Spaces:
Running
Running
File size: 8,216 Bytes
d29b763 | 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 | """Evaluate the locally generated checkpoint against the processed DDInter dataset.
Produces:
- MEDCARE-DDI-AI/models/eval/metrics_summary.json
- MEDCARE-DDI-AI/models/eval/confusion_matrix.png
- MEDCARE-DDI-AI/models/eval/inference_validation_report.md
The script re-uses preprocessing logic from predictor.py to ensure consistency.
"""
from __future__ import annotations
import json
import math
from collections import Counter
from pathlib import Path
from typing import Any
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from preprocessing.artifact_manager import manager
import torch
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, recall_score
from predictor import (
BASE_DIR,
DATA_PATH,
MODEL_PATH,
LABEL_NAMES,
LABEL_TO_INDEX,
INDEX_TO_LABEL,
normalize_name,
canonical_pair_key,
HybridDDIPredictor,
)
OUT_DIR = MODEL_PATH.parent / 'eval'
OUT_DIR.mkdir(parents=True, exist_ok=True)
def build_pairs_from_csv(df: pd.DataFrame) -> pd.DataFrame:
# For evaluation: collapse multiple evidence rows into one canonical pair
pairs = {}
for _, row in df.iterrows():
a = str(row['Drug_A']).strip()
b = str(row['Drug_B']).strip()
level = str(row['Level']).strip().lower()
key = canonical_pair_key(a, b)
if key not in pairs:
pairs[key] = Counter()
pairs[key][level] += 1
records = []
for (a, b), counter in pairs.items():
# majority label
label, _ = counter.most_common(1)[0]
records.append({'drug_a': a, 'drug_b': b, 'label': label, 'support': sum(counter.values())})
return pd.DataFrame(records)
def evaluate(predictor: HybridDDIPredictor, eval_df: pd.DataFrame) -> dict[str, Any]:
y_true = []
y_pred = []
oov_count = 0
for _, row in eval_df.iterrows():
a = row['drug_a']
b = row['drug_b']
label = row['label']
y_true.append(LABEL_TO_INDEX.get(label, 0))
# Use predictor internals to produce logits and probabilities
a_id = predictor._find_vocab_id(a)
b_id = predictor._find_vocab_id(b)
if a_id == 0 or b_id == 0:
oov_count += 1
with torch.no_grad():
logits = predictor.model(torch.tensor([a_id], dtype=torch.long), torch.tensor([b_id], dtype=torch.long))
probs = torch.softmax(logits, dim=-1).squeeze(0).cpu().numpy()
pred_idx = int(np.argmax(probs).item())
y_pred.append(pred_idx)
y_true = np.array(y_true)
y_pred = np.array(y_pred)
acc = float(accuracy_score(y_true, y_pred))
macro_f1 = float(f1_score(y_true, y_pred, average='macro', zero_division=0))
# severe recall corresponds to 'major' label
if 'major' in LABEL_TO_INDEX:
major_idx = LABEL_TO_INDEX['major']
severe_recall = float(recall_score(y_true, y_pred, labels=[major_idx], average='macro', zero_division=0))
else:
severe_recall = 0.0
cm = confusion_matrix(y_true, y_pred, labels=list(range(len(predictor.label_names))))
metrics = {
'accuracy': round(acc, 4),
'macro_f1': round(macro_f1, 4),
'severe_recall': round(severe_recall, 4),
'num_examples': int(len(eval_df)),
'oov_count': int(oov_count),
'oov_rate': round(float(oov_count) / max(1, len(eval_df)), 4),
}
return metrics, cm, y_true, y_pred
def save_confusion_matrix(cm: np.ndarray, labels: list[str], out_path: Path) -> None:
fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]), xticklabels=labels, yticklabels=labels, ylabel='True label', xlabel='Predicted label', title='Confusion Matrix')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(int(cm[i, j]), 'd'), ha='center', va='center', color='white' if cm[i, j] > thresh else 'black')
fig.tight_layout()
fig.savefig(out_path, dpi=150)
plt.close(fig)
def main() -> None:
print('Loading checkpoint and predictor...')
