File size: 7,642 Bytes
bad8293 |
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 |
import numpy as np
import torch
from nlg.bertscore.bertscore import BertScore
from radgraph import RadGraph
from factual.f1chexbert import F1CheXbert
from sklearn.preprocessing import StandardScaler
from nlg.bleu.bleu import Bleu
def radcliq_bertscore(refs, hyps, model_type='distilroberta-base'):
"""
Computes BERTScore for each pair of reference and hypothesis.
Returns:
np.ndarray of shape (N,) with the BERTScore F1 values per pair.
"""
# https://github.com/rajpurkarlab/CXR-Report-Metric/blob/9c9ecad39be6cb2be8e75be1d1c50ef8888a3e40/CXRMetric/run_eval.py#L103
scorer = BertScore(
model_type=model_type,
rescale_with_baseline=True,
idf=False,
num_layers=None
)
_, scores = scorer(refs, hyps)
# scores is a list of torch.Tensor, convert to numpy
return np.array([float(s) for s in scores])
def compute_f1(test_set, retrieved_set):
"""Helper to compute F1 between two sets of items."""
tp = len(test_set & retrieved_set)
fp = len(retrieved_set) - tp
fn = len(test_set) - tp
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall = tp / (tp + fn) if (tp + fn) else 0.0
return 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
def extract_entities(output):
"""Extracts set of (tokens, label) tuples from RadGraph output."""
return {(tuple(ent["tokens"]), ent["label"]) for ent in output.get("entities", {}).values()}
def extract_relations(output):
"""Extracts set of (src, tgt, relation) tuples from RadGraph output."""
rels = set()
entities = output.get("entities", {})
for ent in entities.values():
src = (tuple(ent["tokens"]), ent["label"])
for rel_type, tgt_idx in ent.get("relations", []):
tgt_ent = entities.get(tgt_idx)
if tgt_ent:
tgt = (tuple(tgt_ent["tokens"]), tgt_ent["label"])
rels.add((src, tgt, rel_type))
return rels
def radcliq_radgraph_scores(refs, hyps, model_name='radgraph'):
"""
Computes entity and relation F1 via RadGraph for each report pair and returns their average.
Returns:
np.ndarray of shape (N,) with (entity_f1 + relation_f1)/2 per pair.
"""
rad = RadGraph(model_type=model_name)
gt_outputs = rad(refs)
pred_outputs = rad(hyps)
scores = []
for i in range(len(refs)):
gt_out = gt_outputs.get(str(i), {})
pred_out = pred_outputs.get(str(i), {})
ents_gt = extract_entities(gt_out)
ents_pred = extract_entities(pred_out)
rels_gt = extract_relations(gt_out)
rels_pred = extract_relations(pred_out)
ent_f1 = compute_f1(ents_gt, ents_pred)
rel_f1 = compute_f1(rels_gt, rels_pred)
scores.append((ent_f1 + rel_f1) / 2)
return np.array(scores)
def semantic_embedding_scores(refs, hyps, device='cpu'):
"""
Computes per-pair cosine similarity between embeddings from CheXbert labeler.
Returns:
np.ndarray of shape (N,) with cosine similarities per pair.
"""
if len(refs) != len(hyps):
raise ValueError(f"refs ({len(refs)}) and hyps ({len(hyps)}) must be same length")
labeler = F1CheXbert(device=device)
gt_embs = np.vstack(labeler.get_embeddings(refs))
pred_embs = np.vstack(labeler.get_embeddings(hyps))
# https://github.com/rajpurkarlab/CXR-Report-Metric/blob/9c9ecad39be6cb2be8e75be1d1c50ef8888a3e40/CXRMetric/run_eval.py#L126
dot = np.einsum("nd,nd->n", gt_embs, pred_embs)
norms = np.linalg.norm(gt_embs, axis=1) * np.linalg.norm(pred_embs, axis=1)
with np.errstate(divide='ignore', invalid='ignore'):
sims = np.where(norms > 0, dot / norms, 0.0)
return sims
def radcliq_scores(refs, hyps,
bert_model='distilroberta-base',
radgraph_model='radgraph'):
"""
Computes BERTScore, RadGraph score, and semantic embedding similarity for each ref-hyp pair.
