feat(bokeh): implement line-loss-training

[github-actions]

Implementation: line-loss-training - python/bokeh

Implements the python/bokeh version of line-loss-training.

File: plots/line-loss-training/implementations/python/bokeh.py

Parent Issue: #2860

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays two training curves against a warm off-white background (#FAF8F1). The title "Training and Validation Loss Over Epochs" is rendered in dark text (28pt) at the top. The green training loss curve (#009E73) and orange validation loss curve (#D55E00) are both clearly visible with distinct colors. The x-axis is labeled "Epoch" and the y-axis is labeled "Loss (Cross-Entropy)", both in dark readable text. Tick labels (18pt) are clearly readable against the light background. The legend in the top right shows "Training Loss", "Validation Loss", and "Optimal epoch: 148" with a marker point. Subtle grid lines are visible on both axes. All text is fully readable against the light surface with excellent contrast.

Dark render (plot-dark.png): The same plot rendered on a warm near-black background (#1A1A17) with all text converted to light colors. The data colors remain identical—green (#009E73) for training loss and orange (#D55E00) for validation loss—proving only chrome has flipped. Title and axis labels are now in light text and fully readable against the dark background. Tick labels in light gray are clearly visible. The legend text is light-colored and readable. Grid lines remain subtle and visible. There are no dark-on-dark failures; all text maintains excellent legibility.

Both renders pass theme-readability: light surfaces have dark text, dark surfaces have light text, and data colors are theme-independent. The plot is readable and well-designed in both themes.

Score: 82/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	8	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	5	10
Total	82	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title 28pt, labels 22pt, ticks 18pt); perfectly readable in both themes
• VQ-02: No Overlap (6/6) - No overlapping text elements; legend placement clear
• VQ-03: Element Visibility (6/6) - Line width 4 and marker size 5 are appropriately scaled for 150 data points
• VQ-04: Color Accessibility (2/2) - Green (#009E73) and orange (#D55E00) have excellent contrast; Okabe-Ito is colorblind-safe
• VQ-05: Layout & Canvas (4/4) - Plot fills appropriate portion of canvas with balanced margins
• VQ-06: Axis Labels & Title (2/2) - Y-axis includes unit "Loss (Cross-Entropy)"; clear descriptive labels
• VQ-07: Palette Compliance (2/2) - First series is #009E73 (brand), second is #D55E00 (Okabe-Ito Add Claude Code GitHub Workflow #2); backgrounds are #FAF8F1/#1A1A17; theme-adaptive text colors correct in both renders

Design Excellence (8/20)

• DE-01: Aesthetic Sophistication (4/8) - Well-configured defaults with correct palette; lacks distinctive design enhancements or custom visual choices
• DE-02: Visual Refinement (2/6) - Library defaults with minimal customization; adequate whitespace but no advanced refinement techniques
• DE-03: Data Storytelling (2/6) - Data displayed clearly but no visual hierarchy; overfitting effect present but not emphasized; optimal epoch marker visible but not a focal point

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) - Correct line plot for training curves
• SC-02: Required Features (4/4) - Training and validation loss curves, distinct colors, legend, epochs, loss values, optimal epoch marker
• SC-03: Data Mapping (3/3) - X-axis epochs correct (1-150), Y-axis loss values correct, all data visible
• SC-04: Title & Legend (2/3) - Legend labels correct; title should follow format "line-loss-training · bokeh · anyplot.ai"

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows smooth training decay, noisy validation loss, overfitting effect in later epochs, optimal stopping point
• DQ-02: Realistic Context (5/5) - Authentic neural network training scenario with realistic Keras/PyTorch patterns
• DQ-03: Appropriate Scale (4/4) - 150 epochs typical, loss range 0.15-2.7 realistic, exponential decay pattern factually correct

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → plot → save
• CQ-02: Reproducibility (2/2) - Seed set to 42, fully deterministic
• CQ-03: Clean Imports (2/2) - All imports used; no unused imports
• CQ-04: Code Elegance (2/2) - No fake functionality; appropriate complexity; clean Pythonic code
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html; current API

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) - Uses recommended Bokeh patterns (figure, ColumnDataSource, line, scatter); high-level API; standard but correct
• LM-02: Distinctive Features (2/5) - Uses HoverTool and legend click_policy="mute" (interactive features); theme-adaptive environment tokens; some library-specific features but not extensively leveraged

Score Caps Applied

• None - No conditions trigger score caps

Strengths

• Excellent text legibility with explicitly-sized fonts across all scales (VQ-01 = 8/8)
• Perfect palette compliance: Okabe-Ito colors (#009E73 and #D55E00) with correct theme-adaptive chrome in both renders (VQ-07 = 2/2)
• Realistic training data showing overfitting dynamics with optimal epoch annotation (DQ-01, DQ-02 = 6/5)
• Clean code structure with reproducible seeding and correct output format (CQ = 10/10)
• No overlapping elements or dark-on-dark legibility issues in either theme

Weaknesses

• Title does not follow spec format: should be "line-loss-training · bokeh · anyplot.ai" instead of just descriptive text
• Generic design lacking visual hierarchy or emphasis on the overfitting story
• No visual distinction or emphasis on the optimal epoch marker—it's marked but doesn't guide viewer attention
• Minimal use of Bokeh-specific features beyond basic plot construction

Issues Found

1. SC-04 (Title Format): Title should follow {spec-id} · {library} · anyplot.ai format
2. DE-01/DE-03 (Design): Add visual emphasis to highlight overfitting effect and optimal stopping point through color contrast or annotation

AI Feedback for Next Attempt

Consider emphasizing the overfitting narrative: highlight the divergence point between curves through color intensity shift or a secondary visual cue. Update title format to match spec requirements. These improvements would push DE scores higher and better guide the viewer's interpretation of the training dynamics.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot displays on a warm off-white background (#FAF8F1). Two distinct lines show training loss (green #009E73) and validation loss (orange #D55E00) declining over 150 epochs. The title "line-loss-training · bokeh · anyplot.ai" is bold and prominent. X-axis label "Epoch" and Y-axis label "Loss (Cross-Entropy)" are clearly readable with dark text. A beige shaded region from epoch 80 onward highlights the overfitting area. A larger marker and label at epoch 82 indicate the optimal stopping point ("Epoch 82 Loss 0.6049"). The legend in the top right shows all three entries with readable 16pt text. Grid lines are subtle. All text is clearly readable against the light background.

Dark render (plot-dark.png): The plot displays on a warm near-black background (#1A1A17). The same two lines are rendered with identical colors to the light version (#009E73 and #D55E00), confirming perfect color consistency. All chrome elements (title, axis labels, tick labels, legend text) are rendered in light colors and are clearly readable against the dark background. The shaded overfitting region is visible as a darker overlay. The marker and label at the optimal epoch are clearly visible. All text is readable, with no dark-on-dark failures detected. Data colors are identical to the light render; only chrome is theme-adapted.

Score: 95/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	9	10
Total	95	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title 28pt, labels 22pt, ticks 18pt, legend 16pt, annotation 14pt)
• VQ-02: No Overlap (6/6) - No overlapping elements; all text fully readable
• VQ-03: Element Visibility (6/6) - Lines and markers optimally scaled for 150-point dataset
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is CVD-safe with strong contrast
• VQ-05: Layout & Canvas (4/4) - Perfect layout with balanced margins, 60% canvas utilization
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units: "Loss (Cross-Entropy)"
• VQ-07: Palette Compliance (2/2) - First series #009E73, backgrounds #FAF8F1/#1A1A17 correct, data colors identical across themes

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Intentional design: shaded overfitting region, optimal epoch annotation, professional appearance
• DE-02: Visual Refinement (5/6) - Clean layout with subtle grid, generous whitespace, well-styled legend
• DE-03: Data Storytelling (5/6) - Clear visual hierarchy, color contrast guides viewer, shaded region directs to overfitting zone

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct line plot with dual series
• SC-02: Required Features (4/4) - Training/validation curves, distinct colors, legend, epochs, loss label, optimal point marked
• SC-03: Data Mapping (3/3) - X-axis epoch, Y-axis loss; axes span all data correctly
• SC-04: Title & Legend (3/3) - Title format correct, legend labels accurate

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows training, validation, overfitting, optimal stopping point
• DQ-02: Realistic Context (5/5) - Realistic neural network training with exponential decay, noise, overfitting effect
• DQ-03: Appropriate Scale (4/4) - Loss 0.15–2.5 sensible for cross-entropy, 150 epochs typical

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No functions/classes; straightforward linear flow
• CQ-02: Reproducibility (2/2) - np.random.seed(42) ensures determinism
• CQ-03: Clean Imports (2/2) - All imports used; no dead code
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, no fake UI
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png + plot-{THEME}.html

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - ColumnDataSource, figure API, proper theme handling
• LM-02: Distinctive Features (4/5) - HoverTool, legend muting, Label annotations, Quad shading, hover states

Score Caps Applied

• None

Strengths

• Excellent theme support: both light and dark renders are perfectly legible
• Perfect palette compliance: Okabe-Ito colors identical across themes
• Smart visual design: shaded region guides viewer to overfitting area
• Interactive features: HoverTool, mutable legend, hover states enhance usability
• Explicit typography: all font sizes set for 4800×2700 px canvas
• Realistic synthetic data: proper exponential decay with noise and overfitting effect

Weaknesses

• None significant; the implementation is strong

Verdict: APPROVED

This is a high-quality implementation (95/100) demonstrating strong design thinking, perfect technical compliance, and excellent Bokeh mastery. All visual quality criteria are met with explicit sizing. Ready for publication.