feat: consistent failure case

lab/case/thesis/simplified.py

@@ -79,22 +79,175 @@ def estimate_alpha(session: Session, beta: float = 2.0) -> float:
     return 1.0 / (1.0 + np.exp(-beta * (dh - da))) if (dh + da) > 0 else 0.5
 
 
-def sample_trajectory(rng: np.random.Generator, trans: Dict, prices: np.ndarray, is_agent: bool) -> Tuple[List[Event], int]:
+@dataclass(frozen=True)
+class COIWindow:
+    """Windowed COI metrics computed from realized price exposures.
+
+    COI_policy is the definition-level KPI: E[p_shown] - p_min.
+    COI_agent is the theorem-level object: E[p^(1)] - p_min, where p^(1) is the minimum price realized under agent querying.
+    In this simplified simulator, p^(1) is approximated as the minimum price exposed to any agent in the window (per product).
+    Leak is the observable gap between them.
+    """
+
+    policy: float
+    agent: float
+    leak: float
+    survival_ratio: float
+    policy_by_product: np.ndarray
+    agent_by_product: np.ndarray
+    demand_weights: np.ndarray
+
+
+def _prices_by_product(sessions: List[Session]) -> Dict[int, List[float]]:
+    prices: Dict[int, List[float]] = {}
+    for s in sessions:
+        for e in s.events:
+            prices.setdefault(e.product_idx, []).append(float(e.price_seen))
+    return prices
+
+
+def _min_session_prices_by_product(sessions: List[Session]) -> Dict[int, List[float]]:
+    mins: Dict[int, List[float]] = {}
+    for s in sessions:
+        by_p: Dict[int, float] = {}
+        for e in s.events:
+            pidx = int(e.product_idx)
+            price = float(e.price_seen)
+            by_p[pidx] = price if pidx not in by_p else min(by_p[pidx], price)
+        for pidx, pmin in by_p.items():
+            mins.setdefault(pidx, []).append(pmin)
+    return mins
+
+
+def _min_price_across_sessions_by_product(sessions: List[Session]) -> Dict[int, float]:
+    mins: Dict[int, float] = {}
+    for s in sessions:
+        for e in s.events:
+            pidx = int(e.product_idx)
+            price = float(e.price_seen)
+            mins[pidx] = price if pidx not in mins else min(mins[pidx], price)
+    return mins
+
+
+def _demand_weights_by_product(
+    sessions: List[Session],
+    demand_mapping: Dict[str, float],
+    n_products: int,
+) -> np.ndarray:
+    w = np.zeros(n_products, dtype=float)
+    sessions_by_id = {s.sid: s for s in sessions}
+    for sid, q in demand_mapping.items():
+        sess = sessions_by_id.get(sid)
+        if not sess or not sess.events:
+            continue
+        pidx = int(sess.events[0].product_idx)
+        w[pidx] += float(q)
+    s = float(np.sum(w))
+    return (w / s) if s > 0 else w
+
+
+def compute_coi_window(
+    sessions: List[Session],
+    costs: np.ndarray,
+    demand_mapping: Dict[str, float] | None = None,
+) -> COIWindow:
+    n_products = int(len(costs))
+    prices = _prices_by_product(sessions)
+    agent_min_across = _min_price_across_sessions_by_product([s for s in sessions if s.actor == "A"])
+
+    policy_by_product = np.zeros(n_products, dtype=float)
+    agent_by_product = np.zeros(n_products, dtype=float)
+    seen = np.array([(i in prices) for i in range(n_products)], dtype=bool)
+    agent_seen = np.array([(i in agent_min_across) for i in range(n_products)], dtype=bool)
+
+    for pidx, ps in prices.items():
+        if 0 <= pidx < n_products and ps:
+            policy_by_product[pidx] = float(np.mean(ps) - float(costs[pidx]))
+
+    for pidx, pmin in agent_min_across.items():
+        if 0 <= pidx < n_products:
+            agent_by_product[pidx] = float(pmin - float(costs[pidx]))
+
+    # If no agent exposure exists for a product in the window, there is no realized erosion for that product.
+    agent_by_product[seen & ~agent_seen] = policy_by_product[seen & ~agent_seen]
+
+    demand_weights = (
+        _demand_weights_by_product(sessions, demand_mapping, n_products)
+        if demand_mapping is not None
+        else np.zeros(n_products, dtype=float)
+    )
+
+    has_weights = float(np.sum(demand_weights)) > 0
+    if has_weights:
+        policy = float(np.dot(demand_weights, policy_by_product))
+        agent = float(np.dot(demand_weights, agent_by_product))
+    else:
+        if not bool(np.any(seen)):
+            policy = 0.0
+            agent = 0.0
+        else:
+            policy = float(np.mean(policy_by_product[seen]))
+            agent = float(np.mean(agent_by_product[seen]))
+
+    leak = float(max(policy - agent, 0.0))
+    survival_ratio = float(np.clip(agent / policy, 0.0, 1.0)) if policy > 0 else 0.0
+
+    return COIWindow(
+        policy=policy,
+        agent=agent,
+        leak=leak,
+        survival_ratio=survival_ratio,
+        policy_by_product=policy_by_product,
+        agent_by_product=agent_by_product,
+        demand_weights=demand_weights,
+    )
+
+
+def sample_trajectory(
+    rng: np.random.Generator,
+    trans: Dict,
+    prices: np.ndarray,
+    costs: np.ndarray,
+    theta: Dict[str, float],
+    is_agent: bool,
+    session_price_noise: float = 0.02,
+    surge: float = 0.08,
+    max_markup_mult: float = 1.8,
+) -> Tuple[List[Event], int]:
     """Sample session trajectory from behavioral kernel."""
     state, t, pidx = "start", 0.0, int(rng.integers(0, len(prices)))
+    cost = float(costs[pidx])
+    base_price = float(prices[pidx]) * float(1.0 + rng.normal(0.0, session_price_noise))
+    base_price = float(np.clip(base_price, cost * 1.01, float(prices[pidx]) * 2.0))
+    current_price = base_price
+    signal = 0.0
     events = []
+    # TODO: instead of this very controlled setup implement same session samplin as in models.py
     while state != "end" and len(events) < 30:
-        if state != "start":
-            events.append(Event(action=state, product_idx=pidx, price_seen=float(prices[pidx]), ts=t))
         probs = trans.get(state, {"end": 1.0})
-        state = rng.choice(list(probs.keys()), p=list(probs.values()))
+        nxt = rng.choice(list(probs.keys()), p=list(probs.values()))
+
+        if nxt == "purchase":
+            price_sens = float(theta.get("price_sens", 2.0))
+            base_conv = float(theta.get("base_conv", 0.2))
+            rel = max((current_price - cost) / (cost + 1e-6), 0.0)
+            p_buy = float(np.clip(base_conv * np.exp(-price_sens * rel), 0.0, 1.0))
+            if rng.random() > p_buy:
+                nxt = "end"
+
+        state = nxt
+        if state not in {"start", "end"}:
+            events.append(Event(action=state, product_idx=pidx, price_seen=float(current_price), ts=t))
+            signal += float(ACTION_WEIGHTS.get(state, 0.1))
+            current_price = float(np.clip(base_price * (1.0 + surge * signal), cost * 1.01, base_price * max_markup_mult))
+
         t += max(0.2, rng.gamma(1.5, 0.8) if is_agent else rng.gamma(2.0, 1.2))
     return events, pidx
 
