import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
from torch.utils.tensorboard import SummaryWriter
from earlystoping import Earlystopping
from sklearn import metrics
def MethyOmiVAE(input_path, methy_chr_df_list, random_seed=42, no_cuda=False, model_parallelism=True,
separate_testing=True, batch_size=32, latent_dim=128, learning_rate=1e-3, p1_epoch_num=50,
p2_epoch_num=100, output_loss_record=True, classifier=True, early_stopping=True):
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
device = torch.device('cuda:0' if not no_cuda and torch.cuda.is_available() else 'cpu')
parallel = torch.cuda.device_count() > 1 and model_parallelism
sample_id = np.loadtxt(input_path + 'both_samples.tsv', delimiter='\t', dtype='str')
# Loading label
label = pd.read_csv(input_path + 'both_samples_tumour_type_digit.tsv', sep='\t', header=0, index_col=0)
class_num = len(label.tumour_type.unique())
label_array = label['tumour_type'].values
if separate_testing:
# Get testing set index and training set index
# Separate according to different tumour types
testset_ratio = 0.2
valset_ratio = 0.5
train_index, test_index, train_label, test_label = train_test_split(sample_id, label_array,
test_size=testset_ratio,
random_state=random_seed,
stratify=label_array)
val_index, test_index, val_label, test_label = train_test_split(test_index, test_label, test_size=valset_ratio,
random_state=random_seed, stratify=test_label)
methy_chr_df_test_list = []
methy_chr_df_val_list = []
methy_chr_df_train_list = []
for chrom_index in range(0, 23):
methy_chr_df_test = methy_chr_df_list[chrom_index][test_index]
methy_chr_df_test_list.append(methy_chr_df_test)
methy_chr_df_val = methy_chr_df_list[chrom_index][val_index]
methy_chr_df_val_list.append(methy_chr_df_val)
methy_chr_df_train = methy_chr_df_list[chrom_index][train_index]
methy_chr_df_train_list.append(methy_chr_df_train)
# Get dataset information
sample_num = len(sample_id)
methy_feature_num_list = []
for chrom_index in range(0, 23):
feature_num = methy_chr_df_list[chrom_index].shape[0]
methy_feature_num_list.append(feature_num)
methy_feature_num_array = np.array(methy_feature_num_list)
methy_feature_num = methy_feature_num_array.sum()
print('\nNumber of samples: {}'.format(sample_num))
print('Number of methylation features: {}'.format(methy_feature_num))
if classifier:
print('Number of classes: {}'.format(class_num))
class MethyOmiDataset(Dataset):
def __init__(self, methy_df_list, labels):
self.methy_df_list = methy_df_list
self.labels = labels
def __len__(self):
return self.methy_df_list[0].shape[1]
def __getitem__(self, index):
omics_data = []
# Methylation tensor index 0-22
for methy_chrom_index in range(0, 23):
methy_chr_line = self.methy_df_list[methy_chrom_index].iloc[:, index].values
methy_chr_line_tensor = torch.Tensor(methy_chr_line)
omics_data.append(methy_chr_line_tensor)
label = self.labels[index]
return [omics_data, label]
# DataSets and DataLoaders
if separate_testing:
train_dataset = MethyOmiDataset(methy_df_list=methy_chr_df_train_list, labels=train_label)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
val_dataset = MethyOmiDataset(methy_df_list=methy_chr_df_val_list, labels=val_label)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
test_dataset = MethyOmiDataset(methy_df_list=methy_chr_df_test_list, labels=test_label)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
else:
train_dataset = MethyOmiDataset(methy_df_list=methy_chr_df_list)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
