bioneuralnet.network_embedding.gnn_embedding
Functions
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Retrieves a global logger configured to write to 'bioneuralnet.log' at the project root. |
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Compute the sample skewness of a data set. |
Classes
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alias of |
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Command-line reporter |
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GNNEmbedding Class for Generating Graph Neural Network (GNN) Based Embeddings. |
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PurePath subclass that can make system calls. |
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The year, month and day arguments are required. |
- class bioneuralnet.network_embedding.gnn_embedding.GNNEmbedding(adjacency_matrix: DataFrame, omics_data: DataFrame, phenotype_data: Series | DataFrame, clinical_data: DataFrame | None = None, phenotype_col: str = 'phenotype', model_type: str = 'GAT', hidden_dim: int = 64, layer_num: int = 4, dropout: bool | float = True, num_epochs: int = 100, lr: float = 0.001, weight_decay: float = 0.0001, gpu: bool = False, activation: str = 'relu', seed: int | None = None, tune: bool | None = False, output_dir: str | None = None)[source]
Bases:
objectGNNEmbedding Class for Generating Graph Neural Network (GNN) Based Embeddings.
- adjacency_matrix
pd.DataFrame
- omics_data
pd.DataFrame
- phenotype_data
pd.DataFrame
- clinical_data
Optional[pd.DataFrame]
- phenotype_col
str
- model_type
str
int
- layer_num
int
- dropout
Union[bool, float] (if bool, True maps to 0.5, False to 0.0)
- num_epochs
int
- lr
float
- weight_decay
float
- gpu
bool
- seed
Optional[int]
- tune
Optional[bool]
- embed(as_df: bool = False) torch.Tensor | DataFrame[source]
Generates node embeddings. If tuning is enabled, runs hyperparameter tuning and uses the best configuration.