nannyml.data_quality.unseen.result module

Contains the results of the univariate statistical drift calculation and provides plotting functionality.

class nannyml.data_quality.unseen.result.Result(results_data: DataFrame, column_names: List[str], data_quality_metric: str, timestamp_column_name: Optional[str], chunker: Chunker)

Bases: PerColumnResult, ResultCompareMixin

Contains the results of the univariate statistical drift calculation and provides plotting functionality.

Creates a new AbstractCalculatorResult instance.

Parameters:

results_data (pd.DataFrame) – The data returned by the Calculator.

keys() → List[Key]

plot(*args, **kwargs) → Figure

Returns:

fig – A Figure object containing the requested drift plot.

Can be saved to disk using the write_image() method or shown rendered on screen using the show() method.

Return type:

plotly.graph_objs._figure.Figure

Examples

>>> import nannyml as nml
>>> reference, analysis, _ = nml.load_synthetic_car_price_dataset()
>>> column_names = [col for col in reference.columns if col not in [
....    'car_age', 'km_driven', 'price_new', 'accident_count', 'door_count','timestamp', 'y_pred', 'y_true']]
>>> calc = nml.UnseenValuesCalculator(
...     column_names=column_names,
...     timestamp_column_name='timestamp',
... ).fit(reference)
>>> res = calc.calculate(analysis)
>>> res.filter(period='analysis').plot().show()