feat(pygal): implement contour-decision-boundary

[github-actions]

Implementation: contour-decision-boundary - pygal

Implements the pygal version of contour-decision-boundary.

File: plots/contour-decision-boundary/implementations/pygal.py

Parent Issue: #2921

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a decision boundary visualization with two moon-shaped class regions. The background is divided into two color zones: light blue (Class 0) and light yellow (Class 1), with a curved boundary between them following the characteristic moon pattern from sklearn's make_moons dataset. Training points are overlaid as solid circles - blue circles for Class 0 and yellow circles for Class 1. Misclassified points are marked with red X symbols on top of their class-colored circles. The title "contour-decision-boundary · pygal · pyplots.ai" appears at the top. Axis labels show "Feature 1" (x-axis) and "Feature 2" (y-axis) with clear tick marks ranging from approximately -1.9 to 2.9 on x-axis and -1.4 to 2.0 on y-axis. A legend in the top-right corner shows Class 0, Class 1, and Misclassified markers.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, tick labels, and legend are all clearly readable at the target resolution. Slightly smaller legend text.
• VQ-02: No Overlap (8/8) - No overlapping text elements, all labels fully readable
• VQ-03: Element Visibility (7/8) - Markers are well-sized for the data density (~150 points), misclassified markers clearly distinguishable
• VQ-04: Color Accessibility (5/5) - Blue/yellow palette is colorblind-safe, good contrast between classes
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins, legend placed appropriately
• VQ-06: Axis Labels (1/2) - "Feature 1" and "Feature 2" are descriptive but lack units (acceptable for synthetic ML data)
• VQ-07: Grid & Legend (1/2) - No grid (acceptable for this plot type), legend well placed but could be cleaner

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correct decision boundary visualization with colored regions and overlaid points
• SC-02: Data Mapping (5/5) - X1/X2 correctly mapped to axes, class labels correctly colored
• SC-03: Required Features (5/5) - Decision regions, training points, misclassified point markers, legend all present
• SC-04: Data Range (3/3) - Axes show all data with appropriate padding
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies Class 0, Class 1, and Misclassified
• SC-06: Title Format (1/2) - Uses correct format "contour-decision-boundary · pygal · pyplots.ai" but title font could be slightly larger

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows both classes, decision boundary, and misclassified points well. Good representation of the moon-shaped classification problem.
• DQ-02: Realistic Context (7/7) - Uses sklearn's make_moons, a standard ML teaching dataset, with SVM classifier - excellent educational context
• DQ-03: Appropriate Scale (4/5) - Appropriate feature ranges for synthetic normalized data

Code Quality (8/10 pts)

• CQ-01: KISS Structure (2/3) - Mostly linear structure with helper function for coordinate mapping (acceptable)
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) and random_state=42
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current APIs
• CQ-05: Output Correct (0/1) - Outputs plot.png and plot.svg, but also plot.html

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Creative use of pygal's XY chart as a base with custom SVG injection to create decision boundary regions. This is an excellent workaround for pygal's limited contour capabilities.

Strengths

• Excellent creative solution using pygal's XY chart with custom SVG injection for decision regions
• Clear visualization of the moon-shaped decision boundary with proper color-coded regions
• Good implementation of misclassified point markers with red X overlay
• Well-designed legend showing all three marker types
• Colorblind-safe blue/yellow color scheme
• Proper use of sklearn's make_moons dataset for realistic ML context

Weaknesses

• Helper function data_to_svg() breaks KISS principle slightly, though justified for coordinate mapping
• Axis labels lack units (acceptable for synthetic ML data, but "Feature 1 (normalized)" would be better)

Verdict: APPROVED