Module futureexpert.shared_models

Shared models used across multiple modules.

Classes

class BaseConfig (**data: Any)

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class BaseConfig(BaseModel):
    """Base configuration that is used for most models."""
    model_config = ConfigDict(allow_inf_nan=False,
                              extra='forbid',
                              arbitrary_types_allowed=True)

Base configuration that is used for most models.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• pydantic.main.BaseModel

Subclasses

• ForecastingConfig
• MethodSelectionConfig
• PreprocessingConfig
• ReportConfig
• MatcherConfig

Class variables

var model_config

class Covariate (**data: Any)

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class Covariate(BaseModel):
    """Covariate.

    Parameters
    ----------
    ts: futureexpert.shared_models.TimeSeries
        Time series object of the covariate. Not lagged.
    lag: builtins.int
        Lag by which the covariate was used.
    """
    ts: TimeSeries
    lag: int

Covariate.

Parameters

ts : TimeSeries

Time series object of the covariate. Not lagged.

lag : builtins.int

Lag by which the covariate was used.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• pydantic.main.BaseModel

Class variables

var lag : int

var model_config

var ts : TimeSeries

class CovariateRef (**data: Any)

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class CovariateRef(BaseModel):
    """Covariate reference.

    Parameters
    ----------
    name: builtins.str
        Name of the Covariate
    lag: builtins.int
        Lag by which the covariate was used.
    """
    name: str
    lag: int

Covariate reference.

Parameters

name : builtins.str

Name of the Covariate

lag : builtins.int

Lag by which the covariate was used.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• pydantic.main.BaseModel

Class variables

var lag : int

var model_config

var name : str

class PositiveInt (value: int)

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class PositiveInt(int):
    def __new__(cls, value: int) -> PositiveInt:
        if value < 1:
            raise ValueError('The value must be a positive integer.')
        return super().__new__(cls, value)

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.

>>> int('0b100', base=0)
4

Ancestors

• builtins.int

class TimeSeries (**data: Any)

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class TimeSeries(BaseModel):
    """Time series data.

    Parameters
    ----------
    name: builtins.str
        Name of the time series.
    group: builtins.str
        Group of the time series.
    granularity: builtins.str
        Granularity of the time series.
    values: typing.Sequence
        The actual values of the time series.
    unit: typing.Optional
        The unit of the time .
    unit_factors: typing.Optional
        Factors to convert the time series in another unit.
    grouping: typing.Optional
        Hierarchy levels ot the time series.
    """
    name: Annotated[str, Field(min_length=1)]
    group: str
    granularity: Annotated[str, Field(min_length=1)]
    values: Annotated[Sequence[TimeSeriesValue], Field(min_length=1)]
    unit: Optional[str] = None
    unit_factors: Optional[dict[str, float]] = None
    grouping: Optional[dict[str, Union[str, int]]] = None

Time series data.

Parameters

name : builtins.str

Name of the time series.

group : builtins.str

Group of the time series.

granularity : builtins.str

Granularity of the time series.

values : typing.Sequence

The actual values of the time series.

unit : typing.Optional

The unit of the time .

unit_factors : typing.Optional

Factors to convert the time series in another unit.

grouping : typing.Optional

Hierarchy levels ot the time series.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• pydantic.main.BaseModel

Class variables

var granularity : str

var group : str

var grouping : dict[str, str | int] | None

var model_config

var name : str

var unit : str | None

var unit_factors : dict[str, float] | None

var values : Sequence[TimeSeriesValue]

class TimeSeriesValue (**data: Any)

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class TimeSeriesValue(BaseModel):
    """Value of a time series.

    Parameters
    ----------
    time_stamp_utc: datetime.datetime
        The time stamp of the value.
    value: builtins.float
        The value.
    """
    time_stamp_utc: datetime
    value: Annotated[float, Field(allow_inf_nan=False)]

Value of a time series.

Parameters

time_stamp_utc : datetime.datetime

The time stamp of the value.

value : builtins.float

The value.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• pydantic.main.BaseModel

Class variables

var model_config

var time_stamp_utc : datetime.datetime

var value : float