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Code/RPN_to_pytorch.py

@@ -9,10 +9,6 @@ import torch.nn.functional as F
 import torch.optim as optim 
 import torch.utils.data as utils
 from torch.autograd import Variable
-from sklearn.metrics import roc_curve, auc
-from sklearn.preprocessing import label_binarize
-from sklearn.manifold import TSNE
-import seaborn as sns
 import warnings
 warnings.filterwarnings("ignore")
 import sympy

Code/S_NN_eval.py

@@ -3,7 +3,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 import pandas as pd
 import numpy as np
 import torch

Code/S_NN_train.py

@@ -3,7 +3,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 import pandas as pd
 import numpy as np
 import torch

Code/S_add_bf_on_numbers_on_pareto.py

@@ -9,9 +9,6 @@ import torch.nn.functional as F
 import torch.optim as optim 
 import torch.utils.data as utils
 from torch.autograd import Variable
-from sklearn.metrics import roc_curve, auc
-from sklearn.preprocessing import label_binarize
-from sklearn.manifold import TSNE
 import copy
 import warnings
 warnings.filterwarnings("ignore")

Code/S_add_snap_expr_on_pareto.py

@@ -9,10 +9,6 @@ import torch.nn.functional as F
 import torch.optim as optim 
 import torch.utils.data as utils
 from torch.autograd import Variable
-from sklearn.metrics import roc_curve, auc
-from sklearn.preprocessing import label_binarize
-from sklearn.manifold import TSNE
-import seaborn as sns
 import copy
 import warnings
 warnings.filterwarnings("ignore")

Code/S_final_gd.py

@@ -9,10 +9,6 @@ import torch.nn.functional as F
 import torch.optim as optim 
 import torch.utils.data as utils
 from torch.autograd import Variable
-from sklearn.metrics import roc_curve, auc
-from sklearn.preprocessing import label_binarize
-from sklearn.manifold import TSNE
-import seaborn as sns
 import warnings
 warnings.filterwarnings("ignore")
 import sympy

Code/S_remove_input_neuron.py

@@ -5,7 +5,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 import pandas as pd
 import numpy as np
 import torch
@@ -16,6 +15,8 @@ import torch.utils.data as utils
 import time
 import os
 
+is_cuda = torch.cuda.is_available()
+
 def remove_input_neuron(net,n_inp,idx_neuron,ct_median,save_filename):
     removed_weights = net.linear1.weight[:,idx_neuron]
     # Remove the weights associated with the removed input neuron                                                                                                               
@@ -24,6 +25,9 @@ def remove_input_neuron(net,n_inp,idx_neuron,ct_median,save_filename):
     t = nn.Parameter(t[preserved_ids, :])
     net.linear1.weight = nn.Parameter(torch.transpose(t,0,1))
     # Adjust the biases
-    net.linear1.bias = nn.Parameter(net.linear1.bias+torch.tensor(ct_median*removed_weights).float().cuda())
+    if is_cuda:
+        net.linear1.bias = nn.Parameter(net.linear1.bias+torch.tensor(ct_median*removed_weights).float().cuda())
+    else:
+        net.linear1.bias = nn.Parameter(net.linear1.bias+torch.tensor(ct_median*removed_weights).float())
     torch.save(net.state_dict(), save_filename)
 

Code/S_separability.py

@@ -4,7 +4,6 @@ import os
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 import pandas as pd
 import numpy as np
 import torch

Code/S_symmetry.py

@@ -6,7 +6,6 @@ import os
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 import pandas as pd
 import numpy as np
 import torch