feat(matplotlib): implement elbow-curve

[github-actions]

Implementation: elbow-curve - matplotlib

Implements the matplotlib version of elbow-curve.

File: plots/elbow-curve/implementations/matplotlib.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 on a 16:9 landscape canvas with a white background. The X-axis shows "Number of Clusters (k)" ranging from 1 to 10, and the Y-axis shows "Inertia (Within-Cluster Sum of Squares)" ranging from approximately 500 to 5000. A dark blue line (#306998) connects 10 data points, each marked with yellow (#FFD43B) circular markers with blue borders. The curve shows the characteristic elbow shape with a sharp decline from k=1 to k=4, then a gradual decrease from k=4 onwards. The elbow point at k=4 is highlighted with a larger yellow marker and annotated with "Elbow Point (k=4)" in bold blue text with an arrow pointing to it. A subtle light blue shaded region spans from k=4 to k=10 indicating the "diminishing returns" zone. The title "elbow-curve · matplotlib · pyplots.ai" appears at the top. A subtle dashed grid is visible in the background.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, annotation placed well away from data
• VQ-03: Element Visibility (8/8) - Markers (s=400 for elbow, markersize=12 for line) are appropriately sized for 10 data points
• VQ-04: Color Accessibility (5/5) - Blue/yellow color scheme is colorblind-safe with excellent contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins, good use of space
• VQ-06: Axis Labels (1/2) - Labels are descriptive but Y-axis lacks specific units (though for inertia, unitless is acceptable)
• VQ-07: Grid & Legend (0/2) - Grid is subtle (alpha=0.3), but no legend is shown despite having a label parameter and a labeled axvspan

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct elbow curve line plot with markers
• SC-02: Data Mapping (5/5) - X=k values, Y=inertia correctly mapped
• SC-03: Required Features (5/5) - All spec features present: markers at data points, smooth connecting line, elbow point annotation
• SC-04: Data Range (3/3) - Axes show all data clearly with appropriate range
• SC-05: Legend Accuracy (2/2) - N/A (legend not required for this single-series plot)
• SC-06: Title Format (2/2) - Correct format: "elbow-curve · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows elbow curve shape well with clear elbow point; slight deduction as the curve could show more dramatic variation in the "tail" to better illustrate diminishing returns
• DQ-02: Realistic Context (7/7) - Inertia values (5000 down to ~700) are realistic for K-means clustering scenarios
• DQ-03: Appropriate Scale (4/5) - Values are sensible, though the starting inertia of 5000 is somewhat arbitrary

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)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - All APIs are current
• CQ-05: Output Correct (0/0) - Saves as 'plot.png' ✓

Library Features (3/5 pts)

• LF-01: Uses matplotlib features (3/5) - Good use of annotations with arrowprops, axvspan for shading, marker customization. Could use more distinctive features like spines customization or secondary annotations.

Strengths

• Excellent elbow curve visualization with clear elbow point identification at k=4
• Professional annotation with arrow pointing to the optimal cluster count
• Thoughtful "diminishing returns" shaded region adds educational value
• Color scheme (Python blue #306998 and yellow #FFD43B) is visually appealing and brand-consistent
• Realistic inertia decay pattern that demonstrates the typical K-means behavior
• Clean, readable code following KISS principles

Weaknesses

• Legend is defined but not displayed (ax.legend() call is missing), though the label parameter and axvspan label are set
• Y-axis label could include units or clarify "unitless" nature of inertia

Verdict: APPROVED