mirror of
https://github.com/wassname/Judgemark-v2lp.git
synced 2026-06-27 16:10:14 +08:00
sam-paech
233 lines
9.3 KiB
Python
233 lines
9.3 KiB
Python
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import logging
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import math
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import statistics
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import numpy as np
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import scipy.stats
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from typing import Dict, List
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from utils.stats import normalize
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try:
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from scipy.stats import wasserstein_distance
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HAS_WASSERSTEIN = True
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except ImportError:
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HAS_WASSERSTEIN = False
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def cohen_d(scores1: List[float], scores2: List[float]) -> float:
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"""
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Compute Cohen's d for two sets of scores.
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d = (mean2 - mean1) / pooled_stdev
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"""
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if len(scores1) < 2 or len(scores2) < 2:
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return 0.0
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mean1, mean2 = statistics.mean(scores1), statistics.mean(scores2)
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var1, var2 = statistics.pvariance(scores1), statistics.pvariance(scores2)
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n1, n2 = len(scores1), len(scores2)
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pooled_var = ((n1 - 1)*var1 + (n2 - 1)*var2) / (n1 + n2 - 2)
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if pooled_var <= 1e-12:
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return 0.0
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d = (mean2 - mean1) / math.sqrt(pooled_var)
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return d
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def ci_interval(scores: List[float], ci_level=0.99) -> tuple[float, float]:
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"""
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Compute mean ± z*(stdev/sqrt(n)) for the specified CI level.
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Returns (low, high).
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"""
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if len(scores) < 2:
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# trivial or empty
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mean_ = statistics.mean(scores) if len(scores) == 1 else 0.0
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return (mean_, mean_)
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mean_ = statistics.mean(scores)
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stdev_ = statistics.stdev(scores)
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n = len(scores)
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z = scipy.stats.norm.ppf(0.5 + ci_level/2.0) # ~2.575 for 99% CI
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half_width = z * (stdev_ / math.sqrt(n))
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return (mean_ - half_width, mean_ + half_width)
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def ci_intervals_overlap(ci1: tuple[float, float], ci2: tuple[float, float]) -> bool:
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"""
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Returns True if two confidence intervals overlap.
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"""
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return not (ci1[1] < ci2[0] or ci2[1] < ci1[0])
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def compute_distributions_distance(scores_by_model: Dict[str, List[float]]):
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"""
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Example EMD computation across all pairs, if you still want it.
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Returns an average distance plus each pair's distance.
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"""
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models = list(scores_by_model.keys())
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distances = {}
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sum_dist = 0.0
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pair_count = 0
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for i in range(len(models)):
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for j in range(i+1, len(models)):
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mA, mB = models[i], models[j]
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d = -1.0
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if HAS_WASSERSTEIN and scores_by_model[mA] and scores_by_model[mB]:
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d = wasserstein_distance(scores_by_model[mA], scores_by_model[mB])
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distances[f"{mA}__{mB}"] = d
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if d >= 0.0:
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sum_dist += d
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pair_count += 1
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avg_dist = (sum_dist / pair_count) if pair_count else 0.0
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return {
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"average": avg_dist,
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"pairs": distances
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}
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def compute_average_ci95(model_scores: Dict[str, List[float]]) -> float:
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"""
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Compute the average 95% CI half-width across models.
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"""
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if not model_scores:
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return 0.0
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half_widths = []
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z95 = 1.96
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for scores in model_scores.values():
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if len(scores) < 2:
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half_widths.append(0.0)
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continue
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stdev_ = statistics.stdev(scores)
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mean_ = statistics.mean(scores)
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n = len(scores)
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hw = z95 * (stdev_ / math.sqrt(n))
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half_widths.append(hw)
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return statistics.mean(half_widths) if half_widths else 0.0
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def compute_separability_metrics(
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run_data: dict,
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scores_by_model: Dict[str, List[float]],
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label: str = "raw",
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scale_factor: float = 1.5,
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) -> None:
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"""
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Compute a few custom “separability” metrics:
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• 99% CI overlap only for adjacent models (fraction)
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• The *magnitude* of 99% CI overlap between adjacent models (with optional scaling of intervals)
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• Single summary measure of Cohen’s d (e.g., average of absolute values)
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• EMD across pairs (optional)
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• Weighted or “modulated” metric for average CI95
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Args:
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run_data: A dictionary to store results.
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scores_by_model: Dict of model -> list of scores.
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label: String label for grouping these results in run_data.
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scale_factor: If >1.0, intervals are expanded by that factor when
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computing overlap magnitude. For example, 1.5 means
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you increase the half-width of each interval by 50%.
