Module futureexpert.shaper

Classes

class ResultScenario (**data: Any)

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class ResultScenario(BaseConfig):
    """Configuration of scenarios for one covariate.

    Parameters
    ----------
    ts: futureexpert.shared_models.Covariate
        Information about the covariate.
    high: typing.Sequence[futureexpert.shared_models.TimeSeriesValue]
        High scenario values.
    low: typing.Sequence[futureexpert.shared_models.TimeSeriesValue]
        Low scenario values.
    custom: typing.Optional[typing.Sequence[futureexpert.shared_models.TimeSeriesValue]]
        Custom scenario values (Optional).
    """

    ts: Covariate
    high: Sequence[TimeSeriesValue]
    low: Sequence[TimeSeriesValue]
    custom: Optional[Sequence[TimeSeriesValue]] = None

Configuration of scenarios for one covariate.

Parameters

ts : Covariate

Information about the covariate.

high : typing.Sequence[TimeSeriesValue]

High scenario values.

low : typing.Sequence[TimeSeriesValue]

Low scenario values.

custom : typing.Optional[typing.Sequence[TimeSeriesValue]]

Custom scenario values (Optional).

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

• BaseConfig
• pydantic.main.BaseModel

Class variables

var custom : Sequence[TimeSeriesValue] | None

var high : Sequence[TimeSeriesValue]

var low : Sequence[TimeSeriesValue]

var model_config

var ts : Covariate

class Scenario (**data: Any)

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class Scenario(BaseConfig):
    """Configuration of scenarios for one covariate.

    Parameters
    ----------
    ts: typing.Union[futureexpert.shared_models.Covariate, futureexpert.shared_models.CovariateRef]
        The original covariate time series with lag information.
    ts_version: builtins.str
        Version of the covariate.
    high: builtins.list[futureexpert.shared_models.TimeSeriesValue]
        High scenario values.
    low: builtins.list[futureexpert.shared_models.TimeSeriesValue]
        Low scenario values.
    custom: typing.Optional[builtins.list[futureexpert.shared_models.TimeSeriesValue]]
        Custom scenario values (Optional).
    """
    ts: Union[Covariate, CovariateRef]
    ts_version: str
    high: list[TimeSeriesValue]
    low: list[TimeSeriesValue]
    custom: Optional[list[TimeSeriesValue]] = None

    def add_custom_values(self, values: list[float]) -> None:
        """Add custom scenario values to the Scenario.

    Parameters
    ----------
    values: builtins.list[builtins.float]

    return: builtins.NoneType

    """
        if len(values) != len(self.high):
            raise ValueError('All Scenarios need the same length')
        self.custom = [TimeSeriesValue(time_stamp_utc=x.time_stamp_utc, value=values[idx])
                       for idx, x in enumerate(self.high)]

Configuration of scenarios for one covariate.

Parameters

ts : typing.Union[Covariate, CovariateRef]

The original covariate time series with lag information.

ts_version : builtins.str

Version of the covariate.

high : builtins.list[TimeSeriesValue]

High scenario values.

low : builtins.list[TimeSeriesValue]

Low scenario values.

custom : typing.Optional[builtins.list[TimeSeriesValue]]

Custom scenario values (Optional).

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

• BaseConfig
• pydantic.main.BaseModel

Class variables

var custom : list[TimeSeriesValue] | None

var high : list[TimeSeriesValue]

var low : list[TimeSeriesValue]

var model_config

var ts : Covariate | CovariateRef

var ts_version : str

Methods

def add_custom_values(self, values: list[float]) ‑> None

Expand source code

def add_custom_values(self, values: list[float]) -> None:
    """Add custom scenario values to the Scenario.

Parameters
----------
values: builtins.list[builtins.float]

return: builtins.NoneType

"""
    if len(values) != len(self.high):
        raise ValueError('All Scenarios need the same length')
    self.custom = [TimeSeriesValue(time_stamp_utc=x.time_stamp_utc, value=values[idx])
                   for idx, x in enumerate(self.high)]

Add custom scenario values to the Scenario.

Parameters

values : builtins.list[builtins.float]

 

return : builtins.NoneType

 

class ScenarioValuesConfig (**data: Any)

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class ScenarioValuesConfig(BaseConfig):
    """Configuration for scenario values creation.

