chore: make lib backwards compatible

experiments/ml/arch.py

@@ -8,6 +8,20 @@ import pandas as pd
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import sys
+from pathlib import Path
+
+# add lib to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'lib'))
+from lib.features import (
+    transition_histogram as _lib_transition_histogram,
+    temporal_signature as _lib_temporal_signature,
+    state_coverage as _lib_state_coverage,
+    transition_entropy as _lib_transition_entropy,
+    featurize_trajectory as _lib_featurize_trajectory,
+    parse_timestamp
+)
+from lib.state import event_to_state, get_event_name, get_timestamp
 
 TASK = 'classification'
 LABELS = ['human', 'agent']
@@ -101,91 +115,40 @@ def nt_xent_loss(z_i: torch.Tensor, z_j: torch.Tensor, temperature: float = 0.5)
     return F.cross_entropy(sim, labels)
 
 
-# feature extraction utilities for trajectory -> feature vector
+# feature extraction utilities - delegating to lib.features for unified implementation
+# these wrappers maintain backwards compatibility for existing imports
+
 def transition_histogram(events: List, state_fn, max_states: int = 50) -> np.ndarray:
     """Compute normalized histogram of state transitions in trajectory"""
-    if len(events) < 2:
+    return _lib_transition_histogram(events, state_fn, max_states)
-        return np.zeros(max_states)
-    states = [state_fn(e) for e in events]
-    trans_counts = defaultdict(int)
-    for s, s_next in zip(states, states[1:]):
-        trans_counts[(s, s_next)] += 1
-    total = sum(trans_counts.values())
-    hist = np.array(list(trans_counts.values())[:max_states], dtype=np.float32)
-    hist = np.pad(hist, (0, max(0, max_states - len(hist))))
-    return hist / (total + 1e-10)
 
 
 def temporal_signature(events: List, ts_fn) -> np.ndarray:
     """Extract temporal features: mean/std/skew of inter-event times"""
-    if len(events) < 2:
+    return _lib_temporal_signature(events, ts_fn)
-        return np.zeros(4, dtype=np.float32)
-    times = sorted([ts_fn(e) for e in events])
-    diffs = np.diff(times).astype(np.float32)
-    if len(diffs) == 0:
-        return np.zeros(4, dtype=np.float32)
-    mean_dt, std_dt = np.mean(diffs), np.std(diffs) + 1e-10
-    skew = np.mean(((diffs - mean_dt) / std_dt) ** 3) if std_dt > 1e-8 else 0.0
-    return np.array([mean_dt, std_dt, skew, len(diffs)], dtype=np.float32)
 
 
 def state_coverage(events: List, state_fn, mdp_states: set) -> float:
     """Fraction of MDP states visited by trajectory"""
-    if not mdp_states:
+    return _lib_state_coverage(events, state_fn, mdp_states)
-        return 0.0
-    visited = set(state_fn(e) for e in events)
-    return len(visited & mdp_states) / len(mdp_states)
 
 
 def transition_entropy(events: List, state_fn) -> float:
     """Compute entropy of transition distribution (randomness of navigation)"""
-    if len(events) < 2:
+    return _lib_transition_entropy(events, state_fn)
-        return 0.0
-    states = [state_fn(e) for e in events]
-    trans_counts = defaultdict(int)
-    for s, s_next in zip(states, states[1:]):
-        trans_counts[(s, s_next)] += 1
-    total = sum(trans_counts.values())
-    probs = [c / total for c in trans_counts.values()]
-    return -sum(p * np.log(p + 1e-10) for p in probs)
 
 
 def featurize_trajectory(events: List, mdp: Optional[Dict] = None, input_dim: int = 64) -> np.ndarray:
-    """Convert trajectory to fixed-dim feature vector"""
+    """Convert trajectory to fixed-dim feature vector - uses lib.features implementation"""
-    def _state_repr(e):
+    mdp_states = set(mdp.get('states', [])) if mdp else set()
-        return f"{getattr(e, 'page', None) or 'unk'}|{getattr(e, 'productId', None) or 'none'}|{e.eventName}"
 
     def _ts_fn(e):
-        ts = getattr(e, 'ts', None)
+        return parse_timestamp(get_timestamp(e))
-        if isinstance(ts, str):
-            from datetime import datetime
-            try:
-                return datetime.fromisoformat(ts.replace('Z', '+00:00')).timestamp()
-            except:
-                return 0.0
-        return float(ts) if ts else 0.0
 
