feat(highcharts): implement bar-feature-importance

[github-actions]

Implementation: bar-feature-importance - highcharts

Implements the highcharts version of bar-feature-importance.

File: plots/bar-feature-importance/implementations/highcharts.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 Random Forest model for house price prediction. There are 15 features displayed, sorted from highest importance at the top (Square Footage with 0.215) to lowest at the bottom (Energy Efficiency Score with 0.004). The bars use a gradient color scheme from dark blue (#306998 for high importance) to light blue (#a8d5f2 for low importance). Each bar has a data label showing the precise importance value positioned just to the right of the bar. The title "bar-feature-importance · highcharts · pyplots.ai" is centered at the top with a subtitle "House Price Prediction - Random Forest Feature Importances" below it. The x-axis shows "Importance Score" ranging from 0 to 0.25. Feature names (e.g., "Square Footage", "Number of Bedrooms", "Location Score") are clearly displayed on the y-axis. The chart has a clean white background with subtle gray gridlines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title, subtitle, axis labels, and feature names are all clearly readable with appropriate font sizes for the 4800x2700 canvas
• VQ-02: No Overlap (8/8) - No overlapping text, feature names are well-spaced, data labels do not overlap bars
• VQ-03: Element Visibility (8/8) - Bars are well-sized, gradient colors clearly distinguish importance levels
• VQ-04: Color Accessibility (5/5) - Blue gradient is colorblind-safe, good contrast against white background
• VQ-05: Layout Balance (4/5) - Good use of canvas space, though the right side has significant whitespace due to the x-axis extending to 0.25 while max value is 0.215
• VQ-06: Axis Labels (1/2) - Y-axis has "Importance Score" label but no units (importance scores are unitless, so this is acceptable)
• VQ-07: Grid & Legend (1/2) - Grid is subtle and appropriate; legend disabled (appropriate for single series) but x-axis tick labels are overly dense

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct horizontal bar chart as specified
• SC-02: Data Mapping (5/5) - Features on y-axis, importance on x-axis, correctly assigned
• SC-03: Required Features (5/5) - Sorted bars, gradient coloring, data labels for precision all present
• SC-04: Data Range (3/3) - All 15 features visible with appropriate axis range
• SC-05: Legend Accuracy (2/2) - No legend needed for single-series chart (appropriate)
• SC-06: Title Format (2/2) - Uses exact format "{spec-id} · {library} · pyplots.ai"

Data Quality (17/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows 15 features with good variation in importance values; however, spec mentions optional error bars for ensemble methods which are not shown despite the data being described as "from ensemble averaging"
• DQ-02: Realistic Context (7/7) - House price prediction is a classic ML use case, features are realistic and meaningful
• DQ-03: Appropriate Scale (4/5) - Importance values sum close to 1.0 (0.997), which is realistic; however individual values could show more spread

Code Quality (9/10 pts)

• CQ-01: KISS Structure (2/3) - Mostly linear structure but contains a helper function importance_to_color() which slightly violates KISS principle
• CQ-02: Reproducibility (3/3) - Deterministic data (no random seed needed as data is hardcoded)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current APIs
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses Highcharts bar chart with data labels and custom styling; however, does not use highcharts-core Python library as suggested in the library rules, instead builds JSON config manually

Strengths

• Excellent implementation of the feature importance bar chart with proper sorting (highest at top)
• Clear gradient color mapping that visually emphasizes feature importance levels
• Data labels positioned cleanly at the end of each bar for precise value reading
• Realistic house price prediction scenario with meaningful feature names
• Proper title format following pyplots.ai conventions
• Clean, professional appearance suitable for publication

Weaknesses

• X-axis tick labels are overly dense (increments of 0.005 create visual clutter)
• Contains a helper function importance_to_color() which deviates slightly from KISS structure
• Does not use the highcharts-core Python library as recommended in library rules
• Error bars not included despite spec mentioning them as valuable for ensemble methods

Verdict: APPROVED