def training(data_path, train_datasets, model_dir, batch_size, learning_rate,
num_steps):
"""
Perform training of three a scaden model ensemble consisting of three different models
:param model_dir:
:param batch_size:
:param learning_rate:
:param num_steps:
:return:
"""
# Convert training datasets
if train_datasets == '':
train_datasets = []
else:
train_datasets = train_datasets.split()
print("Training on: " + str(train_datasets))
# M256 model training
print("Training M256 Model ...")
tf.reset_default_graph()
with tf.Session() as sess:
cdn256 = Scaden(sess=sess,
model_dir=model_dir + "/m256",
model_name='m256',
batch_size=batch_size,
learning_rate=learning_rate,
num_steps=num_steps)
cdn256.hidden_units = M256_HIDDEN_UNITS
cdn256.do_rates = M256_DO_RATES
cdn256.train(input_path=data_path, train_datasets=train_datasets)
# Training of mid model
print("Training M512 Model ...")
tf.reset_default_graph()
with tf.Session() as sess:
cdn512 = Scaden(sess=sess,
model_dir=model_dir + "/m512",
model_name='m512',
batch_size=batch_size,
learning_rate=learning_rate,
num_steps=num_steps)
cdn512.hidden_units = M512_HIDDEN_UNITS
cdn512.do_rates = M512_DO_RATES
cdn512.train(input_path=data_path, train_datasets=train_datasets)
# Training of large model
print("Training M1024 Model ...")
tf.reset_default_graph()
with tf.Session() as sess:
cdn1024 = Scaden(sess=sess,
model_dir=model_dir + "/m1024",
model_name='m1024',
batch_size=batch_size,
learning_rate=learning_rate,
num_steps=num_steps)
cdn1024.hidden_units = M1024_HIDDEN_UNITS
cdn1024.do_rates = M1024_DO_RATES
cdn1024.train(input_path=data_path, train_datasets=train_datasets)
print("Training finished.")
def prediction(model_dir, data_path, out_name):
"""
Perform prediction using a trained scaden ensemble
:param model_dir: the directory containing the models
:param data_path: the path to the gene expression file
:param out_name: name of the output prediction file
:return:
"""
# Small model predictions
tf.reset_default_graph()
with tf.Session() as sess:
cdn256 = Scaden(sess=sess,
model_dir=model_dir + "/m256",
model_name='m256')
cdn256.hidden_units = M256_HIDDEN_UNITS
cdn256.do_rates = M256_DO_RATES
# Predict ratios
preds_256 = cdn256.predict(input_path=data_path,
out_name='cdn_predictions_m256.txt')
# Mid model predictions
tf.reset_default_graph()
with tf.Session() as sess:
cdn512 = Scaden(sess=sess,
model_dir=model_dir + "/m512",
model_name='m512')
cdn512.hidden_units = M512_HIDDEN_UNITS
cdn512.do_rates = M512_DO_RATES
# Predict ratios
preds_512 = cdn512.predict(input_path=data_path,
out_name='cdn_predictions_m512.txt')
# Large model predictions
tf.reset_default_graph()
with tf.Session() as sess:
cdn1024 = Scaden(sess=sess,
model_dir=model_dir + "/m1024",
model_name='m1024')
cdn1024.hidden_units = M1024_HIDDEN_UNITS
cdn1024.do_rates = M1024_DO_RATES
# Predict ratios
preds_1024 = cdn1024.predict(input_path=data_path,
out_name='cdn_predictions_m1024.txt')
# Average predictions
preds = (preds_256 + preds_512 + preds_1024) / 3
preds.to_csv(out_name, sep="\t")