File size: 8,931 Bytes
2d12bc4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 |
#!/usr/bin/python
# coding: utf-8
# Author: LE YUAN
# Date: 2020-10-23
import pickle
import sys
import timeit
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.metrics import mean_squared_error,r2_score
class KcatPrediction(nn.Module):
def __init__(self, device, n_fingerprint, n_word, dim, layer_gnn, window, layer_cnn, layer_output):
super(KcatPrediction, self).__init__()
self.embed_fingerprint = nn.Embedding(n_fingerprint, dim)
self.embed_word = nn.Embedding(n_word, dim)
self.W_gnn = nn.ModuleList([nn.Linear(dim, dim)
for _ in range(layer_gnn)])
self.W_cnn = nn.ModuleList([nn.Conv2d(
in_channels=1, out_channels=1, kernel_size=2*window+1,
stride=1, padding=window) for _ in range(layer_cnn)])
self.W_attention = nn.Linear(dim, dim)
self.W_out = nn.ModuleList([nn.Linear(2*dim, 2*dim)
for _ in range(layer_output)])
# self.W_interaction = nn.Linear(2*dim, 2)
self.W_interaction = nn.Linear(2*dim, 1)
self.device = device
self.dim = dim
self.layer_gnn = layer_gnn
self.window = window
self.layer_cnn = layer_cnn
self.layer_output = layer_output
def gnn(self, xs, A, layer):
for i in range(layer):
hs = torch.relu(self.W_gnn[i](xs))
xs = xs + torch.matmul(A, hs)
# return torch.unsqueeze(torch.sum(xs, 0), 0)
return torch.unsqueeze(torch.mean(xs, 0), 0)
def attention_cnn(self, x, xs, layer):
"""The attention mechanism is applied to the last layer of CNN."""
xs = torch.unsqueeze(torch.unsqueeze(xs, 0), 0)
for i in range(layer):
xs = torch.relu(self.W_cnn[i](xs))
xs = torch.squeeze(torch.squeeze(xs, 0), 0)
h = torch.relu(self.W_attention(x))
hs = torch.relu(self.W_attention(xs))
weights = torch.tanh(F.linear(h, hs))
ys = torch.t(weights) * hs
# return torch.unsqueeze(torch.sum(ys, 0), 0)
return torch.unsqueeze(torch.mean(ys, 0), 0)
def forward(self, inputs):
fingerprints, adjacency, words = inputs
layer_gnn = 3
layer_cnn = 3
layer_output = 3
"""Compound vector with GNN."""
fingerprint_vectors = self.embed_fingerprint(fingerprints)
compound_vector = self.gnn(fingerprint_vectors, adjacency, layer_gnn)
"""Protein vector with attention-CNN."""
word_vectors = self.embed_word(words)
protein_vector = self.attention_cnn(compound_vector,
word_vectors, layer_cnn)
"""Concatenate the above two vectors and output the interaction."""
cat_vector = torch.cat((compound_vector, protein_vector), 1)
for j in range(layer_output):
cat_vector = torch.relu(self.W_out[j](cat_vector))
interaction = self.W_interaction(cat_vector)
# print(interaction)
return interaction
def __call__(self, data, train=True):
inputs, correct_interaction = data[:-1], data[-1]
predicted_interaction = self.forward(inputs)
print(predicted_interaction)
if train:
loss = F.mse_loss(predicted_interaction, correct_interaction)
return loss
else:
correct_values = correct_interaction.to('cpu').data.numpy()
predicted_values = predicted_interaction.to('cpu').data.numpy()[0]
# correct_values = np.concatenate(correct_values)
# predicted_values = np.concatenate(predicted_values)
# ys = F.softmax(predicted_interaction, 1).to('cpu').data.numpy()
# predicted_values = list(map(lambda x: np.argmax(x), ys))
print(correct_values)
print(predicted_values)
# predicted_scores = list(map(lambda x: x[1], ys))
return correct_values, predicted_values
class Trainer(object):
def __init__(self, model):
self.model = model
self.optimizer = optim.Adam(self.model.parameters(),
lr=lr, weight_decay=weight_decay)
def train(self, dataset):
np.random.shuffle(dataset)
N = len(dataset)
loss_total = 0
for data in dataset:
loss = self.model(data)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
loss_total += loss.to('cpu').data.numpy()
return loss_total
class Tester(object):
def __init__(self, model):
self.model = model
def test(self, dataset):
N = len(dataset)
SAE = 0 # sum absolute error.