 
-def put_prices_to_market(prices: np.ndarray, alpha: float = 0.2, n_sessions: int = 50,
+def put_prices_to_market(prices: np.ndarray, costs: np.ndarray, alpha: float = 0.2, n_sessions: int = 50,
                          seed: int | None = None) -> Tuple[List[Session], Dict[str, float]]:
-    """Generate sessions from mixture model Q(p) = (1-α)E[d_H] + αE[d_A] (Eq 3).
+    """Generate sessions from mixture model
 
     Returns:
         sessions: list of Session objects with events and product attribution
@@ -108,7 +261,7 @@ def put_prices_to_market(prices: np.ndarray, alpha: float = 0.2, n_sessions: int
         is_agent = rng.random() < alpha
         trans = TRANS_A if is_agent else TRANS_H
         theta = {"price_sens": rng.uniform(0.05, 0.2), "base_conv": 0.01} if is_agent else {"price_sens": rng.uniform(1.5, 4.0), "base_conv": rng.uniform(0.2, 0.5)}
-        events, _ = sample_trajectory(rng, trans, prices, is_agent)
+        events, _ = sample_trajectory(rng, trans, prices, costs=costs, theta=theta, is_agent=is_agent)
         session = Session(sid=sid, events=events, actor="A" if is_agent else "H", theta=theta)
         sessions.append(session)
         demand_mapping[sid] = compute_demand(session)
@@ -167,6 +320,8 @@ class System:
         self.limbo = Limbo()
         self._alpha_est = 0.2  # current contamination estimate
         self._sessions: List[Session] = []
+        self._last_sessions: List[Session] = []
+        self._last_coi: COIWindow | None = None
 
