parser.add_argument("--batch_size", type=int, default=32,
help="size of batch")
parser.add_argument("--ligand_receptor_pair_path", type=str, default="mouse_ligand_receptor_pair",
help="gene ligand receptor pair path")
parser.add_argument("--pretrained_model_path", type=str, default="checkpoints_default.pth",
help="pretrained_model_path")
parser.add_argument("--load_pretrained_model", type=int, default=0,
help="load_pretrained_model")
parser.add_argument("--save_model_path", type=str, default="checkpoints_default.pth",
help="save_model_path")
parser.add_argument("--train_dataset", type=str, default="train_dataset",
help="train dataset")
parser.add_argument("--test_dataset", type=str, default="test_dataset",
help="test dataset")
parser.add_argument("--just_train", type=int, default=0,
help="nothing, for debug")
parser.add_argument("--each_dataset_size", type=int, default=0,
help="0 represent all")
parser.add_argument("--using_mmd", type=int, default=0,
help="if using mmd loss, 0 is not using")
params = parser.parse_args()
print(params)
set_seed(params.random_seed)
# print(random.random())
# print(np.random.random())
# print(torch.rand(2))
trainer = Trainer(params)
trainer.train()
def load_tissue(params=None):
random_seed = params.random_seed
dense_dim = params.dense_dim
set_seed(random_seed)
# 400 0.7895
# 200 0.5117
# 100 0.3203
# 50 0.2083
"""
root = '../data/mammary_gland'
num = 2915
data_path = f'{root}/mouse_Mammary_gland{num}_data.csv'
type_path = f'{root}/mouse_Mammary_gland{num}_celltype.csv'
"""
data_path = '../data/mouse_data/mouse_brain_2915_data.csv'
type_path = '../data/mouse_data/mouse_brain_2915_celltype.csv'
# load celltype file then update labels accordingly
cell2type = pd.read_csv(type_path, index_col=0)
cell2type.columns = ['cell', 'type']
id2label = cell2type['type'].drop_duplicates(keep='first').tolist()
label2id = {label: idx for idx, label in enumerate(id2label)}
print(f'{len(id2label)} classes in total')
cell2type['id'] = cell2type['type'].map(label2id)
assert not cell2type['id'].isnull().any(), 'something wrong about celltype file.'
# load data file
data = pd.read_csv(data_path, index_col=0)
data = data.transpose(copy=True)
assert cell2type['cell'].tolist() == data.index.tolist()
print(f'{data.shape[0]} cells, {data.shape[1]} genes.')
# genes
id2gene = data.columns.tolist()
gene2id = {gene: idx for idx, gene in enumerate(id2gene)}
# construct graph and add nodes and edges
graph = dgl.DGLGraph()
start = time()
# 1. add all genes as nodes
num_genes = len(id2gene)
graph.add_nodes(num_genes)
# maintain a kind of sparse idx for Graph
row_idx, col_idx = data.to_numpy().nonzero()
row_idx = row_idx + num_genes
# 2. add cell nodes and edges
num_cells = data.shape[0]
graph.add_nodes(num_cells)
graph.add_edges(row_idx, col_idx)
graph.add_edges(col_idx, row_idx)
print(f'Added {num_cells} nodes and {len(row_idx)} edges.')
print(f'#Nodes: {graph.number_of_nodes()}, #Edges: {graph.number_of_edges()}.')
print(data.head())
# reduce sparse features to dense features
cell_pca = PCA(n_components=dense_dim, random_state=random_seed)
cell_pca.fit(data.values)
cell_feat = cell_pca.transform(data.values)
cell_feat = torch.FloatTensor(cell_feat)
gene_pca = PCA(n_components=dense_dim, random_state=random_seed)
gene_pca.fit(data.T.values)
gene_feat = gene_pca.transform(data.T.values)
gene_feat = torch.FloatTensor(gene_feat)
feat = torch.cat([gene_feat, cell_feat], dim=0)
# feat = torch.zeros(graph.number_of_nodes(), dense_dim).normal_()
cell_evr = sum(cell_pca.explained_variance_ratio_) * 100
gene_evr = sum(gene_pca.explained_variance_ratio_) * 100
print(f'[PCA] Cell EVR: {cell_evr:.2f}%. Gene EVR: {gene_evr:.2f} %.')
# generate labels for training and testing
labels = torch.LongTensor(cell2type['id'].tolist())
train_mask = torch.zeros(num_cells, dtype=torch.bool)
train_randidx = torch.randperm(num_cells)[:int(num_cells * 0.8)]
# generate mask
train_mask[train_randidx] = True
test_mask = ~train_mask
return num_cells, num_genes, graph, feat, labels, train_mask, test_mask
model_name = type(model).__name__
# TODO: path
save_path = "..."
os.makedirs(save_path, exist_ok=True)
torch.save(model.state_dict(), save_path)
print(f"saving model done!")
return save_path
if __name__ == '__main__':
cfg = cfg()
args = cfg.get_args()
cfg.update_train_configs(args)
set_seed(cfg.random_seed)
pprint.pprint(args)
logger = initialize_exp(cfg)
logger_path = get_dump_path(cfg)
if not cfg.no_tensorboard:
writer = SummaryWriter(
log_dir=os.path.join(logger_path, 'tensorboard'))
# print("print c to continue...")
torch.cuda.set_device(cfg.gpu)
runner = Runner(cfg)
runner.run()