-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathGNN_model_v3_50epc_BCE.py
229 lines (179 loc) · 8.35 KB
/
GNN_model_v3_50epc_BCE.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
# ============================ 1. Environment setup ============================
import os
import dgl
import time
import torch
#import pickle
import numpy as np
import pandas as pd
#import networkx as nx
import torch.nn as nn
from tqdm import tqdm
import torch.nn.functional as F
from dgl.data import DGLDataset
# ========================= 2. Setting up the Dataset =========================
data_time = time.time()
class PolypharmacyDataset(DGLDataset):
def __init__(self):
super().__init__(name='polypharmacy')
def process(self):
features = pd.read_csv('../data/GNN-GSE_full_pkd_norm.csv',index_col = 'ProteinID', sep=',')
drug_comb = pd.read_csv('../data/GNN-TWOSIDE-train-PSE-964.csv', sep=',') # or 3347
#properties = pd.read_csv('../data/GNN_properties.csv')
nodes = pd.read_csv('../data/GNN-GSE_full_pkd_norm.csv', sep=',')
edges = pd.read_csv('../data/GNN-PPI-net.csv', sep=',')
dti = pd.read_csv('../data/GNN-DTI_full.csv', sep=',')
DrugID = pd.read_csv('../data/DrugID.csv', sep = ',')
print('data loaded!')
# generate drug specific ppi subgraph for GNN edges
def drug2ppi(drug):
genes = dti['ProteinID'].loc[dti['DrugID'] == drug].tolist()
df = edges[['protein1','protein2']].loc[edges['protein1'].isin(genes)]
df = df.loc[df['protein2'].isin(genes)]
num_nodes = len(df['protein1'].unique())
df['graph_id'] = DrugID.loc[DrugID['DrugID'] == drug]['GraphID'].tolist()[0] #DrugID
df = df.rename(columns={'protein1': 'src_prot', 'protein2': 'dst_prot'}) # prot: actual protein id
final_genes =df['src_prot'].unique().tolist() # final genes that have ppi data
dic = {gene:final_genes.index(gene) for gene in final_genes} # conversion dic, starts at 0
df['src'] = df['src_prot'].map(dic) #local ids
df['dst'] = df['dst_prot'].map(dic) #local ids
return(df[['graph_id', 'src', 'dst', 'src_prot', 'dst_prot']],num_nodes)
self.graphs = []
self.labels = []
self.comb_graphs = []
self.comb_labels = []
#Node features or PSEs dictionary
feature_dic = {i+1:torch.tensor(features.loc[i+1,]) for i in range(len(features))}
# For each graph ID...
for drug in tqdm(DrugID['DrugID'].tolist()):
# Find the edges as well as the number of nodes and its label.
edges_of_id,num_nodes = drug2ppi(drug)
src = edges_of_id['src'].to_numpy()
dst = edges_of_id['dst'].to_numpy()
label = DrugID.loc[DrugID['DrugID'] == drug]['DrugID'].tolist()[0]
