feat(bokeh): implement silhouette-basic (#2369)

## Implementation: `silhouette-basic` - bokeh

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528202708)*

---------

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/silhouette-basic/implementations/bokeh.py

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+""" pyplots.ai
+silhouette-basic: Silhouette Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Label, Span
+from bokeh.plotting import figure
+
+
+# Data - simulating silhouette analysis of customer segmentation (3 clusters)
+# Realistic scenario: clustering customers by purchase behavior
+np.random.seed(42)
+
+n_clusters = 3
+cluster_sizes = [50, 55, 45]  # Different sized clusters
+
+# Generate realistic silhouette values for each cluster
+# Cluster 0: Well-separated cluster (high silhouette scores)
+cluster0_vals = np.clip(np.random.beta(8, 2, cluster_sizes[0]) * 0.6 + 0.35, 0.1, 0.95)
+# Cluster 1: Good cluster with some overlap (medium-high scores)
+cluster1_vals = np.clip(np.random.beta(5, 2, cluster_sizes[1]) * 0.5 + 0.25, 0.0, 0.85)
+# Cluster 2: Some ambiguous samples (includes negative values)
+cluster2_vals = np.clip(np.random.beta(4, 3, cluster_sizes[2]) * 0.8 - 0.1, -0.15, 0.75)
+
+# Combine all values
+silhouette_vals = np.concatenate([cluster0_vals, cluster1_vals, cluster2_vals])
+cluster_labels = np.concatenate(
+    [
+        np.zeros(cluster_sizes[0], dtype=int),
+        np.ones(cluster_sizes[1], dtype=int),
+        np.full(cluster_sizes[2], 2, dtype=int),
+    ]
+)
+
+# Calculate average silhouette score
+avg_silhouette = float(np.mean(silhouette_vals))
+
+# Prepare data for plotting - sorted silhouette values within each cluster
+y_lower = 15
+bar_data = {"x": [], "y": [], "width": [], "height": [], "color": []}
+cluster_info = []  # For labels
+
+# Colors for clusters - Python blue, yellow, and a colorblind-safe third color
+colors = ["#306998", "#FFD43B", "#2ECC71"]
+
+for i in range(n_clusters):
+    # Get silhouette values for this cluster
+    cluster_mask = cluster_labels == i
+    cluster_silhouette_vals = silhouette_vals[cluster_mask]
+    cluster_silhouette_vals.sort()
+
+    cluster_size = len(cluster_silhouette_vals)
+    y_upper = y_lower + cluster_size
+
+    # Store center position for cluster label
+    cluster_center = (y_lower + y_upper) / 2
+    cluster_avg = float(np.mean(cluster_silhouette_vals))
+    cluster_info.append((cluster_center, cluster_avg, cluster_size, i))
+
+    # Add bars for each sample in cluster
+    for j, val in enumerate(cluster_silhouette_vals):
+        bar_data["x"].append(val / 2)  # Center of bar
+        bar_data["y"].append(y_lower + j + 0.5)  # Y position
+        bar_data["width"].append(abs(val))  # Width = silhouette value
+        bar_data["height"].append(0.85)  # Slightly less than 1 for gap
+        bar_data["color"].append(colors[i])
+
+    y_lower = y_upper + 15  # Gap between clusters
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="silhouette-basic · bokeh · pyplots.ai",
+    x_axis_label="Silhouette Coefficient",
+    y_axis_label="Cluster (samples sorted by silhouette score)",
+    x_range=(-0.3, 1.25),
+    y_range=(0, y_lower + 5),
+    tools="",
+)
+
+# Style the figure - larger text for 4800x2700 canvas
+p.title.text_font_size = "42pt"
+p.title.text_font_style = "bold"
+p.xaxis.axis_label_text_font_size = "32pt"
+p.yaxis.axis_label_text_font_size = "32pt"
+p.xaxis.major_label_text_font_size = "24pt"
+p.yaxis.major_label_text_font_size = "24pt"
+p.xaxis.axis_label_standoff = 25
+p.yaxis.axis_label_standoff = 25
+
+# Create data source for bars
+source = ColumnDataSource(data=bar_data)
+
+# Draw horizontal bars using hbar for proper horizontal bar rendering
+p.rect(x="x", y="y", width="width", height="height", color="color", source=source, line_color=None, alpha=0.85)
+
+# Add vertical line for average silhouette score
+avg_line = Span(location=avg_silhouette, dimension="height", line_color="#E74C3C", line_width=5, line_dash="dashed")
+p.add_layout(avg_line)
+
+# Add average silhouette score label at top
+avg_label = Label(
+    x=avg_silhouette + 0.03,
+    y=y_lower - 5,
+    text=f"Average: {avg_silhouette:.3f}",
+    text_font_size="28pt",
+    text_color="#E74C3C",
+    text_font_style="bold",
+)
+p.add_layout(avg_label)
+
+# Add cluster labels with their average silhouette scores - positioned to avoid overlap
+for center_y, cluster_avg, size, cluster_idx in cluster_info:
+    # Position label to the left side, outside the bars
+    cluster_label = Label(
+        x=-0.22,
+        y=center_y,
+        text=f"Cluster {cluster_idx}",
+        text_font_size="26pt",
+        text_color=colors[cluster_idx],
+        text_font_style="bold",
+        text_align="left",
+        text_baseline="middle",
+    )
+    p.add_layout(cluster_label)
+
+    # Add cluster stats on the right side
+    stats_label = Label(
+        x=1.01,
+        y=center_y,
+        text=f"n={size}, avg={cluster_avg:.2f}",
+        text_font_size="22pt",
+        text_color=colors[cluster_idx],
+        text_font_style="normal",
+        text_align="left",
+        text_baseline="middle",
+    )
+    p.add_layout(stats_label)
+
+# Style grid
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.0  # No horizontal grid
+p.xgrid.grid_line_dash = [6, 4]
+
+# Remove y-axis ticks (sample indices are not meaningful)
+p.yaxis.major_tick_line_color = None
+p.yaxis.minor_tick_line_color = None
+p.yaxis.major_label_text_font_size = "0pt"
+
+# Add vertical line at x=0 for reference
+zero_line = Span(location=0, dimension="height", line_color="#666666", line_width=2, line_alpha=0.5)
+p.add_layout(zero_line)
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+p.outline_line_color = None
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+
+output_file("plot.html")
+save(p)

plots/silhouette-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: silhouette-basic
+created: '2025-12-26T19:38:38Z'
+updated: '2025-12-26T19:46:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528202708
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/silhouette-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent text sizing scaled appropriately for 4800x2700 canvas (42pt title, 32pt
+    labels)
+  - Clean separation between clusters with 15-unit gaps making visual grouping clear
+  - Proper use of Bokeh ColumnDataSource pattern and Span/Label models
+  - Colorblind-safe palette (blue/yellow/green) with good contrast
+  - Sorted bars within clusters exactly as spec requires
+  - Includes both PNG and HTML output leveraging Bokeh dual-format capability
+  - Realistic customer segmentation scenario with varied cluster quality
+  weaknesses:
+  - Stats labels on right side (n=XX, avg=X.XX) appear relatively small compared to
+    other text elements
+  - Could benefit from hover tooltips showing individual sample silhouette values
+    for interactivity
+  - No explicit legend element though cluster labels serve the purpose adequately