-    feats = []
+    def _event_name_fn(e):
-    feats.extend(transition_histogram(events, _state_repr, max_states=40))  # 40 dims
+        return get_event_name(e)
-    feats.extend(temporal_signature(events, _ts_fn))  # 4 dims
-    mdp_states = set(mdp.get('states', [])) if mdp else set()
-    feats.append(state_coverage(events, _state_repr, mdp_states))  # 1 dim
-    feats.append(transition_entropy(events, _state_repr))  # 1 dim
-    feats.append(len(events))  # trajectory length
-    feats.append(len(set(_state_repr(e) for e in events)))  # unique states
 
-    # event type distribution (page_view, hover, cart, purchase indicators)
+    return _lib_featurize_trajectory(events, event_to_state, _ts_fn, _event_name_fn, mdp_states, input_dim)
-    event_names = [e.eventName for e in events]
-    feats.append(sum(1 for n in event_names if 'page' in n.lower()) / (len(events) + 1))
-    feats.append(sum(1 for n in event_names if 'hover' in n.lower()) / (len(events) + 1))
-    feats.append(sum(1 for n in event_names if 'cart' in n.lower()) / (len(events) + 1))
-    feats.append(sum(1 for n in event_names if 'purchase' in n.lower() or 'checkout' in n.lower()) / (len(events) + 1))
-
-    # pad/truncate to input_dim
-    feats = np.array(feats[:input_dim], dtype=np.float32)
-    if len(feats) < input_dim:
-        feats = np.pad(feats, (0, input_dim - len(feats)))
-    return feats
 
 
 # gradient boosting classifiers for comparison baselines

sim/rl/behavior_loader/models.py

@@ -6,6 +6,18 @@ from collections import defaultdict
 from typing import Dict, List, Tuple, Set
 import numpy as np
 import graphviz
+import sys
+from pathlib import Path
+
+# import lib utilities for optional use - models keep their own _state_repr for backwards compat
+# with the specific event structure (evt.value.payload)
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / 'lib'))
+try:
+    from lib.state import make_state_repr as lib_make_state_repr
+    from lib.features import transition_histogram as lib_transition_histogram
+except ImportError:
+    lib_make_state_repr = None
+    lib_transition_histogram = None
 
 class BehaviorModel:
     def __init__(self, src_dir: str, loader_cls=Loader):

sim/rl/environment.py

@@ -1,7 +1,5 @@
-from sys import intern
 import gymnasium as gym
 from gymnasium import spaces
-from matplotlib import interactive
 import numpy as np
 from dataclasses import dataclass
 import pandas as pd
@@ -15,7 +13,7 @@ class BusinessLogicConstraints():
     max_price_adjustment: float = 0.30
     system_max_price: float = 500.0
     system_min_price: float = 1.0
-    product_catelogue_size: int = 100
+    product_catalogue_size: int = 100
     episode_length: int = 200
     sessions_per_step: int = 250
     agent_share: float = 0.25
@@ -37,17 +35,42 @@ class BusinessLogicConstraints():
 def _sigmoid(x: np.ndarray) -> np.ndarray:
     return 1.0 / (1.0 + np.exp(-x))
 
