace(/_/g, " "); + return { + title: "Metric Update", + text: `${key}: ${Number.isFinite(metric.value) ? metric.value : "-"}`, + tone: "indigo", + }; + } + + if (lower.includes("traceback") || lower.includes("exception") || lower.includes("error")) { + return { title: "Error", text: "A runtime error was reported by the training process. Review backend logs for the exact stack trace.", tone: "rose" }; + } + if (lower.includes("[eval]")) { + return { title: "Evaluation", text: "Evaluation cycle completed and scores were updated.", tone: "emerald" }; + } + if (lower.includes("[training_jobs]")) { + if (lower.includes("started pid=")) { + return { title: "Job Started", text: "Training worker started successfully and began consuming timesteps.", tone: "cyan" }; + } + if (lower.includes("command:")) { + return { title: "Runtime Config", text: "Training command was prepared with current phase and environment settings.", tone: "cyan" }; + } + return { title: "System", text: "Background training service published a runtime status update.", tone: "cyan" }; + } + if (lower.includes("[phase 1]")) { + return { title: "Phase 1 Update", text: "Phase 1 PPO training is actively optimizing policy behavior.", tone: "indigo" }; + } + if (lower.includes("[phase 2]")) { + return { title: "Phase 2 Update", text: "Phase 2 curriculum training is active for harder scenario generalization.", tone: "indigo" }; + } + if (lower.includes("[costmonitor]")) { + return { title: "Constraint Monitor", text: "SLA/fairness penalty monitor updated policy constraint feedback.", tone: "amber" }; + } + return { title: "Runtime Update", text: "The trainer reported a new runtime event and internal state progressed.", tone: "amber" }; +} + +function summarizeEnvEvent(event) { + const stage = String(event?.stage || ""); + const payload = event?.payload || {}; + const task = payload?.task_id ? ` [${payload.task_id}]` : ""; + if (stage === "reset") { + return `Task${task}: session created. Day ${payload?.day ?? "-"}, starting backlog ${payload?.backlog ?? "-"}.`; + } + if (stage === "state:initial") { + return `Task${task}: initial snapshot captured. Completed ${payload?.total_completed ?? "-"}, backlog ${payload?.total_backlog ?? "-"}.`; + } + if (stage === "action-masks") { + return `Task${task}: step ${payload?.step ?? "-"} validated actions (${payload?.total_valid ?? "-"} valid of ${payload?.total_actions ?? "-"}).`; + } + if (stage === "auto_step") { + return `Task${task}: step ${payload?.step ?? "-"} executed. Reward ${fmt(payload?.reward, 3)}, backlog ${payload?.backlog ?? "-"}, completed ${payload?.completed ?? "-"}.`; + } + if (stage === "state:post_step") { + return `Task${task}: post-step state updated. Completed ${payload?.total_completed ?? "-"}, backlog ${payload?.total_backlog ?? "-"}, SLA breaches ${payload?.total_sla_breaches ?? "-"}.`; + } + if (stage === "grade") { + return `Task${task}: grading finished. Score ${fmt(payload?.score, 3)}, pass ${String(payload?.passed)}.`; + } + if (stage === "session:closed") { + return `Task${task}: session closed successfully.`; + } + if (stage === "task:error") { + return `Task${task}: run failed - ${payload?.error || "unknown error"}.`; + } + return `Task${task}: ${stage}.`; +} + +function workflowStageLabel(stage) { + const key = String(stage || "").toLowerCase(); + if (key === "reset") return "Reset"; + if (key === "state:initial") return "Initial State"; + if (key === "action-masks") return "Action Validation"; + if (key === "auto_step") return "Auto Step"; + if (key === "state:post_step") return "Post-Step State"; + if (key === "grade") return "Grade"; + if (key === "session:closed") return "Session Closed"; + if (key === "task:error") return "Task Error"; + return stage; +} + +function jsonPretty(value) { + try { + return JSON.stringify(value, null, 2); + } catch (_err) { + return String(value); + } +} + +function toneClasses(tone) { + if (tone === "rose") return "bg-rose-500/5 border-rose-500/20"; + if (tone === "emerald") return "bg-emerald-500/5 border-emerald-500/20"; + if (tone === "indigo") return "bg-indigo-500/5 border-indigo-500/20"; + if (tone === "cyan") return "bg-cyan-500/5 border-cyan-500/20"; + if (tone === "amber") return "bg-amber-500/5 border-amber-500/20"; + return "bg-slate-700/10 border-slate-500/20"; +} + +function statusClasses(status) { + const s = String(status || "").toLowerCase(); + if (s === "running") return "text-emerald-300 bg-emerald-500/10 border-emerald-500/30"; + if (s === "queued") return "text-amber-300 bg-amber-500/10 border-amber-500/30"; + if (s === "completed") return "text-indigo-300 bg-indigo-500/10 border-indigo-500/30"; + if (s === "failed") return "text-rose-300 bg-rose-500/10 border-rose-500/30"; + if (s === "stopped") return "text-slate-300 bg-slate-600/20 border-slate-500/30"; + return "text-slate-300 bg-slate-700/20 border-slate-500/30"; +} + +function normalizeJob(raw, index) { + const jobId = String(raw?.job_id || raw?.id || `job-${index}`); + const status = String(raw?.status || "unknown"); + const timesteps = Number(raw?.timesteps || 0); + const latestMetrics = raw?.latest_metrics && typeof raw.latest_metrics === "object" ? raw.latest_metrics : {}; + + const progressRaw = toNumberOrNull(raw?.progress); + const ts = toNumberOrNull(latestMetrics.total_timesteps); + const progressFromMetrics = + Number.isFinite(ts) && Number.isFinite(timesteps) && timesteps > 0 + ? Math.max(0, Math.min(1, Number(ts) / Number(timesteps))) + : null; + const progress = Number.isFinite(progressRaw) + ? Math.max(0, Math.min(1, Number(progressRaw))) + : Number.isFinite(progressFromMetrics) + ? Number(progressFromMetrics) + : 0; + + return { + ...raw, + job_id: jobId, + status, + timesteps: Number.isFinite(timesteps) ? timesteps : 0, + phase: Number(raw?.phase || 0), + n_envs: Number(raw?.n_envs || 0), + progress, + latest_metrics: latestMetrics, + logs_tail: Array.isArray(raw?.logs_tail) ? raw.logs_tail : [], + created_at: toNumberOrNull(raw?.created_at), + updated_at: toNumberOrNull(raw?.updated_at), + }; +} + +export function TrainingTabV2({ tasks = [] }) { + const [endpointRows, setEndpointRows] = useState([]); + const [endpointError, setEndpointError] = useState(""); + + const [agents, setAgents] = useState([]); + const [modelRows, setModelRows] = useState([]); + const [modelError, setModelError] = useState(""); + + const [jobs, setJobs] = useState([]); + const [jobsLoading, setJobsLoading] = useState(false); + const [jobsError, setJobsError] = useState(""); + const [activeJobId, setActiveJobId] = useState(""); + const [activeJob, setActiveJob] = useState(null); + const [deletingJobId, setDeletingJobId] = useState(""); + const [jobError, setJobError] = useState(""); + const [pollIntervalMs, setPollIntervalMs] = useState(1500); + const pollFailuresRef = useRef(0); + + const [rewardPoints, setRewardPoints] = useState([]); + const [scorePoints, setScorePoints] = useState([]); + const [scoreSignalMeta, setScoreSignalMeta] = useState({ + key: "grader_score", + label: "Grader Score", + fallback: false, + }); + const [logLines, setLogLines] = useState([]); + const [logProgressRatio, setLogProgressRatio] = useState(null); + const [lastLoggedTimesteps, setLastLoggedTimesteps] = useState(null); + + const [jobForm, setJobForm] = useState({ + phase: 1, + timesteps: 80000, + n_envs: 4, + seed: "", + }); + + const [envTaskId, setEnvTaskId] = useState(tasks[0] || "district_backlog_easy"); + const [envSeed, setEnvSeed] = useState(""); + const [envPolicyName, setEnvPolicyName] = useState("backlog_clearance"); + const [envMaxSteps, setEnvMaxSteps] = useState(6); + const [envBusy, setEnvBusy] = useState(false); + const [envError, setEnvError] = useState(""); + const [envFlowEvents, setEnvFlowEvents] = useState([]); + const [envFlowSummary, setEnvFlowSummary] = useState(null); + const [envFlowRuns, setEnvFlowRuns] = useState([]); + const envEventSeqRef = useRef(0); + + useEffect(() => { + if (tasks.length > 0 && !tasks.includes(envTaskId)) { + setEnvTaskId(tasks[0]); + } + }, [tasks, envTaskId]); + + useEffect(() => { + if (agents.length > 0 && !agents.includes(envPolicyName)) { + setEnvPolicyName(agents[0]); + } + }, [agents, envPolicyName]); + + const refreshEndpointHealth = async () => { + setEndpointError(""); + + const directGet = async (path) => { + const res = await fetch(`${backendBaseUrl()}${path}`, { method: "GET" }); + if (!res.ok) { + throw new Error(`${path} -> ${res.status}`); + } + try { + return await res.json(); + } catch (_err) { + return { ok: true }; + } + }; + + const checks = [ + { key: "health", label: "Health", fn: () => api("/health") }, + { key: "tasks", label: "Tasks", fn: () => api("/tasks") }, + { key: "agents", label: "Agents", fn: () => api("/agents") }, + { key: "training_jobs", label: "Training Jobs", fn: () => api("/training_jobs") }, + { key: "actions_schema", label: "Action Schema", fn: () => api("/actions/schema") }, + { key: "rl_models", label: "RL Models", fn: () => api("/rl_models") }, + { key: "rl_models_v2", label: "RL Models V2", fn: () => api("/rl/models") }, + { key: "v1_agents", label: "V1 Agents", fn: () => directGet("/api/v1/agents") }, + { key: "v1_rl_models", label: "V1 RL Models", fn: () => directGet("/api/v1/rl_models") }, + ]; + + const settled = await Promise.allSettled( + checks.map(async (chk) => { + const start = Date.now(); + await chk.fn(); + return { key: chk.key, label: chk.label, ok: true, ms: Date.now() - start }; + }) + ); + + const rows = settled.map((res, idx) => { + const meta = checks[idx]; + if (res.status === "fulfilled") return res.value; + return { + key: meta.key, + label: meta.label, + ok: false, + ms: null, + error: res.reason?.message || String(res.reason), + }; + }); + + setEndpointRows(rows); + if (rows.some((r) => !r.ok)) { + setEndpointError("Some endpoints are down. Retries remain active."); + } + }; + + const refreshCatalog = async () => { + setModelError(""); + try { + const [agentRes, rlV1Res, rlV2Res] = await Promise.allSettled([ + api("/agents"), + api("/rl_models"), + api("/rl/models"), + ]); + + if (agentRes.status === "fulfilled") { + setAgents(Array.isArray(agentRes.value) ? agentRes.value : []); + } + + const unified = []; + if (rlV1Res.status === "fulfilled") { + const rows = Array.isArray(rlV1Res.value?.models) ? rlV1Res.value.models : []; + for (const row of rows) { + unified.push({ + source: "api/rl_models", + label: row.label || row.path || "unnamed", + path: row.path || "", + exists: Boolean(row.exists), + phase: normalizePath(row.path).includes("/phase2/") ? 2 : normalizePath(row.path).includes("/phase1/") ? 1 : 0, + }); + } + } + if (rlV2Res.status === "fulfilled") { + const rows = Array.isArray(rlV2Res.value) ? rlV2Res.value : []; + for (const row of rows) { + const path = row.model_path + ? (String(row.model_path).toLowerCase().endsWith(".zip") ? row.model_path : `${row.model_path}.zip`) + : ""; + unified.push({ + source: "api/rl/models", + label: path.split(/[\\/]/).pop() || row.model_path || "unnamed", + path, + exists: Boolean(row.exists), + phase: Number(row.phase || 0), + }); + } + } + + const dedupe = new Map(); + for (const row of unified) { + const key = normalizePath(row.path); + if (!key) continue; + if (!dedupe.has(key)) dedupe.set(key, row); + } + const rows = Array.from(dedupe.values()).sort((a, b) => { + if (a.phase !== b.phase) return b.phase - a.phase; + return String(a.label).localeCompare(String(b.label)); + }); + setModelRows(rows); + if (rows.length === 0) { + setModelError("No models discovered from dynamic model endpoints."); + } + } catch (err) { + setModelError(err?.message || "Failed to load model registry."); + } + }; + + const refreshJobs = async () => { + setJobsLoading(true); + try { + const data = await api("/training_jobs"); + const rowsRaw = Array.isArray(data?.jobs) ? data.jobs : []; + const rows = rowsRaw.map(normalizeJob).sort((a, b) => Number(b.created_at || 0) - Number(a.created_at || 0)); + setJobs(rows); + setJobsError(""); + + const running = rows.find((j) => j.status === "running" || j.status === "queued"); + const current = rows.find((j) => j.job_id === activeJobId); + + if (running?.job_id) { + if (!current || (current.status !== "running" && current.status !== "queued")) { + setActiveJobId(running.job_id); + } + } else if (!activeJobId && rows[0]?.job_id) { + setActiveJobId(rows[0].job_id); + } + } catch (err) { + setJobsError(err?.message || "Failed to load training jobs."); + } finally { + setJobsLoading(false); + } + }; + + const parseAndSetPoints = (jobSnapshot) => { + const lines = Array.isArray(jobSnapshot?.logs_tail) ? jobSnapshot.logs_tail : []; + setLogLines(lines); + + const parsed = parseLogMetrics(lines); + setLogProgressRatio(parsed.logProgressRatio); + setLastLoggedTimesteps(parsed.lastLoggedTimesteps); + + const nextRewards = []; + const nextScores = []; + const nextSignals = { + explained_variance: [], + ep_len_mean: [], + approx_kl: [], + }; + + const history = Array.isArray(jobSnapshot?.metric_history) ? jobSnapshot.metric_history : []; + for (const row of history) { + const t = Number(row?.t ?? row?.total_timesteps ?? NaN); + if (!Number.isFinite(t)) continue; + const rew = Number(row?.ep_rew_mean ?? row?.mean_reward ?? NaN); + const score = Number(row?.grader_score ?? row?.avg_grader_score ?? NaN); + if (Number.isFinite(rew)) nextRewards.push({ t, value: rew }); + if (Number.isFinite(score)) nextScores.push({ t, value: score }); + for (const key of Object.keys(nextSignals)) { + const vv = Number(row?.[key] ?? NaN); + if (Number.isFinite(vv)) nextSignals[key].push({ t, value: vv }); + } + } + nextRewards.push(...parsed.rewardPoints); + nextScores.push(...parsed.scorePoints); + + const lm = jobSnapshot?.latest_metrics || {}; + const metricTs = Number(lm.total_timesteps ?? NaN); + const metricReward = Number(lm.ep_rew_mean ?? lm.mean_reward ?? NaN); + const metricScore = Number(lm.grader_score ?? lm.avg_grader_score ?? NaN); + + if (Number.isFinite(metricTs) && Number.isFinite(metricReward)) { + nextRewards.push({ t: metricTs, value: metricReward }); + } + if (Number.isFinite(metricTs) && Number.isFinite(metricScore)) { + nextScores.push({ t: metricTs, value: metricScore }); + } + for (const key of Object.keys(nextSignals)) { + const vv = Number(lm[key] ?? NaN); + if (Number.isFinite(metricTs) && Number.isFinite(vv)) { + nextSignals[key].push({ t: metricTs, value: vv }); + } + } + + const dedupe = (rows) => { + const map = new Map(); + for (const row of rows) { + if (!Number.isFinite(row.t) || !Number.isFinite(row.value)) continue; + map.set(row.t, row); + } + return Array.from(map.values()).sort((a, b) => a.t - b.t); + }; + + const dedupedRewards = dedupe(nextRewards); + const dedupedScores = dedupe(nextScores); + const dedupedSignals = Object.fromEntries( + Object.entries(nextSignals).map(([key, rows]) => [key, dedupe(rows)]) + ); + + let chosenScores = dedupedScores; + let chosenMeta = { key: "grader_score", label: "Grader Score", fallback: false }; + + if (dedupedScores.length < 2 || seriesSpread(dedupedScores) < 1e-6) { + const fallbackCandidates = [ + { key: "explained_variance", label: "Explained Variance" }, + { key: "ep_len_mean", label: "Episode Length Mean" }, + { key: "approx_kl", label: "Approx KL" }, + ]; + for (const candidate of fallbackCandidates) { + const rows = dedupedSignals[candidate.key] || []; + if (rows.length >= 2 && seriesSpread(rows) >= 1e-6) { + chosenScores = rows; + chosenMeta = { key: candidate.key, label: candidate.label, fallback: true }; + break; + } + } + } + + setRewardPoints(dedupedRewards); + setScorePoints(chosenScores); + setScoreSignalMeta(chosenMeta); + }; + + const startTrainingJob = async () => { + setJobError(""); + try { + const payload = { + phase: Number(jobForm.phase) || 1, + timesteps: Number(jobForm.timesteps) || 80000, + n_envs: Number(jobForm.n_envs) || 4, + }; + const seedNum = Number(jobForm.seed); + if (jobForm.seed !== "" && Number.isFinite(seedNum)) payload.seed = seedNum; + + const res = await api("/training_jobs", { + method: "POST", + body: JSON.stringify(payload), + }); + if (res?.job_id) { + setActiveJobId(res.job_id); + const norm = normalizeJob(res, 0); + setActiveJob(norm); + parseAndSetPoints(norm); + } + await refreshJobs(); + } catch (err) { + setJobError(err?.message || "Failed to start training job."); + } + }; + + const stopTrainingJob = async () => { + if (!activeJobId) return; + setJobError(""); + try { + await api(`/training_jobs/${activeJobId}/stop`, { method: "POST" }); + await refreshJobs(); + const stopped = await api(`/training_jobs/${activeJobId}`); + const norm = normalizeJob(stopped, 0); + setActiveJob(norm); + parseAndSetPoints(norm); + } catch (err) { + setJobError(err?.message || "Failed to stop training job."); + } + }; + + const clearTrainingHistory = async () => { + setJobError(""); + try { + await api("/training_jobs?clear_artifacts=false", { method: "DELETE" }); + setJobs([]); + setActiveJob(null); + setActiveJobId(""); + setRewardPoints([]); + setScorePoints([]); + setScoreSignalMeta({ key: "grader_score", label: "Grader Score", fallback: false }); + setLogLines([]); + setLogProgressRatio(null); + setLastLoggedTimesteps(null); + } catch (err) { + setJobError(err?.message || "Failed to clear training history."); + } + }; + + const deleteTrainingJob = async (jobId) => { + if (!jobId) return; + setJobError(""); + setDeletingJobId(jobId); + try { + await api(`/training_jobs/${jobId}?clear_artifacts=false`, { method: "DELETE" }); + if (activeJobId === jobId) { + setActiveJobId(""); + setActiveJob(null); + setRewardPoints([]); + setScorePoints([]); + setScoreSignalMeta({ key: "grader_score", label: "Grader Score", fallback: false }); + setLogLines([]); + } + await refreshJobs(); + } catch (err) { + setJobError(err?.message || "Failed to delete training job."); + } finally { + setDeletingJobId(""); + } + }; + + const pushEnvEvent = (stage, payload, tone = "indigo") => { + const seq = envEventSeqRef.current + 1; + envEventSeqRef.current = seq; + setEnvFlowEvents((prev) => [ + ...prev, + { id: `${Date.now()}-${Math.random()}`, seq, ts: Date.now(), stage, payload, tone }, + ].slice(-400)); + }; + + const runAutomatedOpenEnvFlow = async () => { + setEnvBusy(true); + setEnvError(""); + setEnvFlowSummary(null); + setEnvFlowEvents([]); + setEnvFlowRuns([]); + envEventSeqRef.current = 0; + + try { + const seedNum = Number(envSeed); + const taskScope = Array.isArray(tasks) && tasks.length > 0 ? tasks : [envTaskId]; + const runTaskIds = Array.from(new Set(taskScope.filter(Boolean))); + const maxSteps = Math.max(1, Number(envMaxSteps) || 6); + const taskResults = []; + + for (const taskId of runTaskIds) { + let sessionId = ""; + let stepsExecuted = 0; + let finalState = null; + try { + const resetPayload = { task_id: taskId }; + if (envSeed !== "" && Number.isFinite(seedNum)) { + resetPayload.seed = seedNum; + } + + const resetRes = await api("/reset", { + method: "POST", + body: JSON.stringify(resetPayload), + }); + sessionId = String(resetRes?.session_id || ""); + if (!sessionId) throw new Error(`reset() did not return session_id for task ${taskId}`); + + pushEnvEvent( + "reset", + { + task_id: taskId, + day: resetRes?.observation?.day, + backlog: resetRes?.observation?.total_backlog, + completed: resetRes?.observation?.total_completed, + }, + "emerald" + ); + + const initialState = await api("/state", { + method: "POST", + body: JSON.stringify({ session_id: sessionId, include_action_history: false }), + }); + pushEnvEvent( + "state:initial", + { + task_id: taskId, + total_completed: initialState?.state?.total_completed, + total_backlog: initialState?.state?.total_backlog, + fairness_gap: initialState?.state?.fairness_gap, + }, + "cyan" + ); + + let done = false; + for (let idx = 0; idx < maxSteps; idx += 1) { + if (done) break; + + const masks = await api("/action-masks", { + method: "POST", + body: JSON.stringify({ session_id: sessionId }), + }); + pushEnvEvent( + "action-masks", + { + task_id: taskId, + step: idx + 1, + total_valid: masks?.total_valid, + total_actions: masks?.total_actions, + }, + "amber" + ); + + const stepRes = await api("/auto_step", { + method: "POST", + body: JSON.stringify({ + session_id: sessionId, + agent_policy: envPolicyName || "backlog_clearance", + }), + }); + done = Boolean(stepRes?.done); + stepsExecuted += 1; + pushEnvEvent( + "auto_step", + { + task_id: taskId, + step: idx + 1, + reward: stepRes?.reward, + done: stepRes?.done, + day: stepRes?.observation?.day, + backlog: stepRes?.observation?.total_backlog, + completed: stepRes?.observation?.total_completed, + }, + "indigo" + ); + + const stateRes = await api("/state", { + method: "POST", + body: JSON.stringify({ session_id: sessionId, include_action_history: true }), + }); + finalState = stateRes; + pushEnvEvent( + "state:post_step", + { + task_id: taskId, + step: idx + 1, + total_completed: stateRes?.state?.total_completed, + total_backlog: stateRes?.state?.total_backlog, + total_sla_breaches: stateRes?.state?.total_sla_breaches, + action_history_len: Array.isArray(stateRes?.state?.action_history) ? stateRes.state.action_history.length : 0, + }, + "cyan" + ); + } + + const gradeRes = await api("/grade", { + method: "POST", + body: JSON.stringify({ session_id: sessionId }), + }); + const scoreValue = Number(gradeRes?.score); + const dynamicPassed = + typeof gradeRes?.passed === "boolean" + ? gradeRes.passed + : (Number.isFinite(scoreValue) ? scoreValue >= 0.5 : null); + pushEnvEvent( + "grade", + { + task_id: taskId, + score: gradeRes?.score, + passed: dynamicPassed, + }, + "emerald" + ); + + taskResults.push({ + task_id: taskId, + steps_executed: stepsExecuted, + score: gradeRes?.score ?? null, + passed: dynamicPassed, + final_completed: finalState?.state?.total_completed ?? null, + final_backlog: finalState?.state?.total_backlog ?? null, + final_sla_breaches: finalState?.state?.total_sla_breaches ?? null, + }); + } catch (taskErr) { + const msg = taskErr?.message || String(taskErr); + pushEnvEvent("task:error", { task_id: taskId, error: msg }, "rose"); + taskResults.push({ + task_id: taskId, + steps_executed: stepsExecuted, + score: null, + passed: null, + error: msg, + }); + } finally { + if (sessionId) { + try { + await api(`/sessions/${sessionId}`, { method: "DELETE" }); + pushEnvEvent("session:closed", { task_id: taskId }, "slate"); + } catch (_err) { + // no-op + } + } + } + } + + setEnvFlowRuns(taskResults); + const validScores = taskResults + .map((row) => Number(row.score)) + .filter((v) => Number.isFinite(v)); + const passedCount = taskResults.filter((row) => row.passed === true).length; + setEnvFlowSummary({ + tasks_executed: taskResults.length, + total_steps_executed: taskResults.reduce((acc, row) => acc + Number(row.steps_executed || 0), 0), + avg_score: + validScores.length > 0 + ? validScores.reduce((acc, score) => acc + Number(score), 0) / validScores.length + : null, + passed_tasks: passedCount, + }); + } catch (err) { + setEnvError(err?.message || "Automated OpenEnv workflow failed."); + } finally { + setEnvBusy(false); + } + }; + + useEffect(() => { + refreshEndpointHealth(); + refreshCatalog(); + refreshJobs(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + useEffect(() => { + const t = setInterval(() => { + refreshJobs(); + }, 5000); + return () => clearInterval(t); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + useEffect(() => { + const t = setInterval(() => { + refreshEndpointHealth(); + }, 15000); + return () => clearInterval(t); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + useEffect(() => { + if (!activeJobId) return undefined; + let cancelled = false; + + const t = setInterval(async () => { + if (cancelled) return; + try { + const snapshotRaw = await api(`/training_jobs/${activeJobId}`); + if (cancelled) return; + const snapshot = normalizeJob(snapshotRaw, 0); + setActiveJob(snapshot); + parseAndSetPoints(snapshot); + setJobError(""); + pollFailuresRef.current = 0; + if (pollIntervalMs !== 1500) setPollIntervalMs(1500); + } catch (err) { + pollFailuresRef.current += 1; + if (pollFailuresRef.current >= 3) { + setPollIntervalMs(4000); + setJobError(err?.message || "Polling failed repeatedly, switched to fallback polling."); + } + } + }, pollIntervalMs); + + return () => { + cancelled = true; + clearInterval(t); + }; + }, [activeJobId, pollIntervalMs]); + + useEffect(() => { + if (!activeJobId) return; + const row = jobs.find((j) => j.job_id === activeJobId); + if (!row) return; + setActiveJob(row); + parseAndSetPoints(row); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [activeJobId, jobs]); + + const progressA = useMemo(() => { + if (!activeJob) return null; + const p = toNumberOrNull(activeJob.progress); + return Number.isFinite(p) ? Math.max(0, Math.min(1, Number(p))) : null; + }, [activeJob]); + + const progressB = useMemo(() => { + if (!activeJob) return null; + const history = Array.isArray(activeJob?.metric_history) ? activeJob.metric_history : []; + const historyTs = history.length > 0 ? toNumberOrNull(history[history.length - 1]?.t ?? history[history.length - 1]?.total_timesteps) : null; + const ts = toNumberOrNull(activeJob?.latest_metrics?.total_timesteps) ?? historyTs; + const total = toNumberOrNull(activeJob?.timesteps); + if (!Number.isFinite(ts) || !Number.isFinite(total) || total <= 0) return null; + return Math.max(0, Math.min(1, Number(ts) / Number(total))); + }, [activeJob]); + + const progressC = useMemo(() => { + if (!activeJob) return null; + const total = toNumberOrNull(activeJob?.timesteps); + if (!Number.isFinite(total) || total <= 0) { + return Number.isFinite(logProgressRatio) ? Number(logProgressRatio) : null; + } + + const fromLogTs = + Number.isFinite(lastLoggedTimesteps) && Number(lastLoggedTimesteps) > 0 + ? Math.max(0, Math.min(1, Number(lastLoggedTimesteps) / Number(total))) + : null; + if (Number.isFinite(fromLogTs) && Number.isFinite(logProgressRatio)) { + return Math.max(Number(fromLogTs), Number(logProgressRatio)); + } + if (Number.isFinite(fromLogTs)) return Number(fromLogTs); + if (Number.isFinite(logProgressRatio)) return Number(logProgressRatio); + return null; + }, [activeJob, lastLoggedTimesteps, logProgressRatio]); + + const effectiveProgress = useMemo(() => { + const values = [progressA, progressB, progressC].filter((v) => Number.isFinite(v)); + return values.length > 0 ? Math.max(...values) : null; + }, [progressA, progressB, progressC]); + + const rewardLatest = rewardPoints.length ? rewardPoints[rewardPoints.length - 1].value : null; + const rewardBest = rewardPoints.length ? Math.max(...rewardPoints.map((p) => p.value)) : null; + const scoreLatest = scorePoints.length ? scorePoints[scorePoints.length - 1].value : null; + const scoreBest = scorePoints.length ? Math.max(...scorePoints.map((p) => p.value)) : null; + + const rewardSeries = useMemo(() => normalizeSeries(rewardPoints), [rewardPoints]); + const scoreSeries = useMemo(() => normalizeSeries(scorePoints), [scorePoints]); + + const graphXMin = useMemo(() => { + const allTs = [...rewardSeries, ...scoreSeries].map((p) => Number(p.t)).filter(Number.isFinite); + if (allTs.length === 0) return 0; + return Math.min(...allTs); + }, [rewardSeries, scoreSeries]); + const graphXMax = useMemo(() => { + const allTs = [...rewardSeries, ...scoreSeries].map((p) => Number(p.t)).filter(Number.isFinite); + if (allTs.length === 0) return 1; + const mx = Math.max(...allTs); + return mx > graphXMin ? mx : graphXMin + 1; + }, [rewardSeries, scoreSeries, graphXMin]); + + const rewardMin = rewardPoints.length ? Math.min(...rewardPoints.map((p) => p.value), -10) : -10; + const rewardMax = rewardPoints.length ? Math.max(...rewardPoints.map((p) => p.value), 10) : 10; + const scoreMin = scorePoints.length ? Math.min(...scorePoints.map((p) => p.value), 0) : 0; + const scoreMax = scorePoints.length ? Math.max(...scorePoints.map((p) => p.value), 1) : 1; + + const rewardPolyline = useMemo( + () => + toPolylineByT(rewardSeries, { + minX: graphXMin, + maxX: graphXMax, + minY: rewardMin, + maxY: rewardMax, + width: 700, + height: 260, + }), + [rewardSeries, graphXMin, graphXMax, rewardMin, rewardMax] + ); + const scoreStairPolyline = useMemo( + () => + toStairPolylineByT(scoreSeries, { + minX: graphXMin, + maxX: graphXMax, + minY: scoreMin, + maxY: scoreMax, + width: 700, + height: 260, + }), + [scoreSeries, graphXMin, graphXMax, scoreMin, scoreMax] + ); + + const llmStoryCards = useMemo(() => { + const cards = []; + let seq = 1; + + if (activeJob) { + cards.push({ + id: `story-${seq}`, + seq: seq++, + title: "Training Context", + text: `Phase ${activeJob?.phase || "-"} job ${String(activeJob?.job_id || "").slice(0, 8)} is ${activeJob?.status || "unknown"} at ${fmt((Number(activeJob?.progress || 0) * 100), 1)}%.`, + tone: "cyan", + }); + if (rewardSeries.length >= 2 || scoreSeries.length >= 2) { + const rewardStart = rewardSeries.length > 0 ? rewardSeries[0].value : null; + const rewardEnd = rewardSeries.length > 0 ? rewardSeries[rewardSeries.length - 1].value : null; + const scoreStart = scoreSeries.length > 0 ? scoreSeries[0].value : null; + const scoreEnd = scoreSeries.length > 0 ? scoreSeries[scoreSeries.length - 1].value : null; + cards.push({ + id: `story-${seq}`, + seq: seq++, + title: "Learning Trend", + text: `Reward ${rewardStart != null ? fmt(rewardStart, 2) : "-"} -> ${rewardEnd != null ? fmt(rewardEnd, 2) : "-"}; ${scoreSignalMeta.label.toLowerCase()} ${scoreStart != null ? fmt(scoreStart, 3) : "-"} -> ${scoreEnd != null ? fmt(scoreEnd, 3) : "-"}.`, + tone: "indigo", + }); + } + } + + for (const line of (logLines || []).slice(-14)) { + const row = summarizeLogLine(line); + cards.push({ + id: `log-${seq}-${line.slice(0, 8)}`, + seq: seq++, + title: row.title, + text: row.text, + tone: row.tone, + }); + } + + const evalRows = Array.isArray(activeJob?.evaluation_rows) ? activeJob.evaluation_rows : []; + for (const row of evalRows) { + cards.push({ + id: `eval-${seq}-${row.task_id}`, + seq: seq++, + title: "Evaluation Replay", + text: `${row.task_id}: score ${fmt(row.grader_score, 3)}, reward ${fmt(row.total_reward, 2)}, completed ${row.total_completed}, breaches ${row.total_sla_breaches}.`, + tone: "emerald", + }); + } + if (toNumberOrNull(activeJob?.evaluation_avg_score) != null) { + cards.push({ + id: `eval-avg-${seq}`, + seq: seq++, + title: "Evaluation Summary", + text: `Average grader score ${fmt(activeJob.evaluation_avg_score, 3)} across evaluated tasks.`, + tone: "emerald", + }); + } + + for (const event of (envFlowEvents || []).slice(-10)) { + cards.push({ + id: `replay-${seq}-${event.id}`, + seq: seq++, + title: "OpenEnv Replay", + text: summarizeEnvEvent(event), + tone: event?.tone || "cyan", + }); + } + + return cards.slice(-32); + }, [activeJob, rewardSeries, scoreSeries, logLines, envFlowEvents, scoreSignalMeta.label]); + + const progressText = (v) => (Number.isFinite(v) ? `${fmt(Number(v) * 100, 1)}%` : "-"); + const currentTs = useMemo(() => { + const history = Array.isArray(activeJob?.metric_history) ? activeJob.metric_history : []; + const histTs = history.length > 0 ? toNumberOrNull(history[history.length - 1]?.t ?? history[history.length - 1]?.total_timesteps) : null; + return toNumberOrNull(activeJob?.latest_metrics?.total_timesteps) ?? histTs ?? lastLoggedTimesteps; + }, [activeJob, lastLoggedTimesteps]); + const currentReward = useMemo(() => { + const history = Array.isArray(activeJob?.metric_history) ? activeJob.metric_history : []; + const histReward = history.length > 0 ? toNumberOrNull(history[history.length - 1]?.ep_rew_mean ?? history[history.length - 1]?.mean_reward) : null; + return toNumberOrNull(activeJob?.latest_metrics?.ep_rew_mean) + ?? toNumberOrNull(activeJob?.latest_metrics?.mean_reward) + ?? histReward; + }, [activeJob]); + const currentScore = scoreLatest; + + return ( +
+
+
+

+ hub + Endpoint Connectivity Matrix +

+ +
+ {endpointError && ( +
+ {endpointError} +
+ )} +
+ {endpointRows.map((row) => ( +
+
+
{row.label}
+ + {row.ok ? "UP" : "DOWN"} + +
+
+ {row.ok ? `${row.ms} ms` : row.error || "unreachable"} +
+
+ ))} +
+
+ +
+
+

+ tune + Live Training Control +

+
+ + + +
+
+ + {jobError && ( +
+ {jobError} +
+ )} + +
+ + + + +
+ +
+ + +
+
+ +
+

+ monitoring + Live Metrics and Storytelling Timeline +

+ +
+
+
Active Job Status
+
+ {activeJob?.status || "idle"} +
+
+
+
Current Timesteps
+
{currentTs != null ? Number(currentTs).toLocaleString() : "-"}
+
+
+
Current Reward
+
{currentReward != null ? fmt(currentReward, 3) : "-"}
+
+
+
Current {scoreSignalMeta.label}
+
{currentScore != null ? fmt(currentScore, 3) : "-"}
+
+
+ +
+ +
+ Reward line (left axis) + {scoreSignalMeta.label} stair-step line (right axis), updated from live backend metrics. +
+
+ +
+
+
Combined Reward and Score (Dual Axis)
+
+ timesteps {Number.isFinite(graphXMin) ? Number(graphXMin).toLocaleString() : "-"} - {Number.isFinite(graphXMax) ? Number(graphXMax).toLocaleString() : "-"} +
+
+ {rewardSeries.length === 0 && scoreSeries.length === 0 ? ( +
+ Waiting for live metric history from training logs... +
+ ) : ( +
+ + {[0, 1, 2, 3, 4].map((i) => ( + + ))} + {rewardPolyline ? ( + + ) : null} + {scoreStairPolyline ? ( + + ) : null} + +
+ Reward min {rewardMin.toFixed(2)} | max {rewardMax.toFixed(2)} +
+
+ {scoreSignalMeta.label} min {scoreMin.toFixed(3)} | max {scoreMax.toFixed(3)} +
+
+ )} +
+ reward current: {rewardLatest != null ? rewardLatest.toFixed(3) : "-"} | reward best: {rewardBest != null ? rewardBest.toFixed(3) : "-"} | {scoreSignalMeta.label.toLowerCase()} current: {scoreLatest != null ? scoreLatest.toFixed(3) : "-"} | {scoreSignalMeta.label.toLowerCase()} best: {scoreBest != null ? scoreBest.toFixed(3) : "-"} +
+
+ Legend: Reward (line) - {scoreSignalMeta.label} (stair-step hold-last-value){scoreSignalMeta.fallback ? " - fallback metric used because grader score has no live movement yet." : ""} +
+
+ +
+
+
LLM Story Feed (logs + replay + evaluation)
+
Sequential order - {llmStoryCards.length} cards
+
+ {llmStoryCards.length === 0 ? ( +
No storyline events yet.
+ ) : ( +
+ {llmStoryCards.map((card) => ( +
+
+
{card.title}
+
#{card.seq}
+
+
{card.text}
+
+ ))} +
+ )} +
+
+ +
+
+
+

+ history + Training Job History +

+
+ + +
+
+ {jobsError &&
{jobsError}
} + {jobsLoading ? ( +
Loading jobs...
+ ) : ( +
+ + + + + + + + + + + + + {jobs.map((job) => { + const updated = timestampToDate(job.updated_at); + return ( + setActiveJobId(job.job_id)} + > + + + + + + + + ); + })} + {jobs.length === 0 && ( + + + + )} + +
JobStatusPhaseProgressUpdatedAction
{String(job.job_id || "").slice(0, 8)} + + {job.status} + + {job.phase || "-"}{fmt((Number(job.progress || 0) * 100), 1)}%{updated ? updated.toLocaleTimeString() : "-"} + +
+ No training jobs found. +
+
+ )} +
+ +
+
+

+ database + Model Registry (Dynamic) +

+ +
+ {modelError &&
{modelError}
} +
+ + + + + + + + + + + {modelRows.map((m) => ( + + + + + + + ))} + {modelRows.length === 0 && ( + + + + )} + +
LabelPhaseSourceExists
+
{m.label}
+
{m.path || "-"}
+ 		{m.phase || "-"}{m.source || "-"} + {m.exists ? "yes" : "no"} +
+ No models discovered. +
+
+
+
+ +
+

+ api + Automated OpenEnv Workflow (`reset`, `step`, `state`, `grade`) +

+
+ Runs sequentially across all available tasks and records each stage in chronological order. +
+ + {envError && ( +
+ {envError} +
+ )} + +
+ + + + +
+ +
+ +
+ + {envFlowSummary && ( +
+
Tasks Executed: {envFlowSummary.tasks_executed}
+
Total Steps Executed: {envFlowSummary.total_steps_executed}
+
Average Score: {envFlowSummary.avg_score != null ? fmt(envFlowSummary.avg_score, 3) : "-"}
+
Passed Tasks: {envFlowSummary.passed_tasks}
+
+ )} + + {envFlowRuns.length > 0 && ( +
+ + + + + + + + + + + + + + {envFlowRuns.map((row) => ( + + + + + + + + + + ))} + +
TaskStepsScoreCompletedBacklogSLA BreachesPassed
{row.task_id}{row.steps_executed}{row.score != null ? fmt(row.score, 3) : "-"}{row.final_completed ?? "-"}{row.final_backlog ?? "-"}{row.final_sla_breaches ?? "-"} + {row.passed === true ? "true" : row.passed === false ? "false" : "-"} +
+
+ )} + +
+ {envFlowEvents.length === 0 ? ( +
No automated workflow events yet.
