feat(plotnine): implement elbow-curve

[github-actions]

Implementation: elbow-curve - plotnine

Implements the plotnine version of elbow-curve.

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

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays an elbow curve for K-means clustering on a clean white background. A blue line (#306998) connects 10 data points from k=1 to k=10, with circular markers at each discrete k value. The curve starts at approximately 1000 inertia for k=1 and decreases sharply until k=4, then flattens out to around 60-70 for k=10. A yellow/gold dashed vertical line marks the optimal k=4 position, with a yellow "Optimal k = 4" annotation placed to the right of this line. The title "elbow-curve · plotnine · pyplots.ai" appears at the top in bold black text. The x-axis is labeled "Number of Clusters (k)" and the y-axis is labeled "Inertia (Within-Cluster Sum of Squares)". A subtle gray grid is visible in the background.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title is large and bold (24pt), axis titles are clear (20pt), tick labels readable (16pt)
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (8/8) - Line width (size=2) and point size (size=5) are well-suited for 10 data points
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide excellent contrast, colorblind-safe
• VQ-05: Layout Balance (5/5) - Good use of canvas, balanced margins, plot fills appropriate space
• VQ-06: Axis Labels (1/2) - Y-axis label is descriptive but lacks units (inertia is unitless, but could note "arbitrary units")
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), but no legend needed; minor: grid could be slightly more visible

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct elbow curve visualization
• SC-02: Data Mapping (5/5) - X=k values, Y=inertia correctly mapped
• SC-03: Required Features (5/5) - Has markers, connecting line, and annotated optimal k as spec suggests
• SC-04: Data Range (3/3) - All 10 k values visible, y-axis shows full range
• SC-05: Legend Accuracy (2/2) - N/A (no legend needed for single series)
• SC-06: Title Format (2/2) - Correctly uses "elbow-curve · plotnine · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows clear elbow shape with sharp decrease then plateau
• DQ-02: Realistic Context (5/7) - Simulated K-means inertia values are plausible but generic
• DQ-03: Appropriate Scale (5/5) - Inertia values (1000 down to ~60) are realistic for clustering

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) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current plotnine API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Good use of plotnine grammar: ggplot + aes + geom_line + geom_point + geom_vline + annotate + theme_minimal + theme customization. Could have used scale_x_continuous for integer breaks.

Strengths

• Clear visual hierarchy with excellent text sizing at all levels (title, axis labels, ticks)
• Effective use of plotnine grammar of graphics: layered geoms, annotations, and theme customization
• The yellow dashed vertical line with annotation clearly highlights the optimal k value
• Clean, professional appearance using theme_minimal with subtle grid
• Good color contrast between the blue data line and yellow annotation

Weaknesses

• X-axis shows decimal values (2.5, 5.0, 7.5, 10.0) instead of integers (1, 2, 3, ..., 10) which would be more appropriate for discrete k values
• The realistic context is somewhat generic - could use a more specific domain scenario in comments

Verdict: APPROVED