def nll_mtlr(phi, idx_durations, events, reduction='mean', epsilon=1e-7):
"""Negative log-likelihood for the MTLR parametrized model [1] [2].
This is essentially a PMF parametrization with an extra cumulative sum, as explained in [3].
Arguments:
phi {torch.tensor} -- Estimates in (-inf, inf), where pmf = somefunc(phi).
idx_durations {torch.tensor} -- Event times represented as indices.
events {torch.tensor} -- Indicator of event (1.) or censoring (0.).
Same length as 'idx_durations'.
reduction {string} -- How to reduce the loss.
'none': No reduction.
'mean': Mean of tensor.
'sum: sum.
Returns:
torch.tensor -- The negative log-likelihood.
References:
[1] Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, and Vickie Baracos.
Learning patient- specific cancer survival distributions as a sequence of dependent regressors.
In Advances in Neural Information Processing Systems 24, pages 1845–1853.
Curran Associates, Inc., 2011.
https://papers.nips.cc/paper/4210-learning-patient-specific-cancer-survival-distributions-as-a-sequence-of-dependent-regressors.pdf
[2] Stephane Fotso. Deep neural networks for survival analysis based on a multi-task framework.
arXiv preprint arXiv:1801.05512, 2018.
https://arxiv.org/pdf/1801.05512.pdf
[3] Håvard Kvamme and Ørnulf Borgan. Continuous and Discrete-Time Survival Prediction
with Neural Networks. arXiv preprint arXiv:1910.06724, 2019.
https://arxiv.org/pdf/1910.06724.pdf
"""
phi = utils.cumsum_reverse(phi, dim=1)
return nll_pmf(phi, idx_durations, events, reduction, epsilon)
def predict_pmf(self,
input,
batch_size=8224,
numpy=None,
eval_=True,
to_cpu=False,
num_workers=0):
preds = self.predict(input, batch_size, False, eval_, False, to_cpu,
num_workers)
preds = utils.cumsum_reverse(preds, dim=1)
pmf = utils.pad_col(preds).softmax(1)[:, :-1]
return utils.array_or_tensor(pmf, numpy, input)
def test_cumsum_reverse_dim_1():
torch.manual_seed(1234)
x = torch.randn(5, 16)
res_np = x.numpy()[:, ::-1].cumsum(1)[:, ::-1]
res = cumsum_reverse(x, dim=1)
assert np.abs(res.numpy() - res_np).max() < 1e-6
def test_cumsum_reverse_error_dim():
x = torch.randn((5, 3))
with pytest.raises(NotImplementedError):
cumsum_reverse(x, dim=0)
with pytest.raises(NotImplementedError):
cumsum_reverse(x, dim=2)