feat(highcharts): implement confusion-matrix

[github-actions]

Implementation: confusion-matrix - highcharts

Implements the highcharts version of confusion-matrix.

File: plots/confusion-matrix/implementations/highcharts.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 an image classification model with classes: Cat, Dog, Bird, and Fish. The matrix uses a sequential blue colormap ranging from light blue (low values ~0) to dark blue (high values ~85-87). The diagonal shows strong correct predictions (87, 76, 82, 85) in dark blue, while off-diagonal misclassifications appear in lighter shades. Each cell displays its count value in bold black text with white outline. The title "confusion-matrix · highcharts · pyplots.ai" appears at the top with a subtitle "Image Classification Model Performance". The y-axis is labeled "True Label" and x-axis "Predicted Label". A vertical colorbar on the right shows the "Count" scale from 0-100.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - All text is clearly readable: title, subtitle, axis labels, class names, and cell values are all appropriately sized
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (8/8) - Heatmap cells are well-sized, values clearly visible with good contrast
• VQ-04: Color Accessibility (5/5) - Sequential blue colormap is colorblind-safe, good contrast between light/dark values
• VQ-05: Layout Balance (3/5) - Good use of canvas but slight imbalance with colorbar placement; plot is well-proportioned
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("True Label", "Predicted Label") but no units (acceptable for categorical data)
• VQ-07: Grid & Legend (1/2) - Colorbar well placed with "Count" title; white cell borders provide good separation

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct heatmap/confusion matrix visualization
• 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, colorbar, proper axis labels all present
• SC-04: Data Range (3/3) - All data visible, colorbar scale 0-100 appropriate
• SC-05: Legend Accuracy (2/2) - Colorbar correctly labeled as "Count"
• SC-06: Title Format (2/2) - Correct format: "confusion-matrix · highcharts · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows diagonal dominance (correct predictions), varying accuracy per class, realistic misclassification patterns (Cat↔Dog, Bird↔Fish confusion)
• DQ-02: Realistic Context (7/7) - Image classification with animal classes is a plausible ML scenario
• DQ-03: Appropriate Scale (4/5) - Values are reasonable (76-87 correct predictions per class out of 100), though all classes have similar accuracy

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → chart config → export
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set (though data is hardcoded)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (0/1) - Uses current API but includes unused seed
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Good use of Highcharts heatmap series, custom tooltip formatter, color axis with stops, data labels with formatter

Strengths

• Excellent visual clarity with well-sized text and cell annotations
• Sequential blue colormap is colorblind-accessible and effectively shows magnitude
• Proper confusion matrix orientation with True Labels on y-axis and Predicted Labels on x-axis
• Good use of Highcharts-specific features: custom tooltip showing correct/misclassified status, color axis with gradient stops
• White cell borders provide clean visual separation
• Realistic 4-class classification scenario with believable misclassification patterns

Weaknesses

• Layout could better utilize the square canvas - some wasted space around edges
• np.random.seed(42) is set but data is entirely hardcoded, making the seed unnecessary
• Colorbar scale goes to 100 but max value is 87, slightly misleading range

Verdict: APPROVED