feat(pygal): implement bar-feature-importance

[github-actions]

Implementation: bar-feature-importance - pygal

Implements the pygal version of bar-feature-importance.

File: plots/bar-feature-importance/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a horizontal bar chart of feature importances from a house price prediction model. There are 15 features displayed on the y-axis (OverallQual at top, ExterQual at bottom), sorted by importance with highest at top. The x-axis shows "Importance Score" ranging from 0.000 to 0.240. Bars use a gradient from light blue (low importance) to darker Python Blue (high importance). Each bar has its importance value displayed at the end (e.g., 0.245 for OverallQual, 0.182 for GrLivArea). The title reads "bar-feature-importance · pygal · pyplots.ai". The layout has white background with subtle vertical grid lines, and all feature names are clearly readable on the left side.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title, labels, and tick marks all clearly readable at full resolution
• VQ-02: No Overlap (8/8) - No overlapping text, feature names and values all distinct
• VQ-03: Element Visibility (8/8) - Bars appropriately sized for 15 features, good spacing between bars
• VQ-04: Color Accessibility (5/5) - Blue gradient is colorblind-safe, no red-green issues
• VQ-05: Layout Balance (3/5) - Good proportions but x-axis extends far beyond data (0.24 max importance but axis goes to 0.24+), some unused space on right
• VQ-06: Axis Labels (1/2) - X-axis has descriptive label "Importance Score" but no units (dimensionless is acceptable for importance)
• VQ-07: Grid & Legend (1/2) - Grid lines are subtle, no legend needed (single series), but grid extends into unused space

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct horizontal bar chart
• SC-02: Data Mapping (5/5) - Features on y-axis, importance values as bar length
• SC-03: Required Features (5/5) - Sorted by importance (highest at top), gradient coloring, value annotations present
• SC-04: Data Range (3/3) - All 15 features visible, axis accommodates all values
• SC-05: Legend Accuracy (2/2) - No legend needed for single series
• SC-06: Title Format (2/2) - Correct format: "bar-feature-importance · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows 15 features with varied importance values demonstrating the concept well, but spec suggests error bars for ensemble methods (optional, not required)
• DQ-02: Realistic Context (7/7) - House price prediction with RandomForest is a realistic, comprehensible ML scenario using actual Kaggle Ames Housing dataset features
• DQ-03: Appropriate Scale (5/5) - Importance values sum to ~1.0, realistic for tree-based feature importance

Code Quality (8/10 pts)

• CQ-01: KISS Structure (1/3) - Contains a helper function importance_to_color which violates strict KISS principle
• CQ-02: Reproducibility (3/3) - Deterministic data (no random generation)
• CQ-03: Clean Imports (2/2) - Only pygal and Style imported, both used
• CQ-04: No Deprecated API (1/1) - Using current pygal API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (5/5 pts)

• LF-01: Uses distinctive library features - Custom Style, HorizontalBar chart type, per-bar color styling via dicts, print_values for annotations, value_formatter for precision

Strengths

• Excellent use of pygal's per-bar color styling with gradient mapped to importance values
• Clean visual design with appropriate font sizes for 4800x2700 canvas
• Correct sorting with highest importance at top (pygal renders bottom-to-top, code handles this)
• Value annotations positioned clearly at end of each bar with 3 decimal precision
• Realistic house price prediction context using recognizable feature names

Weaknesses

• Contains a helper function importance_to_color which deviates from strict KISS script style (imports → data → plot → save)
• X-axis could be tighter to data range to reduce unused whitespace on right side

Verdict: APPROVED