calibrated-explanations-visualization-plotly 0.3.4

Plotly visualization plugin layouts for calibrated-explanations

calibrated-explanations-visualization-plotly

Family: visualization

Status: mature

Promoted from experimental on 2026-07-21: `calibrated-explanations

=1.0.0rc2(the CE release this package's dispatch model requires) is published on PyPI, and the full no-bridge dispatch proof and compatibility matrix pass against that real release. This package's own PyPI release is the next step after this promotion merges (seedocs/maintainer-release.mdfor the tag-and-publish sequence) — the install command below is the documented target; until the release tag is pushed and published, use the repository-checkout fallback noted in the same section. SeeMATURITY.md` for the full, dated audit trail.

Interactive Plotly visualization layouts for calibrated-explanations (CE).

Purpose

CE's built-in plots are static matplotlib figures. This plugin adds interactive Plotly equivalents and extensions: hover inspection of every rule/contribution, standalone HTML export, searchable feature controls, batch/global instance overviews, and an optional dashboard workspace. It is intended for practitioners who explore CE explanations in notebooks or share self-contained interactive HTML reports.

The plugin preserves CE semantics: calibrated values, intervals, signs, and explanation roles are taken from the CE explanation payload and are never rescaled or re-derived. Where the CE default renderer and Plotly differ, the difference is visual (hover cards, HTML output), not semantic.

Installation

pip install calibrated-explanations-visualization-plotly

with the [live] extra for the optional Dash dashboard:

pip install "calibrated-explanations-visualization-plotly[live]"

Pending this package's own release tag (see docs/maintainer-release.md for the tag-and-publish sequence that follows this promotion), the PyPI distribution does not exist yet; until it does, install from a checkout of this repository instead:

pip install packages/visualization/calibrated-explanations-visualization-plotly

Either way, calibrated-explanations>=1.0.0rc2,<2 (this package's dependency floor) is published on PyPI, so the dependency itself resolves cleanly in a normal environment — verified in a fresh venv (pip check clean, full test suite passing from the installed wheel; see MATURITY.md).

Plotly and NumPy are mandatory dependencies and are installed automatically; Dash is needed only for the optional live dashboard ([live] extra).

Importing the package has no side effects: it does not register plot styles and does not touch CE. CE's entry-point discovery (plugins.registry.load_entrypoint_plugins(), e.g. via the CE plugins CLI) finds and validates the bootstrap, but on CE 1.0.x discovery does not invoke it. To use the styles, call the explicit registration function once:

import ce_visualization_plotly as cevp

cevp.register_plotly_visualization_components()

Registration only adds builders/renderers/styles to CE's public registry. CE >=1.0.0rc2 dispatches explicit third-party styles (e.g. style="plotly.local.factual_bars") natively, with the complete option set forwarded verbatim through context.options — this package never wraps, monkey-patches, or otherwise touches FactualExplanation.plot, AlternativeExplanation.plot, CalibratedExplainer.plot, WrapCalibratedExplainer.plot, or plotting.plot_global.

Quick start

from sklearn.ensemble import RandomForestClassifier
from calibrated_explanations import WrapCalibratedExplainer

explainer = WrapCalibratedExplainer(RandomForestClassifier())
explainer.fit(X_train, y_train)
explainer.calibrate(X_cal, y_cal)

import ce_visualization_plotly as cevp

cevp.register_plotly_visualization_components()  # explicit, idempotent

factual = explainer.explain_factual(X_test)
factual[0].plot(style="plotly.local.factual_bars", show=True)

alternatives = explainer.explore_alternatives(X_test)
alternatives[0].plot(style="plotly.local.alternative_bars", show=True)

explainer.plot(X_test, style="plotly.global.instance_explorer", show=True)

