feat(seaborn): implement bar-permutation-importance (#3024)

## Implementation: `bar-permutation-importance` - seaborn

Implements the **seaborn** version of `bar-permutation-importance`.

**File:** `plots/bar-permutation-importance/implementations/seaborn.py`

**Parent Issue:** #2998

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617498003)*

---------

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

Author: github-actions[bot]

plots/bar-permutation-importance/implementations/seaborn.py

@@ -0,0 +1,67 @@
+""" pyplots.ai
+bar-permutation-importance: Permutation Feature Importance Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from sklearn.datasets import load_wine
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.inspection import permutation_importance
+
+
+# Data - Using wine dataset with permutation importance from Random Forest
+wine = load_wine()
+X, y = wine.data, wine.target
+feature_names = wine.feature_names
+
+# Train a Random Forest model
+np.random.seed(42)
+clf = RandomForestClassifier(n_estimators=100, random_state=42)
+clf.fit(X, y)
+
+# Calculate permutation importance
+perm_importance = permutation_importance(clf, X, y, n_repeats=10, random_state=42)
+
+# Extract importance values
+importance_mean = perm_importance.importances_mean
+importance_std = perm_importance.importances_std
+
+# Sort by importance (descending)
+sorted_idx = np.argsort(importance_mean)[::-1]
+sorted_features = [feature_names[i] for i in sorted_idx]
+sorted_mean = importance_mean[sorted_idx]
+sorted_std = importance_std[sorted_idx]
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Color palette based on importance values (sequential)
+colors = sns.color_palette("viridis", n_colors=len(sorted_features))
+# Map colors to sorted importance (highest importance = darkest)
+color_order = list(reversed(colors))
+
+# Create horizontal bar plot with seaborn
+y_positions = np.arange(len(sorted_features))
+sns.barplot(x=sorted_mean, y=sorted_features, hue=sorted_features, palette=color_order, legend=False, ax=ax, orient="h")
+
+# Add error bars manually (seaborn barplot doesn't support horizontal error bars directly)
+ax.errorbar(
+    sorted_mean, y_positions, xerr=sorted_std, fmt="none", ecolor="#333333", elinewidth=2, capsize=5, capthick=2
+)
+
+# Add vertical reference line at x=0
+ax.axvline(x=0, color="#333333", linestyle="-", linewidth=1.5, alpha=0.7)
+
+# Styling
+ax.set_xlabel("Mean Importance (Decrease in Accuracy)", fontsize=20)
+ax.set_ylabel("Feature", fontsize=20)
+ax.set_title("bar-permutation-importance · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, axis="x", alpha=0.3, linestyle="--")
+
+# Adjust layout
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/bar-permutation-importance/metadata/seaborn.yaml

@@ -0,0 +1,26 @@
+library: seaborn
+specification_id: bar-permutation-importance
+created: '2025-12-31T10:56:07Z'
+updated: '2025-12-31T11:06:35Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617498003
+issue: 2998
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-permutation-importance/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-permutation-importance/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - 'Excellent implementation of the spec with all required features: sorted bars,
+    error bars, color gradient, and x=0 reference line'
+  - Uses real scikit-learn wine dataset with actual permutation importance calculation
+  - Proper seaborn 0.14+ API usage with hue parameter to apply palette
+  - Text sizing follows guidelines perfectly (24/20/16pt)
+  - Viridis palette provides excellent colorblind accessibility
+  weaknesses:
+  - Error bars added via matplotlib errorbar rather than exploring seaborn native
+    error bar capabilities in barplot
+  - Grid uses dashed style which is slightly more visually distracting than solid
+    with low alpha