nannyml.drift.univariate.calculator module
Calculates drift for individual features using the Kolmogorov-Smirnov and chi2-contingency statistical tests.
- class nannyml.drift.univariate.calculator.UnivariateDriftCalculator(column_names: List[str], timestamp_column_name: Optional[str] = None, categorical_methods: Optional[List[str]] = None, continuous_methods: Optional[List[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.
Creates a new UnivariateDriftCalculator instance.
- Parameters:
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) – The name of the column containing the timestamp of the model prediction.
categorical_methods (List[str], default=['jensen_shannon']) – A list of method names that will be performed on categorical columns.
continuous_methods (List[str], default=['jensen_shannon']) – A list of method names that will be performed on continuous columns.
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, analysis, _ = nml.load_synthetic_car_price_dataset() >>> column_names = [col for col in reference.columns if col not in ['timestamp', 'y_pred', 'y_true']] >>> calc = nml.UnivariateDriftCalculator( ... column_names=column_names, ... timestamp_column_name='timestamp', ... continuous_methods=['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein'], ... categorical_methods=['chi2', 'jensen_shannon', 'l_infinity'], ... ).fit(reference) >>> res = calc.calculate(analysis) >>> res = res.filter(period='analysis') >>> for column_name in res.continuous_column_names: ... for method in res.continuous_method_names: ... res.plot(kind='drift', column_name=column_name, method=method).show()