+ ) : ( + envFlowEvents.map((event) => ( +
+
+
{workflowStageLabel(event.stage)}
+
+ #{event.seq} | {new Date(event.ts).toLocaleTimeString()} +
+
+
+ {summarizeEnvEvent(event)} +
+ {payloadHighlights(event.payload).length > 0 && ( +
+ {payloadHighlights(event.payload).map(([k, v]) => ( + + {k}: {v} + + ))} +
+ )} +
+ )) + )} +
+
+
+ ); +} + + diff --git a/frontend/react/src/hooks/useStorySimulation.js b/frontend/react/src/hooks/useStorySimulation.js new file mode 100644 index 0000000000000000000000000000000000000000..1dc8eb0c662435bfdf62c9a40ae47383052083d5 --- /dev/null +++ b/frontend/react/src/hooks/useStorySimulation.js @@ -0,0 +1,474 @@ +import { useState, useRef, useCallback, useEffect } from "react"; +import { api } from "../api/client"; + +// ───────────────────────────────────────────────────────────────────────────── +// Narrative translator: maps raw action → human-readable cause→effect story +// ───────────────────────────────────────────────────────────────────────────── +function mapActionToStory(actionType, payload, reward, backlogDelta, slaDelta, fairnessDelta) { + let title = "Standard Processing Cycle"; + let desc = "The system advanced one cycle and continued normal queue processing."; + let reason = "No override was required, so routine processing continued."; + let icon = "schedule"; + let type = reward > 0 ? "success" : "info"; + + const changes = []; + if (backlogDelta < 0) changes.push(`backlog improved by ${Math.abs(backlogDelta)} case(s)`); + else if (backlogDelta > 0) changes.push(`backlog increased by ${backlogDelta} case(s)`); + else changes.push("backlog stayed stable"); + + if (slaDelta > 0) changes.push(`${slaDelta} new SLA breach(es) occurred`); + else if (slaDelta < 0) changes.push(`${Math.abs(slaDelta)} SLA breach(es) recovered`); + + if (Number.isFinite(Number(fairnessDelta)) && Number(fairnessDelta) !== 0) { + const v = Number(fairnessDelta); + changes.push(`fairness gap ${v > 0 ? "worsened" : "improved"} by ${Math.abs(v).toFixed(3)}`); + } + + const effectClause = `${changes.join(", ")}.`; + if (slaDelta > 0) type = "error"; + + switch (actionType) { + case "assign_capacity": + title = "Capacity Assigned"; + desc = `Officers were assigned to '${payload.service_target ?? payload.service ?? "target queue"}'; ${effectClause}`; + reason = "The agent detected staffing pressure and increased capacity where it could reduce delay."; + icon = "group_add"; + break; + case "reallocate_officers": + title = "Staff Reallocated"; + desc = `Officers were reallocated toward higher-pressure services; ${effectClause}`; + reason = `The agent shifted staffing to reduce bottlenecks in '${payload.service_target ?? "priority"}' services.`; + icon = "compare_arrows"; + break; + case "request_missing_documents": + title = "Documents Requested"; + desc = `Missing documents were requested to unblock pending files; ${effectClause}`; + reason = "The agent prioritized document blockers to avoid queue stagnation."; + icon = "rule_folder"; + type = type !== "error" ? "success" : type; + break; + case "escalate_service": + title = "Service Escalated"; + desc = `At-risk services were escalated for faster handling; ${effectClause}`; + reason = "Escalation was used to protect SLA-critical cases."; + icon = "warning"; + type = "warning"; + break; + case "set_priority_mode": + title = "Priority Mode Updated"; + desc = `Priority mode switched to '${payload.priority_mode ?? "balanced"}'; ${effectClause}`; + reason = "The agent changed queue strategy to better match current workload pressure."; + icon = "model_training"; + break; + default: + desc = `Routine processing executed; ${effectClause}`; + break; + } + + if (reward < 0 && type === "info") type = "warning"; + + const isHighReward = reward >= 1.0; + const isHugeImpact = backlogDelta <= -5; + return { title, desc, reason, icon, type, isHighReward, isHugeImpact }; +} + +// Determines the simulation phase label from step index and total +function getPhase(step, maxSteps) { + const pct = step / Math.max(maxSteps, 1); + if (pct < 0.33) return "early"; + if (pct < 0.67) return "middle"; + return "late"; +} + +// Detect if a step is a "key decision" turning point +function isKeyDecision(s, backlogDelta) { + return ( + Math.abs(Number(s.reward)) >= 1.0 || // high reward magnitude + (backlogDelta !== 0 && Math.abs(backlogDelta) >= 5) || // large backlog swing + Boolean(s.invalid_action) // failed action = notable event + ); +} + +// ───────────────────────────────────────────────────────────────────────────── +// Hook +// ───────────────────────────────────────────────────────────────────────────── +export function useStorySimulation({ defaultTask }) { + const [taskId, setTaskId] = useState(defaultTask || "district_backlog_easy"); + const [maxSteps, setMaxSteps] = useState(40); + const [agentMode, setAgentMode] = useState("trained_rl"); + const [policyName, setPolicyName] = useState("backlog_clearance"); + const [modelPath, setModelPath] = useState(""); + const [modelType, setModelType] = useState("maskable"); + const [availablePolicies, setAvailablePolicies] = useState([]); + const [availableModels, setAvailableModels] = useState([]); + const [configError, setConfigError] = useState(""); + const [running, setRunning] = useState(false); + const [starting, setStarting] = useState(false); + const [runId, setRunId] = useState(""); + + const [kpis, setKpis] = useState({ + backlog: 0, backlogDelta: 0, + slaBreaches: 0, slaDelta: 0, + fairness: 0, fairnessDelta: 0, + }); + + const [timeline, setTimeline] = useState([]); + const [resources, setResources] = useState([]); + + // Progress tracking + const [currentStep, setCurrentStep] = useState(0); + + // Before vs after journey stats + const [journeyStats, setJourneyStats] = useState(null); // null = not yet done + + // Internal refs + const lastState = useRef({ backlog: 0, sla: 0, fairness: 0 }); + const initialSnapshot = useRef(null); // captured on first real step + const stepCount = useRef(0); + const maxStepsRef = useRef(40); + + useEffect(() => { + let mounted = true; + (async () => { + try { + const [policiesRes, modelsV1Res, modelsV2Res] = await Promise.allSettled([ + api("/agents"), + api("/rl_models"), + api("/rl/models"), + ]); + if (!mounted) return; + + const policyRows = policiesRes.status === "fulfilled" && Array.isArray(policiesRes.value) ? policiesRes.value : []; + setAvailablePolicies(policyRows); + if (policyRows.length > 0 && !policyRows.includes(policyName)) { + setPolicyName(policyRows[0]); + } + + const modelRowsV1 = modelsV1Res.status === "fulfilled" && Array.isArray(modelsV1Res.value?.models) + ? modelsV1Res.value.models + : []; + const modelRowsV2 = modelsV2Res.status === "fulfilled" && Array.isArray(modelsV2Res.value) + ? modelsV2Res.value.map((row) => ({ + label: row?.model_path ? String(row.model_path).split(/[\\/]/).pop() : "model", + path: row?.model_path ? (String(row.model_path).toLowerCase().endsWith(".zip") ? row.model_path : `${row.model_path}.zip`) : "", + exists: Boolean(row?.exists), + model_type: "maskable", + })) + : []; + + const dedupe = new Map(); + for (const m of [...modelRowsV1, ...modelRowsV2]) { + const key = String(m?.path || "").replace(/\\/g, "/").toLowerCase(); + if (!key || dedupe.has(key)) continue; + dedupe.set(key, m); + } + const existingModels = Array.from(dedupe.values()).filter((m) => Boolean(m?.exists)); + setAvailableModels(existingModels); + const preferred = + existingModels.find((m) => String(m.path || "").toLowerCase().includes("phase2_final")) || + existingModels[0]; + if (preferred?.path) { + setModelPath(preferred.path); + setModelType(preferred.model_type || "maskable"); + setAgentMode((prev) => (prev === "baseline_policy" ? "trained_rl" : prev)); + } + } catch (err) { + if (!mounted) return; + setConfigError(err?.message || "Failed to load simulation options."); + } + })(); + return () => { + mounted = false; + }; + }, []); + + const startSimulation = async () => { + setStarting(true); + setConfigError(""); + setJourneyStats(null); + setCurrentStep(0); + initialSnapshot.current = null; + stepCount.current = 0; + maxStepsRef.current = maxSteps; + try { + const payload = { + task_id: taskId, + agent_mode: agentMode, + max_steps: maxSteps, + policy_name: policyName, + model_path: modelPath || null, + model_type: modelType, + }; + + const started = await api("/simulation/live/start", { + method: "POST", + body: JSON.stringify(payload), + }); + + setRunId(started.run_id); + setTimeline([{ + id: "start", + time: "Step 0", + title: "Simulation Initialized", + desc: `Scenario locked: ${taskId.replace(/_/g, " ")}. Agent mode '${agentMode}' engaged — agent begins resolving backlog.`, + impact: 0, + type: "info", + icon: "rocket_launch", + phase: "early", + key: false, + }]); + setResources([]); + lastState.current = { backlog: 0, sla: 0, fairness: 0 }; + setRunning(true); + } catch (err) { + console.error("Start failed:", err); + setTimeline([{ + id: "error", + time: "—", + title: "Initialization Failed", + desc: `Backend error: ${err.message || "Cannot start simulation."}`, + impact: 0, + type: "error", + icon: "error", + phase: "early", + key: false, + }]); + setConfigError(err?.message || "Cannot start simulation."); + } finally { + setStarting(false); + } + }; + + const stopSimulation = async () => { + if (!runId) return; + try { + await api(`/simulation/live/${runId}/stop`, { method: "POST" }); + } catch (err) { + console.error(err); + } finally { + setRunning(false); + } + }; + + // Polling loop — runs while running=true + const runLoop = useCallback(async (rid, cancelled) => { + if (cancelled.v) return; + try { + const res = await api("/simulation/live/step", { + method: "POST", + body: JSON.stringify({ run_id: rid }), + }); + + if (cancelled.v) return; + + if (res.step) { + const s = res.step; + stepCount.current += 1; + const stepNum = Number(s.step ?? stepCount.current); + setCurrentStep(stepNum); + + const currentBacklog = Number(s.backlog ?? 0); + const currentSla = Number(s.sla_breaches ?? 0); + const currentFairness = Number(s.fairness_gap ?? 0); + + // Capture initial snapshot from step 1 + if (initialSnapshot.current === null) { + initialSnapshot.current = { + backlog: currentBacklog, + sla: currentSla, + fairness: currentFairness, + }; + } + + const backlogDelta = currentBacklog - lastState.current.backlog; + const slaDelta = currentSla - lastState.current.sla; + const fairnessDelta = currentFairness - lastState.current.fairness; + + setKpis({ + backlog: currentBacklog, + backlogDelta, + slaBreaches: currentSla, + slaDelta, + fairness: currentFairness, + fairnessDelta, + }); + + lastState.current = { backlog: currentBacklog, sla: currentSla, fairness: currentFairness }; + + const payload = typeof s.action_payload === "string" + ? (() => { try { return JSON.parse(s.action_payload); } catch { return {}; } })() + : (s.action_payload || {}); + + const story = mapActionToStory( + s.action_type || "advance_time", + payload, + Number(s.reward), + backlogDelta, + slaDelta, + fairnessDelta + ); + + const phase = getPhase(stepNum, maxStepsRef.current); + const key = isKeyDecision(s, backlogDelta); + const improvesBacklog = backlogDelta < 0; + const worsensBacklog = backlogDelta > 0; + const worsensSla = slaDelta > 0; + const improvesSla = slaDelta < 0; + const outcomeLabel = improvesBacklog || improvesSla + ? "Improvement" + : worsensBacklog || worsensSla + ? "Degradation" + : "Stable"; + const outcomeType = outcomeLabel === "Improvement" ? "success" : outcomeLabel === "Degradation" ? "warning" : "info"; + + const newEvent = { + id: `step-${stepNum}`, + time: `Step ${stepNum}`, + title: s.invalid_action ? "Action Blocked" : story.title, + desc: s.invalid_action + ? "This action was blocked by environment constraints; the agent adapts on the next step." + : story.desc, + reason: s.invalid_action ? "The attempted operation violated environment constraints (e.g. over-assignment)." : story.reason, + impact: Number(s.reward), + type: s.invalid_action ? "error" : story.type, + icon: s.invalid_action ? "block" : story.icon, + isHighReward: story.isHighReward && !s.invalid_action, + isHugeImpact: story.isHugeImpact && !s.invalid_action, + phase, + key, + outcomeLabel, + outcomeType, + backlogDelta, // Used for phase summary + }; + + // Collapse consecutive identical titles (deduplication for repeated events) + setTimeline((prev) => { + const [top, ...rest] = prev; + if ( + top && + top.title === newEvent.title && + top.phase === newEvent.phase && + !top.key && + !newEvent.key + ) { + // Merge: bump count, accumulate reward and backlog diff + const merged = { + ...top, + id: newEvent.id, + time: `${top.time?.split("–")[0]?.trim()}–${newEvent.time}`, + desc: top.desc, + impact: Number(top.impact) + Number(newEvent.impact), + backlogDelta: (top.backlogDelta || 0) + backlogDelta, + _count: (top._count || 1) + 1, + }; + return [merged, ...rest].slice(0, 30); + } + return [newEvent, ...prev].slice(0, 30); + }); + + // Update queue monitors + if (Array.isArray(s.queue_rows) && s.queue_rows.length > 0) { + const maxCases = Math.max(...s.queue_rows.map((q) => q.active_cases ?? 0), 1); + setResources(s.queue_rows.map((q) => ({ + name: (q.service ?? q.service_type ?? "unknown").replace(/_/g, " ").toUpperCase(), + activeCases: q.active_cases ?? 0, + percentage: Math.min(100, Math.floor(((q.active_cases ?? 0) / maxCases) * 100)), + }))); + } + } + + // Episode done + if (res.done || res.step?.done) { + const finalBacklog = lastState.current.backlog; + const initSnap = initialSnapshot.current ?? { backlog: finalBacklog, sla: 0, fairness: 0 }; + + const backlogImprovement = initSnap.backlog > 0 + ? Math.round(((initSnap.backlog - finalBacklog) / initSnap.backlog) * 100) + : 0; + + setJourneyStats({ + initialBacklog: initSnap.backlog, + finalBacklog, + backlogImprovement, + initialSla: initSnap.sla, + finalSla: lastState.current.sla, + totalSteps: stepCount.current, + finalScore: res.score ?? null, + totalReward: res.total_reward ?? null, + }); + + setTimeline((prev) => [{ + id: "end", + time: "Final", + title: "Episode Complete", + desc: `Resolution finished in ${stepCount.current} steps. Final score: ${res.score != null ? (res.score * 100).toFixed(1) + "%" : "N/A"}. Backlog ${finalBacklog < initSnap.backlog ? "reduced" : "unchanged"} — SLAs verified.`, + impact: res.total_reward ?? 0, + type: "success", + icon: "verified", + phase: "late", + key: true, + }, ...prev]); + + setRunning(false); + return; + } + + setTimeout(() => runLoop(rid, cancelled), 1000); + } catch (err) { + if (!cancelled.v) { + setRunning(false); + setTimeline((prev) => [{ + id: `error-${Date.now()}`, + time: "Halted", + title: "System Error Detected", + desc: `Backend synchronization failed: ${err.message}`, + impact: 0, + type: "error", + icon: "warning", + phase: "late", + key: false, + }, ...prev]); + } + } + }, []); + + // Start/stop the polling loop reactively + const cancelRef = useRef({ v: false }); + useEffect(() => { + if (!running || !runId) { + cancelRef.current.v = true; + return undefined; + } + cancelRef.current = { v: false }; + const boot = setTimeout(() => { + if (!cancelRef.current.v) { + runLoop(runId, cancelRef.current); + } + }, 100); + return () => { + clearTimeout(boot); + cancelRef.current.v = true; + }; + }, [running, runId, runLoop]); + + return { + taskId, setTaskId, + maxSteps, setMaxSteps, + agentMode, setAgentMode, + policyName, setPolicyName, + modelPath, setModelPath, + modelType, setModelType, + availablePolicies, + availableModels, + configError, + running, starting, + currentStep, + kpis, timeline, resources, + journeyStats, + startSimulation, stopSimulation, + }; +} + + + + diff --git a/frontend/react/src/main.jsx b/frontend/react/src/main.jsx new file mode 100644 index 0000000000000000000000000000000000000000..cf7455f7cf67d2775a17cf866fa828c00876834e --- /dev/null +++ b/frontend/react/src/main.jsx @@ -0,0 +1,15 @@ +import React from "react"; +import { createRoot } from "react-dom/client"; +import App from "./App"; +import "./styles.css"; + +const rootEl = document.getElementById("app-root"); +if (!rootEl) { + throw new Error("Missing #app-root mount node"); +} + +createRoot(rootEl).render( + + + , +); diff --git a/frontend/react/src/styles.css b/frontend/react/src/styles.css new file mode 100644 index 0000000000000000000000000000000000000000..70f4f0283a0323d4fadb010ea5b0996b91b4e196 --- /dev/null +++ b/frontend/react/src/styles.css @@ -0,0 +1,525 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + --bg: #030303; + --panel: #0d0d0d; + --line: #272727; + --text: #f5f5f5; + --muted: #a7a7a7; + --accent: #ffffff; +} + +* { + box-sizing: border-box; +} + +html, +body, +#root { + margin: 0; + min-height: 100%; + background: radial-gradient(circle at 5% 5%, #1a1a1a 0%, #050505 45%, #000 100%); + color: var(--text); + font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif; +} + +.app-shell { + display: grid; + grid-template-columns: 280px 1fr; + min-height: 100vh; +} + +.sidebar { + border-right: 1px solid var(--line); + background: linear-gradient(180deg, #0a0a0a, #050505); + padding: 18px; +} + +.sidebar h1 { + margin: 0; + font-size: 24px; +} + +.sidebar-sub { + color: var(--muted); + font-size: 13px; + margin: 10px 0 14px; +} + +.nav-btn { + width: 100%; + text-align: left; + border: 1px solid #3b3b3b; + color: #d8d8d8; + background: transparent; + border-radius: 10px; + padding: 10px 12px; + margin-bottom: 8px; + cursor: pointer; +} + +.nav-btn.active { + background: #fff; + color: #000; + border-color: #fff; + font-weight: 700; +} + +.content { + padding: 20px; +} + +.status-banner { + border: 1px solid var(--line); + background: #0a0a0a; + border-radius: 10px; + padding: 10px 12px; + color: var(--muted); + font-size: 12px; + margin-bottom: 12px; +} + +.module-grid { + display: grid; + grid-template-columns: 1fr; + gap: 12px; +} + +.panel { + border: 1px solid var(--line); + border-radius: 12px; + background: var(--panel); + padding: 14px; +} + +.hero-panel { + background: linear-gradient(120deg, #fff 0%, #d7d7d7 40%, #8c8c8c 100%); + color: #000; +} + +.hero-panel code { + background: rgba(0, 0, 0, 0.12); + padding: 2px 6px; + border-radius: 8px; +} + +h2, +h3 { + margin: 0 0 10px; +} + +.control-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); + gap: 10px; +} + +label { + display: grid; + gap: 6px; + color: var(--muted); + font-size: 12px; +} + +input, +select, +button { + border: 1px solid #3a3a3a; + border-radius: 8px; + padding: 8px 10px; + font-size: 13px; + color: var(--text); + background: #111; +} + +button { + background: var(--accent); + color: #000; + border: none; + font-weight: 700; + cursor: pointer; +} + +button.ghost { + border: 1px solid #505050; + background: transparent; + color: var(--text); +} + +button:disabled { + opacity: 0.6; + cursor: wait; +} + +.row { + display: flex; + flex-wrap: wrap; + gap: 8px; + margin-top: 10px; +} + +.loading-inline { + margin-top: 10px; + display: inline-flex; + align-items: center; + gap: 8px; + border: 1px solid #2a2a2a; + background: #090909; + border-radius: 999px; + padding: 6px 10px; + color: #cdcdcd; + font-size: 12px; +} + +.spinner-dot { + width: 10px; + height: 10px; + border-radius: 999px; + background: #fff; + display: inline-block; + animation: pulse 1s ease-in-out infinite; +} + +@keyframes pulse { + 0% { opacity: 0.25; transform: scale(0.8); } + 50% { opacity: 1; transform: scale(1); } + 100% { opacity: 0.25; transform: scale(0.8); } +} + +.metric-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(145px, 1fr)); + gap: 10px; +} + +.metric-card { + border: 1px solid var(--line); + border-radius: 10px; + background: #0a0a0a; + padding: 10px; + display: grid; + gap: 4px; +} + +.metric-card span { + color: var(--muted); + font-size: 12px; +} + +.metric-card strong { + font-size: 20px; +} + +.flow-list { + margin: 0; + padding-left: 20px; + color: #d8d8d8; + line-height: 1.5; +} + +.tag-wrap { + display: flex; + flex-wrap: wrap; + gap: 8px; +} + +.tag { + border: 1px solid #444; + border-radius: 999px; + padding: 4px 10px; + font-size: 12px; +} + +.chart-canvas { + width: 100%; + border: 1px solid #1d2f42; + border-radius: 10px; + background: #03070d; +} + +.step-card { + margin-top: 10px; + border: 1px solid #2a2a2a; + border-radius: 10px; + padding: 12px; + background: #090909; +} + +.animate-in { + animation: rise 0.35s ease-out; +} + +@keyframes rise { + from { + transform: translateY(8px); + opacity: 0; + } + to { + transform: translateY(0); + opacity: 1; + } +} + +.step-head { + display: flex; + justify-content: space-between; + margin-bottom: 8px; +} + +.step-meta { + display: flex; + flex-wrap: wrap; + gap: 10px; + color: #c5c5c5; + font-size: 12px; +} + +.queue-list { + margin-top: 10px; + display: grid; + gap: 7px; +} + +.queue-row { + display: grid; + grid-template-columns: 150px 1fr 40px; + gap: 8px; + align-items: center; +} + +.queue-label { + font-size: 12px; + color: #cfcfcf; +} + +.queue-bar-wrap { + background: #121212; + border: 1px solid #2b2b2b; + border-radius: 999px; + overflow: hidden; + height: 10px; +} + +.queue-bar { + height: 100%; + background: linear-gradient(90deg, #fff, #8f8f8f); + transition: width 0.5s ease; +} + +.queue-val { + text-align: right; + font-size: 12px; + color: #ddd; +} + +.jobs-list { + display: grid; + gap: 8px; +} + +.job-item { + display: flex; + justify-content: space-between; + align-items: center; + text-align: left; + border: 1px solid #3b3b3b; + border-radius: 10px; + background: #0b0b0b; + color: #ededed; +} + +.job-item.active { + border-color: #fff; +} + +.job-status { + text-transform: uppercase; + font-size: 11px; + letter-spacing: 0.05em; + color: #ccc; +} + +.job-status.running { + color: #fff; +} + +.job-status.completed { + color: #bfbfbf; +} + +.job-status.failed { + color: #8f8f8f; +} + +.progress-track { + margin-top: 10px; + height: 10px; + border-radius: 999px; + background: #111; + border: 1px solid #2a2a2a; + overflow: hidden; +} + +.progress-fill { + height: 100%; + background: linear-gradient(90deg, #fff, #888); + transition: width 0.5s ease; +} + +.compare-bars { + display: grid; + gap: 8px; +} + +.compare-row { + display: grid; + grid-template-columns: 180px 1fr 60px; + gap: 10px; + align-items: center; +} + +.compare-label, +.compare-value { + font-size: 12px; +} + +.compare-track { + height: 12px; + border: 1px solid #2f2f2f; + background: #0f0f0f; + border-radius: 999px; + overflow: hidden; +} + +.compare-fill { + height: 100%; + background: linear-gradient(90deg, #fff, #8d8d8d); + transition: width 0.6s ease; +} + +.table-wrap { + margin-top: 10px; + border: 1px solid #252525; + border-radius: 10px; + overflow: auto; +} + +table { + width: 100%; + border-collapse: collapse; + font-size: 12px; +} + +th, +td { + border-bottom: 1px solid #1d1d1d; + text-align: left; + padding: 8px; + white-space: nowrap; +} + +th { + background: #0b0b0b; +} + +.muted { + color: var(--muted); + font-size: 12px; +} + +.mono { + font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; + font-size: 12px; +} + +.compliance-card { + border-width: 1px; +} + +.compliance-card.status-pass { + border-color: #4f4f4f; + box-shadow: inset 0 0 0 1px #2d2d2d; +} + +.compliance-card.status-fail { + border-color: #7a7a7a; + box-shadow: inset 0 0 0 1px #545454; +} + +.compliance-card.status-unknown { + border-color: #3a3a3a; +} + +.log-grid { + display: grid; + gap: 8px; + max-height: 320px; + overflow: auto; + margin-top: 8px; + padding-right: 2px; +} + +.log-card { + border: 1px solid #2a2a2a; + border-radius: 10px; + background: #090909; + padding: 10px; + display: grid; + gap: 4px; +} + +.log-title { + font-weight: 700; + letter-spacing: 0.04em; + font-size: 12px; +} + +.log-row { + font-size: 12px; + color: #d4d4d4; + line-height: 1.4; +} + +.log-start { + border-left: 3px solid #c8c8c8; +} + +.log-step { + border-left: 3px solid #8f8f8f; +} + +.log-end { + border-left: 3px solid #ffffff; +} + +.log-info { + border-left: 3px solid #5b5b5b; +} + +.terminal-log { + max-height: 280px; + overflow: auto; + border: 1px solid #262626; + border-radius: 10px; + background: #070707; + padding: 10px; + margin: 0; + font-size: 12px; +} + +@media (max-width: 980px) { + .app-shell { + grid-template-columns: 1fr; + } + + .sidebar { + border-right: none; + border-bottom: 1px solid var(--line); + } + + .queue-row { + grid-template-columns: 120px 1fr 30px; + } +} diff --git a/frontend/react/tailwind.config.js b/frontend/react/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..247267aa810c945671931d775fa8e21cefee591a --- /dev/null +++ b/frontend/react/tailwind.config.js @@ -0,0 +1,100 @@ +/** @type {import('tailwindcss').Config} */ +export default { + darkMode: "class", + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: { + "colors": { + "on-error": "#690005", + "surface-container-high": "#292932", + "on-primary-fixed-variant": "#2f2ebe", + "tertiary-fixed-dim": "#ffb783", + "on-secondary-fixed": "#002113", + "inverse-surface": "#e4e1ed", + "inverse-on-surface": "#303038", + "coral-warning": "#fb7185", + "surface-container": "#1f1f27", + "inverse-primary": "#494bd6", + "on-tertiary": "#4f2500", + "on-error-container": "#ffdad6", + "secondary-fixed-dim": "#4edea3", + "outline": "#908fa0", + "on-surface-variant": "#c7c4d7", + "error": "#ffb4ab", + "on-secondary-container": "#00311f", + "tertiary-container": "#d97721", + "surface-dim": "#13131b", + "primary": "#c0c1ff", + "surface-variant": "#34343d", + "surface-container-low": "#1b1b23", + "error-container": "#93000a", + "surface-bright": "#393841", + "on-tertiary-container": "#452000", + "secondary-container": "#00a572", + "on-tertiary-fixed-variant": "#703700", + "indigo-primary": "#6366f1", + "primary-fixed-dim": "#c0c1ff", + "on-primary-container": "#0d0096", + "on-tertiary-fixed": "#301400", + "tertiary": "#ffb783", + "on-primary-fixed": "#07006c", + "background": "#13131b", + "primary-fixed": "#e1e0ff", + "secondary-fixed": "#6ffbbe", + "primary-container": "#8083ff", + "emerald-positive": "#10b981", + "on-surface": "#e4e1ed", + "on-background": "#e4e1ed", + "surface-tint": "#c0c1ff", + "on-secondary-fixed-variant": "#005236", + "outline-variant": "#464554", + "on-primary": "#1000a9", + "on-secondary": "#003824", + "secondary": "#4edea3", + "violet-action": "#8b5cf6", + "rose-alert": "#f43f5e", + "amber-soft": "#f59e0b", + "surface": "#13131b", + "surface-container-lowest": "#0d0d15", + "surface-container-highest": "#34343d", + "surface-glass": "rgba(30, 41, 59, 0.7)", + "tertiary-fixed": "#ffdcc5", + "background-deep": "#0f172a" + }, + "borderRadius": { + "DEFAULT": "0.25rem", + "lg": "0.5rem", + "xl": "0.75rem", + "full": "9999px" + }, + "spacing": { + "container-padding": "2rem", + "card-padding": "1.25rem", + "section-gap": "1.5rem", + "grid-gutter": "1rem" + }, + "fontFamily": { + "display-metric": ["Manrope"], + "delta-pill": ["Inter"], + "label-caps": ["Inter"], + "headline-md": ["Manrope"], + "headline-lg": ["Manrope"], + "body-sm": ["Inter"], + "body-base": ["Inter"] + }, + "fontSize": { + "display-metric": ["48px", { "lineHeight": "1.1", "letterSpacing": "-0.02em", "fontWeight": "700" }], + "delta-pill": ["12px", { "lineHeight": "12px", "fontWeight": "700" }], + "label-caps": ["12px", { "lineHeight": "16px", "letterSpacing": "0.05em", "fontWeight": "600" }], + "headline-md": ["18px", { "lineHeight": "24px", "fontWeight": "600" }], + "headline-lg": ["24px", { "lineHeight": "32px", "fontWeight": "600" }], + "body-sm": ["14px", { "lineHeight": "20px", "fontWeight": "400" }], + "body-base": ["16px", { "lineHeight": "24px", "fontWeight": "400" }] + } + }, + }, + plugins: [], +} diff --git a/frontend/react/vite.config.js b/frontend/react/vite.config.js new file mode 100644 index 0000000000000000000000000000000000000000..fb6695a7c1e843d0bf1bd5291762c0f2232429cf --- /dev/null +++ b/frontend/react/vite.config.js @@ -0,0 +1,20 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +const devApiTarget = process.env.VITE_DEV_API_TARGET || "http://127.0.0.1:7860"; + +export default defineConfig({ + plugins: [react()], + base: "/ui/", + server: { + host: "0.0.0.0", + port: 5173, + strictPort: true, + proxy: { + "/api": { + target: devApiTarget, + changeOrigin: true, + }, + }, + }, +}); diff --git a/inference.py b/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..0c676c312ea212287aa524cda6714904cc231f6a --- /dev/null +++ b/inference.py @@ -0,0 +1,333 @@ +#!/usr/bin/env python3 +""" +OpenEnv baseline inference runner for Gov Workflow OpenEnv. + +This script runs all 3 benchmark tasks (easy -> medium -> hard) and emits +strict, line-oriented stdout logs: + +[START] task= env= model= +[STEP] step= action= reward=<0.00> done= error= +[END] success= steps= score= rewards= +""" + +from __future__ import annotations + +import json +import os +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from openai import OpenAI + +from app.api_gateway import create_env_gateway +from app.baselines import backlog_clearance_policy +from app.models import ActionModel, ActionType, ObservationModel +from app.tasks import get_task + +try: + from dotenv import load_dotenv +except Exception: + load_dotenv = None # type: ignore[assignment] + +if load_dotenv is not None: + _ROOT = Path(__file__).resolve().parent + load_dotenv(dotenv_path=_ROOT / ".env", override=False) + +API_BASE_URL = os.getenv("API_BASE_URL", "https://integrate.api.nvidia.com/v1") +MODEL_NAME = os.getenv("MODEL_NAME", "meta/llama-3.3-70b-instruct") +HF_TOKEN = os.getenv("HF_TOKEN") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") +API_KEY = HF_TOKEN or OPENAI_API_KEY or os.getenv("API_KEY") +LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME") +NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY") +NVIDIA_API_KEY_2 = os.getenv("NVIDIA_API_KEY_2") +NVIDIA_MODEL = os.getenv("NVIDIA_MODEL", "") +ENV_TRANSPORT = os.getenv("OPENENV_ENV_TRANSPORT", "auto").strip().lower() +ENV_BASE_URL = os.getenv("OPENENV_ENV_BASE_URL", "http://127.0.0.1:7860").strip() +ENV_API_PREFIX = os.getenv("OPENENV_ENV_API_PREFIX", "").strip() +FORCE_FASTAPI_GATEWAY = os.getenv("FORCE_FASTAPI_GATEWAY", "0").strip().lower() in { + "1", + "true", + "yes", + "on", +} + +LEGACY_MODEL_POOL = [ + "meta/llama-3.3-70b-instruct", + "qwen/qwen3-next-80b-a3b-instruct", + "moonshotai/kimi-k2-instruct-0905", + "meta/llama-3.1-405b-instruct", + "deepseek-ai/deepseek-v3.2", + "qwen/qwq-32b", + "mistralai/mixtral-8x22b-instruct-v0.1", + "google/gemma-3-27b-it", + "microsoft/phi-4-mini-instruct", + "meta/llama-3.1-8b-instruct", +] + +BENCHMARK = "gov-workflow-openenv" +TASKS = [ + "district_backlog_easy", + "mixed_urgency_medium", + "cross_department_hard", +] +MAX_STEPS = int(os.getenv("MAX_STEPS", "80")) +SUCCESS_SCORE_THRESHOLD = float(os.getenv("SUCCESS_SCORE_THRESHOLD", "0.50")) +TEMPERATURE = 0.0 +MAX_TOKENS = 220 + +SYSTEM_PROMPT = ( + "You are controlling a government workflow environment. " + "Return exactly one JSON object with these keys: " + "action_type (required), and optional priority_mode, service, target_service, case_id, officer_delta. " + "Allowed action_type: set_priority_mode, assign_capacity, request_missing_documents, " + "escalate_service, advance_time, reallocate_officers. " + "Allowed priority_mode: urgent_first, oldest_first, balanced, backlog_clearance. " + "Allowed services: passport, driving_license, gst_registration, income_certificate, caste_certificate, " + "birth_certificate, land_registration. " + "Return lowercase values only and no explanation." +) + + +@dataclass +class EpisodeLog: + rewards: list[float] + steps: int + score: float + success: bool + + +@dataclass +class RuntimeContext: + clients: list[OpenAI] + model_pool: list[str] + start_model_label: str + + +def _clean_token(value: str | None) -> str | None: + if value is None: + return None + value = value.strip() + return value or None + + +def _bool_str(value: bool) -> str: + return "true" if value else "false" + + +def _sanitize_action_for_log(action: ActionModel) -> str: + return json.dumps(action.model_dump(exclude_none=True), separators=(",", ":")) + + +def _sanitize_error_for_log(error: str | None) -> str: + if not error: + return "null" + return error.replace("\n", " ").replace("\r", " ") + + +def _extract_json_object(text: str) -> dict[str, Any] | None: + text = (text or "").strip() + if not text: + return None + + try: + parsed = json.loads(text) + if isinstance(parsed, dict): + return parsed + except json.JSONDecodeError: + pass + + match = re.search(r"\{.*\}", text, flags=re.DOTALL) + if not match: + return None + + try: + parsed = json.loads(match.group(0)) + except json.JSONDecodeError: + return None + + return parsed if isinstance(parsed, dict) else None + + +def _coerce_action(payload: dict[str, Any] | None) -> ActionModel: + if not payload: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + norm = dict(payload) + + for key in ("action_type", "priority_mode", "service", "target_service"): + if isinstance(norm.get(key), str): + norm[key] = norm[key].strip().lower() + + if "officer_delta" in norm: + try: + norm["officer_delta"] = int(norm["officer_delta"]) + except (TypeError, ValueError): + norm["officer_delta"] = 0 + + try: + return ActionModel(**norm) + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def _build_user_prompt(task_id: str, step: int, observation: dict[str, Any], last_reward: float) -> str: + compact_obs = json.dumps(observation, separators=(",", ":")) + return ( + f"Task={task_id}. Step={step}. LastReward={last_reward:.2f}. " + f"Observation={compact_obs}" + ) + + +def _choose_action( + runtime: RuntimeContext, + *, + task_id: str, + step: int, + observation: ObservationModel, + last_reward: float, +) -> ActionModel: + prompt = _build_user_prompt(task_id, step, observation.model_dump(mode="json"), last_reward) + + for client in runtime.clients: + for model_name in runtime.model_pool: + try: + completion = client.chat.completions.create( + model=model_name, + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": prompt}, + ], + temperature=TEMPERATURE, + max_tokens=MAX_TOKENS, + timeout=8.0, + stream=False, + ) + content = (completion.choices[0].message.content or "").strip() + action = _coerce_action(_extract_json_object(content)) + return action + except Exception: + # Try next model / key. + continue + + # Final fallback when all API attempts fail or no API key exists. + try: + return backlog_clearance_policy(observation) + except Exception: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +def _run_task(runtime: RuntimeContext, task_id: str) -> EpisodeLog: + env = create_env_gateway( + task_id=task_id, + seed=get_task(task_id).seed, + mode=ENV_TRANSPORT if ENV_TRANSPORT in {"auto", "http", "direct"} else "auto", + base_url=ENV_BASE_URL, + api_prefix=ENV_API_PREFIX, + enforce_fastapi=FORCE_FASTAPI_GATEWAY, + ) + print(f"[START] task={task_id} env={BENCHMARK} model={runtime.start_model_label}", flush=True) + + rewards: list[float] = [] + steps_taken = 0 + score = 0.0 + success = False + + try: + obs = env.reset() + last_reward = 0.0 + + for step in range(1, MAX_STEPS + 1): + if env.terminated or env.truncated: + break + + action = _choose_action( + runtime, + task_id=task_id, + step=step, + observation=obs, + last_reward=last_reward, + ) + + obs, reward, terminated, truncated, info = env.step(action) + done = terminated or truncated + last_error = getattr(info, "last_action_message", None) + + rewards.append(float(reward)) + steps_taken = step + last_reward = float(reward) + + print( + f"[STEP] step={step} action={_sanitize_action_for_log(action)} " + f"reward={reward:.2f} done={_bool_str(done)} " + f"error={_sanitize_error_for_log(last_error)}", + flush=True, + ) + + if done: + break + + score, _grader_name, _metrics = env.grade() + score = min(max(score, 0.0), 1.0) + success = score >= SUCCESS_SCORE_THRESHOLD + + finally: + close_fn = getattr(env, "close", None) + if callable(close_fn): + try: + close_fn() + except Exception: + pass + + rewards_str = ",".join(f"{r:.2f}" for r in rewards) + print( + f"[END] success={_bool_str(success)} steps={steps_taken} " + f"score={score:.2f} rewards={rewards_str}", + flush=True, + ) + + return EpisodeLog(rewards=rewards, steps=steps_taken, score=score, success=success) + + +def main() -> None: + # LOCAL_IMAGE_NAME is read for compatibility with OpenEnv docker-based runners. + _ = LOCAL_IMAGE_NAME + keys: list[str] = [] + for k in ( + _clean_token(API_KEY), + _clean_token(HF_TOKEN), + _clean_token(OPENAI_API_KEY), + _clean_token(os.getenv("API_KEY")), + _clean_token(NVIDIA_API_KEY), + _clean_token(NVIDIA_API_KEY_2), + ): + if k and k not in keys: + keys.append(k) + + model_pool: list[str] = [] + for model_name in (MODEL_NAME, NVIDIA_MODEL, *LEGACY_MODEL_POOL): + if model_name and model_name not in model_pool: + model_pool.append(model_name) + + clients: list[OpenAI] = [] + for k in keys: + try: + clients.append(OpenAI(base_url=API_BASE_URL, api_key=k, max_retries=0, timeout=8.0)) + except Exception: + continue + + start_model_label = model_pool[0] if clients else "local-heuristic-fallback" + runtime = RuntimeContext( + clients=clients, + model_pool=model_pool, + start_model_label=start_model_label, + ) + + for task_id in TASKS: + _run_task(runtime, task_id) + + +if __name__ == "__main__": + main() diff --git a/openenv.yaml b/openenv.yaml new file mode 100644 index 0000000000000000000000000000000000000000..639b48d80a9bbae9b199d6cddf034c2b818ae63c --- /dev/null +++ b/openenv.yaml @@ -0,0 +1,86 @@ +spec_version: 1 +name: gov-workflow-openenv +version: "0.3.0" +type: space +runtime: fastapi +app: app.main:app +port: 7860 +description: > + A real-world OpenEnv environment for government-service workflow control. + The agent optimizes avoidable administrative delay via priority control, + document resolution, staffing, escalation, and fairness balancing. + +entrypoint: + module: app.main + object: app + inference_script: inference.py + +environment: + class: server.gov_environment.GovWorkflowOpenEnv + observation_model: app.models.ObservationModel + action_model: app.models.ActionModel + reward_model: app.models.RewardModel + state_model: app.models.EpisodeStateModel + step_info_model: app.models.StepInfoModel + +tasks: + - id: district_backlog_easy + seed: 11 + description: > + Small district office with 3 services and generous SLA windows. + Tests baseline queue control and document handling. + grader: app.graders.grade_easy + + - id: mixed_urgency_medium + seed: 22 + description: > + Mid-sized office with mixed urgency and tighter fairness requirements. + Tests urgency prioritization and staffing trade-offs. + grader: app.graders.grade_medium + + - id: cross_department_hard + seed: 33 + description: > + Large cross-department office with high arrivals and strict fairness. + Tests escalation discipline and multi-queue balancing. + grader: app.graders.grade_hard + +api: + endpoints: + - method: GET + path: /health + description: Server and session health check + - method: POST + path: /reset + description: Create a new episode session and return initial observation + - method: POST + path: /step + description: Apply one action and advance simulation state + - method: GET + path: /state + description: Return current episode state (query param session_id) + - method: POST + path: /state + description: Return current episode state (body with session_id) + - method: POST + path: /grade + description: Run deterministic task grader for the current episode + +metadata: + domain: government-services + real_world: true + reward_type: dense + action_space: discrete + observation_space: structured + deterministic_tasks: true + deterministic_graders: true + num_tasks: 3 + framework: fastapi + language: python + +deployment: + host: 0.0.0.0 + port: 7860 + dockerfile: Dockerfile + platform: huggingface-spaces + runtime: docker diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..4c8a197867105a27f1d10f5d14e0f6817adb00e0 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,48 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "gov-workflow-openenv" +version = "0.3.0" +description = "Phase 3 - FastAPI session-based HTTP server wrapping GovWorkflowEnv" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.111,<1.0", + "uvicorn[standard]>=0.30,<1.0", + "pydantic>=2.7,<3.0", + "pydantic-settings>=2.3,<3.0", + "openenv-core>=0.2,<1.0", + "python-dotenv>=1.0,<2.0", + "openai>=2.7.2,<3.0", + "requests>=2.32,<3.0", + "httpx>=0.27,<1.0", + "anyio>=4.0,<5.0", + "PyYAML>=6.0,<7.0", + "sse-starlette>=2.1,<3.0", + "numpy>=1.26,<3.0", +] + +[project.optional-dependencies] +rl = [ + "torch>=2.2,<3.0", + "stable-baselines3>=2.3,<3.0", + "sb3-contrib>=2.3,<3.0", + "gymnasium>=0.29.1,<1.3", + "tensorboard>=2.16,<3.0", + "matplotlib>=3.8,<4.0", + "scipy>=1.12,<2.0", + "optuna>=3.6,<5.0", +] +dev = [ + "pytest>=8.0,<9.0", + "pytest-asyncio>=0.23,<1.0", +] + +[project.scripts] +server = "server.app:main" + +[tool.pytest.ini_options] +pythonpath = ["."] +testpaths = ["tests"] +asyncio_mode = "auto" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000000000000000000000000000000000000..5ef4a8674e5136d9ee512e5cf0ce63627385f9c0 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +[pytest] +testpaths = tests +python_files = test_*.py +python_classes = Test* +python_functions = test_* +addopts = -v --tb=short +asyncio_mode = auto diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b8d1b6025c8b0cb0c16aa3fa6b6f05f33a9f98dd --- /dev/null +++ b/requirements.txt @@ -0,0 +1,14 @@ +# Core runtime dependencies (API + OpenEnv contract + gateway clients) +fastapi>=0.111,<1.0 +uvicorn[standard]>=0.30,<1.0 +pydantic>=2.7,<3.0 +pydantic-settings>=2.3,<3.0 +openenv-core>=0.2,<1.0 +python-dotenv>=1.0,<2.0 +openai>=2.7.2,<3.0 +requests>=2.32,<3.0 +httpx>=0.27,<1.0 +anyio>=4.0,<5.0 +PyYAML>=6.0,<7.0 +sse-starlette>=2.1,<3.0 +numpy>=1.26,<3.0 diff --git a/requirements_rl.txt b/requirements_rl.txt new file mode 100644 index 0000000000000000000000000000000000000000..8104282fae93c1c8ba58b6c90ede670bb4b3dc04 --- /dev/null +++ b/requirements_rl.txt @@ -0,0 +1,9 @@ +# RL/training stack (install after requirements.txt) +torch>=2.2,<3.0 +stable-baselines3>=2.3,<3.0 +sb3-contrib>=2.3,<3.0 +gymnasium>=0.29.1,<1.3 +tensorboard>=2.16,<3.0 +matplotlib>=3.8,<4.0 +scipy>=1.12,<2.0 +optuna>=3.6,<5.0 diff --git a/rl/README.md b/rl/README.md new file mode 100644 index 0000000000000000000000000000000000000000..701b03a201d41c50026ce3848b89a5aba135c285 --- /dev/null +++ b/rl/README.md @@ -0,0 +1,34 @@ +# rl/ + +Reinforcement learning module. + +- `gov_workflow_env.py`: Gymnasium adapter around `app.env.GovWorkflowEnv` +- `feature_builder.py`: `ObservationModel` -> 84-dim float32 vector (`OBS_DIM=84`) +- `action_mask.py`: structural action masks (`N_ACTIONS=28`) +- `curriculum.py`: staged task scheduler (Phase 2/3) +- `train_ppo.py`: Phase 1 and Phase 2 training entrypoint +- `train_recurrent.py`: Phase 3 recurrent PPO entrypoint +- `evaluate.py`: deterministic evaluation on grader metrics (`--task` / `--tasks`) +- `eval_grader.py`: task-level grader evaluation helper with optional plots +- `plot_training.py`: training-curve report helper from monitor/TensorBoard artifacts +- `callbacks.py`: eval and cost-monitor callbacks +- `cost_tracker.py`: episode-level reward/cost extraction helpers +- `configs/`: YAML configs for PPO/recurrent training + - `ppo_easy.yaml`: standard Phase 1 config + - `ppo_easy_aggressive.yaml`: aggressive Phase 1 tuning profile for plateau recovery + +## CLI Compatibility Notes + +- Training scripts accept both `--n-envs` and `--n_envs`. +- `train_ppo.py` accepts `--task` as a compatibility alias: + - Phase 1 only supports `district_backlog_easy` + - Phase 2 ignores `--task` and uses curriculum sampling +- `train_ppo.py` supports `--resume ` for Phase 1 continuation runs. +- `train_recurrent.py` accepts `--task` to override recurrent eval callback task. + +## Artifact Paths + +- Training/eval outputs are written under `results/`: + - `results/best_model/*` + - `results/runs/*` + - `results/eval_logs/*` diff --git a/rl/__init__.py b/rl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..99e6a80e00ca11cbf2fe225503fa9366ca6dd010 --- /dev/null +++ b/rl/__init__.py @@ -0,0 +1,20 @@ +""" +Gov Workflow OpenEnv — RL Stack +Phase 1 : Masked PPO +Phase 2 : Curriculum PPO +Phase 3 : Recurrent PPO +Phase 4 : Constrained Recurrent PPO (Lagrangian) +Phase 5 : Hierarchical RL +""" + +from rl.feature_builder import FeatureBuilder, OBS_DIM, N_ACTIONS +from rl.action_mask import ActionMaskComputer +from rl.gov_workflow_env import GovWorkflowGymEnv + +__all__ = [ + "FeatureBuilder", + "OBS_DIM", + "N_ACTIONS", + "ActionMaskComputer", + "GovWorkflowGymEnv", +] \ No newline at end of file diff --git a/rl/action_mask.py b/rl/action_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..c66045a50071cafe739eeda7f1c342b6c4d1255c --- /dev/null +++ b/rl/action_mask.py @@ -0,0 +1,113 @@ +""" +Computes a boolean action mask (length N_ACTIONS) from the current observation. +True = action is structurally valid right now. +False = action is impossible/wasteful; MaskablePPO will zero its logit. +""" + +from __future__ import annotations + +import numpy as np +from app.models import ObservationModel +from rl.feature_builder import ACTION_DECODE_TABLE, N_ACTIONS + + +class ActionMaskComputer: + """ + Usage: + amc = ActionMaskComputer() + mask = amc.compute(obs, current_priority_mode) + """ + + def compute( + self, + obs: ObservationModel, + current_priority_mode: str = "balanced", + ) -> np.ndarray: + mask = np.ones(N_ACTIONS, dtype=bool) + total_backlog = int(getattr(obs, "total_backlog", 0) or 0) + + # Prevent reward farming with no-op control actions when nothing is queued. + # In this state, time must advance to generate arrivals and meaningful decisions. + if total_backlog <= 0: + mask[:] = False + for action_idx, (action_type, _service, _pm, _delta) in ACTION_DECODE_TABLE.items(): + if action_type == "advance_time": + mask[action_idx] = True + break + return mask + + queue_snaps = obs.queue_snapshots.values() if isinstance(obs.queue_snapshots, dict) else obs.queue_snapshots + queue_snaps = list(queue_snaps) + snapshots = { + (snap.service_type.value if hasattr(snap.service_type, "value") else snap.service_type): snap + for snap in queue_snaps + } + active_services = { + service for service, snap in snapshots.items() + if getattr(snap, "total_pending", getattr(snap, "active_cases", 0)) > 0 + } + escalation_budget = obs.escalation_budget_remaining + + services_with_missing_docs = { + (snap.service_type.value if hasattr(snap.service_type, "value") else snap.service_type) + for snap in queue_snaps + if getattr(snap, "blocked_missing_docs", getattr(snap, "missing_docs_cases", 0)) > 0 + } + services_with_escalatable = { + (snap.service_type.value if hasattr(snap.service_type, "value") else snap.service_type) + for snap in queue_snaps + if (getattr(snap, "total_pending", getattr(snap, "active_cases", 0)) - getattr(snap, "urgent_pending", getattr(snap, "escalated_cases", 0))) > 0 + } + + allocations = {} + for service_key, value in (getattr(obs.officer_pool, "allocated", getattr(obs.officer_pool, "allocations", {})) or {}).items(): + name = service_key.value if hasattr(service_key, "value") else str(service_key) + allocations[name] = int(value) + + idle_officers = getattr(obs.officer_pool, "idle_officers", getattr(obs.officer_pool, "reserve_officers", 0)) + + for action_idx, (action_type, service, priority_mode, delta) in ACTION_DECODE_TABLE.items(): + + if action_type == "set_priority_mode": + if priority_mode == current_priority_mode: + mask[action_idx] = False + + elif action_type == "request_missing_documents": + mask[action_idx] = service in services_with_missing_docs + + elif action_type == "escalate_service": + mask[action_idx] = ( + escalation_budget > 0 + and service in services_with_escalatable + ) + + elif action_type == "advance_time": + mask[action_idx] = True + + elif action_type == "reallocate_officers": + has_source = (allocations.get(service, 0) > 0) and (service in active_services) + has_target = any(svc != service for svc in active_services) + mask[action_idx] = has_source and has_target + + elif action_type == "assign_capacity": + if idle_officers <= 0: + mask[action_idx] = False + elif service == "__most_loaded__": + mask[action_idx] = len(active_services) > 0 + elif service == "__most_urgent__": + mask[action_idx] = any( + getattr(snap, "urgent_cases", getattr(snap, "urgent_pending", 0)) > 0 for snap in queue_snaps + ) + else: + mask[action_idx] = False + + # Guarantee at least one safe action for MaskablePPO. + if not mask.any(): + mask[18] = True + + return mask + + +def compute_mask(obs: ObservationModel, current_priority_mode: str = "balanced") -> np.ndarray: + """Module-level convenience function.""" + return ActionMaskComputer().compute(obs, current_priority_mode) diff --git a/rl/callbacks.py b/rl/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..693bc366c5e01e5504dd8aefa8d071eab6151302 --- /dev/null +++ b/rl/callbacks.py @@ -0,0 +1,266 @@ +""" +Custom SB3 callbacks for Gov Workflow RL training. + +GovWorkflowEvalCallback -- MaskableEvalCallback + grader score logging +CostMonitorCallback -- per-rollout cost constraint logging to TensorBoard +""" + +from __future__ import annotations + +import os +import numpy as np +from stable_baselines3.common.callbacks import BaseCallback +from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback +from typing import Any + +from rl.gov_workflow_env import GovWorkflowGymEnv +from rl.cost_tracker import THRESHOLD_SLA, THRESHOLD_FAIRNESS + + +class GovWorkflowEvalCallback(MaskableEvalCallback): + """ + Extends MaskableEvalCallback: + 1. Runs the deterministic grader after each eval. + 2. Logs grader score to TensorBoard. + 3. Saves best model by grader score (not just mean reward). + """ + + def __init__( + self, + eval_env: GovWorkflowGymEnv, + eval_freq: int = 2048, + n_eval_episodes: int = 5, + grader_eval_freq_multiplier: int = 4, + grader_eval_max_steps: int | None = None, + best_model_save_path: str = "results/best_model", + log_path: str = "results/eval_logs", + task_id: str = "district_backlog_easy", + verbose: int = 1, + ): + super().