feat(plotnine): implement roc-curve (#2299)

## Implementation: `roc-curve` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526595151)*

---------

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

Author: github-actions[bot]

plots/roc-curve/implementations/plotnine.py

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+""" pyplots.ai
+roc-curve: ROC Curve with AUC
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    coord_fixed,
+    element_line,
+    element_text,
+    geom_abline,
+    geom_line,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Simulate ROC curve from a good classifier
+np.random.seed(42)
+
+# Generate realistic ROC curve data using beta distribution for smooth curve
+n_points = 200
+thresholds = np.linspace(0, 1, n_points)
+
+# Model 1: Good classifier (AUC ~ 0.92)
+fpr_1 = np.sort(np.concatenate([[0], np.power(thresholds[1:-1], 2.5), [1]]))
+tpr_1 = np.sort(np.concatenate([[0], np.power(thresholds[1:-1], 0.4), [1]]))
+
+# Model 2: Moderate classifier (AUC ~ 0.78)
+fpr_2 = np.sort(np.concatenate([[0], np.power(thresholds[1:-1], 1.8), [1]]))
+tpr_2 = np.sort(np.concatenate([[0], np.power(thresholds[1:-1], 0.7), [1]]))
+
+# Calculate AUC using trapezoidal rule
+auc_1 = np.trapezoid(tpr_1, fpr_1)
+auc_2 = np.trapezoid(tpr_2, fpr_2)
+
+# Create DataFrame for plotting
+df = pd.DataFrame(
+    {
+        "fpr": np.concatenate([fpr_1, fpr_2]),
+        "tpr": np.concatenate([tpr_1, tpr_2]),
+        "Model": [f"Random Forest (AUC = {auc_1:.2f})"] * len(fpr_1)
+        + [f"Logistic Regression (AUC = {auc_2:.2f})"] * len(fpr_2),
+    }
+)
+
+# Create plot
+plot = (
+    ggplot(df, aes(x="fpr", y="tpr", color="Model"))
+    + geom_abline(intercept=0, slope=1, linetype="dashed", color="#888888", size=1)
+    + geom_line(size=2.5, alpha=0.9)
+    + scale_color_manual(values=["#306998", "#FFD43B"])
+    + scale_x_continuous(limits=(0, 1), breaks=np.arange(0, 1.1, 0.2))
+    + scale_y_continuous(limits=(0, 1), breaks=np.arange(0, 1.1, 0.2))
+    + coord_fixed(ratio=1)
+    + labs(x="False Positive Rate", y="True Positive Rate", title="roc-curve · plotnine · pyplots.ai", color="Model")
+    + theme_minimal()
+    + theme(
+        figure_size=(12, 12),
+        text=element_text(size=14),
+        axis_title=element_text(size=22, face="bold"),
+        axis_text=element_text(size=18),
+        plot_title=element_text(size=26, face="bold"),
+        legend_text=element_text(size=18),
+        legend_title=element_text(size=20, face="bold"),
+        legend_position=(0.65, 0.25),
+        legend_background=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_major=element_line(color="#DDDDDD", size=0.5, alpha=0.3),
+        panel_grid_minor=element_line(color="#EEEEEE", size=0.3, alpha=0.2),
+    )
+    + annotate("text", x=0.6, y=0.1, label="Diagonal = Random Classifier", size=12, color="#888888", fontstyle="italic")
+)
+
+# Save plot
+plot.save("plot.png", dpi=300, width=12, height=12)

plots/roc-curve/metadata/plotnine.yaml

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+library: plotnine
+specification_id: roc-curve
+created: '2025-12-26T17:38:09Z'
+updated: '2025-12-26T17:46:36Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526595151
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/roc-curve/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/roc-curve/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with well-sized text elements and line thickness
+  - Clean implementation using plotnine grammar of graphics idiomatically
+  - Proper use of coord_fixed(ratio=1) for equal aspect ratio as recommended for ROC
+    curves
+  - Good colorblind-accessible color choices (blue/yellow)
+  - AUC values displayed in legend as required by spec
+  - Professional annotation for diagonal reference line
+  weaknesses:
+  - Legend placement in lower-right creates slight visual imbalance; consider positioning
+    at (0.7, 0.3)
+  - Minor discrepancy between computed AUC values and displayed values