def build_config_space(self):
'''
Build ray config space from flattened spec.search for ray spec passed to run_experiments()
Specify a config space in spec using `"{key}__{space_type}": {v}`.
Where {space_type} is 'grid_search' of ray.tune, or any function name of np.random.
- grid_search: str, int, float. v = list of choices
- choice: str, int, float. v = list of choices
- randint: int. v = [low, high)
- uniform: float. v = [low, high)
- normal: float. v = [mean, stdev)
E.g. `"lr__uniform": [0.001, 0.1]`, and it will sample `lr` using np.random.uniform(0.001, 0.1)
If any key uses 'grid_search', it will be combined exhaustively in combination with other random sampling.
'''
config_space = {}
for k, v in util.flatten_dict(self.experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type == 'grid_search':
config_space[key] = grid_search(v)
elif space_type == 'choice':
config_space[key] = lambda spec, v=v: random.choice(v)
else:
np_fn = getattr(np.random, space_type)
config_space[key] = lambda spec, v=v: np_fn(*v)
# RaySearch.lab_trial on ray.remote is not thread-safe, we have to carry the trial_index from config, created at the top level on the same thread, ticking once a trial (one samping).
config_space[
'trial_index'] = lambda spec: self.experiment.info_space.tick(
'trial')['trial']
return config_space
def build_config_space(experiment):
'''
Build ray config space from flattened spec.search
Specify a config space in spec using `"{key}__{space_type}": {v}`.
Where `{space_type}` is `grid_search` of `ray.tune`, or any function name of `np.random`:
- `grid_search`: str/int/float. v = list of choices
- `choice`: str/int/float. v = list of choices
- `randint`: int. v = [low, high)
- `uniform`: float. v = [low, high)
- `normal`: float. v = [mean, stdev)
For example:
- `"explore_anneal_epi__randint": [10, 60],` will sample integers uniformly from 10 to 60 for `explore_anneal_epi`,
- `"lr__uniform": [0.001, 0.1]`, and it will sample `lr` using `np.random.uniform(0.001, 0.1)`
If any key uses `grid_search`, it will be combined exhaustively in combination with other random sampling.
'''
config_space = {}
for k, v in util.flatten_dict(experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type == 'grid_search':
config_space[key] = grid_search(v)
elif space_type == 'choice':
config_space[key] = lambda spec, v=v: random.choice(v)
else:
np_fn = getattr(np.random, space_type)
config_space[key] = lambda spec, v=v: np_fn(*v)
return config_space
def build_config_space(experiment):
'''
Build ray config space from flattened spec.search
Specify a config space in spec using `"{key}__{space_type}": {v}`.
Where `{space_type}` is `grid_search` of `ray.tune`, or any function name of `np.random`:
- `grid_search`: str/int/float. v = list of choices
- `choice`: str/int/float. v = list of choices
- `randint`: int. v = [low, high)
- `uniform`: float. v = [low, high)
- `normal`: float. v = [mean, stdev)
For example:
- `"explore_anneal_epi__randint": [10, 60],` will sample integers uniformly from 10 to 60 for `explore_anneal_epi`,
- `"lr__uniform": [0.001, 0.1]`, and it will sample `lr` using `np.random.uniform(0.001, 0.1)`
If any key uses `grid_search`, it will be combined exhaustively in combination with other random sampling.
'''
config_space = {}
for k, v in util.flatten_dict(experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type == 'grid_search':
config_space[key] = grid_search(v)
elif space_type == 'choice':
config_space[key] = lambda spec, v=v: random.choice(v)
else:
np_fn = getattr(np.random, space_type)
config_space[key] = lambda spec, v=v: np_fn(*v)
return config_space
def calc_population_size(experiment):
'''Calculate the population size for RandomSearch or EvolutionarySearch'''
pop_size = 2 # x2 for more search coverage
for k, v in util.flatten_dict(experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type in ('grid_search', 'choice'):
pop_size *= len(v)
else:
pop_size *= 3
return pop_size
def calc_population_size(experiment):
'''Calculate the population size for RandomSearch or EvolutionarySearch'''
pop_size = 2 # x2 for more search coverage
for k, v in util.flatten_dict(experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type in ('grid_search', 'choice'):
pop_size *= len(v)
else:
pop_size *= 3
return pop_size
def calc_population_size(experiment):
'''Calculate the population size for RandomSearch or EvolutionarySearch'''
pop_size = 2 # start with x2 for better sampling coverage
for k, v in util.flatten_dict(experiment.spec['search']).items():
if '__' in k:
key, space_type = k.split('__')
else:
key, space_type = k, 'grid_search'
if space_type in ('grid_search', 'choice'):
pop_size *= len(v)
elif space_type == 'randint':
pop_size *= (v[1] - v[0])
else:
pop_size *= 5
return pop_size
def test_flatten_dict(d, flat_d):
assert util.flatten_dict(d) == flat_d
def test_flatten_dict(d, flat_d):
assert util.flatten_dict(d) == flat_d
def test_flatten_dict(test_dict):
assert util.flatten_dict(test_dict) == test_dict
assert util.flatten_dict({'a': {'b': 1}}) == {'a.b': 1}
assert util.flatten_dict({'a': {'b': 1}}) == {'a.b': 1}
assert util.flatten_dict({'a': {'b': 1}}, sep='_') == {'a_b': 1}
