feat(seaborn): implement bar-feature-importance

[github-actions]

Implementation: bar-feature-importance - seaborn

Implements the seaborn version of bar-feature-importance.

File: plots/bar-feature-importance/implementations/seaborn.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 credit scoring model. There are 15 features sorted by importance, with "Annual Income" at the top (0.180) and "Previous Defaults" at the bottom (0.007). The bars use a sequential "Blues" color palette that transitions from light blue (low importance) to dark blue (high importance). Each bar has error bars (whiskers) showing standard deviation, and importance values are annotated in blue text at the end of each bar. The title reads "bar-feature-importance · seaborn · pyplots.ai" in bold at the top. The x-axis is labeled "Feature Importance" and the y-axis is labeled "Feature". A subtle dashed grid is visible on the x-axis only, and the top/right spines are removed for a cleaner look.

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt bold, axis labels 20pt, tick labels 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, feature names are clearly separated
• VQ-03: Element Visibility (8/8) - Bars are well-sized, error bars clearly visible with appropriate caps
• VQ-04: Color Accessibility (5/5) - Sequential Blues palette is colorblind-safe
• VQ-05: Layout Balance (5/5) - Good proportions, plot fills canvas well, balanced margins
• VQ-06: Axis Labels (1/2) - Descriptive labels but no units (importance is unitless, so acceptable)
• VQ-07: Grid & Legend (1/2) - Grid subtle (alpha=0.3), no legend needed for this plot

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct horizontal bar chart for feature importance
• SC-02: Data Mapping (5/5) - Features on Y-axis, importance on X-axis as specified
• SC-03: Required Features (5/5) - All spec features present: sorted bars, color gradient, error bars, value annotations
• SC-04: Data Range (3/3) - Axes show all data with appropriate padding
• SC-05: Legend Accuracy (2/2) - No legend needed, N/A but colors self-explanatory
• SC-06: Title Format (2/2) - Correct format: bar-feature-importance · seaborn · pyplots.ai

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows full range from high (0.18) to low (0.007) importance with good variability
• DQ-02: Realistic Context (7/7) - Credit scoring model is a real, comprehensible ML scenario
• DQ-03: Appropriate Scale (5/5) - Importance values sum to ~1.0, realistic for tree-based models

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) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Uses current seaborn API with hue parameter correctly
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Uses sns.barplot but error bars added via matplotlib's ax.errorbar rather than using seaborn's built-in capabilities

Strengths

• Excellent visual clarity with proper font sizing and no text overlap
• Color gradient effectively communicates relative importance
• Error bars with caps clearly show ensemble variability
• Realistic credit scoring context with appropriate feature names
• Clean code following KISS principles with proper seaborn 0.14+ API usage
• Value annotations positioned well and aid interpretation

Weaknesses

• Library features score is 0/5: Error bars are added manually via matplotlib instead of leveraging seaborn's built-in errorbar capabilities (seaborn barplot can accept errorbar parameter)

Verdict: APPROVED