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How to use hierarchy_descriptions method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
neural_network.py
Source:neural_network.py 
1# encoding: utf-82"""3@author: Kaiqi Yuan4@software: PyCharm5@file: neural_network.py6@time: 19-1-23 ä¸å9:267@description:8"""9import torch10import torch.nn as nn11import numpy as np12from torch.autograd import Variable13from util.utility import hierarchy_cols_padding, hierarchy_rows_padding, hierarchy_path_align14class Lenet5Classifier(nn.Module):15 def __init__(self, feature_dim):16 super(Lenet5Classifier, self).__init__()17 self.device = torch.device('cuda:1')18 self.num_classes = 219 layer1_out_channels = 2020 layer2_out_channels = 2021 self.layer1 = nn.Sequential(22 nn.Conv2d(1, layer1_out_channels, kernel_size=5, stride=1, padding=2), # Conv1d, https://pytorch.org/docs/stable/nn.html#conv1d23 nn.Dropout(0.3),24 nn.BatchNorm2d(layer1_out_channels), # BatchNorm1d(channel_out), https://pytorch.org/docs/stable/nn.html#batchnorm1d25 nn.ReLU(),26 nn.MaxPool2d(kernel_size=2, stride=2)) # Maxpool1d, https://pytorch.org/docs/stable/nn.html#maxpool1d27 self.layer2 = nn.Sequential(28 nn.Conv2d(layer1_out_channels, layer2_out_channels, kernel_size=5, stride=1, padding=2),29 nn.Dropout(0.3),30 nn.BatchNorm2d(layer2_out_channels),31 nn.ReLU(),32 nn.MaxPool2d(kernel_size=2, stride=2))33 self.fc = nn.Linear(int(feature_dim/4)*1*layer2_out_channels, 2) # https://pytorch.org/docs/stable/nn.html#linear34 print("fc feature_dim: ", int(feature_dim/4)*1*layer2_out_channels)35 def forward(self, in_tensor):36 # in_tensor = x.type(torch.cuda.DoubleTensor)37 # print(in_tensor.type)38 # print("in_tensor: ", in_tensor.shape)39 # in_tensor: torch.Size([16, 1, feature_dim, 1])40 out = self.layer1(in_tensor)41 # print("layer1: ", out.shape)42 out = self.layer2(out)43 # print("layer2: ", out.shape)44 out = out.reshape((out.size(0), -1)) # [100, 7, 7, 32] -> [100, 1568])45 out = self.fc(out)46 return out47class Conv2dReduceDims(nn.Module):48 def __init__(self, H_in, W_in, fc_out_channels, device):49 super(Conv2dReduceDims, self).__init__()50 conv_out_channels = 2051 self.fc_out_channels = fc_out_channels52 self.W_in = W_in if W_in == 1 else int(W_in/2)53 self.device = device54 self.conv_layer = nn.Sequential(55 nn.Conv2d(1, conv_out_channels, kernel_size=5, stride=1, padding=2), # Conv1d, https://pytorch.org/docs/stable/nn.html#conv1d56 nn.BatchNorm2d(conv_out_channels), # BatchNorm1d(channel_out), https://pytorch.org/docs/stable/nn.html#batchnorm1d57 nn.ReLU(),58 nn.MaxPool2d(kernel_size=2, stride=2))59 self.fc = nn.Linear(int(H_in / 2) * self.W_in * conv_out_channels, fc_out_channels)60 # print("Conv2dReduceDims fc feature_dim: ", int(H_in / 2) * self.W_in * conv_out_channels)61 def forward(self, x):62 # print("input: ", x.size())63 x = x.double().to(self.device)64 out = self.conv_layer(x)65 # print("conv_layer out: ", out.size())66 out = out.reshape((out.size(0), -1))67 # print("Conv2dReduceDims: ", out.shape)68 out = self.fc(out)69 return out70 def get_out_dims(self):71 return self.fc_out_channels72class Conv2dPharmacology(nn.Module):73 def __init__(self, feature_dim):74 super(Conv2dPharmacology, self).__init__()75 self.conv_out_channels = 1076 self.feature_dim = feature_dim77 self.cnn1 = nn.Sequential(78 nn.Conv2d(1, self.conv_out_channels, kernel_size=5, stride=2, padding=2),79 nn.BatchNorm2d(self.conv_out_channels),80 nn.ReLU(),81 nn.MaxPool2d(kernel_size=10, stride=5, padding=5))82 self.cnn2 = nn.Sequential(83 nn.Conv2d(self.conv_out_channels, self.conv_out_channels, kernel_size=5, stride=2, padding=2),84 nn.BatchNorm2d(self.conv_out_channels),85 nn.ReLU(),86 nn.MaxPool2d(kernel_size=10, stride=5, padding=5))87 def forward(self, x):88 # TODO: test the size of out89 # x = x.double()90 out = self.cnn1(x)91 # print("cnn1 out:", out.size())92 out = self.cnn2(out)93 # print("cnn2 out:", out.size())94 return out.squeeze()95 def get_flatten_out_dim(self):96 cnn1_out = int(((self.feature_dim-1)/2 + 1) / 5) + 197 cnn2_out = int(((cnn1_out-1)/2 + 1)/5) + 198 return cnn2_out * self.conv_out_channels99def test_Conv2dPharmacology():100 layer1_out_channels = 10101 layer2_out_channels = 10102 conv_out_channels = 10103 inputs = torch.randn((6, 1, 5080, 1))104 # layer1 = nn.Sequential(105 # nn.Conv2d(1, conv_out_channels, kernel_size=5, stride=2, padding=2),106 # nn.BatchNorm2d(conv_out_channels),107 # nn.ReLU(),108 # nn.MaxPool2d(kernel_size=10, stride=5, padding=5))# Maxpool1d, https://pytorch.org/docs/stable/nn.html#maxpool1d109 # layer2 = nn.Sequential(110 # nn.Conv2d(conv_out_channels, conv_out_channels, kernel_size=5, stride=2, padding=2),111 # nn.BatchNorm2d(conv_out_channels),112 # nn.ReLU(),113 # nn.MaxPool2d(kernel_size=10, stride=5, padding=5))114 # outputs = layer1(inputs)115 # print("layer1 outputs: ", outputs.size())116 # outputs = layer2(outputs)117 cnn = Conv2dPharmacology(5080)118 outputs = cnn(inputs)119 print("layer2 outputs: ", outputs.size(), outputs.squeeze().size(), cnn.get_out_dim()) # torch.Size([6, 10, 52, 1]) torch.Size([6, 10, 52]) 52120class BilstmDescription(nn.Module):121 def __init__(self, input_size, device, out_layers=500):122 super(BilstmDescription, self).__init__()123 self.input_size = input_size124 self.hidden_size = 128125 self.num_layers = 2126 self.device = device127 self.batch_first = True128 self.out_layers = out_layers129 self.lstm = nn.LSTM(input_size, self.hidden_size, self.num_layers, batch_first=True, bidirectional=True)130 self.fc = nn.Linear(self.hidden_size * 2, out_layers) # 2 for bidirection131 def forward(self, x):132 x, x_len = hierarchy_cols_padding(x, self.input_size)133 # Set initial states134 h0 = Variable(torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size)).double().to(self.device) # 2 for bidirection135 c0 = Variable(torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size)).double().to(self.device)136 """sort"""137 x_sort_idx = np.argsort(-x_len)138 x_unsort_idx = torch.LongTensor(np.argsort(x_sort_idx))139 x_len = x_len[x_sort_idx]140 x = x[torch.LongTensor(x_sort_idx)]141 """pack"""142 x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first).to(self.device)143 """process using RNN"""144 out_pack, _ = self.lstm(x_emb_p, (h0, c0))145 """unpack: out"""146 out, _ = torch.nn.utils.rnn.pad_packed_sequence(out_pack, batch_first=self.batch_first) # (sequence, lengths)147 out = self.fc(out[:, -1, :])148 """unsort: out"""149 out = out[x_unsort_idx]150 # """unsort: c"""151 # ct = torch.transpose(ct, 0, 1)[152 # x_unsort_idx] # (num_layers * num_directions, batch, hidden_size) -> (batch, ...)153 # ct = torch.transpose(ct, 0, 1)154 # return out, (ht, ct)155 return out156 # def pad_description(self, x):157 # """description_lené¿åº¦ä¸ä¸ï¼éè¦è¿è¡å¯¹é½"""158 # # x.size(): (path_len, description_len, word2vec_len)159 # # max_description_len to pad160 # x_len = []161 # for x_description in x:162 # x_len.append(len(x_description))163 #164 # max_description_len = max(x_len)165 # new_x = []166 # for i in range(len(x)):167 # new_x_description = np.zeros((max_description_len, self.input_size))168 # new_x_description[0: x_len[i]] = x[i]169 # new_x.append(new_x_description)170 #171 # return torch.DoubleTensor(new_x), np.array(x_len)172 def get_out_dims(self):173 return self.out_layers174class GruHierarchy(nn.Module):175 def __init__(self, input_size, hidden_size, device):176 super(GruHierarchy, self).__init__()177 self.input_size = input_size178 self.hidden_size = hidden_size179 self.num_layers = 2180 self.device = device181 self.batch_first = True182 self.bidirectional = True183 self.directions = 2 if self.bidirectional else 1184 self.out_layers = 100185 self.GRU = nn.GRU(input_size,186 self.hidden_size,187 self.num_layers,188 batch_first=self.batch_first,189 bidirectional=self.bidirectional)190 # print("GRU param: ",191 # "\nGRU.hidden_size: ", self.GRU.hidden_size,192 # "\nGRU.bidirectional: ", self.GRU.bidirectional,193 # "\nGRU.input_size: ", self.GRU.input_size,194 # "\nGRU.bidirectional: ", self.GRU.bidirectional,195 # "\nGRU.training: ", self.GRU.training,196 # "\nGRU.batch_first: ", self.GRU.batch_first,197 # "\nGRU.bias: ", self.GRU.bias,198 # "\nGRU.num_layers: ", self.num_layers)199 # print("self.device: ", self.device, "input_size: ", self.input_size)200 def forward(self, x):201 # print("GruHierarchy")202 x, x_len = hierarchy_cols_padding(x, self.input_size)203 batch_size = x.size(0)204 # print("x: ", type(x), x.size())205 h0 = Variable(torch.randn(self.num_layers * self.directions, batch_size, self.hidden_size)).double().to(self.device) # 2 for bidirection206 # print("forward: x:", x.size(), "h0:", h0.size())207 """sort"""208 x_sort_idx = np.argsort(-x_len)209 x_unsort_idx = torch.LongTensor(np.argsort(x_sort_idx))210 x_len = x_len[x_sort_idx]211 x = x[torch.LongTensor(x_sort_idx)]212 """pack"""213 x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first).to(self.device)214 """process using RNN"""215 out_pack, _ = self.GRU(x_emb_p, h0)216 """unpack: out"""217 out, _ = torch.nn.utils.rnn.pad_packed_sequence(out_pack, batch_first=self.batch_first) # (sequence, lengths)218 # out = self.fc(out[:, -1, :])219 """unsort: out"""220 out = out[x_unsort_idx]221 return out, x_len #.permute(1, 0, 2) # (seq_len, batch, input_size) -> (batch, seq_len, input_size)222 def get_out_dims(self):223 return self.hidden_size * self.directions224def process_hierarchy_description(description_lstm, hierarchy_gru_description, hierarchy_descriptions):225 tmp_outs = []226 # print("batch_hierarchy_description: ", len(hierarchy_descriptions))227 for descriptions in hierarchy_descriptions:228 out = description_lstm(descriptions) # path_len * self.description_lstm.get_out_dims()229 # print("description lstm out: ", out.size(), type(out), torch.is_tensor(out))230 tmp_outs.append(out.cpu().detach().numpy())231 batch_gru_outs, x_len = hierarchy_gru_description(tmp_outs) # tmp_outs: [batch_size, 10, num_directions*hidder_size]232 # print("x_len: ", type(x_len), x_len)233 # print("batch_gru_outs, x_len = self.hierarchy_gru(tmp_outs) ", batch_gru_outs.size())234 batch_gru_outs = hierarchy_path_align(batch_gru_outs, x_len) # batchå
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以æé¿çè·¯å¾ä¸ºåï¼235 # print("hierarchy_path_align(batch_gru_outs, x_len", batch_gru_outs.size())236 # æ´ä¸ªæ°æ®éå
对é½237 batch_outs = []238 batch_outs_numpy = batch_gru_outs.cpu().detach().numpy()239 for outs in batch_outs_numpy:240 batch_outs.append(hierarchy_rows_padding(10, hierarchy_gru_description.get_out_dims(), outs))241 return torch.Tensor(batch_outs)242def process_hierarchy_deepwalk(hierarchy_gru_deepwalk, drug_deepwalks):243 batch_gru_outs, x_len = hierarchy_gru_deepwalk(drug_deepwalks)244 batch_gru_outs = hierarchy_path_align(batch_gru_outs, x_len) # batchå
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以æé¿çè·¯å¾ä¸ºåï¼245 # æ´ä¸ªæ°æ®éå
对é½246 batch_outs = []247 batch_outs_numpy = batch_gru_outs.cpu().detach().numpy()248 for outs in batch_outs_numpy:249 batch_outs.append(hierarchy_rows_padding(10, hierarchy_gru_deepwalk.get_out_dims(), outs))250 return torch.Tensor(batch_outs)251def pad_description_test(x):252 tt = []253 tt.append(np.arange(4).reshape((1, 4)))254 tt.append(np.arange(8).reshape((2, 4)))255 tt.append(np.arange(12).reshape((3, 4)))256 tt.append(np.arange(16).reshape((4, 4)))257 tt.append(np.arange(20).reshape((5, 4)))258 def func_test(x):259 x_len = []260 input_size = len(x[0][0])261 for x_description in x:262 x_len.append(len(x_description))263 max_description_len = max(x_len)264 new_x = []265 for i in range(len(x)):266 new_x_description = np.zeros((max_description_len, input_size))267 new_x_description[0: x_len[i]] = x[i]268 new_x.append(new_x_description)269 return new_x, x_len270 print(func_test(tt))271def dynamic_lstm_test():272 """pytorchä¸çå¨ælstmæµè¯æ ·ä¾"""273 sent_1 = [[1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 7], [0, 0, 0, 0]]274 sent_2 = [[10, 11, 12, 13], [12, 13, 14, 15], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]275 sent_3 = [[30, 31, 32, 34], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]276 sent_4 = [[40, 41, 42, 44], [41, 42, 43, 44], [42, 43, 44, 45], [43, 44, 45, 46], [44, 45, 46, 47]]277 sent_5 = [[50, 51, 52, 53], [51, 52, 53, 54], [52, 53, 54, 55], [0, 0, 0, 0], [0, 0, 0, 0]]278 text = np.zeros((5, 5, 4))279 text[0] = sent_1280 text[1] = sent_2281 text[2] = sent_3282 text[3] = sent_4283 text[4] = sent_5284 text = torch.DoubleTensor(text)285 sent_len = np.array([4, 2, 1, 5, 3])286 print("text.shape: ", text.shape)287 def func_test(x, x_len):288 x_sort_idx = np.argsort(-x_len)289 x_unsort_idx = torch.LongTensor(np.argsort(x_sort_idx))290 x_len = x_len[x_sort_idx]291 x = x[torch.LongTensor(x_sort_idx)]292 """pack"""293 x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=True)294 rnn = nn.GRU(4, 3, 2, batch_first=True).double()295 # rnn = nn.LSTM(4, 3, 2, batch_first=True).double()296 h0 = Variable(torch.randn(2, 5, 3)).double()297 c0 = Variable(torch.randn(2, 5, 3)).double()298 out, _ = rnn(x_emb_p, h0)299 # out, _ = rnn(x_emb_p, (h0, c0))300 # unpack301 unpacked, unpacked_len = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)302 print("pad_packed_sequence: ", unpacked.size())303 print("unpacked[0]: ", unpacked[0].size())304 print(unpacked)305 fc = nn.Linear(3, 5).double()306 unpacked = unpacked[:, -1, :]307 print("out[:, -1, :]: ", unpacked.size())308 fc_out = fc(unpacked)309 print("fc_out: ", fc_out.size())310 print(fc_out)311 func_test(text, sent_len)312if __name__ == '__main__':...
