DDR - Deep Distribution Regression¶
The deep distributional regression model.
Class Definition¶
Bases: BaseModel
Functions¶
__init__(cutpoints=None, num_hidden_layers=2, hidden_size=100, dropout_rate=0.2, proportion=0.1, loss_metric='jbce', learning_rate=0.001)
¶
Args: x_train_shape: The shape of the training data, used to define the input size of the first layer. cutpoints: The cutpoints for the DDR model. num_hidden_layers: The number of hidden layers in the network. hidden_size: The number of neurons in each hidden layer.
forward(x)
¶
Forward pass of the DDR model. Args: x: Input tensor. Returns: The cutpoints and probabilities for the DDR model.
Loss Functions¶
JBCE Loss¶
The joint binary cross entropy loss. Args: dists: the predicted distributions y: the observed values alpha: the penalty parameter