feat(highcharts): implement roc-curve

[github-actions]

Implementation: roc-curve - highcharts

Implements the highcharts version of roc-curve.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a ROC curve on a white background. The main ROC curve is displayed as a blue area chart (Python Blue #306998) with a gradient fill from darker blue at the top to lighter blue at the bottom, representing the area under the curve. The curve shows excellent classifier performance, starting near the top-left and reaching the top quickly. A dashed olive/yellow diagonal line represents the random classifier reference (y=x). The title "roc-curve · highcharts · pyplots.ai" appears at the top in bold. The subtitle "Binary Classifier Performance" is shown below. The X-axis is labeled "False Positive Rate" (0 to 1), Y-axis is labeled "True Positive Rate" (0 to 1). A legend in the bottom-right shows "ROC Curve (AUC = 0.967)" and "Random Classifier (AUC = 0.5)". Grid lines are subtle light gray. The layout fills the canvas well with proper margins.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 48px, axis labels at 36px, tick labels at 28px, all perfectly readable at full size
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (8/8) - ROC curve line is thick (6px), diagonal line visible with dashed style, gradient fill clearly shows AUC area
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and olive/yellow (#8B8000) are colorblind-safe, no red-green combinations
• VQ-05: Layout Balance (3/5) - Good margins but legend is positioned in bottom-right corner slightly overlapping the "1" tick mark, could be better positioned
• VQ-06: Axis Labels (2/2) - "False Positive Rate" and "True Positive Rate" are descriptive (units not applicable for rates 0-1)
• VQ-07: Grid & Legend (0/2) - Grid is subtle (alpha 0.1), but legend partially overlaps plot area and the "1" tick label on x-axis

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) - Includes diagonal reference line, AUC score in legend, both required per spec
• SC-04: Data Range (3/3) - Axes range from 0 to 1 as specified
• SC-05: Legend Accuracy (2/2) - Legend correctly shows ROC Curve with AUC value and Random Classifier reference
• SC-06: Title Format (2/2) - Uses correct format "roc-curve · highcharts · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows a good classifier (AUC=0.967) but doesn't show variation like comparing multiple models or a poor classifier
• DQ-02: Realistic Context (7/7) - Binary classification scenario with beta distributions simulating positive/negative class score separation is realistic
• DQ-03: Appropriate Scale (5/5) - 500 samples, 200 thresholds, AUC ~0.97 all sensible values

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear structure: imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current numpy and highcharts APIs
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses AreaSeries with gradient fill which is nice, but doesn't leverage Highcharts' interactive features like tooltips with threshold values

Strengths

• Excellent use of AreaSeries with gradient fill to visually represent the AUC area under the ROC curve
• Clean implementation following the library rules for Highcharts with inline JS embedding
• Proper manual ROC curve calculation demonstrating the algorithm
• Good colorblind-safe color choices (Python Blue and olive yellow)
• Correctly formatted title following pyplots.ai convention

Weaknesses

• Legend position causes slight overlap with the x-axis "1" tick label
• Could leverage Highcharts' tooltip feature to show threshold values at each point on the curve
• Example data shows only one high-performing classifier; could include comparison with a second model to better demonstrate ROC curve usage

Verdict: APPROVED