full_dataset = MethyOmiDataset(methy_df_list=methy_chr_df_list, labels=label_array)
full_loader = DataLoader(full_dataset, batch_size=batch_size, num_workers=6)
# Setting dimensions
latent_space_dim = latent_dim
input_dim_methy_array = methy_feature_num_array
level_2_dim_methy = 256
level_3_dim_methy = 1024
level_4_dim = 512
classifier_1_dim = 128
classifier_2_dim = 64
classifier_out_dim = class_num
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
# ENCODER fc layers
# level 1
# Methy input for each chromosome
self.e_fc1_methy_1 = self.fc_layer(input_dim_methy_array[0], level_2_dim_methy)
self.e_fc1_methy_2 = self.fc_layer(input_dim_methy_array[1], level_2_dim_methy)
self.e_fc1_methy_3 = self.fc_layer(input_dim_methy_array[2], level_2_dim_methy)
self.e_fc1_methy_4 = self.fc_layer(input_dim_methy_array[3], level_2_dim_methy)
self.e_fc1_methy_5 = self.fc_layer(input_dim_methy_array[4], level_2_dim_methy)
self.e_fc1_methy_6 = self.fc_layer(input_dim_methy_array[5], level_2_dim_methy)
self.e_fc1_methy_7 = self.fc_layer(input_dim_methy_array[6], level_2_dim_methy)
self.e_fc1_methy_8 = self.fc_layer(input_dim_methy_array[7], level_2_dim_methy)
self.e_fc1_methy_9 = self.fc_layer(input_dim_methy_array[8], level_2_dim_methy)
self.e_fc1_methy_10 = self.fc_layer(input_dim_methy_array[9], level_2_dim_methy)
self.e_fc1_methy_11 = self.fc_layer(input_dim_methy_array[10], level_2_dim_methy)
self.e_fc1_methy_12 = self.fc_layer(input_dim_methy_array[11], level_2_dim_methy)
self.e_fc1_methy_13 = self.fc_layer(input_dim_methy_array[12], level_2_dim_methy)
self.e_fc1_methy_14 = self.fc_layer(input_dim_methy_array[13], level_2_dim_methy)
self.e_fc1_methy_15 = self.fc_layer(input_dim_methy_array[14], level_2_dim_methy)
self.e_fc1_methy_16 = self.fc_layer(input_dim_methy_array[15], level_2_dim_methy)
self.e_fc1_methy_17 = self.fc_layer(input_dim_methy_array[16], level_2_dim_methy)
self.e_fc1_methy_18 = self.fc_layer(input_dim_methy_array[17], level_2_dim_methy)
self.e_fc1_methy_19 = self.fc_layer(input_dim_methy_array[18], level_2_dim_methy)
self.e_fc1_methy_20 = self.fc_layer(input_dim_methy_array[19], level_2_dim_methy)
self.e_fc1_methy_21 = self.fc_layer(input_dim_methy_array[20], level_2_dim_methy)
self.e_fc1_methy_22 = self.fc_layer(input_dim_methy_array[21], level_2_dim_methy)
self.e_fc1_methy_X = self.fc_layer(input_dim_methy_array[22], level_2_dim_methy)
# Level 2
self.e_fc2_methy = self.fc_layer(level_2_dim_methy*23, level_3_dim_methy)
# self.e_fc2_methy = self.fc_layer(level_2_dim_methy * 23, level_3_dim_methy, dropout=True)
# Level 3
self.e_fc3 = self.fc_layer(level_3_dim_methy, level_4_dim)
# self.e_fc3 = self.fc_layer(level_3_dim_methy, level_4_dim, dropout=True)
# Level 4
self.e_fc4_mean = self.fc_layer(level_4_dim, latent_space_dim, activation=0)
self.e_fc4_log_var = self.fc_layer(level_4_dim, latent_space_dim, activation=0)
# model parallelism
if parallel:
self.e_fc1_methy_1.to('cuda:0')
self.e_fc1_methy_2.to('cuda:0')
self.e_fc1_methy_3.to('cuda:0')
self.e_fc1_methy_4.to('cuda:0')
self.e_fc1_methy_5.to('cuda:0')
self.e_fc1_methy_6.to('cuda:0')
self.e_fc1_methy_7.to('cuda:0')
self.e_fc1_methy_8.to('cuda:0')
self.e_fc1_methy_9.to('cuda:0')
self.e_fc1_methy_10.to('cuda:0')
self.e_fc1_methy_11.to('cuda:0')
self.e_fc1_methy_12.to('cuda:0')
self.e_fc1_methy_13.to('cuda:0')
self.e_fc1_methy_14.to('cuda:0')
self.e_fc1_methy_15.to('cuda:0')
self.e_fc1_methy_16.to('cuda:0')