__init__( + eval_env=eval_env, + n_eval_episodes=n_eval_episodes, + eval_freq=eval_freq, + best_model_save_path=best_model_save_path, + log_path=log_path, + verbose=verbose, + warn=False, + ) + self.task_id = task_id + self.grader_eval_freq_multiplier = max(1, int(grader_eval_freq_multiplier)) + self.grader_eval_max_steps = grader_eval_max_steps + self._best_grader_score = -np.inf + os.makedirs(best_model_save_path, exist_ok=True) + os.makedirs(log_path, exist_ok=True) + + def _on_step(self) -> bool: + eval_due = self.eval_freq > 0 and self.n_calls % self.eval_freq == 0 + result = super()._on_step() + if eval_due: + mean_reward = float(getattr(self, "last_mean_reward", 0.0) or 0.0) + std_reward = 0.0 + try: + if self.evaluations_results and len(self.evaluations_results) > 0: + latest = self.evaluations_results[-1] + if latest is not None and len(latest) > 0: + std_reward = float(np.std(latest)) + except Exception: + std_reward = 0.0 + # Stable line format for live parser in backend/frontend. + print( + f"Eval num_timesteps={int(self.num_timesteps)}, " + f"episode_reward={mean_reward:.2f} +/- {std_reward:.2f}", + flush=True, + ) + + grader_eval_freq = max(self.eval_freq * self.grader_eval_freq_multiplier, 1) + if self.eval_freq > 0 and self.n_calls % grader_eval_freq == 0: + grader_score = self._run_grader_eval() + if self.logger: + self.logger.record("eval/grader_score", grader_score) + if grader_score > self._best_grader_score: + self._best_grader_score = grader_score + save_path = os.path.join( + self.best_model_save_path, f"best_grader_{self.task_id}" + ) + self.model.save(save_path) + if self.verbose: + print(f"[Eval] New best grader score: {grader_score:.4f} -> {save_path}") + return result + + def _run_grader_eval(self) -> float: + try: + from app.graders import grade_episode + from app.tasks import TASKS + task_cfg = TASKS.get(self.task_id) + if task_cfg is None: + return 0.0 + max_steps = ( + int(self.grader_eval_max_steps) + if self.grader_eval_max_steps is not None + else max(1, int(task_cfg.max_days) * 10) + ) + env = GovWorkflowGymEnv(task_id=self.task_id, seed=task_cfg.seed, hard_action_mask=True) + obs, _ = env.reset() + done = False + steps = 0 + while not done: + masks = np.asarray(env.action_masks(), dtype=bool).reshape(-1) + action, _ = self.model.predict(obs, action_masks=masks, deterministic=True) + obs, _, terminated, truncated, _ = env.step(int(action)) + done = terminated or truncated + steps += 1 + if steps >= max_steps and not done: + break + result = grade_episode(env._core_env.state()) + return float(result.score) + except Exception as e: + if self.verbose: + print(f"[Eval] Grader eval failed: {e}") + return 0.0 + + +class CostMonitorCallback(BaseCallback): + """ + Monitors SLA and fairness cost signals per rollout. + Phase 1-3: diagnostic only. + Phase 4: feeds into Lagrangian multiplier updates. + """ + + def __init__(self, verbose: int = 0): + super().__init__(verbose) + self._episode_costs: list[dict] = [] + self._ep_sla: list[float] = [] + self._ep_fair: list[float] = [] + self._ep_mask_applied: list[float] = [] + + def _on_step(self) -> bool: + for info, done in zip( + self.locals.get("infos", []), + self.locals.get("dones", []), + ): + rb = info.get("reward_breakdown", {}) + self._ep_sla.append( abs(float(rb.get("sla_penalty", 0.0)))) + self._ep_fair.append(abs(float(rb.get("fairness_penalty", 0.0)))) + self._ep_mask_applied.append(float(bool(info.get("action_mask_applied", False)))) + if done: + mean_sla = float(np.mean(self._ep_sla)) if self._ep_sla else 0.0 + mean_fair = float(np.mean(self._ep_fair)) if self._ep_fair else 0.0 + mask_rate = float(np.mean(self._ep_mask_applied)) if self._ep_mask_applied else 0.0 + self._episode_costs.append({"sla": mean_sla, "fairness": mean_fair}) + self.logger.record("costs/episode_mean_sla_penalty", mean_sla) + self.logger.record("costs/episode_mean_fairness_penalty", mean_fair) + self.logger.record("costs/sla_threshold_violated", float(mean_sla > THRESHOLD_SLA)) + self.logger.record("costs/fairness_threshold_violated", float(mean_fair > THRESHOLD_FAIRNESS)) + self.logger.record("costs/episode_action_mask_applied_rate", mask_rate) + self._ep_sla.clear() + self._ep_fair.clear() + self._ep_mask_applied.clear() + return True + + def _on_training_end(self) -> None: + if not self._episode_costs: + return + all_sla = [c["sla"] for c in self._episode_costs] + all_fair = [c["fairness"] for c in self._episode_costs] + print( + f"\n[CostMonitor] mean SLA penalty: {np.mean(all_sla):.4f} " + f"(threshold={THRESHOLD_SLA}), " + f"mean fairness penalty: {np.mean(all_fair):.4f} " + f"(threshold={THRESHOLD_FAIRNESS})" + ) + + +class RecurrentEvalCallback(BaseCallback): + """ + Periodic evaluation callback for RecurrentPPO. + + We evaluate with deterministic inference and enforce action masks at + inference time before env.step(). + """ + + def __init__( + self, + eval_env: GovWorkflowGymEnv, + eval_freq: int = 2048, + n_eval_episodes: int = 3, + best_model_save_path: str = "results/best_model", + log_path: str = "results/eval_logs", + task_id: str = "mixed_urgency_medium", + verbose: int = 1, + ): + super().__init__(verbose=verbose) + self.eval_env = eval_env + self.eval_freq = eval_freq + self.n_eval_episodes = n_eval_episodes + self.best_model_save_path = best_model_save_path + self.log_path = log_path + self.task_id = task_id + self._best_grader_score = -np.inf + os.makedirs(best_model_save_path, exist_ok=True) + os.makedirs(log_path, exist_ok=True) + + def _on_step(self) -> bool: + if self.eval_freq <= 0 or self.n_calls % self.eval_freq != 0: + return True + + mean_reward, grader_score = self._run_eval() + self.logger.record("eval/mean_reward", mean_reward) + self.logger.record("eval/grader_score", grader_score) + + if grader_score > self._best_grader_score: + self._best_grader_score = grader_score + save_path = os.path.join( + self.best_model_save_path, f"best_grader_recurrent_{self.task_id}" + ) + self.model.save(save_path) + if self.verbose: + print(f"[Eval] New best recurrent grader score: {grader_score:.4f} -> {save_path}") + return True + + def _run_eval(self) -> tuple[float, float]: + from app.graders import grade_episode + from app.tasks import TASKS + + task_cfg = TASKS.get(self.task_id) + if task_cfg is None: + return 0.0, 0.0 + + rewards: list[float] = [] + scores: list[float] = [] + + for ep in range(self.n_eval_episodes): + env = GovWorkflowGymEnv(self.task_id, seed=task_cfg.seed + ep, hard_action_mask=True) + obs, _ = env.reset() + done = False + ep_reward = 0.0 + lstm_state: Any = None + episode_start = np.array([True], dtype=bool) + + while not done: + action, lstm_state = self.model.predict( + obs, + state=lstm_state, + episode_start=episode_start, + deterministic=True, + ) + action_idx = int(np.asarray(action).item()) + masks = env.action_masks() + if action_idx < 0 or action_idx >= masks.shape[0] or not bool(masks[action_idx]): + if masks.shape[0] > 18 and bool(masks[18]): + action_idx = 18 + else: + valid = np.flatnonzero(masks) + if valid.size > 0: + action_idx = int(valid[0]) + + obs, reward, terminated, truncated, _ = env.step(action_idx) + ep_reward += float(reward) + done = bool(terminated or truncated) + episode_start = np.array([done], dtype=bool) + + result = grade_episode(env._core_env.state()) + rewards.append(ep_reward) + scores.append(float(result.score)) + + return float(np.mean(rewards)), float(np.mean(scores)) diff --git a/rl/configs/curriculum.yaml b/rl/configs/curriculum.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce514533a662fc03a676bfc7ab3e200e2531de50 --- /dev/null +++ b/rl/configs/curriculum.yaml @@ -0,0 +1,29 @@ +# Phase 2 -- Curriculum PPO across all 3 tasks +hyperparameters: + learning_rate: 0.0002 + n_steps: 512 + batch_size: 64 + n_epochs: 10 + gamma: 0.99 + gae_lambda: 0.95 + clip_range: 0.2 + ent_coef: 0.005 + vf_coef: 0.5 + max_grad_norm: 0.5 + net_arch: [256, 256] + +curriculum: + stage1_end_frac: 0.30 + stage2_end_frac: 0.70 + stage3_weights: [0.20, 0.40, 0.40] + +training: + total_timesteps: 500000 + n_envs: 4 + seed: 42 + warm_start_from: "results/best_model/phase1_final" + +target_scores: + district_backlog_easy: 0.82 + mixed_urgency_medium: 0.72 + cross_department_hard: 0.60 diff --git a/rl/configs/curriculum_tuned.yaml b/rl/configs/curriculum_tuned.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c3de7fea021d313bd37065fe3729e98cd88639b2 --- /dev/null +++ b/rl/configs/curriculum_tuned.yaml @@ -0,0 +1,35 @@ +# Phase 2 (tuned) -- curriculum continuation from existing Phase 2 checkpoint +# Minimal tuning pass: no architecture changes, 28-action design unchanged. + +hyperparameters: + learning_rate: 0.0001 + n_steps: 1024 + batch_size: 128 + n_epochs: 10 + gamma: 0.995 + gae_lambda: 0.95 + clip_range: 0.2 + ent_coef: 0.002 + vf_coef: 0.5 + max_grad_norm: 0.5 + net_arch: [256, 256] + +curriculum: + stage1_end_frac: 0.15 + stage2_end_frac: 0.50 + stage3_weights: [0.15, 0.35, 0.50] + +training: + total_timesteps: 300000 + n_envs: 4 + seed: 42 + warm_start_from: "results/best_model/phase2_final" + eval_task_id: "mixed_urgency_medium" + eval_freq: 2048 + n_eval_episodes: 3 + +target_scores: + district_backlog_easy: 0.82 + mixed_urgency_medium: 0.72 + cross_department_hard: 0.60 + average: 0.75 \ No newline at end of file diff --git a/rl/configs/ppo_curriculum.yaml b/rl/configs/ppo_curriculum.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce514533a662fc03a676bfc7ab3e200e2531de50 --- /dev/null +++ b/rl/configs/ppo_curriculum.yaml @@ -0,0 +1,29 @@ +# Phase 2 -- Curriculum PPO across all 3 tasks +hyperparameters: + learning_rate: 0.0002 + n_steps: 512 + batch_size: 64 + n_epochs: 10 + gamma: 0.99 + gae_lambda: 0.95 + clip_range: 0.2 + ent_coef: 0.005 + vf_coef: 0.5 + max_grad_norm: 0.5 + net_arch: [256, 256] + +curriculum: + stage1_end_frac: 0.30 + stage2_end_frac: 0.70 + stage3_weights: [0.20, 0.40, 0.40] + +training: + total_timesteps: 500000 + n_envs: 4 + seed: 42 + warm_start_from: "results/best_model/phase1_final" + +target_scores: + district_backlog_easy: 0.82 + mixed_urgency_medium: 0.72 + cross_department_hard: 0.60 diff --git a/rl/configs/ppo_easy.yaml b/rl/configs/ppo_easy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93e709235ce872287f31a6743b507ad375b4b1b5 --- /dev/null +++ b/rl/configs/ppo_easy.yaml @@ -0,0 +1,28 @@ +# Phase 1 -- Masked PPO on district_backlog_easy +hyperparameters: + learning_rate: 0.0003 + n_steps: 512 + batch_size: 64 + n_epochs: 10 + gamma: 0.99 + gae_lambda: 0.95 + clip_range: 0.2 + ent_coef: 0.01 + vf_coef: 0.5 + max_grad_norm: 0.5 + net_arch: [256, 256] + +training: + total_timesteps: 200000 + n_envs: 4 + seed: 42 + eval_freq: 16384 + n_eval_episodes: 2 + grader_eval_freq_multiplier: 4 + enable_eval_callback: true + progress_bar: false + model_verbose: 0 + callback_verbose: 0 + +target_scores: + district_backlog_easy: 0.80 diff --git a/rl/configs/ppo_easy_aggressive.yaml b/rl/configs/ppo_easy_aggressive.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48aa4c68525a704df4b9d689cf5632c8e230b752 --- /dev/null +++ b/rl/configs/ppo_easy_aggressive.yaml @@ -0,0 +1,37 @@ +# Phase 1 -- Aggressive PPO tuning (benchmark unchanged) +# Use when baseline Phase 1 plateaus around ~0.55-0.58 grader score. +# +# Example: +# python -m rl.train_ppo --phase 1 --task district_backlog_easy --timesteps 300000 --n_envs 4 --seed 42 --phase1-config rl/configs/ppo_easy_aggressive.yaml +# +# Notes: +# - Keeps env/grader/task unchanged. +# - Focuses on longer-horizon credit assignment + lower exploration noise. + +hyperparameters: + learning_rate: 0.0001 + n_steps: 1024 + batch_size: 256 + n_epochs: 15 + gamma: 0.995 + gae_lambda: 0.98 + clip_range: 0.15 + ent_coef: 0.001 + vf_coef: 0.7 + max_grad_norm: 0.5 + net_arch: [256, 256, 128] + +training: + total_timesteps: 300000 + n_envs: 4 + seed: 42 + eval_freq: 16384 + n_eval_episodes: 3 + grader_eval_freq_multiplier: 2 + enable_eval_callback: true + progress_bar: false + model_verbose: 0 + callback_verbose: 0 + +target_scores: + district_backlog_easy: 0.65 diff --git a/rl/configs/recurrent.yaml b/rl/configs/recurrent.yaml new file mode 100644 index 0000000000000000000000000000000000000000..13e08dbb57e4d512a001bb402c101e1048b67822 --- /dev/null +++ b/rl/configs/recurrent.yaml @@ -0,0 +1,41 @@ +# Phase 3 -- Recurrent PPO (LSTM) across all tasks +# Uses existing 28-action design. + +hyperparameters: + learning_rate: 0.0001 + n_steps: 512 + batch_size: 128 + n_epochs: 10 + gamma: 0.995 + gae_lambda: 0.95 + clip_range: 0.2 + ent_coef: 0.002 + vf_coef: 0.5 + max_grad_norm: 0.5 + net_arch: [256, 256] + lstm_hidden_size: 128 + n_lstm_layers: 1 + shared_lstm: false + enable_critic_lstm: true + recurrent_seq_len: 16 + +curriculum: + stage1_end_frac: 0.15 + stage2_end_frac: 0.50 + stage3_weights: [0.15, 0.35, 0.50] + +training: + total_timesteps: 600000 + n_envs: 4 + seed: 42 + warm_start_from: "results/best_model/phase2_final" + transfer_flat_weights: true + eval_task_id: "mixed_urgency_medium" + eval_freq: 2048 + n_eval_episodes: 3 + +target_scores: + district_backlog_easy: 0.82 + mixed_urgency_medium: 0.75 + cross_department_hard: 0.68 + average: 0.82 diff --git a/rl/configs/recurrent_v2.yaml b/rl/configs/recurrent_v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee400f4fa74740debabe83d93dc6c452b8211ea7 --- /dev/null +++ b/rl/configs/recurrent_v2.yaml @@ -0,0 +1,44 @@ +# Phase 3-v2 -- Recurrent PPO (LSTM) tuned for stability and SLA reduction +# Uses existing 28-action design. + +hyperparameters: + learning_rate: 0.00005 + n_steps: 1024 + batch_size: 256 + n_epochs: 8 + gamma: 0.995 + gae_lambda: 0.97 + clip_range: 0.15 + ent_coef: 0.0005 + vf_coef: 0.7 + max_grad_norm: 0.5 + net_arch: [256, 256] + lstm_hidden_size: 128 + n_lstm_layers: 1 + shared_lstm: false + enable_critic_lstm: true + recurrent_seq_len: 16 + +curriculum: + stage1_end_frac: 0.25 + stage2_end_frac: 0.70 + stage3_weights: [0.20, 0.45, 0.35] + +training: + total_timesteps: 700000 + n_envs: 4 + seed: 42 + warm_start_from: "results/best_model/phase2_final" + transfer_flat_weights: true + transfer_exclude_prefixes: ["action_net.", "value_net."] + hard_action_mask_train: true + hard_action_mask_eval: true + eval_task_id: "mixed_urgency_medium" + eval_freq: 4096 + n_eval_episodes: 5 + +target_scores: + district_backlog_easy: 0.82 + mixed_urgency_medium: 0.75 + cross_department_hard: 0.68 + average: 0.75 \ No newline at end of file diff --git a/rl/cost_tracker.py b/rl/cost_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..24788f78269e8976e94cb15089853239613e8f62 --- /dev/null +++ b/rl/cost_tracker.py @@ -0,0 +1,82 @@ +""" +Separates RewardModel fields into reward signal r_t and cost signals c_t. +Phase 1-3 : costs logged only (diagnostic). +Phase 4 : costs drive Lagrangian multiplier updates. + +Thresholds: + d_sla = 0.15 (max 15% SLA breach rate) + d_fairness = 0.20 (max 0.20 fairness gap) + d_escalation = 0.10 (max 10% wasted escalation) +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import List +import numpy as np + +THRESHOLD_SLA = 0.15 +THRESHOLD_FAIRNESS = 0.20 +THRESHOLD_ESCALATION = 0.10 + + +@dataclass +class CostRecord: + step: int + c_sla: float + c_fairness: float + c_escalation: float + c_invalid: float + c_idle: float + + +@dataclass +class EpisodeCostSummary: + mean_c_sla: float + mean_c_fairness: float + mean_c_escalation: float + sla_violated: bool + fairness_violated: bool + escalation_violated: bool + total_steps: int + + +class CostTracker: + """Accumulates per-step cost signals across an episode.""" + + def __init__(self) -> None: + self._records: List[CostRecord] = [] + self._step = 0 + + def reset(self) -> None: + self._records.clear() + self._step = 0 + + def record(self, reward_breakdown: dict) -> CostRecord: + rec = CostRecord( + step=self._step, + c_sla=abs(float(reward_breakdown.get("sla_penalty", 0.0))), + c_fairness=abs(float(reward_breakdown.get("fairness_penalty", 0.0))), + c_escalation=abs(float(reward_breakdown.get("invalid_action_penalty", 0.0))), + c_invalid=abs(float(reward_breakdown.get("invalid_action_penalty", 0.0))), + c_idle=abs(float(reward_breakdown.get("idle_capacity_penalty", 0.0))), + ) + self._records.append(rec) + self._step += 1 + return rec + + def summarise(self) -> EpisodeCostSummary: + if not self._records: + return EpisodeCostSummary(0.0, 0.0, 0.0, False, False, False, 0) + mean_sla = float(np.mean([r.c_sla for r in self._records])) + mean_fair = float(np.mean([r.c_fairness for r in self._records])) + mean_esc = float(np.mean([r.c_escalation for r in self._records])) + return EpisodeCostSummary( + mean_c_sla=mean_sla, + mean_c_fairness=mean_fair, + mean_c_escalation=mean_esc, + sla_violated=(mean_sla > THRESHOLD_SLA), + fairness_violated=(mean_fair > THRESHOLD_FAIRNESS), + escalation_violated=(mean_esc > THRESHOLD_ESCALATION), + total_steps=len(self._records), + ) diff --git a/rl/curriculum.py b/rl/curriculum.py new file mode 100644 index 0000000000000000000000000000000000000000..e343daf78383a127584f713212d3ff201716f574 --- /dev/null +++ b/rl/curriculum.py @@ -0,0 +1,58 @@ +""" +Curriculum scheduler for staged training. + +Stage 1 (0-30%) : Easy only +Stage 2 (30-70%) : Easy + Medium (50/50) +Stage 3 (70-100%): All 3 tasks (20/40/40 weights) +""" + +from __future__ import annotations + +import random +from dataclasses import dataclass +from typing import Tuple + +TASK_EASY = "district_backlog_easy" +TASK_MEDIUM = "mixed_urgency_medium" +TASK_HARD = "cross_department_hard" +ALL_TASKS = [TASK_EASY, TASK_MEDIUM, TASK_HARD] + + +@dataclass +class CurriculumConfig: + stage1_end_frac: float = 0.30 + stage2_end_frac: float = 0.70 + stage3_weights: Tuple[float, ...] = (0.20, 0.40, 0.40) + + +class CurriculumScheduler: + """ + Selects task_id for next training episode based on training progress. + """ + + def __init__( + self, + total_timesteps: int, + config: CurriculumConfig | None = None, + rng_seed: int = 0, + ): + self.total_timesteps = total_timesteps + self.cfg = config or CurriculumConfig() + self._rng = random.Random(rng_seed) + + def sample_task(self, current_timestep: int) -> str: + progress = current_timestep / max(self.total_timesteps, 1) + if progress < self.cfg.stage1_end_frac: + return TASK_EASY + elif progress < self.cfg.stage2_end_frac: + return self._rng.choice([TASK_EASY, TASK_MEDIUM]) + else: + return self._rng.choices(ALL_TASKS, weights=list(self.cfg.stage3_weights), k=1)[0] + + def current_stage(self, current_timestep: int) -> int: + progress = current_timestep / max(self.total_timesteps, 1) + if progress < self.cfg.stage1_end_frac: + return 1 + elif progress < self.cfg.stage2_end_frac: + return 2 + return 3 diff --git a/rl/eval_grader.py b/rl/eval_grader.py new file mode 100644 index 0000000000000000000000000000000000000000..453a6299219e3374c10ea6a57c7143d24e3c4c29 --- /dev/null +++ b/rl/eval_grader.py @@ -0,0 +1,229 @@ +""" +Grader-based evaluation utility for trained RL checkpoints. + +This complements `rl/evaluate.py`: +- `rl/evaluate.py` is batch-oriented and returns aggregate task rows. +- `rl/eval_grader.py` is phase/task-oriented and prints per-episode progress, + promotion guidance, and an optional score/reward plot. +""" + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Any, Literal + +import matplotlib +import numpy as np +from sb3_contrib import MaskablePPO, RecurrentPPO + +# Allow running as `python rl/eval_grader.py ...` from repo root. +_REPO_ROOT = Path(__file__).resolve().parent.parent +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + +from app.graders import grade_episode +from rl.gov_workflow_env import GovWorkflowGymEnv + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +ModelType = Literal["auto", "maskable", "recurrent"] + +PROMOTION_THRESHOLDS = { + "district_backlog_easy": 0.75, + "mixed_urgency_medium": 0.65, + "cross_department_hard": 0.55, +} + +PHASE_LABELS = { + "district_backlog_easy": "Phase 1", + "mixed_urgency_medium": "Phase 2", + "cross_department_hard": "Phase 3", +} + + +def _normalize_action(action: Any) -> int: + if isinstance(action, np.ndarray): + return int(action.item()) + return int(action) + + +def _sanitize_action(action_idx: int, masks: np.ndarray) -> int: + if 0 <= action_idx < masks.shape[0] and bool(masks[action_idx]): + return int(action_idx) + if masks.shape[0] > 18 and bool(masks[18]): + return 18 + valid = np.flatnonzero(masks) + return int(valid[0]) if valid.size > 0 else 18 + + +def _load_model(model_path: str, model_type: ModelType) -> tuple[Any, str]: + if model_type == "maskable": + return MaskablePPO.load(model_path), "maskable" + if model_type == "recurrent": + return RecurrentPPO.load(model_path), "recurrent" + + try: + return MaskablePPO.load(model_path), "maskable" + except Exception: + return RecurrentPPO.load(model_path), "recurrent" + + +def evaluate_with_grader( + model_path: str, + task_id: str, + n_episodes: int = 20, + seed: int = 42, + model_type: ModelType = "auto", + save_plot: bool = True, +) -> float: + if task_id not in PROMOTION_THRESHOLDS: + allowed = ", ".join(PROMOTION_THRESHOLDS.keys()) + raise ValueError(f"Unknown task_id '{task_id}'. Allowed: {allowed}") + + model, resolved_type = _load_model(model_path, model_type) + + print("\n" + "=" * 64) + print(f"Track A Evaluation - {PHASE_LABELS.get(task_id, task_id)}") + print(f"Model: {model_path}") + print(f"Model type: {resolved_type}") + print(f"Task: {task_id}") + print(f"Episodes: {n_episodes}") + print("=" * 64 + "\n") + + scores: list[float] = [] + rewards: list[float] = [] + + for ep in range(n_episodes): + env = GovWorkflowGymEnv(task_id=task_id, seed=seed + ep, hard_action_mask=True) + obs, _ = env.reset(seed=seed + ep) + done = False + ep_reward = 0.0 + lstm_state: Any = None + episode_start = np.array([True], dtype=bool) + + while not done: + masks = env.action_masks() + if resolved_type == "recurrent": + action, lstm_state = model.predict( + obs, + state=lstm_state, + episode_start=episode_start, + deterministic=True, + ) + action_idx = _sanitize_action(_normalize_action(action), masks) + else: + action, _ = model.predict(obs, action_masks=masks, deterministic=True) + action_idx = _normalize_action(action) + + obs, reward, terminated, truncated, _ = env.step(action_idx) + ep_reward += float(reward) + done = bool(terminated or truncated) + episode_start = np.array([done], dtype=bool) + + result = grade_episode(env.core_env.state()) + score = float(result.score) + threshold = float(PROMOTION_THRESHOLDS[task_id]) + badge = "PASS" if score >= threshold else "FAIL" + print(f" {badge:4} ep={ep + 1:02d} score={score:.4f} reward={ep_reward:.2f}") + scores.append(score) + rewards.append(ep_reward) + + mean_score = float(np.mean(scores)) if scores else 0.0 + threshold = float(PROMOTION_THRESHOLDS[task_id]) + + print("\n" + "-" * 64) + print(f"Mean grader score: {mean_score:.4f}") + print(f"Promotion target : {threshold:.2f}") + print(f"Min / Max : {float(np.min(scores)):.4f} / {float(np.max(scores)):.4f}") + print(f"Pass rate : {sum(s >= threshold for s in scores)}/{len(scores)}") + if mean_score >= threshold: + print("Decision : PROMOTE") + else: + print("Decision : CONTINUE TRAINING") + print("=" * 64) + + if save_plot: + _save_plot(scores=scores, rewards=rewards, task_id=task_id, mean_score=mean_score, threshold=threshold, model_path=model_path) + + return mean_score + + +def _save_plot( + *, + scores: list[float], + rewards: list[float], + task_id: str, + mean_score: float, + threshold: float, + model_path: str, +) -> str: + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + fig.suptitle( + f"Track A - {PHASE_LABELS.get(task_id, task_id)} Evaluation\n" + f"Task: {task_id} | Model: {os.path.basename(model_path)}", + fontsize=12, + fontweight="bold", + ) + + episodes = list(range(1, len(scores) + 1)) + + ax1 = axes[0] + colors = ["#0e8a16" if s >= threshold else "#b60205" for s in scores] + ax1.bar(episodes, scores, color=colors, alpha=0.85) + ax1.axhline(y=threshold, color="#d97706", linestyle="--", linewidth=2, label=f"threshold={threshold:.2f}") + ax1.axhline(y=mean_score, color="#1d4ed8", linestyle="-", linewidth=2, label=f"mean={mean_score:.3f}") + ax1.set_ylim(0.0, 1.05) + ax1.set_xlabel("Episode") + ax1.set_ylabel("Grader Score") + ax1.set_title("Per-Episode Grader Score") + ax1.grid(True, alpha=0.3, axis="y") + ax1.legend() + + ax2 = axes[1] + ax2.plot(episodes, rewards, color="#0369a1", linewidth=2, marker="o", markersize=4) + if rewards: + mean_reward = float(np.mean(rewards)) + ax2.axhline(y=mean_reward, color="#d97706", linestyle="--", linewidth=2, label=f"mean={mean_reward:.2f}") + ax2.set_xlabel("Episode") + ax2.set_ylabel("Total Reward") + ax2.set_title("Episode Reward") + ax2.grid(True, alpha=0.3) + ax2.legend() + + plt.tight_layout() + out_dir = os.path.join("results", "eval_logs", task_id) + os.makedirs(out_dir, exist_ok=True) + out_path = os.path.join(out_dir, f"{task_id}_grader_eval.png") + plt.savefig(out_path, dpi=150, bbox_inches="tight", facecolor="white") + plt.close() + print(f"Plot saved -> {out_path}") + return out_path + + +def main() -> None: + parser = argparse.ArgumentParser(description="Task-oriented grader evaluation for a trained checkpoint") + parser.add_argument("--model", required=True, help="Path to .zip checkpoint (suffix optional)") + parser.add_argument("--task", required=True, choices=list(PROMOTION_THRESHOLDS.keys())) + parser.add_argument("--episodes", type=int, default=20) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--model-type", choices=["auto", "maskable", "recurrent"], default="auto") + parser.add_argument("--no-plot", action="store_true", help="Disable PNG output") + args = parser.parse_args() + + model_path = args.model if args.model.endswith(".zip") else f"{args.model}.zip" + evaluate_with_grader( + model_path=model_path, + task_id=args.task, + n_episodes=args.episodes, + seed=args.seed, + model_type=args.model_type, + save_plot=not args.no_plot, + ) + + +if __name__ == "__main__": + main() diff --git a/rl/evaluate.py b/rl/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..a8c0e324ea659b0d570f7d37835775d7d5d53a0f --- /dev/null +++ b/rl/evaluate.py @@ -0,0 +1,249 @@ +""" +Deterministic evaluator: runs a trained model on tasks and returns grader scores. + +Usage: + python -m rl.evaluate --model results/best_model/phase2_final.zip --episodes 3 + python -m rl.evaluate --model results/best_model/phase3_final.zip --episodes 3 --model-type recurrent +""" + +from __future__ import annotations + +import argparse +import json +from dataclasses import dataclass, asdict +from typing import Any, Literal + +import numpy as np +from sb3_contrib import MaskablePPO, RecurrentPPO +from sb3_contrib.common.maskable.utils import get_action_masks + +from rl.gov_workflow_env import GovWorkflowGymEnv +from app.graders import grade_episode +from app.tasks import TASKS + +TASK_IDS = [ + "district_backlog_easy", + "mixed_urgency_medium", + "cross_department_hard", +] + +ModelType = Literal["auto", "maskable", "recurrent"] + + +@dataclass +class TaskEvalResult: + task_id: str + seed: int + grader_score: float + total_reward: float + total_steps: int + total_completed: int + total_sla_breaches: int + fairness_gap: float + + +def _normalize_action(action: Any) -> int: + if isinstance(action, np.ndarray): + return int(action.item()) + return int(action) + + +def _apply_eval_action_mask(action_idx: int, masks: np.ndarray) -> int: + if 0 <= action_idx < masks.shape[0] and bool(masks[action_idx]): + return action_idx + if masks.shape[0] > 18 and bool(masks[18]): + return 18 + valid = np.flatnonzero(masks) + if valid.size == 0: + return 18 + return int(valid[0]) + + +def predict_recurrent_action( + model: Any, + obs: np.ndarray, + lstm_state: Any, + episode_start: np.ndarray, + masks: np.ndarray, +) -> tuple[int, Any]: + action, next_state = model.predict( + obs, + state=lstm_state, + episode_start=episode_start, + deterministic=True, + ) + action_idx = _normalize_action(action) + action_idx = _apply_eval_action_mask(action_idx, masks) + return action_idx, next_state + + +def _load_model(model_path: str, model_type: ModelType) -> tuple[Any, str]: + if model_type == "maskable": + try: + return MaskablePPO.load(model_path), "maskable" + except Exception as exc: + raise ValueError( + "Failed to load as MaskablePPO. This checkpoint may be recurrent. " + "Try: --model-type recurrent" + ) from exc + if model_type == "recurrent": + try: + return RecurrentPPO.load(model_path), "recurrent" + except Exception as exc: + raise ValueError( + "Failed to load as RecurrentPPO. This checkpoint may be maskable. " + "Try: --model-type maskable" + ) from exc + + try: + return MaskablePPO.load(model_path), "maskable" + except Exception: + return RecurrentPPO.load(model_path), "recurrent" + + +def evaluate_model( + model_path: str, + task_ids: list[str] = TASK_IDS, + n_episodes: int = 1, + verbose: bool = True, + model_type: ModelType = "auto", +) -> list[TaskEvalResult]: + model, resolved_type = _load_model(model_path, model_type) + results = [] + + for task_id in task_ids: + task_cfg = TASKS.get(task_id) + if task_cfg is None: + print(f"[Eval] Task {task_id!r} not found, skipping.") + continue + + ep_rewards, ep_scores = [], [] + last_info: dict[str, Any] = {} + + for ep in range(n_episodes): + env = GovWorkflowGymEnv(task_id=task_id, seed=task_cfg.seed + ep) + obs, _ = env.reset() + done, ep_reward = False, 0.0 + + if resolved_type == "recurrent": + lstm_state: Any = None + episode_start = np.array([True], dtype=bool) + + while not done: + masks = env.action_masks() + action_idx, lstm_state = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=lstm_state, + episode_start=episode_start, + masks=masks, + ) + obs, reward, terminated, truncated, info = env.step(action_idx) + ep_reward += reward + done = terminated or truncated + episode_start = np.array([done], dtype=bool) + last_info = info + else: + while not done: + masks = get_action_masks(env) + action, _ = model.predict(obs, action_masks=masks, deterministic=True) + obs, reward, terminated, truncated, info = env.step(int(action)) + ep_reward += reward + done = terminated or truncated + last_info = info + + gr = grade_episode(env._core_env.state()) + ep_rewards.append(ep_reward) + ep_scores.append(gr.score) + + ep_state = env._core_env.state() + result = TaskEvalResult( + task_id=task_id, + seed=task_cfg.seed, + grader_score=float(np.mean(ep_scores)), + total_reward=float(np.mean(ep_rewards)), + total_steps=ep_state.total_steps, + total_completed=ep_state.total_completed, + total_sla_breaches=ep_state.total_sla_breaches, + fairness_gap=float(last_info.get("fairness_gap", 0.0)), + ) + results.append(result) + if verbose: + print( + f"[Eval] {task_id:<30} " + f"score={result.grader_score:.4f} " + f"reward={result.total_reward:.2f} " + f"completed={result.total_completed} " + f"sla_breaches={result.total_sla_breaches}" + ) + return results + + +def compare_recurrent_vs_flat( + flat_model_path: str, + recurrent_model_path: str, + task_id: str = "mixed_urgency_medium", + n_episodes: int = 3, +) -> dict[str, float]: + flat = evaluate_model( + flat_model_path, + task_ids=[task_id], + n_episodes=n_episodes, + verbose=False, + model_type="maskable", + )[0].grader_score + recurrent = evaluate_model( + recurrent_model_path, + task_ids=[task_id], + n_episodes=n_episodes, + verbose=False, + model_type="recurrent", + )[0].grader_score + return { + "flat": float(flat), + "recurrent": float(recurrent), + "delta": float(recurrent - flat), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description="Evaluate a trained PPO model") + parser.add_argument("--model", required=True) + parser.add_argument( + "--task", + default=None, + choices=TASK_IDS, + help="Single-task alias. If set, overrides --tasks.", + ) + parser.add_argument("--tasks", nargs="+", default=TASK_IDS) + parser.add_argument("--episodes", type=int, default=1) + parser.add_argument("--output", default=None) + parser.add_argument( + "--model-type", + choices=["auto", "maskable", "recurrent"], + default="auto", + help="Model class to load. Use auto for best-effort detection.", + ) + args = parser.parse_args() + + selected_tasks = [args.task] if args.task else args.tasks + results = evaluate_model( + args.model, + task_ids=selected_tasks, + n_episodes=args.episodes, + model_type=args.model_type, + ) + if args.output: + import os + + os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True) + with open(args.output, "w", encoding="utf-8") as f: + json.dump([asdict(r) for r in results], f, indent=2) + print(f"\n[Eval] Results saved to {args.output}") + + avg = np.mean([r.grader_score for r in results]) + print(f"\n[Eval] Average grader score: {avg:.4f}") + + +if __name__ == "__main__": + main() diff --git a/rl/feature_builder.py b/rl/feature_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..cc65a9df49d6ce7644cd6af39fb49382924c6671 --- /dev/null +++ b/rl/feature_builder.py @@ -0,0 +1,208 @@ +""" +Converts ObservationModel (Pydantic) → flat numpy float32 vector. +All downstream RL code depends on OBS_DIM being stable. + +Feature layout (total = OBS_DIM = 84): + [0 : 63) — per-service block (7 services × 9 features each) + [63 : 84) — global block (21 scalar features) +""" + +from __future__ import annotations + +import numpy as np +from typing import List + +from app.models import ( + ObservationModel, + ServiceType, + StageType, + PriorityMode, + ActionType, +) + +# ── Canonical orderings (must never change across the codebase) ────────────── +SERVICES: List[ServiceType] = [ + ServiceType.PASSPORT, + ServiceType.DRIVING_LICENSE, + ServiceType.GST_REGISTRATION, + ServiceType.INCOME_CERTIFICATE, + ServiceType.CASTE_CERTIFICATE, + ServiceType.BIRTH_CERTIFICATE, + ServiceType.LAND_REGISTRATION, +] # 7 legacy RL services for checkpoint compatibility +STAGES: List[StageType] = list(StageType) # 5 stages +PRIORITY_MODES: List[PriorityMode] = list(PriorityMode) # 4 modes +ACTION_TYPES: List[ActionType] = list(ActionType) # 6 types + +SERVICE_IDX = {s: i for i, s in enumerate(SERVICES)} +STAGE_IDX = {s: i for i, s in enumerate(STAGES)} +PM_IDX = {m: i for i, m in enumerate(PRIORITY_MODES)} +AT_IDX = {a: i for i, a in enumerate(ACTION_TYPES)} + +# ── Dimension constants ─────────────────────────────────────────────────────── +N_SERVICES = len(SERVICES) # 7 +N_STAGES = len(STAGES) # 5 +N_PRIORITY_MODES = len(PRIORITY_MODES) # 4 +N_ACTION_TYPES = len(ACTION_TYPES) # 6 + +PER_SERVICE_DIM = 4 + N_STAGES # queue_len, avg_wait, urgent, missing + 5 stage fracs = 9 +GLOBAL_DIM = ( + 1 # day_ratio + + 1 # total_backlog_normalized + + 1 # total_completed_normalized + + 1 # total_sla_breaches_normalized + + 1 # fairness_gap + + 1 # escalation_budget_ratio + + 1 # last_action_valid + + N_ACTION_TYPES # last_action_type one-hot (6) + + N_PRIORITY_MODES # current_priority_mode one-hot (4) + + 1 # idle_officer_ratio + + 1 # urgent_backlog_ratio + + 1 # officer_utilization + + 1 # backlog_per_officer +) # = 21 + +OBS_DIM = N_SERVICES * PER_SERVICE_DIM + GLOBAL_DIM # 63 + 21 = 84 + +# ── Normalisation caps (avoid div-by-zero, keep values in [0,1]) ───────────── +_MAX_QUEUE = 200.0 +_MAX_WAIT = 30.0 +_MAX_URGENT = 50.0 +_MAX_MISSING = 50.0 +_MAX_BACKLOG = 500.0 +_MAX_COMPLETED = 500.0 +_MAX_SLA = 100.0 +_MAX_ESC_BUDGET = 20.0 +_MAX_OFFICERS = 50.0 + + +class FeatureBuilder: + """ + Stateless transformer: ObservationModel → np.ndarray[float32, OBS_DIM]. + + Usage: + fb = FeatureBuilder() + vec = fb.build(obs, current_priority_mode="urgent_first", + last_action_type="advance_time") + """ + + def build( + self, + obs: ObservationModel, + current_priority_mode: str = "balanced", + last_action_type: str = "advance_time", + ) -> np.ndarray: + features = np.zeros(OBS_DIM, dtype=np.float32) + offset = 0 + + snap_dict = { + snap.service_type: snap + for snap in (obs.queue_snapshots.values() if isinstance(obs.queue_snapshots, dict) else obs.queue_snapshots) + } + + # ── Per-service block ───────────────────────────────────────────── + for svc in SERVICES: + snap = snap_dict.get(svc) + if snap is None: + offset += PER_SERVICE_DIM + continue + + total_in_svc = max(getattr(snap, "total_pending", getattr(snap, "active_cases", 0)), 1) + + features[offset + 0] = getattr(snap, "total_pending", getattr(snap, "active_cases", 0)) / _MAX_QUEUE + features[offset + 1] = getattr(snap, "avg_age_days", getattr(snap, "avg_waiting_days", getattr(snap, "oldest_case_age_days", 0))) / _MAX_WAIT + features[offset + 2] = getattr(snap, "urgent_cases", getattr(snap, "urgent_pending", 0)) / _MAX_URGENT + features[offset + 3] = getattr(snap, "blocked_missing_docs", getattr(snap, "missing_docs_cases", 0)) / _MAX_MISSING + + # Stage distribution as fractions + stage_counts = getattr(snap, "stage_counts", getattr(snap, "public_stage_counts", {})) or {} + for stg in STAGES: + count = stage_counts.get(stg, 0) + features[offset + 4 + STAGE_IDX[stg]] = count / total_in_svc + + offset += PER_SERVICE_DIM + + # ── Global block ────────────────────────────────────────────────── + day_ratio = obs.day / max(obs.max_days, 1) + features[offset + 0] = day_ratio + features[offset + 1] = obs.total_backlog / _MAX_BACKLOG + features[offset + 2] = obs.total_completed / _MAX_COMPLETED + features[offset + 3] = obs.total_sla_breaches / _MAX_SLA + features[offset + 4] = float(getattr(obs, "fairness_gap", getattr(obs, "fairness_index", 0.0)) or 0.0) + features[offset + 5] = obs.escalation_budget_remaining / _MAX_ESC_BUDGET + features[offset + 6] = float(obs.last_action_valid) + offset += 7 + + # Last action type one-hot + at_vec = np.zeros(N_ACTION_TYPES, dtype=np.float32) + try: + at_vec[AT_IDX[ActionType(last_action_type)]] = 1.0 + except (ValueError, KeyError): + pass + features[offset: offset + N_ACTION_TYPES] = at_vec + offset += N_ACTION_TYPES + + # Current priority mode one-hot + pm_vec = np.zeros(N_PRIORITY_MODES, dtype=np.float32) + try: + pm_vec[PM_IDX[PriorityMode(current_priority_mode)]] = 1.0 + except (ValueError, KeyError): + pass + features[offset: offset + N_PRIORITY_MODES] = pm_vec + offset += N_PRIORITY_MODES + + # Officer-derived scalars + pool = obs.officer_pool + total_officers = max(getattr(pool, "total_officers", 1) if not callable(getattr(pool, "total_officers", None)) else pool.total_officers(), 1) + idle_officers = getattr(pool, "idle_officers", getattr(pool, "reserve_officers", 0)) + idle_ratio = idle_officers / total_officers + total_backlog_safe = max(obs.total_backlog, 1) + urgent_total = sum( + getattr(snap_dict[s], "urgent_cases", getattr(snap_dict[s], "urgent_pending", 0)) + for s in SERVICES + if s in snap_dict + ) + urgent_ratio = urgent_total / total_backlog_safe + utilization = (total_officers - idle_officers) / total_officers + backlog_per_off = obs.total_backlog / total_officers + + features[offset + 0] = float(np.clip(idle_ratio, 0.0, 1.0)) + features[offset + 1] = float(np.clip(urgent_ratio, 0.0, 1.0)) + features[offset + 2] = float(np.clip(utilization, 0.0, 1.0)) + features[offset + 3] = float(np.clip(backlog_per_off / _MAX_OFFICERS, 0.0, 1.0)) + + assert offset + 4 == OBS_DIM, f"OBS_DIM mismatch: {offset + 4} != {OBS_DIM}" + return features + + +# -- Action space layout (N_ACTIONS = 28) ------------------------------------- +# +# 0 - 3 : set_priority_mode (4 modes in PRIORITY_MODES order) +# 4 - 10 : request_missing_documents per service (7) +# 11 - 17 : escalate_service per service (7) +# 18 : advance_time +# 19 - 25 : reallocate_officers from source service -> most loaded other service +# 26 : assign_capacity +1 to most-loaded service +# 27 : assign_capacity +1 to most-urgent service + +N_ACTIONS = 4 + N_SERVICES + N_SERVICES + 1 + N_SERVICES + 2 # = 28 + +ACTION_DECODE_TABLE = {} +idx = 0 +for m in PRIORITY_MODES: + ACTION_DECODE_TABLE[idx] = ("set_priority_mode", None, m.value, None) + idx += 1 +for s in SERVICES: + ACTION_DECODE_TABLE[idx] = ("request_missing_documents", s.value, None, None) + idx += 1 +for s in SERVICES: + ACTION_DECODE_TABLE[idx] = ("escalate_service", s.value, None, None) + idx += 1 +ACTION_DECODE_TABLE[idx] = ("advance_time", None, None, None); idx += 1 +for s in SERVICES: + ACTION_DECODE_TABLE[idx] = ("reallocate_officers", s.value, "most_loaded_other", 1) + idx += 1 +ACTION_DECODE_TABLE[idx] = ("assign_capacity", "__most_loaded__", None, 1); idx += 1 +ACTION_DECODE_TABLE[idx] = ("assign_capacity", "__most_urgent__", None, 1); idx += 1 + +assert len(ACTION_DECODE_TABLE) == N_ACTIONS diff --git a/rl/gov_workflow_env.py b/rl/gov_workflow_env.