coco_pipe.viz.plot_decoding_scores#

coco_pipe.viz.plot_decoding_scores(result, metric=None, model=None, kind='point', aggregate='mean', ax=None, figsize=None)#

Plot aggregate scalar decoding scores by model and metric.

Parameters:

  • result (Any) – Experiment result with get_detailed_scores() or a detailed score DataFrame containing scalar Value rows.

  • metric (str | None) – Optional metric name used to filter scores.

  • model (str | None) – Optional model name used to filter scores.

  • kind (Literal['point', 'bar', 'box']) – Plot type: "point" for aggregate +/- SEM, "bar" for bar summaries, or "box" for fold-level distributions.

  • aggregate (Literal['mean', 'median']) – Summary statistic used by "point" and "bar" plots.

  • ax (Axes | None) – Existing Matplotlib axes to draw into.

  • figsize (tuple[float, float] | None) – Figure size used when creating a new axes.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.decoding.plot_decoding_scores

Interactive Plotly version.

plot_model_comparison

Score differences between model pairs.

plot_fold_score_dispersion

Per-fold score spread.

plot_null_interval_summary

Observed scores versus null bands.

Examples

>>> import pandas as pd
>>> from coco_pipe.viz import decoding as viz
>>> df = pd.DataFrame(
...     {
...         "Model": ["SVM"] * 5,
...         "Metric": ["accuracy"] * 5,
...         "Type": ["detailed_score"] * 5,
...         "Value": [0.71, 0.73, 0.69, 0.74, 0.70],
...     }
... )
>>> fig, ax = viz.plot_decoding_scores(df)

Examples using coco_pipe.viz.plot_decoding_scores#

Basic Decoding: Classification and Model Verification

Basic Decoding: Classification and Model Verification