feat(plotly): implement line-loss-training

[github-actions]

Implementation: line-loss-training - plotly

Implements the plotly version of line-loss-training.

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

Parent Issue: #2860

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays two smooth line curves over a white background with subtle gray gridlines. The title "line-loss-training · plotly · pyplots.ai" is centered at the top. The blue line (Training Loss) and yellow/gold line (Validation Loss) both start around 2.5 and decrease exponentially. The training loss continues decreasing to approximately 0.2, while the validation loss reaches a minimum around epoch 60 (marked with a red star labeled "Best: Epoch 61") and then diverges upward, demonstrating classic overfitting behavior. The x-axis shows "Epoch" (0-100), and the y-axis shows "Cross-Entropy Loss" (0.5-2.5+). A legend in the upper right corner identifies the three elements: Training Loss, Validation Loss, and Best Epoch.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 28pt, axis labels at 22pt, ticks at 18pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements; all labels clearly separated
• VQ-03: Element Visibility (7/8) - Line width of 3 is appropriate; star marker is visible but slightly small for emphasis
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide excellent contrast and are colorblind-safe
• VQ-05: Layout Balance (4/5) - Good canvas utilization; minor issue with large right margin
• VQ-06: Axis Labels (2/2) - "Epoch" and "Cross-Entropy Loss" are descriptive (loss function specified as per spec)
• VQ-07: Grid & Legend (0/2) - Grid is subtle with alpha 0.3, but legend placement partially overlaps with gridlines

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line plot for training curves
• SC-02: Data Mapping (5/5) - Epoch on X-axis, loss values on Y-axis correctly assigned
• SC-03: Required Features (5/5) - Both training and validation curves present, distinct colors (blue/yellow), legend included, minimum validation loss marked with star annotation
• SC-04: Data Range (3/3) - X-axis covers epochs 1-100, Y-axis shows full loss range
• SC-05: Legend Accuracy (2/2) - Legend correctly labels Training Loss, Validation Loss, and Best Epoch
• SC-06: Title Format (2/2) - Follows exact format: "line-loss-training · plotly · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows exponential decay, overfitting divergence, and optimal stopping point; slight noise added for realism
• DQ-02: Realistic Context (7/7) - Neural network training is a perfect, neutral real-world scenario
• DQ-03: Appropriate Scale (4/5) - Loss values 0.1-2.5 are realistic for cross-entropy; 100 epochs is typical

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) ensures reproducibility
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects imported, both used
• CQ-04: No Deprecated API (1/1) - Uses current Plotly API
• 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) - Good use of hover templates for interactivity, but could leverage more Plotly features like annotations, shapes for regions, or rangeslider

Strengths

• Excellent implementation of overfitting visualization with validation loss diverging after epoch 60
• Clean, readable code with proper seed for reproducibility
• Good use of Plotly hover templates showing epoch and loss values on hover
• Appropriate color scheme (blue/yellow) with good accessibility
• Best epoch marker with star symbol effectively highlights the optimal stopping point
• HTML output enabled for full interactivity

Weaknesses

• Legend background slightly blends with grid lines in upper right area
• Star marker for best epoch could be larger (size 16 is adequate but not prominent)
• Could utilize Plotly-specific features like vertical line annotation at best epoch or shaded overfitting region

Verdict: APPROVED