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scdeepsort.py
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scdeepsort.py
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import argparse
import pprint
from typing import get_args
import numpy as np
import torch
from dance import logger
from dance.datasets.singlemodality import CellTypeAnnotationDataset
from dance.modules.single_modality.cell_type_annotation.scdeepsort import ScDeepSort
from dance.typing import LogLevel
from dance.utils import set_seed
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=500)
parser.add_argument("--cache", action="store_true", help="Cache processed data.")
parser.add_argument("--dense_dim", type=int, default=400, help="number of hidden gcn units")
parser.add_argument("--device", type=str, default="cpu", help="Computation device")
parser.add_argument("--dropout", type=float, default=0.1, help="dropout probability")
parser.add_argument("--hidden_dim", type=int, default=200, help="number of hidden gcn units")
parser.add_argument("--log_level", type=str, default="INFO", choices=get_args(LogLevel))
parser.add_argument("--lr", type=float, default=1e-3, help="learning rate")
parser.add_argument("--n_epochs", type=int, default=300, help="number of training epochs")
parser.add_argument("--n_layers", type=int, default=1, help="number of hidden gcn layers")
parser.add_argument("--species", default="mouse", type=str)
parser.add_argument("--test_dataset", nargs="+", type=int, default=[1759], help="Testing dataset IDs")
parser.add_argument("--test_rate", type=float, default=0.2)
parser.add_argument("--tissue", default="Spleen", type=str)
parser.add_argument("--train_dataset", nargs="+", type=int, default=[1970], help="List of training dataset ids.")
parser.add_argument("--weight_decay", type=float, default=5e-4, help="Weight for L2 loss")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num_runs", type=int, default=1)
args = parser.parse_args()
logger.setLevel(args.log_level)
logger.info(f"Running SVM with the following parameters:\n{pprint.pformat(vars(args))}")
scores = []
for seed in range(args.seed, args.seed + args.num_runs):
set_seed(seed)
# Initialize model and get model specific preprocessing pipeline
model = ScDeepSort(args.dense_dim, args.hidden_dim, args.n_layers, args.species, args.tissue,
dropout=args.dropout, batch_size=args.batch_size, device=args.device)
preprocessing_pipeline = model.preprocessing_pipeline(n_components=args.dense_dim)
# Load data and perform necessary preprocessing
dataloader = CellTypeAnnotationDataset(species=args.species, tissue=args.tissue, test_dataset=args.test_dataset,
train_dataset=args.train_dataset)
data = dataloader.load_data(transform=preprocessing_pipeline, cache=args.cache)
# Obtain training and testing data
y_train = data.get_y(split_name="train", return_type="torch").argmax(1)
y_test = data.get_y(split_name="test", return_type="torch")
num_labels = y_test.shape[1]
# Get cell feature graph for scDeepSort
# TODO: make api for the following block?
g = data.data.uns["CellFeatureGraph"]
num_genes = data.shape[1]
gene_ids = torch.arange(num_genes)
train_cell_ids = torch.LongTensor(data.train_idx) + num_genes
test_cell_ids = torch.LongTensor(data.test_idx) + num_genes
g_train = g.subgraph(torch.concat((gene_ids, train_cell_ids)))
g_test = g.subgraph(torch.concat((gene_ids, test_cell_ids)))
# Train and evaluate the model
model.fit(g_train, y_train, epochs=args.n_epochs, lr=args.lr, weight_decay=args.weight_decay,
val_ratio=args.test_rate)
score = model.score(g_test, y_test)
scores.append(score.item())
print(f"{score=:.4f}")
print(f"scDeepSort {args.species} {args.tissue} {args.test_dataset}:")
print(f"{scores}\n{np.mean(scores):.5f} +/- {np.std(scores):.5f}")
"""To reproduce the benchmarking results, please run the following command:
Mouse Brain
$ python scdeepsort.py --species mouse --tissue Brain --train_dataset 753 3285 --test_dataset 2695
Mouse Spleen
$ python scdeepsort.py --species mouse --tissue Spleen --train_dataset 1970 --test_dataset 1759
Mouse Kidney
$ python scdeepsort.py --species mouse --tissue Kidney --train_dataset 4682 --test_dataset 203
"""