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chore: adding simulation logging with wandb

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This commit is contained in:
Daniel Rosel
2026-01-31 16:21:10 +01:00
parent 33cb0d7e95
commit 4abef97bf7
5 changed files with 81 additions and 31 deletions
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33
engine/lib/behavior.py
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@@ -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)