app/env/env_core.py

"""Core PolyGuard environment implementation."""

from __future__ import annotations

import time
import uuid
import os
from pathlib import Path
from typing import Optional

from app.common.constants import (
    DEFAULT_EPISODE_TIMEOUT_SECONDS,
    DEFAULT_MAX_STEPS,
    DEFAULT_SEED,
    DEFAULT_STEP_TIMEOUT_SECONDS,
)
from app.common.enums import Difficulty, SubEnvironment
from app.common.seeding import set_global_seed
from app.common.types import (
    CandidateAction,
    PolyGuardAction,
    PolyGuardObservation,
    PolyGuardState,
    RewardBreakdown,
    StepTrace,
    UncertaintyReport,
)
from app.env.anti_cheat import evaluate_anti_cheat
from app.env.curriculum import pick_difficulty, pick_sub_environment
from app.env.reward_router import compute_reward_breakdown
from app.env.scenario_loader import load_or_generate_scenario
from app.env.termination import check_termination_with_timeout
from app.env.transition import apply_transition
from app.env.verifier import verify_action_legality
from app.knowledge.ddi_knowledge import top_risky_pairs
from app.models.policy.candidate_builder import build_candidates
from app.models.policy.uncertainty import estimate_uncertainty


class PolyGuardEnv:
    def __init__(self, root: Optional[Path] = None) -> None:
        self.root = root or Path(__file__).resolve().parents[2]
        self._episode_index = 0
        self._state: Optional[PolyGuardState] = None
        self._trace: list[StepTrace] = []
        self._last_reward: Optional[RewardBreakdown] = None
        self._episode_started_at: float = 0.0
        self._episode_timeout_seconds: float = float(
            os.getenv("POLYGUARD_EPISODE_TIMEOUT_SECONDS", str(DEFAULT_EPISODE_TIMEOUT_SECONDS))
        )
        self._step_timeout_seconds: float = float(
            os.getenv("POLYGUARD_STEP_TIMEOUT_SECONDS", str(DEFAULT_STEP_TIMEOUT_SECONDS))
        )

    @property
    def state(self) -> PolyGuardState:
        if self._state is None:
            raise RuntimeError("Environment has not been reset.")
        return self._state

    def reset(
        self,
        seed: Optional[int] = None,
        difficulty: Optional[str] = None,
        sub_environment: Optional[str] = None,
        scenario_id: Optional[str] = None,
        patient_id: Optional[str] = None,
    ) -> PolyGuardObservation:
        run_seed = set_global_seed(seed if seed is not None else DEFAULT_SEED)
        diff = Difficulty(difficulty) if difficulty else pick_difficulty(self._episode_index)
        if sub_environment:
            chosen_sub_environment = SubEnvironment(sub_environment)
        else:
            chosen_sub_environment = pick_sub_environment(self._episode_index, diff)
        patient = load_or_generate_scenario(
            root=self.root,
            difficulty=diff,
            scenario_id=scenario_id,
            patient_id=patient_id,
            seed=run_seed,
        )
        scenario_key = scenario_id or patient.patient_id
        max_steps = {
            SubEnvironment.DDI: 3,
            SubEnvironment.REGIMEN_RISK: 6,
            SubEnvironment.BANDIT_MINING: 6,
            SubEnvironment.PRECISION_DOSING: 8,
            SubEnvironment.LONGITUDINAL_DEPRESCRIBING: 10,
            SubEnvironment.WEB_SEARCH_MISSING_DATA: 5,
            SubEnvironment.ALTERNATIVE_SUGGESTION: 6,
            SubEnvironment.NEW_DRUG_DECOMPOSITION: 7,
        }.get(chosen_sub_environment, {
            Difficulty.EASY: 3,
            Difficulty.MEDIUM: 6,
            Difficulty.HARD: 10,
        }.get(diff, DEFAULT_MAX_STEPS))
        risky_pairs = top_risky_pairs([m.drug for m in patient.medications])
        self._state = PolyGuardState(
            episode_id=f"ep_{uuid.uuid4().hex[:8]}",
            seed=run_seed,
            scenario_id=scenario_key,
            difficulty=diff,
            sub_environment=chosen_sub_environment,
            step_count=0,
            max_steps=max_steps,
            patient=patient,
            risk_summary={
                "polypharmacy_count": float(len(patient.medications)),
                "burden_score": len(patient.medications) / 12.0,
                "severe_pair_count": float(len(risky_pairs)),
            },
            burden_score=min(1.0, len(patient.medications) / 12.0),
            precision_dosing_flags=["dose_sensitive_case"] if chosen_sub_environment == SubEnvironment.PRECISION_DOSING else [],
            unresolved_conflicts=list(patient.specialist_conflicts),
        )
        self._trace = []
        self._last_reward = None
        self._episode_started_at = time.monotonic()
        self._episode_index += 1
        obs = self._build_observation()
        self._trace.append(
            StepTrace(
                step=0,
                observation_snapshot=obs,
                reward_components={},
            )
        )
        return obs

