feat(bokeh): implement confusion-matrix

[github-actions]

Implementation: confusion-matrix - bokeh

Implements the bokeh version of confusion-matrix.

File: plots/confusion-matrix/implementations/bokeh.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 sentiment analysis classifier with classes "Negative", "Neutral", "Positive", and "Very Positive". The matrix uses a Blues sequential colormap ranging from light blue (low values near 0) to dark navy blue (high values around 156). Each cell contains bold white or dark blue text showing the count value, with contrasting colors for readability. The diagonal shows strong correct predictions (142, 98, 156, 86), while off-diagonal cells show misclassification patterns. The title "confusion-matrix · bokeh · pyplots.ai" appears at the top. Axis labels clearly show "True Label" (Y-axis) and "Predicted Label" (X-axis), with class names on both axes. A colorbar on the right indicates the count scale from 0-156.

Quality Score: 96/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text clearly readable at full size, appropriate font sizes for 3600x3600 canvas
• VQ-02: No Overlap (8/8) - No overlapping text, well-spaced labels and annotations
• VQ-03: Element Visibility (8/8) - Cell rectangles perfectly sized with white borders for clear separation
• VQ-04: Color Accessibility (5/5) - Blues sequential colormap is colorblind-safe
• VQ-05: Layout Balance (4/5) - Good square layout appropriate for confusion matrix
• VQ-06: Axis Labels (2/2) - Descriptive "True Label" and "Predicted Label"
• VQ-07: Grid & Legend (0/2) - No grid (appropriate), colorbar present but minor styling gap

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
• SC-03: Required Features (5/5) - All spec features: annotations, colorbar, sequential colormap
• SC-04: Data Range (3/3) - All data visible
• SC-05: Legend Accuracy (2/2) - Class names correct
• SC-06: Title Format (2/2) - Uses "confusion-matrix · bokeh · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows diagonal dominance, adjacent class confusion, class imbalance
• DQ-02: Realistic Context (7/7) - Sentiment analysis is a realistic multi-class scenario
• DQ-03: Appropriate Scale (5/5) - Realistic count values (~750 total samples)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save flow
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: No Deprecated API (1/1) - Current Bokeh APIs
• CQ-05: Output Correct (1/1) - Saves plot.png

Library Features (5/5 pts)

• LF-01: Uses distinctive library features - ColumnDataSource, LinearColorMapper, transform(), ColorBar, LabelSet with dynamic text colors

Strengths

• Excellent use of Bokeh-specific features: ColumnDataSource for data management, LinearColorMapper with transform() for color encoding, LabelSet for cell annotations
• Smart dynamic text color calculation (white on dark cells, dark blue on light cells) ensures all annotations are readable
• Square 3600x3600 format is ideal for confusion matrix visualization
• Realistic sentiment analysis scenario with plausible confusion patterns (adjacent classes more likely to be confused)
• Clean code structure following KISS principles with good comments explaining choices

Weaknesses

• Minor: X-axis labels have slight rotation (0.4 rad) which is not strictly necessary for 4 short class names
• VQ-07: Could add slightly more styling to the colorbar or include summary statistics

Verdict: APPROVED