nannyml.performance_calculation.result module
Module containing the results of performance calculations and associated plots.
- class nannyml.performance_calculation.result.PerformanceCalculatorResult(performance_data: pandas.core.frame.DataFrame, model_metadata: nannyml.metadata.base.ModelMetadata)[source]
Bases:
object
Contains the results of performance calculation and adds plotting functionality.
Creates a new PerformanceCalculatorResult instance.
- Parameters
performance_data (pd.DataFrame) – The results of the performance calculation.
model_metadata – The metadata describing the monitored model.
- plot(kind: str = 'performance', metric: Optional[Union[str, nannyml.performance_calculation.metrics.Metric]] = None, *args, **kwargs) plotly.graph_objs._figure.Figure [source]
Render plots based on CBPE estimation results.
This function will return a
plotly.graph_objects.Figure
object. The following kinds of plots are available:performance
: a line plot rendering the estimated performance perChunk
afterapplying the
calculate()
method on a chunked dataset.
- Parameters
kind (str, default='performance') – The kind of plot to render. Only the ‘performance’ plot is currently available.
metric (Union[str, Metric], default=None) – The name of the metric to plot. Value should be one of: - ‘roc_auc’ - ‘f1’ - ‘precision’ - ‘recall’ - ‘specificity’ - ‘accuracy’
Examples
>>> import nannyml.metadata.extraction >>> import nannyml as nml >>> ref_df, ana_df, _ = nml.load_synthetic_binary_classification_dataset() >>> metadata = nannyml.metadata.extraction.extract_metadata(ref_df) >>> calculator = nml.PerformanceCalculator(model_metadata=metadata, chunk_period='W') >>> calculator.fit(ref_df) >>> realized_performance = calculator.calculate(ana_df) >>> # plot the calculated performance metrics >>> for m in calculator.metrics: >>> realized_performance.plot(kind='performance', metric=m).show()