chore: redefined and connected pricers

experiments/procesing/pricers/__init__.py

@@ -1,10 +1,13 @@
 from procesing.pricers.base import PricingFunction
 from procesing.pricers.elasticity import ElasticityBasedPricer
 from procesing.pricers.simple import StaticPricer, RandomPricer
+from procesing.pricers.session_aware import SessionAwarePricer, ProductSpecificSessionPricer
 
 __all__ = [
     'PricingFunction',
     'ElasticityBasedPricer',
     'StaticPricer',
-    'RandomPricer'
+    'RandomPricer',
+    'SessionAwarePricer',
+    'ProductSpecificSessionPricer'
 ]

experiments/procesing/pricers/base.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from typing import Optional, Dict, Any, List
 import numpy as np
 import pandas as pd
 
@@ -6,23 +7,64 @@ import pandas as pd
 class PricingFunction(ABC):
     """
     Abstract base for pricing functions.
-    Defines the mapping f: StateSpace -> prices
+
+    Defines mapping: f(Q_t, P_t, S_t, H_t) -> P_{t+1}
+
+    Where:
+        Q_t ∈ R^n: demand vector at time t
+        P_t ∈ R^n: price vector at time t
+        S_t: session features (behavioral signals, interactions)
+        H_t = {Q_{t-k}, P_{t-k}, S_{t-k}}: historical state trajectory
+
+    Objective:
+        maximize E[R_T] = E[Σ P_t^T · Q_t]
+        subject to:
+            Q_t = g(P_t, S_t)  (demand response via elasticity)
+            P_t ≥ C  (cost floor)
+            minimize L_agent = R_oracle - R_observed
     """
 
     @abstractmethod
-    def fit(self, historical_data: pd.DataFrame):
-        """Train/calibrate the pricing function on historical data"""
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        """
+        Offline training on historical data.
+
+        Args:
+            historical_data: DataFrame with elasticity, prices, demand signals
+            **kwargs: additional training parameters
+        """
         pass
 
     @abstractmethod
     def predict(self, state_space) -> np.ndarray:
         """
-        Generate prices given current state space.
+        Generate optimal prices given current state.
 
         Args:
-            state_space: StateSpace object containing demand, prices, session features
+            state_space: StateSpace object containing Q_t, P_t, S_t, H_t
 
         Returns:
-            prices: price vector P_{t+1} in R^n
+            P_{t+1}: price vector in R^n
         """
         pass
+
+    def update(self, observation: Dict[str, Any]):
+        """
+        Online learning update (optional).
+
+        Args:
+            observation: dict with {state, action, reward, next_state}
+                - state: StateSpace before pricing decision
+                - action: prices shown (P_t)
+                - reward: revenue/conversion signal
+                - next_state: StateSpace after user interaction
+        """
+        pass  # default: no online learning
+
+    def get_params(self) -> Dict[str, Any]:
+        """Return pricing function parameters for serialization."""
+        return {}
+
+    def set_params(self, params: Dict[str, Any]):
+        """Load pricing function parameters from dict."""
+        pass