def setUpClass(cls) -> None:
cls.simple_hypergrid = SimpleHypergrid(
name='simple_adaptee',
dimensions=[
CategoricalDimension(name='categorical_mixed_types', values=['red', True, False, 1]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='zero_to_one', min=0, max=1),
OrdinalDimension(name='ordinal_mixed_types', ordered_values=[1, False, 'two'])
]
)
cls.hierarchical_hypergrid = SimpleHypergrid(
name='hierarchical_adaptee',
dimensions=[
CategoricalDimension(name='categorical_mixed_types', values=['red', True, False, 3]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='zero_to_one', min=0, max=1),
OrdinalDimension(name='ordinal_mixed_types', ordered_values=[3, False, 'two'])
]
).join(
subgrid=SimpleHypergrid(
name="nested_grid",
dimensions=[
CategoricalDimension(name='categorical_mixed_types', values=['red', False, True, 3]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='zero_to_one', min=0, max=1),
OrdinalDimension(name='ordinal_mixed_types', ordered_values=[3, 'two', False])
]
),
on_external_dimension=CategoricalDimension("categorical_mixed_types", values=[True])
)
def decode_ordinal_dimension(serialized: OptimizerService_pb2.OrdinalDimension) -> OrdinalDimension:
assert isinstance(serialized, OptimizerService_pb2.OrdinalDimension)
return OrdinalDimension(
name=serialized.Name,
ascending=serialized.Ascending,
ordered_values=[OptimizerServiceDecoder.decode_primitive_value(value) for value in serialized.OrderedValues]
)
def test_ordinal_dimension(self):
ordinal_dimension = OrdinalDimension(
name='ordinal', ordered_values=['good', 'better', 'best'])
serialized = OptimizerServiceEncoder.encode_ordinal_dimension(
ordinal_dimension)
deserialized = OptimizerServiceDecoder.decode_ordinal_dimension(
serialized)
assert deserialized == ordinal_dimension
assert isinstance(serialized, OptimizerService_pb2.OrdinalDimension)
def setup_class(cls) -> None:
cls.simple_hypergrid = SimpleHypergrid(
name='simple_adaptee',
dimensions=[
CategoricalDimension(name='categorical_mixed_types',
values=['red', True, False, 5]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='z_one', min=-1, max=2),
ContinuousDimension(name='z_two', min=-2, max=1),
ContinuousDimension(name='z_3', min=-2, max=-1),
OrdinalDimension(name='ordinal_mixed_types',
ordered_values=[1, False, 'two'])
])
cls.unbalanced_hierarchical_hypergrid = SimpleHypergrid(
name='hierarchical_adaptee',
dimensions=[
CategoricalDimension(name='categorical_mixed_types',
values=['red', True, False, 3]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='x1', min=-1, max=1),
ContinuousDimension(name='x2', min=-1, max=1),
OrdinalDimension(name='ordinal_mixed_types',
ordered_values=[3, False, 'two'])
]).join(subgrid=SimpleHypergrid(
name="nested_grid",
dimensions=[
CategoricalDimension(name='categorical_mixed_types',
values=['red', False, True, 3]),
DiscreteDimension(name='one_to_ten', min=1, max=10),
ContinuousDimension(name='x1', min=-1, max=1),
ContinuousDimension(name='x2', min=-1, max=1),
OrdinalDimension(name='ordinal_mixed_types',
ordered_values=[3, 'two', False])
]),
on_external_dimension=CategoricalDimension(
"categorical_mixed_types", values=[True]))
cls.balanced_hierarchical_hypergrid = ThreeLevelQuadratic(
).parameter_space
def test_optimization_problem_none_context(self):
parameter_space = SimpleHypergrid(
name="test",
dimensions=[
ContinuousDimension(name="x", min=0, max=1),
OrdinalDimension(name="y", ordered_values=[1, 2, 3, 5, 10]),
CategoricalDimension(name="y2", values=[True, False])
])
objective_space = SimpleHypergrid(name="z",
dimensions=[
ContinuousDimension(
name="z\n special",
min=-50,
max=-49),
ContinuousDimension(name="z1",
min=-1,
max=1)
])
optimization_problem = OptimizationProblem(
parameter_space=parameter_space,
objective_space=objective_space,
objectives=[
Objective(name="z\n special", minimize=True),
Objective(name="z1", minimize=False)
])
encoded_problem = OptimizerServiceEncoder.encode_optimization_problem(
optimization_problem)
decoded_problem = OptimizerServiceDecoder.decode_optimization_problem(
encoded_problem)
print(f"Context space is: {decoded_problem.context_space}")
assert decoded_problem.context_space is None
# Ensure that the parameter space is still valid
# Parameter Space
for _ in range(1000):
assert decoded_problem.parameter_space.random() in parameter_space
assert parameter_space.random() in decoded_problem.parameter_space
# Output Space
for _ in range(1000):
assert decoded_problem.objective_space.random() in objective_space
assert objective_space.random() in decoded_problem.objective_space
# Feature Space
for _ in range(1000):
assert decoded_problem.feature_space.random(
) in optimization_problem.feature_space
assert optimization_problem.feature_space.random(
) in decoded_problem.feature_space
def setUp(self):
self.cache_param_space = SimpleHypergrid(
name='cache_param_space',
dimensions=[
CategoricalDimension(name='cache_implementation_name', values=['lru_cache', 'associative_cache'])
]
)
self.lru_cache_param_space = SimpleHypergrid(
name='lru_cache_config',
dimensions=[
