chore: fixing discretization of actions

engine/lib/__init__.py

@@ -5,3 +5,4 @@ from .wrappers import EconomicMetricsWrapper
 from .callbacks import MetricsCallback, EvalMetricsCallback
 from .providers import ProviderBenchmark, ProviderResult, BenchmarkConfig
 from .coi import compute_uplift_coi, extract_purchases, compute_agent_probability
+from .discrete import EventQTable

engine/lib/behavior.py

@@ -70,7 +70,14 @@ def trajectory_to_events(trajectory: list) -> list:
 
 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)
+    condition = np.asarray(condition, dtype=float)
+    condition = np.nan_to_num(condition, nan=0.0, posinf=0.0, neginf=0.0)
+    condition = np.clip(condition, 0.0, None)
+    s = float(np.sum(condition))
+    if not np.isfinite(s) or s <= 0:
+        cond_norm = np.full(len(condition), 1.0 / max(len(condition), 1), dtype=float)
+    else:
+        cond_norm = condition / s
     n_products = len(condition)
     base_vals = transition_matrix.values
     base_cols, base_rows = (
@@ -91,10 +98,12 @@ def sample_behavior(condition, human=True, max_len=40):
 
     trajectory = [np.random.choice(adjusted_transitions.index)]
     while len(trajectory) < max_len and "checkout" not in trajectory[-1]:
-        probs = adjusted_transitions.loc[trajectory[-1]].values
+        probs = np.asarray(adjusted_transitions.loc[trajectory[-1]].values, dtype=float)
+        probs = np.nan_to_num(probs, nan=0.0, posinf=0.0, neginf=0.0)
+        probs = np.clip(probs, 0.0, None)
+        s = float(np.sum(probs))
         sample = np.random.choice(
-            adjusted_transitions.columns,
-            p=probs / np.sum(probs) if np.sum(probs) > 0 else None,
+            adjusted_transitions.columns, p=(probs / s) if s > 0 else None
         )
         trajectory.append(sample)
     return trajectory