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from dataclasses import dataclass
import os
@dataclass
class FlowConfig:
# Flow model type
model_type: str = 'hierarchical'
# Flow Matching specific parameters
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
# Perturbation related parameters
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' # predict_y, predict_p
result_path: str = './result'
perturbation_fusion_method: str = 'sum' # mlp, sum
fusion_method: str = 'cross' # cross , concat, add
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) |