nannyml.performance_estimation.confidence_based.results module
Module containing CBPE estimation results and plotting implementations.
- class nannyml.performance_estimation.confidence_based.results.CBPEPerformanceEstimatorResult(estimated_data: pandas.core.frame.DataFrame, model_metadata: nannyml.metadata.base.ModelMetadata)[source]
Bases:
nannyml.performance_estimation.base.PerformanceEstimatorResult
Contains results for CBPE estimation and adds plotting functionality.
Creates a new DriftResult instance.
- Parameters
estimated_data (pd.DataFrame) – The results of the
estimate()
call.model_metadata (ModelMetadata) – The metadata describing the monitored model.
- plot(kind: str = 'performance', metric: Optional[str] = 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 (str, default=None) – The metric to plot when rendering a plot of kind ‘performance’.
Examples
>>> import nannyml as nml >>> ref_df, ana_df, _ = nml.load_synthetic_binary_classification_dataset() >>> metadata = nml.extract_metadata(ref_df, model_type=nml.ModelType.CLASSIFICATION_BINARY) >>> estimator = nml.CBPE(model_metadata=metadata, chunk_period='W') >>> estimator.fit(ref_df) >>> estimates = estimator.estimate(ana_df) >>> # plot the estimated performance >>> estimates.plot(kind='performance').show()