feat(letsplot): implement roc-curve

[github-actions]

Implementation: roc-curve - letsplot

Implements the letsplot version of roc-curve.

File: plots/roc-curve/implementations/letsplot.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays an ROC curve visualization with three curves on a 16:9 aspect ratio canvas. The plot uses a clean minimal theme with a light gray grid. The title "roc-curve · letsplot · pyplots.ai" is displayed at the top. The X-axis shows "False Positive Rate" (0 to 1) and the Y-axis shows "True Positive Rate" (0 to 1). Three curves are shown:

• Model A (AUC = 0.97) - Blue curve (#306998) showing excellent classifier performance, hugging the top-left corner
• Model B (AUC = 0.71) - Yellow/gold curve (#FFD43B) showing moderate classifier performance
• Random (AUC = 0.50) - Gray diagonal line (#888888) representing the random baseline
  The legend is positioned at the bottom with the label "Classifier". The plot has equal 1:1 aspect ratio for the axes (coord_fixed). All text is legible and appropriately sized. Lines are thick enough (size=2) to be clearly visible.

Quality Score: 92/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, axis titles 20pt, axis text 16pt, legend text 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, clean layout
• VQ-03: Element Visibility (8/8) - Line size=2 is optimal, curves clearly distinguishable
• VQ-04: Color Accessibility (5/5) - Blue/Yellow/Gray palette is colorblind-safe, excellent contrast
• VQ-05: Layout Balance (5/5) - Good canvas utilization with coord_fixed, legend at bottom well positioned
• VQ-06: Axis Labels (1/2) - Descriptive labels but no units (FPR/TPR are unitless ratios, so acceptable)
• VQ-07: Grid & Legend (1/2) - Grid is subtle but could be lighter (alpha not explicitly set)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct ROC curve visualization
• SC-02: Data Mapping (5/5) - FPR on X-axis, TPR on Y-axis correctly assigned
• SC-03: Required Features (5/5) - Diagonal reference line present, AUC scores in legend, multiple models compared with distinct colors
• SC-04: Data Range (3/3) - Axes range from 0 to 1 as specified
• SC-05: Legend Accuracy (2/2) - Legend labels correctly show model names and AUC values
• SC-06: Title Format (2/2) - Uses correct format "roc-curve · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows good vs moderate classifier, plus random baseline - demonstrates range of performance but could show a poor classifier too
• DQ-02: Realistic Context (7/7) - Binary classification scenario is realistic, synthetic data from beta distributions is appropriate
• DQ-03: Appropriate Scale (4/5) - AUC values plausible (0.97 is very good, 0.71 moderate) though 0.97 is quite high

Code Quality (8/10 pts)

• CQ-01: KISS Structure (1/3) - Has a function compute_roc which violates KISS principle (no functions/classes rule)
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: No Deprecated API (1/1) - Uses current numpy trapezoid function
• 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 ggplot grammar (ggplot, aes, geom_line), coord_fixed for aspect ratio, scale_color_manual, theme_minimal, ggsize - good library usage but nothing particularly unique to lets-plot

Strengths

• Excellent visual clarity with properly sized text elements and colorblind-safe palette
• Correct implementation of ROC curve with all required features (diagonal reference, AUC in legend, multiple models)
• Uses coord_fixed(ratio=1) for equal aspect ratio as recommended in spec
• Clean ggplot grammar of graphics approach with appropriate theme customization
• Proper scale 3x export for 4800x2700 resolution

Weaknesses

• Code contains a function definition (compute_roc) which violates the KISS principle - should be inline calculations
• Grid alpha not explicitly set (could be more subtle)

Verdict: APPROVED