Table Of Contents
Table Of Contents

gluonts.dataset.artificial.recipe module

class gluonts.dataset.artificial.recipe.Add(inputs: List[Union[Any, Callable]])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.BinaryHolidays(dates: List[pandas._libs.tslibs.timestamps.Timestamp], holidays: List[Any])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.BinaryMarkovChain(one_to_zero: Union[Any, Callable], zero_to_one: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Choose(options: Union[Any, Callable], selector: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Concatenate(inputs: List[Union[Any, Callable]], axis: int = 0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Constant(constant)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.ConstantVec(constant: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Convolve(input: Union[Any, Callable], filter: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Debug(print_global=False)[source]

Bases: object

class gluonts.dataset.artificial.recipe.Dilated(source: Callable, dilation: int)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Eval(expr: str)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.EvalRecipe(recipe: List[Tuple[str, Callable]], op: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.ForEachCat(fun, cat_field='cat', cat_idx=0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Lag(input: Union[Any, Callable], lag: Union[Any, Callable] = 0, pad_const: int = 0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Lifted[source]

Bases: object

class gluonts.dataset.artificial.recipe.LiftedAdd(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LiftedBinaryOp(left, right, op)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.LiftedMul(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LiftedSub(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LiftedTruediv(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LinearTrend(slope: Union[Any, Callable] = 1.0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Mul(inputs)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.NanWhere(source: Union[Any, Callable], nan_indicator: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.NormalizeMax(input)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.OneMinus(source: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.OnesLike(other)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomBinary(prob: Union[Any, Callable] = 0.1)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomCat(cardinalities: List[int], prob_fun: Callable = gluonts.dataset.artificial.recipe.RandomSymmetricDirichlet(alpha=1.0, shape=[0]))[source]

Bases: object

class gluonts.dataset.artificial.recipe.RandomChangepoints(max_num_changepoints: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomGaussian(stddev: Union[Any, Callable] = 1.0, shape: Sequence[int] = (0, ))[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomInteger(low: Union[Any, Callable], high: Union[Any, Callable], shape: Optional[Sequence[int]] = (0,))[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomSymmetricDirichlet(alpha: Union[Any, Callable] = 1.0, shape: Sequence[int] = (0, ))[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomUniform(low: Union[Any, Callable] = 0.0, high: Union[Any, Callable] = 1.0, shape=(0, ))[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Ref(field_name: str)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Repeated(pattern: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.SmoothSeasonality(period: Union[Any, Callable], phase: Union[Any, Callable])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Stack(inputs: List[Union[Any, Callable]])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.StackPrefix(prefix: str)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

gluonts.dataset.artificial.recipe.evaluate(funcs: List[Tuple[str, Callable]], length: int, *args, global_state: dict = None, **kwargs) → Dict[str, Any][source]
gluonts.dataset.artificial.recipe.generate(length: int, recipe: Union[Callable, List[Tuple[str, Callable]]], start: pandas._libs.tslibs.timestamps.Timestamp, global_state: Optional[dict] = None, seed: int = 0, item_id_prefix: str = '') → Iterator[Dict[str, Any]][source]
gluonts.dataset.artificial.recipe.make_func(length: int, funcs: List[Tuple[str, Callable]], global_state=None) → Callable[[int, Dict[str, Any]], Dict[str, Any]][source]
gluonts.dataset.artificial.recipe.resolve(val_or_callable: Union[Any, Callable], context: Dict[str, Any], *args, **kwargs)[source]
gluonts.dataset.artificial.recipe.take_as_list(iterator, num)[source]