feat(letsplot): implement elbow-curve

[github-actions]

Implementation: elbow-curve - letsplot

Implements the letsplot version of elbow-curve.

File: plots/elbow-curve/implementations/letsplot.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. It shows a line chart with blue circular markers at each data point, connected by a smooth blue line. The x-axis is labeled "Number of Clusters (k)" ranging from 1 to 10, and the y-axis is labeled "Inertia (Within-Cluster Sum of Squares)" ranging from approximately 1,000 to 13,000. A vertical dashed yellow line marks k=4 as the optimal elbow point, with a yellow diamond marker highlighting that specific data point. The title "elbow-curve · letsplot · pyplots.ai" appears at the top in bold. The curve clearly demonstrates the characteristic elbow shape with sharp decrease from k=1 to k=4, then diminishing returns afterward. The background is clean with subtle gray grid lines and a minimal theme.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title is large and bold, axis labels are clearly readable, tick labels are appropriately sized
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere in the plot
• VQ-03: Element Visibility (7/8) - Markers are well-sized for data density, elbow point highlighted with contrasting diamond marker
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide good contrast, colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, good use of space
• VQ-06: Axis Labels (2/2) - Descriptive labels with context ("Within-Cluster Sum of Squares")
• VQ-07: Grid & Legend (0/2) - Grid is appropriate but no legend present; while no legend is strictly needed, the elbow point annotation could benefit from a small legend explaining the yellow marker

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct elbow curve / line chart with markers
• SC-02: Data Mapping (5/5) - X=k values, Y=inertia correctly mapped
• SC-03: Required Features (5/5) - Has markers at each point, connecting line, elbow point highlighted, vertical indicator line
• SC-04: Data Range (3/3) - All k values (1-10) and inertia values fully visible
• SC-05: Legend Accuracy (2/2) - N/A (no legend needed for single series)
• SC-06: Title Format (2/2) - Uses correct format "elbow-curve · letsplot · pyplots.ai"

Data Quality (17/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows elbow pattern well but could include more dramatic diminishing returns after k=4
• DQ-02: Realistic Context (7/7) - Customer segmentation scenario with plausible inertia values
• DQ-03: Appropriate Scale (4/5) - Values are realistic though slightly simplified for demonstration

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy, pandas, and lets_plot imported
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot2 grammar with ggsize() and theme customization, but could leverage more lets-plot specific features like tooltips or annotations

Strengths

• Excellent visual clarity with properly scaled text elements for 4800x2700 resolution
• Clear elbow point highlighting using contrasting yellow diamond marker and vertical dashed line
• Proper implementation of lets-plot grammar of graphics with layered geoms
• Clean KISS code structure following project guidelines
• Correct title format and descriptive axis labels

Weaknesses

• No legend or annotation text explaining what the yellow marker/line represents (the optimal k value)
• Could use lets-plot's interactive features like tooltips showing exact values on hover
• Grid lines could be slightly more subtle (alpha currently at 0.5, could be 0.3)

Verdict: APPROVED