Forecast Models - Attributes

ForecastModelAttributes(perfdb)

Class used for handling forecast models attributes. Can be accessed via perfdb.forecasts.models.attributes.

Parameters:

• perfdb

  (PerfDB) –

  Top level object carrying all functionality and the connection handler.

Source code in echo_postgres/perfdb_root.py

Python

def __init__(self, perfdb: e_pg.PerfDB) -> None:
    """Base class that all subclasses should inherit from.

    Parameters
    ----------
    perfdb : PerfDB
        Top level object carrying all functionality and the connection handler.

    """
    self._perfdb: e_pg.PerfDB = perfdb

get(forecast_models=None, attribute_names=None, filter_type='and', output_type='dict', values_only=False)

Method to get the attributes of the given data source instance.

The most useful keys/columns returned are:

• attribute_name
• attribute_value
• data_type_name

Parameters:

• forecast_models

  (list[str] | None, default: None ) –

  Names of the forecast models to filter the results. By default None

• attribute_names

  (list[str] | None, default: None ) –

  List of attribute names to filter the results. If set to None will get all. By default None

• filter_type

  (Literal['and', 'or'], default: 'and' ) –

  How to treat multiple filters. Can be one of ["and", "or"]. By default "and"

• output_type

  (Literal['dict', 'DataFrame'], default: 'dict' ) –

  Output type of the data. Can be one of ["dict", "DataFrame"] By default "dict"

• values_only

  (bool, default: False ) –

  If set to True, will only return the values of the attributes, skipping display_name, id, etc.

Returns:

• dict[str, dict[str, dict[str, Any | dict[str, Any]]]] –

  In case output_type is "dict", returns a dictionary in the format {forecast_model_name: {attribute_name: {attribute: value, ...}, ...}, ...} If values_only is True, the innermost dictionary will be {attribute_name: value, ...}

• DataFrame –

  In case output_type is "DataFrame", returns a DataFrame with the following format: index = MultiIndex[feature_name, attribute_name], columns = [attribute, ...] If values_only is True, the columns will be ["attribute_value"]

• DataFrame –

  In case output_type is "pl.DataFrame", returns a Polars DataFrame

Source code in echo_postgres/forecast_model_attributes.py

Python

@validate_call
def get(
    self,
    forecast_models: list[str] | None = None,
    attribute_names: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
    output_type: Literal["dict", "DataFrame", "pl.DataFrame"] = "dict",
    values_only: bool = False,
) -> dict[str, dict[str, dict[str, Any | dict[str, Any]]]] | pd.DataFrame | pl.DataFrame:
    """Method to get the attributes of the given data source instance.

    The most useful keys/columns returned are:

    - attribute_name
    - attribute_value
    - data_type_name

    Parameters
    ----------
    forecast_models : list[str] | None, optional
        Names of the forecast models to filter the results.
        By default None
    attribute_names : list[str] | None, optional
        List of attribute names to filter the results. If set to None will get all. By default None
    filter_type : Literal["and", "or"], optional
        How to treat multiple filters. Can be one of ["and", "or"].
        By default "and"
    output_type : Literal["dict", "DataFrame"], optional
        Output type of the data. Can be one of ["dict", "DataFrame"]
        By default "dict"
    values_only : bool, optional
        If set to True, will only return the values of the attributes, skipping display_name, id, etc.

    Returns
    -------
    dict[str, dict[str, dict[str, Any | dict[str, Any]]]]
        In case output_type is "dict", returns a dictionary in the format {forecast_model_name: {attribute_name: {attribute: value, ...}, ...}, ...}
        If values_only is True, the innermost dictionary will be {attribute_name: value, ...}
    pd.DataFrame
        In case output_type is "DataFrame", returns a DataFrame with the following format: index = MultiIndex[feature_name, attribute_name], columns = [attribute, ...]
        If values_only is True, the columns will be ["attribute_value"]
    pl.DataFrame
        In case output_type is "pl.DataFrame", returns a Polars DataFrame
    """
    # building the WHERE clause
    where = []
    if forecast_models:
        where.append(
            sql.SQL("forecast_model_name IN ({names})").format(names=sql.SQL(", ").join(map(sql.Literal, forecast_models))),
        )
    if attribute_names:
        where.append(
            sql.SQL("attribute_name IN ({names})").format(names=sql.SQL(", ").join(map(sql.Literal, attribute_names))),
        )
    if where:
        where = sql.SQL(f" {filter_type.upper()} ").join(where)

    # building the query
    query = [
        sql.SQL(
            "SELECT {values} FROM performance.v_forecast_model_attributes",
        ).format(
            values=sql.SQL(
                "forecast_model_name, attribute_name, attribute_value::TEXT, data_type_name",
            )
            if values_only
            else sql.SQL(
                "forecast_model_id, forecast_model_name, attribute_id, attribute_name, attribute_value::TEXT, data_type_id, data_type_name, modified_date",
            ),
        ),
    ]
    if where:
        query.append(sql.SQL(" WHERE "))
        query.append(where)
    query.append(sql.SQL(" ORDER BY forecast_model_name, attribute_name"))
    query = sql.Composed(query)

    # executing the query
    df = self._perfdb.conn.read_to_polars(
        query,
        schema_overrides=self._cols_schema,
    )

    # casting the attribute values
    df = cast_attributes(df=df, index_cols=["forecast_model_name"])

    return convert_output(
        df,
        output_type,
        index_col=["forecast_model_name", "attribute_name"],
        nest_by_index=True,
        values_only_key="attribute_value" if values_only else None,
    )