coco_pipe.report.decoding#

Composable section builders for decoding reports.

Attributes#

`DECODING_SECTION_BUILDERS`
`VALID_SECTIONS`

Exceptions#

SectionDataUnavailable

Signal that a report section is inapplicable because its data is absent.

Functions#

`build_decoding_overview_section`(result, *[, name])	Build high-level decoding context.
`build_decoding_summary_section`(result, *[, name])	Build the scalar model-performance summary.
`build_cv_section`(result, *[, metric, model, name, ...])	Build fold scores and score dispersion only.
`build_probability_section`(result, *[, model, name, ...])	Build confusion, ROC, precision-recall, and calibration diagnostics.
`build_statistical_section`(result, *[, metric, model, ...])	Build finite-sample and temporal statistical assessment.
`build_temporal_section`(result, *[, metric, model, ...])	Build temporal score and generalization diagnostics when applicable.
`build_performance_section`(result, *[, metric, name, ...])	Build aggregate score and paired model-comparison figures.
`build_features_section`(result, *[, feature_metadata, ...])	Build feature importance, stability, and sensor-family summaries.
`build_topomaps_section`(result, *[, feature_metadata, ...])	Build feature-family sensor profiles when spatial metadata exists.
`build_fit_diagnostics_section`(result, *[, model, ...])	Build timing and warning diagnostics as supplementary content.
`build_tuning_section`(result, *[, model, name, ...])	Build best-parameter and search-result diagnostics.
`build_neural_section`(result, *[, model, name, ...])	Build neural training artifacts when available.
`build_configuration_section`(result, *[, name])	Build the stored run configuration.
`build_provenance_section`(result, *[, name])	Build environment and provenance metadata.
`build_caveats_section`(result, *[, feature_metadata, name])	Build explicit caveats for unavailable optional diagnostics.
`build_export_inventory_section`(result, *[, name])	Build a compact inventory of available result accessors.
`build_decoding_sections`(result, *[, sections, ...])	Build ordered decoding sections without creating or mutating a report.
`make_decoding_result_report`(result, *[, ...])	Build a report for one `~coco_pipe.decoding.result.ExperimentResult`.
`render_unit_reports`(records, *, modes[, ...])	Render per-unit decoding reports from successful sweep artifacts.
`add_decoding_overview`(self, result, *[, name])
`add_decoding_summary`(self, result[, name])
`add_decoding_statistical_assessment`(self, result[, ...])
`add_decoding_temporal`(self, result[, metric, model, name])
`add_decoding_performance`(self, result, *[, metric, name])
`add_decoding_features`(self, result, *[, ...])
`add_decoding_topomaps`(self, result, *[, ...])
`add_decoding_neural_artifacts`(self, result[, model, name])

Module Contents#

exception coco_pipe.report.decoding.SectionDataUnavailable#

Bases: RuntimeError

Signal that a report section is inapplicable because its data is absent.

coco_pipe.report.decoding.build_decoding_overview_section(result, *, name='Overview')#

Build high-level decoding context.

Parameters:

result (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_decoding_summary_section(result, *, name='Model Performance')#

Build the scalar model-performance summary.

Parameters:

result (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_cv_section(result, *, metric=None, model=None, name='Cross-Validation', include_tables=False, interactive=False)#

Build fold scores and score dispersion only.

Parameters:

result (Any)
metric (str | None)
model (str | None)
name (str)
include_tables (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_probability_section(result, *, model=None, name='Confusion and Probability', include_tables=False, interactive=False)#

Build confusion, ROC, precision-recall, and calibration diagnostics.

Parameters:

result (Any)
model (str | None)
name (str)
include_tables (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_statistical_section(result, *, metric=None, model=None, name='Statistical Assessment', interactive=False)#

Build finite-sample and temporal statistical assessment.

Parameters:

result (Any)
metric (str | None)
model (str | None)
name (str)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_temporal_section(result, *, metric=None, model=None, name='Temporal Decoding', include_tables=False, interactive=False)#

Build temporal score and generalization diagnostics when applicable.

Parameters:

result (Any)
metric (str | None)
model (str | None)
name (str)
include_tables (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_performance_section(result, *, metric=None, name='Performance', interactive=False)#

Build aggregate score and paired model-comparison figures.

Parameters:

result (Any)
metric (str | None)
name (str)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_features_section(result, *, feature_metadata=None, model=None, top_n=20, name='Features', include_tables=False, interactive=False)#

Build feature importance, stability, and sensor-family summaries.

Parameters:

result (Any)
feature_metadata (pandas.DataFrame | None)
model (str | None)
top_n (int)
name (str)
include_tables (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_topomaps_section(result, *, feature_metadata=None, info=None, coords=None, name='Sensor Maps')#

Build feature-family sensor profiles when spatial metadata exists.

Parameters:

result (Any)
feature_metadata (pandas.DataFrame | None)
info (Any)
coords (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_fit_diagnostics_section(result, *, model=None, name='Fit Diagnostics', include_tables=False, supplementary=True, interactive=False)#

Build timing and warning diagnostics as supplementary content.

Parameters:

result (Any)
model (str | None)
name (str)
include_tables (bool)
supplementary (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_tuning_section(result, *, model=None, name='Hyperparameter Tuning', include_tables=False, supplementary=True, interactive=False)#

Build best-parameter and search-result diagnostics.

