flytekitplugins.onnxscikitlearn.ScikitLearn2ONNXConfig¶

class flytekitplugins.onnxscikitlearn.ScikitLearn2ONNXConfig(initial_types, name=None, doc_string='', target_opset=None, custom_conversion_functions=<factory>, custom_shape_calculators=<factory>, custom_parsers=<factory>, options=<factory>, intermediate=False, naming=None, white_op=None, black_op=None, verbose=0, final_types=None)[source]¶

ScikitLearn2ONNXConfig is the config used during the scikitlearn to ONNX conversion.

Parameters:

initial_types (List[Tuple[str, Type]]) – The types of the inputs to the model.
name (str | None) – The name of the graph in the produced ONNX model.
doc_string (str) – A string attached onto the produced ONNX model.
target_opset (int | None) – The ONNX opset number.
custom_conversion_functions (Dict[Callable[[...], Any], Callable[[...], None]]) – A dictionary for specifying the user customized conversion function.
custom_shape_calculators (Dict[Callable[[...], Any], Callable[[...], None]]) – A dictionary for specifying the user customized shape calculator.
custom_parsers (Dict[Callable[[...], Any], Callable[[...], None]]) – Parsers determine which outputs are expected for which particular task.
options (Dict[Any, Any]) – Specific options given to converters.
intermediate (bool) – If True, the function returns the converted model and the instance of Topology used, else, it returns the converted model.
naming (str | Callable[[...], Any] | None) – Change the way intermediates are named.
white_op (Set[str] | None) – White list of ONNX nodes allowed while converting a pipeline.
black_op (Set[str] | None) – Black list of ONNX nodes disallowed while converting a pipeline.
verbose (int) – Display progress while converting a model.
final_types (List[Tuple[str, Type]] | None) – Used to overwrite the type (if type is not None) and the name of every output.

Methods

classmethod from_dict(kvs, *, infer_missing=False)¶

Parameters:: kvs (dict | list | str | int | float | bool | None)
Return type:: A

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)¶

Parameters:: s (str | bytes | bytearray)
Return type:: A

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)¶

Parameters:

infer_missing (bool)
many (bool)
partial (bool)

Return type:

SchemaF[A]

to_dict(encode_json=False)¶

Return type:: Dict[str, dict | list | str | int | float | bool | None]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)¶

Parameters:

skipkeys (bool)
ensure_ascii (bool)
check_circular (bool)
allow_nan (bool)
indent (int | str | None)
separators (Tuple[str, str] | None)
default (Callable | None)
sort_keys (bool)

Return type:

str

Attributes

black_op: Set[str] | None = None

dataclass_json_config = None

doc_string: str = ''

final_types: List[Tuple[str, Type]] | None = None

intermediate: bool = False

name: str | None = None

naming: str | Callable[[...], Any] | None = None

target_opset: int | None = None

verbose: int = 0

white_op: Set[str] | None = None

initial_types: List[Tuple[str, Type]]

custom_conversion_functions: Dict[Callable[[...], Any], Callable[[...], None]]

custom_shape_calculators: Dict[Callable[[...], Any], Callable[[...], None]]

custom_parsers: Dict[Callable[[...], Any], Callable[[...], None]]

options: Dict[Any, Any]