self.e_fc1_methy_17.to('cuda:0')
self.e_fc1_methy_18.to('cuda:0')
self.e_fc1_methy_19.to('cuda:0')
self.e_fc1_methy_20.to('cuda:0')
self.e_fc1_methy_21.to('cuda:0')
self.e_fc1_methy_22.to('cuda:0')
self.e_fc1_methy_X.to('cuda:0')
self.e_fc2_methy.to('cuda:0')
self.e_fc3.to('cuda:0')
self.e_fc4_mean.to('cuda:0')
self.e_fc4_log_var.to('cuda:0')
# DECODER fc layers
# Level 4
self.d_fc4 = self.fc_layer(latent_space_dim, level_4_dim)
# Level 3
self.d_fc3 = self.fc_layer(level_4_dim, level_3_dim_methy)
# self.d_fc3 = self.fc_layer(level_4_dim, level_3_dim_methy, dropout=True)
# Level 2
self.d_fc2_methy = self.fc_layer(level_3_dim_methy, level_2_dim_methy*23)
# self.d_fc2_methy = self.fc_layer(level_3_dim_methy, level_2_dim_methy*23, dropout=True)
# level 1
# Methy output for each chromosome
self.d_fc1_methy_1 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[0], activation=2)
self.d_fc1_methy_2 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[1], activation=2)
self.d_fc1_methy_3 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[2], activation=2)
self.d_fc1_methy_4 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[3], activation=2)
self.d_fc1_methy_5 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[4], activation=2)
self.d_fc1_methy_6 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[5], activation=2)
self.d_fc1_methy_7 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[6], activation=2)
self.d_fc1_methy_8 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[7], activation=2)
self.d_fc1_methy_9 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[8], activation=2)
self.d_fc1_methy_10 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[9], activation=2)
self.d_fc1_methy_11 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[10], activation=2)
self.d_fc1_methy_12 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[11], activation=2)
self.d_fc1_methy_13 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[12], activation=2)
self.d_fc1_methy_14 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[13], activation=2)
self.d_fc1_methy_15 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[14], activation=2)
self.d_fc1_methy_16 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[15], activation=2)
self.d_fc1_methy_17 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[16], activation=2)
self.d_fc1_methy_18 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[17], activation=2)
self.d_fc1_methy_19 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[18], activation=2)
self.d_fc1_methy_20 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[19], activation=2)
self.d_fc1_methy_21 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[20], activation=2)
self.d_fc1_methy_22 = self.fc_layer(level_2_dim_methy, input_dim_methy_array[21], activation=2)
self.d_fc1_methy_X = self.fc_layer(level_2_dim_methy, input_dim_methy_array[22], activation=2)
# model parallelism
if parallel:
self.d_fc4.to('cuda:1')
self.d_fc3.to('cuda:1')
self.d_fc2_methy.to('cuda:1')
self.d_fc1_methy_1.to('cuda:1')
self.d_fc1_methy_2.to('cuda:1')
self.d_fc1_methy_3.to('cuda:1')
self.d_fc1_methy_4.to('cuda:1')
self.d_fc1_methy_5.to('cuda:1')
self.d_fc1_methy_6.to('cuda:1')
self.d_fc1_methy_7.to('cuda:1')
self.d_fc1_methy_8.to('cuda:1')
self.d_fc1_methy_9.to('cuda:1')
self.d_fc1_methy_10.to('cuda:1')
self.d_fc1_methy_11.to('cuda:1')
self.d_fc1_methy_12.to('cuda:1')
self.d_fc1_methy_13.to('cuda:1')
self.d_fc1_methy_14.to('cuda:1')
