nannyml.drift.model_outputs.univariate.statistical.calculator module

Calculates drift for model predictions and model outputs using statistical tests.

class nannyml.drift.model_outputs.univariate.statistical.calculator.StatisticalOutputDriftCalculator(y_pred: str, problem_type: Union[str, ProblemType], y_pred_proba: Optional[Union[str, Dict[str, str]]] = None, timestamp_column_name: Optional[str] = None, chunk_size: Optional[int] = None, chunk_number: Optional[int] = None, chunk_period: Optional[str] = None, chunker: Optional[Chunker] = None)[source]

Bases: AbstractCalculator

Calculates drift for model predictions and model outputs using statistical tests.

Creates a new StatisticalOutputDriftCalculator.

Parameters:

  • y_pred_proba (ModelOutputsType) – Name(s) of the column(s) containing your model output. Pass a single string when there is only a single model output column, e.g. in binary classification cases. Pass a dictionary when working with multiple output columns, e.g. in multiclass classification cases. The dictionary maps a class/label string to the column name containing model outputs for that class/label.

  • y_pred (str) – The name of the column containing your model predictions.

  • timestamp_column_name (str, default=None) – The name of the column containing the timestamp of the model prediction.

  • chunk_size (int, default=None) – Splits the data into chunks containing chunks_size observations. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunk_number (int, default=None) – Splits the data into chunk_number pieces. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunk_period (str, default=None) – Splits the data according to the given period. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunker (Chunker, default=None) – The Chunker used to split the data sets into a lists of chunks.

Examples

>>> import nannyml as nml
>>> from IPython.display import display
>>> reference_df = nml.load_synthetic_binary_classification_dataset()[0]
>>> analysis_df = nml.load_synthetic_binary_classification_dataset()[1]
>>> display(reference_df.head())
>>> calc = nml.StatisticalOutputDriftCalculator(
...     y_pred='y_pred',
...     y_pred_proba='y_pred_proba',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary'
>>> )
>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.data)
>>> score_drift_fig = results.plot(kind='score_drift', plot_reference=True)
>>> score_drift_fig.show()
>>> score_distribution_fig = results.plot(kind='score_distribution', plot_reference=True)
>>> score_distribution_fig.show()
>>> prediction_drift_fig = results.plot(kind='prediction_drift', plot_reference=True)
>>> prediction_drift_fig.show()
>>> prediction_distribution_fig = results.plot(kind='prediction_distribution', plot_reference=True)
>>> prediction_distribution_fig.show()