def _searchSpaceList(self, space: SearchSpaceArray, *,
size: int) -> List[SearchSpaceGrid]:
sub_spaces = space.items(max=size)
param_grids: List[List[SearchSpaceGrid]] = [
nest_all_HPparams(
str(index), self.fixupDegenerateSearchSpaces(accept(sub,
self)))
for index, sub in enumerate(sub_spaces)
]
param_grids_product: Iterable[
Iterable[SearchSpaceGrid]] = itertools.product(*param_grids)
chained_grids: List[SearchSpaceGrid] = [
dict(ChainMap(*gridline, )) for gridline in param_grids_product
]
if space.is_tuple:
st_val = structure_type_tuple
else:
st_val = structure_type_list
discriminated_grids: List[SearchSpaceGrid] = [{
**d, structure_type_name:
SearchSpaceConstant(st_val)
} for d in chained_grids]
return discriminated_grids
def array_single_str_(self,
space: SearchSpaceArray,
path: str,
num,
useCounter=True) -> str:
p = _mk_label(path, num, useCounter=useCounter) + "_"
ret = "(" if space.is_tuple else "["
items: Iterable[SearchSpace] = space.items()
ret += ",".join((accept(sub, self, p, counter=x, useCounter=useCounter)
for x, sub in enumerate(items)))
ret += ")" if space.is_tuple else "]"
return ret
def schemaToSearchSpaceHelper_(
self,
longName,
path: str,
schema: JsonSchema,
relevantFields: Optional[Set[str]],
pgo_freqs: pgo_part = None,
sub_space: bool = True,
) -> Optional[SearchSpace]:
# TODO: handle degenerate cases
# right now, this handles only a very fixed form
if is_false_schema(schema):
return None
typ: Optional[str] = None
typ = schema.get("laleType", None)
if typ is None:
typ = schema.get("type", None)
else:
typ = typ
if "enum" in schema and typ != "operator":
vals = schema["enum"]
return SearchSpaceEnum(vals,
pgo=asFreqs(pgo_freqs),
default=get_default(schema))
if typ is not None:
if typ == "boolean":
return SearchSpaceBool(pgo=asFreqs(pgo_freqs),
default=get_default(schema))
elif typ == "number" or typ == "integer":
exclusive_minimum = False
minimum = schema.get("minimumForOptimizer", None)
if minimum is not None:
exclusive_minimum = schema.get(
"exclusiveMinimumForOptimizer", False)
else:
minimum = schema.get("minimum", None)
if minimum is not None:
exclusive_minimum = schema.get("exclusiveMinimum",
False)
exclusive_maximum = False
maximum = schema.get("maximumForOptimizer", None)
if maximum is not None:
exclusive_maximum = schema.get(
"exclusiveMaximumForOptimizer", False)
else:
maximum = schema.get("maximum", None)
if maximum is not None:
exclusive_maximum = schema.get("exclusiveMaximum",
False)
distribution = schema.get("distribution", None)
laleType = schema.get("laleType", None)
if laleType is None:
laleType = typ
if laleType == "number":
discrete = False
elif laleType == "integer":
discrete = True
else:
raise OperatorSchemaError(
path,
f"specified laleType should be a number or integer, not: {laleType}.",
)
pgo: Freqs
return SearchSpaceNumber(
minimum=minimum,
exclusiveMinimum=exclusive_minimum,
maximum=maximum,
exclusiveMaximum=exclusive_maximum,
discrete=discrete,
distribution=distribution,
pgo=asFreqs(pgo_freqs),
default=get_default(schema),
)
elif typ == "array" or typ == "tuple":
laleType = schema.get("laleType", None)
if laleType is None:
laleType = typ
is_tuple: bool = laleType == "tuple"
min_items = schema.get("minItemsForOptimizer", None)
if min_items is None:
min_items = schema.get("minItems", None)
if min_items is None:
min_items = 0
max_items = schema.get("maxItemsForOptimizer", None)
if max_items is None:
max_items = schema.get("maxItems", None)
items_schema = schema.get("itemsForOptimizer", None)
if items_schema is None:
items_schema = schema.get("items", None)
if items_schema is None:
raise OperatorSchemaError(
path,
f"An array type was found without a provided schema for the items in the schema {schema}. Please provide a schema for the items (consider using itemsForOptimizer)",
)
# we can search an empty list even without schemas
if max_items == 0:
if is_tuple:
return SearchSpaceConstant([()])
else:
return SearchSpaceConstant([[]])
prefix: Optional[List[SearchSpace]] = None
additional: Optional[SearchSpace] = None
if isinstance(items_schema, list):
prefix = []
for i, sub_schema in enumerate(items_schema):
sub = self.schemaToSearchSpaceHelper_(
longName, path + "_" + str(i), sub_schema,
relevantFields)
if sub is None:
return None
else:
prefix.append(sub)
prefix_len = len(prefix)
additional_items_schema = schema.get(
"additionalItemsForOptimizer", None)
if additional_items_schema is None:
additional_items_schema = schema.get(
"additionalItems", None)
if additional_items_schema is None:
if max_items is None or max_items > prefix_len:
raise OperatorSchemaError(
path,
f"An array type was found with provided schemas for {prefix_len} elements, but either an unspecified or too high a maxItems, and no schema for the additionalItems. Please constraing maxItems to <= {prefix_len} (you can set maxItemsForOptimizer), or provide a schema for additionalItems",