self.d_fc1_methy_15.to('cuda:1')
self.d_fc1_methy_16.to('cuda:1')
self.d_fc1_methy_17.to('cuda:1')
self.d_fc1_methy_18.to('cuda:1')
self.d_fc1_methy_19.to('cuda:1')
self.d_fc1_methy_20.to('cuda:1')
self.d_fc1_methy_21.to('cuda:1')
self.d_fc1_methy_22.to('cuda:1')
self.d_fc1_methy_X.to('cuda:1')
# CLASSIFIER fc layers
self.c_fc1 = self.fc_layer(latent_space_dim, classifier_1_dim)
self.c_fc2 = self.fc_layer(classifier_1_dim, classifier_2_dim)
# self.c_fc2 = self.fc_layer(classifier_1_dim, classifier_2_dim, dropout=True)
self.c_fc3 = self.fc_layer(classifier_2_dim, classifier_out_dim, activation=0)
# model parallelism
if parallel:
self.c_fc1.to('cuda:1')
self.c_fc2.to('cuda:1')
self.c_fc3.to('cuda:1')
# Activation - 0: no activation, 1: ReLU, 2: Sigmoid
def fc_layer(self, in_dim, out_dim, activation=1, dropout=False, dropout_p=0.5):
if activation == 0:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim))
elif activation == 2:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.Sigmoid())
else:
if dropout:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.ReLU(),
nn.Dropout(p=dropout_p))
else:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.ReLU())
return layer
def encode(self, x):
methy_1_level2_layer = self.e_fc1_methy_1(x[0])
methy_2_level2_layer = self.e_fc1_methy_2(x[1])
methy_3_level2_layer = self.e_fc1_methy_3(x[2])
methy_4_level2_layer = self.e_fc1_methy_4(x[3])
methy_5_level2_layer = self.e_fc1_methy_5(x[4])
methy_6_level2_layer = self.e_fc1_methy_6(x[5])
methy_7_level2_layer = self.e_fc1_methy_7(x[6])
methy_8_level2_layer = self.e_fc1_methy_8(x[7])
methy_9_level2_layer = self.e_fc1_methy_9(x[8])
methy_10_level2_layer = self.e_fc1_methy_10(x[9])
methy_11_level2_layer = self.e_fc1_methy_11(x[10])
methy_12_level2_layer = self.e_fc1_methy_12(x[11])
methy_13_level2_layer = self.e_fc1_methy_13(x[12])
methy_14_level2_layer = self.e_fc1_methy_14(x[13])
methy_15_level2_layer = self.e_fc1_methy_15(x[14])
methy_16_level2_layer = self.e_fc1_methy_16(x[15])
methy_17_level2_layer = self.e_fc1_methy_17(x[16])
methy_18_level2_layer = self.e_fc1_methy_18(x[17])
methy_19_level2_layer = self.e_fc1_methy_19(x[18])
methy_20_level2_layer = self.e_fc1_methy_20(x[19])
methy_21_level2_layer = self.e_fc1_methy_21(x[20])
methy_22_level2_layer = self.e_fc1_methy_22(x[21])
methy_X_level2_layer = self.e_fc1_methy_X(x[22])
# concat methy tensor together
methy_level2_layer = torch.cat((methy_1_level2_layer, methy_2_level2_layer, methy_3_level2_layer,
methy_4_level2_layer, methy_5_level2_layer, methy_6_level2_layer,
methy_7_level2_layer, methy_8_level2_layer, methy_9_level2_layer,
methy_10_level2_layer, methy_11_level2_layer, methy_12_level2_layer,
methy_13_level2_layer, methy_14_level2_layer, methy_15_level2_layer,
methy_16_level2_layer, methy_17_level2_layer, methy_18_level2_layer,
methy_19_level2_layer, methy_20_level2_layer, methy_21_level2_layer,
methy_22_level2_layer, methy_X_level2_layer), 1)
level_3_layer = self.e_fc2_methy(methy_level2_layer)
level_4_layer = self.e_fc3(level_3_layer)
latent_mean = self.e_fc4_mean(level_4_layer)
latent_log_var = self.e_fc4_log_var(level_4_layer)
return latent_mean, latent_log_var
def reparameterize(self, mean, log_var):
sigma = torch.exp(0.5 * log_var)
eps = torch.randn_like(sigma)
return mean + eps * sigma
def decode(self, z):
level_4_layer = self.d_fc4(z)
level_3_layer = self.d_fc3(level_4_layer)
methy_level3_layer = level_3_layer.narrow(1, 0, level_3_dim_methy)
