def load_task_group(db, options, task_names=None):
if task_names is None:
task_names = options['tasks'].keys()
task_options = { task: options["tasks"][task] for task in task_names }
jobs = load_jobs(db, options['experiment-name'])
task_group = TaskGroup(task_options, options['variables'])
if jobs:
task_group.inputs = np.array([task_group.vectorify(job['params'])
for job in jobs if job['status'] == 'complete'])
task_group.pending = np.array([task_group.vectorify(job['params'])
for job in jobs if job['status'] == 'pending'])
task_group.values = {task : np.array([job['values'][task]
for job in jobs if job['status'] == 'complete'])
for task in task_names}
task_group.add_nan_task_if_nans()
# TODO: record costs
return task_group
def load_task_group(db, options, task_names=None):
if task_names is None:
task_names = options['tasks'].keys()
task_options = { task: options["tasks"][task] for task in task_names }
jobs = load_jobs(db, options['experiment-name'])
task_group = TaskGroup(task_options, options['variables'])
if jobs:
task_group.inputs = np.array([task_group.vectorify(job['params'])
for job in jobs if job['status'] == 'complete'])
task_group.pending = np.array([task_group.vectorify(job['params'])
for job in jobs if job['status'] == 'pending'])
task_group.values = {task : np.array([job['values'][task]
for job in jobs if job['status'] == 'complete'])
for task in task_names}
task_group.add_nan_task_if_nans()
# TODO: record costs
return task_group
def load_task_group(db, options, task_names=None, data_connector=None):
if task_names is None:
task_names = options['tasks'].keys()
task_options = {task: options["tasks"][task] for task in task_names}
jobs = load_jobs(db, options['experiment-name'])
task_group = TaskGroup(task_options, options['variables'])
if jobs:
task_group.inputs = np.array([
task_group.vectorify(job['params']) for job in jobs
if job['status'] == 'complete'
])
task_group.pending = np.array([
task_group.vectorify(job['params']) for job in jobs
if job['status'] == 'pending'
])
task_group.values = {
task: np.array([
job['values'][task] for job in jobs
if job['status'] == 'complete'
])
for task in task_names
}
task_group.add_nan_task_if_nans()
# TODO: record costs
# [KZ]
# Appending the offline collected data to the suggested data.
if data_connector is not None:
if len(task_group.inputs) == 0:
task_group.inputs = data_connector.inputs_
else:
task_group.inputs = np.vstack(
[data_connector.inputs_, task_group.inputs])
inkeys = []
for task_key in data_connector.values_:
if task_key in task_group.values:
inkeys.append(task_key)
# We need to create a new dictionary and feed it to taskgroup.values
# because this is a property...
newvalues = {}
for task_key in inkeys:
if len(task_group.values[task_key]) > 0:
newvalues[task_key] = np.concatenate([
data_connector.values_[task_key],
task_group.values[task_key]
])
else:
newvalues[task_key] = data_connector.values_[task_key]
for task_key in task_group.values:
if task_key not in newvalues:
newvalues[task_key] = task_key
task_group.values = newvalues
return task_group