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f4b267d | 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 | """
Pytest fixtures for conformal protein retrieval tests.
"""
import numpy as np
import pytest
import tempfile
import os
@pytest.fixture
def sample_fasta_file():
"""Create a temporary FASTA file for testing."""
content = """>protein1 | test protein 1
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH
>protein2 | test protein 2
MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYK
>protein3 | short sequence
ACDEFGHIKLMNPQRSTVWY
"""
with tempfile.NamedTemporaryFile(mode='w', suffix='.fasta', delete=False) as f:
f.write(content)
f.flush()
yield f.name
os.unlink(f.name)
@pytest.fixture
def sample_embeddings():
"""Create sample embeddings for testing FAISS operations."""
np.random.seed(42)
# 10 query embeddings, 100 lookup embeddings, 128-dimensional
query_embeddings = np.random.randn(10, 128).astype(np.float32)
lookup_embeddings = np.random.randn(100, 128).astype(np.float32)
return query_embeddings, lookup_embeddings
@pytest.fixture
def scope_like_data():
"""
Create synthetic data similar to SCOPe experiment structure.
Based on notebook: 400 queries x 14777 lookup, but we use smaller
sizes for fast testing: 40 queries x 100 lookup.
"""
np.random.seed(42)
n_queries = 40
n_lookup = 100
# Similarity scores in realistic range (0.999 to 1.0 for protein-vec)
sims = np.random.uniform(0.9993, 0.99999, size=(n_queries, n_lookup)).astype(np.float32)
# Make ~10% exact matches (higher similarity)
labels = np.random.random((n_queries, n_lookup)) < 0.1
# Exact matches should have higher similarity
sims[labels] = np.random.uniform(0.9998, 0.99999, size=labels.sum()).astype(np.float32)
return sims, labels
@pytest.fixture
def calibration_test_split(scope_like_data):
"""Split data into calibration and test sets (like notebooks do 300/100)."""
sims, labels = scope_like_data
n_calib = 30 # 75% for calibration
indices = np.random.permutation(len(sims))
cal_idx = indices[:n_calib]
test_idx = indices[n_calib:]
return {
'cal_sims': sims[cal_idx],
'cal_labels': labels[cal_idx],
'test_sims': sims[test_idx],
'test_labels': labels[test_idx],
}
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