Skip to content

Class SeriesWeightedAverage

  • Jump right in for a hands-on Open In Colab

Import

from NitroFE import SeriesWeightedAverage

SeriesWeightedAverage

series_weighted_average This feature is an implementation of volume weighted moving average

\[ series\_weighted\_average = \frac{\sum (dataframe * dataframe\_for\_weight) }{\sum dataframe\_for\_weight } \]

Methods

fit(self, dataframe, dataframe_for_weight, first_fit=True)

For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False

Parameters:

Name Type Description Default
dataframe Union[pandas.core.frame.DataFrame, pandas.core.series.Series]

dataframe containing column values

 required
dataframe_for_weight Union[pandas.core.frame.DataFrame, pandas.core.series.Series]

dataframe containing column values

 required
first_fit bool

Indicator features require past values for calculation. Use True, when calculating for training data (very first fit) Use False, when calculating for subsequent testing/production data { in which case the values, which were saved during the last phase, will be utilized for calculation }, by default True

 True
Source code in nitrofe\time_based_features\indicator_features\_seriesweightedaverage.py 
def fit(
    self,
    dataframe: Union[pd.DataFrame, pd.Series],
    dataframe_for_weight: Union[pd.DataFrame, pd.Series],
    first_fit: bool = True,
):
    """
    For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False

    Parameters
    ----------
    dataframe : Union[pd.DataFrame, pd.Series]
        dataframe containing column values
    dataframe_for_weight : Union[pd.DataFrame, pd.Series]
        dataframe containing column values
    first_fit : bool, optional
        Indicator features require past values for calculation.
        Use True, when calculating for training data (very first fit)
        Use False, when calculating for subsequent testing/production data { in which case the values, which
        were saved during the last phase, will be utilized for calculation }, by default True
    """
    if isinstance(dataframe, pd.Series):
        dataframe = dataframe.to_frame()
    if isinstance(dataframe_for_weight, pd.Series):
        dataframe_for_weight = dataframe_for_weight.to_frame()

    multiplication_res = (
        pd.DataFrame(
            np.multiply(dataframe.values, dataframe_for_weight.values),
            columns=dataframe.columns,
        )
        if first_fit
        else pd.concat(
            [
                self.multiplication_values_from_last_run,
                pd.DataFrame(
                    np.multiply(dataframe.values, dataframe_for_weight.values),
                    columns=dataframe.columns,
                ),
            ]
        )
    )

    if not first_fit:
        dataframe_for_weight = pd.concat(
            [self.values_from_last_run, dataframe_for_weight]
        )

    cumilative_res = dataframe_for_weight.expanding().sum()

    cumilative_multiplication_res = multiplication_res.expanding().sum()
    self.multiplication_values_from_last_run = cumilative_multiplication_res.iloc[
        -1:
    ]

    cumilative_multiplication_res = (
        cumilative_multiplication_res.iloc[1:]
        if (not first_fit)
        else cumilative_multiplication_res
    )
    cumilative_res = cumilative_res.iloc[1:] if (not first_fit) else cumilative_res
    dataframe_for_weight = (
        dataframe_for_weight.iloc[1:] if (not first_fit) else dataframe_for_weight
    )

    res = pd.DataFrame(
        cumilative_multiplication_res.values / (cumilative_res.values)
    )
    self.values_from_last_run = cumilative_res.iloc[-1:]

    return res

References