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"""
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if "separability_metrics" not in run_data:
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run_data["separability_metrics"] = {}
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run_data["separability_metrics"][label] = {}
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# ----------------------------------------------------------------
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# 1) Basic stats: model means + 99% CI
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# ----------------------------------------------------------------
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model_means = {}
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model_ci99 = {}
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for m, sc in scores_by_model.items():
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if sc:
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model_means[m] = statistics.mean(sc)
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model_ci99[m] = ci_interval(sc, ci_level=0.99)
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else:
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model_means[m] = 0.0
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model_ci99[m] = (0.0, 0.0)
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# Sort models by mean descending
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models_sorted = sorted(model_means.keys(), key=lambda x: model_means[x], reverse=True)
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# ----------------------------------------------------------------
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# 2) Original “adjacent overlap fraction” (no scaling)
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# ----------------------------------------------------------------
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adjacent_overlap = {}
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overlap_count = 0
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for i in range(len(models_sorted) - 1):
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mA, mB = models_sorted[i], models_sorted[i + 1]
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overlap = ci_intervals_overlap(model_ci99[mA], model_ci99[mB])
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adjacent_overlap[f"{mA}__{mB}"] = overlap
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if overlap:
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overlap_count += 1
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adj_frac_overlap = overlap_count / (len(models_sorted) - 1) if len(models_sorted) > 1 else 0.0
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# ----------------------------------------------------------------
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# 3) “Magnitude” of 99% CI overlap between adjacent models
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# with optional scaling factor
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# ----------------------------------------------------------------
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def scale_interval(ci: tuple[float, float], factor: float) -> tuple[float, float]:
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"""
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Given an interval (low, high), expand it about its midpoint by 'factor'.
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E.g. if factor=1.5, the half-width becomes 1.5 * (original half-width).
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"""
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low, high = ci
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mid = (low + high) / 2.0
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half_width = (high - low) / 2.0
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new_half = factor * half_width
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return (mid - new_half, mid + new_half)
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def interval_overlap(ciA: tuple[float, float], ciB: tuple[float, float]) -> float:
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"""Return the length of the overlap between two intervals."""
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return max(0.0, min(ciA[1], ciB[1]) - max(ciA[0], ciB[0]))
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adjacent_overlap_magnitude = {}
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sum_overlap_magnitude = 0.0
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for i in range(len(models_sorted) - 1):
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mA, mB = models_sorted[i], models_sorted[i + 1]
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# Scale each interval before computing overlap
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scaledA = scale_interval(model_ci99[mA], scale_factor)
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scaledB = scale_interval(model_ci99[mB], scale_factor)
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overlap_mag = interval_overlap(scaledA, scaledB)
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adjacent_overlap_magnitude[f"{mA}__{mB}"] = overlap_mag
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sum_overlap_magnitude += overlap_mag
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# ----------------------------------------------------------------
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# 4) Single measure for Cohen’s d (average of absolute Cohen’s d across adjacent pairs)
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# ----------------------------------------------------------------
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d_vals = []
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for i in range(len(models_sorted) - 1):
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mA, mB = models_sorted[i], models_sorted[i + 1]
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d_val = cohen_d(scores_by_model[mA], scores_by_model[mB])
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d_vals.append(abs(d_val))
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avg_cohens_d = sum(d_vals) / len(d_vals) if d_vals else 0.0
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# ----------------------------------------------------------------
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# 5) Optional EMD across all pairs
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# ----------------------------------------------------------------
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emd_data = compute_distributions_distance(scores_by_model)
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# ----------------------------------------------------------------
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# 6) Weighted or modulated average CI95
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# ----------------------------------------------------------------
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avg_ci95 = compute_average_ci95(scores_by_model)
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norm_ci95 = normalize(avg_ci95, 0.15, 0.45, False)
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norm_cohens_d = normalize(avg_cohens_d, 0, 0.4)
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modulated_ci95 = norm_ci95 * norm_cohens_d
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# ----------------------------------------------------------------
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# Store or log results
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# ----------------------------------------------------------------
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metrics_label = run_data["separability_metrics"][label]
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metrics_label["ci99_overlap_adjacent"] = adjacent_overlap
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metrics_label["adjacent_overlap_fraction"] = adj_frac_overlap
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# New overlap magnitude stats (with scaling)
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metrics_label["ci99_overlap_magnitude_adjacent"] = adjacent_overlap_magnitude
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metrics_label["ci99_overlap_magnitude_sum"] = sum_overlap_magnitude
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metrics_label["ci99_overlap_scale_factor"] = scale_factor
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metrics_label["average_cohens_d_adjacent"] = avg_cohens_d
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metrics_label["emd"] = emd_data
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metrics_label["average_ci95"] = avg_ci95
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metrics_label["modulated_ci95"] = modulated_ci95
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# Logging summary
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logging.info(f"\n--- {label.upper()} SEPARABILITY METRICS ---")
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logging.info(f"Adjacent 99% CI Overlap fraction: {adj_frac_overlap:.3f}")
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logging.info(f"Sum of adjacent 99% CI Overlap magnitude (scale={scale_factor}): "
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f"{sum_overlap_magnitude:.3f}")
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logging.info(f"Avg. |Cohen's d| for adjacent pairs: {avg_cohens_d:.3f}")
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logging.info(f"Average EMD across all pairs: {emd_data['average']:.3f}")
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logging.info(f"Avg. CI95 half-width: {avg_ci95:.3f} (modulated: {modulated_ci95:.3f})") |