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

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"]

Source code in echo_postgres/forecast_model_attributes.py

@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"] = "dict",
    values_only: bool = False,
) -> dict[str, dict[str, dict[str, Any | dict[str, Any]]]] | 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, ...}
    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"]
    """
    # 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, data_type_name",
            )
            if values_only
            else sql.SQL("*"),
        ),
    ]
    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
    with self._perfdb.conn.reconnect() as conn:
        # setting attribute_value as object to avoid casting json column as string
        df = conn.read_to_pandas(query, post_convert="pyarrow")

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

    df = df.set_index(["forecast_model_name", "attribute_name"])

    # returning the result
    if output_type == "dict":
        # dropping unwanted columns
        if values_only:
            df = df["attribute_value"]
            temp = df.to_dict()
        else:
            temp = df[["attribute_id", "attribute_value", "data_type_id", "data_type_name", "modified_date"]].to_dict(orient="index")
        # converting from format {(forecast_model_name, attribute_name): value} to {forecast_model_name: {attribute_name: value, ...}, ...}
        output = {}
        for (forecast_model_name, attribute_name), value in temp.items():
            if forecast_model_name not in output:
                output[forecast_model_name] = {}
            output[forecast_model_name][attribute_name] = value
        return output
    if output_type == "DataFrame" and values_only:
        df = df[["attribute_value"]].copy()

    return df