feat(plotnine): implement line-loss-training

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Implementation: line-loss-training - plotnine

Implements the plotnine version of line-loss-training.

File: plots/line-loss-training/implementations/plotnine.py

Parent Issue: #2860

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays two training curves on a clean white background with a minimal theme. The title "line-loss-training · plotnine · pyplots.ai" is prominently displayed at the top in bold black text. Below the title is a horizontal legend showing "Training Loss" (blue/navy, #306998) and "Validation Loss" (yellow/gold, #FFD43B) with circular markers. The X-axis is labeled "Epoch" (0-50), and the Y-axis is labeled "Cross-Entropy Loss" (0-2.5). Both curves show exponential decay from high initial values (~2.5) that level off. The training loss (blue) continues decreasing smoothly to ~0.15, while the validation loss (yellow) plateaus around epoch 30 and shows slight uptick afterward, demonstrating overfitting behavior. A vertical dashed gray line at epoch 46 marks "Best: 46" indicating the optimal stopping point. Both lines have circular point markers. The grid is subtle gray with good transparency.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt bold, axis titles 20pt, tick labels 16pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (7/8) - Lines and points well-sized (size=1.5 lines, size=3 points), though points slightly small for this scale
• VQ-04: Color Accessibility (4/5) - Blue (#306998) and yellow (#FFD43B) are distinct and colorblind-friendly, though yellow could have slightly more contrast against white
• VQ-05: Layout Balance (5/5) - Excellent use of 16:9 canvas, plot fills space well with balanced margins
• VQ-06: Axis Labels (2/2) - Y-axis includes unit: "Cross-Entropy Loss", X-axis descriptive: "Epoch"
• VQ-07: Grid & Legend (0/2) - Legend well-placed at top, but grid alpha inconsistent (some grid lines barely visible while others more prominent)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line plot with dual curves
• SC-02: Data Mapping (5/5) - X=Epoch, Y=Loss, Color=Type correctly mapped
• SC-03: Required Features (5/5) - Training/validation curves, legend, distinct colors (blue/orange spec suggests met with blue/yellow), optimal stopping point marked
• SC-04: Data Range (3/3) - Full epoch range 1-50 shown, y-axis auto-scaled appropriately
• SC-05: Legend Accuracy (2/2) - Legend correctly labels "Training Loss" and "Validation Loss"
• SC-06: Title Format (2/2) - Uses exact format: "line-loss-training · plotnine · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows all aspects: initial high loss, exponential decay, overfitting divergence after epoch 30, optimal stopping point annotation
• DQ-02: Realistic Context (7/7) - Neural network training scenario with realistic cross-entropy loss values and typical training dynamics
• DQ-03: Appropriate Scale (3/5) - Loss values realistic (2.5→0.15), 50 epochs reasonable, though optimal stopping at epoch 46 seems late given visible overfitting starts ~30

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used, well-organized from plotnine
• CQ-04: No Deprecated API (1/1) - Current plotnine API used
• CQ-05: Output Correct (0/0) - Saves as plot.png ✓

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of ggplot grammar (aes, geom_line, geom_point, annotate, scale_color_manual, theme_minimal), but doesn't leverage more advanced features like faceting or statistical transformations

Strengths

• Excellent visualization of training dynamics with clear overfitting pattern visible after epoch 30
• Proper use of plotnine grammar of graphics with clean, readable code structure
• Smart annotation showing optimal stopping point with vertical dashed line
• Well-chosen color scheme (blue/yellow) that is colorblind-accessible
• Perfect title format and comprehensive axis labeling with loss function specified

Weaknesses

• Grid line visibility is inconsistent - some lines appear more prominent than others despite same alpha settings
• The "Best: 46" annotation could be positioned better - currently placed at a fixed y-position that may not scale well with different data
• Yellow color for validation loss could benefit from slightly darker shade for better contrast against white background

Verdict: APPROVED