File size: 4,990 Bytes
4321e7e |
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 |
import os
import glob
import sys
import argparse
import pandas as pd
import torch
from transformers import AutoTokenizer
import shap
sys.path.append(".")
from utils import seed_everything, save_pickle
from models import PLTNUM, PLTNUM_PreTrainedModel
def parse_args():
parser = argparse.ArgumentParser(
description="Calculate SHAP values with a pretrained protein half-life prediction model."
)
parser.add_argument(
"--data_path",
type=str,
required=True,
help="Path to the input data.",
)
parser.add_argument(
"--model",
type=str,
default="westlake-repl/SaProt_650M_AF2",
help="Pretrained model name or path.",
)
parser.add_argument(
"--architecture",
type=str,
default="SaProt",
help="Model architecture: 'ESM2', 'SaProt', or 'LSTM'.",
)
parser.add_argument(
"--folds",
type=int,
default=10,
help="The number of folds for prediction.",
)
parser.add_argument(
"--do_cross_validation",
action="store_true",
default=False,
help="Use cross validation for prediction. If True, you have to specify the 'data_path' that contanins fold information, 'folds' for the number of folds, and 'model_path' for the directory of the model weights.",
)
parser.add_argument(
"--model_path",
type=str,
required=False,
help="Path to the model weight(s).",
)
parser.add_argument("--batch_size", type=int, default=4, help="Batch size.")
parser.add_argument(
"--seed",
type=int,
default=42,
help="Seed for reproducibility.",
)
parser.add_argument(
"--max_length",
type=int,
default=512,
help="Maximum input sequence length. Two tokens are used fo <cls> and <eos> tokens. So the actual length of input sequence is max_length - 2. Padding or truncation is applied to make the length of input sequence equal to max_length.",
)
parser.add_argument(
"--output_dir",
type=str,
default="./output",
help="Output directory.",
)
parser.add_argument(
"--task",
type=str,
default="classification",
help="Task type: 'classification' or 'regression'.",
)
parser.add_argument(
"--sequence_col",
type=str,
default="aa_foldseek",
help="Column name fot the input sequence.",
)
parser.add_argument(
"--max_evals",
type=int,
default=5000,
help="Number of evaluations for SHAP values calculation.",
)
return parser.parse_args()
def calculate_shap_fn(texts, model, cfg):
if len(texts) == 1:
texts = texts[0]
else:
texts = texts.tolist()
inputs = cfg.tokenizer(
texts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=cfg.max_length,
)
inputs = {k: v.to(cfg.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(inputs)
outputs = torch.sigmoid(outputs).detach().cpu().numpy()
return outputs
if __name__ == "__main__":
config = parse_args()
config.device = "cuda" if torch.cuda.is_available() else "cpu"
if not os.path.exists(config.output_dir):
os.makedirs(config.output_dir)
seed_everything(config.seed)
df = pd.read_csv(config.data_path)
config.tokenizer = AutoTokenizer.from_pretrained(config.model)
if config.do_cross_validation:
model_weights = glob.glob(os.path.join(config.model_path, "*.pth"))
for fold in range(config.folds):
model = PLTNUM(config).to(config.device)
model_weight = [w for w in model_weights if f"fold{fold}.pth" in w][0]
model.load_state_dict(torch.load(model_weight, map_location="cpu"))
model.eval()
df_fold = df[df["fold"] == fold].reset_index(drop=True)
explainer = shap.Explainer(lambda x: calculate_shap_fn(x, model, config), config.tokenizer)
shap_values = explainer(
df_fold[config.sequence_col].values.tolist(),
batch_size=config.batch_size,
max_evals=config.max_evals,
)
save_pickle(os.path.join(config.output_dir, f"shap_values_fold{fold}.pickle"), shap_values)
else:
model = PLTNUM_PreTrainedModel.from_pretrained(config.model_path, cfg=config).to(config.device)
model.eval()
# build an explainer using a token masker
explainer = shap.Explainer(lambda x: calculate_shap_fn(x, model, config), config.tokenizer)
shap_values = explainer(
df[config.sequence_col].values.tolist(),
batch_size=config.batch_size,
max_evals=config.max_evals,
)
save_pickle(
os.path.join(config.output_dir, "shap_values.pickle"), shap_values
) |