pharmacology_context_deepwalk.py
Source:pharmacology_context_deepwalk.py
1# encoding: utf-82"""3@author: Kaiqi Yuan4@software: PyCharm5@file: pharmacology_context_deepwalk.py6@time: 2019/2/13 22:287@description: è¯ç©cnnéç»´è³5008 å°è¯ç©æè¿°è¯å¥ç»è¿lstm, æ建context matrix9 æ¼æ¥deepwalk10 æ¼æ¥target_seq11 æ¾å°cnnåç±»å¨ä¸å¤ç12"""13import sys14sys.path.extend(['/home/kqyuan/DDISuccess', '/home/kqyuan/DDISuccess/util',15 '/home/lei/DDISuccess', '/home/lei/DDISuccess/util'])16import torch17from sklearn import metrics18from torch import nn19from torch.utils import data20from model.pharmacology_cnn import BaseModel21from util.data_loader import PharmacologyDescriptionDeepwalkDataset22from util.neural_network import Conv2dReduceDims, BilstmDescription, Lenet5Classifier, GruHierarchy, GruHierarchy_123from util.utility import hierarchy_rows_padding, hierarchy_path_align24class PharmacologyContextDeepwalkModel(BaseModel):25 def __init__(self, dataset, train_data_file, model_save_path='pharmacology_context_model.ckpt'):26 super().__init__(dataset, train_data_file, model_save_path)27 print("self.BATCH_SIZE: ", self.BATCH_SIZE)28 print("self.NUM_EPOCHS: ", self.NUM_EPOCHS)29 self.device_2 = torch.device('cuda:3')30 def train(self):31 pharmacology_feature_dim = self.train_dataset.get_pharmacologicy_feature_dim()32 target_seq_len = self.train_dataset.get_target_seq_len()33 description_wordembedding_dim = self.train_dataset.get_description_wordembedding_dim()34 deepwalk_feature_dim = self.train_dataset.get_deepwalk_feature_dim()35 print("pharmacology_feature_dim: ", pharmacology_feature_dim)36 print("target_seq_len ", target_seq_len)37 print("description_wordembedding_dim: ", description_wordembedding_dim)38 print("hierarchy_feature_dim: ", deepwalk_feature_dim)39 self.pharmacology_cnn = Conv2dReduceDims(pharmacology_feature_dim, 1, 500, self.device)40 self.pharmacology_cnn.double()41 self.pharmacology_cnn.to(self.device)42 self.description_lstm = BilstmDescription(description_wordembedding_dim, self.device)43 self.description_lstm.double()44 self.description_lstm.to(self.device)45 self.hierarchy_gru_description = GruHierarchy(self.description_lstm.get_out_dims(), 25, self.device)46 self.hierarchy_gru_description.double()47 self.hierarchy_gru_description.to(self.device)48 self.hierarchy_gru_deepwalk = GruHierarchy(deepwalk_feature_dim, 25, self.device)49 self.hierarchy_gru_deepwalk.double()50 self.hierarchy_gru_deepwalk.to(self.device)51 # self.hierarchy_gru_deepwalk.to(self.device_2)52 drug_vec_dim = self.pharmacology_cnn.get_out_dims() + 10 * self.hierarchy_gru_description.get_out_dims() + 10 * self.hierarchy_gru_deepwalk.get_out_dims()53 self.target_cnn = Conv2dReduceDims(target_seq_len, self.train_dataset.get_target_seq_width(), drug_vec_dim, self.device_2)54 self.target_cnn.double()55 self.target_cnn.to(self.device_2)56 classifier_in_channels = drug_vec_dim * 257 print("classifier feature dim: ", classifier_in_channels)58 self.classifier = Lenet5Classifier(feature_dim=classifier_in_channels).to(self.device)59 self.classifier.double()60 self.classifier.to(self.device)61 # Loss and optimizer62 criterion = nn.CrossEntropyLoss()63 # classifier.parameters(). Returns an iterator over module parameters. This is typically passed to an optimizer.64 optimizer = torch.optim.SGD(self.classifier.parameters(), lr=self.LEARNING_RATE)65 # Train the classifier66 total_step = len(self.train_loader)67 for epoch in range(self.NUM_EPOCHS):68 correct = 069 total = 070 for i, (phy_features, target_seqs, labels) in enumerate(self.train_loader):71 hierarchy_description, drug_deepwalk = self.train_dataset.feed_batch_data(i, self.BATCH_SIZE)72 phy_features = phy_features.double().to(self.device)73 phy_features = self.pharmacology_cnn(phy_features)74 this_batch_size = len(labels) # torch.Size([100, 1, 28, 28])75 drug_deepwalk = self.process_hierarchy_deepwalk(drug_deepwalk).double().to(self.device).reshape((this_batch_size, -1))76 