File size: 2,033 Bytes
d346d4e | 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 | """Small CPU baseline with JSON weights. Replace with your own model implementation."""
import json
from pathlib import Path
import re
import numpy as np
AMINO_ACIDS = 'ACDEFGHIKLMNPQRSTVWY'
SMILES_CHARS = 'BCNOFPSIcnops[]=#()+-@/\\1234567890'
FEATURE_NAMES = ['log_sequence_length', 'log_smiles_length'] + list(AMINO_ACIDS) + list(SMILES_CHARS)
def features(rows):
result = []
for row in rows:
seq, smi = row['sequence'], row['smiles']
seq_upper = seq.upper()
result.append([np.log1p(len(seq)), np.log1p(len(smi))]
+ [seq_upper.count(a) / max(1, len(seq)) for a in AMINO_ACIDS]
+ [smi.count(a) / max(1, len(smi)) for a in SMILES_CHARS])
return np.asarray(result, dtype=float)
class Model:
def __init__(self, weights=None):
path = Path(weights) if weights else Path(__file__).parent / 'weights' / 'baseline.json'
self.weights = json.loads(path.read_text())
def predict(self, rows):
x = features(rows)
w = self.weights
return (((x - np.asarray(w['mean'])) / np.asarray(w['scale'])) @ np.asarray(w['coef'])
+ w['intercept']).tolist()
def validate_rows(rows, max_rows=256):
if not isinstance(rows, list) or not 1 <= len(rows) <= max_rows:
raise ValueError(f'Provide between 1 and {max_rows} rows.')
ids = set()
for row in rows:
if not isinstance(row, dict) or set(row) != {'id', 'smiles', 'sequence'}:
raise ValueError('Each row must contain exactly id, smiles, sequence.')
if not isinstance(row['id'], str) or not re.fullmatch(r'[A-Za-z0-9_-]{1,80}', row['id']):
raise ValueError('Invalid row ID.')
if row['id'] in ids:
raise ValueError('Duplicate row ID.')
ids.add(row['id'])
for key, limit in [('sequence', 50000), ('smiles', 20000)]:
if not isinstance(row[key], str) or not 1 <= len(row[key]) <= limit:
raise ValueError(f'Invalid {key}.')
return rows
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