Parameters:

result (Any)
model (str | None)
name (str)
include_tables (bool)
supplementary (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_neural_section(result, *, model=None, name='Neural Artifacts', include_tables=False, interactive=False)#

Build neural training artifacts when available.

Parameters:

result (Any)
model (str | None)
name (str)
include_tables (bool)
interactive (bool)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_configuration_section(result, *, name='Configuration')#

Build the stored run configuration.

Parameters:

result (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_provenance_section(result, *, name='Provenance')#

Build environment and provenance metadata.

Parameters:

result (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_caveats_section(result, *, feature_metadata=None, name='Caveats')#

Build explicit caveats for unavailable optional diagnostics.

Parameters:

result (Any)
feature_metadata (pandas.DataFrame | None)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.build_export_inventory_section(result, *, name='Export Inventory')#

Build a compact inventory of available result accessors.

Parameters:

result (Any)
name (str)

Return type:

coco_pipe.report.core.Section

coco_pipe.report.decoding.DECODING_SECTION_BUILDERS: dict[str, collections.abc.Callable[..., coco_pipe.report.core.Section]]#

coco_pipe.report.decoding.VALID_SECTIONS#

coco_pipe.report.decoding.build_decoding_sections(result, *, sections='default', feature_metadata=None, info=None, coords=None, verbose=None, interactive=False, on_error='warn', section_options=None)#

Build ordered decoding sections without creating or mutating a report.

Parameters:

sections (str | collections.abc.Sequence[str]) – A preset name ("compact", "default", "full") or an explicit list of section keys from DECODING_SECTION_BUILDERS.
verbose (bool | None) – Controls the include_tables flag passed to table-bearing sections (cv, probability, temporal, features, fit_diagnostics, tuning, neural). When None it defaults to True only for the "full" preset, so detailed tables appear in full reports and are suppressed in the leaner presets.
section_options (collections.abc.Mapping[str, collections.abc.Mapping[str, Any]] | None) – Per-section keyword overrides, keyed by section name; the inner mapping is forwarded to that section’s builder. For example {"cv": {"metric": "accuracy"}, "features": {"top_n": 30}}. Explicit keys here take precedence over the values derived from feature_metadata, info, coords, verbose, and interactive.
result (Any)
feature_metadata (pandas.DataFrame | None)
info (Any)
coords (Any)
interactive (bool)
on_error (Literal['raise', 'warn', 'placeholder'])

Return type:

list[coco_pipe.report.core.Section]

coco_pipe.report.decoding.make_decoding_result_report(result, *, feature_metadata=None, info=None, coords=None, sections='default', interactive=False, theme='paper', title='Decoding Report', config=None, asset_urls=None, qc_result=None, output_path=None, verbose=None, on_error='warn', section_options=None)#

Build a report for one ~coco_pipe.decoding.result.ExperimentResult.

This is the single-result renderer (one model set on one analysis unit). For a whole classical sweep, see coco_pipe.report.decoding_sweep.make_decoding_report().

With interactive=True, chart-like sections render Plotly figures; topomap and sensor-map sections remain Matplotlib images (no Plotly twin exists).

Parameters:

result (Any)
feature_metadata (pandas.DataFrame | None)
info (Any)
coords (Any)
sections (str | collections.abc.Sequence[str])
interactive (bool)
theme (Literal['paper', 'notebook', 'poster'])
title (str)
config (dict | None)
asset_urls (dict[str, str] | str | None)
qc_result (coco_pipe.io.quality.QCResult | None)
output_path (str | None)
verbose (bool | None)
on_error (Literal['raise', 'warn', 'placeholder'])
section_options (collections.abc.Mapping[str, collections.abc.Mapping[str, Any]] | None)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.render_unit_reports(records, *, modes, feature_metadata=None, asset_urls='inline', title_fn=None, sections='compact', on_error='placeholder')#

Render per-unit decoding reports from successful sweep artifacts.

Parameters:

records (collections.abc.Sequence[collections.abc.Mapping[str, Any]])
modes (collections.abc.Sequence[str])
feature_metadata (pandas.DataFrame | None)
asset_urls (dict[str, str] | str | None)
title_fn (collections.abc.Callable[[collections.abc.Mapping[str, Any]], str] | None)
sections (str | collections.abc.Sequence[str])
on_error (Literal['raise', 'warn', 'placeholder'])

Return type:

None

coco_pipe.report.decoding.add_decoding_overview(self, result, *, name='Overview')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_summary(self, result, name='Decoding Summary')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_statistical_assessment(self, result, metric=None, model=None, name='Statistical Assessment')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
metric (str | None)
model (str | None)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_temporal(self, result, metric=None, model=None, name='Temporal Decoding')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
metric (str | None)
model (str | None)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_performance(self, result, *, metric=None, name='Performance')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
metric (str | None)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_features(self, result, *, feature_metadata=None, name='Features')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
feature_metadata (pandas.DataFrame | None)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_topomaps(self, result, *, feature_metadata=None, info=None, coords=None, name='Sensor Maps')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
feature_metadata (pandas.DataFrame | None)
info (Any)
coords (Any)
name (str)

Return type:

coco_pipe.report.core.Report

coco_pipe.report.decoding.add_decoding_neural_artifacts(self, result, model=None, name='Neural Artifacts')#

Parameters:

self (coco_pipe.report.core.Report)
result (Any)
model (str | None)
name (str)

Return type:

coco_pipe.report.core.Report