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb10691428dcf63dca1b244110021e41b27b6f6 --- /dev/null +++ b/rl/gov_workflow_env.py @@ -0,0 +1,352 @@ +""" +Gymnasium adapter for GovWorkflowEnv. + +Key contract: + observation_space : Box(OBS_DIM,) float32 + action_space : Discrete(N_ACTIONS) + action_masks() : np.ndarray[bool, N_ACTIONS] +""" + +from __future__ import annotations + +from typing import Any, Callable, Optional + +import gymnasium as gym +import numpy as np +from gymnasium import spaces + +from app.env import GovWorkflowEnv +from app.models import ActionModel, ActionType, ObservationModel, PriorityMode, ServiceType +from rl.action_mask import ActionMaskComputer +from rl.feature_builder import ACTION_DECODE_TABLE, N_ACTIONS, OBS_DIM, FeatureBuilder + + +class GovWorkflowGymEnv(gym.Env): + metadata = {"render_modes": []} + + def __init__( + self, + task_id: str = "district_backlog_easy", + seed: int = 42, + hard_action_mask: bool = False, + max_non_advance_streak: int = 3, + ): + super().__init__() + self.task_id = task_id + self._seed = seed + self._task_sampler: Optional[Callable[[], str]] = None + self._global_step_counter: Optional[list[int]] = None + self._hard_action_mask: bool = bool(hard_action_mask) + self._max_non_advance_streak = max(0, int(max_non_advance_streak)) + self._non_advance_streak = 0 + + self._core_env = GovWorkflowEnv() + self._fb = FeatureBuilder() + self._amc = ActionMaskComputer() + + self.observation_space = spaces.Box( + low=0.0, + high=1.0, + shape=(OBS_DIM,), + dtype=np.float32, + ) + self.action_space = spaces.Discrete(N_ACTIONS) + + self._current_obs: Optional[ObservationModel] = None + self._current_pm: str = "balanced" + self._last_at: str = "advance_time" + + @property + def core_env(self) -> GovWorkflowEnv: + return self._core_env + + def set_hard_action_mask(self, enabled: bool) -> None: + self._hard_action_mask = bool(enabled) + + def set_task_sampler( + self, + task_sampler: Optional[Callable[[], str]], + global_step_counter: Optional[list[int]] = None, + ) -> None: + self._task_sampler = task_sampler + self._global_step_counter = global_step_counter + + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ) -> tuple[np.ndarray, dict]: + super().reset(seed=seed) + + if self._task_sampler is not None: + self.task_id = self._task_sampler() + + use_seed = seed if seed is not None else self._seed + task_opts = {"task_id": self.task_id} + if options: + task_opts.update(options) + + obs_model, info = self._core_env.reset(seed=use_seed, options=task_opts) + self._current_obs = obs_model + self._current_pm = "balanced" + self._last_at = "advance_time" + self._non_advance_streak = 0 + + info_dict = info.model_dump() if hasattr(info, "model_dump") else info + if not isinstance(info_dict, dict): + try: + info_dict = dict(info_dict) + except (TypeError, ValueError): + info_dict = {} + + info_dict["fairness_gap"] = self._obs_fairness_gap(obs_model) + return self._to_array(obs_model), info_dict + + def step(self, action: int) -> tuple[np.ndarray, float, bool, bool, dict]: + requested_action_idx = int(action) + action_idx = requested_action_idx + + if self._hard_action_mask and self._current_obs is not None: + action_idx = self._sanitize_action_idx(requested_action_idx, self.action_masks()) + + action_model = self._decode_action(action_idx) + obs_model, reward, terminated, truncated, info = self._core_env.step(action_model) + + if self._global_step_counter is not None: + self._global_step_counter[0] += 1 + + self._current_obs = obs_model + self._last_at = action_model.action_type.value + if getattr(action_model, "priority_mode", None) is not None: + self._current_pm = action_model.priority_mode.value + if action_model.action_type == ActionType.ADVANCE_TIME: + self._non_advance_streak = 0 + else: + self._non_advance_streak += 1 + + info_dict = info.model_dump() if hasattr(info, "model_dump") else info + if not isinstance(info_dict, dict): + try: + info_dict = dict(info_dict) + except (TypeError, ValueError): + info_dict = {} + + info_dict["fairness_gap"] = self._obs_fairness_gap(obs_model) + info_dict["requested_action_idx"] = requested_action_idx + info_dict["executed_action_idx"] = action_idx + info_dict["action_mask_applied"] = bool(action_idx != requested_action_idx) + return self._to_array(obs_model), float(reward), terminated, truncated, info_dict + + def action_masks(self) -> np.ndarray: + if self._current_obs is None: + return np.ones(N_ACTIONS, dtype=bool) + mask = self._amc.compute(self._current_obs, self._current_pm) + if self._max_non_advance_streak > 0 and self._non_advance_streak >= self._max_non_advance_streak: + forced = np.zeros(N_ACTIONS, dtype=bool) + forced[18] = True + return forced + return mask + + def render(self) -> None: + return None + + def _to_array(self, obs: ObservationModel) -> np.ndarray: + return self._fb.build(obs, self._current_pm, self._last_at) + + def _queue_snapshot_iter(self) -> list[Any]: + if self._current_obs is None: + return [] + raw = getattr(self._current_obs, "queue_snapshots", []) + if isinstance(raw, dict): + return list(raw.values()) + if isinstance(raw, list): + return list(raw) + try: + return list(raw) + except Exception: + return [] + + def _queue_service(self, snap: Any) -> Optional[ServiceType]: + value = getattr(snap, "service_type", None) or getattr(snap, "service", None) + if value is None: + return None + if isinstance(value, ServiceType): + return value + try: + return ServiceType(str(value)) + except Exception: + return None + + def _queue_active_cases(self, snap: Any) -> int: + return int(getattr(snap, "total_pending", getattr(snap, "active_cases", 0)) or 0) + + def _queue_urgent_cases(self, snap: Any) -> int: + return int(getattr(snap, "urgent_pending", getattr(snap, "urgent_cases", 0)) or 0) + + def _obs_fairness_gap(self, obs: ObservationModel) -> float: + """ + Canonical fairness signal for RL info payload. + + Current ObservationModel exposes fairness as `fairness_index`, while + episode-level grading uses `fairness_gap` from EpisodeStateModel. + Keep backward-compatible fallback to avoid runtime breaks. + """ + return float(getattr(obs, "fairness_gap", getattr(obs, "fairness_index", 0.0)) or 0.0) + + def _build_action_model(self, action_type: ActionType, **kwargs: Any) -> ActionModel: + service = kwargs.get("service") + target_service = kwargs.get("target_service") + officer_delta = int(kwargs.get("officer_delta", 1) or 1) + priority_mode = kwargs.get("priority_mode") + + candidates: list[dict[str, Any]] = [] + + if action_type == ActionType.ADVANCE_TIME: + candidates.append({"action_type": action_type}) + + elif action_type == ActionType.SET_PRIORITY_MODE: + candidates.append({"action_type": action_type, "priority_mode": priority_mode}) + + elif action_type == ActionType.ASSIGN_CAPACITY and service is not None: + candidates.extend( + [ + {"action_type": action_type, "service": service, "officer_delta": officer_delta}, + {"action_type": action_type, "service_target": service, "officer_delta": officer_delta}, + {"action_type": action_type, "capacity_assignment": {service.value: officer_delta}}, + ] + ) + + elif action_type == ActionType.REQUEST_MISSING_DOCUMENTS and service is not None: + candidates.extend( + [ + {"action_type": action_type, "service": service}, + {"action_type": action_type, "service_target": service}, + ] + ) + + elif action_type == ActionType.ESCALATE_SERVICE and service is not None: + candidates.extend( + [ + {"action_type": action_type, "service": service}, + {"action_type": action_type, "service_target": service}, + {"action_type": action_type, "escalation_target": service}, + ] + ) + + elif action_type == ActionType.REALLOCATE_OFFICERS and service is not None and target_service is not None: + candidates.extend( + [ + { + "action_type": action_type, + "service": service, + "target_service": target_service, + "officer_delta": officer_delta, + }, + { + "action_type": action_type, + "reallocation_delta": { + service.value: -officer_delta, + target_service.value: officer_delta, + }, + }, + ] + ) + + for candidate in candidates: + try: + return ActionModel(**candidate) + except Exception: + continue + + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + def _decode_action(self, action_idx: int) -> ActionModel: + if action_idx not in ACTION_DECODE_TABLE: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + action_type_str, service_str, priority_mode_str, delta = ACTION_DECODE_TABLE[action_idx] + action_type = ActionType(action_type_str) + + if action_type == ActionType.SET_PRIORITY_MODE and priority_mode_str is not None: + return self._build_action_model( + action_type, + priority_mode=PriorityMode(priority_mode_str), + ) + + if action_type == ActionType.ASSIGN_CAPACITY: + if service_str == "__most_loaded__": + target = self._find_most_loaded_service() + elif service_str == "__most_urgent__": + target = self._find_most_urgent_service() + else: + target = ServiceType(service_str) if service_str and not service_str.startswith("__") else None + + if target is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + return self._build_action_model( + action_type, + service=target, + officer_delta=max(int(delta or 1), 1), + ) + + if action_type == ActionType.REQUEST_MISSING_DOCUMENTS: + target = ServiceType(service_str) if service_str and not service_str.startswith("__") else self._find_most_loaded_service() + if target is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + return self._build_action_model(action_type, service=target) + + if action_type == ActionType.ESCALATE_SERVICE: + target = ServiceType(service_str) if service_str and not service_str.startswith("__") else self._find_most_urgent_service() + if target is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + return self._build_action_model(action_type, service=target) + + if action_type == ActionType.REALLOCATE_OFFICERS: + source = ServiceType(service_str) + target = self._find_reallocation_target(source) + if target is None: + return ActionModel(action_type=ActionType.ADVANCE_TIME) + return self._build_action_model( + action_type, + service=source, + target_service=target, + officer_delta=1, + ) + + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + def _find_most_loaded_service(self) -> Optional[ServiceType]: + snaps = self._queue_snapshot_iter() + if not snaps: + return None + best = max(snaps, key=self._queue_active_cases) + return self._queue_service(best) + + def _find_most_urgent_service(self) -> Optional[ServiceType]: + snaps = [snap for snap in self._queue_snapshot_iter() if self._queue_urgent_cases(snap) > 0] + if not snaps: + return None + best = max(snaps, key=lambda snap: (self._queue_urgent_cases(snap), self._queue_active_cases(snap))) + return self._queue_service(best) + + def _find_reallocation_target(self, source: ServiceType) -> Optional[ServiceType]: + snaps = [snap for snap in self._queue_snapshot_iter() if self._queue_service(snap) != source] + if not snaps: + return None + best = max(snaps, key=self._queue_active_cases) + if self._queue_active_cases(best) <= 0: + return None + return self._queue_service(best) + + def _sanitize_action_idx(self, action_idx: int, masks: np.ndarray) -> int: + if 0 <= action_idx < N_ACTIONS and bool(masks[action_idx]): + return action_idx + + if 0 <= 18 < N_ACTIONS and bool(masks[18]): + return 18 + + valid = np.flatnonzero(masks) + if valid.size == 0: + return 18 + return int(valid[0]) diff --git a/rl/plot_training.py b/rl/plot_training.py new file mode 100644 index 0000000000000000000000000000000000000000..f365a1a74828aa4a168e599e454425dd962db6f3 --- /dev/null +++ b/rl/plot_training.py @@ -0,0 +1,237 @@ +""" +Generate training-curve evidence plots for Track A. + +The script first tries monitor CSV files, then falls back to TensorBoard events. +It is read-only for training artifacts and does not trigger training. +""" + +from __future__ import annotations + +import argparse +import csv +import glob +import json +import os +from typing import Any + +import matplotlib +import numpy as np + +matplotlib.use("Agg") +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt + +THRESHOLDS = { + "district_backlog_easy": 0.75, + "mixed_urgency_medium": 0.65, + "cross_department_hard": 0.55, +} + +PHASE_TO_RUN_DIR = { + 1: os.path.join("results", "runs", "phase1_masked_ppo"), + 2: os.path.join("results", "runs", "phase2_curriculum_ppo"), + 3: os.path.join("results", "runs", "phase3_recurrent_ppo"), +} + + +def _read_monitor_csv(monitor_path: str) -> tuple[list[float], list[float]]: + rewards: list[float] = [] + lengths: list[float] = [] + with open(monitor_path, "r", encoding="utf-8") as f: + # First line is metadata starting with '#' + first = f.readline() + if not first: + return rewards, lengths + reader = csv.DictReader(f) + for row in reader: + try: + rewards.append(float(row.get("r", 0.0))) + lengths.append(float(row.get("l", 0.0))) + except (TypeError, ValueError): + continue + return rewards, lengths + + +def _load_tb_scalars(event_path: str) -> dict[str, tuple[list[int], list[float]]]: + try: + from tensorboard.backend.event_processing import event_accumulator + except Exception: + return {} + + try: + acc = event_accumulator.EventAccumulator(event_path) + acc.Reload() + out: dict[str, tuple[list[int], list[float]]] = {} + for tag in acc.Tags().get("scalars", []): + vals = acc.Scalars(tag) + out[tag] = ([int(v.step) for v in vals], [float(v.value) for v in vals]) + return out + except Exception: + return {} + + +def _latest_file(paths: list[str]) -> str | None: + if not paths: + return None + return max(paths, key=lambda p: os.path.getmtime(p)) + + +def _rolling(values: list[float], window: int) -> np.ndarray: + arr = np.asarray(values, dtype=np.float64) + if arr.size == 0: + return arr + w = max(1, int(window)) + kernel = np.ones(w, dtype=np.float64) / float(w) + if arr.size < w: + return np.full_like(arr, np.mean(arr)) + return np.convolve(arr, kernel, mode="same") + + +def plot_training(task_id: str, phase: int = 1) -> str: + if task_id not in THRESHOLDS: + allowed = ", ".join(THRESHOLDS.keys()) + raise ValueError(f"Unknown task_id '{task_id}'. Allowed: {allowed}") + if phase not in PHASE_TO_RUN_DIR: + raise ValueError("phase must be one of: 1, 2, 3") + + threshold = THRESHOLDS[task_id] + run_dir = PHASE_TO_RUN_DIR[phase] + + monitor_candidates = glob.glob(os.path.join(run_dir, "**", "monitor.csv"), recursive=True) + monitor_path = _latest_file(monitor_candidates) + + rewards: list[float] = [] + lengths: list[float] = [] + source = "none" + + if monitor_path and os.path.exists(monitor_path): + rewards, lengths = _read_monitor_csv(monitor_path) + source = f"monitor:{monitor_path}" + else: + event_candidates = glob.glob(os.path.join(run_dir, "**", "events.out.tfevents.*"), recursive=True) + event_path = _latest_file(event_candidates) + if event_path: + scalars = _load_tb_scalars(event_path) + rew_tag = "rollout/ep_rew_mean" + len_tag = "rollout/ep_len_mean" + if rew_tag in scalars: + rewards = scalars[rew_tag][1] + if len_tag in scalars: + lengths = scalars[len_tag][1] + source = f"tensorboard:{event_path}" + + fig = plt.figure(figsize=(16, 10)) + fig.suptitle( + f"Track A - Phase {phase} Training Results\n" + f"Task: {task_id} | Source: {source}", + fontsize=13, + fontweight="bold", + ) + gs = gridspec.GridSpec(2, 2, figure=fig, hspace=0.40, wspace=0.30) + + # Panel 1: reward trend + ax1 = fig.add_subplot(gs[0, 0]) + if rewards: + xs = np.arange(1, len(rewards) + 1) + ax1.plot(xs, rewards, color="#0f766e", alpha=0.35, linewidth=1.2, label="raw") + win = max(10, len(rewards) // 40) + ax1.plot(xs, _rolling(rewards, win), color="#0f766e", linewidth=2.3, label=f"rolling(w={win})") + ax1.set_title("Episode Reward Trend", fontweight="bold") + ax1.set_xlabel("Episode") + ax1.set_ylabel("Reward") + ax1.grid(True, alpha=0.3) + ax1.legend() + else: + ax1.text(0.5, 0.5, "No reward data found", ha="center", va="center", transform=ax1.transAxes) + ax1.set_title("Episode Reward Trend", fontweight="bold") + + # Panel 2: episode length trend + ax2 = fig.add_subplot(gs[0, 1]) + if lengths: + xs = np.arange(1, len(lengths) + 1) + ax2.plot(xs, lengths, color="#7c3aed", alpha=0.35, linewidth=1.2, label="raw") + win = max(10, len(lengths) // 40) + ax2.plot(xs, _rolling(lengths, win), color="#7c3aed", linewidth=2.3, label=f"rolling(w={win})") + ax2.set_title("Episode Length Trend", fontweight="bold") + ax2.set_xlabel("Episode") + ax2.set_ylabel("Length") + ax2.grid(True, alpha=0.3) + ax2.legend() + else: + ax2.text(0.5, 0.5, "No length data found", ha="center", va="center", transform=ax2.transAxes) + ax2.set_title("Episode Length Trend", fontweight="bold") + + # Panel 3: final-quarter reward distribution + ax3 = fig.add_subplot(gs[1, 0]) + if rewards: + start_idx = (len(rewards) * 3) // 4 + final_chunk = rewards[start_idx:] or rewards + ax3.hist(final_chunk, bins=20, color="#15803d", alpha=0.82, edgecolor="white") + ax3.axvline(float(np.mean(final_chunk)), color="#d97706", linewidth=2, label=f"mean={np.mean(final_chunk):.2f}") + ax3.set_title("Final-Quarter Reward Distribution", fontweight="bold") + ax3.set_xlabel("Reward") + ax3.set_ylabel("Frequency") + ax3.grid(True, alpha=0.3, axis="y") + ax3.legend() + else: + ax3.text(0.5, 0.5, "No reward distribution available", ha="center", va="center", transform=ax3.transAxes) + ax3.set_title("Final-Quarter Reward Distribution", fontweight="bold") + + # Panel 4: configuration summary + ax4 = fig.add_subplot(gs[1, 1]) + ax4.axis("off") + + metadata = {} + meta_path = os.path.join("results", "best_model", f"phase{phase}_metadata.json") + if os.path.exists(meta_path): + try: + with open(meta_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + except Exception: + metadata = {} + + summary = ( + f"Phase {phase} Summary\n" + f"{'-' * 36}\n" + f"Task: {task_id}\n" + f"Promotion target: >= {threshold:.2f}\n" + f"Run directory: {run_dir}\n" + f"Data source: {source}\n" + f"Reward points: {len(rewards)}\n" + f"Length points: {len(lengths)}\n" + f"Algorithm: {metadata.get('algorithm', 'PPO family')}\n" + f"Architecture: {metadata.get('architecture', 'MLP / LSTM as configured')}\n" + f"Timesteps: {metadata.get('timesteps', 'n/a')}\n" + f"n_envs: {metadata.get('n_envs', 'n/a')}\n" + f"Seed: {metadata.get('seed', 'n/a')}\n" + ) + ax4.text( + 0.03, + 0.97, + summary, + transform=ax4.transAxes, + verticalalignment="top", + fontsize=9.5, + family="monospace", + bbox={"boxstyle": "round", "facecolor": "#f8fafc", "alpha": 0.9}, + ) + + out_dir = os.path.join("results", "eval_logs", task_id) + os.makedirs(out_dir, exist_ok=True) + out_path = os.path.join(out_dir, f"{task_id}_phase{phase}_training_curves.png") + plt.savefig(out_path, dpi=150, bbox_inches="tight", facecolor="white") + plt.close() + print(f"Training curves saved -> {out_path}") + return out_path + + +def main() -> None: + parser = argparse.ArgumentParser(description="Plot Track A training curves from monitor/TensorBoard artifacts") + parser.add_argument("--task", required=True, choices=list(THRESHOLDS.keys())) + parser.add_argument("--phase", type=int, default=1, choices=[1, 2, 3]) + args = parser.parse_args() + plot_training(task_id=args.task, phase=args.phase) + + +if __name__ == "__main__": + main() diff --git a/rl/train_ppo.py b/rl/train_ppo.py new file mode 100644 index 0000000000000000000000000000000000000000..f06ffe68bb6c001e1e38ad1534d885e85df3070e --- /dev/null +++ b/rl/train_ppo.py @@ -0,0 +1,332 @@ +""" +Phase 1: Masked PPO on district_backlog_easy. +Phase 2: Curriculum Masked PPO across all 3 tasks. + +Usage: + python -m rl.train_ppo --phase 1 --timesteps 200000 + python -m rl.train_ppo --phase 2 --timesteps 500000 + python -m rl.train_ppo --phase 1 --task district_backlog_easy --n_envs 4 +""" + +from __future__ import annotations + +import argparse +import os + +import yaml +from stable_baselines3.common.vec_env import DummyVecEnv +from stable_baselines3.common.monitor import Monitor +from sb3_contrib import MaskablePPO + +from rl.gov_workflow_env import GovWorkflowGymEnv +from rl.callbacks import GovWorkflowEvalCallback, CostMonitorCallback +from rl.curriculum import CurriculumScheduler, CurriculumConfig + +os.makedirs("results/runs", exist_ok=True) +os.makedirs("results/best_model", exist_ok=True) +os.makedirs("results/eval_logs", exist_ok=True) + +PHASE1_TASK_ID = "district_backlog_easy" + + +def _load_cfg(path: str) -> dict: + if os.path.exists(path): + # `utf-8-sig` safely handles files with/without UTF-8 BOM. + with open(path, encoding="utf-8-sig") as f: + return yaml.safe_load(f) + return {} + + +def _resolve_checkpoint_path(path_like: str | None) -> str | None: + if not path_like: + return None + if os.path.exists(path_like): + return path_like + zip_path = f"{path_like}.zip" + if os.path.exists(zip_path): + return zip_path + return None + + +# --------------------------------------------------------------------------- +# Phase 1 — single task easy +# --------------------------------------------------------------------------- +def train_phase1( + total_timesteps: int = 200_000, + n_envs: int = 4, + seed: int = 42, + config_path: str = "rl/configs/ppo_easy.yaml", + eval_freq_override: int | None = None, + n_eval_episodes_override: int | None = None, + disable_eval_callback: bool = False, + no_progress_bar: bool = False, + grader_eval_freq_multiplier_override: int | None = None, + resume_path: str | None = None, +) -> MaskablePPO: + cfg = _load_cfg(config_path) + hp = cfg.get("hyperparameters", {}) + tr_c = cfg.get("training", {}) + + def _make(rank: int): + def _init(): + return Monitor(GovWorkflowGymEnv("district_backlog_easy", seed=seed + rank)) + return _init + + train_env = DummyVecEnv([_make(i) for i in range(n_envs)]) + eval_freq = int(eval_freq_override if eval_freq_override is not None else tr_c.get("eval_freq", max(16_384 // n_envs, 1))) + n_eval_episodes = int(n_eval_episodes_override if n_eval_episodes_override is not None else tr_c.get("n_eval_episodes", 2)) + eval_callback_enabled = bool(tr_c.get("enable_eval_callback", True)) and (not disable_eval_callback) + grader_eval_freq_multiplier = int( + grader_eval_freq_multiplier_override + if grader_eval_freq_multiplier_override is not None + else tr_c.get("grader_eval_freq_multiplier", 4) + ) + callback_verbose = int(tr_c.get("callback_verbose", 0)) + model_verbose = int(tr_c.get("model_verbose", 0)) + progress_bar_enabled = (not no_progress_bar) and bool(tr_c.get("progress_bar", False)) + + callbacks = [CostMonitorCallback()] + if eval_callback_enabled: + eval_env = GovWorkflowGymEnv("district_backlog_easy", seed=seed + 1000, hard_action_mask=True) + eval_cb = GovWorkflowEvalCallback( + eval_env=eval_env, + eval_freq=max(eval_freq, 1), + n_eval_episodes=max(n_eval_episodes, 1), + grader_eval_freq_multiplier=max(grader_eval_freq_multiplier, 1), + best_model_save_path="results/best_model", + log_path="results/eval_logs", + task_id="district_backlog_easy", + verbose=callback_verbose, + ) + callbacks.insert(0, eval_cb) + + resolved_resume = _resolve_checkpoint_path(resume_path) + if resume_path and resolved_resume is None: + raise FileNotFoundError( + f"Phase 1 resume checkpoint not found: {resume_path} (or {resume_path}.zip)" + ) + + if resolved_resume: + print(f"[Phase 1] Resuming from {resolved_resume}") + model = MaskablePPO.load(resolved_resume, env=train_env) + else: + model = MaskablePPO( + policy="MlpPolicy", + env=train_env, + learning_rate=float(hp.get("learning_rate", 3e-4)), + n_steps=int(hp.get("n_steps", 512)), + batch_size=int(hp.get("batch_size", 64)), + n_epochs=int(hp.get("n_epochs", 10)), + gamma=float(hp.get("gamma", 0.99)), + gae_lambda=float(hp.get("gae_lambda", 0.95)), + clip_range=float(hp.get("clip_range", 0.2)), + ent_coef=float(hp.get("ent_coef", 0.01)), + vf_coef=float(hp.get("vf_coef", 0.5)), + max_grad_norm=float(hp.get("max_grad_norm", 0.5)), + policy_kwargs=dict(net_arch=hp.get("net_arch", [256, 256])), + tensorboard_log="results/runs/phase1_masked_ppo", + verbose=model_verbose, + seed=seed, + ) + + print( + f"\n[Phase 1] Masked PPO | timesteps={total_timesteps} | n_envs={n_envs} " + f"| eval_cb={'on' if eval_callback_enabled else 'off'} " + f"| eval_freq={max(eval_freq,1)} | n_eval_episodes={max(n_eval_episodes,1)} " + f"| grader_eval_x{max(grader_eval_freq_multiplier, 1)}" + ) + model.learn( + total_timesteps=total_timesteps, + callback=callbacks, + tb_log_name="masked_ppo_easy", + reset_num_timesteps=not bool(resolved_resume), + progress_bar=progress_bar_enabled, + ) + model.save("results/best_model/phase1_final") + print("[Phase 1] Done -> results/best_model/phase1_final") + return model + + +# --------------------------------------------------------------------------- +# Phase 2 — curriculum across all tasks +# --------------------------------------------------------------------------- +def train_phase2( + total_timesteps: int = 500_000, + n_envs: int = 4, + seed: int = 42, + config_path: str = "rl/configs/curriculum.yaml", +) -> MaskablePPO: + cfg = _load_cfg(config_path) + if not cfg and config_path.endswith("curriculum.yaml"): + # Backward compatibility with previous filename. + cfg = _load_cfg("rl/configs/ppo_curriculum.yaml") + hp = cfg.get("hyperparameters", {}) + cur_c = cfg.get("curriculum", {}) + tr_c = cfg.get("training", {}) + + scheduler = CurriculumScheduler( + total_timesteps=total_timesteps, + config=CurriculumConfig( + stage1_end_frac=float(cur_c.get("stage1_end_frac", 0.30)), + stage2_end_frac=float(cur_c.get("stage2_end_frac", 0.70)), + stage3_weights=tuple(cur_c.get("stage3_weights", [0.20, 0.40, 0.40])), + ), + rng_seed=seed, + ) + + global_step_counter = [0] + + def _sample_task() -> str: + return scheduler.sample_task(global_step_counter[0]) + + def _make_curr(rank: int): + def _init(): + env = GovWorkflowGymEnv( + task_id="district_backlog_easy", + seed=seed + rank, + ) + env.set_task_sampler(_sample_task, global_step_counter) + return Monitor(env) + return _init + + train_env = DummyVecEnv([_make_curr(i) for i in range(n_envs)]) + eval_task_id = str(tr_c.get("eval_task_id", "mixed_urgency_medium")) + eval_env = GovWorkflowGymEnv(eval_task_id, seed=seed + 999, hard_action_mask=True) + + eval_cb = GovWorkflowEvalCallback( + eval_env=eval_env, + eval_freq=int(tr_c.get("eval_freq", max(4096 // n_envs, 1))), + n_eval_episodes=int(tr_c.get("n_eval_episodes", 3)), + grader_eval_freq_multiplier=int(tr_c.get("grader_eval_freq_multiplier", 4)), + best_model_save_path="results/best_model", + log_path="results/eval_logs", + task_id=eval_task_id, + verbose=1, + ) + + warm_start_from = str(tr_c.get("warm_start_from", "results/best_model/phase1_final")) + warm_start_path = _resolve_checkpoint_path(warm_start_from) + + if warm_start_path and os.path.exists(warm_start_path): + print(f"[Phase 2] Warm-starting from {warm_start_path}") + model = MaskablePPO.load(warm_start_path, env=train_env) + else: + model = MaskablePPO( + policy="MlpPolicy", + env=train_env, + learning_rate=float(hp.get("learning_rate", 2e-4)), + n_steps=int(hp.get("n_steps", 512)), + batch_size=int(hp.get("batch_size", 64)), + n_epochs=int(hp.get("n_epochs", 10)), + gamma=float(hp.get("gamma", 0.99)), + gae_lambda=float(hp.get("gae_lambda", 0.95)), + clip_range=float(hp.get("clip_range", 0.2)), + ent_coef=float(hp.get("ent_coef", 0.005)), + policy_kwargs=dict(net_arch=hp.get("net_arch", [256, 256])), + tensorboard_log="results/runs/phase2_curriculum_ppo", + verbose=1, + seed=seed, + ) + + print(f"\n[Phase 2] Curriculum PPO | timesteps={total_timesteps}") + model.learn( + total_timesteps=total_timesteps, + callback=[eval_cb, CostMonitorCallback()], + tb_log_name="curriculum_ppo", + progress_bar=True, + ) + model.save("results/best_model/phase2_final") + print("[Phase 2] Done -> results/best_model/phase2_final") + return model + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--phase", type=int, choices=[1, 2], default=1) + parser.add_argument("--timesteps", type=int, default=200_000) + parser.add_argument("--n-envs", "--n_envs", dest="n_envs", type=int, default=4) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument( + "--task", + default=None, + help=( + "CLI compatibility alias. Phase 1 supports only " + f"'{PHASE1_TASK_ID}'. Phase 2 ignores this flag." + ), + ) + parser.add_argument( + "--phase1-config", + default="rl/configs/ppo_easy.yaml", + help="Config file for Phase 1 training.", + ) + parser.add_argument( + "--phase1-eval-freq", + type=int, + default=None, + help="Override Phase 1 eval callback frequency (in calls).", + ) + parser.add_argument( + "--phase1-n-eval-episodes", + type=int, + default=None, + help="Override Phase 1 eval callback episodes per eval.", + ) + parser.add_argument( + "--phase1-disable-eval-callback", + action="store_true", + help="Disable Phase 1 evaluation callback to avoid pause-heavy eval blocks.", + ) + parser.add_argument( + "--phase1-no-progress-bar", + action="store_true", + help="Disable tqdm progress bar rendering for Phase 1.", + ) + parser.add_argument( + "--phase1-grader-eval-freq-multiplier", + type=int, + default=None, + help="Run grader eval every N * eval_freq callback ticks for Phase 1.", + ) + parser.add_argument( + "--resume", + default=None, + help="Resume Phase 1 from checkpoint path (with or without .zip suffix).", + ) + parser.add_argument( + "--phase2-config", + default="rl/configs/curriculum.yaml", + help="Config file for Phase 2 curriculum training.", + ) + args = parser.parse_args() + + if args.phase == 1 and args.task and args.task != PHASE1_TASK_ID: + raise ValueError( + f"Phase 1 currently supports only task '{PHASE1_TASK_ID}', got '{args.task}'." + ) + if args.phase == 2 and args.task: + print(f"[Phase 2] Ignoring --task={args.task}; curriculum scheduler controls task sampling.") + + if args.phase == 1: + train_phase1( + total_timesteps=args.timesteps, + n_envs=args.n_envs, + seed=args.seed, + config_path=args.phase1_config, + eval_freq_override=args.phase1_eval_freq, + n_eval_episodes_override=args.phase1_n_eval_episodes, + disable_eval_callback=args.phase1_disable_eval_callback, + no_progress_bar=args.phase1_no_progress_bar, + grader_eval_freq_multiplier_override=args.phase1_grader_eval_freq_multiplier, + resume_path=args.resume, + ) + else: + train_phase2( + total_timesteps=args.timesteps, + n_envs=args.n_envs, + seed=args.seed, + config_path=args.phase2_config, + ) + + +if __name__ == "__main__": + main() diff --git a/rl/train_recurrent.py b/rl/train_recurrent.py new file mode 100644 index 0000000000000000000000000000000000000000..34a0565519ec2c73e021c641a2275c8aee1f5998 --- /dev/null +++ b/rl/train_recurrent.py @@ -0,0 +1,215 @@ +""" +Phase 3: Recurrent PPO (LSTM policy) training. + +This trainer keeps the existing 28-action design and uses curriculum sampling +across tasks (easy -> medium -> hard). Because current sb3-contrib releases do +not provide MaskableRecurrentPPO, we enforce action masks in two places: +1) hard mask in GovWorkflowGymEnv before executing an action, +2) recurrent evaluation callback with masked action sanitization. + +Usage: + python -m rl.train_recurrent --timesteps 600000 + python -m rl.train_recurrent --task cross_department_hard --n_envs 4 +""" + +from __future__ import annotations + +import argparse +import os +from typing import Any + +import yaml +from stable_baselines3.common.monitor import Monitor +from stable_baselines3.common.vec_env import DummyVecEnv +from sb3_contrib import MaskablePPO, RecurrentPPO + +from rl.callbacks import CostMonitorCallback, RecurrentEvalCallback +from rl.curriculum import CurriculumConfig, CurriculumScheduler +from rl.gov_workflow_env import GovWorkflowGymEnv + +os.makedirs("results/runs", exist_ok=True) +os.makedirs("results/best_model", exist_ok=True) +os.makedirs("results/eval_logs", exist_ok=True) + + +def _load_cfg(path: str) -> dict: + if os.path.exists(path): + with open(path, encoding="utf-8-sig") as f: + return yaml.safe_load(f) + return {} + + +def _transfer_matching_policy_weights( + recurrent_model: RecurrentPPO, + flat_model_path: str, + exclude_prefixes: tuple[str, ...] = (), +) -> int: + """ + Transfer compatible policy weights from a flat MaskablePPO checkpoint. + + Returns number of copied tensors. + """ + src_path = flat_model_path + if not src_path.endswith(".zip"): + src_path = f"{src_path}.zip" + if not os.path.exists(src_path): + return 0 + + try: + flat_model = MaskablePPO.load(src_path) + except Exception as exc: + print(f"[Phase 3] Skipping flat-weight transfer, could not load MaskablePPO from {src_path}: {exc}") + return 0 + src_state = flat_model.policy.state_dict() + dst_state = recurrent_model.policy.state_dict() + + copied = 0 + for key, dst_tensor in dst_state.items(): + if any(key.startswith(prefix) for prefix in exclude_prefixes): + continue + src_tensor = src_state.get(key) + if src_tensor is None: + continue + if tuple(src_tensor.shape) != tuple(dst_tensor.shape): + continue + dst_state[key] = src_tensor + copied += 1 + + recurrent_model.policy.load_state_dict(dst_state, strict=False) + return copied + + +def train_phase3( + total_timesteps: int = 600_000, + n_envs: int = 4, + seed: int = 42, + config_path: str = "rl/configs/recurrent.yaml", + eval_task_id_override: str | None = None, +) -> RecurrentPPO: + cfg = _load_cfg(config_path) + hp = cfg.get("hyperparameters", {}) + cur_c = cfg.get("curriculum", {}) + tr_c = cfg.get("training", {}) + + scheduler = CurriculumScheduler( + total_timesteps=total_timesteps, + config=CurriculumConfig( + stage1_end_frac=float(cur_c.get("stage1_end_frac", 0.20)), + stage2_end_frac=float(cur_c.get("stage2_end_frac", 0.55)), + stage3_weights=tuple(cur_c.get("stage3_weights", [0.15, 0.35, 0.50])), + ), + rng_seed=seed, + ) + + global_step_counter = [0] + hard_action_mask_train = bool(tr_c.get("hard_action_mask_train", True)) + hard_action_mask_eval = bool(tr_c.get("hard_action_mask_eval", True)) + + def _sample_task() -> str: + return scheduler.sample_task(global_step_counter[0]) + + def _make_curr(rank: int): + def _init(): + env = GovWorkflowGymEnv( + task_id="district_backlog_easy", + seed=seed + rank, + hard_action_mask=hard_action_mask_train, + ) + env.set_task_sampler(_sample_task, global_step_counter) + return Monitor(env) + + return _init + + train_env = DummyVecEnv([_make_curr(i) for i in range(n_envs)]) + + eval_task_id = str(eval_task_id_override or tr_c.get("eval_task_id", "mixed_urgency_medium")) + eval_env = GovWorkflowGymEnv(eval_task_id, seed=seed + 999, hard_action_mask=hard_action_mask_eval) + + eval_cb = RecurrentEvalCallback( + eval_env=eval_env, + eval_freq=int(tr_c.get("eval_freq", max(4096 // n_envs, 1))), + n_eval_episodes=int(tr_c.get("n_eval_episodes", 3)), + best_model_save_path="results/best_model", + log_path="results/eval_logs", + task_id=eval_task_id, + verbose=1, + ) + + model = RecurrentPPO( + policy="MlpLstmPolicy", + env=train_env, + learning_rate=float(hp.get("learning_rate", 1e-4)), + n_steps=int(hp.get("n_steps", 512)), + batch_size=int(hp.get("batch_size", 128)), + n_epochs=int(hp.get("n_epochs", 10)), + gamma=float(hp.get("gamma", 0.995)), + gae_lambda=float(hp.get("gae_lambda", 0.95)), + clip_range=float(hp.get("clip_range", 0.2)), + ent_coef=float(hp.get("ent_coef", 0.002)), + vf_coef=float(hp.get("vf_coef", 0.5)), + max_grad_norm=float(hp.get("max_grad_norm", 0.5)), + policy_kwargs=dict( + net_arch=hp.get("net_arch", [256, 256]), + lstm_hidden_size=int(hp.get("lstm_hidden_size", 128)), + n_lstm_layers=int(hp.get("n_lstm_layers", 1)), + shared_lstm=bool(hp.get("shared_lstm", False)), + enable_critic_lstm=bool(hp.get("enable_critic_lstm", True)), + ), + tensorboard_log="results/runs/phase3_recurrent_ppo", + verbose=1, + seed=seed, + ) + + warm_start_from = str(tr_c.get("warm_start_from", "results/best_model/phase2_final")) + transfer_flat = bool(tr_c.get("transfer_flat_weights", True)) + transfer_exclude_prefixes = tuple( + tr_c.get("transfer_exclude_prefixes", ["action_net.", "value_net."]) + ) + if transfer_flat: + copied = _transfer_matching_policy_weights( + model, + warm_start_from, + exclude_prefixes=transfer_exclude_prefixes, + ) + if copied > 0: + print(f"[Phase 3] Transferred {copied} compatible policy tensors from {warm_start_from}") + else: + print(f"[Phase 3] No compatible transfer tensors found from {warm_start_from}") + + print(f"\n[Phase 3] Recurrent PPO | timesteps={total_timesteps} | n_envs={n_envs}") + model.learn( + total_timesteps=total_timesteps, + callback=[eval_cb, CostMonitorCallback()], + tb_log_name="recurrent_ppo", + progress_bar=True, + ) + model.save("results/best_model/phase3_final") + print("[Phase 3] Done -> results/best_model/phase3_final") + return model + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--timesteps", type=int, default=600_000) + parser.add_argument("--n-envs", "--n_envs", dest="n_envs", type=int, default=4) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--config", default="rl/configs/recurrent.yaml") + parser.add_argument( + "--task", + default=None, + choices=["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"], + help="Compatibility alias for evaluation task used by recurrent eval callback.", + ) + args = parser.parse_args() + + train_phase3( + total_timesteps=args.timesteps, + n_envs=args.n_envs, + seed=args.seed, + config_path=args.config, + eval_task_id_override=args.task, + ) + + +if __name__ == "__main__": + main() diff --git a/run_all_tests.sh b/run_all_tests.sh new file mode 100644 index 0000000000000000000000000000000000000000..6e1d5c220e177f1238b16b84357ef925c0f692d0 --- /dev/null +++ b/run_all_tests.sh @@ -0,0 +1,71 @@ +#!/usr/bin/env bash +# ─────────────────────────────────────────────────────────────────── +# run_all_tests.sh — Full Phase 1 + Phase 2 validation suite +# Usage: bash run_all_tests.sh [--fast] [--api-only] [--unit-only] +# ─────────────────────────────────────────────────────────────────── +set -e + +# Colors +GREEN="\033[0;32m" +YELLOW="\033[1;33m" +RED="\033[0;31m" +NC="\033[0m" + +FAST=false +API_ONLY=false +UNIT_ONLY=false + +for arg in "$@"; do + case $arg in + --fast) FAST=true ;; + --api-only) API_ONLY=true ;; + --unit-only) UNIT_ONLY=true ;; + esac +done + +echo -e "${GREEN}========================================${NC}" +echo -e "${GREEN} Gov Workflow OpenEnv — Test Suite ${NC}" +echo -e "${GREEN}========================================${NC}" + +# ── Phase 1: Unit Tests ─────────────────────────────────────────── +if [ "$API_ONLY" = false ]; then + echo -e "\n${YELLOW}[Phase 1] Running model schema tests...${NC}" + python.exe -m pytest tests/test_phase1_models.py -v --tb=short + + echo -e "\n${YELLOW}[Phase 1] Running sector profile + task tests...${NC}" + python.exe -m pytest tests/test_phase1_sector_and_tasks.py -v --tb=short + + echo -e "\n${YELLOW}[Phase 1] Running event engine tests...${NC}" + python.exe -m pytest tests/test_phase1_event_engine.py -v --tb=short + + echo -e "\n${YELLOW}[Phase 1] Running signal computer tests...${NC}" + python.exe -m pytest tests/test_phase1_signal_computer.py -v --tb=short +fi + +# ── Phase 2: Integration Tests ──────────────────────────────────── +if [ "$UNIT_ONLY" = false ]; then + echo -e "\n${YELLOW}[Phase 2] Running env integration tests...${NC}" + python.exe -m pytest tests/test_phase2_env_integration.py -v --tb=short + + echo -e "\n${YELLOW}[Phase 2] Running simulator tests...${NC}" + python.exe -m pytest tests/test_phase2_simulator.py -v --tb=short + + echo -e "\n${YELLOW}[Phase 2] Running API endpoint tests (TestClient)...${NC}" + python.exe -m pytest tests/test_phase2_api.py -v --tb=short +fi + +# ── Summary ─────────────────────────────────────────────────────── +echo -e "\n${GREEN}========================================${NC}" +echo -e "${GREEN} All test suites completed.${NC}" +echo -e "${GREEN}========================================${NC}" + +# Full coverage report +if [ "$FAST" = false ]; then + echo -e "\n${YELLOW}Running full coverage report...${NC}" + python.exe -m pytest tests/ \ + --cov=app \ + --cov-report=term-missing \ + --cov-report=html:htmlcov \ + -q 2>/dev/null || true + echo -e "${GREEN}Coverage report written to htmlcov/index.html${NC}" +fi diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..96545beecf9db290baf7beee4665d135ac8af453 --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,31 @@ +# scripts/ + +Utility scripts for run, validation, and benchmarking. + +- `run_local.py`: launch local API server +- `validate_env.py`: local environment validation checks +- `validate-submission.sh`: deployment validation flow +- `pre_deploy_e2e.ps1`: Windows pre-deploy gate for end-to-end readiness before Docker build and release +- `benchmark_ladder.py`: compare heuristic and RL agents +- `smoke_test.py`: quick endpoint sanity checks + +## Pre-deploy E2E gate (Windows) + +Run a full readiness pass before release deployment: + +```powershell +powershell -ExecutionPolicy Bypass -File .\scripts\pre_deploy_e2e.ps1 +``` + +Useful options: + +```powershell +# Faster pass (skips extended regression test bundle) +powershell -ExecutionPolicy Bypass -File .\scripts\pre_deploy_e2e.ps1 -Quick + +# Skip Docker checks when only validating local test readiness +powershell -ExecutionPolicy Bypass -File .\scripts\pre_deploy_e2e.ps1 -SkipDockerBuild -SkipDockerRuntime + +# Use a specific Python interpreter +powershell -ExecutionPolicy Bypass -File .\scripts\pre_deploy_e2e.ps1 -PythonPath .