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Model.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 17 11:16:34 2019
+
+@author: anne marie delaney
+         eoin brophy
+         
+Module of the GAN model for time series synthesis.
+
+"""
+
+import torch
+import torch.nn as nn
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+
+""" 
+NN Definitions
+---------------
+Defining the Neural Network Classes to be evaluated in this Notebook
+
+Minibatch Discrimination
+--------------------------
+Creating a module for Minibatch Discrimination to avoid mode collapse as described:
+https://arxiv.org/pdf/1606.03498.pdf
+https://torchgan.readthedocs.io/en/latest/modules/layers.html#minibatch-discrimination
+
+"""
+
+class MinibatchDiscrimination(nn.Module):
+   def __init__(self,input_features,output_features,minibatch_normal_init, hidden_features=16):
+      super(MinibatchDiscrimination,self).__init__()
+
+      self.input_features = input_features
+      self.output_features = output_features
+      self.hidden_features = hidden_features
+      self.T = nn.Parameter(torch.randn(self.input_features,self.output_features, self.hidden_features))
+      if minibatch_normal_init == True:
+        nn.init.normal(self.T, 0,1)
+
+   def forward(self,x):
+      M = torch.mm(x,self.T.view(self.input_features,-1))
+      M = M.view(-1, self.output_features, self.hidden_features).unsqueeze(0)
+      M_t = M.permute(1, 0, 2, 3)
+      # Broadcasting reduces the matrix subtraction to the form desired in the paper
+      out = torch.sum(torch.exp(-(torch.abs(M - M_t).sum(3))), dim=0) - 1
+      return torch.cat([x, out], 1)
+
+"""
+Discriminator Class
+-------------------
+This discriminator has a parameter num_cv which allows the user to specify if 
+they want to have 1 or 2 Convolution Neural Network Layers.
+
+"""
+
+class Discriminator(nn.Module):
+  def __init__(self,seq_length,batch_size,minibatch_normal_init, n_features = 1, num_cv = 1, minibatch = 0, cv1_out= 10, cv1_k = 3, cv1_s = 4, p1_k = 3, p1_s = 3, cv2_out = 10, cv2_k = 3, cv2_s = 3 ,p2_k = 3, p2_s = 3):
+      super(Discriminator,self).__init__()
+      self.n_features = n_features
+      self.seq_length = seq_length
+      self.batch_size = batch_size
+      self.num_cv = num_cv
+      self.minibatch = minibatch
+      self.cv1_dims = int((((((seq_length - cv1_k)/cv1_s) + 1)-p1_k)/p1_s)+1)
+      self.cv2_dims = int((((((self.cv1_dims - cv2_k)/cv2_s) + 1)-p2_k)/p2_s)+1)
+      self.cv1_out = cv1_out
+      self.cv2_out = cv2_out
+
+      #input should be size (batch_size,num_features,seq_length) for the convolution layer
+      self.CV1 = nn.Sequential(
+                  nn.Conv1d(in_channels = self.n_features, out_channels = int(cv1_out),kernel_size = int(cv1_k), stride = int(cv1_s))
+                  ,nn.ReLU()        
+                  ,nn.MaxPool1d(kernel_size = int(p1_k), stride = int(p1_s))   
+                 )
+
+      # 2 convolutional layers
+      if self.num_cv > 1:
+        self.CV2 = nn.Sequential(
+                      nn.Conv1d(in_channels = int(cv1_out), out_channels = int(cv2_out) ,kernel_size =int(cv2_k), stride = int(cv2_s))
+                      ,nn.ReLU()
+                      ,nn.MaxPool1d(kernel_size = int(p2_k), stride = int(p2_s))
+                  )
+
+        #Adding a minibatch discriminator layer to add a cripple affect to the discriminator so that it needs to generate sequences that are different from each other.
+
+        if   self.minibatch > 0:
+          self.mb1 = MinibatchDiscrimination(self.cv2_dims*cv2_out,self.minibatch, minibatch_normal_init)
+          self.out = nn.Sequential(nn.Linear(int(self.cv2_dims*cv2_out)+self.minibatch,1),nn.Sigmoid()) # to make sure the output is between 0 and 1
+        else:
+          self.out = nn.Sequential(nn.Linear(int(self.cv2_dims*cv2_out),1),nn.Sigmoid()) # to make sure the output is between 0 and 1 
+
+      # 1 convolutional layer
+      else:
+
+        #Adding a minibatch discriminator layer to add a cripple affect to the discriminator so that it needs to generate sequences that are different from each other.
