File size: 8,500 Bytes
6e4d123 | 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 | """Evaluate FASTA with a trained composition-features logistic regression (optional labeled CSV)."""
from __future__ import annotations
import argparse
import math
import os
import sys
from pathlib import Path
import joblib
import numpy as np
import pandas as pd
#
# =====================================================================
# Default checkpoint: training output dir or repository `weights/composition_lr/`
# (see evaluation/README.md). Must contain composition_lr_model.joblib (+ calibration CSVs).
# Override via --model-dir.
# =====================================================================
_REPO_ROOT = Path(__file__).resolve().parents[1]
MODEL_CHECKPOINT_DIR = _REPO_ROOT / 'weights' / 'composition_lr'
#
_MODELS_DIR = _REPO_ROOT / 'models'
if str(_MODELS_DIR) not in sys.path:
sys.path.insert(0, str(_MODELS_DIR))
from lr_sequence_composition_baseline import ( # noqa: E402
FEATURE_NAMES,
compute_composition_features,
predict_proba,
standard_amino_acids,
)
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
if _SCRIPT_DIR not in sys.path:
sys.path.insert(0, _SCRIPT_DIR)
import eval_metrics as em # noqa: E402
def resolve_model_dir(cli_value: str | None) -> str:
if cli_value:
return os.path.abspath(cli_value)
return str(MODEL_CHECKPOINT_DIR.resolve())
def load_inference_operating_point(model_dir: str, model_name: str = 'composition_lr') -> tuple[float, float]:
temperature = 1.0
threshold = 0.5
temp_path = os.path.join(model_dir, f'{model_name}_validation_temperature.csv')
if os.path.isfile(temp_path):
try:
df = pd.read_csv(temp_path)
if 'temperature' in df.columns and len(df) > 0:
temperature = float(df['temperature'].iloc[0])
except Exception as e:
print(f'Warning: temperature file: {e}')
summary_path = os.path.join(model_dir, f'{model_name}_validation_validation_diagnostics_summary.csv')
if not os.path.isfile(summary_path):
summary_path = os.path.join(model_dir, f'{model_name}_validation_diagnostics_summary.csv')
if os.path.isfile(summary_path):
try:
sdf = pd.read_csv(summary_path)
if 'optimal_threshold_f1' in sdf.columns and len(sdf) > 0:
threshold = float(sdf['optimal_threshold_f1'].iloc[0])
if 'temperature' in sdf.columns and len(sdf) > 0 and not os.path.isfile(temp_path):
temperature = float(sdf['temperature'].iloc[0])
except Exception as e:
print(f'Warning: diagnostics summary: {e}')
return temperature, threshold
def load_composition_model(model_dir: str):
model_path = os.path.join(model_dir, 'composition_lr_model.joblib')
if not os.path.isfile(model_path):
raise FileNotFoundError(
f'{model_path} not found. Re-run lr_sequence_composition_baseline.py to export '
'composition_lr_model.joblib, or pass --model-dir to a directory that contains it.'
)
bundle = joblib.load(model_path)
clf = bundle['clf']
scaler = bundle['scaler']
feature_names = list(bundle.get('feature_names', FEATURE_NAMES))
if feature_names != FEATURE_NAMES:
print(
f'Warning: checkpoint feature_names differ from current script; '
f'using checkpoint order ({len(feature_names)} features).'