    def _build_observation(self) -> PolyGuardObservation:
        state = self.state
        candidates = build_candidates(state)
        uncertainty = estimate_uncertainty(state)
        risky_pairs = top_risky_pairs([m.drug for m in state.patient.medications])
        warning_summary: list[str] = []
        if state.burden_score >= 0.7:
            warning_summary.append("high_polypharmacy_burden")
        if state.patient.monitoring_gaps:
            warning_summary.extend([f"monitoring_gap:{gap}" for gap in state.patient.monitoring_gaps[:2]])
        if state.sub_environment == SubEnvironment.WEB_SEARCH_MISSING_DATA:
            warning_summary.append("missing_data_web_evidence_recommended")
        if state.sub_environment == SubEnvironment.NEW_DRUG_DECOMPOSITION:
            warning_summary.append("new_drug_component_analysis_recommended")
        return PolyGuardObservation(
            patient_summary={
                "patient_id": state.patient.patient_id,
                "age": state.patient.age,
                "sex": state.patient.sex,
                "adherence_estimate": state.patient.adherence_estimate,
                "sub_environment": state.sub_environment.value,
            },
            medication_table=[m.model_dump(mode="json") for m in state.patient.medications],
            comorbidity_summary=state.patient.comorbidities,
            organ_function_summary={
                "egfr": state.patient.labs.egfr,
                "ast": state.patient.labs.ast,
                "alt": state.patient.labs.alt,
            },
            labs_vitals_summary={**state.patient.labs.model_dump(mode="json"), **state.patient.vitals},
            graph_safety_summary={
                "top_risk_pairs": risky_pairs,
                "polypharmacy_count": len(state.patient.medications),
                "estimated_risk": state.risk_summary.get("burden_score", 0.5),
            },
            burden_score_summary={"burden_score": state.burden_score},
            precision_dosing_flags=state.precision_dosing_flags,
            unresolved_conflicts=state.unresolved_conflicts,
            candidate_action_set=candidates,
            step_budget_remaining=max(0, state.max_steps - state.step_count),
            action_history=state.action_history,
            warning_summary=warning_summary,
            abstention_indicators={"uncertainty": uncertainty, "recommended": uncertainty > 0.65},
            sub_environment=state.sub_environment,
            deterministic_contract={
                "seed": state.seed,
                "scenario_id": state.scenario_id,
                "difficulty": state.difficulty.value,
                "sub_environment": state.sub_environment.value,
            },
        )

    @staticmethod
    def _action_from_payload(action: PolyGuardAction | dict) -> PolyGuardAction:
        if isinstance(action, PolyGuardAction):
            return action
        if not isinstance(action, dict):
            raise ValueError("Action must be a PolyGuardAction or dictionary payload.")
        try:
            return PolyGuardAction.model_validate(action)
        except Exception:  # noqa: BLE001
            candidate = CandidateAction.model_validate(action)
            return PolyGuardAction(
                mode=candidate.mode,
                action_type=candidate.action_type,
                target_drug=candidate.target_drug,
                replacement_drug=candidate.replacement_drug,
                dose_bucket=candidate.dose_bucket,
                taper_days=candidate.taper_days,
                monitoring_plan=candidate.monitoring_plan,
                evidence_query=candidate.evidence_query,
                new_drug_name=candidate.new_drug_name,
                candidate_components=candidate.candidate_components,
                candidate_id=candidate.candidate_id,
                confidence=max(0.45, 1.0 - candidate.uncertainty_score),
                rationale_brief=f"Candidate-selected action ({','.join(candidate.rationale_tags[:2]) or 'rule'})",
            )

    def step(self, action: PolyGuardAction | dict) -> tuple[PolyGuardObservation, float, bool, dict]:
        step_started_at = time.monotonic()
        state = self.state
        parsed = self._action_from_payload(action)
        pre_burden = state.burden_score
        pre_risky_pairs = len(top_risky_pairs([m.drug for m in state.patient.medications]))
        safety_report = verify_action_legality(state, parsed)
        legal_candidate_ids = {c.candidate_id for c in build_candidates(state)}
        anti_cheat = evaluate_anti_cheat(state, parsed, legal_candidate_ids=legal_candidate_ids)

        if safety_report.legal and not anti_cheat.exploit_detected:
            transition_delta = apply_transition(state, parsed)
        else:
            transition_delta = {
                "applied": False,
                "reason": safety_report.violations or anti_cheat.reasons or ["blocked"],
                "rolled_back": True,
            }
            state.action_history.append({"step": state.step_count, "action": parsed.model_dump(mode="json"), "applied": False})
            state.step_count += 1

