feat(highcharts): implement logistic-regression

[github-actions]

Implementation: logistic-regression - python/highcharts

Implements the python/highcharts version of logistic-regression.

File: plots/logistic-regression/implementations/python/highcharts.py

Parent Issue: #3550

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays a warm off-white (#FAF8F1) background with an excellent layout showing the logistic regression analysis. The title "logistic-regression · python · highcharts · anyplot.ai" is clearly visible at the top in dark text (28px). The X-axis is labeled "Study Hours" (0-10 range) and the Y-axis is labeled "Probability" (0-1 range), both with readable labels and tick marks. The data visualization shows green squares (#009E73) at the bottom representing class 0 (Fail), orange triangles (#D55E00) at the top representing class 1 (Pass), a prominent blue logistic curve (#0072B2) displaying the characteristic S-shaped sigmoid function, and a light teal confidence interval band (20% opacity) around the curve. A dashed decision threshold line is drawn at probability = 0.5 with a label. An annotation box in the bottom right displays "Accuracy: 92.7%". The legend shows all series with proper styling and background color. All text is fully readable against the light background with excellent contrast.

Dark render (plot-dark.png): The same visualization is rendered on a warm near-black (#1A1A17) surface with perfect theme adaptation. The title, axis labels, and tick labels are all in light text colors (INK light #F0EFE8, INK_SOFT light #B8B7B0) and remain fully readable against the dark background—no dark-on-dark text failures detected. Critically, the data colors remain identical to the light render: green squares, orange triangles, and blue curve show no color shifts, confirming only the chrome (backgrounds, text, grid, borders) adapts between themes. The confidence interval band darkens slightly with the background while maintaining the same 20% opacity. The decision threshold line and annotation box both adapt their styling appropriately. All visual elements are equally readable and clear in the dark theme.

Score: 91/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	91	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - Font sizes explicitly set (title 28px, labels 22px, ticks 18px), readable at full resolution in both themes
• VQ-02: No Overlap (6/6) - All text well-spaced, data points include y-axis jitter, annotation positioned cleanly without collisions
• VQ-03: Element Visibility (6/6) - Markers, logistic curve, CI band, and decision threshold all clearly visible and distinguishable
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette, inherently colorblind-safe, adequate contrast
• VQ-05: Layout & Canvas (4/4) - 4800x2700 canvas well-utilized with generous margins (spacingBottom: 100, etc.)
• VQ-06: Axis Labels & Title (2/2) - Title format correct, axis labels descriptive (Study Hours, Probability)
• VQ-07: Palette Compliance (2/2) - First categorical series (#009E73), second (#D55E00), curve (#0072B2); backgrounds match (#FAF8F1 light, #1A1A17 dark); only chrome flips between themes

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (6/8) - Explicit Okabe-Ito color management, intentional visual hierarchy, professional polish with theme-adaptive tokens throughout
• DE-02: Visual Refinement (3/6) - Grid lines subtle (10% opacity), legend styled with background and borders, annotation boxes refined, generous spacing
• DE-03: Data Storytelling (4/6) - Clear focal point on sigmoid curve, decision threshold provides interpretive guide, accuracy metric adds model context

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct logistic regression with characteristic S-shaped curve
• SC-02: Required Features (4/4) - All present: data points colored by binary class, smooth logistic curve, 95% CI band, decision threshold at 0.5, y-axis jitter
• SC-03: Data Mapping (3/3) - X-axis (Study Hours, 0-10), Y-axis (Probability, -0.05 to 1.05 for visibility), all data shown
• SC-04: Title & Legend (3/3) - Title format correct, legend labels match series (95% CI, Logistic Curve, Fail/Pass)

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - All logistic regression aspects: binary classes, fitted curve, confidence intervals, decision boundary, model accuracy
• DQ-02: Realistic Context (5/5) - Study hours/pass-fail realistic for academic context, neutral and plausible data distribution
• DQ-03: Appropriate Scale (4/4) - Study hours 0-10 reasonable, probability 0-1 correct, 150 data points within spec range (50-500)

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow, no unnecessary functions/classes
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set, deterministic output
• CQ-03: Clean Imports (2/2) - All imports used (os, tempfile, time, Path, numpy, highcharts_core, selenium, sklearn)
• CQ-04: Code Elegance (2/2) - Bootstrap confidence intervals properly implemented, no fake UI
• CQ-05: Output & API (1/1) - Correct output as plot-{THEME}.html and plot-{THEME}.png

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Proper Chart(container="container") pattern, HighchartsOptions correctly used, appropriate series types (Scatter, Spline, AreaRange)
• LM-02: Distinctive Features (4/5) - AreaRangeSeries for confidence intervals, plotLines for threshold, annotations module for accuracy display—good Highcharts-specific patterns

Score Caps Applied

• None applied

Strengths

• Perfect theme-adaptive implementation with correct INK/INK_SOFT tokens in both renders, no readable text failures
• All specification requirements met with clean implementation: S-shaped curve, two-class data, 95% CI band, decision threshold, accuracy annotation
• Strong palette compliance: Okabe-Ito colors correct across series (Fail #009E73, Pass #D55E00, Curve #0072B2) with identical data colors in light and dark
• Excellent code quality: deterministic with seed, proper bootstrap confidence intervals, clean Highcharts API usage
• Good use of library-specific features: AreaRangeSeries for CI bands, plotLines for threshold line, annotations module for accuracy metric
• All text scaled appropriately for 4800x2700 canvas (title 28px, labels 22px, ticks 18px)

Weaknesses

• Design Excellence moderate: visual refinement and storytelling could be more distinctive (grid styling, visual emphasis could be bolder)

Issues Found

None blocking. Implementation is production-ready.

AI Feedback for Next Attempt

No next attempt needed. Implementation meets all requirements and passes both theme checks.

Verdict: APPROVED