feat(pygal): implement line-loss-training

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Implementation: line-loss-training - pygal

Implements the pygal version of line-loss-training.

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

Parent Issue: #2860

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays two line curves on a white background showing training loss (blue/Python blue #306998) and validation loss (yellow/Python yellow #FFD43B) over 50 epochs. The title "line-loss-training · pygal · pyplots.ai" appears at the top center. The x-axis is labeled "Epoch" with tick marks at intervals of 5 (5, 10, 15... 50). The y-axis is labeled "Cross-Entropy Loss" ranging from approximately 0.2 to 2.4. Both curves start high (~2.3-2.4) and decrease over epochs. The training loss (blue) continues decreasing steadily to ~0.1, while the validation loss (yellow) begins increasing after around epoch 25, demonstrating classic overfitting behavior. A legend appears in the top-left corner outside the plot area with colored squares for "Training Loss" and "Validation Loss". Data points are marked with small dots along each line. Horizontal grid lines are visible at subtle opacity.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are clearly readable. Font sizes are appropriate for 4800x2700 canvas.
• VQ-02: No Overlap (8/8) - No overlapping text elements. X-axis labels spaced at every 5 epochs prevent crowding.
• VQ-03: Element Visibility (7/8) - Lines are clearly visible with good stroke width. Dots are appropriately sized for data density (50 points).
• VQ-04: Color Accessibility (5/5) - Blue and yellow are colorblind-safe and provide excellent contrast against white background.
• VQ-05: Layout Balance (4/5) - Good canvas utilization. Plot fills majority of space. Legend position in top-left is acceptable but slightly isolated.
• VQ-06: Axis Labels (2/2) - Descriptive labels: "Epoch" and "Cross-Entropy Loss" (specifies loss function type as required by spec).
• VQ-07: Grid & Legend (1/2) - Horizontal grid lines are subtle and helpful. Legend is functional but positioned outside plot area which is slightly unusual.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line chart type for training loss curves.
• SC-02: Data Mapping (5/5) - X-axis = epochs (1-50), Y-axis = loss values. Correctly mapped.
• SC-03: Required Features (5/5) - Two curves (training/validation), distinct colors (blue/yellow), clear legend, demonstrates overfitting behavior.
• SC-04: Data Range (3/3) - All data visible, axes appropriately scaled.
• SC-05: Legend Accuracy (2/2) - Legend correctly labels "Training Loss" and "Validation Loss".
• SC-06: Title Format (2/2) - Uses correct format: "line-loss-training · pygal · pyplots.ai".

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows overfitting pattern well (validation loss diverges from training loss after ~epoch 25). Could optionally mark minimum validation loss point.
• DQ-02: Realistic Context (7/7) - Neural network training is a realistic, neutral scenario. Loss curve shapes are authentic.
• DQ-03: Appropriate Scale (4/5) - Loss values (0.1 to 2.5) and 50 epochs are realistic for typical training scenarios.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean structure: imports → data → plot → save. No functions or classes.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for deterministic output.
• CQ-03: Clean Imports (2/2) - Only numpy and pygal imports, all used.
• CQ-04: No Deprecated API (1/1) - Uses current pygal API.
• CQ-05: Output Correct (0/1) - Saves as plot.png AND plot.html (correct for pygal).

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal's custom Style class, Line chart with dots, x_labels, legend configuration. Could leverage more pygal-specific features like tooltips or annotations.

Strengths

• Excellent demonstration of overfitting pattern with training loss continuing to decrease while validation loss increases after epoch ~25
• Clean, readable code following KISS principles with proper random seed
• Appropriate use of Python brand colors (blue #306998, yellow #FFD43B) for visual distinction
• Good font sizing and spacing for 4800x2700 canvas with customized Style
• Y-axis label specifies "Cross-Entropy Loss" as recommended in specification

Weaknesses

• Legend positioned outside plot area in top-left corner appears slightly disconnected from the chart
• Optional feature from spec not implemented: marking the epoch with minimum validation loss (optimal stopping point)
• Grid only shows horizontal lines; vertical guides disabled which reduces readability for epoch identification

Verdict: APPROVED