testY, testPredict = [], []
for data in dataset :
(correct_values, predicted_values) = self.model(data, train=False)
SAE += sum(np.abs(predicted_values-correct_values))
testY.append(correct_values)
testPredict.append(predicted_values)
MAE = SAE / N # mean absolute error.
rmse = np.sqrt(mean_squared_error(testY,testPredict))
r2 = r2_score(testY,testPredict)
return MAE, rmse, r2
def save_MAEs(self, MAEs, filename):
with open(filename, 'a') as f:
f.write('\t'.join(map(str, MAEs)) + '\n')
def save_model(self, model, filename):
torch.save(model.state_dict(), filename)
def load_tensor(file_name, dtype):
return [dtype(d).to(device) for d in np.load(file_name + '.npy', allow_pickle=True)]
def load_pickle(file_name):
with open(file_name, 'rb') as f:
return pickle.load(f)
def shuffle_dataset(dataset, seed):
np.random.seed(seed)
np.random.shuffle(dataset)
return dataset
def split_dataset(dataset, ratio):
n = int(ratio * len(dataset))
dataset_1, dataset_2 = dataset[:n], dataset[n:]
return dataset_1, dataset_2
if __name__ == "__main__":
"""Hyperparameters."""
(DATASET, radius, ngram, dim, layer_gnn, window, layer_cnn, layer_output,
lr, lr_decay, decay_interval, weight_decay, iteration,
setting) = sys.argv[1:]
(dim, layer_gnn, window, layer_cnn, layer_output, decay_interval,
iteration) = map(int, [dim, layer_gnn, window, layer_cnn, layer_output,
decay_interval, iteration])
lr, lr_decay, weight_decay = map(float, [lr, lr_decay, weight_decay])
"""CPU or GPU."""
if torch.cuda.is_available():
device = torch.device('cuda')
print('The code uses GPU...')
else:
device = torch.device('cpu')
print('The code uses CPU!!!')
"""Load preprocessed data."""
dir_input = ('../../Data/input/')
compounds = load_tensor(dir_input + 'compounds', torch.LongTensor)
adjacencies = load_tensor(dir_input + 'adjacencies', torch.FloatTensor)
proteins = load_tensor(dir_input + 'proteins', torch.LongTensor)
interactions = load_tensor(dir_input + 'regression', torch.FloatTensor)
fingerprint_dict = load_pickle(dir_input + 'fingerprint_dict.pickle')
word_dict = load_pickle(dir_input + 'sequence_dict.pickle')
n_fingerprint = len(fingerprint_dict)
n_word = len(word_dict)
"""Create a dataset and split it into train/dev/test."""
dataset = list(zip(compounds, adjacencies, proteins, interactions))
dataset = shuffle_dataset(dataset, 1234)
print(len(dataset))
dataset_train, dataset_ = split_dataset(dataset, 0.8)
dataset_dev, dataset_test = split_dataset(dataset_, 0.5)
"""Set a model."""
torch.manual_seed(1234)
model = KcatPrediction().to(device)
trainer = Trainer(model)
tester = Tester(model)
"""Output files."""
file_MAEs = '../../Results/output/MAEs--' + setting + '.txt'
file_model = '../../Results/output/' + setting
# MAEs = ('Epoch\tTime(sec)\tLoss_train\tMAE_dev\t'
# 'MAE_test\tPrecision_test\tRecall_test')
MAEs = ('Epoch\tTime(sec)\tLoss_train\tMAE_dev\tMAE_test\tRMSE_dev\tRMSE_test\tR2_dev\tR2_test')
with open(file_MAEs, 'w') as f:
f.write(MAEs + '\n')
"""Start training."""
print('Training...')
print(MAEs)
start = timeit.default_timer()
for epoch in range(1, iteration):
if epoch % decay_interval == 0:
trainer.optimizer.param_groups[0]['lr'] *= lr_decay
loss_train = trainer.train(dataset_train)
MAE_dev, RMSE_dev, R2_dev = tester.test(dataset_dev)
MAE_test, RMSE_test, R2_test = tester.test(dataset_test)
end = timeit.default_timer()
time = end - start
MAEs = [epoch, time, loss_train, MAE_dev,
MAE_test, RMSE_dev, RMSE_test, R2_dev, R2_test]
tester.save_MAEs(MAEs, file_MAEs)
tester.save_model(model, file_model)
print('\t'.join(map(str, MAEs)))
|