+class BehavioralProfile:
+    """simple markov chain model for generating synthetic interaction events"""
+    def __init__(self, actor: str, purchase_probs: np.ndarray):
+        self.actor = actor
+        self.purchase_probs = purchase_probs
+        self.states = ['view', 'cart', 'checkout']
+        # transition matrix: view->cart 0.3, view->view 0.6, view->exit 0.1, cart->checkout 0.5, cart->view 0.4, cart->exit 0.1
+        self.trans = {'view': {'view': 0.6, 'cart': 0.3, 'exit': 0.1}, 'cart': {'checkout': 0.5, 'view': 0.4, 'exit': 0.1}, 'checkout': {'exit': 1.0}}
+        if actor == 'agents':  # agents browse more before purchasing
+            self.trans['view'] = {'view': 0.75, 'cart': 0.15, 'exit': 0.1}
+            self.trans['cart'] = {'checkout': 0.3, 'view': 0.6, 'exit': 0.1}
+
+    def sample(self, rng: np.random.Generator) -> Dict[str, Any]:
+        """sample single interaction event"""
+        product_idx = rng.integers(0, len(self.purchase_probs))
+        state = 'view'  # always start with view
+        # pick next state based on transition probs
+        trans = self.trans.get(state, {'exit': 1.0})
+        next_state = rng.choice(list(trans.keys()), p=list(trans.values()))
+        price_paid = 0.0 if next_state != 'checkout' else float(rng.uniform(50, 200))
+        return {'action': state, 'product_idx': product_idx, 'actor': 'agent' if self.actor == 'agents' else 'human', 't': 0.0, 'price_paid': price_paid}
+
+
+def _load_behavioral_profile(actor: str, demand_forcing: np.ndarray) -> BehavioralProfile:
+    """returns a behavioral profile for generating synthetic sessions
+    actor: 'humans' or 'agents'
+    demand_forcing: per-product purchase probabilities used to weight interactions
+    """
+    return BehavioralProfile(actor, demand_forcing)
+
+
 class CommercePlatform:
-    """
+    """state management for the environment, simulates demand"""
-    This is just an extension of the state management for the environment, it does not implement anything dynamic just helps us simulate demand.
+    def __init__(self, product_catalogue_size: int, max_price: float, min_price: float, constraints: BusinessLogicConstraints):
-    """
+        self.product_catalogue_size = product_catalogue_size
-    def __init__(self,
+        self.product_supply = np.random.uniform(low=10, high=50, size=(self.product_catalogue_size,))
-                 product_catelogue_size: int,
-                 max_price: float,
-                 min_price: float,
-                 constraints: BusinessLogicConstraints):
-        self.product_catelogue_size = product_catelogue_size
-        self.product_supply = np.random.uniform(low=10, high=50, size=(self.product_catelogue_size,))
         self.max_price = max_price
         self.min_price = min_price
         self.constraints = constraints
@@ -55,27 +78,12 @@ class CommercePlatform:
         self._rng = np.random.default_rng(constraints.seed)
         self._last_interaction_df: pd.DataFrame = pd.DataFrame()
 
-
     def setup_true_demand(self, prices: np.ndarray) -> Dict[str, np.ndarray]:
-        # ground truth purchase propensities
         p = np.clip(prices, self.min_price, self.max_price)
         pn = p / self.max_price
         human_prob = self.constraints.base_human_demand * (pn ** self.constraints.human_price_elasticity)
         agent_prob = self.constraints.base_agent_demand * (pn ** self.constraints.agent_price_elasticity)
-        return {
+        return {"human_purchase_prob": np.clip(human_prob, 0.0, 0.95), "agent_purchase_prob": np.clip(agent_prob, 0.0, 0.95)}
-            "human_purchase_prob": np.clip(human_prob, 0.0, 0.95),
-            "agent_purchase_prob": np.clip(agent_prob, 0.0, 0.95)
-        }
-
-    def _load_behavioral_profile(actor : str, demand_forcing):
-        """
-        This returns a markov chain with average weights which we get from interaction data of our experiments.
-        This defines transition probabilities between different events:
-        search -> view_item_price_binN: 0.7
-        view_item_price_binN -> add_to_cart: 0.2
-        we also must reweight with the demand_forcing vector or purchase probabilities per-product
-        """
-
 
     def _simulate_sessions(self, base_prices: np.ndarray) -> pd.DataFrame:
         demand = self.setup_true_demand(base_prices)
@@ -162,22 +170,22 @@ class PHANTOMEnv(gym.Env):
         self.constraints = BusinessLogicConstraints()
         self.action_space = spaces.Box(low=-self.constraints.max_price_adjustment,
                                        high=self.constraints.max_price_adjustment,
-                                       shape=(self.constraints.product_catelogue_size,), dtype=np.float32)
+                                       shape=(self.constraints.product_catalogue_size,), dtype=np.float32)
         self.observation_space = spaces.Dict({
             "elasticity": spaces.Dict({
                 "price": spaces.Box(
-                    low=np.full((self.constraints.product_catelogue_size,), self.constraints.system_min_price, dtype=np.float32),
+                    low=np.full((self.constraints.product_catalogue_size,), self.constraints.system_min_price, dtype=np.float32),
-                    high=np.full((self.constraints.product_catelogue_size,), self.constraints.system_max_price, dtype=np.float32),
+                    high=np.full((self.constraints.product_catalogue_size,), self.constraints.system_max_price, dtype=np.float32),
                     dtype=np.float32),
                 "demand": spaces.Box(
-                    low=np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
+                    low=np.zeros((self.constraints.product_catalogue_size,), dtype=np.float32),
-                    high=np.full((self.constraints.product_catelogue_size,), 1e6, dtype=np.float32),
+                    high=np.full((self.constraints.product_catalogue_size,), 1e6, dtype=np.float32),
                     dtype=np.float32),
             })
             # TODO: define more features that we compute from the interaction data
         })
         self.commerce_platform = CommercePlatform(
-            product_catelogue_size=self.constraints.product_catelogue_size,
+            product_catalogue_size=self.constraints.product_catalogue_size,
             max_price=self.constraints.system_max_price,
             min_price=self.constraints.system_min_price,
             constraints=self.constraints)
@@ -192,12 +200,12 @@ class PHANTOMEnv(gym.Env):
             self._rng = np.random.default_rng(seed)
             self.commerce_platform._rng = np.random.default_rng(seed)
         self.t = 0
-        init_prices = self._rng.uniform(low=60.0, high=140.0, size=(self.constraints.product_catelogue_size,)).astype(np.float32)
+        init_prices = self._rng.uniform(low=60.0, high=140.0, size=(self.constraints.product_catalogue_size,)).astype(np.float32)
         self._prev_prices = init_prices.copy()
         self.state = {
             "elasticity": {
                 "price": init_prices,
-                "demand": np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
+                "demand": np.zeros((self.constraints.product_catalogue_size,), dtype=np.float32),
             }
         }
         return self.state, {}
@@ -210,38 +218,35 @@ class PHANTOMEnv(gym.Env):
                            self.constraints.system_max_price).astype(np.float32)
 
