nannyml.drift.multivariate.data_reconstruction.result module

Contains the results of the data reconstruction drift calculation and provides plotting functionality.

class nannyml.drift.multivariate.data_reconstruction.result.Result(results_data: DataFrame, column_names: List[str], categorical_column_names: List[str], continuous_column_names: List[str], timestamp_column_name: str | None = None)[source]

Bases: AbstractCalculatorResult

Contains the results of the data reconstruction drift calculation and provides plotting functionality.

Creates a new AbstractCalculatorResult instance.

Parameters:

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

plot(kind: str = 'drift', *args, **kwargs) → Figure | None[source]

Renders plots for metrics returned by the multivariate data reconstruction calculator.

The different plot kinds that are available:

  • drift

    plots the multivariate reconstruction error over the provided features per Chunk.

Parameters:

  • kind (str, default=`drift`) – The kind of plot you want to have. Value can currently only be drift.

  • plot_reference (bool, default=False) – Indicates whether to include the reference period in the plot or not. Defaults to False.

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_df, analysis_df, _ = nml.load_synthetic_binary_classification_dataset()
>>>
>>> column_names = [col for col in reference_df.columns
>>>                         if col not in ['y_pred', 'y_pred_proba', 'work_home_actual', 'timestamp']]
>>> calc = nml.DataReconstructionDriftCalculator(
>>>     column_names=column_names,
>>>     timestamp_column_name='timestamp'
>>> )
>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> print(results.data)  # access the numbers
                     key  start_index  ...  upper_threshold alert
0       [0:4999]            0  ...         1.511762  True
1    [5000:9999]         5000  ...         1.511762  True
2  [10000:14999]        10000  ...         1.511762  True
3  [15000:19999]        15000  ...         1.511762  True
4  [20000:24999]        20000  ...         1.511762  True
5  [25000:29999]        25000  ...         1.511762  True
6  [30000:34999]        30000  ...         1.511762  True
7  [35000:39999]        35000  ...         1.511762  True
8  [40000:44999]        40000  ...         1.511762  True
9  [45000:49999]        45000  ...         1.511762  True
>>> fig = results.plot(plot_reference=True)
>>> fig.show()