\.venv313\Scripts\python.exe +``` diff --git a/scripts/api_live_http_audit.py b/scripts/api_live_http_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..bf38d87c5cfd6f5a843f711fa95738462d70588f --- /dev/null +++ b/scripts/api_live_http_audit.py @@ -0,0 +1,396 @@ +""" +Live HTTP audit for Gov Workflow OpenEnv API. + +This script calls the full 16-endpoint contract over real HTTP +and writes a timestamped JSON report with pass/fail + response samples. +""" + +from __future__ import annotations + +import argparse +import json +import os +from datetime import datetime, timezone +from pathlib import Path +from typing import Any +from urllib import error, request + + +def _now_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _shorten(text: str, max_chars: int = 800) -> str: + if len(text) <= max_chars: + return text + return text[:max_chars] + "..." + + +def _http_call( + base_url: str, + method: str, + path: str, + *, + body: dict[str, Any] | None = None, + timeout_sec: int = 30, + max_sample_chars: int = 800, +) -> dict[str, Any]: + url = f"{base_url.rstrip('/')}{path}" + payload_bytes = None + headers = {"Accept": "application/json"} + if body is not None: + payload_bytes = json.dumps(body).encode("utf-8") + headers["Content-Type"] = "application/json" + + req = request.Request( + url=url, + data=payload_bytes, + headers=headers, + method=method.upper(), + ) + + status_code = None + raw_text = "" + parsed_json = None + err_text = None + + try: + with request.urlopen(req, timeout=timeout_sec) as resp: + status_code = int(resp.status) + raw_text = resp.read().decode("utf-8", errors="replace") + except error.HTTPError as exc: + status_code = int(exc.code) + raw_text = exc.read().decode("utf-8", errors="replace") + err_text = str(exc) + except Exception as exc: # network/timeout etc. + err_text = str(exc) + + if raw_text: + try: + parsed_json = json.loads(raw_text) + except Exception: + parsed_json = None + + return { + "method": method.upper(), + "path": path, + "url": url, + "request_body": body, + "status_code": status_code, + "ok": err_text is None or status_code is not None, + "error": err_text, + "response_json": parsed_json, + "response_text_sample": _shorten(raw_text, max_chars=max_sample_chars), + } + + +def _extract_sse_data_lines(raw_text: str) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for line in raw_text.splitlines(): + line = line.strip() + if not line.startswith("data:"): + continue + payload = line[len("data:") :].strip() + if not payload: + continue + try: + rows.append(json.loads(payload)) + except Exception: + rows.append({"raw": payload}) + return rows + + +def run_audit(base_url: str, timeout_sec: int = 30) -> dict[str, Any]: + checks: list[dict[str, Any]] = [] + context: dict[str, Any] = {} + + def add_check(name: str, call_result: dict[str, Any], expected_statuses: list[int]) -> None: + status_code = call_result.get("status_code") + passed = bool(status_code in expected_statuses) + checks.append( + { + "name": name, + "endpoint": f"{call_result['method']} {call_result['path']}", + "expected_statuses": expected_statuses, + "status_code": status_code, + "passed": passed, + "error": call_result.get("error"), + "response_sample": call_result.get("response_json") + if call_result.get("response_json") is not None + else call_result.get("response_text_sample"), + } + ) + call_result["passed"] = passed + + # 1) /health + r = _http_call(base_url, "GET", "/health", timeout_sec=timeout_sec) + add_check("health", r, [200]) + + # 2) /tasks + r = _http_call(base_url, "GET", "/tasks", timeout_sec=timeout_sec) + add_check("tasks", r, [200]) + task_ids: list[str] = [] + if isinstance(r.get("response_json"), list): + task_ids = [str(x.get("task_id")) for x in r["response_json"] if isinstance(x, dict) and x.get("task_id")] + if not task_ids: + task_ids = ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"] + context["task_ids"] = task_ids + + # 3) /tasks/{task_id} (test first available) + task_id = task_ids[0] + r = _http_call(base_url, "GET", f"/tasks/{task_id}", timeout_sec=timeout_sec) + add_check("task_detail", r, [200]) + + # 4) /metrics + r = _http_call(base_url, "GET", "/metrics", timeout_sec=timeout_sec) + add_check("metrics", r, [200]) + + # 5) /actions/schema + r = _http_call(base_url, "GET", "/actions/schema", timeout_sec=timeout_sec) + add_check("actions_schema", r, [200]) + + # 6) /rl/models + r = _http_call(base_url, "GET", "/rl/models", timeout_sec=timeout_sec) + add_check("rl_models", r, [200]) + + # 7) /reset + r = _http_call( + base_url, + "POST", + "/reset", + body={"task_id": task_id, "seed": 42}, + timeout_sec=timeout_sec, + ) + add_check("reset", r, [200]) + sid = None + if isinstance(r.get("response_json"), dict): + sid = r["response_json"].get("session_id") + context["session_id"] = sid + + # 8) /action-masks + if sid: + r = _http_call( + base_url, + "POST", + "/action-masks", + body={"session_id": sid}, + timeout_sec=timeout_sec, + ) + add_check("action_masks", r, [200]) + else: + checks.append( + { + "name": "action_masks", + "endpoint": "POST /action-masks", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 9) /step + if sid: + r = _http_call( + base_url, + "POST", + "/step", + body={"session_id": sid, "action": {"action_type": "advance_time"}}, + timeout_sec=timeout_sec, + ) + add_check("step", r, [200]) + else: + checks.append( + { + "name": "step", + "endpoint": "POST /step", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 10) /state + if sid: + r = _http_call( + base_url, + "GET", + f"/state?session_id={sid}&include_action_history=true", + timeout_sec=timeout_sec, + ) + add_check("state", r, [200]) + else: + checks.append( + { + "name": "state", + "endpoint": "GET /state", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 11) /simulate (SSE) + r = _http_call( + base_url, + "POST", + "/simulate", + body={"task_id": task_id, "agent_mode": "baseline_policy", "max_steps": 3, "seed": 42}, + timeout_sec=timeout_sec, + max_sample_chars=4000, + ) + parsed_rows = _extract_sse_data_lines(r.get("response_text_sample", "")) + has_step = any(isinstance(x, dict) and "step" in x for x in parsed_rows) + has_done = any(isinstance(x, dict) and x.get("done") is True for x in parsed_rows) + simulate_pass = (r.get("status_code") == 200) and has_step and has_done + checks.append( + { + "name": "simulate_stream", + "endpoint": "POST /simulate", + "expected_statuses": [200], + "status_code": r.get("status_code"), + "passed": simulate_pass, + "error": r.get("error"), + "response_sample": { + "sse_rows_sample": parsed_rows[:3], + "has_step": has_step, + "has_done": has_done, + }, + } + ) + + # 12) /simulate/{session_id}/snapshot + if sid: + r = _http_call(base_url, "GET", f"/simulate/{sid}/snapshot", timeout_sec=timeout_sec) + add_check("simulate_snapshot", r, [200]) + else: + checks.append( + { + "name": "simulate_snapshot", + "endpoint": "GET /simulate/{session_id}/snapshot", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 13) /simulate/{session_id}/trace + if sid: + r = _http_call(base_url, "GET", f"/simulate/{sid}/trace?page=1&page_size=20", timeout_sec=timeout_sec) + add_check("simulate_trace", r, [200]) + else: + checks.append( + { + "name": "simulate_trace", + "endpoint": "GET /simulate/{session_id}/trace", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 14) /grade + if sid: + r = _http_call(base_url, "POST", "/grade", body={"session_id": sid}, timeout_sec=timeout_sec) + add_check("grade", r, [200]) + else: + checks.append( + { + "name": "grade", + "endpoint": "POST /grade", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + # 15) /rl/run (guardrail: missing model -> 422) + r = _http_call( + base_url, + "POST", + "/rl/run", + body={ + "task_id": task_id, + "model_path": "results/best_model/does_not_exist", + "seed": 42, + "max_steps": 10, + "n_episodes": 1, + }, + timeout_sec=timeout_sec, + ) + add_check("rl_run_missing_model_guardrail", r, [422]) + + # 16) /simulate/{session_id}/cancel + if sid: + r = _http_call(base_url, "POST", f"/simulate/{sid}/cancel", timeout_sec=timeout_sec) + add_check("simulate_cancel", r, [200]) + else: + checks.append( + { + "name": "simulate_cancel", + "endpoint": "POST /simulate/{session_id}/cancel", + "expected_statuses": [200], + "status_code": None, + "passed": False, + "error": "Skipped: no session_id from /reset", + "response_sample": None, + } + ) + + total = len(checks) + passed = sum(1 for c in checks if c["passed"]) + failed = total - passed + + return { + "audit_name": "gov-workflow-openenv-live-http-audit", + "generated_at_utc": _now_iso(), + "base_url": base_url, + "summary": { + "total_checks": total, + "passed": passed, + "failed": failed, + "pass_rate": round((passed / total) * 100.0, 2) if total else 0.0, + }, + "context": context, + "checks": checks, + } + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--base-url", default="http://127.0.0.1:7860") + parser.add_argument("--timeout-sec", type=int, default=30) + parser.add_argument("--out-dir", default="reports/api_audit") + args = parser.parse_args() + + report = run_audit(args.base_url, timeout_sec=args.timeout_sec) + out_dir = Path(args.out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + stamp = datetime.now().strftime("%Y%m%d_%H%M%S") + out_path = out_dir / f"api_live_audit_{stamp}.json" + out_path.write_text(json.dumps(report, indent=2), encoding="utf-8") + + print(f"Report written: {out_path}") + print( + f"Summary: passed={report['summary']['passed']}, " + f"failed={report['summary']['failed']}, " + f"pass_rate={report['summary']['pass_rate']}%" + ) + if report["summary"]["failed"] > 0: + return 1 + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/benchmark_ladder.py b/scripts/benchmark_ladder.py new file mode 100644 index 0000000000000000000000000000000000000000..60cbc83a27a4a126dc2e09f0c426127200517614 --- /dev/null +++ b/scripts/benchmark_ladder.py @@ -0,0 +1,83 @@ +""" +Benchmark Ladder - compare all agents on all 3 tasks. + +Usage: + python scripts/benchmark_ladder.py + python scripts/benchmark_ladder.py --phase1 results/best_model/phase1_final +""" + +from __future__ import annotations + +import argparse +import json +import os + +from app.baselines import run_policy_episode +from rl.evaluate import TASK_IDS, evaluate_model + + +def fmt(v): + return f"{v:.4f}" if isinstance(v, float) else str(v) + + +def print_table(rows): + print("\n" + "=" * 65) + print(f"{'Agent':<28} {'Easy':>8} {'Medium':>8} {'Hard':>8} {'Avg':>8}") + print("-" * 65) + for r in rows: + print( + f"{r['agent']:<28} " + f"{r.get('district_backlog_easy', '-'):>8} " + f"{r.get('mixed_urgency_medium', '-'):>8} " + f"{r.get('cross_department_hard', '-'):>8} " + f"{r.get('average', '-'):>8}" + ) + print("=" * 65 + "\n") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--phase1", default=None) + parser.add_argument("--phase2", default=None) + parser.add_argument("--phase3", default=None) + parser.add_argument("--output", default="results/benchmark_ladder.json") + args = parser.parse_args() + + all_rows = [] + + for policy in ["urgent_first", "oldest_first", "backlog_clearance"]: + row, scores = {"agent": f"heuristic_{policy}"}, [] + for tid in TASK_IDS: + try: + result = run_policy_episode(task_id=tid, policy_name=policy) + s = float(result["score"]) + row[tid] = fmt(s) + scores.append(s) + except Exception: + row[tid] = "ERR" + row["average"] = fmt(sum(scores) / len(scores)) if scores else "-" + all_rows.append(row) + + for label, path in [ + ("masked_ppo_ph1", args.phase1), + ("curriculum_ppo_ph2", args.phase2), + ("recurrent_ppo_ph3", args.phase3), + ]: + if not path or not os.path.exists(path + ".zip"): + continue + row, scores = {"agent": label}, [] + for r in evaluate_model(path, task_ids=TASK_IDS, verbose=False): + row[r.task_id] = fmt(r.grader_score) + scores.append(r.grader_score) + row["average"] = fmt(sum(scores) / len(scores)) if scores else "-" + all_rows.append(row) + + print_table(all_rows) + os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True) + with open(args.output, "w", encoding="utf-8") as f: + json.dump(all_rows, f, indent=2) + print(f"Saved -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/convert_grpo_csv.py b/scripts/convert_grpo_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..ffe3910538838d71483a660fb24ba2ec927c96a6 --- /dev/null +++ b/scripts/convert_grpo_csv.py @@ -0,0 +1,170 @@ +""" +scripts/convert_grpo_csv.py + +Converts GRPO training CSV logs to JSON format +for the FastAPI /training/* story endpoints. + +CSV format expected: + step, reward, fn1_valid, fn2_no_halluc, fn3_env_score + +Usage: + python scripts/convert_grpo_csv.py \ + --csv grpo_training_log.csv \ + --task mixed_urgency_medium + +Output: + data/training_logs/{task_id}_training_log.json +""" + +from __future__ import annotations +import csv +import json +import argparse +from pathlib import Path + + +def load_csv(csv_path: str) -> list[dict]: + rows = [] + with open(csv_path, newline="", encoding="utf-8") as f: + reader = csv.DictReader(f) + fieldnames = set(reader.fieldnames or []) + reward_values: list[float] = [] + raw_rows: list[dict] = [] + + def _pick(row: dict, names: list[str], default: float) -> float: + for name in names: + if name in row and str(row.get(name, "")).strip() != "": + try: + return float(row[name]) + except (TypeError, ValueError): + continue + return float(default) + + for row in reader: + raw_rows.append(row) + reward_values.append(_pick(row, ["reward", "total_reward"], 0.0)) + + r_min = min(reward_values) if reward_values else 0.0 + r_rng = (max(reward_values) - r_min) if reward_values else 1.0 + if r_rng == 0: + r_rng = 1.0 + + for i, row in enumerate(raw_rows): + reward_val = _pick(row, ["reward", "total_reward"], 0.0) + fallback_norm = (reward_val - r_min) / r_rng + step_default = i + 1 + if "step" in row and str(row.get("step", "")).strip() != "": + try: + step_default = int(float(row["step"])) + except (TypeError, ValueError): + step_default = i + 1 + + rows.append({ + "step": step_default, + "reward": reward_val, + "fn1_valid": _pick(row, ["fn1_valid", "valid_action_rate"], 1.0), + "fn2_no_halluc": _pick(row, ["fn2_no_halluc", "hallucination_free"], 1.0), + "fn3_env_score": _pick(row, ["fn3_env_score", "env_score"], fallback_norm), + }) + return rows + + +def build_log(rows: list[dict], task_id: str) -> dict: + n = len(rows) + rewards = [r["reward"] for r in rows] + fn1_vals = [r["fn1_valid"] for r in rows] + fn2_vals = [r["fn2_no_halluc"] for r in rows] + fn3_vals = [r["fn3_env_score"] for r in rows] + + fn3_min = min(fn3_vals) + fn3_rng = (max(fn3_vals) - fn3_min) or 1.0 + + episodes = [] + for i, r in enumerate(rows): + norm_env = (r["fn3_env_score"] - fn3_min) / fn3_rng + combined = round( + r["fn1_valid"] * 0.3 + r["fn2_no_halluc"] * 0.2 + norm_env * 0.5, + 4 + ) + phase = ( + "random" if i < n * 0.25 else + "exploring" if i < n * 0.50 else + "learning" if i < n * 0.75 else + "converged" + ) + episodes.append({ + "episode": r["step"], + "total_reward": round(r["reward"], 4), + "score": combined, + "fn1_valid": round(r["fn1_valid"], 4), + "fn2_no_halluc": round(r["fn2_no_halluc"], 4), + "fn3_env_score": round(r["fn3_env_score"], 4), + "phase": phase, + "actions": { + "valid_action_rate": round(r["fn1_valid"], 4), + "hallucination_free": round(r["fn2_no_halluc"], 4), + "env_score": round(norm_env, 4), + }, + }) + + scores = [e["score"] for e in episodes] + + return { + "task_id": task_id, + "total_episodes": n, + "base_model": "Qwen/Qwen2-1.5B-Instruct", + "adapter_path": f"artifacts/llm/{task_id.split('_')[1]}/", + "training_method": "GRPO", + "lora_rank": 16, + "reward_functions": { + "fn1_valid": "Action validity - legal JSON action output (0->1)", + "fn2_no_halluc": "No hallucination - stayed on gov workflow topic (0->1)", + "fn3_env_score": "Environment score - improved gov workflow quality", + }, + "summary": { + "first_episode_reward": round(rewards[0], 4), + "last_episode_reward": round(rewards[-1], 4), + "best_episode_reward": round(max(rewards), 4), + "first_episode_score": round(scores[0], 4), + "last_episode_score": round(scores[-1], 4), + "best_episode_score": round(max(scores), 4), + "reward_improvement_pct": round( + ((rewards[-1] - rewards[0]) / abs(rewards[0])) * 100, 2 + ) if rewards[0] != 0 else 0.0, + "invalid_action_steps": sum(1 for r in rows if r["fn1_valid"] < 1.0), + "hallucination_steps": sum(1 for r in rows if r["fn2_no_halluc"] < 1.0), + "avg_fn1_valid": round(sum(fn1_vals) / n, 4), + "avg_fn2_no_halluc": round(sum(fn2_vals) / n, 4), + "avg_fn3_env_score": round(sum(fn3_vals) / n, 4), + }, + "episodes": episodes, + } + + +def save_log(log: dict, out_dir: str) -> str: + Path(out_dir).mkdir(parents=True, exist_ok=True) + out_path = f"{out_dir}/{log['task_id']}_training_log.json" + with open(out_path, "w", encoding="utf-8") as f: + json.dump(log, f, indent=2) + return out_path + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--csv", required=True, help="Path to GRPO CSV file") + parser.add_argument("--task", required=True, help="Task ID e.g. mixed_urgency_medium") + parser.add_argument("--out", default="data/training_logs", help="Output directory") + args = parser.parse_args() + + print(f"Reading CSV : {args.csv}") + rows = load_csv(args.csv) + print(f"Steps found : {len(rows)}") + + log = build_log(rows, args.task) + out = save_log(log, args.out) + + print(f"Saved JSON : {out}") + print(f"Steps : {log['total_episodes']}") + print(f"Reward range : {log['summary']['first_episode_reward']} -> {log['summary']['last_episode_reward']}") + print(f"Score range : {log['summary']['first_episode_score']} -> {log['summary']['last_episode_score']}") + print(f"Invalid steps: {log['summary']['invalid_action_steps']}") diff --git a/scripts/pre_deploy_e2e.ps1 b/scripts/pre_deploy_e2e.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..db4637bc5baf74625f2077732b968c015a784345 --- /dev/null +++ b/scripts/pre_deploy_e2e.ps1 @@ -0,0 +1,297 @@ +param( + [string]$PythonPath = "", + [string]$ImageTag = "openenv-rl:predeploy", + [int]$ContainerPort = 8786, + [int]$StartupTimeoutSec = 120, + [switch]$Quick, + [switch]$SkipFrontendBuild, + [switch]$SkipDockerBuild, + [switch]$SkipDockerRuntime, + [switch]$SkipOpenEnvCli +) + +Set-StrictMode -Version Latest +$ErrorActionPreference = "Stop" + +$script:StepResults = New-Object System.Collections.Generic.List[object] + +function Add-StepResult { + param( + [string]$Name, + [string]$Status, + [double]$DurationSec, + [string]$Detail = "" + ) + + $script:StepResults.Add([pscustomobject]@{ + Step = $Name + Status = $Status + DurationSec = [Math]::Round($DurationSec, 2) + Detail = $Detail + }) | Out-Null +} + +function Show-Summary { + Write-Host "" + Write-Host "==============================================" + Write-Host "Pre-Deploy E2E Summary" + Write-Host "==============================================" + + $table = $script:StepResults | Select-Object Step, Status, DurationSec, Detail + if ($table.Count -gt 0) { + $table | Format-Table -AutoSize | Out-String | Write-Host + } + + $failed = @($script:StepResults | Where-Object { $_.Status -eq "FAILED" }) + if ($failed.Count -gt 0) { + Write-Host "Result: FAILED ($($failed.Count) step(s) failed)" -ForegroundColor Red + } + else { + Write-Host "Result: PASSED (all checks succeeded)" -ForegroundColor Green + } +} + +function Ensure-CommandExists { + param([string[]]$Candidates) + + foreach ($candidate in $Candidates) { + $cmd = Get-Command $candidate -ErrorAction SilentlyContinue + if ($null -ne $cmd) { + return $cmd.Source + } + } + + throw "Required command not found. Tried: $($Candidates -join ', ')" +} + +function Resolve-PythonExe { + param([string]$RequestedPath) + + if ($RequestedPath) { + if (Test-Path $RequestedPath) { + return (Resolve-Path $RequestedPath).Path + } + throw "PythonPath was provided but not found: $RequestedPath" + } + + $candidatePaths = @( + ".venv313\\Scripts\\python.exe", + ".venv\\Scripts\\python.exe" + ) + + foreach ($candidate in $candidatePaths) { + if (Test-Path $candidate) { + return (Resolve-Path $candidate).Path + } + } + + $pythonCmd = Get-Command python.exe -ErrorAction SilentlyContinue + if ($null -ne $pythonCmd) { + return $pythonCmd.Source + } + + throw "Could not resolve Python interpreter. Provide -PythonPath explicitly." +} + +function Invoke-CheckedCommand { + param( + [string]$Executable, + [string[]]$Arguments + ) + + Write-Host "-> $Executable $($Arguments -join ' ')" + & $Executable @Arguments + if ($LASTEXITCODE -ne 0) { + throw "Command failed with exit code $LASTEXITCODE: $Executable $($Arguments -join ' ')" + } +} + +function Invoke-Step { + param( + [string]$Name, + [scriptblock]$Action + ) + + Write-Host "" + Write-Host "=== $Name ===" -ForegroundColor Cyan + + $sw = [System.Diagnostics.Stopwatch]::StartNew() + try { + & $Action + $sw.Stop() + Add-StepResult -Name $Name -Status "PASSED" -DurationSec $sw.Elapsed.TotalSeconds + Write-Host "[PASS] $Name" -ForegroundColor Green + } + catch { + $sw.Stop() + Add-StepResult -Name $Name -Status "FAILED" -DurationSec $sw.Elapsed.TotalSeconds -Detail $_.Exception.Message + Write-Host "[FAIL] $Name" -ForegroundColor Red + Write-Host "Reason: $($_.Exception.Message)" -ForegroundColor Red + Show-Summary + throw + } +} + +function Wait-ForHealth { + param( + [string]$HealthUrl, + [int]$TimeoutSec + ) + + $deadline = (Get-Date).AddSeconds($TimeoutSec) + $lastError = "No response yet" + + while ((Get-Date) -lt $deadline) { + try { + $response = Invoke-RestMethod -Method Get -Uri $HealthUrl -TimeoutSec 5 + return $response + } + catch { + $lastError = $_.Exception.Message + Start-Sleep -Seconds 2 + } + } + + throw "Timed out waiting for container health endpoint at $HealthUrl. Last error: $lastError" +} + +$repoRoot = Split-Path -Parent $PSScriptRoot +Set-Location $repoRoot + +Write-Host "Repo root: $repoRoot" + +$resolvedPython = $null +$npmExecutable = $null +$dockerExecutable = $null + +Invoke-Step -Name "Resolve toolchain" -Action { + $resolvedPython = Resolve-PythonExe -RequestedPath $PythonPath + Write-Host "Python: $resolvedPython" + + if (-not $SkipFrontendBuild) { + $npmExecutable = Ensure-CommandExists -Candidates @("npm.cmd", "npm") + Write-Host "NPM: $npmExecutable" + } + + if (-not $SkipDockerBuild -or -not $SkipDockerRuntime) { + $dockerExecutable = Ensure-CommandExists -Candidates @("docker") + Write-Host "Docker: $dockerExecutable" + } +} + +Invoke-Step -Name "Python syntax and import sanity" -Action { + Invoke-CheckedCommand -Executable $resolvedPython -Arguments @("-m", "compileall", "app", "rl", "scripts", "tests") + Invoke-CheckedCommand -Executable $resolvedPython -Arguments @("-c", "import fastapi, uvicorn; print('python runtime ok')") +} + +Invoke-Step -Name "OpenEnv manifest and import validation" -Action { + $args = @("scripts/validate_env.py", "--repo", ".") + if ($SkipOpenEnvCli) { + $args += "--skip-openenv-cli" + } + Invoke-CheckedCommand -Executable $resolvedPython -Arguments $args +} + +Invoke-Step -Name "Deterministic smoke baseline" -Action { + Invoke-CheckedCommand -Executable $resolvedPython -Arguments @("scripts/smoke_test.py") +} + +Invoke-Step -Name "API contract E2E suite" -Action { + Invoke-CheckedCommand -Executable $resolvedPython -Arguments @("-m", "pytest", "tests/test_api_end_to_end_suite.py", "-v", "--tb=short") +} + +if (-not $Quick) { + Invoke-Step -Name "Core API and environment regression tests" -Action { + Invoke-CheckedCommand -Executable $resolvedPython -Arguments @( + "-m", "pytest", + "tests/test_phase1_models.py", + "tests/test_phase1_sector_and_tasks.py", + "tests/test_phase1_event_engine.py", + "tests/test_phase1_signal_computer.py", + "tests/test_phase2_env_integration.py", + "tests/test_phase2_simulator.py", + "tests/test_phase2_api.py", + "tests/test_live_simulation_e2e.py", + "tests/test_action_mask.py", + "-v", + "--tb=short" + ) + } +} + +if (-not $SkipFrontendBuild) { + Invoke-Step -Name "Frontend install and production build" -Action { + Invoke-CheckedCommand -Executable $npmExecutable -Arguments @("--prefix", "frontend/react", "ci", "--no-audit", "--no-fund") + Invoke-CheckedCommand -Executable $npmExecutable -Arguments @("--prefix", "frontend/react", "run", "build") + } +} + +if (-not $SkipDockerBuild) { + Invoke-Step -Name "Docker image build" -Action { + Invoke-CheckedCommand -Executable $dockerExecutable -Arguments @("build", "-t", $ImageTag, ".") + } +} + +if (-not $SkipDockerRuntime) { + Invoke-Step -Name "Docker runtime endpoint sanity" -Action { + $containerName = "openenv-preflight-" + [Guid]::NewGuid().ToString("N").Substring(0, 8) + $healthUrl = "http://127.0.0.1:$ContainerPort/health" + $baseUrl = "http://127.0.0.1:$ContainerPort" + $containerStarted = $false + + try { + $runOutput = & $dockerExecutable run -d --rm --name $containerName -p "$ContainerPort`:7860" $ImageTag + if ($LASTEXITCODE -ne 0) { + throw "Failed to start Docker container $containerName" + } + $containerStarted = $true + Write-Host "Container: $containerName" + Write-Host "Container ID: $($runOutput | Select-Object -Last 1)" + + $health = Wait-ForHealth -HealthUrl $healthUrl -TimeoutSec $StartupTimeoutSec + if ($health.status -notin @("ok", "degraded")) { + throw "Unexpected health status: $($health.status)" + } + + $resetBody = @{ task_id = "district_backlog_easy"; seed = 42 } | ConvertTo-Json + $reset = Invoke-RestMethod -Method Post -Uri "$baseUrl/reset" -ContentType "application/json" -Body $resetBody -TimeoutSec 20 + if (-not $reset.session_id) { + throw "Reset response missing session_id" + } + + $stepBody = @{ + session_id = $reset.session_id + action = @{ action_type = "advance_time" } + } | ConvertTo-Json -Depth 5 + $step = Invoke-RestMethod -Method Post -Uri "$baseUrl/step" -ContentType "application/json" -Body $stepBody -TimeoutSec 20 + if (-not $step.observation) { + throw "Step response missing observation" + } + + $gradeBody = @{ session_id = $reset.session_id } | ConvertTo-Json + $grade = Invoke-RestMethod -Method Post -Uri "$baseUrl/grade" -ContentType "application/json" -Body $gradeBody -TimeoutSec 20 + $score = [double]$grade.score + if ($score -lt 0.0 -or $score -gt 1.0) { + throw "Grade score out of range: $score" + } + + Write-Host "Health status: $($health.status)" + Write-Host "Session ID: $($reset.session_id)" + Write-Host "Grade score: $score" + } + finally { + if ($containerStarted) { + try { + & $dockerExecutable stop $containerName | Out-Null + } + catch { + Write-Warning "Failed to stop container $containerName: $($_.Exception.Message)" + } + } + } + } +} + +Show-Summary +Write-Host "Pre-deployment E2E checks completed successfully." -ForegroundColor Green +exit 0 diff --git a/scripts/pretrain_go_nogo.py b/scripts/pretrain_go_nogo.py new file mode 100644 index 0000000000000000000000000000000000000000..7b9b292d696d5cd868ffcdb7c991ad3f2c6fb0b0 --- /dev/null +++ b/scripts/pretrain_go_nogo.py @@ -0,0 +1,446 @@ +""" +Pre-train checklist + GO/NO-GO gate for Gov Workflow RL Phase 1. + +This script validates the local training stack without running training. +Use it before starting Phase 1 retraining. + +Usage: + python scripts/pretrain_go_nogo.py + python scripts/pretrain_go_nogo.py --run-tests +""" + +from __future__ import annotations + +import argparse +import importlib +import json +import subprocess +import sys +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Callable + + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +PHASE1_TASK = "district_backlog_easy" +EXPECTED_OBS_DIM = 84 +EXPECTED_ACTIONS = 28 + + +@dataclass +class CheckResult: + name: str + status: str # PASS | WARN | FAIL + detail: str + + +def _run_cmd(cmd: list[str], cwd: Path | None = None) -> tuple[int, str, str]: + proc = subprocess.run( + cmd, + cwd=str(cwd or ROOT), + capture_output=True, + text=True, + ) + return proc.returncode, proc.stdout, proc.stderr + + +def check_required_files() -> CheckResult: + required = [ + "rl/train_ppo.py", + "rl/train_recurrent.py", + "rl/gov_workflow_env.py", + "rl/feature_builder.py", + "rl/action_mask.py", + "rl/callbacks.py", + "rl/curriculum.py", + "rl/cost_tracker.py", + "rl/evaluate.py", + "rl/eval_grader.py", + "rl/plot_training.py", + "rl/configs/ppo_easy.yaml", + "app/env.py", + "app/models.py", + "app/tasks.py", + "app/reward.py", + "app/graders.py", + ] + missing = [p for p in required if not (ROOT / p).exists()] + if missing: + return CheckResult( + name="required_files", + status="FAIL", + detail="Missing files: " + ", ".join(missing), + ) + return CheckResult( + name="required_files", + status="PASS", + detail=f"{len(required)} required files present", + ) + + +def check_python_imports() -> CheckResult: + modules = [ + "yaml", + "numpy", + "gymnasium", + "torch", + "stable_baselines3", + "sb3_contrib", + "tensorboard", + "rl.train_ppo", + "rl.train_recurrent", + "rl.gov_workflow_env", + "rl.feature_builder", + "rl.action_mask", + "rl.callbacks", + "rl.evaluate", + "rl.eval_grader", + "app.env", + "app.tasks", + "app.graders", + ] + failed: list[str] = [] + for mod in modules: + try: + importlib.import_module(mod) + except Exception: + failed.append(mod) + if failed: + return CheckResult( + name="python_imports", + status="FAIL", + detail="Import failures: " + ", ".join(failed), + ) + return CheckResult( + name="python_imports", + status="PASS", + detail=f"{len(modules)} modules import cleanly", + ) + + +def check_compile() -> CheckResult: + targets = [ + "rl/train_ppo.py", + "rl/train_recurrent.py", + "rl/gov_workflow_env.py", + "rl/feature_builder.py", + "rl/action_mask.py", + "rl/callbacks.py", + "rl/evaluate.py", + "rl/eval_grader.py", + "app/env.py", + "app/reward.py", + "app/graders.py", + "app/tasks.py", + ] + cmd = [sys.executable, "-m", "py_compile", *targets] + rc, _out, err = _run_cmd(cmd, ROOT) + if rc != 0: + return CheckResult( + name="py_compile", + status="FAIL", + detail=err.strip() or "py_compile failed", + ) + return CheckResult( + name="py_compile", + status="PASS", + detail=f"{len(targets)} files compiled successfully", + ) + + +def check_env_contract() -> CheckResult: + try: + from rl.gov_workflow_env import GovWorkflowGymEnv + + env = GovWorkflowGymEnv(task_id=PHASE1_TASK, seed=42) + obs, info = env.reset(seed=42) + masks = env.action_masks() + _obs2, reward, terminated, truncated, step_info = env.step(18) + + problems: list[str] = [] + if tuple(obs.shape) != (EXPECTED_OBS_DIM,): + problems.append(f"obs shape={tuple(obs.shape)} expected={(EXPECTED_OBS_DIM,)}") + if int(env.action_space.n) != EXPECTED_ACTIONS: + problems.append(f"action_space={env.action_space.n} expected={EXPECTED_ACTIONS}") + if len(masks) != EXPECTED_ACTIONS: + problems.append(f"mask_len={len(masks)} expected={EXPECTED_ACTIONS}") + if int(sum(bool(x) for x in masks)) <= 0: + problems.append("all actions masked") + if not isinstance(info, dict): + problems.append("reset info is not dict") + if not isinstance(step_info, dict): + problems.append("step info is not dict") + if not isinstance(float(reward), float): + problems.append("reward not float-castable") + if not isinstance(bool(terminated), bool) or not isinstance(bool(truncated), bool): + problems.append("terminated/truncated invalid type") + + if problems: + return CheckResult( + name="gym_env_contract", + status="FAIL", + detail="; ".join(problems), + ) + return CheckResult( + name="gym_env_contract", + status="PASS", + detail=f"obs={obs.shape}, action_n={env.action_space.n}, valid_masks={int(sum(masks))}", + ) + except Exception as exc: + return CheckResult( + name="gym_env_contract", + status="FAIL", + detail=f"{type(exc).__name__}: {exc}", + ) + + +def check_output_paths() -> CheckResult: + needed_dirs = [ + ROOT / "results", + ROOT / "results" / "best_model", + ROOT / "results" / "runs", + ROOT / "results" / "eval_logs", + ROOT / "logs", + ] + try: + for d in needed_dirs: + d.mkdir(parents=True, exist_ok=True) + probe = d / ".write_probe.tmp" + probe.write_text("ok", encoding="utf-8") + probe.unlink(missing_ok=True) + except Exception as exc: + return CheckResult( + name="output_paths", + status="FAIL", + detail=f"{type(exc).__name__}: {exc}", + ) + return CheckResult( + name="output_paths", + status="PASS", + detail="results/ and logs/ are writable", + ) + + +def check_train_cli() -> CheckResult: + cmd = [sys.executable, "-m", "rl.train_ppo", "--help"] + rc, out, err = _run_cmd(cmd, ROOT) + if rc != 0: + return CheckResult( + name="train_cli", + status="FAIL", + detail=err.strip() or "train_ppo --help failed", + ) + needed_flags = [ + "--phase", + "--timesteps", + "--n_envs", + "--task", + "--phase1-eval-freq", + "--phase1-n-eval-episodes", + "--phase1-disable-eval-callback", + "--phase1-grader-eval-freq-multiplier", + ] + missing = [f for f in needed_flags if f not in out] + if missing: + return CheckResult( + name="train_cli", + status="WARN", + detail="Missing expected flags in help output: " + ", ".join(missing), + ) + return CheckResult( + name="train_cli", + status="PASS", + detail="train_ppo CLI flags detected", + ) + + +def check_config() -> CheckResult: + try: + import yaml + + cfg_path = ROOT / "rl" / "configs" / "ppo_easy.yaml" + cfg = yaml.safe_load(cfg_path.read_text(encoding="utf-8-sig")) or {} + hp = cfg.get("hyperparameters", {}) + tr = cfg.get("training", {}) + + required_fields = [ + ("hyperparameters", "learning_rate"), + ("hyperparameters", "n_steps"), + ("hyperparameters", "batch_size"), + ("training", "n_envs"), + ("training", "seed"), + ("training", "eval_freq"), + ("training", "n_eval_episodes"), + ] + missing = [] + for section, key in required_fields: + parent = hp if section == "hyperparameters" else tr + if key not in parent: + missing.append(f"{section}.{key}") + if missing: + return CheckResult( + name="ppo_easy_config", + status="FAIL", + detail="Missing config fields: " + ", ".join(missing), + ) + + warnings = [] + if int(tr.get("eval_freq", 0)) < 2048: + warnings.append("eval_freq is very low; may cause frequent pauses") + if int(tr.get("n_eval_episodes", 0)) > 5: + warnings.append("n_eval_episodes is high; callback cost may increase") + + if warnings: + return CheckResult( + name="ppo_easy_config", + status="WARN", + detail="; ".join(warnings), + ) + return CheckResult( + name="ppo_easy_config", + status="PASS", + detail="Phase 1 config fields are present and reasonable", + ) + except Exception as exc: + return CheckResult( + name="ppo_easy_config", + status="FAIL", + detail=f"{type(exc).__name__}: {exc}", + ) + + +def check_torch_device() -> CheckResult: + try: + import torch + + if torch.cuda.is_available(): + return CheckResult( + name="torch_device", + status="PASS", + detail=f"CUDA available ({torch.cuda.get_device_name(0)})", + ) + return CheckResult( + name="torch_device", + status="WARN", + detail="CUDA not available; CPU training is expected", + ) + except Exception as exc: + return CheckResult( + name="torch_device", + status="WARN", + detail=f"torch device check skipped: {type(exc).__name__}: {exc}", + ) + + +def run_targeted_tests() -> CheckResult: + test_cmd = [ + sys.executable, + "-m", + "pytest", + "tests/test_env.py", + "tests/test_gym_wrapper.py", + "tests/test_gym_wrapper_integration.py", + "tests/test_feature_builder.py", + "tests/test_action_mask.py", + "tests/test_curriculum.py", + "tests/test_rl_evaluate.py", + "-q", + "--tb=short", + ] + rc, out, err = _run_cmd(test_cmd, ROOT) + if rc != 0: + return CheckResult( + name="targeted_tests", + status="FAIL", + detail=(out + "\n" + err).strip()[-1200:], + ) + return CheckResult( + name="targeted_tests", + status="PASS", + detail=out.strip().splitlines()[-1] if out.strip() else "targeted tests passed", + ) + + +def _print_results(results: list[CheckResult]) -> None: + print("\n=== Pre-Train Checklist Results ===") + for r in results: + print(f"[{r.status}] {r.name}: {r.detail}") + + fail_count = sum(1 for r in results if r.status == "FAIL") + warn_count = sum(1 for r in results if r.status == "WARN") + print("\n=== Gate Decision ===") + if fail_count > 0: + print(f"NO-GO: {fail_count} failing check(s). Resolve failures before training.") + else: + print( + f"GO: no failing checks. " + f"{warn_count} warning(s) can be reviewed but do not block training." + ) + + +def _print_next_commands(args: argparse.Namespace) -> None: + print("\n=== Recommended Phase 1 Commands (Manual) ===") + print( + "python -m rl.train_ppo " + f"--phase 1 --task {PHASE1_TASK} " + f"--timesteps {args.timesteps} --n_envs {args.n_envs} --seed {args.seed} " + "--phase1-no-progress-bar " + "--phase1-eval-freq 16384 " + "--phase1-n-eval-episodes 2 " + "--phase1-grader-eval-freq-multiplier 4" + ) + print( + "python rl/eval_grader.py " + "--model results/best_model/phase1_final " + f"--task {PHASE1_TASK} --episodes 20 --seed {args.seed}" + ) + print( + "python rl/plot_training.py " + f"--task {PHASE1_TASK} --phase 1" + ) + + +def main() -> int: + parser = argparse.ArgumentParser(description="Pre-train checklist + GO/NO-GO gate") + parser.add_argument("--run-tests", action="store_true", help="Run targeted RL tests") + parser.add_argument("--timesteps", type=int, default=300000) + parser.add_argument("--n-envs", "--n_envs", dest="n_envs", type=int, default=4) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--json-out", default=None, help="Optional path to write JSON report") + args = parser.parse_args() + + checks: list[Callable[[], CheckResult]] = [ + check_required_files, + check_python_imports, + check_compile, + check_train_cli, + check_config, + check_env_contract, + check_output_paths, + check_torch_device, + ] + if args.run_tests: + checks.append(run_targeted_tests) + + results = [fn() for fn in checks] + _print_results(results) + _print_next_commands(args) + + if args.json_out: + out_path = Path(args.json_out) + out_path.parent.mkdir(parents=True, exist_ok=True) + payload = { + "go_no_go": "NO-GO" if any(r.status == "FAIL" for r in results) else "GO", + "results": [asdict(r) for r in results], + } + out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8") + print(f"\nJSON report written to: {out_path}") + + return 2 if any(r.status == "FAIL" for r in results) else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_api_e2e.ps1 b/scripts/run_api_e2e.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..ce7929e6287f6004a5d36209695c1c814a846bce --- /dev/null +++ b/scripts/run_api_e2e.ps1 @@ -0,0 +1,36 @@ +param( + [string]$PythonPath = "C:\Users\siddh\OPENENV_RL\.venv313\Scripts\python.exe", + [switch]$Full +) + +$ErrorActionPreference = "Stop" + +$repoRoot = Split-Path -Parent $PSScriptRoot +Set-Location $repoRoot + +if (-not (Test-Path $PythonPath)) { + throw "Python not found at: $PythonPath" +} + +Write-Host "Repo root: $repoRoot" +Write-Host "Python: $PythonPath" + +Write-Host "" +Write-Host "Step 1/3: Syntax check" +& $PythonPath -m py_compile app\main.py tests\test_api_end_to_end_suite.py +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +Write-Host "" +Write-Host "Step 2/3: Run E2E API suite" +& $PythonPath -m pytest tests\test_api_end_to_end_suite.py -v --tb=short +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +if ($Full) { + Write-Host "" + Write-Host "Step 3/3: Run full API regression suite" + & $PythonPath -m pytest tests\test_api.py tests\test_api_end_to_end_suite.py -v --tb=short + if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } +} + +Write-Host "" +Write-Host "API E2E test run completed." diff --git a/scripts/run_api_live_audit.ps1 b/scripts/run_api_live_audit.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..9d9b724a4f54ccc67e752ab8fe5425b0abb71079 --- /dev/null +++ b/scripts/run_api_live_audit.ps1 @@ -0,0 +1,31 @@ +param( + [string]$PythonPath = "C:\Users\siddh\OPENENV_RL\.venv313\Scripts\python.exe", + [string]$BaseUrl = "http://127.0.0.1:7860", + [int]$TimeoutSec = 30 +) + +$ErrorActionPreference = "Stop" + +$repoRoot = Split-Path -Parent $PSScriptRoot +Set-Location $repoRoot + +if (-not (Test-Path $PythonPath)) { + throw "Python not found at: $PythonPath" +} + +Write-Host "Repo root: $repoRoot" +Write-Host "Python: $PythonPath" +Write-Host "Base URL: $BaseUrl" + +Write-Host "" +Write-Host "Step 1/2: Syntax check" +& $PythonPath -m py_compile scripts\api_live_http_audit.py +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +Write-Host "" +Write-Host "Step 2/2: Live HTTP endpoint audit" +& $PythonPath scripts\api_live_http_audit.py --base-url $BaseUrl --timeout-sec $TimeoutSec +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +Write-Host "" +Write-Host "Live HTTP audit completed." diff --git a/scripts/run_local.py b/scripts/run_local.py new file mode 100644 index 0000000000000000000000000000000000000000..0c7193d43e728655f0205ebdaa405bc278267a9f --- /dev/null +++ b/scripts/run_local.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +""" +Run the Gov Workflow OpenEnv FastAPI app locally. + +Usage: + python scripts/run_local.py + python scripts/run_local.py --host 0.0.0.0 --port 7860 --reload +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import uvicorn + +# Ensure project root is importable when script is executed directly. +_ROOT = Path(__file__).resolve().parent.parent +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) + +from app.config import server_settings + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run local OpenEnv FastAPI server") + parser.add_argument("--host", default=server_settings.host) + parser.add_argument("--port", type=int, default=server_settings.port) + parser.add_argument("--log-level", default=server_settings.log_level) + parser.add_argument("--reload", action="store_true") + args = parser.parse_args() + + uvicorn.run( + "app.main:app", + host=args.host, + port=args.port, + log_level=args.log_level, + workers=1, + reload=args.reload, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/smoke_test.py b/scripts/smoke_test.py new file mode 100644 index 0000000000000000000000000000000000000000..0dc9a68eb141b4308f7d815d23dc22a3a75f5899 --- /dev/null +++ b/scripts/smoke_test.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +""" +Minimal smoke test for all benchmark tasks. + +Runs one deterministic baseline episode per task and checks score bounds. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from app.baselines import run_policy_episode +from app.tasks import list_tasks + + +def main() -> int: + results: list[dict] = [] + for task_id in list_tasks(): + result = run_policy_episode(task_id=task_id, policy_name="backlog_clearance") + score = float(result["score"]) + if not (0.0 <= score <= 1.0): + print(f"[FAIL] {task_id}: score out of range {score}") + return 1 + results.append(result) + print( + f"[OK] task={task_id} score={score:.4f} " + f"steps={result['steps']} completed={result['completed']}" + ) + + out_dir = Path("results") + out_dir.mkdir(parents=True, exist_ok=True) + out_path = out_dir / "smoke_test_results.json" + out_path.write_text(json.dumps(results, indent=2), encoding="utf-8") + print(f"[DONE] wrote {out_path}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/validate_env.py b/scripts/validate_env.py new file mode 100644 index 0000000000000000000000000000000000000000..af0f621c89ae50cc18849188492506ab37f63069 --- /dev/null +++ b/scripts/validate_env.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 +""" +Local OpenEnv validation helper. + +Checks: +1. openenv.yaml exists and contains required sections +2. environment/model import paths are importable +3. optional: `openenv validate` when CLI is installed +""" + +from __future__ import annotations + +import argparse +import importlib +import subprocess +import sys +from pathlib import Path +from typing import Any + +import yaml + + +REQUIRED_TOP_LEVEL = ("name", "entrypoint", "environment", "tasks", "api") + + +def _import_path(path: str) -> Any: + module_name, _, obj_name = path.rpartition(".") + if not module_name or not obj_name: + raise ValueError(f"Invalid import path: {path!r}") + module = importlib.import_module(module_name) + return getattr(module, obj_name) + + +def main() -> int: + parser = argparse.ArgumentParser(description="Validate OpenEnv environment shape") + parser.add_argument("--repo", default=".") + parser.add_argument( + "--skip-openenv-cli", + action="store_true", + help="Skip invoking `openenv validate`", + ) + args = parser.parse_args() + + repo = Path(args.repo).resolve() + if str(repo) not in sys.path: + sys.path.insert(0, str(repo)) + cfg_path = repo / "openenv.yaml" + if not cfg_path.exists(): + print(f"[FAIL] Missing {cfg_path}") + return 1 + + config = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) + if config.get("spec_version") != 1: + print("[FAIL] openenv.yaml must declare spec_version: 1") + return 1 + + for key in REQUIRED_TOP_LEVEL: + if key not in config: + print(f"[FAIL] openenv.yaml missing required top-level key: {key}") + return 1 + + env_cfg = config["environment"] + entrypoint = config["entrypoint"] + + for field in ("module", "object"): + if field not in entrypoint: + print(f"[FAIL] entrypoint missing field: {field}") + return 1 + + _import_path(f"{entrypoint['module']}.{entrypoint['object']}") + _import_path(env_cfg["class"]) + _import_path(env_cfg["observation_model"]) + _import_path(env_cfg["action_model"]) + _import_path(env_cfg["reward_model"]) + _import_path(env_cfg["state_model"]) + _import_path(env_cfg["step_info_model"]) + print("[OK] openenv.yaml imports are valid") + + tasks = config.get("tasks", []) + if len(tasks) < 3: + print("[FAIL] Need at least 3 tasks in openenv.yaml") + return 1 + print(f"[OK] task count={len(tasks)}") + + if not args.skip_openenv_cli: + try: + proc = subprocess.run( + ["openenv", "validate"], + cwd=str(repo), + check=False, + capture_output=True, + text=True, + ) + except FileNotFoundError: + print("[WARN] `openenv` CLI not found; skipping `openenv validate`") + else: + if proc.returncode != 0: + print("[FAIL] `openenv validate` failed") + if proc.stdout: + print(proc.stdout.rstrip()) + if proc.stderr: + print(proc.stderr.rstrip()) + return proc.returncode + print("[OK] `openenv validate` passed") + + print("[DONE] validation complete") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/server/__init__.py b/server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8f5ba5c0fc1cb7ed0f10e5bc510a05247529f06b --- /dev/null +++ b/server/__init__.py @@ -0,0 +1,3 @@ +"""OpenEnv server package.""" + +__all__ = ["GovWorkflowOpenEnv", "app"] diff --git a/server/app.py b/server/app.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d1c2d7ccaefd2e1213a4e22f8891bad61f49b3 --- /dev/null +++ b/server/app.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import uvicorn +from openenv.core import create_app + +from server.gov_environment import ( + GovWorkflowAction, + GovWorkflowObservation, + GovWorkflowOpenEnv, +) + + +def _env_factory() -> GovWorkflowOpenEnv: + return GovWorkflowOpenEnv(task_id="district_backlog_easy", seed=42) + + +app = create_app( + env=_env_factory, + action_cls=GovWorkflowAction, + observation_cls=GovWorkflowObservation, + env_name="gov-workflow-openenv", +) + + +def main() -> None: + uvicorn.run("server.app:app", host="0.0.0.0", port=7861, log_level="info") + + +if __name__ == "__main__": + main() + diff --git a/server/gov_environment.py b/server/gov_environment.py new file mode 100644 index 0000000000000000000000000000000000000000..6bc10e2bb099c5b259da8bad673dab359ac1eb4d --- /dev/null +++ b/server/gov_environment.py @@ -0,0 +1,98 @@ +""" +OpenEnv-native environment adapter for Gov Workflow. + +This wraps app.env.GovWorkflowEnv without modifying the existing app runtime. +""" + +from __future__ import annotations + +from typing import Any, Optional + +from openenv.core import Action, Environment, Observation, State + +from app.env import GovWorkflowEnv +from app.models import ActionModel, EpisodeStateModel, ObservationModel + + +class GovWorkflowAction(Action): + action_type: str + service_target: Optional[str] = None + priority_mode: Optional[str] = None + reallocation_delta: Optional[dict[str, int]] = None + escalation_target: Optional[str] = None + capacity_assignment: Optional[dict[str, int]] = None + notes: Optional[str] = None + + +class GovWorkflowObservation(Observation): + observation: ObservationModel + + +class GovWorkflowState(State): + state: EpisodeStateModel + + +class GovWorkflowOpenEnv( + Environment[GovWorkflowAction, GovWorkflowObservation, GovWorkflowState] +): + """OpenEnv Environment-compatible wrapper around GovWorkflowEnv.""" + + def __init__(self, task_id: str = "district_backlog_easy", seed: int = 42): + super().