     @property
     def alpha(self) -> float:
@@ -190,24 +345,27 @@ class System:
                     agg_demand[pidx] += q
         return float(np.dot(prices, agg_demand))
 
-    def _coi_leakage(self, prices: np.ndarray, n_agents: int = 1) -> float:
-        """COI leakage tied to Theorem 1: erosion from order statistic collapse.
-
-        As N agents query, min(p_1..p_N) → p_min and COI → 0.
-        Leakage = erosion_rate × margin_at_risk
-        """
-        price_std = float(np.std(prices))
-        erosion = coi_erosion(max(1, n_agents), price_std)
-        margin_at_risk = float(np.mean(prices - self.costs))
-        return erosion * margin_at_risk
+    def _compute_coi_window(self, demand: Dict[str, float]) -> COIWindow:
+        if not self._last_sessions:
+            zeros = np.zeros(self.n, dtype=float)
+            return COIWindow(
+                policy=0.0,
+                agent=0.0,
+                leak=0.0,
+                survival_ratio=0.0,
+                policy_by_product=zeros,
+                agent_by_product=zeros,
+                demand_weights=zeros,
+            )
+        return compute_coi_window(self._last_sessions, self.costs, demand_mapping=demand)
 
     def _objective(self, prices: np.ndarray, demand: Dict[str, float]) -> float:
         """Robust objective: R(p,d) - λ·COI_leak (Eq 23 simplified)."""
         revenue = self._revenue_under_demand(prices, demand)
         cost = float(np.sum(self.costs))  # fixed cost approximation
         profit = revenue - cost
-        coi_penalty = self.lambda_coi * self._coi_leakage(prices) * float(np.mean(prices - self.costs))
-        return profit - coi_penalty
+        self._last_coi = self._compute_coi_window(demand)
+        return profit - self.lambda_coi * self._last_coi.leak
 
     def compute_prices(self, demand: Dict[str, float] | None = None) -> np.ndarray:
         """Compute next prices via simple gradient-like update on robust objective.
@@ -230,28 +388,44 @@ class System:
 
     def observe_demand(self, prices: np.ndarray, alpha_true: float = 0.2, n_sessions: int = 50) -> Dict[str, float]:
         """Observe market response to prices."""
-        sessions, demand_map = put_prices_to_market(prices, alpha=alpha_true, n_sessions=n_sessions, seed=int(self.rng.integers(0, 10000)))
+        sessions, demand_map = put_prices_to_market(prices, costs=self.costs, alpha=alpha_true, n_sessions=n_sessions, seed=int(self.rng.integers(0, 10000)))
+        self._last_sessions = sessions
         self._sessions.extend(sessions)  # store actual sessions for correct product attribution
         self.limbo.add_update("demand", demand_map)
         return demand_map
 
-    def step(self, alpha_true: float = 0.2, n_sessions: int = 50) -> Tuple[np.ndarray, Dict[str, float], float]:
+    def step(self, alpha_true: float = 0.2, n_sessions: int = 50) -> Tuple[np.ndarray, Dict[str, float], float, COIWindow]:
         """Single simulation step: prices -> demand -> reward."""
         demand_hist = self.limbo.get_demand_history()
         prices = self.compute_prices(demand_hist[-1] if demand_hist else None)
         demand = self.observe_demand(prices, alpha_true, n_sessions)
         reward = self._objective(prices, demand)
-        return prices, demand, reward
+        coi = self._last_coi or self._compute_coi_window(demand)
+        return prices, demand, reward, coi
 
