feat(letsplot): implement contour-decision-boundary

[github-actions]

Implementation: contour-decision-boundary - letsplot

Implements the letsplot version of contour-decision-boundary.

File: plots/contour-decision-boundary/implementations/letsplot.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 classifier visualization using the make_moons dataset. The feature space is divided into two regions: a light blue region (predicted class 0) primarily covering the left and upper portions, and a light yellow/gold region (predicted class 1) covering the right and lower portions. The curved decision boundary between regions clearly shows the KNN classifier's ability to separate the two moon-shaped clusters. Training data points are overlaid as circles (correctly classified) and X markers (incorrectly classified). Blue circles/X marks represent true class 0, yellow circles/X marks represent true class 1. Three separate legends on the right side explain: Predicted Class, True Class, and Classification status. The title "contour-decision-boundary · letsplot · pyplots.ai" appears at top. Axis labels show "Feature X1" (x-axis) and "Feature X2" (y-axis).

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, and tick marks are all clearly readable at full size with appropriate font sizes
• VQ-02: No Overlap (8/8) - No overlapping text elements, all labels clearly visible
• VQ-03: Element Visibility (7/8) - Points are well-sized (size=5) with appropriate visibility, though some overlap near the decision boundary
• VQ-04: Color Accessibility (5/5) - Blue and yellow color scheme is colorblind-safe, good contrast between regions and points
• VQ-05: Layout Balance (3/5) - Good overall layout, but three separate legends take up significant space on the right
• VQ-06: Axis Labels (1/2) - Descriptive labels ("Feature X1", "Feature X2") but no units (though not applicable for synthetic features)
• VQ-07: Grid & Legend (2/2) - Clean minimal theme, legends are well-positioned

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct decision boundary visualization with filled regions
• SC-02: Data Mapping (5/5) - X1 and X2 correctly mapped to feature dimensions
• SC-03: Required Features (5/5) - All spec features present: colored regions, overlaid training points, marker styles for correct/incorrect, legend
• SC-04: Data Range (3/3) - All data visible with appropriate padding
• SC-05: Legend Accuracy (2/2) - Legends accurately describe classes and classification status
• SC-06: Title Format (2/2) - Correct format: "contour-decision-boundary · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows both classes, decision boundary, correct and incorrect classifications; make_moons is a standard ML dataset
• DQ-02: Realistic Context (6/7) - Make_moons is a well-known synthetic classification dataset; plausible ML demonstration scenario
• DQ-03: Appropriate Scale (5/5) - Appropriate feature range for normalized synthetic data

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• 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 lets-plot API
• CQ-05: Output Correct (0/1) - Saves as plot.png but includes unnecessary plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot grammar (geom_tile, geom_point, scale_*_manual), theme_minimal; could leverage more lets-plot specific interactive features

Strengths

• Excellent visual representation of the decision boundary using geom_tile for filled regions
• Clear distinction between correctly and incorrectly classified points using shape encoding
• Appropriate use of lets-plot's grammar of graphics with proper layering (background regions + foreground points)
• Good color scheme with Python colors (#306998 blue, #FFD43B yellow) that is colorblind-accessible
• Comprehensive legend system showing all three aspects: predicted class, true class, and classification accuracy

Weaknesses

• Three separate legends consume significant horizontal space; could potentially be consolidated
• Axis labels lack meaningful domain context (just "Feature X1/X2" since using synthetic data)

Verdict: APPROVED