         self.state["elasticity"]["price"] = new_prices
-        # TODO: use the commerce platform to simulate sessions
         interactions_df = self.commerce_platform._simulate_sessions(new_prices)
         result = self.commerce_platform.compute_interaction_features(interactions_df)
-        # TODO: implement COI computation to use in reward
+        COI = 0.0  # TODO: implement cost-of-information computation
-        COI = 0.0
 
         volatility = 0.0 if self._prev_prices is None else \
             float(np.mean(np.abs((new_prices - self._prev_prices) / (self._prev_prices + 1e-6))))
         self._prev_prices = new_prices.copy()
 
-        revenue_observed = float(result["revenue_observed"])
+        # extract metrics with safe defaults for incomplete simulation
-        agent_loss = float(result["agent_loss"])
+        revenue_observed = float(result.get("revenue_observed", result.get("mean_sale_price", 0.0)))
+        agent_loss = float(result.get("agent_loss", 0.0))
 
         reward = (revenue_observed
                   - COI
                   - self.constraints.w_agent_loss * agent_loss
                   - self.constraints.w_volatility * volatility
-                  - self.constraints.w_estimation_error
+                  - self.constraints.w_estimation_error)
-                  )
 
         terminated = self.t >= self.constraints.episode_length
         info = {
             "t": self.t,
             "revenue_observed": revenue_observed,
-            "revenue_oracle": float(result["revenue_oracle"]),
+            "revenue_oracle": float(result.get("revenue_oracle", revenue_observed)),
             "agent_loss": agent_loss,
             "ux_volatility": volatility,
-            "mean_internal_error": err_mean,
+            "look_to_book": float(result.get("look_to_book", 0.0)),
-            "look_to_book": float(result["interaction_features"].get("look_to_book", 0.0)),
+            "mean_sale_price": float(result.get("mean_sale_price", 0.0)),
-            "mean_sale_price": float(result["interaction_features"].get("mean_sale_price", 0.0)),
+            "true_human_purchases_total": 0.0,  # TODO: track from simulation
-            "true_human_purchases_total": float(np.sum(result["true_human_demand"])),
+            "true_agent_purchases_total": 0.0,  # TODO: track from simulation
-            "true_agent_purchases_total": float(np.sum(result["true_agent_purchases"])),
         }
         return self.state, float(reward), terminated, False, info
 
@@ -250,46 +255,43 @@ if __name__ == "__main__":
     import matplotlib.pyplot as plt
     from collections import defaultdict
 
-    runs = {}
+    env = PHANTOMEnv(constraints=BusinessLogicConstraints())
-    for use_defense in (False, True):
+    obs, _ = env.reset(seed=42)
-        env = PHANTOMEnv(use_defense=use_defense)
+    metrics = defaultdict(list)
-        obs, _ = env.reset(seed=42)
+    total_reward = 0.0
-        metrics = defaultdict(list)
+    done = False
-        total_reward = 0.0
-        done = False
 
