feat(letsplot): implement chernoff-basic

[github-actions]

Implementation: chernoff-basic - letsplot

Implements the letsplot version of chernoff-basic.

File: plots/chernoff-basic/implementations/letsplot.py

Parent Issue: #3003

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays 12 Chernoff faces arranged in a 3×4 grid layout. Each row represents a different iris species: Setosa (top row, blue faces), Versicolor (middle row, yellow faces), and Virginica (bottom row, red/pink faces). Each face has distinct facial features including eyes with pupils, eyebrows, a nose, and a mouth. The faces show variation in face width, eye size, mouth curvature (happy/sad), and eyebrow slant across different samples. Setosa faces appear "happier" with upward-curved mouths, while Virginica faces appear "sadder" with downward-curved mouths. Each face is labeled with its species name below. A legend on the right shows the three species colors. The title reads "Iris Species Comparison · chernoff-basic · lets-plot · pyplots.ai".

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title is large and readable, species labels are bold and clear
• VQ-02: No Overlap (8/8) - No overlapping elements, faces well-spaced in grid
• VQ-03: Element Visibility (7/8) - Faces are well-sized and visible, facial features clear
• VQ-04: Color Accessibility (4/5) - Blue, yellow, red palette distinguishable but not ideal for colorblind users
• VQ-05: Layout Balance (5/5) - Excellent grid layout utilizing canvas well
• VQ-06: Axis Labels (N/A → 2/2) - Not applicable for Chernoff faces (no axes needed)
• VQ-07: Grid & Legend (0/2) - Legend is small and positioned far right, somewhat isolated

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct Chernoff faces visualization
• SC-02: Data Mapping (5/5) - Variables correctly mapped to facial features (face width, eye size, mouth curvature, eyebrow slant)
• SC-03: Required Features (4/5) - Has grid layout, color by species, labels; missing nose length variation
• SC-04: Data Range (3/3) - All data properly normalized and displayed
• SC-05: Legend Accuracy (2/2) - Legend correctly shows species colors
• SC-06: Title Format (1/2) - Uses "lets-plot" instead of "letsplot" in title

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variation across species and within species, demonstrates face differences effectively
• DQ-02: Realistic Context (7/7) - Uses real Iris dataset, a classic multivariate dataset
• DQ-03: Appropriate Scale (5/5) - Normalized values (0-1) as recommended in spec

Code Quality (8/10 pts)

• CQ-01: KISS Structure (0/3) - Uses functions (create_face), violates KISS principle
• CQ-02: Reproducibility (3/3) - np.random.seed(42) is set
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Current lets-plot API
• CQ-05: Output Correct (2/1) - Saves as plot.png and plot.html (bonus for HTML)

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Effectively uses ggplot grammar with geom_polygon, geom_path, geom_text, scale_fill_manual, comprehensive theme customization

Strengths

• Excellent implementation of Chernoff faces using lets-plot's grammar of graphics approach
• Clear visual distinction between species through face colors and facial feature variations
• Well-organized 3×4 grid layout with proper spacing
• Good use of geom_polygon for face outlines and eyes, geom_path for mouth and eyebrows
• Real Iris dataset provides meaningful multivariate data demonstration
• Proper normalization of data to 0-1 range as specified
• Facial features effectively encode data: face width (sepal length), eye size (sepal width), mouth curvature (petal length), eyebrow slant (petal width)

Weaknesses

• Code uses a helper function (create_face) which violates the KISS structure requirement
• Title uses "lets-plot" with hyphen instead of "letsplot" (single word)
• Legend is somewhat small and isolated on the right side
• Nose does not vary based on any data variable (static for all faces)

Verdict: APPROVED