)
return clf, scaler, feature_names
def parse_arguments():
p = argparse.ArgumentParser(
description='Evaluate FASTA with composition LR (gravy, pI, charge, complexity).',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
p.add_argument('--fasta', required=True, help='Input FASTA file')
p.add_argument(
'--model-dir',
default=None,
help='Overrides MODEL_CHECKPOINT_DIR defined at the top of this script',
)
p.add_argument('--output', required=True, help='Output directory')
p.add_argument(
'--csv',
default=None,
help='Optional CSV with id column (variant/sequence_id/id) and label (highFRET/lowFRET)',
)
p.add_argument(
'--no-detailed-metrics',
action='store_true',
help='With --csv: one-line summary only; skip diagnostic CSV/report output',
)
p.add_argument(
'--include-features',
action='store_true',
help='Add per-sequence composition feature columns to the predictions CSV',
)
return p.parse_args()
def read_fasta_robust(fasta_path):
h = None
buf = []
with open(fasta_path, 'r') as f:
for line in f:
line = line.strip()
if not line:
continue
if line.startswith('>'):
if h is not None:
s = ''.join(buf)
if s:
yield (h, s)
h = line[1:].strip()
buf = []
elif h is not None:
buf.append(line)
if h is not None:
s = ''.join(buf)
if s:
yield (h, s)
def main():
args = parse_arguments()
model_dir = resolve_model_dir(args.model_dir)
detailed = args.csv and not args.no_detailed_metrics
os.makedirs(args.output, exist_ok=True)
print(f'Checkpoint directory: {model_dir}')
clf, scaler, feature_names = load_composition_model(model_dir)
temperature, threshold = load_inference_operating_point(model_dir)
print(f'Operating point: temperature={temperature:.4f}, threshold={threshold:.4f}')
sequence_ids: list[str] = []
sequences: list[str] = []
feature_rows: list[dict[str, float]] = []
skipped: list[tuple[str, int, str]] = []
for header, seq in read_fasta_robust(args.fasta):
feats = compute_composition_features(seq)
row = {name: feats[name] for name in feature_names}
if any(math.isnan(v) for v in row.values()):
skipped.append((header, len(standard_amino_acids(seq)), 'nan_composition_features'))
continue
sequence_ids.append(header)
sequences.append(seq)
feature_rows.append(row)
for header, length, reason in skipped:
print(f'Warning: skip {header} ({reason}, canonical_aa_len={length})')
if not sequences:
print('ERROR: No sequences to process.')
return
X = np.array([[row[n] for n in feature_names] for row in feature_rows], dtype=float)
proba = predict_proba(clf, scaler, X)
proba = em.apply_temperature_scaling(proba, temperature)
results = pd.DataFrame(
{
'sequence_id': sequence_ids,
'sequence_length': [len(standard_amino_acids(s)) for s in sequences],
'prediction_probability': proba,
'predicted_class': np.where(proba >= threshold, 'highFRET', 'lowFRET'),
}
)
if args.include_features:
feat_df = pd.DataFrame(feature_rows)
results = pd.concat([results, feat_df], axis=1)
out_predictions = os.path.join(args.output, 'composition_lr_predictions.csv')
results.to_csv(out_predictions, index=False)
print(f'Predictions saved: {out_predictions}')
if skipped:
pd.DataFrame(skipped, columns=['sequence_id', 'length', 'reason']).to_csv(
os.path.join(args.output, 'skipped_sequences.csv'), index=False
)
if not args.csv or not os.path.isfile(args.csv):
print('No labeled CSV; predictions only.')
return
try:
_, _, label_map = em.build_label_map_int(pd.read_csv(args.csv))
except ValueError as e:
print(f'Skipping metrics: {e}')
return
y_true_list: list[int] = []
y_prob_list: list[float] = []
for sid in sequence_ids:
lab = label_map.get(str(sid).strip())
if lab is None:
continue
y_true_list.append(lab)
y_prob_list.append(
float(results.loc[results['sequence_id'] == sid, 'prediction_probability'].iloc[0])
)
if not y_true_list:
print('No overlapping labels between FASTA IDs and CSV. Skipping metrics.')
return
y_true = np.asarray(y_true_list)
y_prob = np.asarray(y_prob_list)
y_pred = (y_prob >= threshold).astype(int)
em.run_supervised_evaluation(
y_true, y_prob, y_pred, 'composition_lr', args.output, threshold, detailed_metrics=detailed
)
if detailed:
print(f'Detailed diagnostics written under {args.output}')
if __name__ == '__main__':
main()
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