python/methylnet/predictions.py
def launch_hyperparameter_scan(hyperparameter_input_csv, hyperparameter_output_log, generate_input, job_chunk_size, interest_cols, categorical, reset_all, torque, gpu, gpu_node, nohup, n_jobs_relaunch, crossover_p, model_complexity_factor,n_jobs, val_loss_column, add_softmax, additional_command, cuda, hyperparameter_yaml, randomseed, additional_opts, max_epochs):
	"""Run randomized hyperparameter scan of neural network hyperparameters."""
	from methylnet.hyperparameter_scans import coarse_scan, find_top_jobs
	custom_jobs=[]
	if n_jobs_relaunch:
		custom_jobs=find_top_jobs(hyperparameter_input_csv, hyperparameter_output_log,n_jobs_relaunch, crossover_p, val_loss_column)
	if os.path.exists(hyperparameter_yaml):
		from ruamel.yaml import safe_load as load
		with open(hyperparameter_yaml) as f:
			new_grid = load(f.read())
	else:
		new_grid = {}
	coarse_scan(hyperparameter_input_csv, hyperparameter_output_log, generate_input, job_chunk_size, interest_cols, reset_all, torque, gpu, gpu_node, nohup, mlp=True, custom_jobs=custom_jobs, model_complexity_factor=model_complexity_factor,n_jobs=n_jobs, categorical=categorical,add_softmax=add_softmax, additional_command=additional_command, cuda=cuda, new_grid=new_grid, randomseed=randomseed, additional_opts=additional_opts, max_epochs=max_epochs)
Run randomized hyperparameter scan of neural network hyperparameters.
Run randomized hyperparameter scan of neural network hyperparameters.
Run randomized hyperparameter scan of neural network hyperparameters .
def launch_hyperparameter_scan(hyperparameter_input_csv, hyperparameter_output_log, generate_input, job_chunk_size, interest_cols, categorical, reset_all, torque, gpu, gpu_node, nohup, n_jobs_relaunch, crossover_p, model_complexity_factor,n_jobs, val_loss_column, add_softmax, additional_command, cuda, hyperparameter_yaml, randomseed, additional_opts, max_epochs):
	from methylnet.hyperparameter_scans import coarse_scan, find_top_jobs
	custom_jobs=[]
	if n_jobs_relaunch:
		custom_jobs=find_top_jobs(hyperparameter_input_csv, hyperparameter_output_log,n_jobs_relaunch, crossover_p, val_loss_column)
	if os.path.exists(hyperparameter_yaml):
		from ruamel.yaml import safe_load as load
		with open(hyperparameter_yaml) as f:
			new_grid = load(f.read())
	else:
		new_grid = {}
	coarse_scan(hyperparameter_input_csv, hyperparameter_output_log, generate_input, job_chunk_size, interest_cols, reset_all, torque, gpu, gpu_node, nohup, mlp=True, custom_jobs=custom_jobs, model_complexity_factor=model_complexity_factor,n_jobs=n_jobs, categorical=categorical,add_softmax=add_softmax, additional_command=additional_command, cuda=cuda, new_grid=new_grid, randomseed=randomseed, additional_opts=additional_opts, max_epochs=max_epochs)
def launch_hyperparameter_scan ( hyperparameter_input_csv , hyperparameter_output_log , generate_input , job_chunk_size , interest_cols , categorical , reset_all , torque , gpu , gpu_node , nohup , n_jobs_relaunch , crossover_p , model_complexity_factor , n_jobs , val_loss_column , add_softmax , additional_command , cuda , hyperparameter_yaml , randomseed , additional_opts , max_epochs ) : from methylnet . hyperparameter_scans import coarse_scan , find_top_jobs custom_jobs = [ ] if n_jobs_relaunch : custom_jobs = find_top_jobs ( hyperparameter_input_csv , hyperparameter_output_log , n_jobs_relaunch , crossover_p , val_loss_column ) if os . path . exists ( hyperparameter_yaml ) : from ruamel . yaml import safe_load as load with open ( hyperparameter_yaml ) as f : new_grid = load ( f . read ( ) ) else : new_grid = { } coarse_scan ( hyperparameter_input_csv , hyperparameter_output_log , generate_input , job_chunk_size , interest_cols , reset_all , torque , gpu , gpu_node , nohup , mlp = True , custom_jobs = custom_jobs , model_complexity_factor = model_complexity_factor , n_jobs = n_jobs , categorical = categorical , add_softmax = add_softmax , additional_command = additional_command , cuda = cuda , new_grid = new_grid , randomseed = randomseed , additional_opts = additional_opts , max_epochs = max_epochs )
Run randomized hyperparameter scan of neural network hyperparameters.
Run randomized hyperparameter scan of neural network hyperparameters .
"""Run randomized hyperparameter scan of neural network hyperparameters."""
hyperparameter_input_csv
hyperparameter_output_log
generate_input
job_chunk_size
interest_cols
categorical
reset_all
torque
gpu
gpu_node
nohup
n_jobs_relaunch
crossover_p
model_complexity_factor
n_jobs
val_loss_column
add_softmax
additional_command
cuda
hyperparameter_yaml
randomseed
additional_opts
max_epochs