|
|
| from dataclasses import dataclass |
| import os |
| @dataclass |
| class FlowConfig: |
| |
| model_type: str = 'hierarchical' |
|
|
| |
| batch_size: int = 32 |
| ntoken: int = 512 |
| d_model: int = 512 |
| lr: float = 1e-5 |
| steps: int = 5000 |
| eta_min: float = 1e-7 |
| devices: str = "1" |
| test_only: bool = False |
| |
| data_name: str = "combosciplex" |
| perturbation_function: str = 'crisper' |
| noise_type: str = "Gaussian" |
| poisson_alpha: float = 0.8 |
| poisson_target_sum: int = -1 |
|
|
| print_every: int = 5000 |
| mode: str = 'predict_y' |
| result_path: str = './result' |
| perturbation_fusion_method: str = 'sum' |
| fusion_method: str = 'cross' |
| infer_top_gene: int = 1000 |
| n_top_genes: int = 5000 |
| checkpoint_path: str = '' |
| gamma: float = 0.0 |
| split_method: str = 'additive' |
| use_mmd_loss: bool = False |
| fold: int = 0 |
| use_negative_edge: bool = False |
| topk: int = 15 |
| |
| def __post_init__(self): |
| if self.data_name == 'norman_umi_go_filtered': |
| self.n_top_genes = 5054 |
| if self.data_name == 'norman': |
| self.n_top_genes = 5000 |
| path = self.make_path() |
|
|
| def make_path(self): |
| exp_name = '-'.join(['flow', |
| f'fusion_{self.fusion_method}', |
| f'{self.data_name}', |
| self.model_type, |
| self.mode, |
| f'gamma_{self.gamma}', |
| f'perturbation_function_{self.perturbation_function}', |
| f'lr_{self.lr}', |
| f'dim_model_{self.d_model}', |
| f'infer_top_gene_{self.infer_top_gene}', |
| f'split_method_{self.split_method}', |
| f'use_mmd_loss_{self.use_mmd_loss}', |
| f'fold_{self.fold}', |
| f'use_negative_edge_{self.use_negative_edge}', |
| f'topk_{self.topk}', |
| ]) |
| return os.path.join(self.result_path, exp_name) |
