howso.client.typing#

Classes

`Cases`	Representation of a table of cases.
`Distances`	Representation of a case distances result.
`Evaluation`	Representation of an Evaluate result.
`TrainStatus`	Representation of a status output from AbstractHowsoClient.train.

Attributes

`CaseIndices`	Sequence of `case_indices` tuples.
`GenerateNewCases`	Valid values for `generate_new_cases` parameters.
`LibraryType`	Valid values for `library_type` parameters.
`Mode`	Valid values for `mode` parameters.
`NewCaseThreshold`	Valid values for `new_case_threshold` parameters.
`NormalizeMethod`	Valid values for `normalize_method` parameters.
`PathLike`	Objects which can be interpreted as paths.
`Persistence`	Valid values for `persistence` parameters.
`Precision`	Valid values for `precision` parameters.
`SeriesIDTracking`	Valid values for `series_id_tracking` parameters.
`TabularData2D`	2-dimensional tabular data.
`TabularData3D`	3-dimensional tabular (i.e., time-series) data.
`TargetedModel`	Valid values for `targeted_model` parameters.
`AblationThresholdMap`	Threshold map(s) for auto-ablation and data reduction.

class howso.client.typing.Cases#

Bases: TypedDict

Representation of a table of cases.

cases: list[list[Any]]#: Matrix of row and column values.

features: list[str]#: The feature column names.

class howso.client.typing.Distances#

Bases: TypedDict

Representation of a case distances result.

case_indices: Sequence[tuple[str, int]]#: The corresponding distances case indices.

distances: DataFrame#: The matrix of computed distances.

class howso.client.typing.Evaluation#

Bases: TypedDict

Representation of an Evaluate result.

aggregated: Any#: The aggregated evaluation output.

evaluated: dict[str, list[Any]]#: A mapping of feature names to lists of values.

class howso.client.typing.TrainStatus#

Bases: TypedDict

Representation of a status output from AbstractHowsoClient.train.

needs_analyze: NotRequired[bool]#: Indicates whether the Trainee needs an analyze.

needs_data_reduction: NotRequired[bool]#: Indicates whether the Trainee recommends a call to reduce_data.

howso.client.typing.AblationThresholdMap#

Threshold map(s) for auto-ablation and data reduction.

alias of dict[Literal[‘accuracy’, ‘adjusted_smape’, ‘mcc’, ‘missing_value_accuracy’, ‘precision’, ‘r2’, ‘recall’, ‘rmse’, ‘smape’, ‘spearman_coeff’], dict[str, float]]

howso.client.typing.CaseIndices#

Sequence of case_indices tuples.

alias of Sequence[tuple[str, int]]

howso.client.typing.GenerateNewCases#

Valid values for generate_new_cases parameters.

alias of Literal[‘always’, ‘attempt’, ‘no’]

howso.client.typing.LibraryType#

Valid values for library_type parameters.

alias of Literal[‘st’, ‘mt’]

howso.client.typing.Mode#

Valid values for mode parameters.

alias of Literal[‘robust’, ‘full’]

howso.client.typing.NewCaseThreshold#

Valid values for new_case_threshold parameters.

alias of Literal[‘max’, ‘min’, ‘most_similar’]

howso.client.typing.NormalizeMethod#

Valid values for normalize_method parameters.

alias of Literal[‘fractional_absolute’, ‘fractional’, ‘relative’]

howso.client.typing.PathLike#

Objects which can be interpreted as paths.

alias of str | PathLike

howso.client.typing.Persistence#

Valid values for persistence parameters.

alias of Literal[‘allow’, ‘always’, ‘never’]

howso.client.typing.Precision#

Valid values for precision parameters.

alias of Literal[‘exact’, ‘similar’]

howso.client.typing.SeriesIDTracking#

Valid values for series_id_tracking parameters.

alias of Literal[‘fixed’, ‘dynamic’, ‘no’]

howso.client.typing.TabularData2D#

2-dimensional tabular data.

alias of DataFrame | list[list[Any]]

howso.client.typing.TabularData3D#

3-dimensional tabular (i.e., time-series) data.

alias of list[DataFrame] | list[list[list[Any]]]

howso.client.typing.TargetedModel#

Valid values for targeted_model parameters.

alias of Literal[‘single_targeted’, ‘omni_targeted’, ‘targetless’]