methy_level2_layer = self.d_fc2_methy(methy_level3_layer)
methy_1_level2_layer = methy_level2_layer.narrow(1, 0, level_2_dim_methy)
methy_2_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy, level_2_dim_methy)
methy_3_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*2, level_2_dim_methy)
methy_4_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*3, level_2_dim_methy)
methy_5_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*4, level_2_dim_methy)
methy_6_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*5, level_2_dim_methy)
methy_7_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*6, level_2_dim_methy)
methy_8_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*7, level_2_dim_methy)
methy_9_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*8, level_2_dim_methy)
methy_10_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*9, level_2_dim_methy)
methy_11_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*10, level_2_dim_methy)
methy_12_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*11, level_2_dim_methy)
methy_13_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*12, level_2_dim_methy)
methy_14_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*13, level_2_dim_methy)
methy_15_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*14, level_2_dim_methy)
methy_16_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*15, level_2_dim_methy)
methy_17_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*16, level_2_dim_methy)
methy_18_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*17, level_2_dim_methy)
methy_19_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*18, level_2_dim_methy)
methy_20_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*19, level_2_dim_methy)
methy_21_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*20, level_2_dim_methy)
methy_22_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*21, level_2_dim_methy)
methy_X_level2_layer = methy_level2_layer.narrow(1, level_2_dim_methy*22, level_2_dim_methy)
recon_x1 = self.d_fc1_methy_1(methy_1_level2_layer)
recon_x2 = self.d_fc1_methy_2(methy_2_level2_layer)
recon_x3 = self.d_fc1_methy_3(methy_3_level2_layer)
recon_x4 = self.d_fc1_methy_4(methy_4_level2_layer)
recon_x5 = self.d_fc1_methy_5(methy_5_level2_layer)
recon_x6 = self.d_fc1_methy_6(methy_6_level2_layer)
recon_x7 = self.d_fc1_methy_7(methy_7_level2_layer)
recon_x8 = self.d_fc1_methy_8(methy_8_level2_layer)
recon_x9 = self.d_fc1_methy_9(methy_9_level2_layer)
recon_x10 = self.d_fc1_methy_10(methy_10_level2_layer)
recon_x11 = self.d_fc1_methy_11(methy_11_level2_layer)
recon_x12 = self.d_fc1_methy_12(methy_12_level2_layer)
recon_x13 = self.d_fc1_methy_13(methy_13_level2_layer)
recon_x14 = self.d_fc1_methy_14(methy_14_level2_layer)
recon_x15 = self.d_fc1_methy_15(methy_15_level2_layer)
recon_x16 = self.d_fc1_methy_16(methy_16_level2_layer)
recon_x17 = self.d_fc1_methy_17(methy_17_level2_layer)
recon_x18 = self.d_fc1_methy_18(methy_18_level2_layer)
recon_x19 = self.d_fc1_methy_19(methy_19_level2_layer)
recon_x20 = self.d_fc1_methy_20(methy_20_level2_layer)
recon_x21 = self.d_fc1_methy_21(methy_21_level2_layer)
recon_x22 = self.d_fc1_methy_22(methy_22_level2_layer)
recon_x23 = self.d_fc1_methy_X(methy_X_level2_layer)
return [recon_x1, recon_x2, recon_x3, recon_x4, recon_x5, recon_x6, recon_x7, recon_x8, recon_x9,
recon_x10, recon_x11, recon_x12, recon_x13, recon_x14, recon_x15, recon_x16, recon_x17, recon_x18,
recon_x19, recon_x20, recon_x21, recon_x22, recon_x23]
