Evaluation updated to competing risks (#29)

* Implemented computation of Negative Log-Likelihood.

Author: Jeanselme

dsm/dsm_api.py

@@ -32,7 +32,9 @@
 from dsm.dsm_torch import DeepConvolutionalSurvivalMachinesTorch
 from dsm.losses import predict_cdf
 import dsm.losses as losses
-from dsm.utilities import train_dsm, _get_padded_features, _get_padded_targets
+from dsm.utilities import train_dsm
+from dsm.utilities import _get_padded_features, _get_padded_targets
+from dsm.utilities import _reshape_tensor_with_nans
 
 import torch
 import numpy as np
@@ -120,6 +122,40 @@ def fit(self, x, t, e, vsize=0.15,
 
     return self
 
+  def _eval_nll(self, x, t, e):
+    r"""This function computes the negative log likelihood of the given data.
+    In case of competing risks, the negative log likelihoods are summed over
+    the different events' type.
+
+    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, \( \delta \).
+        \( \delta = r \) means the event r took place.
+    
+    Returns:
+      float: Negative log likelihood.
+    """
+    if not(self.fitted):
+      raise Exception("The model has not been fitted yet. Please fit the " +
+                      "model using the `fit` method on some training data " +
+                      "before calling `_eval_nll`.")
+    processed_data = self._prepocess_training_data(x, t, e, 0, 0)
+    _, _, _, x_val, t_val, e_val = processed_data
+    x_val, t_val, e_val = x_val,\
+        _reshape_tensor_with_nans(t_val),\
+        _reshape_tensor_with_nans(e_val)
+    loss = 0
+    for r in range(self.torch_model.risks):
+        loss += float(losses.conditional_loss(self.torch_model,
+                      x_val, t_val, e_val, elbo=False,
+                      risk=str(r+1)).detach().numpy())
+    return loss
+
   def _prepocess_test_data(self, x):
     return torch.from_numpy(x)