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 >>> >>> reference_df, analysis_df, target_df = nml.load_synthetic_binary_classification_dataset() >>> >>> calc = nml.TargetDistributionCalculator( >>> y_true='work_home_actual', >>> timestamp_column_name='timestamp' >>> ) >>> calc.fit(reference_df) >>> results = calc.calculate(analysis_df.merge(target_df, on='identifier')) >>> print(results.data) # check the numbers key start_index end_index ... thresholds alert significant 0 [0:4999] 0 4999 ... 0.05 True True 1 [5000:9999] 5000 9999 ... 0.05 False False 2 [10000:14999] 10000 14999 ... 0.05 False False 3 [15000:19999] 15000 19999 ... 0.05 False False 4 [20000:24999] 20000 24999 ... 0.05 False False 5 [25000:29999] 25000 29999 ... 0.05 False False 6 [30000:34999] 30000 34999 ... 0.05 False False 7 [35000:39999] 35000 39999 ... 0.05 False False 8 [40000:44999] 40000 44999 ... 0.05 False False 9 [45000:49999] 45000 49999 ... 0.05 False False >>> >>> results.plot(kind='target_drift', plot_reference=True).show() >>> results.plot(kind='target_distribution', plot_reference=True).show()