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https://github.com/wassname/Judgemark-v2lp.git
synced 2026-06-27 16:10:14 +08:00
wassname
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
def clamp(x: float, low: float = 0.0, high: float = 1.0) -> float:
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"""Clamp a value between low and high."""
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return max(low, min(x, high))
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def normalize(val, min_val, max_val, bigger_is_better=True):
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if max_val <= min_val:
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return 0.0
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norm = (val - min_val) / (max_val - min_val)
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if not bigger_is_better:
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norm = 1.0 - norm
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return clamp(norm)
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def modulate_x_by_y(x, y):
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"""
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Modulate x so that it is sharply reduced if y is < 0.3, tapering to no effect as y approaches 1.
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Use case example:
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Modulate a confidence interval based on Cohen's d value.
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Weak judges have low ci95 ranges for each model they score.
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For a strong judge that's a good thing, but for the weak judge
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it just means they score *everything* within a narrow band.
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Here we compensate for this by modulating the ci95 range by a
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different measure of separability (cohen's d). When ci95 is
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large but cohen's d is small, the modulated value is also small.
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"""
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def modulation_factor(d):
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if d <= 0.3:
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# Steeper rise to reach ~0.95 by 0.3
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return 3.17 * d * (1 - 0.15 * d)
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else:
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# Smooth curve approaching 1 after 0.3
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t = (d - 0.3) / 0.7 # normalize remaining part to 0-1
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base = 3.17 * 0.3 * (1 - 0.15 * 0.3) # value at d=0.3
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return base + (1 - base) * (t * (2 - t))
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return x * modulation_factor(y) |