hierarchy_description = self.process_hierarchy_description(hierarchy_description).double().to(self.device).reshape((this_batch_size, -1))77 # drug_deepwalk = drug_deepwalk.double().to(self.device).reshape((this_batch_size, -1))78 # target_seqs = target_seqs.double().to(self.device_2)79 target_seqs = self.target_cnn(target_seqs).to(self.device)80 labels = labels.to(self.device) # torch.Size([100])81 # Forward pass82 # print("phy_features.size()", phy_features.size())83 # print("target_seqs.size()", target_seqs.size())84 # print("hierarchy_description.size()", hierarchy_description.size())85 inputs = torch.cat((phy_features, target_seqs, hierarchy_description, drug_deepwalk), 1).reshape(86 (this_batch_size, 1, -1, 1))87 # print("inputs:", inputs.size())88 outputs = self.classifier(inputs)89 loss = criterion(outputs, labels)90 # Backward and optimize91 # æ ¹æ®pytorchä¸çbackward()å½æ°ç计ç®ï¼å½ç½ç»åéè¿è¡åé¦æ¶ï¼æ¢¯åº¦æ¯è¢«ç§¯ç´¯çèä¸æ¯è¢«æ¿æ¢æï¼92 # ä½æ¯å¨æ¯ä¸ä¸ªbatchæ¶æ¯«æ çé®å¹¶ä¸éè¦å°ä¸¤ä¸ªbatchç梯度混åèµ·æ¥ç´¯ç§¯ï¼å æ¤è¿éå°±éè¦æ¯ä¸ªbatch设置ä¸ézero_grad äº93 optimizer.zero_grad() # Sets gradients of all classifier parameters to zero.94 loss.backward()95 optimizer.step()96 total, correct = self.calculate_accuracy(outputs, labels, total, correct)97 if (i + 1) % 32 == 0:98 print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'99 .format(epoch + 1, self.NUM_EPOCHS, i + 1, total_step, loss.item(), correct / total * 100))100 # # Test the classifier101 # self.classifier.eval()102 def test(self, test_data_file):103 test_dataset = self.dataset_func(test_data_file)104 test_loader = data.DataLoader(dataset=test_dataset,105 batch_size=self.BATCH_SIZE,106 shuffle=True)107 with torch.no_grad():108 correct = 0109 total = 0110 y_true = None111 y_pred = None112 pred_score = None113 for i, (phy_features, target_seqs, labels) in enumerate(test_loader):114 hierarchy_description, drug_deepwalk = test_dataset.feed_batch_data(i, self.BATCH_SIZE)115 phy_features = phy_features.double().to(self.device)116 phy_features = self.pharmacology_cnn(phy_features)117 # print("phy_features.size()", phy_features.size())118 # print("target_seqs.size()", target_seqs.size())119 # print("hierarchy_description.size()", len(hierarchy_description))120 # print("drug_deepwalk.size()", len(drug_deepwalk))121 # print("this_batch_size: ", len(labels))122 # print("labels: ",labels)123 this_batch_size = len(labels)124 hierarchy_description = self.process_hierarchy_description(hierarchy_description)125 # print("self.process_hierarchy_description(hierarchy_description): ", hierarchy_description.size())126 hierarchy_description = hierarchy_description.double().to(self.device).reshape((this_batch_size, -1))127 drug_deepwalk = self.process_hierarchy_deepwalk(drug_deepwalk).double().to(self.device).reshape((this_batch_size, -1))128 # target_seqs = target_seqs.double().to(self.device).reshape((this_batch_size, -1))129 target_seqs = self.target_cnn(target_seqs).to(self.device)130 labels = labels.to(self.device) # torch.Size([100])131 # Forward pass132 inputs = torch.cat((phy_features, target_seqs, hierarchy_description, drug_deepwalk), 1).reshape(133 (this_batch_size, 1, -1, 1))134 outputs = self.classifier(inputs)135 y_true, total, correct, y_pred, pred_score = self.eval_model(outputs, labels, total, correct, y_true,136 y_pred, pred_score)137 print("************test**************")138 accuracy = correct / total139 precision, recall, f1, _ = metrics.precision_recall_fscore_support(y_true, y_pred)140 auroc = metrics.roc_auc_score(y_true,141 pred_score) # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score142 aupr_precision, aupr_recall, _ = metrics.precision_recall_curve(y_true, pred_score, pos_label=1)143 aupr = metrics.auc(aupr_recall, aupr_precision)144 print("test dataset: ", test_data_file)145 print("auroc: ", auroc)146 print("accuracy: ", accuracy)147 print("precision: ", precision)148 