feat(pygal): implement elbow-curve

[github-actions]

Implementation: elbow-curve - pygal

Implements the pygal version of elbow-curve.

File: plots/elbow-curve/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays an elbow curve for K-means clustering with a blue line connecting data points from k=1 to k=10. The y-axis shows "Inertia (Within-cluster Sum of Squares)" ranging from 0 to approximately 4400, while the x-axis shows "Number of Clusters (k)" with integer values 1-10. Blue circular markers appear at each k value, connected by a smooth blue line. A larger yellow/gold marker highlights the optimal elbow point at k=4. The title "elbow-curve · pygal · pyplots.ai" appears at the top. A legend at the bottom shows "Inertia" (blue) and "Optimal k" (yellow). The background is clean white with subtle horizontal grid lines. The characteristic elbow shape is clearly visible with a steep decline from k=1 (4200) to k=4 (680), then flattening out.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are all clearly readable at full size. Font sizes are appropriate.
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere in the plot.
• VQ-03: Element Visibility (7/8) - Markers are well-sized and visible. The elbow point marker is distinctively larger. Line connecting points is clear.
• VQ-04: Color Accessibility (5/5) - Blue and yellow/gold colors are colorblind-safe and provide good contrast.
• VQ-05: Layout Balance (4/5) - Good canvas utilization with balanced margins. Plot area fills approximately 60% of canvas.
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with context: "Number of Clusters (k)" and "Inertia (Within-cluster Sum of Squares)".
• VQ-07: Grid & Legend (1/2) - Horizontal grid lines are subtle. Legend at bottom is clear but placed quite far from the plot area.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line chart with markers showing elbow curve.
• SC-02: Data Mapping (5/5) - X-axis correctly shows k values, Y-axis shows inertia.
• SC-03: Required Features (5/5) - Has markers at each data point, smooth connecting line, and highlighted optimal k value as specified.
• SC-04: Data Range (3/3) - Y-axis starts at 0, X-axis shows all k values 1-10, proper range set.
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies "Inertia" and "Optimal k" series.
• SC-06: Title Format (2/2) - Title follows exact format: "elbow-curve · pygal · pyplots.ai".

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Data clearly demonstrates the elbow pattern with steep initial decline and gradual flattening. The elbow point at k=4 is evident.
• DQ-02: Realistic Context (6/7) - Customer segmentation scenario is plausible. Inertia values follow realistic K-means behavior.
• DQ-03: Appropriate Scale (5/5) - Inertia values (365-4200) are realistic for clustering analysis.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean structure: imports → data → style → chart → save. No functions or classes.
• CQ-02: Reproducibility (2/3) - Data is deterministic (hardcoded values), but no random seed comment needed since no randomness used.
• CQ-03: Clean Imports (2/2) - Only pygal and Style are imported, both are used.
• CQ-04: No Deprecated API (1/1) - Uses current pygal API.
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html correctly.

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal XY chart, custom Style, legend positioning, dots_size customization. Could have used more pygal-specific features like tooltips or value formatting.

Strengths

• Clear visualization of the elbow curve with excellent readability
• Optimal k value (elbow point) is highlighted with a distinct larger yellow marker
• Proper title format and descriptive axis labels with full context
• Clean KISS code structure following pygal best practices
• Good color contrast between the main line and elbow highlight
• Data realistically demonstrates the diminishing returns pattern

Weaknesses

• Legend positioned quite far from the plot area at the bottom
• Could use pygal's built-in annotation or tooltip features to enhance the elbow point indication

Verdict: APPROVED