src.wic.model

Module Contents

Classes

NumpyEncoder	Extensible JSON <https://json.org> encoder for Python data structures.
WICModel	Helper class that provides a standard way to create an ABC using

class src.wic.model.NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: json.JSONEncoder

Extensible JSON <https://json.org> encoder for Python data structures.

Supports the following objects and types by default:

Python	JSON
dict	object
list, tuple	array
str	string
int, float	number
True	true
False	false
None	null

To extend this to recognize other objects, subclass and implement a .default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError).

default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

class src.wic.model.WICModel(**data: Any)

Bases: pydantic.BaseModel, abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

class Config

json_encoders

_cache: pandas.DataFrame

_cache_path: pathlib.Path

scaler: Any

predictions: dict[tuple[src.use.UseID, src.use.UseID], float]

abstract as_df() → pandas.DataFrame

abstract predict(use_pairs: Iterable[tuple[src.use.Use, src.use.Use]], **kwargs) → list[float]

predict_all(use_pairs: list[tuple[src.use.Use, src.use.Use]]) → list[float]