DiscreteDimension(name='size', min=1, max=2**20),
OrdinalDimension(name='color', ordered_values=['green', 'orange', 'red'])
]
)
self.associative_cache_implementation_root_param_space = SimpleHypergrid(
name='associative_cache_config',
dimensions=[
CategoricalDimension(name='hash_function_name', values=['mod_prime_hash_function', 'lowest_bits']),
CategoricalDimension(name='bucket_implementation', values=['single_value', 'binary_search_tree', 'linked_list'])
]
)
self.mod_prime_hash_function_param_space = SimpleHypergrid(
name='mod_prime_hash_function',
dimensions=[
OrdinalDimension(name='prime', ordered_values=[1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59])
]
)
self.lowest_bits_param_space = SimpleHypergrid(
name='lowest_bits',
dimensions=[
DiscreteDimension(name='num_bits', min=1, max=64)
]
)
self.binary_search_tree_param_space = SimpleHypergrid(
name='binary_search_tree',
dimensions=[
DiscreteDimension(name='max_depth', min=1, max=2**10)
]
)
self.linked_list_param_space = SimpleHypergrid(
name='linked_list',
dimensions=[
DiscreteDimension(name='max_length', min=1, max=2**10)
]
)
self.associative_cache_implementation_param_space = self.associative_cache_implementation_root_param_space.join(
subgrid=self.mod_prime_hash_function_param_space,
on_external_dimension=CategoricalDimension(name='hash_function_name', values=['mod_prime_hash_function'])
).join(
subgrid=self.lowest_bits_param_space,
on_external_dimension=CategoricalDimension(name='hash_function_name', values='lowest_bits')
).join(
subgrid=self.binary_search_tree_param_space,
on_external_dimension=CategoricalDimension(name='bucket_implementation', values=['binary_search_tree'])
)
self.cache_param_space = self.cache_param_space.join(
subgrid=self.lru_cache_param_space,
on_external_dimension=CategoricalDimension(name='cache_implementation_name', values=['lru_cache'])
).join(
subgrid=self.associative_cache_implementation_param_space,
on_external_dimension=CategoricalDimension(name='cache_implementation_name', values=['associative_cache'])
).join(
subgrid=self.linked_list_param_space,
on_external_dimension=CategoricalDimension(name='associative_cache_config.bucket_implementation', values=['linked_list'])
)
import json
import os
import sys
mlos_root_path = os.environ['MLOS_ROOT']
mlos_python_root_path = os.path.join(mlos_root_path, 'Source', 'Mlos.Python')
sys.path.append(mlos_python_root_path)
from mlos.Spaces import SimpleHypergrid, EmptyDimension, CategoricalDimension, ContinuousDimension, DiscreteDimension, OrdinalDimension
from mlos.Spaces.HypergridsJsonEncoderDecoder import HypergridJsonEncoder, HypergridJsonDecoder
continuous = ContinuousDimension(name='continuous', min=1, max=10)
discrete = DiscreteDimension(name='discrete', min=1, max=10)
ordinal = OrdinalDimension(name='ordinal', ordered_values=[1,2,3,4,5,6,7,8,9,10])
categorical = CategoricalDimension(name='categorical', values=[1,2,3,4,5,6,7,8,9,10])
simple_hypergrid = SimpleHypergrid(
name='all_kinds_of_dimensions',
dimensions = [
continuous,
discrete,
ordinal,
categorical
]
)
py_simple_hypergrid_json_string = json.dumps(simple_hypergrid, cls=HypergridJsonEncoder)
def object_hook(self, obj):
type_names_to_types = {
"EmptyDimension": EmptyDimension,
"CategoricalDimension": CategoricalDimension,
"ContinuousDimension": ContinuousDimension,
"DiscreteDimension": DiscreteDimension,
"OrdinalDimension": OrdinalDimension,
"CompositeDimension": CompositeDimension,
}
if 'ObjectType' not in obj:
return obj
object_type = obj['ObjectType']
if object_type == 'EmptyDimension':
return EmptyDimension(
name=obj['Name'],
type=type_names_to_types[obj['Type']]
)
if object_type == "CategoricalDimension":
return CategoricalDimension(
name=obj['Name'],
values=obj['Values']
)
if object_type == "ContinuousDimension":
return ContinuousDimension(
name=obj['Name'],
min=obj.get('Min', None),
max=obj.get('Max', None),
include_min=obj.get('IncludeMin', None),
include_max=obj.get('IncludeMax', None)
)
if object_type == "DiscreteDimension":
return DiscreteDimension(
name=obj['Name'],
min=obj['Min'],
max=obj['Max']
)
if object_type == "OrdinalDimension":
return OrdinalDimension(
name=obj['Name'],
ordered_values=obj.get('OrderedValues', None),
ascending=obj.get('Ascending', True) # TODO - this looks risky
)
if object_type == "CompositeDimension":
return CompositeDimension(
name=obj['Name'],
chunks_type=type_names_to_types[obj['ChunksType']],
chunks=obj['Chunks']
)
if object_type == "SimpleHypergrid":
simple_hypergrid = SimpleHypergrid(
name=obj['Name'],
dimensions=obj.get('Dimensions', [])
)
for _, subgrids_joined_on_dimension in obj.get('GuestSubgrids', dict()).items():
for joined_subgrid in subgrids_joined_on_dimension:
simple_hypergrid.add_subgrid_on_external_dimension(
other_hypergrid=joined_subgrid.subgrid,
external_dimension=joined_subgrid.join_dimension
)
return simple_hypergrid
if object_type == "GuestSubgrid":
return SimpleHypergrid.JoinedSubgrid(
subgrid=obj['Subgrid'],
join_dimension=obj['ExternalPivotDimension']
)
if object_type == "set":
return set(obj['Values'])
return obj
