velocitatem/PHANTOM
1
0
Fork 0
mirror of https://github.com/velocitatem/PHANTOM.git synced 2026-05-31 16:43:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: consistent failure case

Browse Source
This commit is contained in:
Daniel Rosel
2026-01-24 15:16:41 +01:00
parent bae51daa1c
commit 4033e73ba1
3 changed files with 264 additions and 54 deletions
Download Patch File Download Diff File Expand all files Collapse all files

73
lab/case/thesis/simplified_env.py
View File

@@ -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,
}