chore: adding simulation logging with wandb

engine/lib/__init__.py

@@ -1,3 +1,6 @@
 from .demand import estimate_demand, generate_demand_for_actor
 from .behavior import sample_behavior
 from .render import DashboardRenderer, style_axis
+from .wrappers import EconomicMetricsWrapper
+from .callbacks import MetricsCallback, EvalMetricsCallback
+from .providers import ProviderBenchmark, ProviderResult, BenchmarkConfig

engine/lib/behavior.py

@@ -1,27 +1,39 @@
-from sim.rl.behavior_loader.models import BehaviorModel, AgentBehaviorModel, aggregate_event_transitions
+from sim.rl.behavior_loader.models import (
+    BehaviorModel,
+    AgentBehaviorModel,
+    aggregate_event_transitions,
+)
 import pandas as pd
 import numpy as np
 from .demand import generate_demand_for_actor
 
 base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
-human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
+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'
+    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):
     # 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()
+    base_cols, base_rows = (
+        transition_matrix.columns.tolist(),
+        transition_matrix.index.tolist(),
+    )
 
     # 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))
@@ -29,19 +41,24 @@ def adjust_behavior_to_condition(condition, transition_matrix):
     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):
     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]:
+    while len(trajectory) < max_len and "checkout" not in trajectory[-1]:
         probs = adjusted_transitions.loc[trajectory[-1]].values
-        sample = np.random.choice(adjusted_transitions.columns, p=probs/np.sum(probs) if np.sum(probs) > 0 else None)
+        sample = np.random.choice(
+            adjusted_transitions.columns,
+            p=probs / np.sum(probs) if np.sum(probs) > 0 else None,
+        )
         trajectory.append(sample)
     return trajectory
 
+
 if __name__ == "__main__":
-    t=sample_behavior(generate_demand_for_actor(np.array([10,20,30])), human=True)
+    t = sample_behavior(generate_demand_for_actor(np.array([10, 20, 30])), human=True)
     print(t)
-    t=sample_behavior(generate_demand_for_actor(np.array([10,20,30])), human=False)
+    t = sample_behavior(generate_demand_for_actor(np.array([10, 20, 30])), human=False)
     print(t)