bioneuralnet.downstream_task.subject_representation

Functions

generate_hidden_dims(init_dim[, min_dim])	Generates a list of hidden dimensions by halving the initial dimension until the minimum is reached.
get_activation(activation)	Maps a string to a PyTorch activation module.
get_logger(name)	Retrieves a global logger configured to write to 'bioneuralnet.log' at the project root.

Classes

ASHAScheduler	alias of AsyncHyperBandScheduler
Any(*args, **kwargs)	Special type indicating an unconstrained type.
AutoEncoder(*args, **kwargs)	Generic Autoencoder for configurable reduction.
CLIReporter(*[, metric_columns, ...])	Command-line reporter
Path(*args, **kwargs)	PurePath subclass that can make system calls.
SubjectRepresentation(omics_data, embeddings)	SubjectRepresentation Class for Integrating Network Embeddings into Omics Data.
datetime(year, month, day[, hour[, minute[, ...)	The year, month and day arguments are required.

Exceptions

TuneError

General error class raised by ray.tune.

class bioneuralnet.downstream_task.subject_representation.AutoEncoder(*args: Any, **kwargs: Any)

Bases: Module

Generic Autoencoder for configurable reduction. Builds encoder and decoder layers based on a list of hidden dimensions. Allows tuning of dropout, activation, and network architecture.

forward(x)

class bioneuralnet.downstream_task.subject_representation.SubjectRepresentation(omics_data: DataFrame, embeddings: DataFrame, phenotype_data: DataFrame | None = None, phenotype_col: str = 'phenotype', reduce_method: str = 'AE', seed: int | None = None, tune: bool | None = False, output_dir: str | None = None)

Bases: object

SubjectRepresentation Class for Integrating Network Embeddings into Omics Data.

run() → DataFrame

Executes the Subject Representation workflow. If tuning is enabled, runs hyperparameter tuning and uses the best config to reduce embeddings. Otherwise, uses the default reduction method. :returns:

• Enhanced omics data as a DataFrame.

bioneuralnet.downstream_task.subject_representation.generate_hidden_dims(init_dim: int, min_dim: int = 2) → List[int]

Generates a list of hidden dimensions by halving the initial dimension until the minimum is reached. For example, if init_dim is 64, this returns [64, 32, 16, 8, 4, 2].

bioneuralnet.downstream_task.subject_representation.get_activation(activation: str)

Maps a string to a PyTorch activation module.