print("recall: ", recall)149 print("f1: ", f1)150 print("aupr: ", aupr)151 # # Save the classifier checkpoint152 torch.save(self.classifier.state_dict(), self.model_save_path)153 def process_hierarchy_description(self, hierarchy_descriptions):154 tmp_outs = []155 # print("batch_hierarchy_description: ", len(hierarchy_descriptions))156 for descriptions in hierarchy_descriptions:157 out = self.description_lstm(descriptions) # path_len * self.description_lstm.get_out_dims()158 # print("description lstm out: ", out.size(), type(out), torch.is_tensor(out))159 tmp_outs.append(out.cpu().detach().numpy())160 batch_gru_outs, x_len = self.hierarchy_gru_description(tmp_outs) # tmp_outs: [batch_size, 10, num_directions*hidder_size]161 # print("x_len: ", type(x_len), x_len)162 # print("batch_gru_outs, x_len = self.hierarchy_gru(tmp_outs) ", batch_gru_outs.size())163 batch_gru_outs = hierarchy_path_align(batch_gru_outs, x_len) # batchå
对é½ï¼æ¯ä¸ªbatchå
以æé¿çè·¯å¾ä¸ºåï¼164 # print("hierarchy_path_align(batch_gru_outs, x_len", batch_gru_outs.size())165 # æ´ä¸ªæ°æ®éå
对é½166 batch_outs = []167 batch_outs_numpy = batch_gru_outs.cpu().detach().numpy()168 for outs in batch_outs_numpy:169 batch_outs.append(hierarchy_rows_padding(10, self.hierarchy_gru_description.get_out_dims(), outs))170 return torch.Tensor(batch_outs)171 def process_hierarchy_deepwalk(self, drug_deepwalks):172 batch_gru_outs, x_len = self.hierarchy_gru_deepwalk(drug_deepwalks) # drug_deepwalks: [batch_size, path_len, num_directions*hidder_size]173 # print("x_len: ", type(x_len), x_len)174 batch_gru_outs = hierarchy_path_align(batch_gru_outs, x_len) # batchå
对é½ï¼æ¯ä¸ªbatchå
以æé¿çè·¯å¾ä¸ºåï¼175 # æ´ä¸ªæ°æ®éå
对é½176 batch_outs = []177 batch_outs_numpy = batch_gru_outs.cpu().detach().numpy()178 for outs in batch_outs_numpy:179 batch_outs.append(hierarchy_rows_padding(10, self.hierarchy_gru_deepwalk.get_out_dims(), outs))180 return torch.Tensor(batch_outs)181 # # [this_batch_size, path_len, 128]182 # batch_outs = []183 # for hierarchy_deepwalk in drug_deepwalks:184 # print("hierarchy_deepwalk is numpy: ", hierarchy_deepwalk.shape)185 # hierarchy_deepwalk = hierarchy_rows_padding(hierarchy_deepwalk.shape[0], 500, hierarchy_deepwalk)186 # hierarchy_deepwalk = torch.Tensor(hierarchy_deepwalk).to(self.device_2)187 # hierarchy_deepwalk = hierarchy_deepwalk.reshape((1, hierarchy_deepwalk.size(0), hierarchy_deepwalk.size(1)))188 # print("hierarchy_deepwalk is tensor: ", hierarchy_deepwalk.size())189 # out = self.hierarchy_gru_deepwalk(hierarchy_deepwalk)190 # print("out size: ", out.size())191 # print("out type: ", type(out))192 # out = out.squeeze().cpu().detach().numpy()193 # out_dims = self.hierarchy_gru_description.get_out_dims()194 # print("before pad: ", out.shape, "out_dims: ", out_dims)195 # out_vec = hierarchy_rows_padding(10, out_dims, out)196 # out_vec = out_vec.reshape(10 * out_dims) # multiple dimension to single dimension197 # batch_outs.append(out_vec)198 #199 # # batch_outs = hierarchy_padding(10, self.hierarchy_gru.out_dims, batch_outs)200 # batch_outs = torch.Tensor(batch_outs)201 # # print("batch_out size: ", batch_outs.size())202 # return batch_outs203if __name__ == '__main__':204 # model = PharmacologyContextDeepwalkModel(PharmacologyDescriptionDeepwalkDataset, "../Data/DTI/e_train.pickle", "e_pharmacology_context_deepwalk_model.ckpt")205 # model.train()206 # model.classifier.eval()207 # model.test("../Data/DTI/e_test.pickle")208 # model = PharmacologyContextDeepwalkModel(PharmacologyDescriptionDeepwalkDataset, "../Data/DTI/ic_train.pickle", "ic_pharmacology_context_deepwalk_model.ckpt")209 # model.train()210 # model.classifier.eval()211 # model.test("../Data/DTI/ic_test.pickle")212 #213 model = PharmacologyContextDeepwalkModel(PharmacologyDescriptionDeepwalkDataset, "../Data/DTI/gpcr_train.pickle", "gpcr_pharmacology_context_deepwalk_model.ckpt")214 model.train()215 model.classifier.eval()...
