feat(letsplot): implement bar-permutation-importance (#3031)

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

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

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

**Parent Issue:** #2998

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617503531)*

---------

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

Author: github-actions[bot]

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

@@ -0,0 +1,97 @@
+""" pyplots.ai
+bar-permutation-importance: Permutation Feature Importance Plot
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    coord_flip,
+    element_line,
+    element_text,
+    geom_bar,
+    geom_errorbar,
+    geom_vline,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_fill_gradient,
+    scale_x_discrete,
+    theme,
+    theme_minimal,
+)
+
+
+LetsPlot.setup_html()
+
+# Data: Simulated permutation importance from a Random Forest model
+np.random.seed(42)
+
+features = [
+    "Income Level",
+    "Credit Score",
+    "Employment Years",
+    "Debt Ratio",
+    "Account Age",
+    "Payment History",
+    "Loan Amount",
+    "Interest Rate",
+    "Property Value",
+    "Monthly Expenses",
+    "Savings Balance",
+    "Number of Accounts",
+    "Recent Inquiries",
+    "Education Level",
+    "Region Code",
+]
+
+# Generate importance values - higher for more predictive features
+base_importance = np.array(
+    [0.15, 0.12, 0.09, 0.08, 0.06, 0.05, 0.04, 0.03, 0.025, 0.02, 0.015, 0.01, 0.008, 0.005, 0.002]
+)
+# Add some noise
+importance_mean = base_importance + np.random.uniform(-0.005, 0.005, len(features))
+importance_std = np.random.uniform(0.003, 0.02, len(features))
+
+# Create DataFrame and sort by importance
+df = pd.DataFrame({"feature": features, "importance_mean": importance_mean, "importance_std": importance_std})
+df = df.sort_values("importance_mean", ascending=True).reset_index(drop=True)
+
+# Create ordered categorical for proper y-axis ordering
+df["feature"] = pd.Categorical(df["feature"], categories=df["feature"].tolist(), ordered=True)
+
+# Calculate error bar positions
+df["ymin"] = df["importance_mean"] - df["importance_std"]
+df["ymax"] = df["importance_mean"] + df["importance_std"]
+
+# Create the plot with horizontal bars using geom_bar + coord_flip
+plot = (
+    ggplot(df, aes(x="feature", y="importance_mean", fill="importance_mean"))
+    + geom_bar(stat="identity", width=0.7, alpha=0.9)
+    + geom_errorbar(aes(ymin="ymin", ymax="ymax"), width=0.25, size=0.8, color="#333333")
+    + geom_vline(xintercept=0, color="#888888", size=0.8, linetype="dashed")
+    + coord_flip()
+    + scale_fill_gradient(low="#FFD43B", high="#306998", guide="none")
+    + scale_x_discrete()
+    + labs(x="Feature", y="Mean Decrease in Model Score", title="bar-permutation-importance · letsplot · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        axis_text_y=element_text(size=14),
+        panel_grid_major_x=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor_x=element_line(color="#EEEEEE", size=0.3),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x for 4800x2700) - path="." saves to current directory
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save as HTML for interactive view
+ggsave(plot, "plot.html", path=".")

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

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+library: letsplot
+specification_id: bar-permutation-importance
+created: '2025-12-31T10:56:21Z'
+updated: '2025-12-31T11:20:08Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617503531
+issue: 2998
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-permutation-importance/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-permutation-importance/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/bar-permutation-importance/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with well-sized text and properly spaced bars
+  - Color gradient from yellow to blue effectively emphasizes importance ranking
+  - Error bars are clearly visible and informative with appropriate styling
+  - Realistic credit/lending domain data that demonstrates the plot type well
+  - Clean, KISS-compliant code structure with proper reproducibility seed
+  - Good use of lets-plot grammar of graphics with theme_minimal and scale_fill_gradient
+  weaknesses:
+  - The vertical reference line at x=0 is not rendering correctly with coord_flip()
+    - geom_vline with xintercept=0 creates a horizontal line instead of vertical after
+    the coordinate flip
+  - HTML output could leverage lets-plot interactivity features like tooltips