feat(pygal): implement bar-feature-importance (#2321)

## Implementation: `bar-feature-importance` - pygal

Implements the **pygal** version of `bar-feature-importance`.

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526639091)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

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

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+""" pyplots.ai
+bar-feature-importance: Feature Importance Bar Chart
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import pygal
+from pygal.style import Style
+
+
+# Data - Feature importances from a RandomForest classifier (house price prediction)
+features = [
+    "OverallQual",
+    "GrLivArea",
+    "TotalBsmtSF",
+    "GarageCars",
+    "YearBuilt",
+    "FullBath",
+    "TotRmsAbvGrd",
+    "Fireplaces",
+    "BsmtQual",
+    "LotArea",
+    "GarageArea",
+    "YearRemodAdd",
+    "KitchenQual",
+    "Neighborhood",
+    "ExterQual",
+]
+importances = [0.245, 0.182, 0.098, 0.087, 0.076, 0.054, 0.048, 0.042, 0.038, 0.035, 0.031, 0.024, 0.019, 0.012, 0.009]
+
+# Sort by importance (descending) - pygal renders bottom to top, so reverse for highest at top
+sorted_pairs = sorted(zip(features, importances, strict=True), key=lambda x: x[1], reverse=False)
+sorted_features = [p[0] for p in sorted_pairs]
+sorted_importances = [p[1] for p in sorted_pairs]
+
+# Generate colors from light blue to Python Blue based on importance
+min_imp = min(sorted_importances)
+max_imp = max(sorted_importances)
+
+
+def importance_to_color(value):
+    """Map importance to color gradient: light blue -> Python Blue (#306998)"""
+    if max_imp == min_imp:
+        ratio = 1
+    else:
+        ratio = (value - min_imp) / (max_imp - min_imp)
+    # Interpolate from light blue (180, 210, 240) to Python Blue (48, 105, 152)
+    r = int(180 - ratio * (180 - 48))
+    g = int(210 - ratio * (210 - 105))
+    b = int(240 - ratio * (240 - 152))
+    return f"#{r:02x}{g:02x}{b:02x}"
+
+
+# Custom style for large canvas (4800 x 2700 px)
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998",),
+    font_family="sans-serif",
+    title_font_size=72,
+    label_font_size=42,
+    major_label_font_size=38,
+    legend_font_size=40,
+    value_font_size=36,
+    value_colors=("#333333",),
+    stroke_width=0,
+)
+
+# Create horizontal bar chart
+chart = pygal.HorizontalBar(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="bar-feature-importance · pygal · pyplots.ai",
+    x_title="Importance Score",
+    show_legend=False,
+    show_y_guides=False,
+    show_x_guides=True,
+    print_values=True,
+    print_values_position="top",
+    value_formatter=lambda x: f"{x:.3f}",
+    margin=60,
+    spacing=15,
+    truncate_label=-1,
+    x_label_rotation=0,
+)
+
+# Set feature names as y-axis labels (x_labels become y-axis in horizontal chart)
+chart.x_labels = sorted_features
+
+# Add data with individual colors per bar
+bar_data = [{"value": imp, "color": importance_to_color(imp)} for imp in sorted_importances]
+chart.add("Importance", bar_data)
+
+# Save outputs
+chart.render_to_file("plot.html")
+chart.render_to_png("plot.png")

plots/bar-feature-importance/metadata/pygal.yaml

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+library: pygal
+specification_id: bar-feature-importance
+created: '2025-12-26T17:41:36Z'
+updated: '2025-12-26T17:52:21Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526639091
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/pygal/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of pygal 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
+  - X-axis could be tighter to data range to reduce unused whitespace on right side