def make_config_tensorflow(
name: str,
cls: Type,
properties: Optional[Dict[str, Tuple[Type, field]]] = None,
):
"""
Given a tensorflow class, read its docstring and ``__init__`` parameters to
generate a config class with properties containing the correct types,
and default values.
"""
if properties is None:
properties = {}
doc_params = tensorflow_docstring_args(cls)
properties.update(doc_params)
return make_config(
name, [tuple([key] + list(value)) for key, value in properties.items()]
)
def make_pytorch_config(
name: str,
cls: Type,
properties: Optional[Dict[str, Tuple[Type, field]]] = None,
):
"""
Given a class or function, read its docstring and ``__init__`` parameters to
generate a config class with properties containing the correct types,
and default values.
"""
if properties is None:
properties = {}
properties.update(inspect_pytorch_params(cls))
return make_config(
name,
[tuple([key] + list(value)) for key, value in properties.items()])
def mkscikit_config_cls(
name: str,
cls: Type,
properties: Optional[Dict[str, Tuple[Type, field]]] = None,
):
"""
Given a scikit class, read its docstring and ``__init__`` parameters to
generate a config class with properties containing the correct types,
and default values.
"""
if properties is None:
properties = {}
parameters = inspect.signature(cls).parameters
docstring = inspect.getdoc(cls)
docparams = {}
# Parse parameters and their datatypes from docstring
last_param_name = None
for line in docstring.split("\n"):
if not ":" in line:
continue
param_name, dtypes = line.split(":", maxsplit=1)
param_name = param_name.strip()
dtypes = dtypes.strip()
if not param_name in parameters or param_name in docparams:
continue
docparams[param_name] = dtypes
last_param_name = param_name
# Ensure all required parameters are present in docstring
for param_name, param in parameters.items():
if param_name in ["args", "kwargs"]:
continue
if not param_name in docparams:
raise ParameterNotInDocString(
f"{param_name} for {cls.__qualname__}")
properties[param_name] = scikit_doc_to_field(docparams[param_name],
param)
return make_config(
name,
[tuple([key] + list(value)) for key, value in properties.items()])
dffml_cls_ctx = type(
name + "ScorerContext",
(parentContext, ),
{},
)
dffml_cls = type(
name + "Scorer",
(parentScorer, ),
{
"CONTEXT":
dffml_cls_ctx,
"CONFIG":
make_config(
name + "Config",
[
tuple([key] + list(value))
for key, value in properties.items()
],
),
"SCIKIT_SCORER":
functools.partial(method),
},
)
# Add the ENTRY_POINT_ORIG_LABEL
dffml_cls = entrypoint(entrypoint_name)(dffml_cls)
setattr(sys.modules[__name__], dffml_cls_ctx.__qualname__, dffml_cls_ctx)
setattr(sys.modules[__name__], dffml_cls.__qualname__, dffml_cls)