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fix&perf: correct power law R² calculation in structure evaluator #142

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Merged
ChenZiHong-Gavin merged 4 commits into from
Dec 26, 2025
Merged

fix&perf: correct power law R² calculation in structure evaluator #142

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7ec1d07
fix: correct power law R² calculation in structure evaluator
CHERRY-ui8 Dec 26, 2025
6870dc4
docs: add KG evaluation metrics to READM
CHERRY-ui8 Dec 26, 2025
48e11c6
simplify StructureEvaluator
CHERRY-ui8 Dec 26, 2025
b026053
pylint: clean up whitespace
CHERRY-ui8 Dec 26, 2025
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28 changes: 21 additions & 7 deletions graphgen/models/evaluator/kg/structure_evaluator.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
from collections import Counter
from typing import Any, Dict, Optional

import numpy as np
Expand Down Expand Up @@ -81,14 +82,27 @@ def _calculate_powerlaw_r2(degree_map: Dict[str, int]) -> Optional[float]:
return None

try:
# Fit power law: log(y) = a * log(x) + b
log_degrees = np.log(degrees)
sorted_log_degrees = np.sort(log_degrees)
x = np.arange(1, len(sorted_log_degrees) + 1)
log_x = np.log(x)

degree_counts = Counter(degrees)
degree_values = []
frequencies = []
for deg, freq in sorted(degree_counts.items()):
if deg > 0 and freq > 0:
degree_values.append(deg)
frequencies.append(freq)
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@gemini-code-assist gemini-code-assist bot Dec 26, 2025

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medium

This block for populating degree_values and frequencies can be made more concise and efficient. The if deg > 0 and freq > 0: check is redundant because degrees are already filtered for positive values on line 78, and Counter does not store items with a frequency of zero. You can use the zip(*...) idiom for a more Pythonic and performant one-liner to replace this entire block.

Suggested change
degree_values = []
frequencies = []
for deg, freq in sorted(degree_counts.items()):
if deg > 0 and freq > 0:
degree_values.append(deg)
frequencies.append(freq)
degree_values, frequencies = zip(*sorted(degree_counts.items()))


if len(degree_values) < 3:
logger.warning(
f"Insufficient unique degrees ({len(degree_values)}) for power law fitting. "
f"Graph may be too uniform."
)
return None

# Fit power law: log(frequency) = a * log(degree) + b
log_degrees = np.log(degree_values)
log_frequencies = np.log(frequencies)

# Linear regression on log-log scale
r_value, *_ = stats.linregress(log_x, sorted_log_degrees)
r_value, *_ = stats.linregress(log_degrees, log_frequencies)
r2 = r_value**2

return float(r2)
Expand Down
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