pharmacology_context.py
Source:pharmacology_context.py
1# encoding: utf-82"""3@author: Kaiqi Yuan4@software: PyCharm5@file: pharmacology_context.py6@time: 19-1-24 ä¸å8:507@description: è¯ç©cnnéç»´è³5008 å°è¯ç©æè¿°è¯å¥ç»è¿lstm, æ建context matrix9 æ¼æ¥target_seq10 æ¾å°cnnåç±»å¨ä¸å¤ç11"""12import sys13sys.path.extend(['/home/kqyuan/DDISuccess', '/home/kqyuan/DDISuccess/util',14 '/home/lei/DDISuccess', '/home/lei/DDISuccess/util'])15import torch16from sklearn import metrics17from torch import nn18from torch.utils import data19from model.pharmacology_cnn import BaseModel20from util.data_loader import PharmacologyDescriptionDataset21from util.neural_network import Conv2dReduceDims, BilstmDescription, Lenet5Classifier, GruHierarchy22from util.utility import hierarchy_rows_padding, hierarchy_path_align23class PharmacologyContextModel(BaseModel):24 def __init__(self, dataset, train_data_file, model_save_path='pharmacology_context_model.ckpt'):25 super().__init__(dataset, train_data_file, model_save_path)26 # self.NUM_EPOCHS = 127 print("self.NUM_EPOCHS: ", self.NUM_EPOCHS)28 def train(self):29 pharmacology_feature_dim = self.train_dataset.get_pharmacologicy_feature_dim()30 target_seq_len = self.train_dataset.get_target_seq_len()31 description_wordembedding_dim = self.train_dataset.get_description_wordembedding_dim()32 print("pharmacology_feature_dim: ", pharmacology_feature_dim)33 print("target_seq_len ", target_seq_len)34 print("description_wordembedding_dim: ", description_wordembedding_dim)35 self.pharmacology_cnn = Conv2dReduceDims(pharmacology_feature_dim)36 self.pharmacology_cnn.double()37 self.pharmacology_cnn.to(self.device)38 self.description_lstm = BilstmDescription(description_wordembedding_dim, self.device)39 self.description_lstm.double()40 self.description_lstm.to(self.device)41 self.hierarchy_gru = GruHierarchy(self.description_lstm.get_out_dims(), self.device)42 self.hierarchy_gru.double()43 self.hierarchy_gru.to(self.device)44 classifier_in_channels = 500 + target_seq_len + 10*self.hierarchy_gru.get_out_dims() # the output of cnn for reduce dims is self.description_lstm.get_out_dims()45 print("classifier_in_channels: ", classifier_in_channels)46 self.classifier = Lenet5Classifier(feature_dim=classifier_in_channels).to(self.device)47 self.classifier.double()48 self.classifier.to(self.device)49 # Loss and optimizer50 criterion = nn.CrossEntropyLoss()51 # classifier.parameters(). Returns an iterator over module parameters. This is typically passed to an optimizer.52 optimizer = torch.optim.SGD(self.classifier.parameters(), lr=self.LEARNING_RATE)53 # Train the classifier54 total_step = len(self.train_loader)55 for epoch in range(self.NUM_EPOCHS):56 correct = 057 total = 058 for i, (phy_features, target_seqs, labels) in enumerate(self.train_loader):59 phy_features = phy_features.double().to(self.device)60 phy_features = self.pharmacology_cnn(phy_features)61 this_batch_size = len(labels)62 hierarchy_description = self.train_dataset.feed_batch_data(i, self.BATCH_SIZE)63 hierarchy_description = self.process_hierarchy_description(hierarchy_description).double().to(self.device).reshape((this_batch_size, -1)) # torch.Size([100, 1, 28, 28])64 target_seqs = target_seqs.double().to(self.device).reshape((this_batch_size, -1)) # torch.Size([100, 1, 28, 28])65 labels = labels.to(self.device) # torch.Size([100])66 # Forward pass67 # print("phy_features.size()", phy_features.size())68 # print("target_seqs.size()", target_seqs.size())69 # print("hierarchy_description.size()", hierarchy_description.size())70 inputs = torch.cat((phy_features, target_seqs, hierarchy_description), 1).reshape((this_batch_size, 1, -1, 1))71 # print("inputs:", inputs.size())72 outputs = self.classifier(inputs)73 loss = criterion(outputs, labels)74 # Backward and optimize75 # æ ¹æ®pytorchä¸çbackward()å½æ°ç计ç®ï¼å½ç½ç»åéè¿è¡åé¦æ¶ï¼æ¢¯åº¦æ¯è¢«ç§¯ç´¯çèä¸æ¯è¢«æ¿æ¢æï¼76 # ä½æ¯å¨æ¯ä¸ä¸ªbatchæ¶æ¯«æ çé®å¹¶ä¸éè¦å°ä¸¤ä¸ªbatchç梯度混åèµ·æ¥ç´¯ç§¯ï¼å æ¤è¿éå°±éè¦æ¯ä¸ªbatch设置ä¸ézero_grad