Module `auton_survival.models.dcm`

Deep Cox Mixtures

The Cox Mixture involves the assumption that the survival function 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.

For full details on Deep Cox Mixture, refer to the paper [1].

References

[1] Deep Cox Mixtures for Survival Regression. Machine Learning in Health Conference (2021)

  @article{nagpal2021dcm,
  title={Deep Cox mixtures for survival regression},
  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
  journal={arXiv preprint arXiv:2101.06536},
  year={2021}
  }

Browse git

Sub-modules

auton_survival.models.dcm.dcm_torch
auton_survival.models.dcm.dcm_utilities

Classes

class DeepCoxMixtures (k=3, layers=None, gamma=10, smoothing_factor=0.0001, use_activation=False, random_seed=0)

A Deep Cox Mixture model.

This is the main interface to a Deep Cox Mixture model. A model is instantiated with approporiate set of hyperparameters and fit on numpy arrays consisting of the features, event/censoring times and the event/censoring indicators.

For full details on Deep Cox Mixture, refer to the paper [1].

References

[1] Deep Cox Mixtures for Survival Regression. Machine Learning in Health Conference (2021)

Parameters

k : int: The number of underlying Cox distributions.
layers : list: A list of integers consisting of the number of neurons in each hidden layer.
random_seed : int: Controls the reproducibility of called functions.

Example

>>> from auton_survival.models.dcm import DeepCoxMixtures
>>> model = DeepCoxMixtures()
>>> model.fit(x, t, e)

Browse git

Methods

def fit(self, x, t, e, vsize=0.15, val_data=None, iters=1, learning_rate=0.001, batch_size=100, optimizer='Adam')

This method is used to train an instance of the DSM model.

Parameters

x : np.ndarray: A numpy array of the input features, $x$ .
t : np.ndarray: A numpy array of the event/censoring times, $t$ .
e : np.ndarray: A numpy array of the event/censoring indicators, $\delta$ . $\delta = 1$ means the event took place.
vsize : float: Amount of data to set aside as the validation set.
val_data : tuple: A tuple of the validation dataset. If passed vsize is ignored.
iters : int: The maximum number of training iterations on the training dataset.
learning_rate : float: The learning rate for the Adam optimizer.
batch_size : int: learning is performed on mini-batches of input data. this parameter specifies the size of each mini-batch.
optimizer : str: The choice of the gradient based optimization method. One of 'Adam', 'RMSProp' or 'SGD'.

Browse git

def predict_survival(self, x, t)

Returns the estimated survival probability at time $t$ , $\widehat{\mathbb{P}}(T > t|X)$ for some input data $x$ .

Parameters

x : np.ndarray: A numpy array of the input features, $x$ .
t : list or float: a list or float of the times at which survival probability is to be computed

Returns

np.array: numpy array of the survival probabilites at each time in t.

Browse git

def predict_latent_z(self, x)

Browse git