+        if self.minibatch > 0 :
+
+          self.mb1 = MinibatchDiscrimination(int(self.cv1_dims*cv1_out),self.minibatch, minibatch_normal_init)
+          self.out = nn.Sequential(nn.Linear(int(self.cv1_dims*cv1_out)+self.minibatch,1),nn.Dropout(0.2),nn.Sigmoid()) # to make sure the output is between 0 and 1
+        else:
+          self.out = nn.Sequential(nn.Linear(int(self.cv1_dims*cv1_out),1),nn.Sigmoid())  
+
+
+
+  def forward(self,x):
+     # print("Calculated Output dims after CV1: "+str(self.cv1_dims))
+     # print("input: "+str(x.size()))
+      x = self.CV1(x.view(self.batch_size,1,self.seq_length))
+     # print("CV1 Output: "+str(x.size()))
+
+      #2 Convolutional Layers
+      if self.num_cv > 1:   
+
+        x = self.CV2(x)
+        x = x.view(self.batch_size,-1)
+
+      #  print("CV2 Output: "+str(x.size()))
+        if self.minibatch > 0:
+             x = self.mb1(x.squeeze())
+       #      print("minibatch output: "+str(x.size()))
+             x = self.out(x.squeeze())
+        else:
+
+             x = self.out(x.squeeze())
+
+      # 1 convolutional layers
+      else:
+
+        x = x.view(self.batch_size,-1)
+
+        #1 convolutional Layer and minibatch discrimination
+        if self.minibatch > 0:
+             x = self.mb1(x)
+             x = self.out(x)
+        #1 convolutional Layer and no minibatch discrimination
+        else:
+             x = self.out(x)
+
+
+
+      return x
+
+"""
+Generator Class
+---------------
+This defines the Generator for evaluation. The Generator consists of two LSTM 
+layers with a final fully connected layer.
+
+"""
+
+class Generator(nn.Module):
+  def __init__(self,seq_length,batch_size,n_features = 1, hidden_dim = 50, 
+               num_layers = 2, tanh_output = False):
+      super(Generator,self).__init__()
+      self.n_features = n_features
+      self.hidden_dim = hidden_dim
+      self.num_layers = num_layers
+      self.seq_length = seq_length
+      self.batch_size = batch_size
+      self.tanh_output = tanh_output
+
+
+
+      self.layer1 = nn.LSTM(input_size = self.n_features, hidden_size = self.hidden_dim, 
+                                  num_layers = self.num_layers,batch_first = True#,dropout = 0.2,
+                                 )
+      if self.tanh_output == True:
+        self.out = nn.Sequential(nn.Linear(self.hidden_dim,1),nn.Tanh()) # to make sure the output is between 0 and 1 - removed ,nn.Sigmoid()
+      else:
+        self.out = nn.Linear(self.hidden_dim,1) 
+
+  def init_hidden(self):
+      weight = next(self.parameters()).data
+      hidden = (weight.new(self.num_layers, self.batch_size, self.hidden_dim).zero_().to(device), weight.new(self.num_layers, self.batch_size, self.hidden_dim).zero_().to(device))
+      return hidden
+
+  def forward(self,x,hidden):
+
+      x,hidden = self.layer1(x.view(self.batch_size,self.seq_length,1),hidden)
+
+      x = self.out(x)
+
+      return x #,hidden

data.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 17 11:09:33 2019
+
+@author: anne marie delaney
+         eoin brophy
+
+Data Loading module for GAN training
+------------------------------------
+
+Creating the Training Set
+
+Creating the pytorch dataset class for use with Data Loader to enable batch training of the GAN
+"""
+import torch
+from torch.utils.data import Dataset
+import pandas as pd
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+class ECGData(Dataset):
+  #This is the class for the ECG Data that we need to load, transform and then use in the dataloader.
+  def __init__(self,source_file,class_id, transform = None):
+    self.source_file = source_file
+    data = pd.read_csv(source_file, header = None)
+    class_data = data[data[187]==class_id]
+    self.data = class_data.drop(class_data.iloc[:,187],axis=1)
+    self.transform = transform
+    self.class_id = class_id
+
+  def __len__(self):
+    return self.data.shape[0]
+
+  def __getitem__(self,idx):
+    sample = self.data.iloc[idx]
+    if self.transform:
+        sample = self.transform(sample)
+    return sample
+
+"""Including the function that will transform the dataframe to a pytorch tensor"""
+
+class PD_to_Tensor(object):
+    def __call__(self,sample):
+      return torch.tensor(sample.values).to(device)
+