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updaing coi definition

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This commit is contained in:
Daniel Rosel
2026-02-05 12:47:13 +01:00
parent ebd2378859
commit e44feb7da0
3 changed files with 27 additions and 29 deletions
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2
engine/lib/__init__.py
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@@ -4,4 +4,4 @@ from .render import DashboardRenderer, style_axis
from .wrappers import EconomicMetricsWrapper from .wrappers import EconomicMetricsWrapper
from .callbacks import MetricsCallback, EvalMetricsCallback from .callbacks import MetricsCallback, EvalMetricsCallback
from .providers import ProviderBenchmark, ProviderResult, BenchmarkConfig from .providers import ProviderBenchmark, ProviderResult, BenchmarkConfig
from .coi import compute_coi_leakage, compute_erosion_metrics, compute_agent_probability from .coi import compute_uplift_coi, extract_purchases, compute_agent_probability

2
engine/train.py
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@@ -31,7 +31,7 @@ model.save("phantom_sac")
wandb.finish() wandb.finish()
# test trained policy # test trained policy
env = PHANTOM(n_products=10, alpha=0.3, render_mode="human") env = PHANTOM(n_products=10, alpha=0.3, render_mode=None)
obs, _ = env.reset() obs, _ = env.reset()
for _ in range(100): for _ in range(100):
action, _ = model.predict(obs, deterministic=True) action, _ = model.predict(obs, deterministic=True)

52
engine/wrapper.py
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@@ -4,8 +4,8 @@ import numpy as np
from .engine import Limbo, MarketEngine, PricingEngine from .engine import Limbo, MarketEngine, PricingEngine
from .lib.render import DashboardRenderer from .lib.render import DashboardRenderer
from .lib.coi import ( from .lib.coi import (
compute_coi_leakage, compute_uplift_coi,
compute_erosion_metrics, extract_purchases,
compute_agent_probability, compute_agent_probability,
) )
from .lib.behavior import get_transition_models, trajectory_to_events from .lib.behavior import get_transition_models, trajectory_to_events
@@ -84,6 +84,7 @@ class PHANTOM(gym.Env):
self._renderer = None self._renderer = None
self._initial_episode_prices = None self._initial_episode_prices = None
self._trajectories = [] # session trajectories for agent prob calculation self._trajectories = [] # session trajectories for agent prob calculation
self.baseline_prices = np.full(self.n_products, self.price_bounds[0])
# load behavioral models for agent probability estimation # load behavioral models for agent probability estimation
try: try:
@@ -119,19 +120,30 @@ class PHANTOM(gym.Env):
all_events, self._human_trans, self._agent_trans all_events, self._human_trans, self._agent_trans
) )
def _compute_reward(self, prices: np.ndarray, demand: dict) -> float: def _compute_reward(self, prices: np.ndarray, demand: dict) -> tuple[float, dict]:
revenue = np.sum( revenue = sum(prices[i] * demand.get(i, 0.0) for i in range(self.n_products))
prices * np.array([demand.get(i, 0.0) for i in range(self.n_products)])
)
# compute agent probability from behavioral trajectories trajs_mix = self.market.last_trajectories
agent_prob = self._compute_agent_prob() purchases_mix = extract_purchases(trajs_mix)
coi_mix = compute_uplift_coi(prices, purchases_mix, self.baseline_prices)
# COI leakage: minimal implementation per thesis old_state = (self.market.alpha, self.market.Nagents, self.market.Nhumans)
coi_leakage = compute_coi_leakage(prices, agent_prob) self.market.alpha, self.market.Nagents, self.market.Nhumans = 0.0, 0, self.N
self.market.act(prices)
purchases_base = extract_purchases(self.market.last_trajectories)
coi_base = compute_uplift_coi(prices, purchases_base, self.baseline_prices)
self.market.alpha, self.market.Nagents, self.market.Nhumans = old_state
coi_leakage = max(0.0, coi_base - coi_mix)
coi_penalty = self.lambda_coi * coi_leakage coi_penalty = self.lambda_coi * coi_leakage
return float(revenue - coi_penalty) return float(revenue - coi_penalty), {
"revenue": float(revenue),
"coi_mix": float(coi_mix),
"coi_base": float(coi_base),
"coi_leakage": float(coi_leakage),
"coi_penalty": float(coi_penalty),
}
def _record_history(self): def _record_history(self):
demand_arr = np.array( demand_arr = np.array(
@@ -163,27 +175,13 @@ class PHANTOM(gym.Env):
self._trajectories.extend(self.market.last_trajectories) self._trajectories.extend(self.market.last_trajectories)
agent_prob = self._compute_agent_prob() agent_prob = self._compute_agent_prob()
coi_leakage = compute_coi_leakage(self._prices, agent_prob) reward, metrics = self._compute_reward(self._prices, self._demand)
reward = self._compute_reward(self._prices, self._demand)
terminated = self._step_count >= 100 terminated = self._step_count >= 100
# legacy erosion metrics for comparison
erosion = compute_erosion_metrics(
self._price_history,
self._demand_history,
self._initial_episode_prices,
self._prices,
self.price_bounds,
self.alpha,
self.coi_window,
)
info = { info = {
"step": self._step_count, "step": self._step_count,
"agent_prob": agent_prob, "agent_prob": agent_prob,
"coi_leakage": coi_leakage, **metrics,
"coi_penalty": self.lambda_coi * coi_leakage,
"erosion_metrics": erosion,
"raw_revenue": np.sum( "raw_revenue": np.sum(
self._prices self._prices
* np.array([self._demand.get(i, 0.0) for i in range(self.n_products)]) * np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])