def test_workon(database):
"""Test scenario having a configured experiment already setup."""
try:
Database(of_type='MongoDB',
name='orion_test',
username='******',
password='******')
except (TypeError, ValueError):
pass
experiment = Experiment('voila_voici')
config = experiment.configuration
config['algorithms'] = {'gradient_descent': {'learning_rate': 0.1}}
config['pool_size'] = 1
config['max_trials'] = 100
config['metadata']['user_script'] = os.path.abspath(
os.path.join(os.path.dirname(__file__), "black_box.py"))
config['metadata']['user_args'] = ["-x~uniform(-50, 50)"]
experiment.configure(config)
workon(experiment)
exp = list(database.experiments.find({'name': 'voila_voici'}))
assert len(exp) == 1
exp = exp[0]
assert '_id' in exp
exp_id = exp['_id']
assert exp['name'] == 'voila_voici'
assert exp['pool_size'] == 1
assert exp['max_trials'] == 100
assert exp['algorithms'] == {
'gradient_descent': {
'learning_rate': 0.1,
'dx_tolerance': 1e-7
}
}
assert 'user' in exp['metadata']
assert 'datetime' in exp['metadata']
assert 'user_script' in exp['metadata']
assert os.path.isabs(exp['metadata']['user_script'])
assert exp['metadata']['user_args'] == ['-x~uniform(-50, 50)']
trials = list(database.trials.find({'experiment': exp_id}))
assert len(trials) <= 15
trials = list(sorted(trials, key=lambda trial: trial['submit_time']))
assert trials[-1]['status'] == 'completed'
for result in trials[-1]['results']:
assert result['type'] != 'constraint'
if result['type'] == 'objective':
assert abs(result['value'] - 23.4) < 1e-6
assert result['name'] == 'example_objective'
elif result['type'] == 'gradient':
res = numpy.asarray(result['value'])
assert 0.1 * numpy.sqrt(res.dot(res)) < 1e-7
assert result['name'] == 'example_gradient'
params = trials[-1]['params']
assert len(params) == 1
assert params[0]['name'] == '/x'
assert params[0]['type'] == 'real'
assert (params[0]['value'] - 34.56789) < 1e-5
def main(args):
"""Build experiment and execute hunt command"""
args['root'] = None
args['leafs'] = []
# TODO: simplify when parameter parsing is refactored
worker_trials = ExperimentBuilder().fetch_full_config(args)['worker_trials']
experiment = EVCBuilder().build_from(args)
workon(experiment, worker_trials)
def test_workon():
"""Test scenario having a configured experiment already setup."""
name = "voici_voila"
config = {"name": name}
config["algorithms"] = {"gradient_descent": {"learning_rate": 0.1}}
config["pool_size"] = 1
config["max_trials"] = 100
config["exp_max_broken"] = 5
config["user_args"] = [
os.path.abspath(os.path.join(os.path.dirname(__file__),
"black_box.py")),
"-x~uniform(-50, 50, precision=None)",
]
with OrionState():
experiment = experiment_builder.build_from_args(config)
workon(experiment, 100, 100, 100, 100, 100)
storage = get_storage()
exp = list(storage.fetch_experiments({"name": name}))
assert len(exp) == 1
exp = exp[0]
assert "_id" in exp
assert exp["name"] == name
assert exp["pool_size"] == 1
assert exp["max_trials"] == 100
assert exp["max_broken"] == 5
assert exp["algorithms"] == {
"gradient_descent": {
"learning_rate": 0.1,
"dx_tolerance": 1e-7
}
}
assert "user" in exp["metadata"]
assert "datetime" in exp["metadata"]
assert "user_script" in exp["metadata"]
assert exp["metadata"]["user_args"] == config["user_args"]
trials = list(storage.fetch_trials(experiment))
assert len(trials) <= 15
trials = list(sorted(trials, key=lambda trial: trial.submit_time))
assert trials[-1].status == "completed"
for result in trials[-1].results:
assert result.type != "constraint"
if result.type == "objective":
assert abs(result.value - 23.4) < 1e-6
assert result.name == "example_objective"
elif result.type == "gradient":
res = numpy.asarray(result.value)
assert 0.1 * numpy.sqrt(res.dot(res)) < 1e-7
assert result.name == "example_gradient"
params = trials[-1].params
assert len(params) == 1
px = params["/x"]
assert isinstance(px, float)
assert (px - 34.56789) < 1e-5
def test_workon():
"""Test scenario having a configured experiment already setup."""
name = 'voici_voila'
config = {'name': name}
config['algorithms'] = {
'gradient_descent': {
'learning_rate': 0.1
}
}
config['pool_size'] = 1
config['max_trials'] = 100
config['user_args'] = [
os.path.abspath(os.path.join(os.path.dirname(__file__), "black_box.py")),
"-x~uniform(-50, 50, precision=None)"]
with OrionState():
experiment = experiment_builder.build_from_args(config)
workon(experiment, 100, 100, 100, 100, 100)
storage = get_storage()
exp = list(storage.fetch_experiments({'name': name}))
assert len(exp) == 1
exp = exp[0]
assert '_id' in exp
assert exp['name'] == name
assert exp['pool_size'] == 1
assert exp['max_trials'] == 100
assert exp['algorithms'] == {'gradient_descent': {'learning_rate': 0.1,
'dx_tolerance': 1e-7}}
assert 'user' in exp['metadata']
assert 'datetime' in exp['metadata']
assert 'user_script' in exp['metadata']
assert exp['metadata']['user_args'] == config['user_args']
trials = list(storage.fetch_trials(experiment))
assert len(trials) <= 15
trials = list(sorted(trials, key=lambda trial: trial.submit_time))
assert trials[-1].status == 'completed'
for result in trials[-1].results:
assert result.type != 'constraint'
if result.type == 'objective':
assert abs(result.value - 23.4) < 1e-6
assert result.name == 'example_objective'
elif result.type == 'gradient':
res = numpy.asarray(result.value)
assert 0.1 * numpy.sqrt(res.dot(res)) < 1e-7
assert result.name == 'example_gradient'
params = trials[-1]._params
assert len(params) == 1
assert params[0].name == '/x'
assert params[0].type == 'real'
assert (params[0].value - 34.56789) < 1e-5
def main(args):
"""Build experiment and execute hunt command"""
args['root'] = None
args['leafs'] = []
# TODO: simplify when parameter parsing is refactored
experiment = experiment_builder.build_from_args(args)
config = experiment_builder.get_cmd_config(args)
worker_config = orion.core.config.worker.to_dict()
if config.get('worker'):
worker_config.update(config.get('worker'))
workon(experiment, **worker_config)
def main(args):
"""Build experiment and execute hunt command"""
args["root"] = None
args["leafs"] = []
# TODO: simplify when parameter parsing is refactored
experiment = experiment_builder.build_from_args(args)
if args["init_only"]:
return
config = experiment_builder.get_cmd_config(args)
worker_config = orion.core.config.worker.to_dict()
if config.get("worker"):
worker_config.update(config.get("worker"))
workon(experiment,
ignore_code_changes=config["branching"].get("ignore_code_changes"),
**worker_config)
def main():
"""Entry point for `orion.core` functionality."""
experiment = infer_experiment()
workon(experiment)
return 0
def main(args):
"""Build experiment and execute hunt command"""
args['root'] = None
args['leafs'] = []
experiment = EVCBuilder().build_from(args)
workon(experiment)
def _execute(cmdargs, cmdconfig):
experiment = _infer_experiment(cmdargs, cmdconfig)
workon(experiment)
