chore: properly developing

engine/lib/behavior.py

@@ -0,0 +1,48 @@
+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
+
+base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
+human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
+
+
+def adjust_behavior_to_condition(condition, transition_matrix):
+    # transition matrix just maps probability of eventA to eventB
+    # we enhance this that eventA-productI to eventB-productJ... based on the condition of interest
+    # this is to simulate the impact of demand onto the behavior
+    # NxN -> (N*(P + 1))x(N*(P + 1)) where P is number of products
+    new_transitions = transition_matrix.copy()
+    for col in new_transitions.columns:
+        for product in range(len(condition)):
+            # adjust the transition probability based on the demand condition
+            newname = f"{col}_product{product}"
+            new_transitions[newname] = new_transitions[col] * (condition[product] / np.sum(condition))
+    for row in transition_matrix.index:
+        for product in range(len(condition)):
+            newname = f"{row}_product{product}"
+            new_transitions.loc[newname] = new_transitions.loc[row] * (condition[product] / np.sum(condition))
+
+    return new_transitions.fillna(0.0)
+
+def sample_behavior(condition, human=True, max_len=40):
+    model = BehaviorModel(human_dir) if human else AgentBehaviorModel(agent_dir)
+    mdp = model.build_MDP()
+    raw_events = aggregate_event_transitions(mdp) # this gets us transtition between events (blind to products or prices)
+    # staet: {state: p} is raw_events we needc a matrix a pivot table
+    events_pivot = pd.DataFrame(raw_events).fillna(0.0)
+    # now adjust the transition matrix based on the condition to get a more informed transition matrix
+    adjusted_transitions = adjust_behavior_to_condition(condition, events_pivot)
+
+    trajectory = [np.random.choice(adjusted_transitions.index)]
+    while len(trajectory) < max_len or 'checkout' 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)
+        trajectory.append(sample)
+    return trajectory
+
+if __name__ == "__main__":
+    t=sample_behavior(generate_demand(np.array([10,20,30])), human=True)
+    print(t)
+    t=sample_behavior(generate_demand(np.array([10,20,30])), human=False)
+    print(t)