velocitatem/PHANTOM
1
0
Fork 0
mirror of https://github.com/velocitatem/PHANTOM.git synced 2026-05-31 08:33:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

chore: better wrapping amd more performant

Browse Source
This commit is contained in:
Daniel Rosel
2026-01-29 10:01:53 +01:00
parent 6e06081d60
commit 772772b5b9
3 changed files with 162 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
engine/lib/behavior.py
View File

@@ -6,33 +6,32 @@ 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/"
_cache = {} # lazy cache for models and base pivots
def _get_base_pivot(human: bool):
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):
# 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))
# 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()
return new_transitions.fillna(0.0)
# 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))
new_cols = [f"{c}_product{p}" for c in base_cols for p in range(n_products)]
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):
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)
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]: