feat(matplotlib): implement bar-feature-importance

[github-actions]

Implementation: bar-feature-importance - matplotlib

Implements the matplotlib version of bar-feature-importance.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a horizontal bar chart showing feature importances from a machine learning model (credit/loan prediction context). The chart shows 12 features sorted by importance from bottom to top, with "Income" being the most important (0.182) and "Number of Dependents" the least important (0.015). The bars use a sequential blue color gradient (Blues colormap) that transitions from lighter blue for lower values to darker blue for higher importance values. Each bar has error bars showing standard deviation and value annotations displayed to the right of each bar. The title uses the correct format "bar-feature-importance · matplotlib · pyplots.ai". The x-axis is labeled "Importance Score" and y-axis is labeled "Feature". A subtle dashed grid is visible on the x-axis only. The top and right spines are hidden for a cleaner appearance.

Quality Score: 94/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, labels at 20pt, ticks at 16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, all feature names fully visible
• VQ-03: Element Visibility (8/8) - Bars are well-sized, error bars clearly visible with good capsize
• VQ-04: Color Accessibility (5/5) - Uses Blues colormap which is colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins, tight_layout applied
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("Importance Score", "Feature") but missing units
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3, dashed), but there is no legend (not needed here)

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 on x-axis as specified
• SC-03: Required Features (5/5) - Sorted bars, color gradient, error bars, value annotations all present
• SC-04: Data Range (3/3) - X-axis shows full range from 0 to beyond max importance + std
• SC-05: Legend Accuracy (2/2) - No legend needed for single series, N/A
• SC-06: Title Format (2/2) - Correct format: "bar-feature-importance · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows 12 features with good variation in importance values, but all features trend consistently (could show some mid-range clustering)
• DQ-02: Realistic Context (7/7) - Credit/loan prediction model with realistic feature names (Income, Credit Score, Age, etc.)
• DQ-03: Appropriate Scale (5/5) - Importance values sum to ~1.0, realistic for sklearn feature_importances_

Code Quality (10/10 pts)

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

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses barh with error bars, colormap, spine customization. Good but could use additional matplotlib features like annotate() for richer annotations

Strengths

• Excellent implementation of all spec requirements: sorted bars, color gradient, error bars, and value annotations
• Clean code structure following KISS principles with proper seed for reproducibility
• Professional styling with hidden top/right spines and subtle grid
• Realistic credit scoring context with appropriate feature names and importance values
• Good use of Blues colormap for sequential color gradient mapped to importance values

Weaknesses

• Axis labels could include units (e.g., "Importance Score (normalized)" or similar)
• Could leverage matplotlib's annotate() for more styled value labels with arrows or backgrounds

Verdict: APPROVED