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import os
import h5py
from pathlib import Path
import yaml
import pickle
import concurrent.futures
from tqdm import tqdm
import json
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple, Optional
import torch
from .constants import *
# load combined epitopes csv
def load_epitopes_csv(csv_name: str = "epitopes.csv") -> pd.DataFrame:
epitopes_csv = Path(BASE_DIR) / "data" / 'epitopes' / csv_name
if not epitopes_csv.exists():
raise FileNotFoundError(f"[Error] Epitopes CSV not found at {epitopes_csv}")
df = pd.read_csv(epitopes_csv)
if df.empty:
print(f"[Warning] The CSV {epitopes_csv} is empty.")
return None
unique_protein_chains = set()
epitope_dict = {}
for _, row in df.iterrows():
antigen_name = row.get("antigen_name", "N/A")
binary_label = row.get("binary_label", "")
# Split antigen name into pdb and chain
if "_" in antigen_name:
pdb, chain = antigen_name.split("_", 1)
else:
pdb, chain = antigen_name, "A" # Default to chain A if no underscore
unique_protein_chains.add((pdb, chain))
# Convert binary_label string to list of integers
if isinstance(binary_label, str):
binary_list = [int(char) for char in binary_label if char in ['0', '1']]
else:
binary_list = []
epitope_dict[antigen_name] = binary_list
return df, list(unique_protein_chains), epitope_dict
def load_epitopes_csv_single(
csv_path: str = None
) -> pd.DataFrame:
if csv_path is None:
epitopes_csv = Path(BASE_DIR)/ "data" / "epitopes" / "epitopes_13.csv"
else:
epitopes_csv = Path(csv_path)
if not epitopes_csv.exists():
raise FileNotFoundError(f"[Error] Epitopes CSV not found at {epitopes_csv}")
df = pd.read_csv(epitopes_csv)
if df.empty:
print(f"[Warning] The CSV {epitopes_csv} is empty.")
return None
unique_protein_chains = set()
epitope_dict = {}
for _, row in df.iterrows():
antigen = row.get("antigen_chain", "N/A")
epitopes = row.get("epitopes", "")
pdb, chain = antigen.split("_")
unique_protein_chains.add((pdb, chain))
# Parse out epitope residue numbers
epitope_nums = []
if isinstance(epitopes, str):
for e in epitopes.split(","):
e = e.strip()
if "_" in e:
parts = e.split("_", 1)
try:
ep_num = int(parts[0])
epitope_nums.append(ep_num)
except ValueError:
pass
epitope_dict[antigen] = epitope_nums
return df, list(unique_protein_chains), epitope_dict
def load_species(species_path: str = f"{BASE_DIR}/data/species.json") -> Dict[str, Dict[str, str]]:
with open(species_path, "r") as f:
species = json.load(f)
return species
def load_data_split(data_split: str, verbose: bool = True) -> List[Tuple[str, str]]:
"""
Load the antigens for the specified data split.
Args:
data_split: Data split name ('train', 'val', 'test')
Returns:
List of (pdb_id, chain_id) tuples for the split
"""
splits_file = Path(BASE_DIR) / "data" / "epitopes" / "data_splits.json"
if not splits_file.exists():
if verbose:
print(f"Data splits file not found: {splits_file}")
print("Using all antigens from load_epitopes_csv()")
_, antigens, _ = load_epitopes_csv()
return antigens
try:
with open(splits_file, 'r') as f:
splits = json.load(f)
if data_split not in splits:
raise KeyError(f"Split '{data_split}' not found in splits file")
antigens = [(item[0], item[1]) for item in splits[data_split]]
if verbose:
print(f"Loaded {len(antigens)} antigens for {data_split} split")
return antigens
except Exception as e:
if verbose:
print(f"Error loading data splits: {str(e)}")
print("Falling back to load_epitopes_csv()")
_, antigens, _ = load_epitopes_csv()
return antigens
def load_split_antigens(base_dir=DISK_DIR / "data", split="train"):
"""
Load the antigen list for a specific data split.
Args:
base_dir (str): Base directory where split files are stored
split (str): One of "train", "val", or "test"
Returns:
list: List of (pdb_id, chain_id) tuples for the specified split
"""
base_dir = Path(base_dir)
pickle_path = base_dir / f"{split}_antigens.pkl"
if not pickle_path.exists():
raise FileNotFoundError(f"Split file not found: {pickle_path}")
with open(pickle_path, "rb") as f:
antigens = pickle.load(f)
print(f"[INFO] Loaded {len(antigens)} antigens for {split} split")
return antigens
# load protein embedding extracted by ESM-C
def load_protein_embedding(
pdb_chain: Tuple[str, str],
embedding_dir: Optional[str] = None,
mode: str = "full"
) -> np.ndarray:
"""
Retrieve either full or mean embeddings for a given (PDB ID, Chain ID) pair.
Args:
pdb_chain (Tuple[str, str]): Protein identifier (pdb_id, chain_id).
embedding_dir (Optional[str]): Directory where embeddings are stored.
mode (str): "mean" to retrieve mean embedding (from .npy) or
"full" to retrieve full embedding (from .h5).
