nannyml.drift.model_inputs.univariate.statistical.calculator module
Calculates drift for individual features using the Kolmogorov-Smirnov and chi2-contingency statistical tests.
- class nannyml.drift.model_inputs.univariate.statistical.calculator.UnivariateStatisticalDriftCalculator(feature_column_names: List[str], 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 individual features using statistical tests.
Creates a new UnivariateStatisticalDriftCalculator instance.
- Parameters:
feature_column_names (List[str]) – A list containing the names of features in the provided data set. A drift score will be calculated for each entry in this list.
timestamp_column_name (str, default=None) – The name of the column containing the timestamp of the model prediction.
chunk_size (int) – 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) – Splits the data into chunk_number pieces. Only one of chunk_size, chunk_number or chunk_period should be given.
chunk_period (str) – Splits the data according to the given period. Only one of chunk_size, chunk_number or chunk_period should be given.
chunker (Chunker) – The Chunker used to split the data sets into a lists of chunks.
Examples
>>> import nannyml as nml >>> >>> reference_df, analysis_df, _ = nml.load_synthetic_binary_classification_dataset() >>> >>> feature_column_names = [col for col in reference_df.columns >>> if col not in ['y_pred', 'y_pred_proba', 'work_home_actual', 'timestamp']] >>> calc = nml.UnivariateStatisticalDriftCalculator( >>> feature_column_names=feature_column_names, >>> timestamp_column_name='timestamp' >>> ) >>> calc.fit(reference_df) >>> results = calc.calculate(analysis_df) >>> print(results.data) # check the numbers key start_index ... identifier_alert identifier_threshold 0 [0:4999] 0 ... True 0.05 1 [5000:9999] 5000 ... True 0.05 2 [10000:14999] 10000 ... True 0.05 3 [15000:19999] 15000 ... True 0.05 4 [20000:24999] 20000 ... True 0.05 5 [25000:29999] 25000 ... True 0.05 6 [30000:34999] 30000 ... True 0.05 7 [35000:39999] 35000 ... True 0.05 8 [40000:44999] 40000 ... True 0.05 9 [45000:49999] 45000 ... True 0.05 >>> fig = results.plot(kind='feature_drift', plot_reference=True, feature_column_name='distance_from_office') >>> fig.show()