# Create a graph and add it to the list of graphs and labels.
g = dgl.graph((src, dst), num_nodes=num_nodes)
# Need to convert proteinsIDs for feature assigning
prot_ids = edges_of_id['src_prot'].unique().tolist()
for prot in edges_of_id['dst_prot'].unique().tolist():
if prot not in prot_ids:
prot_ids.append(prot)
convert_prot = {prot_ids.index(prot):prot for prot in prot_ids}
#Adding features of each node
g.ndata['PSE'] = torch.zeros(g.num_nodes(), 964)
for node in g.nodes().tolist():
g.ndata['PSE'][node] = feature_dic[convert_prot[node]]
self.graphs.append(g)
self.labels.append(label)
# conver drugid to their respective graph id
#drug2graph = {properties['label'][i]:i for i in range(len(properties))}
#drug2graph = {self.labels[i]:i for i in range(len(self.labels))}
for i in range(len(drug_comb)):
row = drug_comb.loc[i]
g1 = self.graphs[self.labels.index(row[0])] # Drug1 graph
g2 = self.graphs[self.labels.index(row[1])] # Drug2 graph
self.comb_graphs.append([g1,g2])
self.comb_labels.append(torch.tensor(row[2:])) # PSE values
def __getitem__(self, i):
return self.comb_graphs[i], self.comb_labels[i]
#return self.graphs[i], self.labels[i]
def __len__(self):
return len(self.comb_graphs)
print('\nCreating the PolypharmacyDataset ...\n')
dataset = PolypharmacyDataset()
print('\nPolypharmacyDataset created!\n')
end = time.time()
hours, rem = divmod(end-data_time , 3600)
minutes, seconds = divmod(rem, 60)
print('\ndataset is compiled! \ncompiling time = {:0>2}:{:0>2}:{:05.2f}'.format(int(hours),int(minutes),seconds))
# ========================= 3. Data loading and batch =========================
print('\nCreating train and test batches ...\n')
from dgl.dataloading import GraphDataLoader
from torch.utils.data.sampler import SubsetRandomSampler
num_examples = len(dataset)*0.15
num_train = int(num_examples * 0.1)
train_sampler = SubsetRandomSampler(torch.arange(num_train))
test_sampler = SubsetRandomSampler(torch.arange(num_train, num_examples))
train_dataloader = GraphDataLoader(
dataset, sampler=train_sampler, batch_size=35 ,drop_last=False)
test_dataloader = GraphDataLoader(
dataset, sampler=test_sampler, batch_size=35 ,drop_last=False)
print('\nTrain and test batches are created!\n')
# ========================= 4. GNN Model: Siamese GCN =========================
from dgl.nn import GraphConv
class GCN(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
h = self.conv2(g, h)
g.ndata['h'] = h
out = F.relu(dgl.mean_nodes(g, 'h'))
#out = F.relu(dgl.max_nodes(g, 'h'))
return out
# =============================== 5. Training =================================
train_time = time.time()
#print('\nCreating the SiameseGCN model ...\n')
# Create the model with given dimensions
model = GCN(964, 200, 964)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
print('\n======================== Trainig ========================\n')
f = open("log_model_V3_BCE.txt", "a")
f.write('\n======================== Trainig ========================\n')
for epoch in range(50):
batch = 0
for batched_graph, labels in train_dataloader:
start_time = time.time()
g1 = batched_graph[0]
g2 = batched_graph[1]
pred1 = model(g1, g1.ndata['PSE'].float())
pred2 = model(g2, g2.ndata['PSE'].float())
pred = F.normalize(pred1+pred2)/2
loss = F.binary_cross_entropy(torch.sigmoid(pred).float(),labels.float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
run_time = round(time.time() - start_time,3)
msg = 'epoch%s, batch%s | loss = %s | time: %s s\n' % (epoch,batch,loss.tolist(),run_time)
f.write(msg)
print (msg)
batch += 1
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(labels)):
for j in range(len(labels[i])):
if labels[i][j] == 1 and pred[i][j] != 0:
TP += 1
elif labels[i][j] == 1 and pred[i][j] == 0:
FN += 1
elif labels[i][j] == 0 and pred[i][j] != 0:
FP += 1
elif labels[i][j] == 0 and pred[i][j] == 0:
TN += 1
else:
pass
# Validation metrics
acc = ((TP+TN)*100)/(TP+FP+FN+TN)
prec = (TP*100)/(TP+FP)
recall = (TP*100)/(TP+FN)
F1 = 2*(recall*prec)/(recall+prec)
sim = ((F.cosine_similarity(pred.float(),labels.float())).mean().tolist())*100
msg2 = 'Accuracy: %s | Precision: %s | Recall: %s | F1: %s | Similarity: %s\n' %(round(acc,4),round(prec,4),round(recall,4),round(F1,4),round(sim,4))
f.write(msg2)
print(msg2)
end = time.time()
hours, rem = divmod(end-train_time , 3600)
minutes, seconds = divmod(rem, 60)
msg_tiem = '\ntotal training time = {:0>2}:{:0>2}:{:05.2f}'.format(int(hours),int(minutes),seconds)
f.write(msg_tiem)
print(msg_tiem)
torch.save(model.state_dict(), 'state_dict_model_V3_BCE.pt')
torch.save(model, 'entire_model_V3_BCE.pt')
print('\nmodel is saved!\n')
f.write('\nmodel saved!\n')
f.close()