def random_sample(self):
"""
Creates a random point within the bounds of the space.
"""
params = np.empty(self.dim)
for col, _bound in enumerate(self._bounds):
if _bound['_type'] == 'choice':
params[col] = parameter_expressions.choice(
_bound['_value'], self.random_state)
elif _bound['_type'] == 'randint':
params[col] = self.random_state.randint(_bound['_value'][0],
_bound['_value'][1],
size=1)
elif _bound['_type'] == 'uniform':
params[col] = parameter_expressions.uniform(
_bound['_value'][0], _bound['_value'][1],
self.random_state)
elif _bound['_type'] == 'quniform':
params[col] = parameter_expressions.quniform(
_bound['_value'][0], _bound['_value'][1],
_bound['_value'][2], self.random_state)
elif _bound['_type'] == 'loguniform':
params[col] = parameter_expressions.loguniform(
_bound['_value'][0], _bound['_value'][1],
self.random_state)
elif _bound['_type'] == 'qloguniform':
params[col] = parameter_expressions.qloguniform(
_bound['_value'][0], _bound['_value'][1],
_bound['_value'][2], self.random_state)
return params
def _random_config(search_space, random_state):
chosen_config = {}
for key, val in search_space.items():
if val['_type'] == 'choice':
choices = val['_value']
index = random_state.randint(len(choices))
if all([isinstance(c, (int, float)) for c in choices]):
chosen_config[key] = choices[index]
else:
raise ValueError('Choices with type other than int and float is not supported.')
elif val['_type'] == 'uniform':
chosen_config[key] = random_state.uniform(val['_value'][0], val['_value'][1])
elif val['_type'] == 'randint':
chosen_config[key] = random_state.randint(
val['_value'][0], val['_value'][1])
elif val['_type'] == 'quniform':
chosen_config[key] = parameter_expressions.quniform(
val['_value'][0], val['_value'][1], val['_value'][2], random_state)
elif val['_type'] == 'loguniform':
chosen_config[key] = parameter_expressions.loguniform(
val['_value'][0], val['_value'][1], random_state)
elif val['_type'] == 'qloguniform':
chosen_config[key] = parameter_expressions.qloguniform(
val['_value'][0], val['_value'][1], val['_value'][2], random_state)
else:
raise ValueError('Unknown key %s and value %s' % (key, val))
return chosen_config
def _send_new_trial(self):
self.parameters_count += 1
new_trial = {
"parameter_id": self.parameters_count,
"parameters": {
"optimizer": param.choice(self.searchspace_json["optimizer"], self.random_state),
"learning_rate": param.loguniform(self.searchspace_json["learning_rate"][0],
self.searchspace_json["learning_rate"][1],
self.random_state)
},
"parameter_source": "algorithm"
}
logger.info("New trial sent: {}".format(new_trial))
send(CommandType.NewTrialJob, json_tricks.dumps(new_trial))
