nannyml.performance_calculation.calculator module
Module containing base classes for performance calculation.
- class nannyml.performance_calculation.calculator.PerformanceCalculator(model_metadata: nannyml.metadata.base.ModelMetadata, metrics: List[str], chunk_size: Optional[int] = None, chunk_number: Optional[int] = None, chunk_period: Optional[str] = None, chunker: Optional[nannyml.chunk.Chunker] = None)[source]
Bases:
object
Base class for performance metric calculation.
Creates a new performance calculator.
- Parameters
model_metadata (ModelMetadata) – The metadata describing the monitored model.
metrics (List[str]) – A list of metrics to calculate.
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 >>> ref_df, ana_df, _ = nml.load_synthetic_binary_classification_dataset() >>> metadata = nml.extract_metadata(ref_df) >>> # create a new calculator, chunking by week >>> calculator = nml.PerformanceCalculator(model_metadata=metadata, chunk_period='W')
- calculate(analysis_data: pandas.core.frame.DataFrame) nannyml.performance_calculation.result.PerformanceCalculatorResult [source]
Calculates performance on the analysis data, using the metrics specified on calculator creation.
- Parameters
analysis_data (pd.DataFrame) – Analysis data for the model, i.e. model inputs and predictions.
Examples
>>> import nannyml as nml >>> ref_df, ana_df, _ = nml.load_synthetic_binary_classification_dataset() >>> metadata = nml.extract_metadata(ref_df) >>> calculator = nml.PerformanceCalculator(model_metadata=metadata, chunk_period='W') >>> calculator.fit(ref_df) >>> # calculate realized performance on analysis data >>> realized_performance = calculator.calculate(ana_df)
- fit(reference_data: pandas.core.frame.DataFrame) nannyml.performance_calculation.calculator.PerformanceCalculator [source]
Fits the calculator on the reference data, calibrating it for further use on the full dataset.
- Parameters
reference_data (pd.DataFrame) – Reference data for the model, i.e. model inputs and predictions enriched with target data.
Examples
>>> import nannyml as nml >>> ref_df, ana_df, _ = nml.load_synthetic_binary_classification_dataset() >>> metadata = nml.extract_metadata(ref_df) >>> calculator = nml.PerformanceCalculator(model_metadata=metadata, chunk_period='W') >>> # fit the calculator on reference data >>> calculator.fit(ref_df)