feat(pygal): implement confusion-matrix

[github-actions]

Implementation: confusion-matrix - pygal

Implements the pygal version of confusion-matrix.

File: plots/confusion-matrix/implementations/pygal.py

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 4×4 confusion matrix heatmap for a sentiment analysis classifier with classes: Positive, Neutral, Negative, and Mixed. The matrix uses a sequential blue colormap ranging from very light blue (#f7fbff) for low values to dark blue (#08306b) for high values. The title "confusion-matrix · pygal · pyplots.ai" appears at the top. "True Label" is shown on the y-axis (left side, rotated), "Predicted Label" on the x-axis (bottom). Class labels are displayed on both axes - row labels on the left and column labels rotated 45° at the bottom. Each cell contains the count value with white text on dark cells and dark text on light cells. A colorbar on the right shows the scale from 5 to 142 with a "Count" title. The diagonal cells (correct predictions) have a subtle blue border highlighting them. The layout is clean and well-balanced on a square canvas.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - all text clearly readable at full size, appropriate font sizes
• VQ-02: No Overlap (8/8) - no overlapping text elements, rotated labels prevent overlap
• VQ-03: Element Visibility (8/8) - cells well-sized, values clearly visible
• VQ-04: Color Accessibility (5/5) - sequential blue colormap is colorblind-safe
• VQ-05: Layout Balance (3/5) - good proportions, but some unused space in corners
• VQ-06: Axis Labels (2/2) - "True Label" and "Predicted Label" are descriptive
• VQ-07: Grid & Legend (0/2) - no grid (acceptable for heatmap), but no legend since it's a matrix

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - correct confusion matrix heatmap
• SC-02: Data Mapping (5/5) - true labels on y-axis, predicted on x-axis as specified
• SC-03: Required Features (5/5) - annotations present, colorbar included, diagonal highlighted
• SC-04: Data Range (3/3) - all data visible, colorbar shows full range
• SC-05: Legend Accuracy (2/2) - colorbar correctly labeled "Count"
• SC-06: Title Format (2/2) - uses correct format "{spec-id} · {library} · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - shows varying performance across classes, different confusion patterns
• DQ-02: Realistic Context (7/7) - sentiment analysis is a real-world classification scenario
• DQ-03: Appropriate Scale (5/5) - counts are realistic for a test dataset

Code Quality (5/10 pts)

• CQ-01: KISS Structure (0/3) - uses custom class (necessary for pygal limitation, but deviates from KISS)
• CQ-02: Reproducibility (3/3) - np.random.seed(42) present
• CQ-03: Clean Imports (2/2) - all imports used
• CQ-04: No Deprecated API (0/1) - uses internal pygal.graph.graph.Graph (not public API)
• CQ-05: Output Correct (0/1) - saves to plot.svg and plot.png (spec requires plot.png, which is correct)

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - custom SVG generation, pygal styling system, smooth color interpolation

Strengths

• Excellent visual design with proper colorbar, cell annotations, and diagonal highlighting
• Creative solution to implement confusion matrix in pygal which lacks native heatmap support
• Proper contrast handling for text on varying background colors
• Good use of sequential blue colormap matching the specification requirements
• Realistic sentiment analysis scenario with meaningful class confusion patterns

Weaknesses

• Uses custom class extending pygal.graph.graph.Graph which deviates from KISS principle (though necessary for pygal)
• Imports from internal module (pygal.graph.graph) rather than public API
• The sys.path manipulation for import is a workaround that adds complexity

Verdict: APPROVED