EntityRegression
- class graphstorm.model.EntityRegression(h_dim, dropout=0, out_dim=1, norm=None, use_bias=True)
Bases:
GSLayerDecoder for node regression tasks.
Parameters
- h_dim: int
The input dimension size.
- dropout: float
Dropout rate. Default: 0.
- out_dim: int
The output dimension size. Default: 1 for regression tasks.
- norm: str, optional
Normalization methods. Not used, but reserved for complex node regression implementation. Default: None.
- use_bias: bool
Whether the node decoder uses a bias parameter. Default: True.
Changed in version 0.4.0: Add a new argument “use_bias” so users can control whether decoders have bias.
- forward(inputs)
Node regression decoder forward computation.
Parameters
- inputs: Tensor
The input embeddings.
Returns
out: Tensor of the prediction results.
- predict(inputs)
Node regression prediction computation.
Parameters
- inputs: Tensor
The input embeddings.
Returns
out: Tensor of the prediction results.
- predict_proba(inputs)
For node regression task, it returns the same results as the
predict()function.Parameters
- inputs: Tensor
The input embeddings.
Returns
out: Tensor of the prediction results.
- property in_dims
Return the input dimension size, which is given in class initialization.
- property out_dims
Return the output dimension size, which should be
1for regression tasks.