File size: 8,057 Bytes
358d3bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 | """
Distribution Normalization for cMSCI.
Scores from different embedding spaces (CLIP vs CLAP) and different
pairwise channels (st_i, st_a, gram_volume) have different natural
distributions. Z-score normalization makes them comparable.
The ReferenceDistribution class fits mean/std from existing experiment
data and normalizes new scores to z-scores or percentile ranks.
"""
from __future__ import annotations
import json
import logging
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from scipy import stats as sp_stats
logger = logging.getLogger(__name__)
class ReferenceDistribution:
"""
Stores mean/std for a single score channel and provides normalization.
Usage:
ref = ReferenceDistribution()
ref.fit(list_of_scores)
z = ref.normalize(new_score) # z-score
p = ref.percentile(new_score) # percentile rank [0, 1]
"""
def __init__(self, name: str = ""):
self.name = name
self.mean: float = 0.0
self.std: float = 1.0
self.n: int = 0
self._sorted_values: Optional[np.ndarray] = None
def fit(self, scores: List[float]) -> None:
"""Fit the distribution from a list of observed scores."""
arr = np.array(scores, dtype=np.float64)
self.n = len(arr)
self.mean = float(np.mean(arr))
self.std = float(np.std(arr, ddof=1)) if self.n > 1 else 1.0
if self.std < 1e-10:
self.std = 1.0
self._sorted_values = np.sort(arr)
def normalize(self, score: float) -> float:
"""Z-score normalization: (score - mean) / std."""
return float((score - self.mean) / self.std)
def percentile(self, score: float) -> float:
"""
Percentile rank of score within the reference distribution.
Returns a value in [0, 1] where 0.5 = median of reference.
"""
if self._sorted_values is None or len(self._sorted_values) == 0:
return 0.5
rank = np.searchsorted(self._sorted_values, score, side="right")
return float(rank / len(self._sorted_values))
def to_dict(self) -> Dict:
return {
"name": self.name,
"mean": self.mean,
"std": self.std,
"n": self.n,
}
@classmethod
def from_dict(cls, d: Dict) -> "ReferenceDistribution":
obj = cls(name=d.get("name", ""))
obj.mean = d["mean"]
obj.std = d["std"]
obj.n = d.get("n", 0)
return obj
def save(self, path: str) -> None:
with open(path, "w") as f:
json.dump(self.to_dict(), f, indent=2)
@classmethod
def load(cls, path: str) -> "ReferenceDistribution":
with open(path) as f:
return cls.from_dict(json.load(f))
class CalibrationStore:
"""
Collection of ReferenceDistributions for all score channels.
Provides save/load for the full calibration state.
"""
def __init__(self):
self.distributions: Dict[str, ReferenceDistribution] = {}
def add(self, name: str, scores: List[float]) -> ReferenceDistribution:
ref = ReferenceDistribution(name=name)
ref.fit(scores)
self.distributions[name] = ref
logger.info(
"Calibration[%s]: mean=%.4f, std=%.4f, n=%d",
name, ref.mean, ref.std, ref.n,
)
return ref
def normalize(self, name: str, score: float) -> float:
if name not in self.distributions:
return score
return self.distributions[name].normalize(score)
def percentile(self, name: str, score: float) -> float:
if name not in self.distributions:
return 0.5
return self.distributions[name].percentile(score)
def save(self, path: str) -> None:
data = {name: ref.to_dict() for name, ref in self.distributions.items()}
Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
json.dump(data, f, indent=2)
logger.info("Calibration saved to %s", path)
@classmethod
def load(cls, path: str) -> "CalibrationStore":
store = cls()
with open(path) as f:
data = json.load(f)
for name, d in data.items():
store.distributions[name] = ReferenceDistribution.from_dict(d)
logger.info("Calibration loaded from %s (%d channels)", path, len(store.distributions))
return store
def has_channel(store: CalibrationStore, name: str) -> bool:
"""Check if a calibration channel exists in the store."""
return name in store.distributions
def extend_calibration_with_exmcr(
store: CalibrationStore,
gram_coh_ia_scores: List[float],
gram_coh_tia_scores: Optional[List[float]] = None,
) -> CalibrationStore:
"""
Extend calibration store with ExMCR-derived channels.
Args:
store: Existing CalibrationStore to extend.
gram_coh_ia_scores: Gram coherence of (image_clip, ExMCR(audio_clap)) pairs.
gram_coh_tia_scores: Optional 3-way gram coherence of (text, image, ExMCR(audio)).
Returns:
Extended CalibrationStore (same object, modified in place).
"""
if gram_coh_ia_scores:
store.add("gram_coh_ia_exmcr", gram_coh_ia_scores)
if gram_coh_tia_scores:
store.add("gram_coh_tia", gram_coh_tia_scores)
return store
def extend_calibration_with_uncertainty(
store: CalibrationStore,
uncertainty_ti_scores: List[float],
uncertainty_ta_scores: Optional[List[float]] = None,
) -> CalibrationStore:
"""
Extend calibration store with ProbVLM uncertainty channels.
Args:
store: Existing CalibrationStore to extend.
uncertainty_ti_scores: Per-sample mean uncertainty for text-image (CLIP adapter).
uncertainty_ta_scores: Per-sample mean uncertainty for text-audio (CLAP adapter).
Returns:
Extended CalibrationStore (same object, modified in place).
"""
if uncertainty_ti_scores:
store.add("uncertainty_ti", uncertainty_ti_scores)
if uncertainty_ta_scores:
store.add("uncertainty_ta", uncertainty_ta_scores)
# Combined uncertainty channel
if uncertainty_ti_scores and uncertainty_ta_scores:
combined = [
(ti + ta) / 2.0
for ti, ta in zip(uncertainty_ti_scores, uncertainty_ta_scores)
]
store.add("uncertainty_mean", combined)
return store
def build_reference_distributions(
rq1_results_path: str,
) -> CalibrationStore:
"""
Build reference distributions from existing RQ1 baseline results.
Extracts st_i, st_a, and msci scores from baseline condition only
(matched image + audio), fitting a distribution for each channel.
Args:
rq1_results_path: Path to rq1_results.json
Returns:
CalibrationStore with fitted distributions for st_i, st_a, msci
"""
with open(rq1_results_path) as f:
data = json.load(f)
st_i_scores = []
st_a_scores = []
msci_scores = []
for r in data["results"]:
if r.get("condition") != "baseline":
continue
if r.get("st_i") is not None:
st_i_scores.append(r["st_i"])
if r.get("st_a") is not None:
st_a_scores.append(r["st_a"])
if r.get("msci") is not None:
msci_scores.append(r["msci"])
store = CalibrationStore()
if st_i_scores:
store.add("st_i", st_i_scores)
if st_a_scores:
store.add("st_a", st_a_scores)
if msci_scores:
store.add("msci", msci_scores)
# GRAM coherence distributions (1 - gram_volume) for gram calibration mode
# gram_volume = sqrt(1 - cos^2), so gram_coherence = 1 - sqrt(1 - cos^2)
if st_i_scores:
gram_coh_ti = [1.0 - np.sqrt(max(0, 1 - s**2)) for s in st_i_scores]
store.add("gram_coh_ti", gram_coh_ti)
if st_a_scores:
gram_coh_ta = [1.0 - np.sqrt(max(0, 1 - s**2)) for s in st_a_scores]
store.add("gram_coh_ta", gram_coh_ta)
return store
|