feat(matplotlib): implement confusion-matrix

[github-actions]

Implementation: confusion-matrix - matplotlib

Implements the matplotlib version of confusion-matrix.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 4x4 confusion matrix heatmap for a multi-class classification scenario with classes: Cat, Dog, Bird, and Fish. The diagonal cells (correct predictions) are dark blue with high values (85, 78, 88, 90), while off-diagonal cells (misclassifications) are lighter shades of blue with lower values. Each cell shows both the raw count and the row percentage in parentheses. The y-axis is labeled "True Label" and x-axis is labeled "Predicted Label". A colorbar on the right shows the "Count" scale from approximately 10 to 90. The title follows the required format "confusion-matrix · matplotlib · pyplots.ai". Text uses white color on dark backgrounds and black on light backgrounds for contrast. X-axis labels are rotated 45 degrees. White grid lines separate the cells.

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, labels 20pt, tick labels 18pt, cell annotations 16pt bold - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Cells are well-sized, annotations clear with proper contrast (white on dark, black on light)
• VQ-04: Color Accessibility (5/5) - Blues colormap is colorblind-safe, sequential palette appropriate for count data
• VQ-05: Layout Balance (5/5) - Square format (12x12 inches) appropriate for symmetric matrix, good margins, colorbar well-positioned
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("True Label", "Predicted Label") but lack units (not applicable for categorical data, but could indicate "Class")
• VQ-07: Grid & Legend (1/2) - White grid lines between cells are functional; colorbar is well-placed though grid is slightly prominent

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 labels on x-axis as specified
• SC-03: Required Features (5/5) - Cell annotations with counts and percentages, Blues colormap, colorbar, clear axis labels
• SC-04: Data Range (3/3) - All cells visible and readable
• SC-05: Legend Accuracy (2/2) - Colorbar correctly labeled "Count"
• SC-06: Title Format (2/2) - Correct format: "confusion-matrix · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows both correct predictions (diagonal) and misclassifications (off-diagonal), varying accuracy levels across classes
• DQ-02: Realistic Context (7/7) - Animal classification is a common real-world ML scenario, class confusion patterns are realistic (Cat/Dog more confused than Fish)
• DQ-03: Appropriate Scale (5/5) - Values are realistic for a test set (~100 samples per class, 78-90% accuracy range)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean sequential structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib APIs
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Uses basic imshow() for heatmap; could use matplotlib's more advanced features like ConfusionMatrixDisplay from sklearn integration, or custom annotations with bounding boxes

Strengths

• Excellent text contrast with adaptive white/black text based on background intensity
• Row-normalized percentages provide additional insight beyond raw counts
• Realistic multi-class classification scenario with plausible confusion patterns
• Clean KISS code structure with proper seed for reproducibility
• Square aspect ratio perfect for symmetric confusion matrix
• Professional colorbar with clear "Count" label

Weaknesses

• Does not leverage matplotlib-specific advanced features (just uses basic imshow)
• Grid lines at width=2 are slightly prominent compared to subtle alpha=0.3 guidelines

Verdict: APPROVED