Returns:
np.ndarray: The requested embedding (mean: (embed_dim,), full: (seq_len, embed_dim)).
"""
pdb_id, chain_id = pdb_chain
# Handle default embedding directory selection
if embedding_dir is None:
embedding_dir = EMBEDDING_DIR if mode == "mean" else FULL_EMBEDDING_DIR
embedding_dir = Path(embedding_dir) / "esmc-6b"
embedding_dir = Path(embedding_dir) # Ensure it's a Path object
if mode == "mean":
mean_file = embedding_dir / f"{pdb_id}_{chain_id}.npy"
if not mean_file.exists():
raise FileNotFoundError(f"Mean embedding file not found: {mean_file}")
return np.load(mean_file) # Shape: (embed_dim,)
elif mode == "full":
full_file = embedding_dir / f"{pdb_id}_{chain_id}.h5"
if not full_file.exists():
raise FileNotFoundError(f"Full embedding file not found: {full_file}")
with h5py.File(full_file, "r") as h5f:
return h5f["embedding"][:] # Shape: (seq_len, embed_dim)
else:
raise ValueError("Invalid mode. Use 'mean' or 'full'.")
# load EGNN model
def load_egnn_model(model_name: str = "best_large_egnn.bin", layer_type: str = "EGNNWithAngleLayer", model_type: str = "Angle",
verbose: bool = True):
"""
Load the best trained EGNN model from a checkpoint.
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = BASE_DIR / "models" / model_name
model_path = Path(model_path)
if 'small' in model_name:
hidden_dim = 640
n_layers = 1
else:
hidden_dim = 1280
n_layers = 2
# Load the trained EGNN model
if model_type == "Angle":
model = EGNNWithAngle(in_dim=2560, hidden_dim=hidden_dim, n_layers=n_layers, layer_type=layer_type).to(device)
elif model_type == "Attention":
model = EGNNWithAttention(in_dim=2560, hidden_dim=hidden_dim, n_layers=n_layers).to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
if verbose:
print(f"[INFO] Successfully loaded EGNN model from {model_path}")
return model, device
# load SBEP model
def load_sbep(model_name: str = "best_model.bin", timestamp: str = None, version: int = 3, device_id: int = 1, verbose: bool = True):
"""
Load the trained SBEP model from a checkpoint with enhanced architecture mismatch handling.
Args:
model_name (str): Name of the model file (default: "best_model.bin")
timestamp (str): Timestamp of the model run (e.g., "20250326_084627")
verbose (bool): Whether to print loading information
Returns:
model (SBEP): The trained model ready for inference
device (torch.device): The device (CPU/GPU) used for inference
"""
from bce.model.model import SBEP # Import here to avoid circular imports
import torch
from pathlib import Path
from bce.utils.constants import BASE_DIR
# Detect device
# Check available GPUs and their memory
if torch.cuda.is_available() and device_id >= 0:
device = torch.device(f"cuda:{device_id}")
else:
device = torch.device("cpu")
# Construct model path
if timestamp:
model_path = BASE_DIR / "results" / "sbep" / timestamp / model_name.replace('.bin', '')
else:
# If no timestamp provided, use the latest model
results_dir = BASE_DIR / "results" / "sbep"
if not results_dir.exists():
raise FileNotFoundError(f"Results directory not found: {results_dir}")
# Get the latest timestamp directory
timestamps = [d for d in results_dir.iterdir() if d.is_dir()]
if not timestamps:
raise FileNotFoundError("No model runs found")
latest_dir = max(timestamps, key=lambda x: x.name)
model_path = latest_dir / model_name.replace('.bin', '')
if verbose:
print(f"Using latest model from: {latest_dir.name}")
# Check if either the path or path with .bin extension exists
if not model_path.exists():
model_path = model_path.with_suffix('.bin')
if not model_path.exists():
raise FileNotFoundError(f"Model file not found at {model_path}")
# Load model with architecture mismatch handling
try:
if verbose:
print(f"Loading model from: {model_path}")
# First try loading with strict=False to handle size mismatches
if version == 1:
model = SBEP.load(model_path, device=device, strict=False, verbose=verbose)
elif version == 2:
model = SBEP_v2.load(model_path, device=device, strict=False, verbose=verbose)
elif version == 3:
model = SBEP_v3.load(model_path, device=device, strict=False, verbose=verbose)
elif version == 4:
model = SBEP_v4.load(model_path, device=device, strict=False, verbose=verbose)
else:
raise ValueError(f"Invalid version: {version}")
model.eval()
if verbose:
print(f"[INFO] Model loaded (some parameters may not match current architecture)")
print(f"[INFO] Model architecture: {model.__class__.__name__}")
print(f"[INFO] Model device: {next(model.parameters()).device}")
return model, device
except Exception as e:
error_msg = f"Failed to load model from {model_path}: {str(e)}"
if verbose:
print(f"[ERROR] {error_msg}")
raise RuntimeError(error_msg) |