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1d6f391 | 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 | #!/usr/bin/env python3
"""Analyze confidence distribution for ambiguous BPE tokens."""
import sys
import torch
import torch.nn.functional as F
from pathlib import Path
import pickle
import json
import numpy as np
from collections import Counter
from tqdm import tqdm
sys.path.insert(0, str(Path(__file__).parent.parent))
from model.multimodal_glycan_bert_v3 import MultimodalGlycanBERT, MultimodalGlycanBERTConfig
def main():
device = torch.device("cuda")
# Load vocab and ambiguity
with open('../data/bpe_vocabulary_clean.json', 'r') as f:
vocab = json.load(f)
token_to_id = vocab.get('token_to_id', vocab)
id_to_token = {v: k for k, v in token_to_id.items()}
with open('../data/bpe_ambiguity_tokens.json', 'r') as f:
ambig_data = json.load(f)
ambig_ids = set(ambig_data.get('ambiguous_ids', []))
# Load model
print("Loading model...")
checkpoint = torch.load('../checkpoints_v4_bpe_topology/best_v4_bpe_model.pt', map_location=device)
cfg = checkpoint['config']['model'] if 'config' in checkpoint else None
if cfg:
config = MultimodalGlycanBERTConfig(
seq_vocab_size=cfg['sequence']['vocab_size'],
seq_hidden_size=cfg['sequence']['hidden_size'],
seq_num_layers=cfg['sequence']['num_hidden_layers'],
seq_num_heads=cfg['sequence']['num_attention_heads'],
seq_max_length=cfg['sequence']['max_length'],
ms_vocab_size=cfg['mass_spectrometry']['vocab_size'],
ms_hidden_size=cfg['mass_spectrometry']['hidden_size'],
ms_num_layers=cfg['mass_spectrometry']['num_hidden_layers'],
ms_num_heads=cfg['mass_spectrometry']['num_attention_heads'],
ms_max_length=cfg['mass_spectrometry']['max_length'],
struct_vocab_size=cfg['structure_3d']['vocab_size'],
struct_hidden_size=cfg['structure_3d']['hidden_size'],
struct_num_layers=cfg['structure_3d']['num_hidden_layers'],
struct_num_heads=cfg['structure_3d']['num_attention_heads'],
struct_max_length=cfg['structure_3d']['max_length'],
fusion_hidden_size=cfg['fusion']['fusion_hidden_size'],
fusion_num_layers=cfg['fusion']['fusion_num_layers'],
)
model = MultimodalGlycanBERT(config)
model.load_state_dict(checkpoint['model_state_dict'] if 'model_state_dict' in checkpoint else checkpoint)
model.to(device)
model.eval()
# Load sequences (sample)
print("Loading sequences...")
with open('../data/sequences_bpe.pkl', 'rb') as f:
seqs = pickle.load(f)
if isinstance(seqs, dict):
seqs = list(seqs.values())
# Sample 10k sequences for analysis
import random
random.seed(42)
sample = random.sample([s for s in seqs if any(t in ambig_ids for t in s.get('token_ids', []))], min(10000, len(seqs)))
print(f"Analyzing {len(sample)} sequences...")
all_confidences = []
max_length = 256
pad_id = token_to_id.get('[PAD]', 0)
mask_id = token_to_id.get('[MASK]', 4)
with torch.no_grad():
for seq in tqdm(sample):
token_ids = list(seq.get('token_ids', []))[:max_length]
ambig_positions = [i for i, t in enumerate(token_ids) if t in ambig_ids]
if not ambig_positions:
continue
# Pad
attention_mask = [1] * len(token_ids) + [0] * (max_length - len(token_ids))
token_ids = token_ids + [pad_id] * (max_length - len(token_ids))
# Mask ambiguous
masked = token_ids.copy()
for pos in ambig_positions:
masked[pos] = mask_id
# Forward
seq_t = torch.tensor([masked], dtype=torch.long, device=device)
att_t = torch.tensor([attention_mask], dtype=torch.long, device=device)
res_t = torch.zeros_like(seq_t)
outputs = model(
seq_token_ids=seq_t, seq_attention_mask=att_t, seq_residue_ids=res_t,
ms_token_ids=torch.zeros(1, 150, dtype=torch.long, device=device),
ms_attention_mask=torch.zeros(1, 150, dtype=torch.long, device=device),
has_ms=torch.zeros(1, dtype=torch.bool, device=device),
struct_token_ids=torch.zeros(1, 200, dtype=torch.long, device=device),
struct_attention_mask=torch.zeros(1, 200, dtype=torch.long, device=device),
struct_residue_ids=torch.full((1, 200), -1, dtype=torch.long, device=device),
has_3d=torch.zeros(1, dtype=torch.bool, device=device),
return_dict=True,
)
logits = outputs['seq_logits'][0]
for pos in ambig_positions:
probs = F.softmax(logits[pos], dim=-1)
conf = probs.max().item()
pred = probs.argmax().item()
all_confidences.append({
'confidence': conf,
'original': token_ids[pos],
'predicted': pred,
'pred_is_valid': pred not in ambig_ids
})
# Analyze
confs = [c['confidence'] for c in all_confidences]
valid_confs = [c['confidence'] for c in all_confidences if c['pred_is_valid']]
print("\n" + "="*60)
print("CONFIDENCE DISTRIBUTION ANALYSIS")
print("="*60)
print(f"Total ambiguous tokens analyzed: {len(confs)}")
print(f"Predictions to valid tokens: {len(valid_confs)} ({100*len(valid_confs)/len(confs):.1f}%)")
# Histogram
bins = [0.0, 0.3, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0]
print("\nConfidence Distribution (valid predictions only):")
for i in range(len(bins)-1):
cnt = sum(1 for c in valid_confs if bins[i] <= c < bins[i+1])
pct = 100 * cnt / len(valid_confs) if valid_confs else 0
bar = "█" * int(pct/2)
print(f" [{bins[i]:.2f}-{bins[i+1]:.2f}): {cnt:6d} ({pct:5.1f}%) {bar}")
# Cumulative
print("\nCumulative Resolution by Threshold:")
for thresh in [0.5, 0.6, 0.7, 0.8, 0.9]:
above = sum(1 for c in valid_confs if c >= thresh)
pct = 100 * above / len(confs) if confs else 0
print(f" >= {thresh}: {above:6d} tokens ({pct:5.1f}% of total)")
print("="*60)
if __name__ == "__main__":
main()
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