     def run(self, n_steps: int = 100, alpha_true: float = 0.2) -> Dict:
         """Run simulation for n_steps, return trajectory."""
-        trajectory = {"prices": [], "demand": [], "rewards": [], "alpha_est": [], "alpha_true": alpha_true}
+        trajectory = {
+            "prices": [],
+            "demand": [],
+            "rewards": [],
+            "alpha_est": [],
+            "alpha_true": alpha_true,
+            "coi_policy": [],
+            "coi_agent": [],
+            "coi_leak": [],
+            "coi_survival": [],
+        }
         for _ in range(n_steps):
-            p, d, r = self.step(alpha_true)
+            p, d, r, coi = self.step(alpha_true)
             trajectory["prices"].append(p)
             trajectory["demand"].append(d)
             trajectory["rewards"].append(r)
             trajectory["alpha_est"].append(self._alpha_est)
+            trajectory["coi_policy"].append(coi.policy)
+            trajectory["coi_agent"].append(coi.agent)
+            trajectory["coi_leak"].append(coi.leak)
+            trajectory["coi_survival"].append(coi.survival_ratio)
         return trajectory
 
 
@@ -268,10 +442,17 @@ if __name__ == "__main__":
     # quick demo
     sys = System(n_products=5, seed=42)
     traj = sys.run(n_steps=20, alpha_true=0.25)
-    print(f"avg reward: {np.mean(traj['rewards']):.2f}, final α̂: {traj['alpha_est'][-1]:.3f}")
+    print(
+        f"avg reward: {np.mean(traj['rewards']):.2f}, "
+        f"final α̂: {traj['alpha_est'][-1]:.3f}, "
+        f"COI_policy: {np.mean(traj['coi_policy']):.3f}, "
+        f"COI_agent: {np.mean(traj['coi_agent']):.3f}, "
+        f"leak: {np.mean(traj['coi_leak']):.3f}"
+    )
 
     prices = np.array([20.0, 35.0, 50.0, 25.0, 40.0])
-    sessions, demand = put_prices_to_market(prices, alpha=0.3, n_sessions=20, seed=123)
+    costs = np.array([15.0, 28.0, 40.0, 18.0, 30.0])
+    sessions, demand = put_prices_to_market(prices, costs=costs, alpha=0.3, n_sessions=20, seed=123)
     print(f'sessions: {len(sessions)}, agents: {sum(1 for s in sessions if s.actor=="A")}')
 
     for n in [1, 5, 10, 50, 100]:

lab/case/thesis/simplified_env.py

@@ -19,8 +19,19 @@ try:
 except ImportError:
     HAS_GYM = False
 
-from .simplified import (System, Session, Event, Limbo, put_prices_to_market,
-                         compute_demand, estimate_alpha, coi_erosion, TRANS_H, TRANS_A)
+from .simplified import (
+    System,
+    Session,
+    Event,
+    Limbo,
+    put_prices_to_market,
+    compute_coi_window,
+    compute_demand,
+    estimate_alpha,
+    coi_erosion,
+    TRANS_H,
+    TRANS_A,
+)
 
 
 @dataclass
@@ -116,9 +127,19 @@ class PricingEnv(gym.Env if HAS_GYM else object):
                 agg[pidx] += q
         self._demand_agg = agg
 
-        revenue = float(np.dot(prices, agg))
-        cost = float(np.dot(sys.costs, np.clip(agg, 0, 1)))  # simplified cost model
-        profit = revenue - cost
+        revenue = 0.0
+        cost = 0.0
+        purchases = np.zeros(self.n, dtype=float)
+        for sess in sys._last_sessions:
+            for e in sess.events:
+                if e.action != "purchase":
+                    continue
+                pidx = int(e.product_idx)
+                if 0 <= pidx < self.n:
+                    purchases[pidx] += 1.0
+                    revenue += float(e.price_seen)
+                    cost += float(sys.costs[pidx])
+        profit = float(revenue - cost)
 
         # volatility penalty (price changes)
         vol_penalty = 0.0
@@ -126,9 +147,8 @@ class PricingEnv(gym.Env if HAS_GYM else object):
             price_change = np.abs(prices - self._last_prices) / (sys.refs + 1e-6)
             vol_penalty = cfg.lambda_vol * float(np.mean(price_change))
 
-        # COI leakage penalty
-        avg_margin = float(np.mean(prices - sys.costs))
-        coi_leak = sys.alpha * avg_margin
+        coi = compute_coi_window(sys._last_sessions, sys.costs, demand_mapping=demand)
+        coi_leak = float(coi.leak)
 
         if cfg.reward_mode == "revenue":
             r = revenue
@@ -181,11 +201,11 @@ class PricingEnv(gym.Env if HAS_GYM else object):
         prices = self._sys.refs * action.astype(np.float64)
         prices = np.clip(prices, self._sys.costs * 1.01, self._sys.refs * 2.0)
 