Available styles

Canonical style id	Input	Meaning
plotly.local.factual_bars	one factual explanation	Signed local feature/rule contributions around zero, with a calibrated prediction header.
plotly.local.factual_simple	one factual explanation	Compact hub-style weight bars in payload order (conjunctions included): sign-coloured bars, optional interval error bars, no prediction header or ranking.
plotly.local.alternative_bars	one alternative explanation	Independent alternative scenarios as prediction deltas (not additive components).
plotly.local.ensured	alternative explanation collection	CE's ensured plot: prediction vs. uncertainty with alternative rule points and movement arrows.
plotly.local.alternative_feature_summary	one alternative explanation	Which features appear in emitted alternatives, per role and quality flags (not global importance).
plotly.local.uncertainty_quadrant	one factual explanation	Absolute local impact vs. calibrated uncertainty width, bucketed into quadrants.
plotly.global.instance_explorer	batch of instances	Hover-only prediction/uncertainty overview of many instances (not a global explanation method).
plotly.dashboard.instance_workspace	batch of instances	Standalone-HTML (or live Dash) workspace combining the instance explorer with per-instance local cards.

plotly.local.ensured_triangular is retained as a deprecated alias for plotly.local.ensured; it resolves to the same builder and renderer with no semantic change. New code should use the canonical id.

Support matrix

Style	Binary clf	Multiclass clf	Thresholded/probabilistic regression	Conformal/percentile regression	Factual	Alternative	Batch input
factual_bars	✅	⚠️ one-vs-rest header only	✅	✅	✅	❌ error	per instance
factual_simple	✅	⚠️	✅	✅	✅	❌ error	per instance
alternative_bars	✅	⚠️ one-vs-rest	✅	✅	❌ error	✅	per instance
ensured	✅	⚠️ one-vs-rest	✅	✅	❌	✅	collection-level
alternative_feature_summary	✅	⚠️	✅	✅	❌	✅	per instance
uncertainty_quadrant	✅	⚠️ one-vs-rest weights	✅	✅	✅	❌ error	per instance
instance_explorer	✅	✅	✅	✅	n/a	n/a	✅
instance_workspace	✅	⚠️	✅	✅	via cards	via cards	✅

• ✅ supported and tested. ⚠️ supported with limitations: multiclass renders as the predicted class versus its complement (one-vs-rest); there is no per-class panel. The executable definition of this limitation lives in tests/test_multiclass.py (real 3-class CE workflow: every ⚠️ style must build and render the one-vs-rest view; factual headers caption the predicted class as P(y=<label>)/P(y!=<label>)).
• ❌ unsupported inputs raise a clear ValueError early (no silent fallback to another style).
• Uncertainty display for one-sided explanations raises Warning, matching CE core behaviour.
• uncertainty_quadrant has a fixed contract independent of task: x is the absolute calibrated feature weight (|weight|), y is the calibrated weight interval width (weight_high − weight_low). Rules without a weight and a two-sided weight interval are omitted with a visible UserWarning; if no rule qualifies (e.g. one-sided explanations) the builder raises ValueError. Quadrant labels bucket rules against thresholds (median/quantile_75/explicit) whose values and provenance are recorded in the artifact — they are presentation thresholds, not statistical claims.

Configuration

Common options accepted by the local bar styles (filter_top, sort_by, show_uncertainty, hover_uncertainty, show_prediction_header, hover_detail) and style-specific options are validated on entry; invalid values raise ValueError naming the allowed values. Highlights:

