netmap.utils.netmap_config.NetmapConfig

class netmap.utils.netmap_config.NetmapConfig(input_data: str = 'data.h5ad', layer: str = 'X', output_directory: str = 'netmap', transcription_factors: str = '', tf_only: bool = True, penalize_error: bool = True, adata_filename: str = 'grn_lrp.h5ad', grn: str = 'grn_lrp.tsv', masking_percentage: float = 0.1, print_every: int = 100, optimizer: str = 'Adam', learning_rate: float = 0.005, epochs: int = 10000, n_models: int = 20, validation_size: float = 0.2, model: str = 'NegativeBinomialAutoencoder', xai_method: str = 'GradientShap', aggregation_strategy: str = 'mean')

__init__(input_data: str = 'data.h5ad', layer: str = 'X', output_directory: str = 'netmap', transcription_factors: str = '', tf_only: bool = True, penalize_error: bool = True, adata_filename: str = 'grn_lrp.h5ad', grn: str = 'grn_lrp.tsv', masking_percentage: float = 0.1, print_every: int = 100, optimizer: str = 'Adam', learning_rate: float = 0.005, epochs: int = 10000, n_models: int = 20, validation_size: float = 0.2, model: str = 'NegativeBinomialAutoencoder', xai_method: str = 'GradientShap', aggregation_strategy: str = 'mean') → None

Methods

__init__([input_data, layer, ...])
read_yaml(yaml_file)
write_yaml(yaml_file)

Attributes

adata_filename
aggregation_strategy
epochs
grn
input_data
layer
learning_rate
masking_percentage
model
n_models
optimizer
output_directory
penalize_error
print_every
tf_only
transcription_factors
validation_size
xai_method