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Evaluation module: compute SHD, F1, Precision, Recall between predicted and true CPDAGs.
"""
import numpy as np
import logging
logger = logging.getLogger(__name__)
def compute_shd(pred_adj, true_adj):
"""Compute Structural Hamming Distance between two CPDAGs/DAGs.
Both inputs are adjacency matrices where:
adj[i,j]=1 and adj[j,i]=0 means i->j (directed)
adj[i,j]=1 and adj[j,i]=1 means i--j (undirected)
SHD counts: missing edges + extra edges + wrongly oriented edges
"""
n = pred_adj.shape[0]
assert pred_adj.shape == true_adj.shape, "Adjacency matrices must have same shape"
shd = 0
for i in range(n):
for j in range(i + 1, n):
# True edge state
t_ij, t_ji = true_adj[i, j], true_adj[j, i]
# Predicted edge state
p_ij, p_ji = pred_adj[i, j], pred_adj[j, i]
true_has_edge = (t_ij == 1 or t_ji == 1)
pred_has_edge = (p_ij == 1 or p_ji == 1)
if true_has_edge and not pred_has_edge:
# Missing edge
shd += 1
elif not true_has_edge and pred_has_edge:
# Extra edge
shd += 1
elif true_has_edge and pred_has_edge:
# Both have edge - check if same type
true_type = (t_ij, t_ji) # (1,0)=directed, (1,1)=undirected, (0,1)=reverse
pred_type = (p_ij, p_ji)
if true_type != pred_type:
# Wrong orientation
shd += 1
return shd
def compute_edge_metrics(pred_adj, true_adj):
"""Compute precision, recall, F1 on edges (skeleton-level and directed).
Returns dict with:
- skeleton_precision, skeleton_recall, skeleton_f1: ignoring direction
- directed_precision, directed_recall, directed_f1: including direction
- shd: structural hamming distance
- n_true_edges, n_pred_edges: edge counts
"""
n = pred_adj.shape[0]
# Skeleton comparison (ignoring direction)
true_skeleton = ((true_adj + true_adj.T) > 0).astype(int)
pred_skeleton = ((pred_adj + pred_adj.T) > 0).astype(int)
# Only upper triangle for skeleton (undirected)
skel_tp = skel_fp = skel_fn = 0
for i in range(n):
for j in range(i + 1, n):
t = true_skeleton[i, j]
p = pred_skeleton[i, j]
if t == 1 and p == 1:
skel_tp += 1
elif t == 0 and p == 1:
skel_fp += 1
elif t == 1 and p == 0:
skel_fn += 1
skel_precision = skel_tp / (skel_tp + skel_fp) if (skel_tp + skel_fp) > 0 else 0
skel_recall = skel_tp / (skel_tp + skel_fn) if (skel_tp + skel_fn) > 0 else 0
skel_f1 = (2 * skel_precision * skel_recall / (skel_precision + skel_recall)
if (skel_precision + skel_recall) > 0 else 0)
# Directed comparison (full adjacency)
dir_tp = dir_fp = dir_fn = 0
for i in range(n):
for j in range(n):
if i == j:
continue
t = true_adj[i, j]
p = pred_adj[i, j]
if t == 1 and p == 1:
dir_tp += 1
elif t == 0 and p == 1:
dir_fp += 1
elif t == 1 and p == 0:
dir_fn += 1
dir_precision = dir_tp / (dir_tp + dir_fp) if (dir_tp + dir_fp) > 0 else 0
dir_recall = dir_tp / (dir_tp + dir_fn) if (dir_tp + dir_fn) > 0 else 0
dir_f1 = (2 * dir_precision * dir_recall / (dir_precision + dir_recall)
if (dir_precision + dir_recall) > 0 else 0)
shd = compute_shd(pred_adj, true_adj)
# Count edges
n_true_edges = 0
n_pred_edges = 0
for i in range(n):
for j in range(i + 1, n):
if true_adj[i, j] or true_adj[j, i]:
n_true_edges += 1
if pred_adj[i, j] or pred_adj[j, i]:
n_pred_edges += 1
return {
'shd': shd,
'skeleton_precision': skel_precision,
'skeleton_recall': skel_recall,
'skeleton_f1': skel_f1,
'directed_precision': dir_precision,
'directed_recall': dir_recall,
'directed_f1': dir_f1,
'n_true_edges': n_true_edges,
'n_pred_edges': n_pred_edges,
}
def dag_to_cpdag(dag_adjmat):
"""Import from data.generator to avoid circular dependency."""
from causal_selection.data.generator import dag_to_cpdag as _dag_to_cpdag
return _dag_to_cpdag(dag_adjmat)
def evaluate_algorithm_result(result, true_cpdag):
"""Evaluate a single algorithm result against ground truth CPDAG.
Args:
result: dict from run_algorithm (must have 'adjmat', 'output_type', 'status')
true_cpdag: ground truth CPDAG adjacency matrix
Returns:
dict with all metrics, or penalty metrics if algorithm failed
"""
n = true_cpdag.shape[0]
max_possible_shd = n * (n - 1) // 2 # maximum possible SHD
if result['status'] != 'success' or result['adjmat'] is None:
return {
'shd': max_possible_shd,
'normalized_shd': 1.0,
'skeleton_precision': 0.0,
'skeleton_recall': 0.0,
'skeleton_f1': 0.0,
'directed_precision': 0.0,
'directed_recall': 0.0,
'directed_f1': 0.0,
'n_true_edges': int(((true_cpdag + true_cpdag.T) > 0).sum() // 2),
'n_pred_edges': 0,
'runtime': result['runtime'],
'status': result['status'],
}
pred_adj = result['adjmat']
# If the algorithm outputs a DAG, convert to CPDAG for fair comparison
if result['output_type'] == 'dag':
pred_cpdag = dag_to_cpdag(pred_adj)
else:
pred_cpdag = pred_adj # Already CPDAG or PAG-derived
# Compute metrics
metrics = compute_edge_metrics(pred_cpdag, true_cpdag)
metrics['normalized_shd'] = metrics['shd'] / max_possible_shd if max_possible_shd > 0 else 0
metrics['runtime'] = result['runtime']
metrics['status'] = result['status']
return metrics
if __name__ == '__main__':
# Test with Asia network
from causal_selection.data.generator import load_bn_model, get_true_dag_adjmat, dag_to_cpdag as gen_dag_to_cpdag, sample_dataset
from causal_selection.discovery.algorithms import run_algorithm, ALGORITHM_POOL
import warnings
warnings.filterwarnings('ignore')
model = load_bn_model('asia')
true_dag, nodes = get_true_dag_adjmat(model)
true_cpdag = gen_dag_to_cpdag(true_dag)
df = sample_dataset(model, 1000, seed=0)
print(f"ASIA (N=1000) - True edges: {int(((true_cpdag + true_cpdag.T) > 0).sum() // 2)}")
print(f"{'Algorithm':15s} {'SHD':>5s} {'nSHD':>6s} {'Skel_F1':>8s} {'Dir_F1':>7s} {'Runtime':>8s} {'Status'}")
print("-" * 70)
for algo_name in ALGORITHM_POOL:
result = run_algorithm(algo_name, df, timeout_sec=60)
metrics = evaluate_algorithm_result(result, true_cpdag)
print(f"{algo_name:15s} {metrics['shd']:5d} {metrics['normalized_shd']:6.3f} "
f"{metrics['skeleton_f1']:8.3f} {metrics['directed_f1']:7.3f} "
f"{metrics['runtime']:7.2f}s {metrics['status']}")
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