feature_types

Feature types module.

Classes:

Name	Description
NumericalFeature	Numerical feature.
CategoricalFeature	Categorical feature.
MultivariateNumericalFeature	MultivariateNumericalFeature feature.
UnivariateTimeSeries	UnivariateTimeSerie feature.
MultivariateTimeSeries	MultivariateTimeSeries feature.
ImageFeature	Image feature.
FeatureTypeConvertor	Class for converting exposed features type into feature types.

NumericalFeature

Numerical feature.

It represents quantifiable values that can be measured and ordered. Its values may be continuous (e.g., real numbers) or discrete (e.g., integers). For instance, "age" or "price" could be set as numerical features.

Parameters:

Name	Type	Description	Default
name	Literal[str]		'numerical'

Methods:

Name	Description
from_exposed	Instantiate a NumericalFeature from ExposedNumerical.

Attributes:

Name	Type	Description
name	Literal['numerical']
as_exposed	ExposedNumerical	NumericalFeature as ExposedNumericalFeature or ExposedFeatureWithAugmentation.

name: Literal['numerical'] = 'numerical'

as_exposed: ExposedNumerical

NumericalFeature as ExposedNumericalFeature or ExposedFeatureWithAugmentation.

from_exposed(exposed_feature: ExposedNumerical) -> NumericalFeature

Instantiate a NumericalFeature from ExposedNumerical.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedNumerical) -> "NumericalFeature":
    """Instantiate a NumericalFeature from ExposedNumerical."""
    return NumericalFeature(name=cast("Literal['numerical']", exposed_feature.name))

CategoricalFeature

Categorical feature.

It represents data (bool, int or str only) that can be divided into distinct groups or categories.

It may be nominal or ordinal. For instance, "Education level" or "gender" could be set as categorical features.

Parameters:

Name	Type	Description	Default
categories	list[str | int | bool]	Categories, automatically inferred from the preprocessor after the fitting step. None if not inferred yet.	None
name	Literal[str]		'categorical'

Methods:

Name	Description
from_exposed	Instantiate a CategoricalFeature from ExposedCategorical.

Attributes:

Name	Type	Description
name	Literal['categorical']
categories	list[str | int | bool] | None
as_exposed	ExposedCategorical	As ExposedCategorical.

name: Literal['categorical'] = 'categorical'

categories: list[str | int | bool] | None = field(default=None, kw_only=True)

as_exposed: ExposedCategorical

As ExposedCategorical.

from_exposed(exposed_feature: ExposedCategorical) -> CategoricalFeature

Instantiate a CategoricalFeature from ExposedCategorical.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedCategorical) -> "CategoricalFeature":
    """Instantiate a CategoricalFeature from ExposedCategorical."""
    return CategoricalFeature(
        categories=exposed_feature.categories,
    )

MultivariateNumericalFeature

MultivariateNumericalFeature feature.

It represents numerical data points divided into several channels. Oppositely as time serie features, there is no time relationship between points here.

Parameters:

Name	Type	Description	Default
channel_names	List[str] | None	List of channel names, optional.	None
name	Literal[str]		'multivariate_numerical'

Methods:

Name	Description
from_exposed	Instantiate a MultivariateNumericalFeature from an ExposedVectorNumerical.

Attributes:

Name	Type	Description
name	Literal['multivariate_numerical']
channel_names	list[str] | None
as_exposed	ExposedVectorNumerical	As ExposedVectorNumerical.

name: Literal['multivariate_numerical'] = 'multivariate_numerical'

channel_names: list[str] | None = field(default=None, kw_only=True)

as_exposed: ExposedVectorNumerical

As ExposedVectorNumerical.

from_exposed(exposed_feature: ExposedVectorNumerical) -> MultivariateNumericalFeature

Instantiate a MultivariateNumericalFeature from an ExposedVectorNumerical.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedVectorNumerical) -> "MultivariateNumericalFeature":
    """Instantiate a MultivariateNumericalFeature from an ExposedVectorNumerical."""
    return MultivariateNumericalFeature(
        channel_names=exposed_feature.channel_names,
    )

UnivariateTimeSeries

UnivariateTimeSerie feature.

It represents a time serie with a single channel, synchronized (no dynamic time warping required) or not.

The DTW will be applied automatically if this feature is required.

Parameters:

Name	Type	Description	Default
asynchronous	int	Whether the time serie is asynchronous (dynamic time warping will be automatically applied server side) or not.	False.
mirrored_channel	str | None	Used in XpViz. Indeed, if the same channel is used for both input (as a lookback, under a first feature object) and target (as a horizon to predict, under a second feature object), this parameter may be specified to visualize on the same curve both features, (the lookback and its corresponding horizon).	None
name	Literal[str]		'univariate_time_series'

Methods:

Name	Description
from_exposed	Instantiate a UnivariateTimeSeries from an ExposedUnivariateTimeSeries.

