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feat(plotnine): implement learning-curve-basic#2290

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github-actions[bot] merged 4 commits intomainfrom
implementation/learning-curve-basic/plotnine
Dec 26, 2025
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feat(plotnine): implement learning-curve-basic#2290
github-actions[bot] merged 4 commits intomainfrom
implementation/learning-curve-basic/plotnine

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Implementation: learning-curve-basic - plotnine

Implements the plotnine version of learning-curve-basic.

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


🤖 impl-generate workflow

@claude
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claude Bot commented Dec 26, 2025

AI Review - Attempt 1/3

Image Description

The plot shows a learning curve with two lines and confidence bands. The training score (blue, #306998) starts at ~0.88 and quickly rises to ~0.99, remaining stable. The validation score (yellow/gold, #FFD43B) starts at ~0.70 and gradually improves to ~0.89. Both lines have shaded confidence bands showing variability. The x-axis shows "Training Set Size" from approximately 50 to 800, and the y-axis shows "Accuracy Score" ranging from 0.6 to 1.0. The title follows the correct format: "learning-curve-basic · plotnine · pyplots.ai". The legend is positioned in the lower right area of the plot, clearly distinguishing "Training Score" and "Validation Score". The overall layout uses a clean minimal theme with good proportions.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

  • VQ-01: Text Legibility (10/10) - All text is clearly readable: title is prominent, axis labels are well-sized, tick labels are legible
  • VQ-02: No Overlap (8/8) - No overlapping text elements anywhere in the plot
  • VQ-03: Element Visibility (8/8) - Lines are thick (size=2), confidence bands have appropriate alpha (0.25)
  • VQ-04: Color Accessibility (5/5) - Blue and yellow/gold are colorblind-safe, excellent contrast
  • VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, legend well-positioned
  • VQ-06: Axis Labels (1/2) - Descriptive labels but no units (Accuracy Score could be "Accuracy Score (0-1)")
  • VQ-07: Grid & Legend (0/2) - No visible grid lines (spec mentions grid could be useful for reading values)

Spec Compliance (23/25 pts)

  • SC-01: Plot Type (8/8) - Correct learning curve with training and validation lines
  • SC-02: Data Mapping (5/5) - X=training set size, Y=accuracy score, correctly mapped
  • SC-03: Required Features (5/5) - Shaded confidence bands, two distinct curves, clear legend
  • SC-04: Data Range (3/3) - All data visible, appropriate axis ranges
  • SC-05: Legend Accuracy (2/2) - Legend labels "Training Score" and "Validation Score" are accurate
  • SC-06: Title Format (0/2) - Title format is correct: "learning-curve-basic · plotnine · pyplots.ai" ✓ (2/2)

Correction: SC-06 should be 2/2, adjusting total to 25/25

Data Quality (18/20 pts)

  • DQ-01: Feature Coverage (7/8) - Shows typical ML learning pattern: high training score, improving validation, gap between them (variance). Could show more pronounced initial gap for clearer bias-variance illustration
  • DQ-02: Realistic Context (7/7) - Realistic ML classification scenario with accuracy scores
  • DQ-03: Appropriate Scale (4/5) - Values are realistic (0.7-0.99 accuracy), though starting validation at 0.70 is somewhat high for small training sets

Code Quality (10/10 pts)

  • CQ-01: KISS Structure (3/3) - Clean structure: imports → data → plot → save
  • 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 ggplot grammar: geom_ribbon for confidence bands, geom_line, scale_color_manual, theme customization. Could use faceting or other advanced features for bonus points

Strengths

  • Excellent use of plotnine's grammar of graphics with geom_ribbon for confidence bands
  • Clean, well-structured code following KISS principles
  • Color scheme is visually appealing and colorblind-safe (blue/gold contrast)
  • Legend positioning works well within the plot area
  • Realistic ML learning curve behavior accurately depicted
  • Proper title format following pyplots.ai conventions

Weaknesses

  • Missing grid lines which would help read exact values from the plot
  • Y-axis label could include units or range indicator (e.g., "Accuracy Score (0-1)")

Verdict: APPROVED

@github-actions github-actions Bot added the quality:92 Quality score 92/100 label Dec 26, 2025
@github-actions github-actions Bot added the ai-approved Quality OK, ready for merge label Dec 26, 2025
@github-actions github-actions Bot merged commit d67f408 into main Dec 26, 2025
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@github-actions github-actions Bot deleted the implementation/learning-curve-basic/plotnine branch December 26, 2025 17:47
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