-        # drift contamination
-        if self.cfg.alpha_drift != 0:
-            self._alpha = np.clip(
-                self._alpha + self.cfg.alpha_drift * self._sys.rng.normal(),
-                *self.cfg.alpha_bounds)
+        # # drift contamination
+        # if self.cfg.alpha_drift != 0:
+        #     self._alpha = np.clip(
+        #         self._alpha + self.cfg.alpha_drift * self._sys.rng.normal(),
+        #         *self.cfg.alpha_bounds)
 
         # observe demand
         demand = self._sys.observe_demand(prices, alpha_true=self._alpha, n_sessions=self.cfg.sessions_per_step)
@@ -205,25 +225,38 @@ class PricingEnv(gym.Env if HAS_GYM else object):
         truncated = False
 
         # compute metrics for tracking
-        revenue = float(np.dot(prices, self._demand_agg))
-        cost = float(np.dot(self._sys.costs, np.clip(self._demand_agg, 0, 1)))
-        profit = revenue - cost
+        revenue = 0.0
+        cost = 0.0
+        n_purchases = 0
+        for sess in self._sys._last_sessions:
+            for e in sess.events:
+                if e.action != "purchase":
+                    continue
+                n_purchases += 1
+                revenue += float(e.price_seen)
+                cost += float(self._sys.costs[int(e.product_idx)])
+        profit = float(revenue - cost)
         n_agents = int(self._alpha * self.cfg.sessions_per_step)
         price_std = float(np.std(prices))
+        coi = compute_coi_window(self._sys._last_sessions, self._sys.costs, demand_mapping=demand)
 
         info = {
             "alpha_true": self._alpha,
             "alpha_est": self._sys.alpha,
             "alpha_error": abs(self._alpha - self._sys.alpha),
-            "revenue": revenue,
-            "profit": profit,
-            "cost": cost,
+            "revenue": float(revenue),
+            "profit": float(profit),
+            "cost": float(cost),
+            "n_purchases": int(n_purchases),
             "avg_margin": float(np.mean((prices - self._sys.costs) / self._sys.costs)),
             "n_sessions": len(demand),
             "n_agents": n_agents,
             "price_std": price_std,
             "coi_erosion": coi_erosion(max(1, n_agents), price_std),
-            "coi_leakage": self._sys.alpha * float(np.mean(prices - self._sys.costs)),
+            "coi_policy": float(coi.policy),
+            "coi_agent": float(coi.agent),
+            "coi_leakage": float(coi.leak),
+            "coi_survival": float(coi.survival_ratio),
             "cumulative_reward": sum(self._episode_rewards),
             "step": self._t,
         }

lab/case/thesis/train.py

@@ -2,11 +2,6 @@
 
 Trains pricing policies using stable-baselines3 with TensorBoard logging.
 Tracks COI erosion, alpha estimation error, and economic KPIs per thesis formulation.
-
-Usage:
-    python -m lab.case.thesis.train --algo ppo --alpha 0.3 --steps 100000
-    python -m lab.case.thesis.train --algo adaptive --sweep  # run alpha sweep
-    tensorboard --logdir lab/case/thesis/runs
 """
 from __future__ import annotations
 import argparse
@@ -41,9 +36,9 @@ class EpisodeMetrics:
     reward: float = 0.0
     revenue: float = 0.0
     profit: float = 0.0
-    coi_erosion: float = 0.0      # theorem 1: order statistic erosion
-    coi_leakage: float = 0.0      # per-step leakage penalty
-    alpha_error: float = 0.0      # |α - α̂|
+    coi_erosion: float = 0.0
+    coi_leakage: float = 0.0
+    alpha_error: float = 0.0
     avg_margin: float = 0.0
     n_agents: int = 0
     steps: int = 0
@@ -213,6 +208,7 @@ def train(cfg: ExperimentConfig) -> Dict[str, Any]:
         if algo_cls is None:
             raise ValueError(f"unknown algo: {cfg.algo}")
         common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto")
+        # TODO: setup hyper parameter passing to train different variations (no free lunch)
         if cfg.algo.lower() == "ppo":
             model = PPO("MlpPolicy", train_env, learning_rate=3e-4, n_steps=2048,
                         batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95,