feat: joint loader

sim/rl/behavior_loader/loader.py

@@ -71,13 +71,44 @@ class AgentLoader(Loader):
             sessions[entry] = [i for i in ints if not self._is_admin_page_simple(i)]
         return sessions
 
-if __name__ == "__main__":
-    DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/agents/collected_data/"
-    loader = AgentLoader(DIR)
-    _, n = loader.get_entries()
-    print(f"Loaded {n} sessions from {DIR}")
+class JointLoader:
+    """Loader for combined human (Kafka) and agent (direct) data without discrimination"""
 
-    DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
-    loader = Loader(DIR)
+    def __init__(self, human_dir: str, agent_dir: str):
+        self.human_dir = human_dir
+        self.agent_dir = agent_dir
+        self.human_loader = Loader(human_dir)
+        self.agent_loader = AgentLoader(agent_dir)
+        self.data = self._load_joint_sessions()
+        self.entries = list(self.data.keys())
+
+    def _load_joint_sessions(self) -> dict:
+        sessions = {}
+        # load human sessions (unwrap from Kafka format to PayloadModel)
+        for sid, evts in self.human_loader.get_data().items():
+            sessions[f"human_{sid}"] = [evt.value.payload for evt in evts]
+        # load agent sessions (already PayloadModel)
+        for sid, evts in self.agent_loader.get_data().items():
+            sessions[f"agent_{sid}"] = evts
+        return sessions
+
+    def get_data(self) -> dict:
+        return self.data
+
+    def get_entries(self) -> tuple[list[str], int]:
+        return self.entries, len(self.entries)
+
+if __name__ == "__main__":
+    AGENT_DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/agents/collected_data/"
+    loader = AgentLoader(AGENT_DIR)
     _, n = loader.get_entries()
-    print(f"Loaded {n} sessions from {DIR}")
+    print(f"Loaded {n} agent sessions from {AGENT_DIR}")
+
+    HUMAN_DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
+    loader = Loader(HUMAN_DIR)
+    _, n = loader.get_entries()
+    print(f"Loaded {n} human sessions from {HUMAN_DIR}")
+
+    joint_loader = JointLoader(HUMAN_DIR, AGENT_DIR)
+    _, n = joint_loader.get_entries()
+    print(f"Loaded {n} total sessions (combined) from joint loader")

sim/rl/behavior_loader/models.py

@@ -1,5 +1,5 @@
 from experiments.agents.base import Agent
-from loader import Loader, AgentLoader
+from loader import Loader, AgentLoader, JointLoader
 from collections import defaultdict
 from typing import Dict, List, Tuple, Set
 import numpy as np
@@ -109,6 +109,28 @@ class AgentBehaviorModel(BehaviorModel):
             trajectories.append(states)
         return trajectories
 
+class JointBehaviorModel(BehaviorModel):
+    """behavior model for combined human+agent data (flat PayloadModel distribution)"""
+
+    def __init__(self, human_dir: str = DIR, agent_dir: str = AGENT_DIR):
+        self.loader = JointLoader(human_dir, agent_dir)
+        self.data = self.loader.get_data()
+        self.entries, self.num_entries = self.loader.get_entries()
+        self.mdp = None
+
+    def _state_repr(self, evt) -> str:
+        # direct access to PayloadModel fields (JointLoader unwraps to PayloadModel)
+        return f"{evt.page or 'unk'}|{evt.productId or 'none'}|{evt.eventName}"
+
+    def _extract_sessions(self):
+        trajectories = []
+        for sid, evts in self.data.items():
+            if len(evts) < 2: continue
+            # sort by timestamp string (ISO format sorts lexicographically)
+            states = [self._state_repr(e) for e in sorted(evts, key=lambda x: x.ts)]
+            trajectories.append(states)
+        return trajectories
+
 def aggregate_event_transitions(mdp: Dict) -> Dict[str, Dict[str, float]]:
     """aggregate state transitions by event type and normalize"""
     evt_trans = defaultdict(lambda: defaultdict(float))
@@ -209,3 +231,11 @@ if __name__ == "__main__":
     print(f"\nMost divergent event types:")
     for evt, kl in kl_divs:
         print(f"  {evt}: {kl:.4f}")
+
+    # build joint model (combined distribution)
+    print("\n=== Joint Model (Human + Agent Combined) ===")
+    joint_model = JointBehaviorModel()
+    joint_mdp = joint_model.build_MDP()
+    print(f"Built joint MDP: {joint_mdp['num_states']} states, {sum(len(t) for t in joint_mdp['transitions'].values())} transitions")
+    if joint_mdp['states']:
+        visualize_mdp(joint_model, threshold=0.05, output="joint_mdp_viz", fmt="pdf", export_dot=True)