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GNN_model2.py
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GNN_model2.py
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# ============================ 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
#torch.cuda.set_device(0)
# ========================= 2. Setting up the Dataset =========================
data_time = time.time()
file = open('../data/PolypharmacyDataset.obj', 'r')
dataset = pickle.load(file)
end = time.time()
hours, rem = divmod(end-data_time , 3600)
minutes, seconds = divmod(rem, 60)
print('\nPolypharmacyDataset Created!\n')
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)
num_train = int(num_examples * 0.8)
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=5, drop_last=False)
test_dataloader = GraphDataLoader(
dataset, sampler=test_sampler, batch_size=5, 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(dataset.dim_nfeats, 450, dataset.gclasses)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
print('\n======================== Trainig ========================\n')
for epoch in range(20):
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.relu((pred1+pred2)/2)
loss = F.binary_cross_entropy(torch.sigmoid(pred).float(),labels.float())
#loss = 1- F.cosine_similarity(torch.sigmoid(pred),labels).mean()
#loss = F.triplet_margin_loss(labels,pred1,pred2)
optimizer.zero_grad()
loss.backward()
optimizer.step()
run_time = round(time.time() - start_time,3)
print ('epoch%s, batch%s | loss = %s | time: %s s' % (epoch,batch,loss.tolist(),run_time))
batch += 1
end = time.time()
hours, rem = divmod(end-train_time , 3600)
minutes, seconds = divmod(rem, 60)
print('\ntotal training time = {:0>2}:{:0>2}:{:05.2f}'.format(int(hours),int(minutes),seconds))
torch.save(model.state_dict(), 'state_dict_model.pt')
torch.save(model, 'entire_model.pt')
print('\nmodel is saved!\n')
print('\n======================== Testing ========================\n')
num_correct = 0
num_correct_0 = 0
num_incorrect = 0
num_tests = 0
for batched_graph, labels in test_dataloader:
pred1 = model(batched_graph[0], batched_graph[0].ndata['PSE'].float())
pred2 = model(batched_graph[1], batched_graph[1].ndata['PSE'].float())
pred = F.relu((pred1+pred2)/2)
for i in range(len(labels)):
for j in range(len(labels[i])):
num_tests += 1
if labels[i][j] == 1 and pred[i][j] != 0:
num_correct += 1
elif labels[i][j] == 1 and pred[i][j] == 0:
num_incorrect += 1
elif labels[i][j] == 0 and pred[i][j] != 0:
num_incorrect += 1
elif labels[i][j] == 0 and pred[i][j] == 0:
num_correct_0 += 1
pass
acc = ((num_correct+num_correct_0)*100)/(num_tests)
acc2 = ((num_correct_0)*100)/(num_tests)
prec = ((num_correct)*100)/(num_incorrect+num_correct)
sim = ((F.cosine_similarity(pred.float(),labels.float())).mean().tolist())*100
print('Accuracy: %s, %s | Precision: %s | Similarity: %s' %(round(acc,5),round(acc2,5),round(prec,5),round(sim,5)))