autoprognosis.plugins.pipeline package

Pipeline(plugins_str: List[str]) → Any

class PipelineMeta(name: str, plugins: Tuple[Type, ...], dct: dict)

Bases: type

change_output(output: str) → None

fit(X: DataFrame, *args: Any, **kwargs: Any) → Any

get_args(**kwargs: Any) → Dict

static hyperparameter_space(*args: Any, **kwargs: Any) → Dict

static hyperparameter_space_for_layer(name: str, *args: Any, **kwargs: Any) → Dict

is_fitted() → Any

static load(buff: bytes) → PipelineMeta

static load_template(buff: bytes) → PipelineMeta

mro()

Return a type’s method resolution order.

static name(*args: Any) → str

predict(**kwargs: Any) → DataFrame

predict_proba(**kwargs: Any) → DataFrame

sample_params(*args: Any, **kwargs: Any) → Dict

save(**kwargs: Any) → bytes

save_template(**kwargs: Any) → bytes

static type(*args: Any) → str

Submodules

• autoprognosis.plugins.pipeline.generators module