# Module auton_survival.models.cmhe.cmhe_torch

## Classes

 class DeepCMHETorch (k, g, inputdim, layers=None, gamma=100, smoothing_factor=0.0001, gate_l2_penalty=0.0001, optimizer='Adam') 

PyTorch model definition of the Cox Mixture with Hereogenous Effects Model.

Cox Mixtures with Heterogenous Effects involves the assuming that the base survival rates are independent of the treatment effect. of the individual to be a mixture of K Cox Models. Conditioned on each subgroup Z=k; the PH assumptions are assumed to hold and the baseline hazard rates is determined non-parametrically using an spline-interpolated Breslow's estimator.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

### Class variables

var dump_patches : bool
var training : bool

### Methods

 def forward(self, x, a) ‑> Callable[..., Any] 
 class IdentifiableLinear (in_features, out_features, bias=True) 

Softmax and LogSoftmax with K classes in pytorch are over specfied and lead to issues of mis-identifiability. This class is a wrapper for linear layers that are correctly specified with K-1 columns. The output of this layer for the Kth class is all zeros. This allows direct application of pytorch.nn.LogSoftmax and pytorch.nn.Softmax.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

### Class variables

var dump_patches : bool
var training : bool

### Instance variables

var weight

### Methods

 def forward(self, x) ‑> Callable[..., Any]