QuantileCalibrationBoxVisualization#

class openstef_beam.analysis.visualizations.quantile_calibration_box_visualization.QuantileCalibrationBoxVisualization(**data: Any) → None[source]

Bases: VisualizationProvider

Creates boxplot visualization for quantile calibration across multiple targets.

Inherits from QuantileProbabilityVisualization to reuse data extraction and validation logic, but overrides the plotting methods to create boxplots.

Boxplots are particularly useful for:

  • Comparing calibration across multiple targets

  • Showing distribution of calibration errors

  • Identifying outlier targets or systematic biases

  • Evaluating consistency of uncertainty estimates

Example

Basic usage in analysis pipeline

>>> from openstef_beam.analysis import AnalysisConfig
>>> from openstef_beam.analysis.visualizations import QuantileCalibrationBoxVisualization
>>>
>>> # Configure quantile calibration boxplot analysis
>>> analysis_config = AnalysisConfig(
...     visualization_providers=[
...         QuantileCalibrationBoxVisualization(
...             name="quantile_calibration_boxplot",
...         ),
...     ]
... )
>>>
>>> # The visualization will generate boxplots showing calibration error
>>> # distributions across multiple targets for comparative analysis
Parameters:

data (Any)

property supported_aggregations: set[AnalysisAggregation]

Boxplot visualization requires multiple targets, so NONE aggregation is excluded.

create_by_target(reports: list[tuple[TargetMetadata, EvaluationSubsetReport]]) → VisualizationOutput[source]

Creates visualization comparing multiple targets from the same run.

Groups reports by target metadata and creates visualizations showing performance differences across individual targets within the same model run.

Parameters:
Returns:

Visualization comparing performance across different targets.

Return type:

VisualizationOutput

create_by_group(reports: dict[GroupName, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) → VisualizationOutput[source]

Creates visualization comparing multiple targets from the same run.

Groups targets by their group_name and creates comparative visualizations showing performance differences across target categories or types.

Parameters:

  • reports (dict[TypeAliasType, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) – Dictionary mapping group names to lists of (metadata, report) tuples for that group.

  • reports

Returns:

Visualization comparing performance across different target groups.

Return type:

VisualizationOutput

create_by_run_and_target(reports: dict[RunName, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) → VisualizationOutput[source]

Creates visualization comparing multiple runs on the same target group.

Groups reports by run_name and creates comparative visualizations showing how different models or configurations perform on the same targets.

Parameters:

  • reports (dict[TypeAliasType, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) – Dictionary mapping run names to lists of (metadata, report) tuples for that run.

  • reports

Returns:

Visualization comparing different model runs on the same targets.

Return type:

VisualizationOutput

create_by_run_and_group(reports: dict[tuple[RunName, GroupName], list[tuple[TargetMetadata, EvaluationSubsetReport]]]) → VisualizationOutput[source]

Creates visualization across multiple runs and target groups.

Creates matrix-style comparisons showing how different models perform across different target categories, enabling full comparative analysis.

Parameters:

  • reports (dict[tuple[TypeAliasType, TypeAliasType], list[tuple[TargetMetadata, EvaluationSubsetReport]]]) – Dictionary mapping (run_name, group_name) tuples to lists of (metadata, report) tuples for that combination.

  • reports

Returns:

Visualization matrix comparing runs across target groups.

Return type:

VisualizationOutput

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str