feat(plotly): implement contour-decision-boundary

[github-actions]

Implementation: contour-decision-boundary - python/plotly

Implements the python/plotly version of contour-decision-boundary.

File: plots/contour-decision-boundary/implementations/python/plotly.py

Parent Issue: #2921

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays a decision boundary visualization on a warm off-white background (#FAF8F1). A filled contour region shows class probability from green (#009E73, probability 0) to orange (#D55E00, probability 1), with opacity 0.4 for transparency. A dashed yellow line marks the crisp decision boundary at probability 0.5. Training data points are overlaid: green circles for Class 0 and orange diamonds for Class 1, with white edges for definition. The colorbar on the right shows the "Class Probability" scale. Axis titles are "Feature 1 (Standardized)" and "Feature 2 (Standardized)", with the main title at the top. The legend is positioned in the bottom-right corner with a light background. All text (title, axis labels, tick labels) is rendered in dark ink (#1A1A17 and #4A4A44) and is clearly readable against the light background. Grid lines are subtle and do not compete with data. Overall layout has generous margins and balanced whitespace. Legibility: PASS - All text is readable.

Dark render (plot-dark.png): The same visualization renders on a warm near-black background (#1A1A17). The contour colors are identical to the light render (green to orange gradient), proving theme-independent data colors. The decision boundary dashed line is clearly visible as yellow. Training points retain their colors and are fully distinguishable. All text elements (title, axis labels, tick labels) are rendered in light ink (#F0EFE8 and #B8B7B0) and are clearly readable against the dark background. The legend background uses the elevated dark color (#242420) with a visible border. The colorbar displays properly. Grid lines are visible but subtle. Legibility: PASS - All text is readable, no dark-on-dark failures. Data colors match the light render exactly, confirming proper theme adaptation.

Score: 95/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	17	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	95	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - Font sizes correct (28px title, 22px axis labels, 18px ticks); all text readable in both themes
• VQ-02: No Overlap (6/6) - No overlapping text; legend and colorbar properly positioned without collision
• VQ-03: Element Visibility (6/6) - Contour regions, training points, and decision boundary all clearly visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette (green/orange) is colorblind-safe; high contrast
• VQ-05: Layout & Canvas (4/4) - Good proportions; 1600×900 with 3× scale; margins (80,100,100,80) provide generosity
• VQ-06: Axis Labels & Title (2/2) - Title format correct; axis labels include units "(Standardized)"
• VQ-07: Palette Compliance (2/2) - First series #009E73 (green), second #D55E00 (orange, Okabe-Ito order); colorscale endpoints use these colors; plot backgrounds #FAF8F1/#1A1A17; both renders theme-correct

Design Excellence (17/20)

• DE-01: Aesthetic Sophistication (7/8) - Custom colorscale with Okabe-Ito endpoints; thoughtful opacity (0.4); well-composed layers (contours + boundary + points); professional execution, though could explore more visual distinctiveness
• DE-02: Visual Refinement (5/6) - Clean, minimal design; subtle grid; proper legend background and border; colorbar well-styled; could benefit from enhanced visual polish
• DE-03: Data Storytelling (5/6) - Clear narrative: contour regions show class partition, dashed line shows decision threshold, training points show ground truth; visual hierarchy is evident, could strengthen with additional emphasis

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct decision boundary contour visualization
• SC-02: Required Features (4/4) - All spec requirements met: decision boundary visualization, training data overlay with distinct colors, legend with class labels
• SC-03: Data Mapping (3/3) - X1/X2 correctly mapped to axes; y shown via marker color and shape; all data visible
• SC-04: Title & Legend (3/3) - Title "contour-decision-boundary · plotly · anyplot.ai"; legend labels match data classes

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Comprehensive: probability gradient, decision boundary line, training points, decision threshold at 0.5
• DQ-02: Realistic Context (5/5) - make_moons is realistic ML scenario; noise=0.25 shows real-world classification challenge
• DQ-03: Appropriate Scale (4/4) - 200 samples, 150×150 mesh grid, standardized features—appropriate for visualization

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple procedural code; no unnecessary functions or classes
• CQ-02: Reproducibility (2/2) - Uses np.random.seed(42); KNeighborsClassifier(n_neighbors=15) is deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports: os, numpy, plotly, sklearn components
• CQ-04: Code Elegance (2/2) - No fake UI; appropriate complexity; clear intent
• CQ-05: Output & API (1/1) - Correct output: plot-{THEME}.png and plot-{THEME}.html

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Proper use of go.Figure(), add_trace(), Contour traces; theme adaptation via environment variable follows plotly conventions
• LM-02: Distinctive Features (4/5) - Good use of double Contour trace (filled + boundary line), custom colorscale, hover templates, interactive outputs; solid distinctive features, could leverage more plotly-specific capabilities

Score Caps Applied

• None applied

Strengths

• Perfect theme adaptation with proper color tokens for both light and dark renders
• Clean, reproducible code with deterministic random seed
• Excellent visual quality in both themes with all text clearly readable
• Proper implementation of decision boundary visualization using Contour traces
• Okabe-Ito palette correctly applied throughout
• Well-designed layout with appropriate margins and whitespace
• Comprehensive specification compliance

Weaknesses

• Design Excellence could be elevated with additional visual refinement or distinctive styling choices
• Could explore more advanced plotly features for enhanced library mastery

Issues Found

None requiring rejection.

AI Feedback for Next Attempt

This is an excellent implementation that fully meets requirements. No repair needed. If pursuing higher design excellence in future iterations, consider: distinctive typography choices, enhanced visual hierarchy through size/opacity variation, or additional interactive elements unique to plotly (annotations, custom buttons).

Verdict: APPROVED