    Parameters
    ----------
    actuals_version: builtins.str
        Version from the time series that should get forecasted.
    actuals_name: builtins.str
        Name of the actuals time series.
    covariate_versions: builtins.list[builtins.str]
        All the covariate versions from the covariates listed in `covariates`.
    covariates: builtins.list[futureexpert.shared_models.CovariateRef]
        List of covariates to create scenarios for.
    fc_horizon: builtins.int
        The number of forecast periods.
    """
    actuals_version: str
    actuals_name: str
    covariate_versions: list[str]
    covariates: list[CovariateRef]
    fc_horizon: int

Configuration for scenario values creation.

Parameters

actuals_version : builtins.str

Version from the time series that should get forecasted.

actuals_name : builtins.str

Name of the actuals time series.

covariate_versions : builtins.list[builtins.str]

All the covariate versions from the covariates listed in covariates.

covariates : builtins.list[CovariateRef]

List of covariates to create scenarios for.

fc_horizon : builtins.int

The number of forecast periods.

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

• BaseConfig
• pydantic.main.BaseModel

Class variables

var actuals_name : str

var actuals_version : str

var covariate_versions : list[str]

var covariates : list[CovariateRef]

var fc_horizon : int

var model_config

class ShaperConfig (**data: Any)

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class ShaperConfig(BaseConfig):
    """Configuration of a shaper run.

    Parameters
    ----------
    report_note: builtins.str
        Title of the report.
    actuals_version: builtins.str
        Version of the actuals.
    actuals_name: builtins.str
        Name of the time series.
    scenarios: builtins.list[futureexpert.shaper.Scenario]
        Information about the covariates and their scenario values.
    db_name: typing.Optional[builtins.str]
        Only accessible for internal use. Name of the database to use for storing the results.
    """
    report_note: str
    actuals_version: str
    actuals_name: str
    scenarios: list[Scenario]
    db_name: Optional[str] = None

Configuration of a shaper run.

Parameters

report_note : builtins.str

Title of the report.

actuals_version : builtins.str

Version of the actuals.

actuals_name : builtins.str

Name of the time series.

scenarios : builtins.list[Scenario]

Information about the covariates and their scenario values.

db_name : typing.Optional[builtins.str]

Only accessible for internal use. Name of the database to use for storing the results.

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

• BaseConfig
• pydantic.main.BaseModel

Class variables

var actuals_name : str

var actuals_version : str

var db_name : str | None

var model_config

var report_note : str

var scenarios : list[Scenario]

class ShaperInput (**data: Any)

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class ShaperInput(BaseModel):
    """Input of the shaper service.

    Parameters
    ----------
    actuals: futureexpert.shared_models.TimeSeries
        Time series for which the forecasts where performed.
    scenarios: builtins.list[futureexpert.shaper.ResultScenario]
        The Scenario Information.
    """
    actuals: TimeSeries
    scenarios: list[ResultScenario]

Input of the shaper service.

Parameters

actuals : TimeSeries

Time series for which the forecasts where performed.

scenarios : builtins.list[ResultScenario]

The Scenario Information.

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 actuals : TimeSeries

var model_config

var scenarios : list[ResultScenario]

class ShaperResult (**data: Any)

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class ShaperResult(BaseModel):
    """Result of the shaper service.

   Parameters
    ----------
    input: futureexpert.shaper.ShaperInput
        Input Information.
    forecast_low: typing.Sequence[futureexpert.forecast.ForecastValue]
        Forecast values of the low scenario.
    forecast_high: typing.Sequence[futureexpert.forecast.ForecastValue]
        Forecast values of the high scenario.
    forecast_custom: typing.Sequence[futureexpert.forecast.ForecastValue]
        Forecast values of the custom scenario.
    """
    input: ShaperInput
    forecast_low: Sequence[ForecastValue]
    forecast_high: Sequence[ForecastValue]
    forecast_custom: Sequence[ForecastValue]

Result of the shaper service.

Parameters

---

input: futureexpert.shaper.ShaperInput Input Information. forecast_low: typing.Sequence[futureexpert.forecast.ForecastValue] Forecast values of the low scenario. forecast_high: typing.Sequence[futureexpert.forecast.ForecastValue] Forecast values of the high scenario. forecast_custom: typing.Sequence[futureexpert.forecast.ForecastValue] Forecast values of the custom scenario.

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 forecast_custom : Sequence[ForecastValue]

var forecast_high : Sequence[ForecastValue]

var forecast_low : Sequence[ForecastValue]

var input : ShaperInput

var model_config