nannyml.drift.target.target_distribution.calculator module

Calculates drift for model targets and target distributions using statistical tests.

class nannyml.drift.target.target_distribution.calculator.TargetDistributionCalculator(y_true: str, problem_type: Union[str, ProblemType], 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 targets and target distributions using statistical tests.

creates a new TargetDistributionCalculator.

Parameters:

  • y_true (str) – The name of the column containing your model target values.

  • 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]
>>> analysis_target_df = nml.load_synthetic_binary_classification_dataset()[2]
>>> analysis_df = analysis_df.merge(analysis_target_df, on='identifier')
>>> display(reference_df.head(3))
>>> calc = nml.TargetDistributionCalculator(
...     y_true='work_home_actual',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary'
>>> )
>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.data.head(3))
>>> target_drift_fig = results.plot(kind='target_drift', plot_reference=True)
>>> target_drift_fig.show()
>>> target_distribution_fig = results.plot(kind='target_distribution', plot_reference=True)
>>> target_distribution_fig.show()