        uncertainty_report = self.get_uncertainty_report()
        reward = compute_reward_breakdown(
            state=state,
            action=parsed,
            safety_report=safety_report,
            anti_cheat_detected=anti_cheat.exploit_detected,
            uncertainty=uncertainty_report.overall_uncertainty,
            pre_burden=pre_burden,
            pre_risky_pairs=pre_risky_pairs,
        )
        self._last_reward = reward
        state.cumulative_reward += reward.total_reward

        elapsed = time.monotonic() - self._episode_started_at
        done, reason = check_termination_with_timeout(
            state=state,
            action=parsed,
            exploit_detected=anti_cheat.exploit_detected,
            elapsed_seconds=elapsed,
            wall_clock_limit_seconds=self._episode_timeout_seconds,
        )
        step_elapsed = time.monotonic() - step_started_at
        step_timeout = step_elapsed >= self._step_timeout_seconds
        if step_timeout and not done:
            done = True
            reason = "step_timeout"

        state.done = done
        invalid_action_count = sum(1 for item in state.action_history if item.get("applied") is False)
        transition_failures = transition_delta.get("reason", [])
        if isinstance(transition_failures, str):
            transition_failures = [transition_failures]
        failure_reasons = list(dict.fromkeys([*safety_report.violations, *anti_cheat.reasons, *transition_failures]))
        observation = self._build_observation()
        self._trace.append(
            StepTrace(
                step=state.step_count,
                observation_snapshot=observation,
                selected_action=parsed,
                critic_output={"safety_report": safety_report.model_dump(mode="json"), "anti_cheat": anti_cheat.reasons},
                reward_components=reward.model_dump(mode="json"),
                transition_delta=transition_delta,
                uncertainty_report=uncertainty_report,
                failure_reasons=failure_reasons,
                timeout=bool(step_timeout or reason == "wall_clock_timeout"),
            )
        )
        info = {
            "termination_reason": reason,
            "safety_report": safety_report.model_dump(mode="json"),
            "anti_cheat_reasons": anti_cheat.reasons,
            "reward_breakdown": reward.model_dump(mode="json"),
            "primary_reward_channels": {
                "safety_legality": reward.primary_safety_legality,
                "clinical_improvement": reward.primary_clinical_improvement,
                "dosing_quality": reward.primary_dosing_quality,
                "process_integrity": reward.primary_process_integrity,
            },
            "failure_reasons": failure_reasons,
            "transition_delta": transition_delta,
            "step_timeout": step_timeout,
            "episode_elapsed_seconds": round(elapsed, 3),
            "step_elapsed_seconds": round(step_elapsed, 3),
            "invalid_action_count": invalid_action_count,
            "checks": {
                "anti_cheat": bool(anti_cheat.reasons),
                "timeout": bool(step_timeout or reason == "wall_clock_timeout"),
                "parser_exploit": "parser_exploit_pattern" in anti_cheat.reasons,
                "legality_gate": bool(safety_report.legal),
            },
        }
        return observation, reward.total_reward, done, info

    def get_state(self) -> dict:
        return self.state.model_dump(mode="json")

    def get_reward_breakdown(self) -> dict:
        return self._last_reward.model_dump(mode="json") if self._last_reward else {}

    def get_trace(self) -> list[dict]:
        return [item.model_dump(mode="json") for item in self._trace]

    def get_legal_actions(self) -> list[dict]:
        obs = self._build_observation()
        return [
            self._action_from_payload(candidate.model_dump(mode="json")).model_dump(mode="json")
            for candidate in obs.candidate_action_set
            if candidate.legality_precheck
        ]

    def get_candidate_actions(self) -> list[dict]:
        obs = self._build_observation()
        return [candidate.model_dump(mode="json") for candidate in obs.candidate_action_set]

    def get_metadata(self) -> dict[str, object]:
        return {
            "name": "polyguard-openenv",
            "description": (
                "Polypharmacy safety and optimization environment with constrained "
                "actions, reward decomposition, and OpenEnv-compatible APIs."
            ),
            "version": "0.2.0",
            "openenv_mode": "simulation",
            "reward_range": [0.001, 0.999],
            "reward_precision": 3,
            "action_schema": "PolyGuardAction (strict)",
            "observation_schema": "PolyGuardObservation",
            "state_schema": "PolyGuardState",
            "step_timeout_seconds": self._step_timeout_seconds,
            "episode_timeout_seconds": self._episode_timeout_seconds,
        }

    def get_uncertainty_report(self) -> UncertaintyReport:
        state = self.state
        uncertainty = estimate_uncertainty(state)
        missing_flags: list[str] = []
        if state.patient.labs.egfr is None:
            missing_flags.append("missing_egfr")
        if state.patient.labs.ast is None or state.patient.labs.alt is None:
            missing_flags.append("missing_liver_enzymes")
        return UncertaintyReport(
            overall_uncertainty=uncertainty,
            missing_data_flags=missing_flags,
            abstention_recommended=uncertainty > 0.65,
        )