• plotly.local.factual_bars — filter_top, sort_by (abs|value|interval_width|label|original; default: CE core ranking via rnk_metric/rnk_weight), show_uncertainty (default False), show_prediction_header (default True), hover_detail (compact|full), show_y_labels, show_rule_labels. Only orientation="horizontal" is supported.
• plotly.local.factual_simple — show_uncertainty (default False; also accepts CE's uncertainty=True alias). Rules (including conjunctions) render in payload order with axis labels truncated at 32 characters; the full rule text is kept in the artifact and shown in hover. There is no prediction header, ranking, or filtering — the style intentionally mirrors the explainable-ai-hub factual figure.
• plotly.local.alternative_bars — filter_top, sort_by (original|prediction_delta|interval_width|role|feature), show_uncertainty (accepted but ignored with a visible UserWarning — CE core has no such toggle for alternative plots and calibrated intervals are always drawn), hover_uncertainty, show_prediction_header, hover_detail, include_conjunctive_components (default True), unknown_policy (show|hide).
• plotly.local.ensured — filter_top/max_points, sort_by, show_arrows, show_original, show_triangle_reference, hover_detail, include_missing_rule_points, feature_checklist, side_panel, infer_roles (default False; opt-in role heuristics, marked role_source="heuristic"), pareto_cost.
• plotly.local.uncertainty_quadrant — threshold_strategy (median|quantile_75|explicit, default median), impact_threshold, uncertainty_threshold, sort_by (default absolute_impact), filter_top.
• plotly.local.alternative_feature_summary — filter_top_features, include_conjunctions (default False), normalize (count|share), infer_roles (default False), unknown_policy, sort_by, hover_detail, role_mapping. Only horizontal orientation is supported.
• plotly.global.instance_explorer — aggregate_positions (default True), position_precision (default 3), aggregation_strategy (round|bin), marker_size_min/marker_size_max, task (classification|probabilistic_regression|conformal_regression|auto), class_id, threshold, low_high_percentiles, include_instance_records, show_triangle_reference.
• plotly.dashboard.instance_workspace — dashboard_mode (standalone_html), precompute (selected), card selection via available_cards, plus global_options, factual_options, alternative_options forwarded to the underlying builders. Live mode is started with ce_visualization_plotly.dashboard.launch_instance_workspace(...) and requires the [live] extra.

All styles accept show (default True) and filename/path for HTML export. Saving with filename= coerces the suffix to .html and disables auto-show unless show is passed explicitly.

Interpretation

• Factual bars are signed local contributions around a zero line; positive and negative contributions are coloured distinctly (classification: red/blue; regression: blue/red, matching CE defaults). The prediction header (probability bars or regression interval) uses an independent x-axis from the contribution bars — do not compare bar widths across the two panels. The complement probability bar spans [1 − high, 1 − low].
• Alternative bars are independent candidate scenarios: each bar is "if this condition held, the prediction would move to X". They must not be summed or stacked. Conjunctive rules are expanded into indented component sub-bars that all share the same prediction delta because CE provides no per-feature decomposition for conjunctions.
• Ensured plot: x = probability (probabilistic) or prediction value (regression); y = uncertainty; red marker = original prediction; blue markers = alternative rule points; arrows show predictive movement.
• Uncertainty intervals are calibrated CE intervals; interval width is high − low and is never inverted or rescaled. Regression predictions and intervals are shown on the data scale and never presented as probabilities.
• Explanation roles (counter, super, semi) come from CE metadata. When metadata is unavailable the plots record role="unknown" with role_source="unavailable"; heuristic inference is opt-in (infer_roles=True) and always marked role_source="heuristic".
• Arrows and prediction movements are predictive statements only. They never indicate causal actionability.

Compatibility

Dependency	Declared range	Tested versions
Python	>=3.11	3.11.9, 3.14.4
calibrated-explanations	>=1.0.0rc2,<2	1.0.0rc2 (installed from real PyPI, pip check clean)
numpy	>=1.24 (CE's own floor; direct runtime import)	2.4.6, 2.5.1
plotly	>=5.18	5.18.0, 6.7.0, 6.9.0
dash (optional, [live])	>=3.1	3.1.0, 4.4.0 (base install verified without dash)

The dash floor is >=3.1 because dash 2.x/3.0 pin Flask/Werkzeug versions with known published vulnerabilities; dash 3.1 is the first release whose dependency range admits the patched Flask 3.1.3+/Werkzeug 3.1.4+.

