iguanas.metrics.classification.Revenue

class iguanas.metrics.classification.Revenue(y_type: str, chargeback_multiplier: int)[source]

Calculates the revenue for either a single or set of binary predictors.

Parameters
y_typestr

Dictates whether the binary target column flags fraud (y_type = ‘Fraud’) or non-fraud (y_type = ‘NonFraud’).

chargeback_multiplierint

Multiplier to apply to chargeback transactions.

fit(y_true: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series], y_preds: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series, iguanas.utils.typing.pandas.core.frame.DataFrame, iguanas.utils.typing.databricks.koalas.frame.DataFrame], sample_weight: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series]) Union[float, iguanas.utils.typing.numpy.ndarray][source]

Calculates the revenue for either a single or set of binary predictors.

Parameters
y_trueUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType]

The target column.

y_predsUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType, PandasDataFrameType, KoalasDataFrameType]

The binary predictor column.

sample_weightUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType]

Row-wise transaction amounts to apply.

Returns
Union[float, NumpyArrayType]

Revenue(s).