)
elif additional_items_schema is False:
if max_items is None:
max_items = prefix_len
else:
max_items = min(max_items, prefix_len)
else:
additional = self.schemaToSearchSpaceHelper_(
longName,
path + "-",
additional_items_schema,
relevantFields,
)
# if items_schema is None:
# raise ValueError(f"an array type was found without a provided schema for the items in the schema {schema}. Please provide a schema for the items (consider using itemsForOptimizer)")
else:
additional = self.schemaToSearchSpaceHelper_(
longName, path + "-", items_schema, relevantFields)
if max_items is None:
raise OperatorSchemaError(
path,
f"An array type was found without a provided maximum number of items in the schema {schema}, and it is not a list with 'additionalItems' set to False. Please provide a maximum (consider using maxItemsForOptimizer), or, if you are using a list, set additionalItems to False",
)
return SearchSpaceArray(
prefix=prefix,
minimum=min_items,
maximum=max_items,
additional=additional,
is_tuple=is_tuple,
)
elif typ == "object":
if "properties" not in schema:
return SearchSpaceObject(longName, [], [])
o = self.JsonSchemaToSearchSpaceHelper(
longName,
path,
schema,
relevantFields,
pgo_freqs=pgo_freqs,
sub_space=sub_space,
)
if sub_space:
return SearchSpaceDict(o)
else:
all_keys = list(o.keys())
all_keys.sort()
o_choice = tuple([o.get(k, None) for k in all_keys])
return SearchSpaceObject(longName, all_keys, [o_choice])
elif typ == "string":
pass
elif typ == "operator":
# TODO: If there is a default, we could use it
vals = schema.get("enum", None)
if vals is None:
logger.error(
"An operator is required by the schema but was not provided"
)
return None
sub_schemas: List[SearchSpace] = [
accept(op, self)
if isinstance(op, Operator) else SearchSpaceConstant(op)
for op in vals
]
combined_sub_schema: SearchSpace
if len(sub_schemas) == 1:
combined_sub_schema = sub_schemas[0]
if isinstance(combined_sub_schema, SearchSpaceConstant):
return combined_sub_schema
else:
combined_sub_schema = SearchSpaceSum(sub_schemas)
if all((isinstance(x, SearchSpaceConstant)
for x in sub_schemas)):
return combined_sub_schema
return SearchSpaceOperator(combined_sub_schema)
elif typ == "Any":
raise OperatorSchemaError(
path,
f"A search space was found with laleType ({typ}), which is not searchable. Please mark the relevant hyperparameter as not relevant for the optimizer. schema: {schema}",
)
else:
raise OperatorSchemaError(
path,
f"An unknown type ({typ}) was found in the schema {schema}"
)
if "anyOf" in schema:
objs = []
for s_obj in schema["anyOf"]:
if "type" in s_obj and s_obj["type"] == "object":
o = self.JsonSchemaToSearchSpaceHelper(
longName,
path,
s_obj,
relevantFields,
pgo_freqs=pgo_freqs,
sub_space=sub_space,
)
if o:
objs.append(o)
if objs:
# First, gather a list of all the properties
keys_list = [set(o.keys()) for o in objs]
# make sure the iterator is deterministic
all_keys = list(set.union(*keys_list))
# and we might as well make it sorted
all_keys.sort()
def as_str(k, c):
if c is None:
return "None"
else:
return search_space_to_str_for_comparison(
c, path + "_" + k)
anys: Dict[str, Any] = {}
for o in objs:
o_choice = tuple([o.get(k, None) for k in all_keys])
k = str([
as_str(all_keys[idx], c)
for idx, c in enumerate(o_choice)
])
if k in anys:
logger.info(
f"Ignoring Duplicate SearchSpace entry {k}")
anys[k] = o_choice
return SearchSpaceObject(longName, all_keys, anys.values())
else:
return SearchSpaceObject(longName, [], [])
if "allOf" in schema:
# if all but one are negated constraints, we will just ignore them
pos_sub_schema: List[JsonSchema] = []
for sub_schema in schema["allOf"]:
if "not" not in sub_schema:
pos_sub_schema.append(sub_schema)
if len(pos_sub_schema) > 1:
raise OperatorSchemaError(
path,
f"schemaToSearchSpaceHelper does not yet know how to compile the given schema {schema}, because it is an allOf with more than one non-negated schemas ({pos_sub_schema})",
)
if len(pos_sub_schema) == 0:
raise OperatorSchemaError(
path,
f"schemaToSearchSpaceHelper does not yet know how to compile the given schema {schema}, because it is an allOf with only negated schemas",
)
logger.debug(
f"[{path}]: schemaToSearchSpaceHelper: ignoring negated schemas in the conjunction {schema}"
)
return self.schemaToSearchSpaceHelper_(
longName,
path,
pos_sub_schema[0],
relevantFields,
pgo_freqs=pgo_freqs,
sub_space=sub_space,
)
# TODO: handle degenerate cases
raise OperatorSchemaError(
path,
f"schemaToSearchSpaceHelper does not yet know how to compile the given schema {schema}",
)
def array_single_expr_(self, space: SearchSpaceArray, path: str, num):
p = _mk_label(path, num) + "_"
items: Iterable[SearchSpace] = space.items()
ret = [accept(sub, self, p, counter=x) for x, sub in enumerate(items)]
return tuple(ret) if space.is_tuple else ret
