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training.py
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import os, sys
# sys.path.append(os.path.abspath("/home/zh/codes/rnn_virus_source_code"))
import models
import train_model
import make_dataset
import build_features
import utils
import torch
import numpy as np
def main():
if subtype_flag == 0:
# data_path = '/home/zh/codes/rnn_virus_source_code/data/raw/H1N1_cluster/'
# data_set = '/home/zh/codes/rnn_virus_source_code/data/processed/H1N1/triplet_cluster'
data_path = '/home/zh/codes/transformer_virus/data/raw/H1N1_cluster/'
data_set = '/home/zh/codes/transformer_virus/data/processed/H1N1_drop_duplicates/triplet_cluster'
elif subtype_flag == 1:
data_path = '/home/zh/codes/transformer_virus/data/raw/H3N2_cluster/'
data_set = '/home/zh/codes/transformer_virus/data/processed/H3N2/triplet_cluster'
elif subtype_flag == 2:
data_path = '/home/zh/codes/transformer_virus/data/raw/H5N1_cluster/'
data_set = '/home/zh/codes/transformer_virus/data/processed/H5N1/triplet_cluster'
elif subtype_flag == 3:
data_path = '/home/zh/codes/transformer_virus/data/processed/COV19/'
data_set = '/home/zh/codes/transformer_virus/data/processed/COV19/triplet_cluster'
# data_path = '/home/zh/codes/rnn_virus_source_code/data/processed/COV19/'
# data_set = '/home/zh/codes/rnn_virus_source_code/data/processed/COV19/triplet_cluster'
parameters = {
# Exlude _train/_test and file ending
'data_set': data_set,
# raw data path
'data_path': data_path,
# 'svm', lstm', 'gru', 'attention' (only temporal) or 'da-rnn' (input and temporal attention)
'model': model,
# Number of hidden units in the encoder
'hidden_size': 512,
# Droprate (applied at input)
'dropout_p': 0.0001,
# Note, no learning rate decay implemented
'learning_rate': 0.001,
# Size of mini batch
'batch_size': 256,
# Number of training iterations
'num_of_epochs': 100
}
torch.manual_seed(1)
np.random.seed(1)
train_trigram_vecs, train_labels = utils.read_dataset(parameters['data_set'] + '_train.csv',
parameters['data_path'], concat=False)
test_trigram_vecs, test_labels = utils.read_dataset(parameters['data_set'] + '_test.csv',
parameters['data_path'], concat=False)
X_train = torch.tensor(train_trigram_vecs, dtype=torch.float32)
Y_train = torch.tensor(train_labels, dtype=torch.int64)
X_test = torch.tensor(test_trigram_vecs, dtype=torch.float32)
Y_test = torch.tensor(test_labels, dtype=torch.int64)
# give weights for imbalanced dataset
_, counts = np.unique(Y_train, return_counts=True)
train_counts = max(counts)
train_imbalance = max(counts) / Y_train.shape[0]
_, counts = np.unique(Y_test, return_counts=True)
test_counts = max(counts)
test_imbalance = max(counts) / Y_test.shape[0]
print('Class imbalances:')
print(' Training %.3f' % train_imbalance)
print(' Testing %.3f' % test_imbalance)
if parameters['model'] == 'svm':
window_size = 1
train_model.svm_baseline(
build_features.reshape_to_linear(train_trigram_vecs, window_size=window_size), train_labels,
build_features.reshape_to_linear(test_trigram_vecs, window_size=window_size), test_labels)
elif parameters['model'] == 'random forest':
window_size = 1
train_model.random_forest_baseline(
build_features.reshape_to_linear(train_trigram_vecs, window_size=window_size), train_labels,
build_features.reshape_to_linear(test_trigram_vecs, window_size=window_size), test_labels)
elif parameters['model'] == 'logistic regression':
window_size = 1
train_model.logistic_regression_baseline(
build_features.reshape_to_linear(train_trigram_vecs, window_size=window_size), train_labels,
build_features.reshape_to_linear(test_trigram_vecs, window_size=window_size), test_labels)
else:
input_dim = X_train.shape[2]
seq_length = X_train.shape[0]
output_dim = 2
if parameters['model'] == 'lstm':
net = models.RnnModel(input_dim, output_dim, parameters['hidden_size'], parameters['dropout_p'],
cell_type='LSTM')
elif parameters['model'] == 'gru':
net = models.RnnModel(input_dim, output_dim, parameters['hidden_size'], parameters['dropout_p'],
cell_type='GRU')
elif parameters['model'] == 'rnn':
net = models.RnnModel(input_dim, output_dim, parameters['hidden_size'], parameters['dropout_p'],
cell_type='RNN')
elif parameters['model'] == 'attention':
net = models.AttentionModel(seq_length, input_dim, output_dim, parameters['hidden_size'],
parameters['dropout_p'])
elif parameters['model'] == 'da-rnn':
net = models.DaRnnModel(seq_length, input_dim, output_dim, parameters['hidden_size'],
parameters['dropout_p'])
elif parameters['model'] == 'transformer':
net = models.TransformerModel(100, 2, parameters['dropout_p'])
# use gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = net.to(device)
X_train = X_train.to(device)
Y_train = Y_train.to(device)
X_test = X_test.to(device)
Y_test = Y_test.to(device)
# use gpu
train_model.train_rnn(net, False, parameters['num_of_epochs'], parameters['learning_rate'],
parameters['batch_size'], X_train, Y_train, X_test, Y_test, True, parameters['model'])
if __name__ == '__main__':
subtype = ['H1N1', 'H3N2', 'H5N1', 'COV19']
subtype_flag = make_dataset.subtype_selection(subtype[3])
# model = ['gru', 'lstm', 'attention', 'rnn', 'svm', 'logistic regression']
# model = ['svm', 'logistic regression', 'transformer']
# model = ['logistic regression','random forest','rnn','lstm']
model = ['transformer']
# model = ['attention', 'gru', 'lstm', 'rnn','logistic regression']
# model = ['logistic regression', 'random forest', 'rnn']
# model = ['rnn']
for model in model:
print('\n')
print("Experimental results with model %s on subtype_flag %s:" % (model, subtype_flag))
main()