def classifier(self, mean):
level_1_layer = self.c_fc1(mean)
level_2_layer = self.c_fc2(level_1_layer)
output_layer = self.c_fc3(level_2_layer)
return output_layer
def forward(self, x):
mean, log_var = self.encode(x)
z = self.reparameterize(mean, log_var)
classifier_x = mean
if parallel:
z = z.to('cuda:1')
classifier_x = classifier_x.to('cuda:1')
recon_x = self.decode(z)
pred_y = self.classifier(classifier_x)
return z, recon_x, mean, log_var, pred_y
# Instantiate VAE
if parallel:
vae_model = VAE()
else:
vae_model = VAE().to(device)
# Early Stopping
if early_stopping:
early_stop_ob = Earlystopping()
# Tensorboard writer
train_writer = SummaryWriter(log_dir='logs/train')
val_writer = SummaryWriter(log_dir='logs/val')
# print the model information
# print('\nModel information:')
# print(vae_model)
total_params = sum(params.numel() for params in vae_model.parameters())
print('Number of parameters: {}'.format(total_params))
optimizer = optim.Adam(vae_model.parameters(), lr=learning_rate)
def methy_recon_loss(recon_x, x):
loss = F.binary_cross_entropy(recon_x[1], x[1], reduction='sum')
for i in range(1, 23):
loss += F.binary_cross_entropy(recon_x[i], x[i], reduction='sum')
loss /= 23
return loss
def kl_loss(mean, log_var):
loss = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
return loss
def classifier_loss(pred_y, y):
loss = F.cross_entropy(pred_y, y, reduction='sum')
return loss
# k_methy_recon = 1
# k_kl = 1
# k_class = 1
# loss record
loss_array = np.zeros(shape=(9, p1_epoch_num+p2_epoch_num+1))
# performance metrics
metrics_array = np.zeros(4)
def train(e_index, e_num, k_methy_recon, k_kl, k_c):
vae_model.train()
train_methy_recon = 0
train_kl = 0
train_classifier = 0
train_correct_num = 0
train_total_loss = 0
for batch_index, sample in enumerate(train_loader):
data = sample[0]
y = sample[1]
for chr_i in range(23):
data[chr_i] = data[chr_i].to(device)
y = y.to(device)
optimizer.zero_grad()
_, recon_data, mean, log_var, pred_y = vae_model(data)
if parallel:
for chr_i in range(23):
recon_data[chr_i] = recon_data[chr_i].to('cuda:0')
pred_y = pred_y.to('cuda:0')
methy_recon = methy_recon_loss(recon_data, data)
kl = kl_loss(mean, log_var)
class_loss = classifier_loss(pred_y, y)
loss = k_methy_recon * methy_recon + k_kl * kl + k_c * class_loss
loss.backward()
with torch.no_grad():
pred_y_softmax = F.softmax(pred_y, dim=1)
_, predicted = torch.max(pred_y_softmax, 1)
correct = (predicted == y).sum().item()
train_methy_recon += methy_recon.item()
train_kl += kl.item()
train_classifier += class_loss.item()
train_correct_num += correct
train_total_loss += loss.item()
optimizer.step()
# if batch_index % 15 == 0:
# print('Epoch {:3d}/{:3d} --- [{:5d}/{:5d}] ({:2d}%)\n'
# 'Methy Recon Loss: {:.2f} KL Loss: {:.2f} '
# 'Classification Loss: {:.2f}\nACC: {:.2f}%'.format(
# e_index + 1, e_num, batch_index * len(data[0]), len(train_dataset),
# round(100. * batch_index / len(train_loader)), methy_recon.item() / len(data[0]),
# kl.item() / len(data[0]), class_loss.item() / len(data[0]),
# correct / len(data[0]) * 100))
train_methy_recon_ave = train_methy_recon / len(train_dataset)
train_kl_ave = train_kl / len(train_dataset)
train_classifier_ave = train_classifier / len(train_dataset)
train_accuracy = train_correct_num / len(train_dataset) * 100
train_total_loss_ave = train_total_loss / len(train_dataset)
print('Epoch {:3d}/{:3d}\n'
'Training\n'
'Methy Recon Loss: {:.2f} KL Loss: {:.2f} '
'Classification Loss: {:.2f}\nACC: {:.2f}%'.