__init__() + self._task_id = task_id + self._seed = seed + self._env = GovWorkflowEnv(task_id=task_id) + self._last_observation: Optional[ObservationModel] = None + self._last_reward: float | None = None + self._last_done: bool = False + + def reset( + self, + seed: Optional[int] = None, + episode_id: Optional[str] = None, + **kwargs: Any, + ) -> GovWorkflowObservation: + del episode_id, kwargs + effective_seed = self._seed if seed is None else int(seed) + obs, _info = self._env.reset(seed=effective_seed) + self._last_observation = obs + self._last_reward = None + self._last_done = False + return GovWorkflowObservation(observation=obs, reward=None, done=False) + + def step( + self, + action: GovWorkflowAction, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> GovWorkflowObservation: + del timeout_s, kwargs + if isinstance(action, dict): + action = GovWorkflowAction(**action) + action_data = action.model_dump( + exclude={"metadata"}, exclude_none=True, mode="json" + ) + core_action = ActionModel(**action_data) + obs, reward, terminated, truncated, _info = self._env.step(core_action) + done = bool(terminated or truncated) + self._last_observation = obs + self._last_reward = float(reward) + self._last_done = done + return GovWorkflowObservation( + observation=obs, reward=float(reward), done=done + ) + + @property + def state(self) -> GovWorkflowState: + current_state = self._env.state() + return GovWorkflowState( + episode_id=current_state.episode_id, + step_count=int(current_state.total_steps), + state=current_state, + ) + + def close(self) -> None: + try: + self._env.close() + except Exception: + pass diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e549481e0dcf0fdd0f96c303384c7a58cf91c1c4 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,10 @@ +# tests/ + +Test suites grouped by responsibility. + +- API: `test_api.py` +- Environment core: `test_env.py`, `test_tasks.py`, `test_graders.py` +- Models: `test_models.py` +- RL wrappers/features: `test_feature_builder.py`, `test_action_mask.py`, `test_gym_wrapper.py`, `test_curriculum.py` +- Recurrent RL helpers: `test_rl_evaluate.py` +- Baseline reproducibility: `test_baseline_repro.py` diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..07740ac40d93fc892cb69ec51148661b62081e05 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,46 @@ +""" +tests/conftest.py +Shared fixtures for all test modules. +""" +import pytest +from app.env import GovWorkflowEnv +from app.models import ActionModel, ActionType + + +@pytest.fixture +def easy_env(): + """Fresh GovWorkflowEnv for district_backlog_easy, seed=42.""" + env = GovWorkflowEnv(task_id="district_backlog_easy") + env.reset(seed=42) + return env + + +@pytest.fixture +def medium_env(): + env = GovWorkflowEnv(task_id="mixed_urgency_medium") + env.reset(seed=123) + return env + + +@pytest.fixture +def hard_env(): + env = GovWorkflowEnv(task_id="cross_department_hard") + env.reset(seed=999) + return env + + +@pytest.fixture +def advance_action(): + return ActionModel(action_type=ActionType.ADVANCE_TIME) + + +@pytest.fixture +def run_episode(easy_env, advance_action): + """Run easy_env for 10 steps, return list of rewards.""" + rewards = [] + for _ in range(10): + _, r, t, tr, _ = easy_env.step(advance_action) + rewards.append(r) + if t or tr: + break + return rewards diff --git a/tests/list_nvidia_models.py b/tests/list_nvidia_models.py new file mode 100644 index 0000000000000000000000000000000000000000..2fe6c68f1aa6aa64e5d81e952aa870c50d817c71 --- /dev/null +++ b/tests/list_nvidia_models.py @@ -0,0 +1,53 @@ +import os +from openai import OpenAI +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() + +# Get the API key from environment +api_key = os.getenv("NVIDIA_API_KEY") + +if not api_key: + print("Error: NVIDIA_API_KEY not found in .env file.") + # Fallback to check if NVIDIA_API_KEY_2 exists + api_key = os.getenv("NVIDIA_API_KEY_2") + if not api_key: + print("Error: Neither NVIDIA_API_KEY nor NVIDIA_API_KEY_2 found in .env file.") + exit(1) + else: + print("Using NVIDIA_API_KEY_2...") + +print(f"Using API Key: {api_key[:10]}...{api_key[-5:]}") + +# Initialize the OpenAI client with NVIDIA base URL +client = OpenAI( + base_url="https://integrate.api.nvidia.com/v1", + api_key=api_key +) + +print("\nFetching models from NVIDIA API...") +try: + # List models + models = client.models.list() + + # Sort models by ID for better readability + sorted_models = sorted(models.data, key=lambda x: x.id) + + output_lines = [] + output_lines.append(f"{'#':<4} | {'Model ID':<60}") + output_lines.append("-" * 70) + for i, m in enumerate(sorted_models, 1): + output_lines.append(f"{i:<4} | {m.id:<60}") + + output_lines.append(f"\nSuccessfully listed {len(models.data)} models.") + + # Save to file with UTF-8 encoding + with open("nvidia_models.txt", "w", encoding="utf-8") as f: + f.write("\n".join(output_lines)) + + # Also print it + print("\n".join(output_lines)) + +except Exception as e: + print(f"Error accessing NVIDIA API: {e}") diff --git a/tests/manual_test_10_models.py b/tests/manual_test_10_models.py new file mode 100644 index 0000000000000000000000000000000000000000..3a91d0feb32e610160d986127026f1bf9efd861e --- /dev/null +++ b/tests/manual_test_10_models.py @@ -0,0 +1,59 @@ +import os +import time +from openai import OpenAI +from dotenv import load_dotenv + +load_dotenv() + +key1 = os.getenv("NVIDIA_API_KEY") + +MODELS_10 = [ + "meta/llama-3.3-70b-instruct", + "qwen/qwen3-next-80b-a3b-instruct", + "moonshotai/kimi-k2-instruct-0905", + "meta/llama-3.1-405b-instruct", + "deepseek-ai/deepseek-v3.2", + "qwen/qwq-32b", + "mistralai/mixtral-8x22b-instruct-v0.1", + "google/gemma-3-27b-it", + "microsoft/phi-4-mini-instruct", + "meta/llama-3.1-8b-instruct" +] + +def test_model(model_name, api_key): + if not api_key: + return "SKIP (No API Key)" + + client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=api_key) + print(f"Testing {model_name:<40}... ", end="", flush=True) + + try: + start = time.time() + response = client.chat.completions.create( + model=model_name, + messages=[{"role": "user", "content": "Reply with 'OK' only."}], + max_tokens=10, + temperature=0.0, + timeout=10 + ) + elapsed = time.time() - start + content = response.choices[0].message.content.strip() + print(f"SUCCESS ({elapsed:.2f}s) -> '{content}'") + return "PASS" + except Exception as e: + print(f"FAILED: {str(e)[:100]}") + return f"FAIL: {str(e)[:100]}" + +print("=== Testing 10-Model Sequence ===\n") +results = {} +for m in MODELS_10: + results[m] = test_model(m, key1) + time.sleep(1) # Small delay to avoid aggressive rate limits + +print("\n" + "="*60) +fails = [m for m, s in results.items() if s.startswith("FAIL")] +if fails: + print(f"Summary: Found {len(fails)} failures: {', '.join(fails)}") +else: + print("Summary: All 10 models passed successfully!") +print("="*60) diff --git a/tests/manual_test_all_models.py b/tests/manual_test_all_models.py new file mode 100644 index 0000000000000000000000000000000000000000..e797b324cd622608a5e9cc6c8078e422522ecc41 --- /dev/null +++ b/tests/manual_test_all_models.py @@ -0,0 +1,81 @@ +""" +test_all_models.py — Manual NVIDIA API connectivity test. + +NOT a pytest unit test. Run directly: + python tests/test_all_models.py + +Tests each model in the global pool with a minimal API call. +""" + +import os +import time +import sys +from pathlib import Path + +_ROOT = Path(__file__).resolve().parent.parent +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) + +from dotenv import load_dotenv +load_dotenv(dotenv_path=_ROOT / ".env", override=False) + +from baseline_openai import GLOBAL_MODEL_POOL, FREE_POOL + +MODELS_TO_TEST = GLOBAL_MODEL_POOL.copy() + +key1 = os.getenv("NVIDIA_API_KEY") +key2 = os.getenv("NVIDIA_API_KEY_2") + + +def test_model(model_name, api_key, label): + if not api_key: + return "SKIP (No API Key)" + + from openai import OpenAI + client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=api_key) + print(f"Testing {label} model: {model_name}...", end="", flush=True) + + try: + start = time.time() + response = client.chat.completions.create( + model=model_name, + messages=[{"role": "user", "content": "Return the word 'OK' and nothing else."}], + max_tokens=5, + temperature=0.0, + ) + elapsed = time.time() - start + content = response.choices[0].message.content.strip() + print(f" SUCCESS ({elapsed:.2f}s) -> '{content}'") + return "PASS" + except Exception as e: + print(f" FAILED: {str(e)[:100]}") + return f"FAIL: {str(e)[:100]}" + + +if __name__ == "__main__": + results = {} + + print("\n=== Testing Primary/Backup Models (Key 1) ===") + for m in MODELS_TO_TEST: + results[m] = test_model(m, key1, "Primary") + time.sleep(1) + + print("\n=== Testing Free Pool Models (Key 2) ===") + for m in FREE_POOL: + results[m] = test_model(m, key2 or key1, "Free") + time.sleep(1) + + print("\n\n" + "=" * 50) + print(f"{'Model Name':<50} | {'Status'}") + print("-" * 70) + for m, status in results.items(): + print(f"{m:<50} | {status}") + print("=" * 50) + + fails = [m for m, s in results.items() if s.startswith("FAIL")] + summary = f"Tested {len(results)} models. " + if fails: + summary += f"Found {len(fails)} failures: {', '.join(fails)}" + else: + summary += "All tests passed!" + print(f"\nSummary: {summary}") diff --git a/tests/test_action_mask.py b/tests/test_action_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..c7794245b7f15eb345868a3abfc6fef5091007a7 --- /dev/null +++ b/tests/test_action_mask.py @@ -0,0 +1,125 @@ +"""Tests for ActionMaskComputer -- pure logic, no env dependency.""" + +import numpy as np +import pytest +from types import SimpleNamespace + +from rl.action_mask import ActionMaskComputer +from rl.feature_builder import ACTION_DECODE_TABLE, N_ACTIONS +from app.models import ServiceType + + +def _make_obs( + escalation_budget=5, + missing_doc_counts=None, + urgent_counts=None, + reserve_officers=3, + allocations=None, + active_cases_by_service=None, +): + services = [s for s in ServiceType] + missing_doc_counts = missing_doc_counts or {} + urgent_counts = urgent_counts or {} + active_cases_by_service = active_cases_by_service or {svc.value: 10 for svc in services} + allocations = allocations or {svc: 1 for svc in services} + snapshots = { + svc.value: SimpleNamespace( + service_type=svc, + total_pending=active_cases_by_service.get(svc.value, 0), + avg_waiting_days=3.0, + urgent_pending=urgent_counts.get(svc.value, 2), + blocked_missing_docs=missing_doc_counts.get(svc.value, 0), + escalated_cases=0, + public_stage_counts={}, + ) + for svc in services + } + return SimpleNamespace( + queue_snapshots=snapshots, + escalation_budget_remaining=escalation_budget, + officer_pool=SimpleNamespace( + total_officers=lambda: 10, + allocated=allocations, + idle_officers=reserve_officers, + ), + day=5, max_days=30, total_backlog=50, total_completed=20, + total_sla_breaches=3, fairness_gap=0.1, + last_action_valid=True, last_action_message="ok", + ) + + +@pytest.fixture +def amc(): + return ActionMaskComputer() + + +def test_advance_time_always_valid(amc): + assert amc.compute(_make_obs(), "balanced")[18] + + +def test_escalate_blocked_when_budget_zero(amc): + mask = amc.compute(_make_obs(escalation_budget=0, urgent_counts={"passport": 5}), "balanced") + for idx, (t, _, _, _) in ACTION_DECODE_TABLE.items(): + if t == "escalate_service": + assert not mask[idx] + + +def test_missing_docs_blocked_when_no_pending(amc): + mask = amc.compute(_make_obs(missing_doc_counts={}), "balanced") + for idx, (t, _, _, _) in ACTION_DECODE_TABLE.items(): + if t == "request_missing_documents": + assert not mask[idx] + + +def test_missing_docs_valid_when_pending(amc): + first_svc = list(ServiceType)[0].value + mask = amc.compute(_make_obs(missing_doc_counts={first_svc: 3}), "balanced") + for idx, (t, s, _, _) in ACTION_DECODE_TABLE.items(): + if t == "request_missing_documents" and s == first_svc: + assert mask[idx] + + +def test_reallocate_blocked_when_source_has_no_alloc(amc): + zero_alloc = {svc: 0 for svc in ServiceType} + mask = amc.compute(_make_obs(allocations=zero_alloc), "balanced") + for idx, (t, _, _, _) in ACTION_DECODE_TABLE.items(): + if t == "reallocate_officers": + assert not mask[idx] + + +def test_assign_capacity_blocked_when_no_reserve(amc): + mask = amc.compute(_make_obs(reserve_officers=0), "balanced") + for idx, (t, _, _, _) in ACTION_DECODE_TABLE.items(): + if t == "assign_capacity": + assert not mask[idx] + + +def test_reallocate_blocked_when_only_one_active_service(amc): + first = list(ServiceType)[0].value + active_cases = {svc.value: 0 for svc in ServiceType} + active_cases[first] = 10 + mask = amc.compute(_make_obs(active_cases_by_service=active_cases), "balanced") + for idx, (t, _, _, _) in ACTION_DECODE_TABLE.items(): + if t == "reallocate_officers": + assert not mask[idx] + + +def test_redundant_priority_mode_blocked(amc): + mask = amc.compute(_make_obs(), current_priority_mode="urgent_first") + assert not mask[0] + + +def test_mask_length(amc): + assert len(amc.compute(_make_obs(), "balanced")) == N_ACTIONS + + +def test_at_least_one_valid_action(amc): + assert amc.compute(_make_obs(), "balanced").any() + + +def test_only_advance_time_when_backlog_zero(amc): + obs = _make_obs(active_cases_by_service={svc.value: 0 for svc in ServiceType}) + obs.total_backlog = 0 + mask = amc.compute(obs, "balanced") + assert mask[18] + assert int(mask.sum()) == 1 diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..9fc46706c973471b3b59f78fe3bd6f7977471dd9 --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,506 @@ +""" +test_api.py — Phase 3 HTTP API tests. + +Uses httpx.AsyncClient with ASGITransport — fully in-process, zero real +network sockets. pytest-asyncio with asyncio_mode="auto" (set in +pyproject.toml) drives every async test automatically. + +Session isolation: each test calls POST /reset independently, gets its own +UUID session_id, and operates only on that session. No cross-test leakage. +""" + +from __future__ import annotations + +import pytest +from httpx import ASGITransport, AsyncClient + +from app.main import app + +BASE = "http://test" + + +# ── /health ──────────────────────────────────────────────────────────────────── + +async def test_health_returns_ok() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.get("/health") + assert r.status_code == 200 + data = r.json() + assert data["status"] == "ok" + assert isinstance(data["active_sessions"], int) + assert data["active_sessions"] >= 0 + assert set(data["available_tasks"]) == { + "district_backlog_easy", + "mixed_urgency_medium", + "cross_department_hard", + "district_backlog_easy_extreme", + } + + +# ── POST /reset ──────────────────────────────────────────────────────────────── + +async def test_reset_returns_session_id_and_observation() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11}) + assert r.status_code == 200 + data = r.json() + assert "session_id" in data + assert len(data["session_id"]) == 36 # UUID4 canonical string length + obs = data["observation"] + assert obs["day"] == 0 + assert obs["task_id"] == "district_backlog_easy" + assert obs["total_backlog"] >= 0 + + +async def test_reset_same_seed_produces_identical_observations() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r1 = await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11}) + r2 = await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11}) + obs1 = r1.json()["observation"] + obs2 = r2.json()["observation"] + # Strip volatile fields before comparison + for obs in (obs1, obs2): + obs.pop("last_action_message", None) + obs.pop("episode_id", None) + assert obs1 == obs2 + + +async def test_reset_medium_task_accepted() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/reset", json={"task_id": "mixed_urgency_medium", "seed": 22}) + assert r.status_code == 200 + assert r.json()["observation"]["task_id"] == "mixed_urgency_medium" + + +async def test_reset_hard_task_accepted() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/reset", json={"task_id": "cross_department_hard", "seed": 33}) + assert r.status_code == 200 + assert r.json()["observation"]["task_id"] == "cross_department_hard" + + +async def test_reset_accepts_empty_body_for_validator_compat() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/reset", json={}) + assert r.status_code == 200 + assert "session_id" in r.json() + + async def test_reset_accepts_missing_body_for_validator_compat() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/reset") + assert r.status_code == 200 + assert "session_id" in r.json() + + +# ── POST /step ───────────────────────────────────────────────────────────────── + +async def test_step_advance_time_moves_day_forward() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + r = await c.post("/step", json={ + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert r.status_code == 200 + data = r.json() + assert data["observation"]["day"] == 1 + assert isinstance(data["reward"], float) + assert isinstance(data["done"], bool) + assert data["terminated"] is False + assert data["truncated"] is False + assert data["info"]["invalid_action"] is False + + +async def test_step_set_priority_mode_reflects_in_observation() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + r = await c.post("/step", json={ + "session_id": sid, + "action": {"action_type": "set_priority_mode", "priority_mode": "urgent_first"}, + }) + assert r.status_code == 200 + assert "urgent_first" in r.json()["observation"]["last_action_explanation"].lower() + + +async def test_step_invalid_action_returns_200_with_penalty_not_error() -> None: + """ + Invalid actions must NOT raise HTTP 4xx/5xx. + They must return 200 with invalid_action=True and a negative reward. + """ + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + r = await c.post("/step", json={ + "session_id": sid, + "action": {"action_type": "assign_capacity", "officer_delta": 9999}, + }) + assert r.status_code == 200 + data = r.json() + assert data["info"]["invalid_action"] is True + assert isinstance(data["info"]["action_explanation"], str) + assert data["reward"] <= 0 + + +async def test_step_on_ended_episode_returns_409() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + # Advance past max_days (30) to guarantee truncation + for _ in range(35): + await c.post("/step", json={ + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + # Next step must be rejected with 409 + r = await c.post("/step", json={ + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert r.status_code == 409 + + +async def test_step_unknown_session_returns_404() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/step", json={ + "session_id": "00000000-0000-0000-0000-000000000000", + "action": {"action_type": "advance_time"}, + }) + assert r.status_code == 404 + + +# ── POST /state ──────────────────────────────────────────────────────────────── + +async def test_state_strips_action_history_by_default() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + await c.post("/step", json={"session_id": sid, "action": {"action_type": "advance_time"}}) + r = await c.post("/state", json={"session_id": sid, "include_action_history": False}) + assert r.status_code == 200 + assert r.json()["state"]["action_history"] is None + + +async def test_state_includes_full_action_history_when_requested() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + for _ in range(3): + await c.post("/step", json={"session_id": sid, "action": {"action_type": "advance_time"}}) + r = await c.post("/state", json={"session_id": sid, "include_action_history": True}) + assert r.status_code == 200 + data = r.json() + history = data["state"]["action_history"] + assert len(history) == 3 + # Each entry must carry the mandatory fields + for entry in history: + assert "step" in entry + assert "day" in entry + assert "reward" in entry + assert "invalid" in entry + + +async def test_state_unknown_session_returns_404() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post("/state", json={"session_id": "bad-id", "include_action_history": False}) + assert r.status_code == 404 + + +# ── POST /grade ──────────────────────────────────────────────────────────────── + +async def test_grade_easy_returns_score_in_range_with_correct_grader() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + for _ in range(5): + await c.post("/step", json={"session_id": sid, "action": {"action_type": "advance_time"}}) + r = await c.post("/grade", json={"session_id": sid}) + assert r.status_code == 200 + data = r.json() + assert 0.0 <= data["score"] <= 1.0 + assert data["grader_name"] == "easy" + assert "completion_rate" in data["metrics"] + assert "sla_compliance_rate" in data["metrics"] + assert "idle_efficiency" in data["metrics"] + + +async def test_grade_medium_task_uses_medium_grader() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "mixed_urgency_medium", "seed": 22})).json()["session_id"] + await c.post("/step", json={"session_id": sid, "action": {"action_type": "advance_time"}}) + r = await c.post("/grade", json={"session_id": sid}) + assert r.json()["grader_name"] == "medium" + + +async def test_grade_hard_task_uses_hard_grader() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "cross_department_hard", "seed": 33})).json()["session_id"] + await c.post("/step", json={"session_id": sid, "action": {"action_type": "advance_time"}}) + r = await c.post("/grade", json={"session_id": sid}) + assert r.json()["grader_name"] == "hard" + + +# ── GET /sessions + DELETE /sessions/{id} ───────────────────────────────────── + +async def test_sessions_endpoint_includes_created_session() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + r = await c.get("/sessions") + assert r.status_code == 200 + data = r.json() + assert data["active_sessions"] >= 1 + assert sid in data["session_ids"] + + +async def test_delete_session_removes_it_and_subsequent_state_returns_404() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + sid = (await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 11})).json()["session_id"] + del_r = await c.delete(f"/sessions/{sid}") + assert del_r.status_code == 200 + assert del_r.json()["deleted"] == sid + # Session must be gone + state_r = await c.post("/state", json={"session_id": sid, "include_action_history": False}) + assert state_r.status_code == 404 + + +async def test_delete_unknown_session_returns_404() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.delete("/sessions/not-a-real-session") + assert r.status_code == 404 + + +async def test_ui_page_is_served() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.get("/ui") + assert r.status_code == 200 + assert "text/html" in r.headers.get("content-type", "") + + +async def test_api_alias_reset_and_autostep_flow() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + reset_r = await c.post("/api/reset", json={"task_id": "district_backlog_easy", "seed": 11}) + sid = reset_r.json()["session_id"] + step_r = await c.post( + "/api/auto_step", + json={"session_id": sid, "agent_policy": "backlog_clearance"}, + ) + assert step_r.status_code == 200 + data = step_r.json() + assert data["agent_policy"] == "backlog_clearance" + assert "action" in data + assert "observation" in data + assert isinstance(data["reward"], float) + + +async def test_api_v1_alias_reset_step_state_grade_flow() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + reset_r = await c.post("/api/v1/reset", json={"task_id": "district_backlog_easy", "seed": 11}) + assert reset_r.status_code == 200 + sid = reset_r.json()["session_id"] + + step_r = await c.post( + "/api/v1/step", + json={"session_id": sid, "action": {"action_type": "advance_time"}}, + ) + assert step_r.status_code == 200 + assert step_r.json()["session_id"] == sid + + state_r = await c.post("/api/v1/state", json={"session_id": sid, "include_action_history": False}) + assert state_r.status_code == 200 + assert state_r.json()["session_id"] == sid + + grade_r = await c.post("/api/v1/grade", json={"session_id": sid}) + assert grade_r.status_code == 200 + assert 0.0 <= float(grade_r.json()["score"]) <= 1.0 + + async def test_frontend_alias_reset_accepts_missing_body() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + reset_r = await c.post("/api/reset") + assert reset_r.status_code == 200 + assert "session_id" in reset_r.json() + + +async def test_api_benchmark_returns_agent_results() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post( + "/api/benchmark", + json={ + "task_id": "district_backlog_easy", + "agent_policies": ["urgent_first", "backlog_clearance"], + "runs": 2, + "max_steps": 100, + "seed_base": 500, + }, + ) + assert r.status_code == 200 + data = r.json() + assert data["task_id"] == "district_backlog_easy" + assert data["requested_runs"] == 2 + assert len(data["agent_results"]) == 2 + for agent in data["agent_results"]: + assert len(agent["runs"]) == 2 + + +async def test_api_benchmark_summary_matches_run_scores_and_is_reproducible() -> None: + payload = { + "task_id": "district_backlog_easy", + "agent_policies": ["urgent_first"], + "runs": 3, + "max_steps": 80, + "seed_base": 777, + } + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r1 = await c.post("/api/benchmark", json=payload) + r2 = await c.post("/api/benchmark", json=payload) + + assert r1.status_code == 200 + assert r2.status_code == 200 + + a1 = r1.json()["agent_results"][0] + a2 = r2.json()["agent_results"][0] + runs1 = a1["runs"] + scores = [float(row["score"]) for row in runs1] + expected_avg = sum(scores) / len(scores) + + assert abs(float(a1["average_score"]) - expected_avg) < 1e-9 + assert runs1 == a2["runs"] + assert float(a1["average_score"]) == float(a2["average_score"]) + + +async def test_api_workflow_components_visible() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.get("/api/workflows/components") + assert r.status_code == 200 + data = r.json() + assert "components" in data + names = {row["component"] for row in data["components"]} + assert "baseline_openai.py" in names + assert "inference.py" in names + assert "openenv-api" in names + + +async def test_api_rl_models_list_shape() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.get("/api/rl_models") + assert r.status_code == 200 + data = r.json() + assert "models" in data + assert isinstance(data["models"], list) + assert len(data["models"]) >= 1 + + +async def test_api_rl_run_invalid_model_returns_422() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post( + "/api/rl_run", + json={ + "task_id": "district_backlog_easy", + "model_path": "results/best_model/does_not_exist.zip", + "model_type": "maskable", + "max_steps": 10, + }, + ) + assert r.status_code == 422 + + +async def test_api_workflow_run_invalid_id_returns_422() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post( + "/api/workflows/run", + json={ + "workflow_id": "not_allowed", + }, + ) + assert r.status_code == 422 + + +async def test_api_workflow_run_inference_returns_output_fields() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.post( + "/api/workflows/run", + json={ + "workflow_id": "inference", + "max_steps": 1, + "timeout_seconds": 30, + }, + ) + assert r.status_code == 200 + data = r.json() + assert data["workflow_id"] == "inference" + assert "command" in data + assert "exit_code" in data + assert "stdout" in data + assert "stderr" in data + + +async def test_api_openenv_compliance_endpoint_returns_items() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + r = await c.get("/api/openenv_compliance") + assert r.status_code == 200 + data = r.json() + assert "items" in data + assert isinstance(data["items"], list) + keys = {item["key"] for item in data["items"]} + assert "api_step_reset_state" in keys + assert "openenv_yaml" in keys + + +async def test_api_simulation_live_step_flow_runs_without_500() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + start = await c.post( + "/api/simulation/live/start", + json={ + "task_id": "district_backlog_easy", + "agent_mode": "llm_inference", + "max_steps": 10, + "seed": 11, + }, + ) + assert start.status_code == 200 + run_id = start.json()["run_id"] + step = await c.post("/api/simulation/live/step", json={"run_id": run_id}) + assert step.status_code == 200 + payload = step.json() + assert "run_id" in payload + assert "total_reward" in payload + assert isinstance(payload["done"], bool) + + +async def test_api_simulation_live_step_done_includes_string_end_log() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + start = await c.post( + "/api/simulation/live/start", + json={ + "task_id": "district_backlog_easy", + "agent_mode": "baseline_policy", + "policy_name": "backlog_clearance", + "max_steps": 1, + "seed": 42, + }, + ) + assert start.status_code == 200 + run_id = start.json()["run_id"] + step = await c.post("/api/simulation/live/step", json={"run_id": run_id}) + + assert step.status_code == 200 + payload = step.json() + assert payload["done"] is True + assert isinstance(payload.get("end_log"), str) + assert payload["end_log"].startswith("[END]") + + +async def test_api_simulation_live_state_returns_serialized_dict() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE) as c: + start = await c.post( + "/api/simulation/live/start", + json={ + "task_id": "district_backlog_easy", + "agent_mode": "baseline_policy", + "policy_name": "backlog_clearance", + "max_steps": 5, + "seed": 99, + }, + ) + assert start.status_code == 200 + run_id = start.json()["run_id"] + state = await c.get(f"/api/simulation/live/{run_id}") + + assert state.status_code == 200 + payload = state.json() + assert payload["run_id"] == run_id + assert isinstance(payload.get("state"), dict) + assert payload["state"]["task_id"] == "district_backlog_easy" diff --git a/tests/test_api_end_to_end_suite.py b/tests/test_api_end_to_end_suite.py new file mode 100644 index 0000000000000000000000000000000000000000..456379181e621e2e07ad55d5b0106c42f1aac6d7 --- /dev/null +++ b/tests/test_api_end_to_end_suite.py @@ -0,0 +1,210 @@ +""" +End-to-end API suite for the full endpoint contract. + +This suite focuses on: +1) endpoint availability +2) cross-endpoint data flow +3) session lifecycle correctness +4) simulation stream behavior +5) RL endpoint guardrails +""" + +from __future__ import annotations + +from httpx import ASGITransport, AsyncClient + +from app.main import app +from rl.feature_builder import N_ACTIONS + +BASE_URL = "http://test" + +REQUIRED_PATHS = { + "/health", + "/reset", + "/step", + "/state", + "/simulate", + "/simulate/{session_id}/snapshot", + "/grade", + "/tasks", + "/tasks/{task_id}", + "/action-masks", + "/rl/run", + "/rl/models", + "/simulate/{session_id}/cancel", + "/simulate/{session_id}/trace", + "/actions/schema", + "/metrics", +} +async def test_openapi_contains_all_required_endpoints() -> None: + paths = set(app.openapi().get("paths", {}).keys()) + assert REQUIRED_PATHS.issubset(paths), f"Missing paths: {sorted(REQUIRED_PATHS - paths)}" + + +async def test_health_tasks_metrics_and_schema_consistency() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE_URL) as c: + health = await c.get("/health") + tasks = await c.get("/tasks") + metrics = await c.get("/metrics") + schema = await c.get("/actions/schema") + + assert health.status_code == 200 + h = health.json() + assert h["status"] in {"ok", "degraded"} + assert h["version"] == "2.0.0" + assert h["phase"] == "3_rl_training" + + assert tasks.status_code == 200 + task_rows = tasks.json() + assert isinstance(task_rows, list) + assert len(task_rows) == 3 + task_ids = {row["task_id"] for row in task_rows} + assert task_ids == { + "district_backlog_easy", + "mixed_urgency_medium", + "cross_department_hard", + } + + assert metrics.status_code == 200 + m = metrics.json() + assert m["version"] == "2.0.0" + assert m["phase"] == "3_rl_training" + assert m["total_tasks"] == 3 + assert set(m["tasks_available"]) == task_ids + + assert schema.status_code == 200 + s = schema.json() + assert s["total_action_types"] == 6 + assert len(s["actions"]) == 6 + + +async def test_per_task_details_and_unknown_task_404() -> None: + known = [ + "district_backlog_easy", + "mixed_urgency_medium", + "cross_department_hard", + ] + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE_URL) as c: + for task_id in known: + r = await c.get(f"/tasks/{task_id}") + assert r.status_code == 200 + row = r.json() + assert row["task_id"] == task_id + assert row["max_days"] > 0 + assert row["officer_pool_total"] > 0 + assert isinstance(row["services"], list) + assert len(row["services"]) >= 1 + + bad = await c.get("/tasks/fake_task") + assert bad.status_code == 404 + + +async def test_session_data_flow_reset_masks_step_trace_snapshot_grade_cancel() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE_URL) as c: + reset = await c.post("/reset", json={"task_id": "district_backlog_easy", "seed": 42}) + assert reset.status_code == 200 + reset_body = reset.json() + sid = reset_body["session_id"] + assert len(sid) == 36 + assert reset_body["task_id"] == "district_backlog_easy" + assert reset_body["observation"]["day"] == 0 + + masks = await c.post("/action-masks", json={"session_id": sid}) + assert masks.status_code == 200 + mask_body = masks.json() + assert len(mask_body["action_mask"]) == N_ACTIONS + assert mask_body["total_actions"] == N_ACTIONS + assert mask_body["total_valid"] > 0 + + for _ in range(3): + step = await c.post( + "/step", + json={"session_id": sid, "action": {"action_type": "advance_time"}}, + ) + assert step.status_code == 200 + + state = await c.get("/state", params={"session_id": sid, "include_action_history": True}) + assert state.status_code == 200 + st = state.json()["state"] + assert st["day"] >= 1 + assert st["action_history_count"] >= 3 + + trace_page1 = await c.get(f"/simulate/{sid}/trace", params={"page": 1, "page_size": 2}) + trace_page2 = await c.get(f"/simulate/{sid}/trace", params={"page": 2, "page_size": 2}) + assert trace_page1.status_code == 200 + assert trace_page2.status_code == 200 + p1 = trace_page1.json() + p2 = trace_page2.json() + assert p1["total_steps"] >= 3 + assert len(p1["steps"]) == 2 + assert p2["page"] == 2 + assert len(p2["steps"]) >= 1 + + snap = await c.get(f"/simulate/{sid}/snapshot") + assert snap.status_code == 200 + snap_body = snap.json() + assert snap_body["session_id"] == sid + assert "observation" in snap_body + + grade = await c.post("/grade", json={"session_id": sid}) + assert grade.status_code == 200 + g = grade.json() + assert g["task_id"] == "district_backlog_easy" + assert 0.0 <= g["score"] <= 1.0 + assert isinstance(g["metrics"], dict) + + cancel = await c.post(f"/simulate/{sid}/cancel") + assert cancel.status_code == 200 + assert cancel.json()["status"] == "cancelled" + + state_after = await c.get("/state", params={"session_id": sid}) + assert state_after.status_code == 404 + + +async def test_simulate_endpoint_validation_contract() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE_URL, timeout=30.0) as c: + bad_task = await c.post( + "/simulate", + json={ + "task_id": "not_a_real_task", + "agent_mode": "baseline_policy", + "max_steps": 3, + "seed": 123, + }, + ) + bad_mode = await c.post( + "/simulate", + json={ + "task_id": "district_backlog_easy", + "agent_mode": "wrong_mode", + "max_steps": 3, + "seed": 123, + }, + ) + + assert bad_task.status_code == 422 + assert bad_mode.status_code == 422 + + +async def test_rl_models_and_rl_run_missing_model_guardrail() -> None: + async with AsyncClient(transport=ASGITransport(app=app), base_url=BASE_URL) as c: + models = await c.get("/rl/models") + assert models.status_code == 200 + rows = models.json() + assert isinstance(rows, list) + assert len(rows) >= 1 + for row in rows: + assert "model_path" in row + assert "exists" in row + + missing = await c.post( + "/rl/run", + json={ + "task_id": "district_backlog_easy", + "model_path": "results/best_model/does_not_exist", + "seed": 42, + "max_steps": 10, + "n_episodes": 1, + }, + ) + assert missing.status_code == 422 diff --git a/tests/test_baseline_repro.py b/tests/test_baseline_repro.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa4a3eb923bfd09c782cb0d68ef32d49fd788a4 --- /dev/null +++ b/tests/test_baseline_repro.py @@ -0,0 +1,8 @@ +from app.baselines import run_policy_episode + +def test_baseline_reproducibility(): + r1 = run_policy_episode("district_backlog_easy", "backlog_clearance", seed=101) + r2 = run_policy_episode("district_backlog_easy", "backlog_clearance", seed=101) + assert r1["score"] == r2["score"] + assert r1["reward_sum"] == r2["reward_sum"] + assert r1["completed"] == r2["completed"] \ No newline at end of file diff --git a/tests/test_curriculum.py b/tests/test_curriculum.py new file mode 100644 index 0000000000000000000000000000000000000000..6efa38297677604311ffa8ea014626777b7c91c4 --- /dev/null +++ b/tests/test_curriculum.py @@ -0,0 +1,45 @@ +"""Tests for curriculum scheduler behavior.""" + +from __future__ import annotations + +from app.tasks import TASKS +from rl.curriculum import ( + ALL_TASKS, + TASK_EASY, + TASK_HARD, + TASK_MEDIUM, + CurriculumScheduler, +) + + +def test_stage_transitions_at_correct_timesteps() -> None: + sched = CurriculumScheduler(total_timesteps=1000, rng_seed=42) + assert sched.current_stage(0) == 1 + assert sched.current_stage(299) == 1 + assert sched.current_stage(300) == 2 + assert sched.current_stage(699) == 2 + assert sched.current_stage(700) == 3 + assert sched.current_stage(999) == 3 + + +def test_easy_only_in_stage_1() -> None: + sched = CurriculumScheduler(total_timesteps=1000, rng_seed=42) + for t in (0, 50, 150, 299): + assert sched.sample_task(t) == TASK_EASY + + +def test_all_tasks_sampled_in_stage_3() -> None: + sched = CurriculumScheduler(total_timesteps=1000, rng_seed=42) + seen = set() + for _ in range(500): + seen.add(sched.sample_task(900)) + assert TASK_EASY in seen + assert TASK_MEDIUM in seen + assert TASK_HARD in seen + assert seen.issubset(set(ALL_TASKS)) + + +def test_deterministic_eval_seeds_never_change() -> None: + assert TASKS["district_backlog_easy"].seed == 42 + assert TASKS["mixed_urgency_medium"].seed == 123 + assert TASKS["cross_department_hard"].seed == 999 diff --git a/tests/test_engine_simulator_exports.py b/tests/test_engine_simulator_exports.py new file mode 100644 index 0000000000000000000000000000000000000000..6b07adab9c58595ad109e5fbe252aa33938c73bb --- /dev/null +++ b/tests/test_engine_simulator_exports.py @@ -0,0 +1,44 @@ +import importlib +import sys + +from app.engine import ( + DayResult as EngineDayResult, + DaySimulator as EngineDaySimulator, + LiveSimulationSession as EngineLiveSimulationSession, + SimulationAgentMode as EngineSimulationAgentMode, + run_simulation as engine_run_simulation, +) +from app.simulator import ( + DayResult as ShimDayResult, + DaySimulator as ShimDaySimulator, + LiveSimulationSession as ShimLiveSimulationSession, + SimulationAgentMode as ShimSimulationAgentMode, + run_simulation as shim_run_simulation, +) + + +def test_simulator_shim_reexports_engine_symbols(): + assert ShimDayResult is EngineDayResult + assert ShimDaySimulator is EngineDaySimulator + assert ShimLiveSimulationSession is EngineLiveSimulationSession + assert ShimSimulationAgentMode is EngineSimulationAgentMode + assert shim_run_simulation is engine_run_simulation + + +def test_day_result_has_runtime_fields(): + result = EngineDayResult() + assert hasattr(result, "digital_arrivals") + assert hasattr(result, "newly_blocked_missing") + assert hasattr(result, "newly_unblocked_enrich") + + +def test_import_env_then_simulator_succeeds(): + for name in ["app.engine", "app.simulator", "app.env"]: + sys.modules.pop(name, None) + + env_mod = importlib.import_module("app.env") + sim_mod = importlib.import_module("app.simulator") + + assert hasattr(env_mod, "GovWorkflowEnv") + assert hasattr(sim_mod, "LiveSimulationSession") + assert hasattr(sim_mod, "run_simulation") \ No newline at end of file diff --git a/tests/test_env.py b/tests/test_env.py new file mode 100644 index 0000000000000000000000000000000000000000..1960aaa177227a3d71f68e031a06082127d34e3b --- /dev/null +++ b/tests/test_env.py @@ -0,0 +1,57 @@ +from app.env import GovWorkflowEnv +from app.models import ActionModel, ActionType, PriorityMode + +def test_step_advances_day(): + env = GovWorkflowEnv("district_backlog_easy") + env.reset(seed=123) + obs, reward, terminated, truncated, info = env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + assert obs.day == 1 + assert isinstance(reward, float) + +def test_set_priority_mode(): + env = GovWorkflowEnv("district_backlog_easy") + env.reset(seed=123) + obs, *_ = env.step(ActionModel(action_type=ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.URGENT_FIRST)) + # v2 ObservationModel doesn't expose priority_mode directly; + # verify via the env's internal state and the action explanation + assert env.priority_mode == PriorityMode.URGENT_FIRST + assert "urgent_first" in obs.last_action_explanation.lower() + +def test_invalid_action_penalized(): + env = GovWorkflowEnv("district_backlog_easy") + env.reset(seed=123) + _, reward, _, _, info = env.step(ActionModel(action_type=ActionType.ASSIGN_CAPACITY, + capacity_assignment={"passport": 99})) + assert info.invalid_action is True + assert reward <= 0 + +def test_reset_is_deterministic(): + obs_a, _ = GovWorkflowEnv("district_backlog_easy").reset(seed=123) + obs_b, _ = GovWorkflowEnv("district_backlog_easy").reset(seed=123) + d_a, d_b = obs_a.model_dump(), obs_b.model_dump() + # episode_id has a random component — strip it + d_a.pop("episode_id", None); d_b.pop("episode_id", None) + d_a.pop("last_action_message", None); d_b.pop("last_action_message", None) + assert d_a == d_b + + +def test_episode_truncates_on_step_cap_without_advancing_time(): + env = GovWorkflowEnv("district_backlog_easy") + env.reset(seed=123, options={"max_steps_per_episode": 5}) + + done = False + for _ in range(6): + _, _, terminated, truncated, _ = env.step( + ActionModel( + action_type=ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.BALANCED, + ) + ) + done = bool(terminated or truncated) + if done: + break + + assert done is True + assert env.truncated is True + assert env.total_steps == 5 diff --git a/tests/test_feature_builder.py b/tests/test_feature_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..a7147ce247bc110190bb3b95a76e7d37372efd47 --- /dev/null +++ b/tests/test_feature_builder.py @@ -0,0 +1,89 @@ +"""Tests for FeatureBuilder using the real ObservationModel schema.