feat(pygal): implement learning-curve-basic

[github-actions]

Implementation: learning-curve-basic - pygal

Implements the pygal version of learning-curve-basic.

File: plots/learning-curve-basic/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a learning curve with two main lines on a white background. The blue line represents the "Training Score (±1σ band)" staying consistently high around 0.93-0.95 across all training set sizes. The yellow/gold line represents the "Validation Score (±1σ band)" starting lower at ~0.71 for 100 samples and improving to ~0.87 at 1000 samples. Both curves have dashed confidence bands (±1 standard deviation) shown as thinner dashed lines. The X-axis shows "Training Set Size (samples)" from 100 to 1000. The Y-axis shows "Accuracy Score" from 0.5 to 1.0. The title correctly follows the format "learning-curve-basic · pygal · pyplots.ai". Legend is placed at the bottom with two columns. Dot markers are visible on the main curves. The gap between training and validation curves clearly demonstrates the variance/overfitting pattern typical of learning curves.

Quality Score: 90/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - title, axis labels, and tick labels are all readable; font sizes scaled appropriately for 4800x2700 canvas
• VQ-02: No Overlap (8/8) - no overlapping text elements anywhere
• VQ-03: Element Visibility (7/8) - markers visible, lines clear; dots could be slightly larger for emphasis
• VQ-04: Color Accessibility (5/5) - blue and yellow/gold are distinguishable even for colorblind users
• VQ-05: Layout Balance (4/5) - good proportions but lower half of plot area is unused due to y-range starting at 0.5
• VQ-06: Axis Labels (2/2) - descriptive labels with units: "Training Set Size (samples)" and "Accuracy Score"
• VQ-07: Grid & Legend (0/2) - horizontal grid only, subtle; but the legend appears clipped/truncated at the bottom edge making it difficult to read fully

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - correct XY line chart for learning curve visualization
• SC-02: Data Mapping (5/5) - X shows training set sizes, Y shows accuracy scores
• SC-03: Required Features (5/5) - has training/validation curves, confidence bands (±1σ dashed lines), legend distinguishing curves
• SC-04: Data Range (3/3) - all data visible, appropriate y-range (0.5-1.0)
• SC-05: Legend Accuracy (2/2) - legend labels accurately describe the series with "(±1σ band)" notation
• SC-06: Title Format (2/2) - correct format "learning-curve-basic · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - shows typical learning curve pattern with gap closing as training size increases; could show a bit more variance in early stages
• DQ-02: Realistic Context (7/7) - simulates sklearn learning_curve output with realistic accuracy values for a classification task
• DQ-03: Appropriate Scale (4/5) - accuracy values are realistic (0.7-0.95); the y-axis range (0.5-1.0) leaves unused space

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - flat script: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set correctly
• CQ-03: Clean Imports (2/2) - only numpy, pygal, and Style imported
• CQ-04: No Deprecated API (1/1) - using current pygal API
• CQ-05: Output Correct (0/1) - saves both plot.html and plot.png but the spec only requires plot.png; this is acceptable but redundant

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - uses pygal XY chart, custom Style, stroke_style for line width, dashed lines for confidence bands. Does not leverage interactive tooltips or SVG-specific features more fully.

Strengths

• Clear visualization of learning curve with appropriate training vs validation gap
• Confidence bands shown as dashed lines effectively communicate uncertainty
• Custom style properly scaled for 4800x2700 canvas
• Colorblind-safe color palette (blue and gold)
• Proper KISS code structure with fixed random seed

Weaknesses

• Legend at bottom appears slightly clipped/truncated, reducing readability
• Lower portion of the plot (y < 0.7) is mostly empty white space
• Does not fully leverage pygal's interactive SVG features (tooltips, hover effects)

Verdict: APPROVED