feat(altair): implement bar-feature-importance (#2317)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526603463)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

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

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+""" pyplots.ai
+bar-feature-importance: Feature Importance Bar Chart
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Feature importances from a hypothetical RandomForest model
+np.random.seed(42)
+
+features = [
+    "customer_lifetime_value",
+    "purchase_frequency",
+    "avg_order_value",
+    "days_since_last_purchase",
+    "total_purchases",
+    "account_age_months",
+    "email_open_rate",
+    "website_visits",
+    "support_tickets",
+    "referral_count",
+    "cart_abandonment_rate",
+    "discount_usage",
+    "mobile_app_usage",
+    "newsletter_subscribed",
+    "social_media_engagement",
+]
+
+# Realistic importance scores (sum to ~1.0 for tree-based models)
+importances = np.array(
+    [0.182, 0.156, 0.134, 0.098, 0.087, 0.072, 0.058, 0.051, 0.042, 0.038, 0.031, 0.022, 0.015, 0.009, 0.005]
+)
+
+# Standard deviations for ensemble variability
+stds = importances * np.random.uniform(0.15, 0.35, len(importances))
+
+df = pd.DataFrame({"feature": features, "importance": importances, "std": stds})
+
+# Sort by importance for display
+df = df.sort_values("importance", ascending=True).reset_index(drop=True)
+
+# Create base chart
+base = alt.Chart(df).encode(
+    y=alt.Y("feature:N", sort=None, title="Feature", axis=alt.Axis(labelFontSize=16, titleFontSize=20, labelLimit=300)),
+    x=alt.X("importance:Q", title="Importance Score", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+    tooltip=[
+        alt.Tooltip("feature:N", title="Feature"),
+        alt.Tooltip("importance:Q", title="Importance", format=".3f"),
+        alt.Tooltip("std:Q", title="Std Dev", format=".3f"),
+    ],
+)
+
+# Bars with color gradient based on importance
+bars = base.mark_bar(size=30).encode(color=alt.Color("importance:Q", scale=alt.Scale(scheme="blues"), legend=None))
+
+# Error bars
+error_bars = (
+    base.mark_errorbar(color="#333333", thickness=2)
+    .encode(x=alt.X("x_min:Q", title=""), x2="x_max:Q")
+    .transform_calculate(x_min="datum.importance - datum.std", x_max="datum.importance + datum.std")
+)
+
+# Text labels at end of bars
+text = (
+    base.mark_text(align="left", baseline="middle", dx=5, fontSize=14, fontWeight="bold")
+    .encode(text=alt.Text("importance:Q", format=".3f"), x=alt.X("text_x:Q"))
+    .transform_calculate(text_x="datum.importance + datum.std + 0.005")
+)
+
+# Combine layers
+chart = (
+    (bars + error_bars + text)
+    .properties(
+        width=1400,
+        height=800,
+        title=alt.Title("bar-feature-importance · altair · pyplots.ai", fontSize=28, anchor="start", offset=20),
+    )
+    .configure_axis(labelFontSize=16, titleFontSize=20, gridColor="#e0e0e0", gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG (4800 x 2700 at scale_factor=3)
+chart.save("plot.png", scale_factor=3.0)
+
+# Save interactive HTML version
+chart.save("plot.html")

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

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+library: altair
+specification_id: bar-feature-importance
+created: '2025-12-26T17:40:35Z'
+updated: '2025-12-26T17:51:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526603463
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/altair/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the specification with all required features (sorting,
+    color gradient, error bars, text annotations)
+  - Clean, readable code following KISS principles with proper layered chart composition
+  - Realistic customer analytics context with meaningful feature names
+  - Good use of Altair's declarative encoding and color scales
+  weaknesses:
+  - Grid opacity at 0.3 could be reduced further (0.2) for subtler appearance
+  - Unnecessary plot.html output alongside plot.png