chore: better wrapping amd more performant

engine/lib/behavior.py

@@ -6,33 +6,32 @@ from .demand import generate_demand
 base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
 human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
 
+_cache = {}  # lazy cache for models and base pivots
+
+def _get_base_pivot(human: bool):
+    key = 'human' if human else 'agent'
+    if key not in _cache:
+        model = BehaviorModel(human_dir) if human else AgentBehaviorModel(agent_dir)
+        mdp = model.build_MDP()
+        _cache[key] = pd.DataFrame(aggregate_event_transitions(mdp)).fillna(0.0)
+    return _cache[key]
 
 def adjust_behavior_to_condition(condition, transition_matrix):
-    # transition matrix just maps probability of eventA to eventB
-    # we enhance this that eventA-productI to eventB-productJ... based on the condition of interest
-    # this is to simulate the impact of demand onto the behavior
-    # NxN -> (N*(P + 1))x(N*(P + 1)) where P is number of products
-    new_transitions = transition_matrix.copy()
-    for col in new_transitions.columns:
-        for product in range(len(condition)):
-            # adjust the transition probability based on the demand condition
-            newname = f"{col}_product{product}"
-            new_transitions[newname] = new_transitions[col] * (condition[product] / np.sum(condition))
-    for row in transition_matrix.index:
-        for product in range(len(condition)):
-            newname = f"{row}_product{product}"
-            new_transitions.loc[newname] = new_transitions.loc[row] * (condition[product] / np.sum(condition))
+    # expand NxN transition matrix to (N*P)x(N*P) weighted by demand condition
+    cond_norm = condition / np.sum(condition)
+    n_products = len(condition)
+    base_vals = transition_matrix.values
+    base_cols, base_rows = transition_matrix.columns.tolist(), transition_matrix.index.tolist()
 
-    return new_transitions.fillna(0.0)
+    # expand via kronecker-like tiling: each cell becomes a P*P block weighted by outer product of cond_norm
+    expanded = np.kron(base_vals, np.outer(cond_norm, cond_norm))
+    new_cols = [f"{c}_product{p}" for c in base_cols for p in range(n_products)]
+    new_rows = [f"{r}_product{p}" for r in base_rows for p in range(n_products)]
+    return pd.DataFrame(expanded, index=new_rows, columns=new_cols)
 
 def sample_behavior(condition, human=True, max_len=40):
-    model = BehaviorModel(human_dir) if human else AgentBehaviorModel(agent_dir)
-    mdp = model.build_MDP()
-    raw_events = aggregate_event_transitions(mdp) # this gets us transtition between events (blind to products or prices)
-    # staet: {state: p} is raw_events we needc a matrix a pivot table
-    events_pivot = pd.DataFrame(raw_events).fillna(0.0)
-    # now adjust the transition matrix based on the condition to get a more informed transition matrix
-    adjusted_transitions = adjust_behavior_to_condition(condition, events_pivot)
+    base_pivot = _get_base_pivot(human)
+    adjusted_transitions = adjust_behavior_to_condition(condition, base_pivot)
 
     trajectory = [np.random.choice(adjusted_transitions.index)]
     while len(trajectory) < max_len or 'checkout' in trajectory[-1]:

engine/lib/demand.py

@@ -8,7 +8,8 @@ def generate_demand(prices, distribution_method = np.random.normal, distribution
     product_valuations = distribution_method(*distribution_params, size=len(prices))
     # assumption 2: demand decreases as price increases, following a simple linear model
     demand = np.maximum(0, product_valuations - prices) # demand cannot be negative
-    demand = demand / np.sum(demand) * 100  # normalize to total demand of 1000 units so demand output is within [0, 100]
+    total = np.sum(demand)
+    demand = demand / total * 100 if total > 0 else demand  # normalize to percentage, avoid div by zero
     logger.info(f"Generated demand for prices {prices}: {demand} with valuations from distribution {distribution_params}")
     return demand
 

engine/wrapper.py

@@ -1,5 +1,142 @@
 import gymnasium as gym
 from gymnasium import spaces
-from engine import Limbo
+import numpy as np
+import matplotlib.pyplot as plt
+from .engine import Limbo, MarketEngine, PricingEngine
 
