modified: dsm/__init__.py

	modified:   dsm/dsm_api.py
	modified:   dsm/dsm_torch.py
	modified:   dsm/losses.py

dsm/__init__.py

@@ -17,16 +17,12 @@
 # along with Deep Survival Machines.
 # If not, see <https://www.gnu.org/licenses/>.
 
-"""
-[![Build Status](https://travis-ci.org/autonlab/DeepSurvivalMachines.svg?\
-branch=master)](https://travis-ci.org/autonlab/DeepSurvivalMachines)
-&nbsp;&nbsp;&nbsp;\
-[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)]\
-(https://www.gnu.org/licenses/gpl-3.0)
-&nbsp;&nbsp;&nbsp;\
-[![GitHub Repo stars](https://img.shields.io/github/stars/autonlab/Deep\
-SurvivalMachines?style=social)](https://github.com/autonlab/DeepSurvival\
-Machines)
+r"""
+[![Build Status](https://travis-ci.org/autonlab/DeepSurvivalMachines.svg?branch=master)](https://travis-ci.org/autonlab/DeepSurvivalMachines)
+&nbsp;&nbsp;&nbsp;
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
+&nbsp;&nbsp;&nbsp;
+[![GitHub Repo stars](https://img.shields.io/github/stars/autonlab/DeepSurvivalMachines?style=social)](https://github.com/autonlab/DeepSurvivalMachines)
 
 
 Python package `dsm` provides an API to train the Deep Survival Machines
@@ -77,7 +73,7 @@
 data like vital signs, degradation monitoring signals in predictive
 maintainance. **DRSM** allows the learnt representations at each time step to
 involve historical context from previous time steps. **DRSM** implementation in
-`dsm` is carried out through an easy to use API, 
+`dsm` is carried out through an easy to use API,
 `DeepRecurrentSurvivalMachines` that accepts lists of data streams and
 corresponding failure times. The module automatically takes care of appropriate
 batching and padding of variable length sequences.

dsm/dsm_api.py

@@ -62,7 +62,7 @@ def fit(self, x, t, e, vsize=0.15,
           iters=1, learning_rate=1e-3, batch_size=100,
           elbo=True, optimizer="Adam", random_state=100):
 
-    """This method is used to train an instance of the DSM model.
+    r"""This method is used to train an instance of the DSM model.
 
     Parameters
     ----------
@@ -137,7 +137,7 @@ def _prepocess_training_data(self, x, t, e, vsize, random_state):
 
 
   def predict_risk(self, x, t):
-    """Returns the estimated risk of an event occuring before time \( t \)
+    r"""Returns the estimated risk of an event occuring before time \( t \)
       \( \widehat{\mathbb{P}}(T\leq t|X) \) for some input data \( x \).
 
     Parameters
@@ -161,7 +161,7 @@ def predict_risk(self, x, t):
 
 
   def predict_survival(self, x, t):
-    """Returns the estimated survival probability at time \( t \),
+    r"""Returns the estimated survival probability at time \( t \),
       \( \widehat{\mathbb{P}}(T > t|X) \) for some input data \( x \).
 
     Parameters
@@ -249,15 +249,23 @@ class DeepRecurrentSurvivalMachines(DSMBase):
 
   """
 
+  def __init__(self, k=3, layers=None, hidden=None, 
+               distribution='Weibull', temp=1000., discount=1.0, typ='LSTM'):
+    super(DeepRecurrentSurvivalMachines, self).__init__(k=k, layers=layers,
+                                                        distribution=distribution)
+    self.hidden = hidden
+    self.typ = typ
   def _gen_torch_model(self, inputdim, optimizer):
     """Helper function to return a torch model."""
     return DeepRecurrentSurvivalMachinesTorch(inputdim,
                                               k=self.k,
                                               layers=self.layers,
+                                              hidden=self.hidden,
                                               dist=self.dist,
                                               temp=self.temp,
                                               discount=self.discount,
-                                              optimizer=optimizer)
+                                              optimizer=optimizer,
+                                              typ=self.typ)
 
   def _prepocess_test_data(self, x):
     return torch.from_numpy(_get_padded_features(x))
@@ -273,6 +281,8 @@ def _prepocess_training_data(self, x, t, e, vsize, random_state):
     t = _get_padded_targets(t)
     e = _get_padded_targets(e)
 
+    print (x.shape)
+
     x_train, t_train, e_train = x[idx], t[idx], e[idx]
 
     x_train = torch.from_numpy(x_train).double()

dsm/dsm_torch.py

@@ -42,7 +42,7 @@
 
 
 def create_representation(inputdim, layers, activation):
-  """Helper function to generate the representation function for DSM.
+  r"""Helper function to generate the representation function for DSM.
 
   Deep Survival Machines learns a representation (\ Phi(X) \) for the input
   data. This representation is parameterized using a Non Linear Multilayer
@@ -258,10 +258,12 @@ def __init__(self, inputdim, k, typ='LSTM', layers=1,
                                bias=False, batch_first=True)
     if self.typ == 'RNN':
       self.embedding = nn.RNN(inputdim, hidden, layers,
+                              bias=False, batch_first=True,
+                              nonlinearity='relu')
+    if self.typ == 'GRU':
+      self.embedding = nn.GRU(inputdim, hidden, layers,
                               bias=False, batch_first=True)
 
-
-
   def forward(self, x):
     """The forward function that is called when data is passed through DSM.
 
@@ -280,7 +282,7 @@ def forward(self, x):
     xrep, _ = self.embedding(x)
     xrep = xrep.contiguous().view(-1, self.hidden)
     xrep = xrep[inputmask]
-    #xrep = nn.ReLU6()(xrep)
+    xrep = nn.ReLU6()(xrep)
     return(self.act(self.shapeg(xrep))+self.shape.expand(xrep.shape[0], -1),
            self.act(self.scaleg(xrep))+self.scale.expand(xrep.shape[0], -1),
            self.gate(xrep)/self.temp)

dsm/losses.py

@@ -288,4 +288,4 @@ def predict_cdf(model, x, t_horizon):
     return _lognormal_cdf(model, x, t_horizon)
   else:
     raise NotImplementedError('Distribution: '+model.dist+
-                              ' not implemented yet.')
+                              ' not implemented yet.')