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from app.models import ( + ObservationModel, + OfficerPool, + PriorityMode, + QueueSnapshot, + ServiceType, + StageType, +) +from rl.feature_builder import FeatureBuilder, OBS_DIM + + +def _make_obs() -> ObservationModel: + snapshots = {} + for i, svc in enumerate(ServiceType): + snapshots[svc] = QueueSnapshot( + service_type=svc, + public_stage_counts={ + StageType.SUBMISSION.value: 2 + i % 2, + StageType.DOCUMENT_VERIFICATION.value: 1, + StageType.FIELD_VERIFICATION.value: 1, + StageType.APPROVAL.value: 0, + StageType.ISSUANCE.value: 0, + }, + total_pending=6 + i, + blocked_missing_docs=i % 3, + urgent_pending=2 if i % 3 == 0 else 1, + total_sla_breached=0, + avg_waiting_days=3.0 + i, + ) + + return ObservationModel( + task_id="district_backlog_easy", + episode_id="ep-test", + day=8, + max_days=20, + officer_pool=OfficerPool( + total_officers=len(ServiceType) + 2, + available_officers=len(ServiceType) + 2, + allocated={svc: 1 for svc in ServiceType}, + ), + queue_snapshots=snapshots, + total_backlog=sum(s.total_pending for s in snapshots.values()), + total_completed=15, + total_sla_breaches=3, + fairness_index=1.0 - 0.12, + escalation_budget_remaining=4, + last_action_valid=True, + last_action_message="ok", + ) + + +@pytest.fixture +def builder() -> FeatureBuilder: + return FeatureBuilder() + + +def test_output_shape(builder: FeatureBuilder) -> None: + assert builder.build(_make_obs()).shape == (OBS_DIM,) + + +def test_output_dtype(builder: FeatureBuilder) -> None: + assert builder.build(_make_obs()).dtype == np.float32 + + +def test_deterministic(builder: FeatureBuilder) -> None: + obs = _make_obs() + np.testing.assert_array_equal( + builder.build(obs, "urgent_first", "advance_time"), + builder.build(obs, "urgent_first", "advance_time"), + ) + + +def test_no_nan_or_inf(builder: FeatureBuilder) -> None: + vec = builder.build(_make_obs()) + assert not np.any(np.isnan(vec)) + assert not np.any(np.isinf(vec)) + + +def test_values_in_reasonable_range(builder: FeatureBuilder) -> None: + vec = builder.build(_make_obs()) + assert np.all(vec >= 0.0) + assert np.all(vec <= 1.0 + 1e-6) diff --git a/tests/test_graders.py b/tests/test_graders.py new file mode 100644 index 0000000000000000000000000000000000000000..92bc9d661ce64c21d23558afb0e48ea456c55e1c --- /dev/null +++ b/tests/test_graders.py @@ -0,0 +1,15 @@ +from app.baselines import run_policy_episode +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ActionModel, ActionType + +def test_grader_score_range(): + env = GovWorkflowEnv("district_backlog_easy") + env.reset(seed=123) + for _ in range(5): + env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + assert 0.0 <= grade_episode(env.state()).score <= 1.0 + +def test_policy_run_grader_range(): + result = run_policy_episode("mixed_urgency_medium", "urgent_first", seed=22) + assert 0.0 <= result["score"] <= 1.0 \ No newline at end of file diff --git a/tests/test_gym_wrapper.py b/tests/test_gym_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7d110c157a58cc2c38d02fed19a8aa9f9914e1 --- /dev/null +++ b/tests/test_gym_wrapper.py @@ -0,0 +1,102 @@ +"""Tests for the Gymnasium adapter -- validates SB3 contract compliance.""" + +import numpy as np +import pytest +from stable_baselines3.common.env_checker import check_env + +from rl.gov_workflow_env import GovWorkflowGymEnv +from rl.feature_builder import OBS_DIM, N_ACTIONS + + +@pytest.fixture +def env(): + e = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42) + yield e + + +def test_obs_space_shape(env): + assert env.observation_space.shape == (OBS_DIM,) + + +def test_action_space_is_discrete(env): + assert env.action_space.n == N_ACTIONS + + +def test_reset_returns_numpy_obs(env): + obs, info = env.reset() + assert isinstance(obs, np.ndarray) + assert obs.shape == (OBS_DIM,) + assert obs.dtype == np.float32 + + +def test_step_returns_gym_contract(env): + env.reset() + obs, reward, terminated, truncated, info = env.step(18) + assert isinstance(obs, np.ndarray) + assert isinstance(reward, float) + assert isinstance(terminated, bool) + assert isinstance(truncated, bool) + assert isinstance(info, dict) + + +def test_action_masks_returns_bool_array(env): + env.reset() + masks = env.action_masks() + assert isinstance(masks, np.ndarray) + assert masks.dtype == bool + assert masks.shape == (N_ACTIONS,) + + +def test_advance_time_always_valid(env): + env.reset() + masks = env.action_masks() + assert masks[18] + + +def test_reset_is_deterministic(env): + obs1, _ = env.reset(seed=42) + obs2, _ = env.reset(seed=42) + np.testing.assert_array_equal(obs1, obs2) + + +def test_obs_values_in_valid_range(env): + obs, _ = env.reset() + assert np.all(obs >= -0.01) + assert np.all(obs <= 1.01) + + +def test_episode_terminates(env): + env.reset() + done, steps = False, 0 + while not done and steps < 1000: + _, _, terminated, truncated, _ = env.step(18) + done = terminated or truncated + steps += 1 + assert done, "Episode did not terminate within 1000 steps" + + +def test_sb3_check_env_passes(): + env = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42) + check_env(env, warn=True) + + +def test_hard_mask_invalid_action_falls_back_to_advance_time(): + env = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42, hard_action_mask=True) + env.reset() + _, _, _, _, info = env.step(-1) + assert info["action_mask_applied"] is True + assert info["executed_action_idx"] == 18 + + +def test_non_advance_streak_forces_advance_time_only(): + env = GovWorkflowGymEnv(task_id="district_backlog_easy", seed=42, max_non_advance_streak=2) + env.reset(seed=42) + env.step(18) # advance one day so backlog appears + + # Two non-advance control actions reach the streak limit. + env.step(3) + env.step(2) + masks = env.action_masks() + + assert masks[18] + assert int(masks.sum()) == 1 diff --git a/tests/test_gym_wrapper_integration.py b/tests/test_gym_wrapper_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..2de770206b0327503884efd0a188455bee03e0fe --- /dev/null +++ b/tests/test_gym_wrapper_integration.py @@ -0,0 +1,59 @@ +import numpy as np + +from rl.gov_workflow_env import GovWorkflowGymEnv + + +def test_gym_wrapper_reset_step_and_core_env_access(): + env = GovWorkflowGymEnv( + task_id="district_backlog_easy", + seed=101, + hard_action_mask=True, + ) + + obs, info = env.reset(seed=101) + + assert isinstance(obs, np.ndarray) + assert obs.shape == env.observation_space.shape + assert isinstance(info, dict) + assert env.core_env is not None + + masks = env.action_masks() + assert isinstance(masks, np.ndarray) + assert masks.dtype == bool + assert masks.shape == (env.action_space.n,) + + valid_actions = np.flatnonzero(masks) + assert valid_actions.size > 0 + + obs2, reward, terminated, truncated, info2 = env.step(int(valid_actions[0])) + + assert isinstance(obs2, np.ndarray) + assert obs2.shape == env.observation_space.shape + assert isinstance(reward, float) + assert isinstance(terminated, bool) + assert isinstance(truncated, bool) + assert isinstance(info2, dict) + assert "requested_action_idx" in info2 + assert "executed_action_idx" in info2 + assert "action_mask_applied" in info2 + + +def test_gym_wrapper_hard_mask_sanitizes_invalid_action_when_available(): + env = GovWorkflowGymEnv( + task_id="district_backlog_easy", + seed=202, + hard_action_mask=True, + ) + env.reset(seed=202) + masks = env.action_masks() + + invalid_actions = np.flatnonzero(~masks) + if invalid_actions.size == 0: + return + + invalid_idx = int(invalid_actions[0]) + _, _, _, _, info = env.step(invalid_idx) + + assert info["requested_action_idx"] == invalid_idx + assert info["executed_action_idx"] != invalid_idx + assert info["action_mask_applied"] is True \ No newline at end of file diff --git a/tests/test_live_simulation_e2e.py b/tests/test_live_simulation_e2e.py new file mode 100644 index 0000000000000000000000000000000000000000..3eab5cb9aca3a3faaf41d8f9b37c2c22f7fde638 --- /dev/null +++ b/tests/test_live_simulation_e2e.py @@ -0,0 +1,39 @@ +from app.engine import LiveSimulationSession, SimulationAgentMode, run_simulation + + +def test_run_simulation_baseline_policy_end_to_end(): + result = run_simulation( + task_id="district_backlog_easy", + agent_mode=SimulationAgentMode.BASELINE_POLICY, + max_steps=12, + seed=123, + policy_name="backlog_clearance", + ) + + assert result.task_id == "district_backlog_easy" + assert result.agent_mode == SimulationAgentMode.BASELINE_POLICY + assert result.seed == 123 + assert isinstance(result.total_reward, float) + assert 0.0 <= result.score <= 1.0 + assert isinstance(result.summary, dict) + assert isinstance(result.trace, list) + assert len(result.trace) > 0 + + +def test_live_session_step_once_smoke(): + session = LiveSimulationSession( + task_id="district_backlog_easy", + agent_mode=SimulationAgentMode.BASELINE_POLICY, + max_steps=5, + seed=7, + policy_name="backlog_clearance", + ) + try: + row, log_line, finished = session.step_once() + assert isinstance(row, dict) + assert isinstance(log_line, str) + assert "[STEP]" in log_line + assert "reward" in row + assert isinstance(finished, bool) + finally: + session.close() \ No newline at end of file diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000000000000000000000000000000000000..d417285b53b01130bc8ae14317f9382d6ca73dcf --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,11 @@ +import pytest +from app.models import ActionModel, ActionType, PriorityMode, ApplicationCase, ServiceType + +def test_action_model_validation(): + a = ActionModel(action_type=ActionType.SET_PRIORITY_MODE, priority_mode=PriorityMode.URGENT_FIRST) + assert a.priority_mode == PriorityMode.URGENT_FIRST + +def test_service_case_bounds(): + with pytest.raises(Exception): + ApplicationCase(case_id="x", service_type=ServiceType.PASSPORT, + arrival_day=-1, sla_deadline_day=10) \ No newline at end of file diff --git a/tests/test_persistence_history.py b/tests/test_persistence_history.py new file mode 100644 index 0000000000000000000000000000000000000000..b292e701a4321e843fcf27cf849a5a1b068d8643 --- /dev/null +++ b/tests/test_persistence_history.py @@ -0,0 +1,66 @@ +from fastapi.testclient import TestClient + +from app.main import app + + +def test_simulation_history_persists_completed_runs() -> None: + client = TestClient(app) + + run_resp = client.post( + "/api/simulation/run", + json={ + "task_id": "district_backlog_easy", + "agent_mode": "baseline_policy", + "policy_name": "backlog_clearance", + "max_steps": 5, + "seed": 123, + }, + ) + assert run_resp.status_code == 200 + + history_resp = client.get("/api/history/simulations") + assert history_resp.status_code == 200 + runs = history_resp.json().get("runs", []) + assert isinstance(runs, list) + assert any(row.get("task_id") == "district_backlog_easy" for row in runs) + + run_id = next((row.get("run_id") for row in runs if row.get("run_id")), None) + assert run_id + detail_resp = client.get(f"/api/history/simulations/{run_id}") + assert detail_resp.status_code == 200 + detail = detail_resp.json() + assert detail.get("run_id") == run_id + + +def test_comparison_history_roundtrip() -> None: + client = TestClient(app) + + payload = { + "task_id": "district_backlog_easy", + "baseline_policy": "backlog_clearance", + "model_path": "results/best_model/phase2_final.zip", + "model_type": "maskable", + "include_llm": True, + "runs": 2, + "steps": 10, + "episodes": 1, + "seed_base": 100, + "result": { + "baselineScore": 0.6, + "trainedScore": 0.7, + "llmScore": 0.5, + }, + } + create_resp = client.post("/api/history/comparisons", json=payload) + assert create_resp.status_code == 200 + comparison_id = create_resp.json().get("comparison_id") + assert comparison_id + + list_resp = client.get("/api/history/comparisons") + assert list_resp.status_code == 200 + rows = list_resp.json().get("comparisons", []) + assert any(row.get("comparison_id") == comparison_id for row in rows) + + detail_resp = client.get(f"/api/history/comparisons/{comparison_id}") + assert detail_resp.status_code == 200 + assert detail_resp.json().get("comparison_id") == comparison_id diff --git a/tests/test_persistence_store.py b/tests/test_persistence_store.py new file mode 100644 index 0000000000000000000000000000000000000000..37e1670a12e3e23a1ea539ff546030e36591f747 --- /dev/null +++ b/tests/test_persistence_store.py @@ -0,0 +1,35 @@ +from pathlib import Path + +from app.persistence import PersistenceStore + + +def test_persistence_falls_back_when_configured_path_is_not_directory( + tmp_path: Path, + monkeypatch, +) -> None: + blocked_path = tmp_path / "blocked-target" + blocked_path.write_text("not-a-directory", encoding="utf-8") + + monkeypatch.setenv("STORAGE_ENABLED", "true") + monkeypatch.setenv("OPENENV_DATA_DIR", str(blocked_path)) + monkeypatch.delenv("STORAGE_DATA_DIR", raising=False) + + store = PersistenceStore(repo_root=tmp_path) + + expected_dir = (tmp_path / "outputs" / "persist").resolve() + assert store.enabled is True + assert store.data_dir == expected_dir + assert store.db_path.exists() + assert store.training_runs_dir.exists() + + +def test_persistence_is_disabled_via_env(tmp_path: Path, monkeypatch) -> None: + monkeypatch.setenv("STORAGE_ENABLED", "false") + monkeypatch.delenv("OPENENV_DATA_DIR", raising=False) + monkeypatch.delenv("STORAGE_DATA_DIR", raising=False) + + store = PersistenceStore(repo_root=tmp_path) + + assert store.enabled is False + assert store.data_dir == (tmp_path / "outputs" / "persist").resolve() + assert not store.db_path.exists() diff --git a/tests/test_phase1_event_engine.py b/tests/test_phase1_event_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..4e45ee5cd16590b64fced82f565b58317e73b2ac --- /dev/null +++ b/tests/test_phase1_event_engine.py @@ -0,0 +1,220 @@ +""" +tests/test_phase1_event_engine.py +Phase 1 validation: event_engine.py — determinism, scenario scaling, event effects +Run: pytest tests/test_phase1_event_engine.py -v +""" +import pytest +from app.models import EventType, ScenarioMode +from app.event_engine import EventEngine, DayEventParams, SCENARIO_MULTIPLIER, BASE_PROBS +from app.tasks import get_task + + +# ─── DayEventParams defaults ───────────────────────────────────────────────── +class TestDayEventParams: + def test_default_arrival_multiplier_one(self): + p = DayEventParams() + assert p.arrival_multiplier == 1.0 + + def test_default_officer_reduction_zero(self): + p = DayEventParams() + assert p.officer_reduction == 0 + + def test_default_no_active_events(self): + p = DayEventParams() + assert p.active_events == [] + + def test_has_events_false_by_default(self): + p = DayEventParams() + assert p.has_events() is False + + def test_has_events_true_when_populated(self): + p = DayEventParams() + p.active_events.append(EventType.SURGE_APPLICATIONS) + assert p.has_events() is True + + +# ─── ScenarioMultiplier constants ──────────────────────────────────────────── +class TestScenarioMultipliers: + def test_normal_multiplier_one(self): + assert SCENARIO_MULTIPLIER[ScenarioMode.NORMAL] == 1.0 + + def test_crisis_multiplier_greater_than_normal(self): + assert SCENARIO_MULTIPLIER[ScenarioMode.CRISIS] > SCENARIO_MULTIPLIER[ScenarioMode.NORMAL] + + def test_extreme_multiplier_greatest(self): + assert (SCENARIO_MULTIPLIER[ScenarioMode.EXTREME_OVERLOAD] > + SCENARIO_MULTIPLIER[ScenarioMode.CRISIS]) + + def test_all_multipliers_positive(self): + for mode, mult in SCENARIO_MULTIPLIER.items(): + assert mult > 0, f"Multiplier for {mode} should be positive" + + +# ─── EventEngine construction ──────────────────────────────────────────────── +class TestEventEngineConstruction: + def test_engine_initialises_with_seed_and_mode(self): + engine = EventEngine(seed=42, scenario_mode=ScenarioMode.NORMAL) + assert engine.seed == 42 + assert engine.scenario_mode == ScenarioMode.NORMAL + + def test_engine_stores_correct_multiplier(self): + engine = EventEngine(seed=0, scenario_mode=ScenarioMode.CRISIS) + assert engine._multiplier == SCENARIO_MULTIPLIER[ScenarioMode.CRISIS] + + +# ─── Determinism guarantee ──────────────────────────────────────────────────── +class TestEventEngineDeterminism: + def test_same_seed_same_day_same_events(self): + task = get_task("cross_department_hard") + engine1 = EventEngine(seed=999, scenario_mode=ScenarioMode.CRISIS) + engine2 = EventEngine(seed=999, scenario_mode=ScenarioMode.CRISIS) + for day in range(1, 10): + e1 = engine1.get_events_for_day(day, task) + e2 = engine2.get_events_for_day(day, task) + assert e1 == e2, f"Day {day}: non-deterministic events {e1} vs {e2}" + + def test_different_seeds_can_produce_different_events(self): + task = get_task("cross_department_hard") + engine_a = EventEngine(seed=1, scenario_mode=ScenarioMode.CRISIS) + engine_b = EventEngine(seed=2, scenario_mode=ScenarioMode.CRISIS) + results_a = [engine_a.get_events_for_day(d, task) for d in range(1, 30)] + results_b = [engine_b.get_events_for_day(d, task) for d in range(1, 30)] + # They should differ for at least some days (with high probability) + assert results_a != results_b + + def test_day_independence(self): + """Calling day 5 after day 3 gives same result as calling day 5 directly.""" + task = get_task("cross_department_hard") + engine = EventEngine(seed=42, scenario_mode=ScenarioMode.CRISIS) + # Call day 3 first, then day 5 + engine.get_events_for_day(3, task) + day5_after = engine.get_events_for_day(5, task) + # Fresh engine, only call day 5 + engine2 = EventEngine(seed=42, scenario_mode=ScenarioMode.CRISIS) + day5_direct = engine2.get_events_for_day(5, task) + assert day5_after == day5_direct + + +# ─── Event output format ───────────────────────────────────────────────────── +class TestEventEngineOutput: + def test_returns_list_of_event_types(self): + task = get_task("cross_department_hard") + engine = EventEngine(seed=42, scenario_mode=ScenarioMode.CRISIS) + events = engine.get_events_for_day(1, task) + assert isinstance(events, list) + for e in events: + assert isinstance(e, EventType) + + def test_no_event_returned_when_none_active(self): + """Easy task with NO_EVENT allowed — must return [NO_EVENT] not [].""" + task = get_task("district_backlog_easy") + engine = EventEngine(seed=42, scenario_mode=ScenarioMode.NORMAL) + events = engine.get_events_for_day(1, task) + assert len(events) >= 1 + + def test_events_only_from_allowed_list(self): + task = get_task("district_backlog_easy") + engine = EventEngine(seed=42, scenario_mode=ScenarioMode.NORMAL) + for day in range(1, 31): + events = engine.get_events_for_day(day, task) + for e in events: + assert e in task.allowed_events or e == EventType.NO_EVENT + + def test_hard_task_can_produce_surge_event(self): + """With crisis mode + 60 days, a surge event must appear at least once.""" + task = get_task("cross_department_hard") + engine = EventEngine(seed=999, scenario_mode=ScenarioMode.CRISIS) + all_events = [] + for day in range(1, 61): + all_events.extend(engine.get_events_for_day(day, task)) + non_null = [e for e in all_events if e != EventType.NO_EVENT] + assert len(non_null) > 0, "Crisis mode should produce at least one real event" + + +# ─── Apply events effects ───────────────────────────────────────────────────── +class TestApplyEvents: + def _engine(self): + return EventEngine(seed=42, scenario_mode=ScenarioMode.CRISIS) + + def test_no_event_gives_no_modification(self): + engine = self._engine() + task = get_task("district_backlog_easy") + params = engine.apply_events([EventType.NO_EVENT], task) + assert params.arrival_multiplier == 1.0 + assert params.officer_reduction == 0 + + def test_surge_event_increases_arrival_multiplier(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.SURGE_APPLICATIONS], task) + assert params.arrival_multiplier > 1.0 + + def test_officer_unavailable_reduces_officers(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.OFFICER_UNAVAILABLE], task) + assert params.officer_reduction >= 1 + + def test_doc_rejection_spike_boosts_defect_rate(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.DOCUMENT_REJECTION_SPIKE], task) + assert params.doc_defect_rate_boost > 0.0 + + def test_revenue_db_delay_boosts_system_dependency(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.REVENUE_DB_DELAY], task) + assert params.system_dependency_boost > 0.0 + + def test_sla_escalation_reduces_sla_window(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.SLA_ESCALATION_ORDER], task) + assert params.sla_window_multiplier <= 1.0 + + def test_multiple_events_compound(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events( + [EventType.SURGE_APPLICATIONS, EventType.OFFICER_UNAVAILABLE], task + ) + assert params.arrival_multiplier > 1.0 + assert params.officer_reduction >= 1 + + def test_active_events_populated_correctly(self): + engine = self._engine() + task = get_task("cross_department_hard") + params = engine.apply_events([EventType.SURGE_APPLICATIONS], task) + assert EventType.SURGE_APPLICATIONS in params.active_events + + def test_no_event_gives_no_event_in_active_list(self): + engine = self._engine() + task = get_task("district_backlog_easy") + params = engine.apply_events([EventType.NO_EVENT], task) + assert params.active_events == [EventType.NO_EVENT] + + +# ─── Describe events ────────────────────────────────────────────────────────── +class TestDescribeEvents: + def _engine(self): + return EventEngine(seed=42, scenario_mode=ScenarioMode.NORMAL) + + def test_no_event_description(self): + engine = self._engine() + desc = engine.describe_events([EventType.NO_EVENT]) + assert "No active events" in desc + + def test_surge_description(self): + engine = self._engine() + desc = engine.describe_events([EventType.SURGE_APPLICATIONS]) + assert isinstance(desc, str) + assert len(desc) > 0 + + def test_multiple_events_description(self): + engine = self._engine() + desc = engine.describe_events([ + EventType.SURGE_APPLICATIONS, + EventType.OFFICER_UNAVAILABLE, + ]) + assert ";" in desc # Two events joined by semicolon diff --git a/tests/test_phase1_models.py b/tests/test_phase1_models.py new file mode 100644 index 0000000000000000000000000000000000000000..e945920db1a78fd10ed6719db929e8c336c917af --- /dev/null +++ b/tests/test_phase1_models.py @@ -0,0 +1,398 @@ +""" +tests/test_phase1_models.py +Gov Workflow OpenEnv — Phase 1 Model Schema Tests +FIXED VERSION — matches real codebase exactly: + - InternalSubstate includes 'blocked_enrichment' + - GraderResult uses 'score' not 'final_score' + - GraderResult uses 'grader_name' and 'metrics' dict (not individual float fields) +""" +import pytest + + +# ══════════════════════════════════════════════════════ +# ENUM TESTS +# ══════════════════════════════════════════════════════ + +class TestEnums: + + def test_service_types_count(self): + from app.models import ServiceType + assert len(ServiceType) == 8 + + def test_all_service_types_present(self): + from app.models import ServiceType + expected = { + "passport", "driving_license", "gst_registration", + "income_certificate", "caste_certificate", + "birth_certificate", "land_registration", "aadhaar_card", + } + assert {s.value for s in ServiceType} == expected + + def test_stage_types_count(self): + from app.models import StageType + assert len(StageType) == 5 + + def test_all_stage_types_present(self): + from app.models import StageType + expected = { + "submission", "document_verification", "field_verification", + "approval", "issuance", + } + assert {s.value for s in StageType} == expected + + def test_internal_substates(self): + from app.models import InternalSubstate + expected = { + "pre_scrutiny", + "doc_validation", + "service_specific_validation", + "field_verification_pending", + "decision_pending", + "issuance_ready", + "blocked_missing_docs", + "blocked_enrichment", + "completed", + "rejected", + } + assert {s.value for s in InternalSubstate} == expected + + def test_priority_modes(self): + from app.models import PriorityMode + expected = {"urgent_first", "oldest_first", "balanced", "backlog_clearance"} + assert {p.value for p in PriorityMode} == expected + + def test_action_types(self): + from app.models import ActionType + expected = { + "set_priority_mode", "assign_capacity", "request_missing_documents", + "escalate_service", "advance_time", "reallocate_officers", + } + assert {a.value for a in ActionType} == expected + + def test_event_types(self): + from app.models import EventType + assert "no_event" in {e.value for e in EventType} + + def test_scenario_modes(self): + from app.models import ScenarioMode + expected = {"normal", "crisis", "extreme_overload"} + assert {s.value for s in ScenarioMode} == expected + + +# ══════════════════════════════════════════════════════ +# OFFICER POOL TESTS +# ══════════════════════════════════════════════════════ + +class TestOfficerPool: + + def test_idle_officers_calculation(self): + from app.models import OfficerPool + pool = OfficerPool( + total_officers=10, + available_officers=10, + allocated={"income_certificate": 6}, + ) + assert pool.idle_officers == 4 + + def test_idle_officers_zero_when_fully_allocated(self): + from app.models import OfficerPool + pool = OfficerPool( + total_officers=8, + available_officers=8, + allocated={"income_certificate": 8}, + ) + assert pool.idle_officers == 0 + + def test_idle_officers_fully_idle(self): + from app.models import OfficerPool + pool = OfficerPool( + total_officers=5, available_officers=5, allocated={} + ) + assert pool.idle_officers == 5 + + def test_deep_copy_does_not_share_allocated_dict(self): + from app.models import OfficerPool + pool = OfficerPool( + total_officers=6, + available_officers=6, + allocated={"income_certificate": 3}, + ) + copy = pool.model_copy(deep=True) + copy.allocated["income_certificate"] = 99 + assert pool.allocated["income_certificate"] == 3 + + +# ══════════════════════════════════════════════════════ +# APPLICATION CASE TESTS +# ══════════════════════════════════════════════════════ + +class TestApplicationCase: + + def _make_case(self, arrival=0, deadline=30, current=0): + from app.models import ApplicationCase, ServiceType + return ApplicationCase( + service_type=ServiceType.INCOME_CERTIFICATE, + arrival_day=arrival, + current_day=current, + sla_deadline_day=deadline, + ) + + def test_days_until_sla_positive(self): + c = self._make_case(arrival=0, deadline=30, current=5) + assert c.days_until_sla == 25 + + def test_days_until_sla_zero_when_past(self): + c = self._make_case(arrival=0, deadline=10, current=15) + assert c.days_until_sla == 0 + + def test_sla_risk_zero_on_arrival(self): + c = self._make_case(arrival=0, deadline=30, current=0) + assert c.sla_risk == 0.0 + + def test_sla_risk_one_when_at_deadline(self): + c = self._make_case(arrival=0, deadline=10, current=10) + assert c.sla_risk == 1.0 + + def test_sla_risk_midpoint(self): + c = self._make_case(arrival=0, deadline=20, current=10) + assert abs(c.sla_risk - 0.5) < 1e-6 + + def test_sla_risk_capped_at_one(self): + c = self._make_case(arrival=0, deadline=5, current=100) + assert c.sla_risk == 1.0 + + def test_unique_case_ids(self): + from app.models import ApplicationCase, ServiceType + ids = { + ApplicationCase( + service_type=ServiceType.INCOME_CERTIFICATE, + arrival_day=0, current_day=0, sla_deadline_day=21, + ).case_id + for _ in range(50) + } + assert len(ids) == 50 + + def test_default_substate_is_pre_scrutiny(self): + from app.models import InternalSubstate + c = self._make_case() + assert c.internal_substate == InternalSubstate.PRE_SCRUTINY + + def test_default_public_stage_is_submission(self): + from app.models import StageType + c = self._make_case() + assert c.public_stage == StageType.SUBMISSION + + +# ══════════════════════════════════════════════════════ +# QUEUE SNAPSHOT TESTS +# ══════════════════════════════════════════════════════ + +class TestQueueSnapshot: + + def test_construction_with_defaults(self): + from app.models import QueueSnapshot, ServiceType + snap = QueueSnapshot(service_type=ServiceType.INCOME_CERTIFICATE) + assert snap.total_pending == 0 + assert snap.total_completed_today == 0 + assert snap.total_sla_breached == 0 + + def test_sla_risk_bounded(self): + from app.models import QueueSnapshot, ServiceType + snap = QueueSnapshot( + service_type=ServiceType.INCOME_CERTIFICATE, + current_sla_risk=0.75, + ) + assert 0.0 <= snap.current_sla_risk <= 1.0 + + +# ══════════════════════════════════════════════════════ +# OBSERVATION MODEL TESTS +# ══════════════════════════════════════════════════════ + +class TestObservationModel: + + def _make_obs(self): + from app.models import ObservationModel, OfficerPool, ScenarioMode + return ObservationModel( + task_id="district_backlog_easy", + episode_id="ep-001", + day=0, + max_days=30, + scenario_mode=ScenarioMode.NORMAL, + officer_pool=OfficerPool( + total_officers=8, available_officers=8, + allocated={"income_certificate": 8}, + ), + ) + + def test_default_signals_in_range(self): + obs = self._make_obs() + for field in ( + "backlog_pressure", "sla_risk_score", "fairness_index", + "resource_utilization", "digital_intake_ratio", + ): + val = getattr(obs, field) + assert 0.0 <= val <= 1.0, f"{field}={val} out of [0,1] range" + + def test_last_action_valid_defaults_true(self): + obs = self._make_obs() + assert obs.last_action_valid is True + + def test_escalation_budget_remaining_default_zero(self): + obs = self._make_obs() + assert obs.escalation_budget_remaining == 0 + + def test_serialisation_round_trip(self): + obs = self._make_obs() + data = obs.model_dump() + from app.models import ObservationModel + obs2 = ObservationModel.model_validate(data) + assert obs2.task_id == obs.task_id + assert obs2.day == obs.day + + +# ══════════════════════════════════════════════════════ +# ACTION MODEL TESTS +# ══════════════════════════════════════════════════════ + +class TestActionModel: + + def test_advance_time_action(self): + from app.models import ActionModel, ActionType + a = ActionModel(action_type=ActionType.ADVANCE_TIME) + assert a.action_type == ActionType.ADVANCE_TIME + + def test_set_priority_mode_action(self): + from app.models import ActionModel, ActionType, PriorityMode + a = ActionModel( + action_type=ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.URGENT_FIRST, + ) + assert a.priority_mode == PriorityMode.URGENT_FIRST + + def test_escalate_action(self): + from app.models import ActionModel, ActionType, ServiceType + a = ActionModel( + action_type=ActionType.ESCALATE_SERVICE, + escalation_target=ServiceType.INCOME_CERTIFICATE, + ) + assert a.escalation_target == ServiceType.INCOME_CERTIFICATE + + def test_reallocate_action(self): + from app.models import ActionModel, ActionType + a = ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + reallocation_delta={"income_certificate": 2, "land_registration": -2}, + ) + assert sum(a.reallocation_delta.values()) == 0 + + def test_json_serialisation(self): + from app.models import ActionModel, ActionType + a = ActionModel(action_type=ActionType.ADVANCE_TIME) + j = a.model_dump_json() + assert "advance_time" in j + + +# ══════════════════════════════════════════════════════ +# REWARD MODEL TESTS +# ══════════════════════════════════════════════════════ + +class TestRewardModel: + + def test_default_total_reward_zero(self): + from app.models import RewardModel + r = RewardModel() + assert r.total_reward == 0.0 + + def test_all_components_default_zero(self): + from app.models import RewardModel + r = RewardModel() + for field in ( + "progress_reward", "completion_reward", "waiting_penalty", + "sla_penalty", "fairness_penalty", "invalid_action_penalty", + "idle_capacity_penalty", + ): + assert getattr(r, field) == 0.0, f"{field} should default to 0.0" + + +# ══════════════════════════════════════════════════════ +# GRADER RESULT TESTS +# ══════════════════════════════════════════════════════ + +class TestGraderResult: + """ + FIXED: Real GraderResult has: + result.score -> float [0.0, 1.0] + result.grader_name -> str + result.metrics -> dict[str, float] + NOT: final_score, document_rework_rate (those were old spec names). + """ + + def _get_cls(self): + from app.models import GraderResult + return GraderResult + + def _score_attr(self): + fields = self._get_cls().model_fields + return "score" if "score" in fields else "final_score" + + def _make(self): + GraderResult = self._get_cls() + fields = GraderResult.model_fields + score_attr = self._score_attr() + kwargs = { + "task_id": "district_backlog_easy", + "episode_id": "ep-test-001", + score_attr: 0.75, + } + if "grader_name" in fields: + kwargs["grader_name"] = "easy_grader" + if "metrics" in fields: + kwargs["metrics"] = { + "completion_rate": 0.80, + "sla_compliance_rate": 0.90, + "idle_efficiency": 0.70, + } + return GraderResult(**kwargs) + + def test_score_bounds(self): + result = self._make() + score_val = getattr(result, self._score_attr()) + assert 0.0 <= score_val <= 1.0, ( + f"{self._score_attr()}={score_val} not in [0.0, 1.0]" + ) + + def test_optional_fields_none(self): + GraderResult = self._get_cls() + fields = GraderResult.model_fields + result = self._make() + + if "metrics" in fields: + metrics = result.metrics + assert isinstance(metrics, dict), "metrics must be a dict" + for key in ( + "document_rework_rate", "fairness_gap", + "urgent_cases_served_rate", "wasted_escalation_ratio", + ): + val = metrics.get(key) + assert val is None or isinstance(val, (int, float)), ( + f"metrics['{key}'] should be None or numeric, got {type(val)}" + ) + else: + for field_name in ( + "document_rework_rate", "fairness_gap", + "urgent_cases_served_rate", "wasted_escalation_ratio", + ): + if field_name in fields: + val = getattr(result, field_name) + assert val is None or isinstance(val, float) + + def test_grader_result_has_score_field(self): + fields = list(self._get_cls().model_fields.keys()) + assert any(f in fields for f in ("score", "final_score")), ( + f"GraderResult must have score or final_score. Got: {fields}" + ) + + def test_grader_result_score_is_float(self): + result = self._make() + assert isinstance(getattr(result, self._score_attr()), float) diff --git a/tests/test_phase1_sector_and_tasks.py b/tests/test_phase1_sector_and_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..9ea357b3d60539f59f68e05bd2a9bd238449a6cf --- /dev/null +++ b/tests/test_phase1_sector_and_tasks.py @@ -0,0 +1,301 @@ +""" +tests/test_phase1_sector_and_tasks.py +Phase 1 validation: sector_profiles.py + tasks.py +Run: pytest tests/test_phase1_sector_and_tasks.py -v +""" +import pytest +from app.models import ServiceType, ScenarioMode, EventType +from app.sector_profiles import ( + get_sector_profile, + SECTOR_REGISTRY, + INCOME_CERTIFICATE_PROFILE, + LAND_REGISTRATION_PROFILE, + BIRTH_CERTIFICATE_PROFILE, + PASSPORT_PROFILE, + GST_REGISTRATION_PROFILE, + CASTE_CERTIFICATE_PROFILE, + DRIVING_LICENSE_PROFILE, +) +from app.tasks import ( + get_task, + list_tasks, + list_benchmark_tasks, + TASK_EASY, + TASK_MEDIUM, + TASK_HARD, + TASK_REGISTRY, + make_extreme_variant, +) + + +# ─── Sector Profiles Registry ──────────────────────────────────────────────── +class TestSectorRegistry: + def test_all_services_have_profiles(self): + for svc in ServiceType: + assert svc in SECTOR_REGISTRY, f"Missing profile for {svc}" + + def test_get_sector_profile_all_services(self): + for svc in ServiceType: + profile = get_sector_profile(svc) + assert profile.service_type == svc + + def test_unknown_service_raises_key_error(self): + with pytest.raises(KeyError): + get_sector_profile("nonexistent_service") # type: ignore + + def test_registry_has_seven_entries(self): + assert len(SECTOR_REGISTRY) == 8 + + +# ─── Individual Sector Profile Values ──────────────────────────────────────── +class TestIncomeCertificateProfile: + def test_sla_days(self): + assert INCOME_CERTIFICATE_PROFILE.sla_days == 21 + + def test_missing_docs_probability_range(self): + p = INCOME_CERTIFICATE_PROFILE.missing_docs_probability + assert 0.0 <= p <= 1.0 + + def test_field_verification_probability_range(self): + p = INCOME_CERTIFICATE_PROFILE.field_verification_probability + assert 0.0 <= p <= 1.0 + + def test_base_processing_rate_positive(self): + assert INCOME_CERTIFICATE_PROFILE.base_processing_rate > 0 + + def test_field_verification_days_positive(self): + assert INCOME_CERTIFICATE_PROFILE.field_verification_days >= 1 + + def test_doc_defect_rate_paper_higher_than_digital(self): + assert (INCOME_CERTIFICATE_PROFILE.doc_defect_rate_paper > + INCOME_CERTIFICATE_PROFILE.doc_defect_rate_digital) + + +class TestLandRegistrationProfile: + def test_sla_days_thirty(self): + assert LAND_REGISTRATION_PROFILE.sla_days == 30 + + def test_field_verification_heavy(self): + # Land registration has the highest field verification probability + assert LAND_REGISTRATION_PROFILE.field_verification_probability > 0.5 + + def test_field_verification_days_longer(self): + # Land should require more field verification days than income cert + assert (LAND_REGISTRATION_PROFILE.field_verification_days >= + INCOME_CERTIFICATE_PROFILE.field_verification_days) + + +class TestBirthCertificateProfile: + def test_sla_days_seven(self): + assert BIRTH_CERTIFICATE_PROFILE.sla_days == 7 + + def test_fast_processing_rate(self): + # Birth certificate should process faster than land registration + assert (BIRTH_CERTIFICATE_PROFILE.base_processing_rate > + LAND_REGISTRATION_PROFILE.base_processing_rate) + + def test_low_missing_docs_probability(self): + assert BIRTH_CERTIFICATE_PROFILE.missing_docs_probability < 0.30 + + +class TestGSTProfile: + def test_sla_days_seven(self): + assert GST_REGISTRATION_PROFILE.sla_days == 7 + + def test_all_probabilities_in_range(self): + p = GST_REGISTRATION_PROFILE + for attr in ["missing_docs_probability", "doc_defect_rate_digital", + "doc_defect_rate_paper", "field_verification_probability"]: + val = getattr(p, attr) + assert 0.0 <= val <= 1.0, f"{attr} out of range: {val}" + + +class TestAllProfileConstraints: + @pytest.mark.parametrize("service", list(ServiceType)) + def test_probabilities_in_range(self, service): + p = get_sector_profile(service) + for attr in ["missing_docs_probability", "doc_defect_rate_digital", + "doc_defect_rate_paper", "field_verification_probability", + "manual_scrutiny_intensity", "decision_backlog_sensitivity", + "system_dependency_risk"]: + val = getattr(p, attr) + assert 0.0 <= val <= 1.0, ( + f"{service.value}.{attr} = {val} is outside [0, 1]" + ) + + @pytest.mark.parametrize("service", list(ServiceType)) + def test_sla_days_positive(self, service): + p = get_sector_profile(service) + assert p.sla_days >= 1 + + @pytest.mark.parametrize("service", list(ServiceType)) + def test_processing_rate_positive(self, service): + p = get_sector_profile(service) + assert p.base_processing_rate >= 0.1 + + @pytest.mark.parametrize("service", list(ServiceType)) + def test_field_verification_days_positive(self, service): + p = get_sector_profile(service) + assert p.field_verification_days >= 1 + + @pytest.mark.parametrize("service", list(ServiceType)) + def test_paper_defect_rate_higher_than_digital(self, service): + p = get_sector_profile(service) + assert p.doc_defect_rate_paper >= p.doc_defect_rate_digital, ( + f"{service.value}: paper defect rate should be >= digital" + ) + + +# ─── Tasks ──────────────────────────────────────────────────────────────────── +class TestTaskRegistry: + def test_three_benchmark_tasks_exist(self): + tasks = list_benchmark_tasks() + assert len(tasks) == 3 + + def test_benchmark_task_ids(self): + tasks = set(list_benchmark_tasks()) + assert "district_backlog_easy" in tasks + assert "mixed_urgency_medium" in tasks + assert "cross_department_hard" in tasks + + def test_all_tasks_retrievable(self): + for tid in list_tasks(): + task = get_task(tid) + assert task.task_id == tid + + def test_unknown_task_raises_value_error(self): + with pytest.raises(ValueError): + get_task("nonexistent_task_id_xyz") + + def test_registry_has_at_least_three_entries(self): + assert len(TASK_REGISTRY) >= 3 + + +class TestTaskEasy: + def test_task_id(self): + assert TASK_EASY.task_id == "district_backlog_easy" + + def test_difficulty(self): + assert TASK_EASY.difficulty == "easy" + + def test_scenario_mode_normal(self): + assert TASK_EASY.scenario_mode == ScenarioMode.NORMAL + + def test_seed_deterministic(self): + assert TASK_EASY.seed == 42 + + def test_max_days_thirty(self): + assert TASK_EASY.max_days == 30 + + def test_single_service(self): + assert len(TASK_EASY.enabled_services) == 1 + assert ServiceType.INCOME_CERTIFICATE in TASK_EASY.enabled_services + + def test_arrival_rate_positive(self): + for svc, rate in TASK_EASY.arrival_rate_per_day.items(): + assert rate > 0, f"Arrival rate for {svc} should be positive" + + def test_officer_pool_valid(self): + pool = TASK_EASY.initial_officer_pool + assert pool.total_officers >= 1 + assert pool.available_officers >= 1 + + def test_escalation_budget_nonnegative(self): + assert TASK_EASY.escalation_budget >= 0 + + def test_no_fairness_threshold(self): + assert TASK_EASY.fairness_threshold is None + + def test_low_event_probability(self): + assert TASK_EASY.event_probability <= 0.10 + + +class TestTaskMedium: + def test_task_id(self): + assert TASK_MEDIUM.task_id == "mixed_urgency_medium" + + def test_difficulty(self): + assert TASK_MEDIUM.difficulty == "medium" + + def test_five_services(self): + assert len(TASK_MEDIUM.enabled_services) == 5 + assert ServiceType.PASSPORT in TASK_MEDIUM.enabled_services + assert ServiceType.DRIVING_LICENSE in TASK_MEDIUM.enabled_services + assert ServiceType.AADHAAR_CARD in TASK_MEDIUM.enabled_services + + def test_max_days_forty_five(self): + assert TASK_MEDIUM.max_days == 45 + + def test_higher_event_probability_than_easy(self): + assert TASK_MEDIUM.event_probability > TASK_EASY.event_probability + + def test_arrival_rates_for_all_services(self): + for svc in TASK_MEDIUM.enabled_services: + key = svc if svc in TASK_MEDIUM.arrival_rate_per_day else svc.value + rate = TASK_MEDIUM.arrival_rate_per_day.get(svc, + TASK_MEDIUM.arrival_rate_per_day.get(svc.value, None)) + assert rate is not None and rate > 0 + + def test_officer_pool_covers_both_services(self): + pool = TASK_MEDIUM.initial_officer_pool + allocated_services = set(pool.allocated.keys()) + # At least one service should have officers + assert len(allocated_services) >= 1 + + +class TestTaskHard: + def test_task_id(self): + assert TASK_HARD.task_id == "cross_department_hard" + + def test_difficulty(self): + assert TASK_HARD.difficulty == "hard" + + def test_scenario_mode_crisis(self): + assert TASK_HARD.scenario_mode == ScenarioMode.CRISIS + + def test_max_days_sixty(self): + assert TASK_HARD.max_days == 60 + + def test_fairness_threshold_set(self): + assert TASK_HARD.fairness_threshold is not None + assert 0.0 <= TASK_HARD.fairness_threshold <= 1.0 + + def test_has_escalation_events(self): + assert EventType.SLA_ESCALATION_ORDER in TASK_HARD.allowed_events + + def test_event_probability_highest(self): + assert TASK_HARD.event_probability > TASK_MEDIUM.event_probability + + def test_escalation_budget_higher_than_easy(self): + assert TASK_HARD.escalation_budget >= TASK_EASY.escalation_budget + + +class TestExtremeVariant: + def test_extreme_variant_creation(self): + extreme = make_extreme_variant(TASK_EASY) + assert "_extreme" in extreme.task_id + + def test_extreme_scenario_mode(self): + extreme = make_extreme_variant(TASK_MEDIUM) + assert extreme.scenario_mode == ScenarioMode.EXTREME_OVERLOAD + + def test_extreme_event_probability_higher(self): + extreme = make_extreme_variant(TASK_EASY) + assert extreme.event_probability > TASK_EASY.event_probability + + def test_extreme_does_not_mutate_original(self): + original_mode = TASK_EASY.scenario_mode + make_extreme_variant(TASK_EASY) + assert TASK_EASY.scenario_mode == original_mode + + +class TestTaskDeterminism: + def test_same_seed_same_task(self): + t1 = get_task("district_backlog_easy") + t2 = get_task("district_backlog_easy") + assert t1.seed == t2.seed + assert t1.max_days == t2.max_days + + def test_tasks_have_different_seeds(self): + seeds = {get_task(tid).seed for tid in list_benchmark_tasks()} + assert len(seeds) == 3, "Each benchmark task must have a unique seed" diff --git a/tests/test_phase1_signal_computer.py b/tests/test_phase1_signal_computer.py new file mode 100644 index 0000000000000000000000000000000000000000..4c3ed08931f84e1845a983ad5b7697d81f34517e --- /dev/null +++ b/tests/test_phase1_signal_computer.py @@ -0,0 +1,255 @@ +""" +tests/test_phase1_signal_computer.py +Phase 1 validation: signal_computer.py +Run: pytest tests/test_phase1_signal_computer.py -v +""" +import pytest +from app.models import ServiceType, OfficerPool, QueueSnapshot +from app.signal_computer import SignalComputer, ComputedSignals + + +def make_snapshot( + service: ServiceType, + total_pending: int = 0, + completed_today: int = 0, + sla_breached: int = 0, + urgent: int = 0, + blocked_missing: int = 0, + field_pending: int = 0, + sla_risk: float = 0.0, +) -> QueueSnapshot: + return QueueSnapshot( + service_type=service, + total_pending=total_pending, + total_completed_today=completed_today, + total_sla_breached=sla_breached, + urgent_pending=urgent, + blocked_missing_docs=blocked_missing, + field_verification_pending=field_pending, + current_sla_risk=sla_risk, + ) + + +def make_pool(total=10, available=10, allocated=None) -> OfficerPool: + return OfficerPool( + total_officers=total, + available_officers=available, + allocated=allocated or {}, + ) + + +class TestComputedSignalsDefaults: + def test_defaults_all_zero_or_reasonable(self): + s = ComputedSignals() + assert s.backlog_pressure == 0.0 + assert s.sla_risk_score == 0.0 + assert s.fairness_index == 1.0 + assert s.resource_utilization == 0.0 + assert s.digital_intake_ratio == 0.5 + assert s.blocked_cases_missing_docs == 0 + assert s.field_verification_load == 0.0 + + +class TestSignalComputerEmpty: + def test_empty_snapshots_returns_defaults(self): + sc = SignalComputer() + pool = make_pool() + signals = sc.compute({}, pool) + assert signals.backlog_pressure == 0.0 + assert signals.fairness_index == 1.0 + assert signals.blocked_cases_missing_docs == 0 + + +class TestBacklogPressure: + def test_no_backlog_gives_zero_pressure(self): + sc = SignalComputer() + pool = make_pool(total=10, available=10) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=0)} + signals = sc.compute(snap, pool, capacity_per_day=10.0) + assert signals.backlog_pressure == 0.0 + + def test_high_backlog_gives_high_pressure(self): + sc = SignalComputer() + pool = make_pool(total=5, available=5) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=1000)} + signals = sc.compute(snap, pool, capacity_per_day=5.0) + assert signals.backlog_pressure > 0.8 + + def test_backlog_pressure_bounded_at_one(self): + sc = SignalComputer() + pool = make_pool(total=1, available=1) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=99999)} + signals = sc.compute(snap, pool, capacity_per_day=1.0) + assert signals.backlog_pressure <= 1.0 + + +class TestSLARiskScore: + def test_zero_risk_when_all_cases_fresh(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=10, sla_risk=0.0)} + signals = sc.compute(snap, pool) + assert signals.sla_risk_score == 0.0 + + def test_full_risk_when_all_cases_at_deadline(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=10, sla_risk=1.0)} + signals = sc.compute(snap, pool) + assert abs(signals.sla_risk_score - 1.0) < 0.01 + + def test_sla_risk_bounded(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=5, sla_risk=0.99)} + signals = sc.compute(snap, pool) + assert 0.0 <= signals.sla_risk_score <= 1.0 + + +class TestFairnessIndex: + def test_single_service_fairness_is_one(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=5, completed_today=3)} + signals = sc.compute(snap, pool) + assert signals.fairness_index == 1.0 + + def test_equal_completion_rates_fairness_is_one(self): + sc = SignalComputer() + pool = make_pool() + snaps = { + ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=5, completed_today=5), + ServiceType.LAND_REGISTRATION.value: + make_snapshot(ServiceType.LAND_REGISTRATION, + total_pending=5, completed_today=5), + } + signals = sc.compute(snaps, pool) + assert abs(signals.fairness_index - 1.0) < 0.05 + + def test_unequal_completion_rates_reduce_fairness(self): + sc = SignalComputer() + pool = make_pool() + snaps = { + ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=10, completed_today=10), + ServiceType.LAND_REGISTRATION.value: + make_snapshot(ServiceType.LAND_REGISTRATION, + total_pending=10, completed_today=0), + } + signals = sc.compute(snaps, pool) + assert signals.fairness_index < 1.0 + + def test_fairness_bounded(self): + sc = SignalComputer() + pool = make_pool() + snaps = { + "a": make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=100, completed_today=100), + "b": make_snapshot(ServiceType.LAND_REGISTRATION, + total_pending=100, completed_today=0), + } + signals = sc.compute(snaps, pool) + assert 0.0 <= signals.fairness_index <= 1.0 + + +class TestResourceUtilization: + def test_fully_allocated_gives_one(self): + sc = SignalComputer() + pool = make_pool(total=10, available=10, + allocated={"income_certificate": 10}) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=5)} + signals = sc.compute(snap, pool) + assert abs(signals.resource_utilization - 1.0) < 0.01 + + def test_zero_allocation_gives_zero_utilization(self): + sc = SignalComputer() + pool = make_pool(total=10, available=10, allocated={}) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=5)} + signals = sc.compute(snap, pool) + assert signals.resource_utilization == 0.0 + + def test_utilization_bounded(self): + sc = SignalComputer() + pool = make_pool(total=10, available=10, + allocated={"income_certificate": 99}) + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=5)} + signals = sc.compute(snap, pool) + assert 0.0 <= signals.resource_utilization <= 1.0 + + +class TestDigitalIntakeRatio: + def test_all_digital_gives_one(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, total_pending=5)} + signals = sc.compute(snap, pool, + todays_arrivals=10, digital_arrivals=10) + assert signals.digital_intake_ratio == 1.0 + + def test_no_arrivals_gives_half(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE)} + signals = sc.compute(snap, pool, todays_arrivals=0, digital_arrivals=0) + assert signals.digital_intake_ratio == 0.5 + + def test_ratio_bounded(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE)} + signals = sc.compute(snap, pool, todays_arrivals=5, digital_arrivals=5) + assert 0.0 <= signals.digital_intake_ratio <= 1.0 + + +class TestBlockedAndFieldLoad: + def test_blocked_cases_aggregated_across_services(self): + sc = SignalComputer() + pool = make_pool() + snaps = { + ServiceType.INCOME_CERTIFICATE.value: + make_snapshot(ServiceType.INCOME_CERTIFICATE, + total_pending=10, blocked_missing=3), + ServiceType.LAND_REGISTRATION.value: + make_snapshot(ServiceType.LAND_REGISTRATION, + total_pending=8, blocked_missing=2), + } + signals = sc.compute(snaps, pool) + assert signals.blocked_cases_missing_docs == 5 + + def test_field_verification_load_fraction(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.PASSPORT.value: + make_snapshot(ServiceType.PASSPORT, + total_pending=10, field_pending=4)} + signals = sc.compute(snap, pool) + assert abs(signals.field_verification_load - 0.4) < 0.05 + + def test_field_load_bounded(self): + sc = SignalComputer() + pool = make_pool() + snap = {ServiceType.PASSPORT.value: + make_snapshot(ServiceType.PASSPORT, + total_pending=5, field_pending=5)} + signals = sc.compute(snap, pool) + assert 0.0 <= signals.field_verification_load <= 1.0 diff --git a/tests/test_phase2_api.py b/tests/test_phase2_api.py new file mode 100644 index 0000000000000000000000000000000000000000..20e0e66b6974d0af1c76f646cc4cf540b5018485 --- /dev/null +++ b/tests/test_phase2_api.py @@ -0,0 +1,295 @@ +""" +tests/test_phase2_api.py +Phase 2 API: FastAPI endpoints /health /reset /step /state /grade /sessions +Run (server must be running on localhost:7860): + pytest tests/test_phase2_api.py -v +OR against the TestClient (no server needed): + pytest tests/test_phase2_api.py -v --use-testclient +""" +import pytest +import sys + +# ── Use TestClient by default — no running server needed ───────────────────── +try: + from fastapi.testclient import TestClient + from app.main import app + client = TestClient(app) + USE_TESTCLIENT = True +except Exception: + import requests + BASE = "http://localhost:7860" + USE_TESTCLIENT = False + + +def post(path: str, body: dict) -> dict: + if USE_TESTCLIENT: + r = client.post(path, json=body) + else: + import requests + r = requests.post(f"{BASE}{path}", json=body) + return r.status_code, r.json() + + +def get(path: str, params: dict = None) -> dict: + if USE_TESTCLIENT: + r = client.get(path, params=params) + else: + import requests + r = requests.get(f"{BASE}{path}", params=params) + return r.status_code, r.json() + + +def delete(path: str) -> dict: + if USE_TESTCLIENT: + r = client.delete(path) + else: + import requests + r = requests.delete(f"{BASE}{path}") + return r.status_code, r.json() + + +# ─── /health ────────────────────────────────────────────────────────────────── +class TestHealth: + def test_health_returns_200(self): + code, body = get("/health") + assert code == 200 + + def test_health_status_ok(self): + _, body = get("/health") + assert body.get("status") == "ok" + + def test_health_has_version(self): + _, body = get("/health") + assert "version" in body + + def test_health_has_active_sessions(self): + _, body = get("/health") + assert "active_sessions" in body + assert isinstance(body["active_sessions"], int) + + +# ─── POST /reset ────────────────────────────────────────────────────────────── +class TestReset: + def test_reset_returns_200(self): + code, _ = post("/reset", {"task_id": "district_backlog_easy"}) + assert code == 200 + + def test_reset_returns_session_id(self): + _, body = post("/reset", {"task_id": "district_backlog_easy"}) + assert "session_id" in body + assert isinstance(body["session_id"], str) + assert len(body["session_id"]) > 0 + + def test_reset_returns_observation(self): + _, body = post("/reset", {"task_id": "district_backlog_easy"}) + assert "observation" in body + obs = body["observation"] + assert obs["day"] == 0 + assert obs["task_id"] == "district_backlog_easy" + + def test_reset_returns_info_dict(self): + _, body = post("/reset", {"task_id": "district_backlog_easy"}) + assert "info" in body + assert isinstance(body["info"], dict) + + def test_reset_with_seed(self): + code, body = post("/reset", {"task_id": "district_backlog_easy", "seed": 42}) + assert code == 200 + assert "session_id" in body + + def test_reset_different_tasks(self): + for tid in ["district_backlog_easy", "mixed_urgency_medium", "cross_department_hard"]: + code, body = post("/reset", {"task_id": tid}) + assert code == 200, f"Reset failed for task {tid}" + assert body["observation"]["task_id"] == tid + + def test_two_resets_give_different_session_ids(self): + _, b1 = post("/reset", {"task_id": "district_backlog_easy"}) + _, b2 = post("/reset", {"task_id": "district_backlog_easy"}) + assert b1["session_id"] != b2["session_id"] + + +# ─── POST /step ─────────────────────────────────────────────────────────────── +class TestStep: + def _session(self): + _, body = post("/reset", {"task_id": "district_backlog_easy", "seed": 42}) + return body["session_id"] + + def test_step_returns_200(self): + sid = self._session() + code, _ = post("/step", { + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert code == 200 + + def test_step_returns_all_fields(self): + sid = self._session() + _, body = post("/step", { + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert "observation" in body + assert "reward" in body + assert "terminated" in body + assert "truncated" in body + assert "info" in body + + def test_step_reward_is_number(self): + sid = self._session() + _, body = post("/step", { + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert isinstance(body["reward"], (int, float)) + + def test_step_observation_day_increments(self): + sid = self._session() + _, b = post("/step", {"session_id": sid, + "action": {"action_type": "advance_time"}}) + assert b["observation"]["day"] == 1 + + def test_step_set_priority_mode(self): + sid = self._session() + _, body = post("/step", { + "session_id": sid, + "action": {"action_type": "set_priority_mode", + "priority_mode": "urgent_first"}, + }) + assert body["info"]["invalid_action"] is False + + def test_step_invalid_action_flagged(self): + sid = self._session() + _, body = post("/step", { + "session_id": sid, + "action": {"action_type": "set_priority_mode"}, # missing priority_mode + }) + assert body["info"]["invalid_action"] is True + + def test_step_on_unknown_session_returns_404(self): + code, _ = post("/step", { + "session_id": "no-such-session-xyz", + "action": {"action_type": "advance_time"}, + }) + assert code == 404 + + def test_step_terminated_episode_returns_409(self): + sid = self._session() + # Run until termination + for _ in range(200): + _, b = post("/step", {"session_id": sid, + "action": {"action_type": "advance_time"}}) + if b.get("terminated") or b.get("truncated"): + break + # One more step should be 409 + code, _ = post("/step", { + "session_id": sid, + "action": {"action_type": "advance_time"}, + }) + assert code in [409, 422] + + +# ─── GET/POST /state ────────────────────────────────────────────────────────── +class TestState: + def _session(self): + _, body = post("/reset", {"task_id": "district_backlog_easy", "seed": 42}) + return body["session_id"] + + def test_state_post_returns_200(self): + sid = self._session() + code, _ = post("/state", {"session_id": sid}) + assert code == 200 + + def test_state_get_returns_200(self): + sid = self._session() + code, _ = get("/state", {"session_id": sid}) + assert code == 200 + + def test_state_has_episode_state(self): + sid = self._session() + _, body = post("/state", {"session_id": sid}) + assert "state" in body + + def test_state_day_zero_at_start(self): + sid = self._session() + _, body = post("/state", {"session_id": sid}) + assert body["state"]["day"] == 0 + + def test_state_unknown_session_404(self): + code, _ = post("/state", {"session_id": "ghost-session"}) + assert code == 404 + + def test_state_action_history_excluded_by_default(self): + sid = self._session() + _, body = post("/state", {"session_id": sid, + "include_action_history": False}) + state = body["state"] + assert "action_history" not in state or state.get("action_history") is None + + +# ─── POST /grade ────────────────────────────────────────────────────────────── +class TestGrade: + def _run_session(self, steps=5): + _, body = post("/reset", {"task_id": "district_backlog_easy", "seed": 42}) + sid = body["session_id"] + for _ in range(steps): + r = post("/step", {"session_id": sid, + "action": {"action_type": "advance_time"}}) + if r[1].get("terminated") or r[1].get("truncated"): + break + return sid + + def test_grade_returns_200(self): + sid = self._run_session() + code, _ = post("/grade", {"session_id": sid}) + assert code == 200 + + def test_grade_score_in_range(self): + sid = self._run_session() + _, body = post("/grade", {"session_id": sid}) + assert 0.0 <= body["score"] <= 1.0 + + def test_grade_has_grader_name(self): + sid = self._run_session() + _, body = post("/grade", {"session_id": sid}) + assert "grader_name" in body + assert isinstance(body["grader_name"], str) + + def test_grade_has_metrics(self): + sid = self._run_session() + _, body = post("/grade", {"session_id": sid}) + assert "metrics" in body + + def test_grade_unknown_session_404(self): + code, _ = post("/grade", {"session_id": "dead-session"}) + assert code == 404 + + +# ─── GET /sessions / DELETE /sessions/{id} ─────────────────────────────────── +class TestSessions: + def test_list_sessions_returns_200(self): + code, _ = get("/sessions") + assert code == 200 + + def test_list_sessions_has_count(self): + _, body = get("/sessions") + assert "active_sessions" in body + + def test_delete_session(self): + _, r = post("/reset", {"task_id": "district_backlog_easy"}) + sid = r["session_id"] + code, body = delete(f"/sessions/{sid}") + assert code == 200 + assert body.get("deleted") == sid + + def test_delete_nonexistent_session_404(self): + code, _ = delete("/sessions/nonexistent-id-xyz") + assert code == 404 + + def test_session_count_increases_after_reset(self): + _, b1 = get("/sessions") + count_before = b1["active_sessions"] + post("/reset", {"task_id": "district_backlog_easy"}) + _, b2 = get("/sessions") + count_after = b2["active_sessions"] + assert count_after >= count_before diff --git a/tests/test_phase2_env_integration.py b/tests/test_phase2_env_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c4d3061224b8a862d00db67d704fd492d3cbdf --- /dev/null +++ b/tests/test_phase2_env_integration.py @@ -0,0 +1,333 @@ +""" +tests/test_phase2_env_integration.py +Phase 2 integration: env.py end-to-end episode lifecycle +Tests reset(), step(), state(), advance_time loop, action dispatch +Run: pytest tests/test_phase2_env_integration.py -v +""" +import pytest +from app.env import GovWorkflowEnv +from app.models import ( + ActionModel, ActionType, PriorityMode, ServiceType, + ObservationModel, EpisodeStateModel, StepInfoModel, RewardModel, + InternalSubstate, +) + + +def make_env(task_id="district_backlog_easy") -> GovWorkflowEnv: + return GovWorkflowEnv(task_id=task_id) + + +# ─── reset() API ───────────────────────────────────────────────────────────── +class TestReset: + def test_reset_returns_tuple(self): + env = make_env() + result = env.reset() + assert isinstance(result, tuple) + assert len(result) == 2 + + def test_reset_returns_observation_and_info(self): + env = make_env() + obs, info = env.reset() + assert isinstance(obs, ObservationModel) + assert isinstance(info, dict) + + def test_reset_observation_day_zero(self): + env = make_env() + obs, _ = env.reset() + assert obs.day == 0 + + def test_reset_episode_id_set(self): + env = make_env() + obs, _ = env.reset() + assert obs.episode_id != "" + assert len(obs.episode_id) > 0 + + def test_reset_not_terminated(self): + env = make_env() + env.reset() + assert env.terminated is False + assert env.truncated is False + + def test_reset_deterministic_with_same_seed(self): + env1 = make_env() + env2 = make_env() + obs1, _ = env1.reset(seed=42) + obs2, _ = env2.reset(seed=42) + assert obs1.day == obs2.day + assert obs1.task_id == obs2.task_id + assert obs1.officer_pool.total_officers == obs2.officer_pool.total_officers + + def test_reset_with_explicit_seed(self): + env = make_env() + obs, _ = env.reset(seed=99) + assert obs.day == 0 + + def test_reset_info_contains_task_id(self): + env = make_env() + _, info = env.reset() + assert "task_id" in info + + def test_reset_task_id_in_observation(self): + env = make_env() + obs, _ = env.reset() + assert obs.task_id == "district_backlog_easy" + + def test_double_reset_gives_fresh_episode(self): + env = make_env() + obs1, _ = env.reset(seed=42) + ep1 = obs1.episode_id + obs2, _ = env.reset(seed=42) + ep2 = obs2.episode_id + assert ep1 != ep2 # New episode ID each reset + + def test_reset_officer_pool_matches_task_config(self): + from app.tasks import get_task + env = make_env() + obs, _ = env.reset() + task = get_task("district_backlog_easy") + assert obs.officer_pool.total_officers == task.initial_officer_pool.total_officers + + +# ─── step() API ─────────────────────────────────────────────────────────────── +class TestStep: + def _ready_env(self): + env = make_env() + env.reset(seed=42) + return env + + def test_step_returns_five_tuple(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + result = env.step(action) + assert len(result) == 5 + + def test_step_returns_correct_types(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + obs, reward, terminated, truncated, info = env.step(action) + assert isinstance(obs, ObservationModel) + assert isinstance(reward, float) + assert isinstance(terminated, bool) + assert isinstance(truncated, bool) + assert isinstance(info, StepInfoModel) + + def test_step_advances_day(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + obs, _, _, _, _ = env.step(action) + assert obs.day == 1 + + def test_step_on_terminated_raises(self): + env = self._ready_env() + env.terminated = True + with pytest.raises(RuntimeError): + env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + + def test_advance_time_increases_day_each_step(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + days = [] + for _ in range(5): + obs, _, terminated, truncated, _ = env.step(action) + days.append(obs.day) + if terminated or truncated: + break + assert days == sorted(days) + + def test_reward_is_finite_number(self): + env = self._ready_env() + _, reward, _, _, _ = env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + assert not (reward != reward) # not NaN + assert reward != float("inf") + + def test_step_info_has_reward_breakdown(self): + env = self._ready_env() + _, _, _, _, info = env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + assert isinstance(info.reward_breakdown, RewardModel) + + +# ─── state() API ────────────────────────────────────────────────────────────── +class TestState: + def test_state_returns_episode_state_model(self): + env = make_env() + env.reset(seed=42) + s = env.state() + assert isinstance(s, EpisodeStateModel) + + def test_state_task_id_correct(self): + env = make_env() + env.reset(seed=42) + s = env.state() + assert s.task_id == "district_backlog_easy" + + def test_state_day_matches_env_day(self): + env = make_env() + env.reset(seed=42) + env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + s = env.state() + assert s.day == env.day + + def test_state_not_terminated_at_start(self): + env = make_env() + env.reset(seed=42) + s = env.state() + assert s.terminated is False + + def test_state_episode_id_matches_obs(self): + env = make_env() + obs, _ = env.reset(seed=42) + s = env.state() + assert s.episode_id == obs.episode_id + + def test_state_total_steps_increments(self): + env = make_env() + env.reset(seed=42) + env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + s = env.state() + assert s.total_steps == 2 + + +# ─── Action dispatch ────────────────────────────────────────────────────────── +class TestActionDispatch: + def _ready_env(self, task="district_backlog_easy"): + env = make_env(task) + env.reset(seed=42) + return env + + def test_set_priority_mode_urgent_first(self): + env = self._ready_env() + action = ActionModel( + action_type=ActionType.SET_PRIORITY_MODE, + priority_mode=PriorityMode.URGENT_FIRST, + ) + _, _, _, _, info = env.step(action) + assert not info.invalid_action + assert env.priority_mode == PriorityMode.URGENT_FIRST + + def test_set_priority_mode_without_mode_is_invalid(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.SET_PRIORITY_MODE) + _, _, _, _, info = env.step(action) + assert info.invalid_action + + def test_advance_time_valid(self): + env = self._ready_env() + _, _, _, _, info = env.step(ActionModel(action_type=ActionType.ADVANCE_TIME)) + assert not info.invalid_action + + def test_escalate_without_budget_is_invalid(self): + env = self._ready_env() + env.escalation_budget_remaining = 0 + action = ActionModel( + action_type=ActionType.ESCALATE_SERVICE, + escalation_target=ServiceType.INCOME_CERTIFICATE, + ) + _, _, _, _, info = env.step(action) + assert info.invalid_action + + def test_reallocate_with_bad_delta_is_invalid(self): + env = self._ready_env() + action = ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + reallocation_delta={"income_certificate": 2}, # doesn't sum to 0 + ) + _, _, _, _, info = env.step(action) + assert info.invalid_action + + def test_reallocate_with_one_entry_is_invalid(self): + env = self._ready_env() + action = ActionModel( + action_type=ActionType.REALLOCATE_OFFICERS, + reallocation_delta={"income_certificate": 0}, + ) + _, _, _, _, info = env.step(action) + assert info.invalid_action + + def test_assign_capacity_without_dict_is_invalid(self): + env = self._ready_env() + action = ActionModel(action_type=ActionType.ASSIGN_CAPACITY) + _, _, _, _, info = env.step(action) + assert info.invalid_action + + def test_request_missing_docs_no_blocked_cases_is_invalid(self): + env = self._ready_env() + # At day 0 no cases are blocked yet + action = ActionModel( + action_type=ActionType.REQUEST_MISSING_DOCUMENTS, + service_target=ServiceType.INCOME_CERTIFICATE, + ) + _, _, _, _, info = env.step(action) + # Either valid (if cases exist) or invalid (if none blocked) — must not crash + assert isinstance(info.invalid_action, bool) + + +# ─── Full episode lifecycle ──────────────────────────────────────────────────── +class TestFullEpisode: + def test_episode_terminates_within_max_days(self): + env = make_env("district_backlog_easy") + env.reset(seed=42) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + steps = 0 + while steps < 200: + _, _, terminated, truncated, _ = env.step(action) + steps += 1 + if terminated or truncated: + break + assert terminated or truncated, "Episode must terminate" + + def test_completed_cases_nonneg_at_end(self): + env = make_env("district_backlog_easy") + env.reset(seed=42) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(35): + _, _, t, tr, _ = env.step(action) + if t or tr: + break + s = env.state() + assert s.total_completed >= 0 + + def test_cumulative_reward_is_float(self): + env = make_env("district_backlog_easy") + env.reset(seed=42) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(5): + env.step(action) + s = env.state() + assert isinstance(s.cumulative_reward, float) + + def test_episode_deterministic_same_seed_same_actions(self): + def run(seed): + env = make_env("district_backlog_easy") + env.reset(seed=seed) + rewards = [] + for _ in range(10): + _, r, t, tr, _ = env.step( + ActionModel(action_type=ActionType.ADVANCE_TIME) + ) + rewards.append(round(r, 6)) + if t or tr: + break + return rewards + + r1 = run(42) + r2 = run(42) + assert r1 == r2, "Same seed + same actions must give same rewards" + + def test_medium_task_episode_does_not_crash(self): + env = make_env("mixed_urgency_medium") + env.reset(seed=123) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(50): + _, _, t, tr, _ = env.step(action) + if t or tr: + break + + def test_hard_task_episode_does_not_crash(self): + env = make_env("cross_department_hard") + env.reset(seed=999) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(65): + _, _, t, tr, _ = env.step(action) + if t or tr: + break diff --git a/tests/test_phase2_graders.py b/tests/test_phase2_graders.py new file mode 100644 index 0000000000000000000000000000000000000000..d12c1ca086a487b65b071364211291b53cf01854 --- /dev/null +++ b/tests/test_phase2_graders.py @@ -0,0 +1,116 @@ +""" +tests/test_phase2_graders.py +Phase 2: graders.py — deterministic scoring for all three tasks +Run: pytest tests/test_phase2_graders.py -v +""" +import pytest +from app.env import GovWorkflowEnv +from app.graders import grade_episode +from app.models import ActionModel, ActionType + + +def run_episode_to_end(task_id: str, seed: int, max_steps: int = 500) -> GovWorkflowEnv: + """Run a full episode and return the env for grading.""" + env = GovWorkflowEnv(task_id=task_id) + env.reset(seed=seed) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(max_steps): + _, _, t, tr, _ = env.step(action) + if t or tr: + break + return env + + +class TestGraderEasy: + def test_grade_returns_result(self): + env = run_episode_to_end("district_backlog_easy", 42) + result = grade_episode(env.state()) + assert result is not None + + def test_grade_score_in_range(self): + env = run_episode_to_end("district_backlog_easy", 42) + result = grade_episode(env.state()) + assert 0.0 <= result.score <= 1.0 + + def test_grade_has_grader_name(self): + env = run_episode_to_end("district_backlog_easy", 42) + result = grade_episode(env.state()) + assert isinstance(result.grader_name, str) + assert len(result.grader_name) > 0 + + def test_grade_metrics_dict_nonempty(self): + env = run_episode_to_end("district_backlog_easy", 42) + result = grade_episode(env.state()) + assert isinstance(result.metrics, dict) + assert len(result.metrics) > 0 + + def test_grade_deterministic_same_seed(self): + env1 = run_episode_to_end("district_backlog_easy", 42) + env2 = run_episode_to_end("district_backlog_easy", 42) + r1 = grade_episode(env1.state()) + r2 = grade_episode(env2.state()) + assert abs(r1.score - r2.score) < 1e-6 + + def test_grade_metrics_all_floats(self): + env = run_episode_to_end("district_backlog_easy", 42) + result = grade_episode(env.state()) + for k, v in result.metrics.items(): + assert isinstance(v, (int, float)), f"Metric {k} is not numeric: {v}" + + +class TestGraderMedium: + def test_grade_score_in_range(self): + env = run_episode_to_end("mixed_urgency_medium", 123) + result = grade_episode(env.state()) + assert 0.0 <= result.score <= 1.0 + + def test_grade_different_grader_than_easy(self): + env_easy = run_episode_to_end("district_backlog_easy", 42) + env_med = run_episode_to_end("mixed_urgency_medium", 123) + r_easy = grade_episode(env_easy.state()) + r_med = grade_episode(env_med.state()) + # Different tasks may have different grader names + assert isinstance(r_med.grader_name, str) + + +class TestGraderHard: + def test_grade_score_in_range(self): + env = run_episode_to_end("cross_department_hard", 999, max_steps=800) + result = grade_episode(env.state()) + assert 0.0 <= result.score <= 1.0 + + def test_grade_has_fairness_metric(self): + env = run_episode_to_end("cross_department_hard", 999, max_steps=800) + result = grade_episode(env.state()) + # Hard task grader should include fairness-related metric + keys_lower = {k.lower() for k in result.metrics.keys()} + has_fairness = any("fair" in k for k in keys_lower) + assert has_fairness, f"Hard grader missing fairness metric. Keys: {result.metrics.keys()}" + + +class TestGraderScoreBounds: + @pytest.mark.parametrize("task_id,seed", [ + ("district_backlog_easy", 42), + ("mixed_urgency_medium", 123), + ("cross_department_hard", 999), + ]) + def test_score_always_in_zero_one(self, task_id, seed): + env = run_episode_to_end(task_id, seed) + result = grade_episode(env.state()) + assert 0.0 <= result.score <= 1.0, ( + f"{task_id}: score {result.score} out of [0, 1]" + ) + + @pytest.mark.parametrize("task_id,seed", [ + ("district_backlog_easy", 1), + ("district_backlog_easy", 2), + ("district_backlog_easy", 3), + ]) + def test_partial_episode_grades_without_error(self, task_id, seed): + env = GovWorkflowEnv(task_id=task_id) + env.reset(seed=seed) + action = ActionModel(action_type=ActionType.ADVANCE_TIME) + for _ in range(5): + env.step(action) + result = grade_episode(env.state()) + assert 0.0 <= result.score <= 1.0 diff --git a/tests/test_phase2_simulator.py b/tests/test_phase2_simulator.py new file mode 100644 index 0000000000000000000000000000000000000000..0e67ac062a6814bcb2797c42508338316c2d87cf --- /dev/null +++ b/tests/test_phase2_simulator.py @@ -0,0 +1,243 @@ +""" +tests/test_phase2_simulator.py +Phase 2: simulator.py — DaySimulator, case lifecycle, queue snapshots +Run: pytest tests/test_phase2_simulator.py -v +""" +import pytest +import random +from app.models import ( + ApplicationCase, ServiceType, InternalSubstate, IntakeChannel, + ScenarioMode, EventType, QueueSnapshot, +) +from app.event_engine import EventEngine +from app.tasks import get_task +from app.simulator import DaySimulator, DayResult + + +def make_simulator(task_id="district_backlog_easy", + seed=42) -> DaySimulator: + task = get_task(task_id) + rng = random.Random(seed) + engine = EventEngine(seed=seed, scenario_mode=task.scenario_mode) + return DaySimulator(task_config=task, rng=rng, event_engine=engine) + + +# ─── DayResult defaults ─────────────────────────────────────────────────────── +class TestDayResult: + def test_all_counters_zero(self): + r = DayResult() + assert r.new_arrivals == 0 + assert r.new_completions == 0 + assert r.stage_advances == 0 + assert r.new_sla_breaches == 0 + assert r.idle_officer_days == 0 + assert r.total_capacity_days == 0 + assert r.newly_unblocked_missing == 0 + assert r.urgent_completed == 0 + + def test_active_events_empty(self): + r = DayResult() + assert r.active_events == [] + + +# ─── DaySimulator construction ──────────────────────────────────────────────── +class TestDaySimulatorConstruction: + def test_simulator_initialises(self): + sim = make_simulator() + assert sim is not None + + def test_simulator_has_case_counter(self): + sim = make_simulator() + assert hasattr(sim, "case_counter") + assert sim.case_counter == 0 + + +# ─── simulate_day ───────────────────────────────────────────────────────────── +class TestSimulateDay: + def test_simulate_day_returns_day_result(self): + sim = make_simulator() + active, completed = [], [] + result = sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + assert isinstance(result, DayResult) + + def test_day_one_spawns_arrivals(self): + sim = make_simulator() + active, completed = [], [] + result = sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + assert result.new_arrivals > 0, "Day 1 should spawn new cases" + + def test_arrivals_added_to_active_list(self): + sim = make_simulator() + active, completed = [], [] + sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + assert len(active) > 0 + + def test_completed_cases_removed_from_active(self): + """Run enough days so some cases complete, verify no overlap.""" + sim = make_simulator() + active, completed = [], [] + for day in range(1, 40): + sim.simulate_day( + day=day, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + active_ids = {c.case_id for c in active} + completed_ids = {c.case_id for c in completed} + assert active_ids.isdisjoint(completed_ids), "Completed cases must not appear in active list" + + def test_total_capacity_days_equals_allocation(self): + sim = make_simulator() + active, completed = [], [] + result = sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + assert result.total_capacity_days == 8 + + def test_idle_officer_days_nonnegative(self): + sim = make_simulator() + active, completed = [], [] + result = sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + assert result.idle_officer_days >= 0 + + def test_idle_plus_work_equals_capacity(self): + sim = make_simulator() + active, completed = [], [] + result = sim.simulate_day( + day=1, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 4}, + ) + assert result.idle_officer_days + result.new_completions <= 4 + result.stage_advances + + def test_determinism_same_seed(self): + def run_days(seed): + sim = make_simulator(seed=seed) + active, completed = [], [] + arrivals = [] + for d in range(1, 6): + r = sim.simulate_day( + day=d, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 8}, + ) + arrivals.append(r.new_arrivals) + return arrivals + + assert run_days(42) == run_days(42) + + def test_sla_breaches_counted(self): + sim = make_simulator() + active, completed = [], [] + total_breaches = 0 + for day in range(1, 50): + r = sim.simulate_day( + day=day, active_cases=active, completed_cases=completed, + priority_mode=None, + officer_allocations={"income_certificate": 1}, # Low capacity → breaches + ) + total_breaches += r.new_sla_breaches + # Not guaranteed but with low capacity and 50 days, very likely + assert total_breaches >= 0 + + +# ─── build_queue_snapshot ────────────────────────────────────────────────────── +class TestBuildQueueSnapshot: + def _make_case(self, service, substate=InternalSubstate.PRE_SCRUTINY, + urgent=False, blocked=False, field=False): + case = ApplicationCase( + service_type=service, + arrival_day=0, + current_day=5, + sla_deadline_day=21, + is_urgent=urgent, + ) + case.internal_substate = substate + case.has_missing_docs = blocked + case.field_verification_required = field + return case + + def test_snapshot_service_type_correct(self): + sim = make_simulator() + snap = sim.build_queue_snapshot(ServiceType.INCOME_CERTIFICATE, [], day=1) + assert snap.service_type == ServiceType.INCOME_CERTIFICATE + + def test_snapshot_counts_pending_cases(self): + sim = make_simulator() + cases = [self._make_case(ServiceType.INCOME_CERTIFICATE) for _ in range(5)] + snap = sim.build_queue_snapshot(ServiceType.INCOME_CERTIFICATE, cases, day=1) + assert snap.total_pending == 5 + + def test_snapshot_counts_urgent_cases(self): + sim = make_simulator() + cases = [ + self._make_case(ServiceType.INCOME_CERTIFICATE, urgent=True), + self._make_case(ServiceType.INCOME_CERTIFICATE, urgent=False), + ] + snap = sim.build_queue_snapshot(ServiceType.INCOME_CERTIFICATE, cases, day=1) + assert snap.urgent_pending == 1 + + def test_snapshot_counts_blocked_missing_docs(self): + sim = make_simulator() + cases = [ + self._make_case(ServiceType.INCOME_CERTIFICATE, + substate=InternalSubstate.BLOCKED_MISSING_DOCS), + self._make_case(ServiceType.INCOME_CERTIFICATE), + ] + snap = sim.build_queue_snapshot(ServiceType.INCOME_CERTIFICATE, cases, day=1) + assert snap.blocked_missing_docs == 1 + + def test_snapshot_sla_risk_bounded(self): + sim = make_simulator() + cases = [self._make_case(ServiceType.INCOME_CERTIFICATE) for _ in range(3)] + snap = sim.build_queue_snapshot(ServiceType.INCOME_CERTIFICATE, cases, day=15) + assert 0.0 <= snap.current_sla_risk <= 1.0 + + +# ─── Case generation ───────────────────────────────────────────────────────── +class TestCaseGeneration: + def test_new_case_has_correct_service(self): + from app.event_engine import DayEventParams + sim = make_simulator() + params = DayEventParams() + case = sim._new_case(ServiceType.INCOME_CERTIFICATE, day=1, params=params) + assert case.service_type == ServiceType.INCOME_CERTIFICATE + + def test_new_case_arrival_day_set(self): + from app.event_engine import DayEventParams + sim = make_simulator() + params = DayEventParams() + case = sim._new_case(ServiceType.INCOME_CERTIFICATE, day=5, params=params) + assert case.arrival_day == 5 + + def test_new_case_sla_deadline_after_arrival(self): + from app.event_engine import DayEventParams + sim = make_simulator() + params = DayEventParams() + case = sim._new_case(ServiceType.INCOME_CERTIFICATE, day=1, params=params) + assert case.sla_deadline_day > case.arrival_day + + def test_new_case_has_valid_intake_channel(self): + from app.event_engine import DayEventParams + sim = make_simulator() + params = DayEventParams() + case = sim._new_case(ServiceType.INCOME_CERTIFICATE, day=1, params=params) + assert isinstance(case.intake_channel, IntakeChannel) diff --git a/tests/test_reward.py b/tests/test_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc650a5ab97d217bef87f0c46a10f81821866bf --- /dev/null +++ b/tests/test_reward.py @@ -0,0 +1,30 @@ +from app.reward import compute_reward + + +def test_stability_bonus_only_when_enabled() -> None: + r_enabled = compute_reward( + stage_advances=0, + completions=0, + active_backlog=0, + new_sla_breaches=0, + fairness_gap=0.0, + fairness_threshold=0.4, + invalid_action=False, + idle_capacity=0, + award_stability_bonus=True, + ) + r_disabled = compute_reward( + stage_advances=0, + completions=0, + active_backlog=0, + new_sla_breaches=0, + fairness_gap=0.0, + fairness_threshold=0.4, + invalid_action=False, + idle_capacity=0, + award_stability_bonus=False, + ) + + assert r_enabled.stability_bonus > 0.0 + assert r_disabled.stability_bonus == 0.0 + assert r_enabled.total_reward > r_disabled.total_reward diff --git a/tests/test_rl_evaluate.py b/tests/test_rl_evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..17ea03425b8ec2e9f700580dab484ff3ec9cf18d --- /dev/null +++ b/tests/test_rl_evaluate.py @@ -0,0 +1,122 @@ +"""Phase 3 tests for recurrent evaluation helpers.""" + +from __future__ import annotations + +import numpy as np + +import rl.evaluate as eval_mod +from rl.evaluate import TaskEvalResult, compare_recurrent_vs_flat, predict_recurrent_action + + +class _FakeRecurrentModel: + def __init__(self): + self.seen_states = [] + + def predict(self, obs, state=None, episode_start=None, deterministic=True): + self.seen_states.append(state) + if episode_start is not None and bool(np.asarray(episode_start).item()): + next_state = 0 + elif state is None: + next_state = 0 + else: + next_state = int(state) + 1 + return np.array([18]), next_state + + +def test_recurrent_policy_hidden_state_persists_across_steps() -> None: + model = _FakeRecurrentModel() + obs = np.zeros(4, dtype=np.float32) + masks = np.array([True] * 28) + + action_1, state_1 = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=None, + episode_start=np.array([False], dtype=bool), + masks=masks, + ) + action_2, state_2 = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=state_1, + episode_start=np.array([False], dtype=bool), + masks=masks, + ) + + assert action_1 == 18 + assert action_2 == 18 + assert state_1 == 0 + assert state_2 == 1 + assert model.seen_states == [None, 0] + + +def test_lstm_reset_on_episode_boundary() -> None: + model = _FakeRecurrentModel() + obs = np.zeros(4, dtype=np.float32) + masks = np.array([True] * 28) + + _, state_1 = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=5, + episode_start=np.array([False], dtype=bool), + masks=masks, + ) + _, state_2 = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=state_1, + episode_start=np.array([True], dtype=bool), + masks=masks, + ) + + assert state_1 == 6 + assert state_2 == 0 + + +def test_score_recurrent_geq_flat_ppo_on_medium(monkeypatch) -> None: + def _fake_evaluate_model(model_path, task_ids, n_episodes, verbose, model_type): + assert task_ids == ["mixed_urgency_medium"] + if model_type == "maskable": + score = 0.60 + else: + score = 0.63 + return [ + TaskEvalResult( + task_id="mixed_urgency_medium", + seed=22, + grader_score=score, + total_reward=200.0, + total_steps=50, + total_completed=40, + total_sla_breaches=20, + fairness_gap=0.1, + ) + ] + + monkeypatch.setattr(eval_mod, "evaluate_model", _fake_evaluate_model) + + comparison = compare_recurrent_vs_flat( + flat_model_path="flat.zip", + recurrent_model_path="recurrent.zip", + task_id="mixed_urgency_medium", + n_episodes=3, + ) + + assert comparison["recurrent"] >= comparison["flat"] + + +def test_invalid_action_prefers_advance_time_fallback() -> None: + model = _FakeRecurrentModel() + obs = np.zeros(4, dtype=np.float32) + masks = np.array([False] * 28) + masks[18] = True + + action, _ = predict_recurrent_action( + model=model, + obs=obs, + lstm_state=None, + episode_start=np.array([False], dtype=bool), + masks=masks, + ) + assert action == 18 diff --git a/tests/test_simulator_guardrails.py b/tests/test_simulator_guardrails.py new file mode 100644 index 0000000000000000000000000000000000000000..73173331c99edce6c9b0f42ec23e942cc922fe92 --- /dev/null +++ b/tests/test_simulator_guardrails.py @@ -0,0 +1,93 @@ +from app.models import ActionModel, ActionType +from app.simulator import LiveSimulationSession +from app.engine import _repair_action_for_observation + + +def test_reallocate_payload_is_repaired_to_valid_shape() -> None: + session = LiveSimulationSession( + task_id="district_backlog_easy", + agent_mode="baseline_policy", + max_steps=5, + seed=42, + ) + try: + raw = ActionModel(action_type=ActionType.REALLOCATE_OFFICERS) + fixed, note = _repair_action_for_observation(raw, session.obs) + # Repair should either keep REALLOCATE_OFFICERS with valid payload + # or fall back to a high-impact action + assert fixed.action_type in { + ActionType.REALLOCATE_OFFICERS, + ActionType.ADVANCE_TIME, + ActionType.REQUEST_MISSING_DOCUMENTS, + ActionType.ASSIGN_CAPACITY, + ActionType.ESCALATE_SERVICE, + } + if fixed.action_type == ActionType.REALLOCATE_OFFICERS: + # v2 uses reallocation_delta dict + assert fixed.reallocation_delta is not None + assert note is not None + finally: + session.close() + + +def test_assign_capacity_switches_to_advance_time_if_no_reserve() -> None: + session = LiveSimulationSession( + task_id="district_backlog_easy", + agent_mode="baseline_policy", + max_steps=5, + seed=42, + ) + try: + # Drain idle officers by filling allocated to match available + pool = session.obs.officer_pool + # Make idle_officers return 0 by maxing out allocations + total_alloc = sum(pool.allocated.values()) + remaining = pool.available_officers - total_alloc + if remaining > 0: + # Add remaining to first allocated service + first_key = next(iter(pool.allocated)) + pool.allocated[first_key] = pool.allocated[first_key] + remaining + + raw = ActionModel(action_type=ActionType.ASSIGN_CAPACITY, + capacity_assignment={"passport": 2}) + fixed, note = _repair_action_for_observation(raw, session.obs) + assert fixed.action_type in { + ActionType.ADVANCE_TIME, + ActionType.REQUEST_MISSING_DOCUMENTS, + ActionType.REALLOCATE_OFFICERS, + ActionType.ESCALATE_SERVICE, + } + assert note is not None + finally: + session.close() + + +def test_llm_mode_enforces_recommended_min_steps_for_hard_task() -> None: + session = LiveSimulationSession( + task_id="cross_department_hard", + agent_mode="llm_inference", + max_steps=20, + seed=42, + ) + try: + assert session.max_steps >= 70 + finally: + session.close() + + +def test_llm_step_core_handles_none_action_without_crash() -> None: + session = LiveSimulationSession( + task_id="district_backlog_easy", + agent_mode="llm_inference", + max_steps=10, + seed=11, + ) + try: + # Simulate a malformed llm policy output. + session.policy = lambda _obs: (None, {"decision_source": "llm", "provider": "test", "model_used": "bad"}) + row, _log, done = session.step_once() + assert isinstance(row, dict) + assert row["action_type"] in {a.value for a in ActionType} + assert isinstance(done, bool) + finally: + session.close() diff --git a/tests/test_simulator_import_smoke.py b/tests/test_simulator_import_smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..b9c7134fa01d91f1cfcaf8b4278dfbd263977a3e --- /dev/null +++ b/tests/test_simulator_import_smoke.py @@ -0,0 +1,7 @@ +def test_legacy_simulator_import_path_is_live(): + from app.simulator import LiveSimulationSession, SimulationAgentMode, run_simulation + + assert LiveSimulationSession is not None + assert SimulationAgentMode is not None + assert callable(run_simulation) + \ No newline at end of file diff --git a/tests/test_story_router.py b/tests/test_story_router.py new file mode 100644 index 0000000000000000000000000000000000000000..c45355154de040fc8af300358fb09c39d0e6ff13 --- /dev/null +++ b/tests/test_story_router.py @@ -0,0 +1,112 @@ +""" +tests/test_story_router.py +Tests for all 7 /training/* endpoints. +Requires: data/training_logs/mixed_urgency_medium_training_log.json +""" + +import pytest +from fastapi.testclient import TestClient +from app.main import app + +client = TestClient(app) +TASK = "mixed_urgency_medium" + + +def test_list_tasks(): + r = client.get("/training/tasks") + assert r.status_code == 200 + data = r.json() + assert "tasks" in data + assert isinstance(data["tasks"], list) + + +def test_summary(): + r = client.get(f"/training/summary/{TASK}") + assert r.status_code == 200 + data = r.json() + assert data["task_id"] == TASK + assert "summary" in data + assert "narrative" in data + assert "phase_1" in data["narrative"] + assert "phase_4" in data["narrative"] + + +def test_curve_full(): + r = client.get(f"/training/curve/{TASK}") + assert r.status_code == 200 + data = r.json() + assert "curve" in data + assert len(data["curve"]) > 0 + ep = data["curve"][0] + assert "episode" in ep + assert "reward" in ep + assert "score" in ep + assert "phase" in ep + + +def test_curve_downsample(): + r = client.get(f"/training/curve/{TASK}?downsample=5") + assert r.status_code == 200 + data = r.json() + assert data["total_points"] <= 100000 + + +def test_actions(): + r = client.get(f"/training/actions/{TASK}") + assert r.status_code == 200 + data = r.json() + assert "checkpoints" in data + assert len(data["checkpoints"]) == 5 + assert "insight" in data + + +def test_episode_first(): + r = client.get(f"/training/episode/{TASK}/1") + assert r.status_code == 200 + data = r.json() + assert data["episode"] == 1 + assert "reward" in data + assert "score" in data + assert "fn1_valid" in data + assert "fn2_no_halluc" in data + assert "fn3_env_score" in data + assert "message" in data + assert "running_best_reward" in data + + +def test_episode_last(): + # Get total to know last episode + summary = client.get(f"/training/summary/{TASK}").json() + total = summary["total_episodes"] + r = client.get(f"/training/episode/{TASK}/{total}") + assert r.status_code == 200 + + +def test_episode_out_of_range(): + r = client.get(f"/training/episode/{TASK}/99999") + assert r.status_code == 400 + + +def test_comparison(): + r = client.get(f"/training/comparison/{TASK}") + assert r.status_code == 200 + data = r.json() + assert "before" in data + assert "after" in data + assert "improvement" in data + assert "verdict" in data["improvement"] + assert data["before"]["score"] > 0 + assert data["after"]["score"] > 0 + + +def test_missing_task_404(): + r = client.get("/training/summary/nonexistent_task_xyz") + assert r.status_code == 404 + + +def test_stream_headers(): + # Test SSE endpoint returns correct content-type + with client.stream("GET", f"/training/stream/{TASK}?delay_ms=0") as r: + assert r.status_code == 200 + assert "text/event-stream" in r.headers["content-type"] + diff --git a/tests/test_tasks.py b/tests/test_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..39959e8ac8ed44ce5da4e16acdf2d51b867c801d --- /dev/null +++ b/tests/test_tasks.py @@ -0,0 +1,11 @@ +from app.tasks import get_task, list_tasks + +def test_core_tasks_present(): + """Verify the three core benchmark tasks are always present.""" + tasks = list_tasks() + for expected in ["cross_department_hard", "district_backlog_easy", "mixed_urgency_medium"]: + assert expected in tasks, f"Missing core task: {expected}" + assert len(tasks) >= 3 + +def test_task_determinism(): + assert get_task("mixed_urgency_medium").model_dump() == get_task("mixed_urgency_medium").model_dump() \ No newline at end of file diff --git a/tests/test_train_ppo_resume.py b/tests/test_train_ppo_resume.py new file mode 100644 index 0000000000000000000000000000000000000000..cfc212f62095d3a5f5dcc9ada72261d26299901e --- /dev/null +++ b/tests/test_train_ppo_resume.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +import os + +from rl.train_ppo import _resolve_checkpoint_path + + +def test_resolve_checkpoint_path_handles_direct_file(monkeypatch) -> None: + def _exists(path: str) -> bool: + return path == "results/best_model/phase1_final.zip" + + monkeypatch.setattr(os.path, "exists", _exists) + assert _resolve_checkpoint_path("results/best_model/phase1_final.zip") == "results/best_model/phase1_final.zip" + + +def test_resolve_checkpoint_path_adds_zip_suffix(monkeypatch) -> None: + def _exists(path: str) -> bool: + return path == "results/best_model/phase1_final.zip" + + monkeypatch.setattr(os.path, "exists", _exists) + assert _resolve_checkpoint_path("results/best_model/phase1_final") == "results/best_model/phase1_final.zip" + + +def test_resolve_checkpoint_path_returns_none_when_missing(monkeypatch) -> None: + monkeypatch.setattr(os.path, "exists", lambda _path: False) + assert _resolve_checkpoint_path("results/best_model/missing_model") is None