-class PHANTOM(gym.Env, Limbo):
+
+class PHANTOM(gym.Env):
+    """Gymnasium wrapper for the Limbo pricing-market simulation. Platform sets prices, market responds with demand."""
+    metadata = {"render_modes": ["human", "ansi"]}
+
+    def __init__(self,
+                 n_products: int = 10,
+                 alpha: float = 0.3,
+                 N: int = 100,
+                 price_bounds: tuple = (10.0, 150.0),
+                 lambda_coi: float = 0.1,  # coi leakage penalty weight
+                 render_mode: str = None):
+        super().__init__()
+        self.n_products = n_products
+        self.price_bounds = price_bounds
+        self.lambda_coi = lambda_coi
+        self.render_mode = render_mode
+
+        self.market = MarketEngine(alpha=alpha, N=N)
+        self._platform_stub = PricingEngine()
+        self._limbo = Limbo(self._platform_stub, self.market)
+
+        # action: continuous prices for each product
+        self.action_space = spaces.Box(
+            low=price_bounds[0], high=price_bounds[1],
+            shape=(n_products,), dtype=np.float32
+        )
+        # observation: demand estimate + previous prices
+        self.observation_space = spaces.Dict({
+            "demand": spaces.Box(low=0.0, high=100.0, shape=(n_products,), dtype=np.float32),
+            "prices": spaces.Box(low=price_bounds[0], high=price_bounds[1], shape=(n_products,), dtype=np.float32),
+        })
+
+        self._prices = None
+        self._demand = None
+        self._step_count = 0
+        self._demand_history = []  # list of demand arrays over time
+        self._price_history = []   # list of price arrays over time
+        self._fig, self._axes = None, None
+
+    def _get_obs(self) -> dict:
+        demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)], dtype=np.float32)
+        return {"demand": demand_arr, "prices": self._prices.astype(np.float32)}
+
+    def _compute_reward(self, prices: np.ndarray, demand: dict) -> float:
+        demand_arr = np.array([demand.get(i, 0.0) for i in range(self.n_products)])
+        revenue = np.sum(prices * demand_arr)  # revenue = price * quantity proxy
+        base_price = self.price_bounds[0]
+        return float(revenue)# - self.lambda_coi * coi_leak)
+
+    def _record_history(self):
+        demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
+        self._demand_history.append(demand_arr)
+        self._price_history.append(self._prices.copy())
+
+    def reset(self, seed=None, options=None):
+        super().reset(seed=seed)
+        self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
+        self._demand = self.market.act(self._prices)
+        self._step_count = 0
+        self._demand_history, self._price_history = [], []
+        self._record_history()
+        if self._fig: plt.close(self._fig)
+        self._fig, self._axes = None, None
+        return self._get_obs(), {}
+
+    def step(self, action: np.ndarray):
+        self._prices = np.clip(action, *self.price_bounds)
+        self._demand = self.market.act(self._prices)
+        self._step_count += 1
+        self._record_history()
+
+        reward = self._compute_reward(self._prices, self._demand)
+        terminated = self._step_count >= 100
+        truncated = False
+
+        return self._get_obs(), reward, terminated, truncated, {"step": self._step_count}
+
+    def render(self):
+        if self.render_mode == "human":
+            if self._fig is None:
+                plt.ion()
+                self._fig, self._axes = plt.subplots(2, 2, figsize=(12, 8))
+                self._fig.suptitle("PHANTOM Environment")
+
+            demand_mat = np.array(self._demand_history).T  # shape: (n_products, timesteps)
+            price_mat = np.array(self._price_history).T
+            revenue_per_step = np.sum(demand_mat * price_mat, axis=0)  # revenue = demand * price
+            demand_variance = np.var(demand_mat, axis=0)  # how spread demand is across products
+
+            for row in self._axes:
+                for ax in row: ax.clear()
+
+            self._axes[0, 0].imshow(demand_mat, aspect='auto', cmap='viridis', origin='lower')
+            self._axes[0, 0].set_xlabel("Time Step")
+            self._axes[0, 0].set_ylabel("Product")
+            self._axes[0, 0].set_title("Demand Over Time")
+
+            self._axes[0, 1].imshow(price_mat, aspect='auto', cmap='plasma', origin='lower')
+            self._axes[0, 1].set_xlabel("Time Step")
+            self._axes[0, 1].set_ylabel("Product")
+            self._axes[0, 1].set_title("Price Over Time")
+
+            self._axes[1, 0].plot(revenue_per_step, color='teal', linewidth=1.5)
+            self._axes[1, 0].set_xlabel("Time Step")
+            self._axes[1, 0].set_ylabel("Revenue")
+            self._axes[1, 0].set_title("Revenue per Step")
+
+            self._axes[1, 1].plot(demand_variance, color='orangered', linewidth=1.5)
+            self._axes[1, 1].set_xlabel("Time Step")
+            self._axes[1, 1].set_ylabel("Variance")
+            self._axes[1, 1].set_title("Demand Variance")
+
+            self._fig.tight_layout()
+            self._fig.canvas.draw()
+            self._fig.canvas.flush_events()
+            plt.pause(0.01)
+
+        elif self.render_mode == "ansi":
+            return f"step={self._step_count}, prices={self._prices}, demand={self._demand}"
+        return None
+
+    def close(self):
+        if self._fig: plt.close(self._fig)
+        self._fig, self._axes = None, None
+
+
+if __name__ == "__main__":
+    env = PHANTOM(n_products=100, render_mode="human")
+    obs, _ = env.reset()
+    for _ in range(100):
+        action = env.action_space.sample()
+        obs, reward, term, trunc, info = env.step(action)
+        env.render()
+        print(f"Reward: {reward:.2f}")
+        if term: break