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48 lines
2.2 KiB
Python
48 lines
2.2 KiB
Python
from sim.rl.behavior_loader.models import BehaviorModel, AgentBehaviorModel, aggregate_event_transitions
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import pandas as pd
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import numpy as np
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from .demand import generate_demand_for_actor
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base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
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human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
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_cache = {} # lazy cache for models and base pivots
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def _get_base_pivot(human: bool):
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key = 'human' if human else 'agent'
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if key not in _cache:
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model = BehaviorModel(human_dir) if human else AgentBehaviorModel(agent_dir)
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mdp = model.build_MDP()
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_cache[key] = pd.DataFrame(aggregate_event_transitions(mdp)).fillna(0.0)
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return _cache[key]
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def adjust_behavior_to_condition(condition, transition_matrix):
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# expand NxN transition matrix to (N*P)x(N*P) weighted by demand condition
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cond_norm = condition / np.sum(condition)
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n_products = len(condition)
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base_vals = transition_matrix.values
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base_cols, base_rows = transition_matrix.columns.tolist(), transition_matrix.index.tolist()
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# expand via kronecker-like tiling: each cell becomes a P*P block weighted by outer product of cond_norm
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expanded = np.kron(base_vals, np.outer(cond_norm, cond_norm))
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new_cols = [f"{c}_product{p}" for c in base_cols for p in range(n_products)]
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new_rows = [f"{r}_product{p}" for r in base_rows for p in range(n_products)]
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return pd.DataFrame(expanded, index=new_rows, columns=new_cols)
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def sample_behavior(condition, human=True, max_len=40):
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base_pivot = _get_base_pivot(human)
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adjusted_transitions = adjust_behavior_to_condition(condition, base_pivot)
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trajectory = [np.random.choice(adjusted_transitions.index)]
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while len(trajectory) < max_len or 'checkout' in trajectory[-1]:
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probs = adjusted_transitions.loc[trajectory[-1]].values
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sample = np.random.choice(adjusted_transitions.columns, p=probs/np.sum(probs) if np.sum(probs) > 0 else None)
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trajectory.append(sample)
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return trajectory
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if __name__ == "__main__":
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t=sample_behavior(generate_demand_for_actor(np.array([10,20,30])), human=True)
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print(t)
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t=sample_behavior(generate_demand_for_actor(np.array([10,20,30])), human=False)
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print(t)
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