Args:
refs: List of reference report strings.
hyps: List of hypothesis report strings.
device: Device for embedding model ('cpu' or 'cuda').
bert_model: HuggingFace model name for BERTScore.
radgraph_model: Model name for RadGraph inference.
Returns:
Dict with keys 'bertscore', 'radgraph', 'semantic', each mapping to a numpy array of shape (N,).
"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# BERTScore
bert_scores = radcliq_bertscore(refs, hyps, model_type=bert_model)
# RadGraph
rad_scores = radcliq_radgraph_scores(refs, hyps, model_name=radgraph_model)
# Semantic embeddings
sem_scores = semantic_embedding_scores(refs, hyps, device=device)
# BLEU
bleu_scorer = Bleu()
bleu_scores = bleu_scorer(refs, hyps)[1]
return {
'bertscore': bert_scores,
'radgraph': rad_scores,
'semb_score': sem_scores,
'bleu_score': bleu_scores
}
class CompositeMetric:
def __init__(self):
scaler = StandardScaler(with_mean=True, with_std=True)
# learnt parameters, infered from
# https://github.com/rajpurkarlab/CXR-Report-Metric/blob/main/CXRMetric/run_eval.py#L219
scaler.mean_ = np.array([0.53792312, 0.61757256, 0.76479421, 0.44738335])
scaler.scale_ = np.array([0.30282584, 0.22430938, 0.25394391, 0.29892717])
scaler.var_ = np.array([0.09170349, 0.05031470, 0.06448751, 0.08935745])
scaler.n_samples_seen_ = 160 # integer
scaler.n_features_in_ = 4 # integer
self.scaler = scaler
self.coefs = np.array([
-3.77083683e-01, # radgraph weight
-3.70300100e-01, # bertscore weight
-2.52616218e-01, # s-emb weight
4.31504841e-12, # bleu weight
2.46655256e-10 # intercept / bias
])
self.cols = ["radgraph", "bertscore", "semb_score", "bleu_score"]
def predict(self, X):
Xn = self.scaler.transform(X)
Xn = np.hstack([Xn, np.ones((Xn.shape[0], 1))])
return Xn @ self.coefs
def _build_matrix(self, metrics: dict[str, np.ndarray]) -> np.ndarray:
"""Stack features in the canonical column order."""
return np.column_stack([metrics[c] for c in self.cols])
def predict(self, refs, hyps) -> np.ndarray:
"""
Args
----
metrics : dict returned by `radcliq_scores`
Returns
-------
np.ndarray of shape (N,) – RadCliQ-v1 score for each ref/hyp pair.
"""
metrics = radcliq_scores(refs, hyps)
X = self._build_matrix(metrics)
Xn = self.scaler.transform(X)
# Append bias term
Xn = np.hstack([Xn, np.ones((Xn.shape[0], 1))])
scores = Xn @ self.coefs
return 1/scores.mean(), scores
if __name__ == "__main__":
refs = [
"No evidence of pneumothorax following chest tube removal.",
"There is a left pleural effusion.",
"There is a left pleural effusion."
]
hyps = [
"No pneumothorax detected.",
"Left pleural effusion is present.",
"No pneumothorax detected.",
]
# Step-1: compute the four individual metrics
# Step-2: get the RadCliQ-v1 composite
radcliq = CompositeMetric()
mean_scores, detail_scores = radcliq.predict(refs, hyps)
for i, s in enumerate(detail_scores, 1):
print(f"Pair {i}: RadCliQ-v1 = {s:.4f}")
print(f"RadCliQ-v1 score: {mean_scores:.4f}") |