CE >=1.0.0rc2 is required, and is not optional or backward-compatible to rc1. CE 1.0.0rc1's public plotting API drops or rewrites several options (filter_top, uncertainty, rnk_metric, rnk_weight, style itself on the alternative path) before this package's plugin is ever invoked, and provides no context.runtime for the dashboard. There is no compatibility shim for rc1 in this package (an earlier, since-removed version had one); installing against rc1 will silently drop options rather than error, so the dependency floor is enforced rather than merely recommended.

calibrated-explanations==1.0.0rc2 is now published on PyPI (verified 2026-07-20); this package's own dependency floor resolves and installs cleanly from a normal pip install. See MATURITY.md for the full reproducible evidence, including the earlier-that-day audit trail from before the release existed.

Assumptions and limitations

• Role metadata is metadata-dependent; without it, roles are reported as unknown rather than guessed (unless infer_roles=True, which marks its output as heuristic).
• Interactive output requires a browser or a notebook front-end able to render Plotly HTML. Very large batches degrade interactive performance; the instance explorer aggregates positions by default to compensate.
• plotly.global.instance_explorer is hover-only in this release: click panels and embedded local drill-down are not implemented.
• Image (PNG/SVG) export is not part of the supported scope; output is figure objects and standalone HTML. Use Plotly's own export tooling (e.g. kaleido) at your own discretion.
• Only horizontal bar orientations are supported for the bar styles.
• Registration (register_plotly_visualization_components()) only adds builders, renderers, and styles to CE's public registry. It never replaces, wraps, or otherwise touches any CE plotting callable — FactualExplanation.plot, AlternativeExplanation.plot, CalibratedExplainer.plot, WrapCalibratedExplainer.plot, and plotting.plot_global all keep their original identity before and after registration, and after rendering every style (see tests/test_no_bridge_proof.py). CE >=1.0.0rc2 dispatches explicit third-party styles through its own public registry-resolution machinery with the complete option set, so no adapter or compatibility layer is needed on this package's side.
• CE's collection-level .plot(...) (e.g. explanations.plot(...)) warns visibly (UserWarning) on an unrecognised keyword argument. A single indexed explanation's .plot(...) (e.g. explanations[0].plot(...)) currently does not — an unrecognised kwarg is silently dropped. This is a CE-core behaviour, not something this plugin can change; see MATURITY.md "Known limitations" for the executable proof.

Failure modes

• Unsupported explanation kind (e.g. alternative explanation passed to factual_bars): immediate ValueError.
• Missing rule/contribution payload: ValueError ("does not expose factual rule contributions" / "No … available for plotting").
• Invalid option values: ValueError naming the accepted values.
• Plotly missing (broken installation): RuntimeError with an actionable install message from every renderer.
• Dash missing when launching the live dashboard: RuntimeError instructing to install the [live] extra.
• One-sided explanations with show_uncertainty=True: Warning (CE-core-compatible behaviour).
• Ranking is CE's own code: both bar styles rank via CE's public rank_features/calculate_metrics; there is no plugin-side replica, and ranking failures propagate as errors rather than silently reordering.
• Rules without two-sided weight intervals in uncertainty_quadrant: omitted with a visible UserWarning; ValueError if nothing remains.
• Rule labels, feature names, and hover text are rendered as Plotly text (not raw HTML). Standalone HTML shells escape user-controlled labels server-side, and client-side scripts assign data values to the DOM via textContent only (never innerHTML concatenation), so hostile labels, class names, or targets render as text. This is a reviewed threat boundary for package-generated HTML — not a security certification, and it does not extend to third-party plugins composed into the same page.

Support

• Issues: https://github.com/kristinebergs/calibrated_explanations/issues (public intake for the CE plugin ecosystem).
• Maintainer: Tuwe Löfström ([e-mail]).
• Security reports: see SECURITY.md at the repository root.

Examples

The package-local examples/ directory contains notebooks for factual bars, alternative bars, the alternative feature summary, the ensured plot, the uncertainty quadrant, the global instance explorer, and both dashboard modes. Notebooks are supplementary documentation; the automated test suite is the authoritative behaviour reference.