format(e_index + 1, e_num, train_methy_recon_ave, train_kl_ave, train_classifier_ave, train_accuracy))
loss_array[0, e_index] = train_methy_recon_ave
loss_array[1, e_index] = train_kl_ave
loss_array[2, e_index] = train_classifier_ave
loss_array[3, e_index] = train_accuracy
# TB
train_writer.add_scalar('Total loss', train_total_loss_ave, e_index)
train_writer.add_scalar('Methy recon loss', train_methy_recon_ave, e_index)
train_writer.add_scalar('KL loss', train_kl_ave, e_index)
train_writer.add_scalar('Classification loss', train_classifier_ave, e_index)
train_writer.add_scalar('Accuracy', train_accuracy, e_index)
if separate_testing:
def val(e_index, get_metrics=False):
vae_model.eval()
val_methy_recon = 0
val_kl = 0
val_classifier = 0
val_correct_num = 0
val_total_loss = 0
y_store = torch.tensor([0])
predicted_store = torch.tensor([0])
with torch.no_grad():
for batch_index, sample in enumerate(val_loader):
data = sample[0]
y = sample[1]
for chr_i in range(23):
data[chr_i] = data[chr_i].to(device)
y = y.to(device)
_, recon_data, mean, log_var, pred_y = vae_model(data)
if parallel:
for chr_i in range(23):
recon_data[chr_i] = recon_data[chr_i].to('cuda:0')
pred_y = pred_y.to('cuda:0')
methy_recon = methy_recon_loss(recon_data, data)
kl = kl_loss(mean, log_var)
class_loss = classifier_loss(pred_y, y)
loss = methy_recon + kl + class_loss
pred_y_softmax = F.softmax(pred_y, dim=1)
_, predicted = torch.max(pred_y_softmax, 1)
correct = (predicted == y).sum().item()
y_store = torch.cat((y_store, y.cpu()))
predicted_store = torch.cat((predicted_store, predicted.cpu()))
val_methy_recon += methy_recon.item()
val_kl += kl.item()
val_classifier += class_loss.item()
val_correct_num += correct
val_total_loss += loss.item()
output_y = y_store[1:].numpy()
output_pred_y = predicted_store[1:].numpy()
if get_metrics:
metrics_array[0] = metrics.accuracy_score(output_y, output_pred_y)
metrics_array[1] = metrics.precision_score(output_y, output_pred_y, average='weighted')
metrics_array[2] = metrics.recall_score(output_y, output_pred_y, average='weighted')
metrics_array[3] = metrics.f1_score(output_y, output_pred_y, average='weighted')
val_methy_recon_ave = val_methy_recon / len(val_dataset)
val_kl_ave = val_kl / len(val_dataset)
val_classifier_ave = val_classifier / len(val_dataset)
val_accuracy = val_correct_num / len(val_dataset) * 100
val_total_loss_ave = val_total_loss / len(val_dataset)
print('Validation\n'
'Methy Recon Loss: {:.2f} KL Loss: {:.2f} Classification Loss: {:.2f}'
'\nACC: {:.2f}%\n'.