if not MODEL_PATH.exists():
raise FileNotFoundError(f'Checkpoint not found at {MODEL_PATH}')
predictor = HybridDDIPredictor.from_default_paths()
print('Loading processed dataset...')
df = manager.load_artifact('ddinter_combined')
eval_df = build_pairs_from_csv(df)
print(f'Prepared {len(eval_df)} canonical pairs for evaluation')
metrics, cm, y_true, y_pred = evaluate(predictor, eval_df)
# Additional checks: preprocessing consistency
metadata = {
'model_version': predictor.model_version,
'vocab_size_checkpoint': len(predictor.vocab),
'vocab_size_used_by_model': predictor.model.embedding.num_embeddings - 1,
'embedding_dim_checkpoint': predictor.embedding_dim,
'model_embedding_dim': predictor.model.embedding.embedding_dim,
'label_names': predictor.label_names,
'index_to_label': predictor.index_to_label,
'num_eval_pairs': len(eval_df),
}
metrics.update(metadata)
# Save metrics JSON
metrics_path = OUT_DIR / 'metrics_summary.json'
with metrics_path.open('w', encoding='utf-8') as fh:
json.dump(metrics, fh, indent=2)
# Save confusion matrix PNG
cm_path = OUT_DIR / 'confusion_matrix.png'
save_confusion_matrix(cm, predictor.label_names, cm_path)
# Generate simple report
report_lines = []
report_lines.append('# Inference Validation Report')
report_lines.append('')
report_lines.append(f'- Model version: {predictor.model_version}')
report_lines.append(f'- Eval pairs: {len(eval_df)}')
report_lines.append(f"- Vocab size (checkpoint): {metadata['vocab_size_checkpoint']}")
report_lines.append(f"- Vocab size (model): {metadata['vocab_size_used_by_model']}")
report_lines.append(f"- Embedding dim (checkpoint): {metadata['embedding_dim_checkpoint']}")
report_lines.append(f"- Embedding dim (model): {metadata['model_embedding_dim']}")
report_lines.append('')
report_lines.append('## Metrics')
report_lines.append('')
report_lines.append(f"- Accuracy: {metrics['accuracy']}")
report_lines.append(f"- Macro F1: {metrics['macro_f1']}")
report_lines.append(f"- Severe (major) recall: {metrics['severe_recall']}")
report_lines.append(f"- OOV count: {metrics['oov_count']} (rate: {metrics['oov_rate']})")
report_lines.append('')
report_lines.append('## Confusion matrix')
report_lines.append(f'Confusion matrix saved to `{cm_path}`')
report_lines.append('')
report_lines.append('## Preprocessing & Consistency Checks')
report_lines.append('- Label ordering (checkpoint): ' + ', '.join(predictor.label_names))
report_lines.append('- Index to label mapping:')
report_lines.append('')
for idx, label in predictor.index_to_label.items():
report_lines.append(f'- {idx} -> {label}')
report_lines.append('')
report_lines.append('## Observations & drift checks')
if metrics['oov_rate'] > 0.05:
report_lines.append('- Warning: OOV rate exceeds 5% — incoming drug names differ from training vocabulary.')
else:
report_lines.append('- OOV rate within expected bounds.')
# Healthcare grade quick pass/fail on severe recall
threshold_severe_recall = 0.90
if metrics['severe_recall'] >= threshold_severe_recall:
report_lines.append(f'- Severe recall >= {threshold_severe_recall} (PASS)')
else:
report_lines.append(f'- Severe recall < {threshold_severe_recall} (FAIL) — consider retraining or calibration for higher sensitivity on critical events')
report_path = OUT_DIR / 'inference_validation_report.md'
report_path.write_text('\n'.join(report_lines), encoding='utf-8')
print('Evaluation complete.')
print(f'Metrics JSON: {metrics_path}')
print(f'Confusion matrix: {cm_path}')
print(f'Report: {report_path}')
if __name__ == '__main__':
main()
|