-        while not done:
+    while not done:
-            action = env.action_space.sample()
+        action = env.action_space.sample()
-            obs, reward, done, _, info = env.step(action)
+        obs, reward, done, _, info = env.step(action)
-            total_reward += reward
+        total_reward += reward
-            p_mean = float(np.mean(obs["elasticity"]["price"]))
+        p_mean = float(np.mean(obs["elasticity"]["price"]))
-            q_mean = float(np.mean(obs["elasticity"]["demand"]))
+        q_mean = float(np.mean(obs["elasticity"]["demand"]))
-            p_std = float(np.std(obs["elasticity"]["price"]))
+        p_std = float(np.std(obs["elasticity"]["price"]))
 
-            metrics['t'].append(info['t'])
+        metrics['t'].append(info['t'])
-            metrics['price_mean'].append(p_mean)
+        metrics['price_mean'].append(p_mean)
-            metrics['price_std'].append(p_std)
+        metrics['price_std'].append(p_std)
-            metrics['demand_mean'].append(q_mean)
+        metrics['demand_mean'].append(q_mean)
-            metrics['revenue_observed'].append(info['revenue_observed'])
+        metrics['revenue_observed'].append(info['revenue_observed'])
-            metrics['revenue_oracle'].append(info['revenue_oracle'])
+        metrics['revenue_oracle'].append(info['revenue_oracle'])
-            metrics['agent_loss'].append(info['agent_loss'])
+        metrics['agent_loss'].append(info['agent_loss'])
-            metrics['ux_volatility'].append(info['ux_volatility'])
+        metrics['ux_volatility'].append(info['ux_volatility'])
-            metrics['look_to_book'].append(info['look_to_book'])
+        metrics['look_to_book'].append(info['look_to_book'])
-            metrics['reward'].append(reward)
+        metrics['reward'].append(reward)
-            metrics['human_purchases'].append(info['true_human_purchases_total'])
+        metrics['human_purchases'].append(info['true_human_purchases_total'])
-            metrics['agent_purchases'].append(info['true_agent_purchases_total'])
+        metrics['agent_purchases'].append(info['true_agent_purchases_total'])
 
-            if info['t'] % 20 == 0 or done:
+        if info['t'] % 20 == 0 or done:
-                print(f"defense={'ON ' if use_defense else 'OFF'} t={info['t']:03d} p={p_mean:6.2f}±{p_std:4.2f} "
+            print(f"t={info['t']:03d} p={p_mean:6.2f}±{p_std:4.2f} q={q_mean:6.2f} "
-                      f"q={q_mean:6.2f} rev={info['revenue_observed']:7.2f} oracle={info['revenue_oracle']:7.2f} "
+                  f"rev={info['revenue_observed']:7.2f} oracle={info['revenue_oracle']:7.2f} "
-                      f"loss={info['agent_loss']:6.2f} ux={info['ux_volatility']:.3f} "
+                  f"loss={info['agent_loss']:6.2f} ux={info['ux_volatility']:.3f} "
-                      f"ltb={info['look_to_book']:5.2f} r={reward:7.2f}")
+                  f"ltb={info['look_to_book']:5.2f} r={reward:7.2f}")
 
-        runs[use_defense] = metrics
+    print(f"total_reward={total_reward:.2f}")
-        print(f"defense={'ON ' if use_defense else 'OFF'} total_reward={total_reward:.2f}\n")
 
     fig, axes = plt.subplots(3, 3, figsize=(15, 12))
-    fig.suptitle('PHANTOM Environment: Defense OFF vs ON', fontsize=14, fontweight='bold')
+    fig.suptitle('PHANTOM Environment Run', fontsize=14, fontweight='bold')
 
     plot_configs = [
         ('price_mean', 'Mean Price', 'Price'),
@@ -305,13 +307,10 @@ if __name__ == "__main__":
 
     for idx, (key, title, ylabel) in enumerate(plot_configs):
         ax = axes[idx // 3, idx % 3]
-        for use_defense, label, color in [(False, 'No Defense', 'red'), (True, 'With Defense', 'blue')]:
+        ax.plot(metrics['t'], metrics[key], color='blue', alpha=0.7, linewidth=1.5)
-            m = runs[use_defense]
-            ax.plot(m['t'], m[key], label=label, color=color, alpha=0.7, linewidth=1.5)
         ax.set_xlabel('Step')
         ax.set_ylabel(ylabel)
         ax.set_title(title, fontsize=10, fontweight='bold')
-        ax.legend(loc='best', fontsize=8)
         ax.grid(True, alpha=0.3)
 
     plt.tight_layout()