format(val_methy_recon_ave, val_kl_ave, val_classifier_ave, val_accuracy))
loss_array[4, e_index] = val_methy_recon_ave
loss_array[5, e_index] = val_kl_ave
loss_array[6, e_index] = val_classifier_ave
loss_array[7, e_index] = val_accuracy
# TB
val_writer.add_scalar('Total loss', val_total_loss_ave, e_index)
val_writer.add_scalar('Methy recon loss', val_methy_recon_ave, e_index)
val_writer.add_scalar('KL loss', val_kl_ave, e_index)
val_writer.add_scalar('Classification loss', val_classifier_ave, e_index)
val_writer.add_scalar('Accuracy', val_accuracy, e_index)
return val_accuracy, output_pred_y
print('\nUNSUPERVISED PHASE\n')
# unsupervised phase
for epoch_index in range(p1_epoch_num):
train(e_index=epoch_index, e_num=p1_epoch_num+p2_epoch_num, k_methy_recon=1, k_kl=1, k_c=0)
if separate_testing:
_, out_pred_y = val(epoch_index)
print('\nSUPERVISED PHASE\n')
# supervised phase
epoch_number = p1_epoch_num
for epoch_index in range(p1_epoch_num, p1_epoch_num+p2_epoch_num):
epoch_number += 1
train(e_index=epoch_index, e_num=p1_epoch_num+p2_epoch_num, k_methy_recon=0, k_kl=0, k_c=1)
if separate_testing:
if epoch_index == p1_epoch_num+p2_epoch_num-1:
val_classification_acc, out_pred_y = val(epoch_index, get_metrics=True)
else:
val_classification_acc, out_pred_y = val(epoch_index)
if early_stopping:
early_stop_ob(vae_model, val_classification_acc)
if early_stop_ob.stop_now:
print('Early stopping\n')
break
if early_stopping:
best_epoch = p1_epoch_num + early_stop_ob.best_epoch_num
loss_array[8, 0] = best_epoch
print('Load model of Epoch {:d}'.format(best_epoch))
vae_model.load_state_dict(torch.load('../ssd/checkpoint.pt'))
_, out_pred_y = val(epoch_number, get_metrics=True)
# Encode all of the data into the latent space
print('Encoding all the data into latent space...')
vae_model.eval()
with torch.no_grad():
d_z_store = torch.zeros(1, latent_dim).to(device)
for i, sample in enumerate(full_loader):
d = sample[0]
for chr_i in range(23):
d[chr_i] = d[chr_i].to(device)
_, _, d_z, _, _ = vae_model(d)
d_z_store = torch.cat((d_z_store, d_z), 0)
all_data_z = d_z_store[1:]
all_data_z_np = all_data_z.cpu().numpy()
# Output file
print('Preparing the output files... ')
input_path_name = input_path.split('/')[-1]
latent_space_path = 'results/' + input_path_name + str(latent_dim) + 'D_latent_space.tsv'
all_data_z_df = pd.DataFrame(all_data_z_np, index=sample_id)
all_data_z_df.to_csv(latent_space_path, sep='\t')
if separate_testing:
pred_y_path = 'results/' + input_path_name + str(latent_dim) + 'D_pred_y.tsv'
np.savetxt(pred_y_path, out_pred_y, delimiter='\t')
metrics_record_path = 'results/' + input_path_name + str(latent_dim) + 'D_metrics.tsv'
np.savetxt(metrics_record_path, metrics_array, delimiter='\t')
if output_loss_record:
loss_record_path = 'results/' + input_path_name + str(latent_dim) + 'D_loss_record.tsv'
np.savetxt(loss_record_path, loss_array, delimiter='\t')
return all_data_z_df
Datasets
Models