Attributes:

Name	Type	Description
name	Literal['univariate_time_series']
asynchronous	bool
mirrored_channel	str | None
as_exposed	ExposedUnivariateTimeSeries	As ExposedUnivariateTimeSeries.

name: Literal['univariate_time_series'] = 'univariate_time_series'

asynchronous: bool = field(default=False, kw_only=True)

mirrored_channel: str | None = field(default=None, kw_only=True)

as_exposed: ExposedUnivariateTimeSeries

As ExposedUnivariateTimeSeries.

from_exposed(exposed_feature: ExposedUnivariateTimeSeries) -> UnivariateTimeSeries

Instantiate a UnivariateTimeSeries from an ExposedUnivariateTimeSeries.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedUnivariateTimeSeries) -> "UnivariateTimeSeries":
    """Instantiate a UnivariateTimeSeries from an ExposedUnivariateTimeSeries."""
    return UnivariateTimeSeries(
        asynchronous=exposed_feature.asynchronous,
        mirrored_channel=exposed_feature.mirrored_channel,
    )

MultivariateTimeSeries

MultivariateTimeSeries feature.

This class represents a multivariate time series (multiple channels), synchronized (no dynamic time warping required) or not.

The DTW will be applied automatically if this feature is required.

Parameters:

Name	Type	Description	Default
asynchronous	int	Whether the time serie is asynchronous (dynamic time warping will be automatically applied server side) or not.	False.
channel_names	List[str] | None	List of channel names, optional.	None
name	Literal[str]		'multivariate_time_series'

Methods:

Name	Description
from_exposed	Instantiate a MultivariateTimeSeries from a ExposedMultivariateTimeSeries.

Attributes:

Name	Type	Description
name	Literal['multivariate_time_series']
asynchronous	bool
channel_names	list[str] | None
as_exposed	ExposedMultivariateTimeSeries	As ExposedMultivariateTimeSeries.

name: Literal['multivariate_time_series'] = 'multivariate_time_series'

asynchronous: bool = field(default=False, kw_only=True)

channel_names: list[str] | None = field(default=None, kw_only=True)

as_exposed: ExposedMultivariateTimeSeries

As ExposedMultivariateTimeSeries.

from_exposed(exposed_feature: ExposedMultivariateTimeSeries) -> MultivariateTimeSeries

Instantiate a MultivariateTimeSeries from a ExposedMultivariateTimeSeries.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedMultivariateTimeSeries) -> "MultivariateTimeSeries":
    """Instantiate a MultivariateTimeSeries from a ExposedMultivariateTimeSeries."""
    return MultivariateTimeSeries(
        asynchronous=exposed_feature.asynchronous,
        channel_names=exposed_feature.channel_names,
    )

ImageFeature

Image feature.

It represents image feature objects.

Parameters:

Name	Type	Description	Default
name	Literal[str]		'image'
serialized_width	int | None		None
serialized_height	int | None		None
serialized_byte_format	str		'PNG'

Methods:

Name	Description
from_exposed	Instantiate an ImageFeature from an ExposedImage.

Attributes:

Name	Type	Description
name	Literal['image']
serialized_width	int | None
serialized_height	int | None
serialized_byte_format	str
as_exposed	ExposedImage	As ExposedImage.

name: Literal['image'] = 'image'

serialized_width: int | None = None

serialized_height: int | None = None

serialized_byte_format: str = 'PNG'

as_exposed: ExposedImage

As ExposedImage.

from_exposed(exposed_feature: ExposedImage) -> ImageFeature

Instantiate an ImageFeature from an ExposedImage.

Source code in src/xpdeep/dataset/feature/feature_types.py

@staticmethod
def from_exposed(exposed_feature: ExposedImage) -> "ImageFeature":
    """Instantiate an ImageFeature from an ExposedImage."""
    return ImageFeature(
        serialized_width=exposed_feature.serialized_width,
        serialized_height=exposed_feature.serialized_height,
        serialized_byte_format=exposed_feature.serialized_byte_format,
    )

FeatureTypeConvertor

Class for converting exposed features type into feature types.

For instance, it recognizes an ExposedNumerical and instantiate corresponding NumericalFeature.

Methods:

Name	Description
from_exposed	Convert exposed feature type into feature type.

from_exposed(exposed_feature_type: ExposedNumerical | ExposedVectorNumerical | ExposedCategorical | ExposedUnivariateTimeSeries | ExposedMultivariateTimeSeries | ExposedImage) -> NumericalFeature | MultivariateNumericalFeature | CategoricalFeature | UnivariateTimeSeries | MultivariateTimeSeries | ImageFeature

Convert exposed feature type into feature type.

Source code in src/xpdeep/dataset/feature/feature_types.py

@classmethod
def from_exposed(
    cls,
    exposed_feature_type: ExposedNumerical
    | ExposedVectorNumerical
    | ExposedCategorical
    | ExposedUnivariateTimeSeries
    | ExposedMultivariateTimeSeries
    | ExposedImage,
) -> (
    NumericalFeature
    | MultivariateNumericalFeature
    | CategoricalFeature
    | UnivariateTimeSeries
    | MultivariateTimeSeries
    | ImageFeature
):
    """Convert exposed feature type into feature type."""
    match exposed_feature_type:
        case ExposedNumerical():
            return NumericalFeature.from_exposed(exposed_feature_type)
        case ExposedVectorNumerical():
            return MultivariateNumericalFeature.from_exposed(exposed_feature_type)
        case ExposedCategorical():
            return CategoricalFeature.from_exposed(exposed_feature_type)
        case ExposedUnivariateTimeSeries():
            return UnivariateTimeSeries.from_exposed(exposed_feature_type)
        case ExposedMultivariateTimeSeries():
            return MultivariateTimeSeries.from_exposed(exposed_feature_type)
        case ExposedImage():
            return ImageFeature.from_exposed(exposed_feature_type)