nirs4all.visualization.branch_diagram module

Branch Diagram - DAG visualization for pipeline branching structure.

DEPRECATED: This module is deprecated in favor of pipeline_diagram.py which provides a more comprehensive visualization of the entire pipeline structure.

The BranchDiagram and plot_branch_diagram are now aliases for PipelineDiagram and plot_pipeline_diagram respectively.

Example

>>> from nirs4all.visualization.pipeline_diagram import PipelineDiagram
>>> diagram = PipelineDiagram(pipeline_steps, predictions)
>>> fig = diagram.render()
>>> fig.savefig('pipeline_diagram.png')
class nirs4all.visualization.branch_diagram.BranchDiagram(predictions: Any = None, config: Dict[str, Any] | None = None)[source]

Bases: PipelineDiagram

DEPRECATED: Use PipelineDiagram instead.

This class is kept for backward compatibility only. It wraps PipelineDiagram with the old API.

render(show_metrics: bool | None = None, metric: str | None = None, partition: str | None = None, figsize: Tuple[float, float] | None = None, title: str | None = None) → Figure[source]

Render the branch diagram (deprecated API).

Parameters:

  • show_metrics – Override config’s show_metrics setting.

  • metric – Override metric to display.

  • partition – Override partition for metrics.

  • figsize – Override figure size.

  • title – Optional title for the diagram.

Returns:

matplotlib Figure object.

class nirs4all.visualization.branch_diagram.PipelineDiagram(pipeline_steps: List[Any] | None = None, predictions: Any = None, execution_trace: Any = None, config: Dict[str, Any] | None = None)[source]

Bases: object

Create DAG visualization for pipeline execution structure.

Renders a visual diagram showing the complete pipeline topology, including all steps, shapes, branches, and models.

pipeline_steps

List of pipeline step definitions

predictions

Optional Predictions object with execution data

execution_trace

Optional ExecutionTrace with actual runtime shapes

config

Optional dict for customization

NODE_STYLES = {'branch': ('#E0F2F1', '#00796B'), 'concat_transform': ('#F3E5F5', '#7B1FA2'), 'default': ('#ECEFF1', '#455A64'), 'feature_augmentation': ('#E0F2F1', '#00796B'), 'input': ('#FAFAFA', '#616161'), 'merge': ('#E0F2F1', '#00796B'), 'merge_sources': ('#E0F2F1', '#00796B'), 'model': ('#FFEBEE', '#D32F2F'), 'output': ('#FAFAFA', '#616161'), 'preprocessing': ('#E3F2FD', '#1976D2'), 'sample_augmentation': ('#E8F5E9', '#388E3C'), 'source_branch': ('#E0F2F1', '#00796B'), 'splitter': ('#F3E5F5', '#7B1FA2'), 'y_processing': ('#FFF8E1', '#FFA000')}
edges: List[Tuple[str, str]]
classmethod from_trace(execution_trace: Any, predictions: Any = None, config: Dict[str, Any] | None = None) → PipelineDiagram[source]

Create a PipelineDiagram from an ExecutionTrace.

This builds the diagram using actual runtime data including measured shapes at each step.

Parameters:

  • execution_trace – ExecutionTrace object from pipeline execution

  • predictions – Optional Predictions object to enrich nodes with scores

  • config – Optional configuration dict

Returns:

PipelineDiagram instance ready for rendering

Example

>>> from nirs4all.visualization import PipelineDiagram
>>> diagram = PipelineDiagram.from_trace(trace)
>>> fig = diagram.render(title="Execution Trace")
nodes: Dict[str, PipelineNode]
render(show_shapes: bool | None = None, figsize: Tuple[float, float] | None = None, title: str | None = None, initial_shape: Tuple[int, int, int] | None = None) → Figure[source]

Render the pipeline diagram.

Parameters:

  • show_shapes – Override config’s show_shapes setting

  • figsize – Override figure size

  • title – Optional title for the diagram

  • initial_shape – Initial dataset shape (samples, processings, features)

Returns:

matplotlib Figure object

nirs4all.visualization.branch_diagram.plot_branch_diagram(predictions: Any = None, show_metrics: bool = True, metric: str = 'rmse', partition: str = 'test', figsize: Tuple[float, float] | None = None, title: str | None = None, config: Dict[str, Any] | None = None) → Figure[source]

DEPRECATED: Use plot_pipeline_diagram instead.

Parameters:

  • predictions – Predictions object with branch metadata.

  • show_metrics – Whether to show metrics in nodes.

  • metric – Metric to display (default: ‘rmse’).

  • partition – Partition for metrics (default: ‘test’).

  • figsize – Figure size tuple.

  • title – Optional title for the diagram.

  • config – Additional configuration dict.

Returns:

matplotlib Figure object.

nirs4all.visualization.branch_diagram.plot_pipeline_diagram(pipeline_steps: List[Any] | None = None, predictions: Any = None, show_shapes: bool = True, figsize: Tuple[float, float] | None = None, title: str | None = None, initial_shape: Tuple[int, int, int] | None = None, config: Dict[str, Any] | None = None, execution_trace: Any = None) → Figure[source]

Convenience function to create a pipeline diagram.

Parameters:

  • pipeline_steps – List of pipeline step definitions

  • predictions – Optional Predictions object with execution data

  • show_shapes – Whether to show shape info in nodes

  • figsize – Figure size tuple

  • title – Optional title for the diagram

  • initial_shape – Initial dataset shape (samples, processings, features)

  • config – Additional configuration dict

  • execution_trace – Optional ExecutionTrace object

Returns:

matplotlib Figure object

Example

>>> from nirs4all.visualization.pipeline_diagram import plot_pipeline_diagram
>>> fig = plot_pipeline_diagram(pipeline, initial_shape=(189, 1, 2151))
>>> fig.savefig('pipeline_diagram.png')