feat(letsplot): implement silhouette-basic (#2371)

## Implementation: `silhouette-basic` - letsplot

Implements the **letsplot** version of `silhouette-basic`.

**File:** `plots/silhouette-basic/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528205803)*

---------

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

Author: github-actions[bot]

plots/silhouette-basic/implementations/letsplot.py

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+""" pyplots.ai
+silhouette-basic: Silhouette Plot
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+from sklearn.cluster import KMeans
+from sklearn.datasets import load_iris
+from sklearn.metrics import silhouette_samples, silhouette_score
+
+
+LetsPlot.setup_html()
+
+# Data - Clustering iris dataset into 3 groups
+np.random.seed(42)
+iris = load_iris()
+X = iris.data
+n_clusters = 3
+
+# Perform K-means clustering
+kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+cluster_labels = kmeans.fit_predict(X)
+
+# Calculate silhouette scores
+silhouette_vals = silhouette_samples(X, cluster_labels)
+avg_silhouette = silhouette_score(X, cluster_labels)
+
+# Build dataframe for plotting - sort samples within each cluster by silhouette score
+data_rows = []
+y_position = 0
+cluster_centers = []
+cluster_avg_scores = []
+
+colors = ["#306998", "#FFD43B", "#DC2626"]  # Python Blue, Python Yellow, Red
+
+for cluster_idx in range(n_clusters):
+    # Get samples in this cluster
+    mask = cluster_labels == cluster_idx
+    cluster_silhouettes = silhouette_vals[mask]
+    cluster_silhouettes_sorted = np.sort(cluster_silhouettes)
+
+    # Calculate cluster average
+    cluster_avg = cluster_silhouettes.mean()
+    cluster_avg_scores.append(cluster_avg)
+
+    # Track the center position for annotation
+    cluster_start = y_position
+
+    # Add each sample as a row
+    for sil_val in cluster_silhouettes_sorted:
+        data_rows.append(
+            {"y": y_position, "silhouette": sil_val, "cluster": f"Cluster {cluster_idx}", "cluster_idx": cluster_idx}
+        )
+        y_position += 1
+
+    cluster_end = y_position - 1
+    cluster_centers.append((cluster_start + cluster_end) / 2)
+
+    # Add small gap between clusters
+    y_position += 5
+
+df = pd.DataFrame(data_rows)
+df["x_start"] = 0  # Starting x position for horizontal bars
+
+# Create annotation dataframe for cluster labels
+annotation_df = pd.DataFrame(
+    {
+        "y": cluster_centers,
+        "x": [-0.12] * n_clusters,
+        "label": [f"Cluster {i}\n(avg: {cluster_avg_scores[i]:.2f})" for i in range(n_clusters)],
+    }
+)
+
+# Create the silhouette plot using horizontal bars
+plot = (
+    ggplot()
+    + geom_segment(aes(x="x_start", xend="silhouette", y="y", yend="y", color="cluster"), data=df, size=1.5)
+    + geom_vline(xintercept=avg_silhouette, color="#333333", linetype="dashed", size=1)
+    + geom_text(aes(x="x", y="y", label="label"), data=annotation_df, size=14, hjust=1)
+    + scale_color_manual(values=colors)
+    + labs(
+        x="Silhouette Coefficient",
+        y="Sample Index (sorted within cluster)",
+        title="silhouette-basic · letsplot · pyplots.ai",
+    )
+    + xlim(-0.3, 1.0)
+    + theme_minimal()
+    + theme(
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        legend_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_position="right",
+        axis_text_y=element_blank(),
+        axis_ticks_y=element_blank(),
+        panel_grid_major_y=element_blank(),
+        panel_grid_minor_y=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Add annotation for average silhouette line
+avg_label_df = pd.DataFrame(
+    {"x": [avg_silhouette + 0.03], "y": [max(df["y"]) * 0.95], "label": [f"Avg: {avg_silhouette:.2f}"]}
+)
+plot = plot + geom_text(aes(x="x", y="y", label="label"), data=avg_label_df, size=14, hjust=0)
+
+# Save as PNG (scale 3x to get 4800 x 2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save as HTML for interactive version
+ggsave(plot, "plot.html", path=".")

plots/silhouette-basic/metadata/letsplot.yaml

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+library: letsplot
+specification_id: silhouette-basic
+created: '2025-12-26T19:39:00Z'
+updated: '2025-12-26T19:44:40Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528205803
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of geom_segment to create horizontal bars sorted within each cluster,
+    producing the characteristic silhouette shape
+  - Clear cluster annotations with average scores positioned appropriately on the
+    left side
+  - Well-implemented dashed vertical line showing overall average silhouette score
+    with label
+  - Uses realistic Iris dataset which is a classic clustering example
+  - Good color scheme with Python branding colors that are also colorblind-friendly
+  - Proper ggplot2 grammar style with theme customization for clean appearance
+  weaknesses:
+  - X-axis extends to -0.3 but no negative silhouette values exist in the data, creating
+    unnecessary whitespace
+  - Legend is somewhat redundant since cluster labels are already annotated directly
+    on the plot
+  - The Iris dataset with 3 clusters produces all positive silhouette values; showing
+    some negative values would better demonstrate the plot ability to highlight potential
+    misclassifications