new file: dsm.py

	new file:   dsm_loss.py
	new file:   dsm_utilites.py

dsm.py

@@ -0,0 +1,128 @@
+import torch.nn as nn
+import torch 
+
+class DeepSurvivalMachines(nn.Module):
+
+    def __init__(self, inputdim, k, mlptyp=1, HIDDEN=False, init=False, dist='Weibull'):
+
+        super(DeepSurvivalMachines, self).__init__()
+
+        shape = 1.
+        scale = 1.
+
+        self.k = k
+
+        self.mlptype = mlptyp
+        self.scale = nn.Parameter(-torch.ones(k))
+        self.shape = nn.Parameter(-torch.ones(k))
+
+        self.HIDDEN = HIDDEN
+
+
+        if mlptyp == 1:
+
+            self.gate = nn.Sequential(nn.Linear(inputdim, k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(inputdim, k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(inputdim, k, bias=True))
+
+        if mlptyp == 2:
+
+            self.gate = nn.Sequential(nn.Linear(HIDDEN[0], k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(HIDDEN[0], k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(HIDDEN[0], k, bias=True))
+
+            self.embedding = nn.Sequential(nn.Linear(inputdim, HIDDEN[0], bias=False),
+                                      nn.ReLU6())
+
+        if mlptyp == 3:
+
+            self.gate = nn.Sequential(nn.Linear(HIDDEN[1], k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(HIDDEN[1], k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(HIDDEN[1], k, bias=True))
+
+            self.embedding = nn.Sequential(nn.Linear(inputdim, HIDDEN[0], bias=False),
+                                        nn.ReLU6(),
+                                        nn.Linear(HIDDEN[0], HIDDEN[1], bias=False),
+                                        nn.ReLU6())
+
+        if mlptyp == 4:
+
+            self.gate = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+
+            self.embedding = nn.Sequential(nn.Linear(inputdim, HIDDEN[0], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[0], HIDDEN[1], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[1], HIDDEN[2], bias=False),
+                                      nn.ReLU6())
+
+        if mlptyp == 5:
+
+            self.gate = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+
+            self.embedding = nn.Sequential(nn.Linear(inputdim, HIDDEN[0], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[0], HIDDEN[1], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[1], HIDDEN[2], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[2], HIDDEN[3], bias=False),
+                                      nn.ReLU6())
+
+        if mlptyp == 6:
+
+            self.gate = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=False))
+            self.scaleg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+            self.shapeg = nn.Sequential(nn.Linear(HIDDEN[2], k, bias=True))
+
+            self.embedding = nn.Sequential(nn.Linear(inputdim, HIDDEN[0], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[0], HIDDEN[1], bias=False),
+                                      nn.ReLU6(),
+                                      nn.Linear(HIDDEN[1], HIDDEN[2], bias=False),
+                                      nn.ReLU6(),
+                                    nn.Linear(HIDDEN[2], HIDDEN[3], bias=False),
+                                      nn.ReLU6(),
+                                    nn.Linear(HIDDEN[3], HIDDEN[4], bias=False),
+                                      nn.ReLU6())
+
+        if init is not False:
+
+            self.shape.data.fill_(init[0])
+            self.scale.data.fill_(init[1])
+
+
+        self.dist = dist
+
+        if self.dist == 'Weibull':
+
+            self.act = nn.SELU()
+
+        elif self.dist == 'LogNormal':
+
+            self.act = nn.Tanh()
+
+    def forward(self, x, adj=True):
+
+        if self.mlptype == 1:
+
+            embed = x
+
+        else:
+
+            embed = self.embedding(x)
+
+        if adj:
+
+            return self.act(self.shapeg(embed))+self.shape.expand(x.shape[0],-1),  self.act(self.scaleg(embed))+self.scale.expand(x.shape[0],-1), self.gate(embed)/1000
+
+        else:
+
+            return  self.shape, self.scale, self.gate(embed)/1000
+
+
+