äº77 optimizer.zero_grad() # Sets gradients of all classifier parameters to zero.78 loss.backward()79 optimizer.step()80 total, correct = self.calculate_accuracy(outputs, labels, total, correct)81 if (i + 1) % 32 == 0:82 print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'83 .format(epoch + 1, self.NUM_EPOCHS, i + 1, total_step, loss.item(), correct / total * 100))84 # # Test the classifier85 # self.classifier.eval()86 def test(self, test_data_file):87 test_dataset = self.dataset_func(test_data_file)88 test_loader = data.DataLoader(dataset=test_dataset,89 batch_size=self.BATCH_SIZE,90 shuffle=True)91 with torch.no_grad():92 correct = 093 total = 094 y_true = None95 y_pred = None96 pred_score = None97 for i, (phy_features, target_seqs, labels) in enumerate(test_loader):98 phy_features = phy_features.double().to(self.device)99 phy_features = self.pharmacology_cnn(phy_features)100 this_batch_size = len(labels)101 hierarchy_description = test_dataset.feed_batch_data(i, self.BATCH_SIZE)102 hierarchy_description = self.process_hierarchy_description(hierarchy_description).reshape((this_batch_size, -1)).double().to(self.device) # torch.Size([100, 1, 28, 28])103 target_seqs = target_seqs.double().to(self.device).reshape((this_batch_size, -1)) # torch.Size([100, 1, 28, 28])104 labels = labels.to(self.device) # torch.Size([100])105 # Forward pass106 inputs = torch.cat((phy_features, target_seqs, hierarchy_description), 1).reshape(107 (this_batch_size, 1, -1, 1))108 outputs = self.classifier(inputs)109 y_true, total, correct, y_pred, pred_score = self.eval_model(outputs, labels, total, correct, y_true,110 y_pred, pred_score)111 print("************test**************")112 accuracy = correct / total113 precision, recall, f1, _ = metrics.precision_recall_fscore_support(y_true, y_pred)114 auroc = metrics.roc_auc_score(y_true,115 pred_score) # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score116 aupr_precision, aupr_recall, _ = metrics.precision_recall_curve(y_true, pred_score, pos_label=1)117 aupr = metrics.auc(aupr_recall, aupr_precision)118 print("test dataset: ", test_data_file)119 print("auroc: ", auroc)120 print("accuracy: ", accuracy)121 print("precision: ", precision)122 print("recall: ", recall)123 print("f1: ", f1)124 print("aupr: ", aupr)125 # # Save the classifier checkpoint126 torch.save(self.classifier.state_dict(), self.model_save_path)127 def process_hierarchy_description(self, hierarchy_descriptions):128 tmp_outs = []129 for descriptions in hierarchy_descriptions:130 out = self.description_lstm(descriptions) # path_len * self.description_lstm.get_out_dims()131 # print("description lstm out: ", out.size(), type(out), torch.is_tensor(out))132 tmp_outs.append(out.cpu().detach().numpy())133 batch_gru_outs, x_len = self.hierarchy_gru(tmp_outs) # tmp_outs: [batch_size, 10, num_directions*hidder_size]134 # print("x_len: ", type(x_len), x_len)135 batch_gru_outs = hierarchy_path_align(batch_gru_outs, x_len) # batchå
对é½ï¼æ¯ä¸ªbatchå
以æé¿çè·¯å¾ä¸ºåï¼136 # æ´ä¸ªæ°æ®éå
对é½137 batch_outs = []138 batch_outs_numpy = batch_gru_outs.cpu().detach().numpy()139 for outs in batch_outs_numpy:140 batch_outs.append(hierarchy_rows_padding(10, self.hierarchy_gru.get_out_dims(), outs))141 return torch.tensor(batch_outs)142if __name__ == '__main__':143 # model = PharmacologyContextModel(PharmacologyDescriptionDataset, "../Data/DTI/e_train.pickle", "e_pharmacology_context_model.ckpt")144 # model.train()145 # model.classifier.eval()146 # model.test("../Data/DTI/e_test.pickle")147 # model = PharmacologyContextModel(PharmacologyDescriptionDataset, "../Data/DTI/ic_train.pickle", "ic_pharmacology_context_model.ckpt")148 # model.train()149 # model.classifier.eval()150 # model.test("../Data/DTI/ic_test.pickle")151 #152 model = PharmacologyContextModel(PharmacologyDescriptionDataset, "../Data/DTI/gpcr_train.pickle",153 "gpcr_pharmacology_context_model.ckpt")154 model.train()155 model.classifier.eval()...
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