nirs4all.controllers.charts package

Submodules

Module contents

Chart/visualization controllers.

Controllers for chart and visualization operators.

class nirs4all.controllers.charts.ExclusionChartController[source]

Bases: OperatorController

Controller for visualizing sample exclusions.

Creates 2D scatter plots using PCA to show the relationship between included and excluded samples. Supports coloring by: - Exclusion status (included vs excluded) - Target values (y) - Exclusion reason

Pipeline syntax:

“exclusion_chart” # Basic exclusion visualization

{“exclusion_chart”: {“color_by”: “y”}} # Color by target values

{“exclusion_chart”: {“color_by”: “reason”}} # Color by exclusion reason

{“exclusion_chart”: {

“n_components”: 3, # Use 3D PCA “show_legend”: True, “title”: “Custom Title”

}}

execute(step_info: ParsedStep, dataset: SpectroDataset, context: ExecutionContext, runtime_context: RuntimeContext, source: int = -1, mode: str = 'train', loaded_binaries: List[Tuple[str, Any]] | None = None, prediction_store: Any | None = None) → Tuple[ExecutionContext, Any][source]

Execute exclusion visualization.

Creates a 2D (or 3D) scatter plot showing included vs excluded samples using PCA for dimensionality reduction.

Parameters:

  • step_info – Parsed step containing operator and configuration

  • dataset – Dataset to visualize

  • context – Pipeline execution context

  • runtime_context – Runtime infrastructure context

  • source – Data source index (unused)

  • mode – Execution mode

  • loaded_binaries – Pre-loaded binaries (unused)

  • prediction_store – External prediction store (unused)

Returns:

Tuple of (context, StepOutput with chart image)

classmethod matches(step: Any, operator: Any, keyword: str) → bool[source]

Match exclusion_chart keyword.

priority: int = 10
classmethod supports_prediction_mode() → bool[source]

Chart controllers skip during prediction.

classmethod use_multi_source() → bool[source]

Operates at dataset level.