scdynomics.bimodal_pretrain
- scdynomics.bimodal_pretrain(config_path: str, data_corpus_path: str, mod1_layer: str = 'X', mod2_layer: str = 'pan_promoter', valid_fraction: float = 0.001, dataloader_worker: int = 10, pretrainer_ckpt: str = None, token_dict_path: str = '../data/gene_dict/token_dict.json.gz', default_root_dir: str = '../logs', seed: int = 42, ndevices: int = 1, accelerator: str = 'cpu') tuple
Pretraining pipeline for bimodal data
- Parameters:
- config_path:
str The path to the JSON configuration file containing dataloader and trainer hyperparameters.
- data_corpus_path:
str The path to the preprocessed data corpus file.
- mod1_layer:
str(default:"X") The layer name of modality 1 in the data corpus file.
- mod2_layer:
str(default:"pan_promoter") The layer name of modality 2 in the data corpus file.
- valid_fraction:
float(default:0.001) The fraction of the data to be reserved for validation.
- dataloader_worker:
int(default:10) The number of workers to be used for data loading.
- pretrainer_ckpt:
str(default:None) The path to an existing pretrainer checkpoint file. If None, the model will be randomly initialized and trained from scratch.
- token_dict_path:
str(default:"../data/gene_dict/token_dict.json.gz") The path to the token dictionary file used to map genes to tokens.
- default_root_dir:
str(default:"../logs") The default root directory for saving PyTorch Lightning logs and checkpoints.
- seed:
int(default:42) The random seed for reproducibility across runs.
- ndevices:
int(default:1) The number of accelerator devices to be used for training.
- accelerator:
str(default:"cpu") The accelerator to be used for training. It can be “auto”, “cuda”, “mps”, or “cpu”.
- config_path:
- Returns:
tuple - trainer:
pytorch_lightning.Trainer The instantiated and executed pytorch_lightning.Trainer object.
- model:
Masked_Pretrainer The trained pretrainer model object.
- trainer: