feat(bokeh): implement roc-curve (#2313)

## Implementation: `roc-curve` - bokeh

Implements the **bokeh** version of `roc-curve`.

**File:** `plots/roc-curve/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526594112)*

---------

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/roc-curve/implementations/bokeh.py

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+""" pyplots.ai
+roc-curve: ROC Curve with AUC
+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, save
+from bokeh.models import ColumnDataSource, Legend
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+
+
+# Data - Simulated ROC curves for three classifiers with different performance levels
+np.random.seed(42)
+
+# Generate ROC curve points using sklearn-like simulation
+# Using the parametric approach: FPR = t, TPR = t^(1/k) where k controls curve shape
+n_points = 200
+t = np.linspace(0, 1, n_points)
+
+# Model 1: Strong classifier (AUC ~0.95) - curve bows far towards top-left
+k1 = 0.15
+fpr_1 = t
+tpr_1 = np.power(t, k1)
+auc_1 = np.trapezoid(tpr_1, fpr_1)
+
+# Model 2: Medium classifier (AUC ~0.82) - moderate curve
+k2 = 0.35
+fpr_2 = t
+tpr_2 = np.power(t, k2)
+auc_2 = np.trapezoid(tpr_2, fpr_2)
+
+# Model 3: Weak classifier (AUC ~0.68) - closer to diagonal
+k3 = 0.6
+fpr_3 = t
+tpr_3 = np.power(t, k3)
+auc_3 = np.trapezoid(tpr_3, fpr_3)
+
+# Random classifier reference line
+fpr_random = np.array([0, 1])
+tpr_random = np.array([0, 1])
+
+# Create ColumnDataSources
+source_1 = ColumnDataSource(data={"fpr": fpr_1, "tpr": tpr_1})
+source_2 = ColumnDataSource(data={"fpr": fpr_2, "tpr": tpr_2})
+source_3 = ColumnDataSource(data={"fpr": fpr_3, "tpr": tpr_3})
+source_random = ColumnDataSource(data={"fpr": fpr_random, "tpr": tpr_random})
+
+# Create figure - Square format preferred for equal aspect ratio
+p = figure(
+    width=3600,
+    height=3600,
+    title="roc-curve · bokeh · pyplots.ai",
+    x_axis_label="False Positive Rate",
+    y_axis_label="True Positive Rate",
+    x_range=(-0.02, 1.02),
+    y_range=(-0.02, 1.02),
+    tools="",
+    toolbar_location=None,
+)
+
+# Plot random classifier reference line (diagonal)
+random_line = p.line(
+    x="fpr", y="tpr", source=source_random, line_width=3, line_dash="dashed", line_color="#888888", alpha=0.8
+)
+
+# Plot ROC curves with distinct colors
+# Using Python Blue (#306998) for best model, then complementary colors
+line_1 = p.line(x="fpr", y="tpr", source=source_1, line_width=5, line_color="#306998", alpha=0.9)
+line_2 = p.line(x="fpr", y="tpr", source=source_2, line_width=5, line_color="#FFD43B", alpha=0.9)
+line_3 = p.line(x="fpr", y="tpr", source=source_3, line_width=5, line_color="#E74C3C", alpha=0.9)
+
+# Create legend with AUC scores
+legend = Legend(
+    items=[
+        (f"Random Forest (AUC = {auc_1:.2f})", [line_1]),
+        (f"Logistic Regression (AUC = {auc_2:.2f})", [line_2]),
+        (f"Decision Tree (AUC = {auc_3:.2f})", [line_3]),
+        ("Random Classifier", [random_line]),
+    ],
+    location="bottom_right",
+)
+
+p.add_layout(legend)
+legend.label_text_font_size = "20pt"
+legend.glyph_height = 30
+legend.glyph_width = 30
+legend.spacing = 15
+legend.padding = 20
+legend.background_fill_alpha = 0.8
+legend.border_line_alpha = 0
+
+# Style the plot
+p.title.text_font_size = "32pt"
+p.title.align = "center"
+p.xaxis.axis_label_text_font_size = "24pt"
+p.yaxis.axis_label_text_font_size = "24pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_font_size = "18pt"
+
+# Grid styling - subtle
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = [6, 4]
+
+# Background and border
+p.background_fill_color = "#FAFAFA"
+p.border_fill_color = "white"
+p.outline_line_color = "#CCCCCC"
+
+# Axis styling
+p.xaxis.axis_line_width = 2
+p.yaxis.axis_line_width = 2
+p.xaxis.major_tick_line_width = 2
+p.yaxis.major_tick_line_width = 2
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="ROC Curve with AUC")

plots/roc-curve/metadata/bokeh.yaml

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+library: bokeh
+specification_id: roc-curve
+created: '2025-12-26T17:39:45Z'
+updated: '2025-12-26T17:50:15Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526594112
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/roc-curve/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/roc-curve/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/roc-curve/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with appropriate line widths and font sizes for the 3600x3600
+    canvas
+  - Correct implementation of ROC curve mathematics using parametric approach
+  - Clean, well-organized code following KISS principles
+  - Proper use of ColumnDataSource for data management
+  - Good color choices that distinguish the three classifiers
+  - Produces both PNG and HTML output showcasing Bokeh's dual output capability
+  weaknesses:
+  - Legend positioned in extreme bottom-right corner; could be better placed inside
+    the plot area or with more padding
+  - Does not utilize Bokeh's interactive features like HoverTool to show threshold
+    values on hover