feat(seaborn): implement roc-curve (#2305)

## Implementation: `roc-curve` - seaborn

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526593384)*

---------

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plots/roc-curve/implementations/seaborn.py

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+""" pyplots.ai
+roc-curve: ROC Curve with AUC
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+# Generate synthetic classification data for three models with different performance levels
+n_samples = 1000
+
+# True labels (binary)
+y_true = np.concatenate([np.zeros(500), np.ones(500)])
+
+# Model 1: Good classifier (AUC ~0.92)
+y_scores_model1 = np.concatenate(
+    [
+        np.random.beta(2, 5, 500),  # Low scores for class 0
+        np.random.beta(5, 2, 500),  # High scores for class 1
+    ]
+)
+
+# Model 2: Moderate classifier (AUC ~0.78)
+y_scores_model2 = np.concatenate([np.random.beta(2, 3, 500), np.random.beta(3, 2, 500)])
+
+# Model 3: Weak classifier (AUC ~0.65)
+y_scores_model3 = np.concatenate([np.random.beta(2, 2.5, 500), np.random.beta(2.5, 2, 500)])
+
+# Compute ROC curves manually at various thresholds
+n_thresholds = 200
+thresholds = np.linspace(0, 1, n_thresholds)
+
+# Model 1 ROC
+tpr1, fpr1 = [], []
+for thresh in thresholds:
+    y_pred = (y_scores_model1 >= thresh).astype(int)
+    tp = np.sum((y_pred == 1) & (y_true == 1))
+    fp = np.sum((y_pred == 1) & (y_true == 0))
+    fn = np.sum((y_pred == 0) & (y_true == 1))
+    tn = np.sum((y_pred == 0) & (y_true == 0))
+    tpr1.append(tp / (tp + fn) if (tp + fn) > 0 else 0)
+    fpr1.append(fp / (fp + tn) if (fp + tn) > 0 else 0)
+fpr1, tpr1 = np.array(fpr1), np.array(tpr1)
+
+# Model 2 ROC
+tpr2, fpr2 = [], []
+for thresh in thresholds:
+    y_pred = (y_scores_model2 >= thresh).astype(int)
+    tp = np.sum((y_pred == 1) & (y_true == 1))
+    fp = np.sum((y_pred == 1) & (y_true == 0))
+    fn = np.sum((y_pred == 0) & (y_true == 1))
+    tn = np.sum((y_pred == 0) & (y_true == 0))
+    tpr2.append(tp / (tp + fn) if (tp + fn) > 0 else 0)
+    fpr2.append(fp / (fp + tn) if (fp + tn) > 0 else 0)
+fpr2, tpr2 = np.array(fpr2), np.array(tpr2)
+
+# Model 3 ROC
+tpr3, fpr3 = [], []
+for thresh in thresholds:
+    y_pred = (y_scores_model3 >= thresh).astype(int)
+    tp = np.sum((y_pred == 1) & (y_true == 1))
+    fp = np.sum((y_pred == 1) & (y_true == 0))
+    fn = np.sum((y_pred == 0) & (y_true == 1))
+    tn = np.sum((y_pred == 0) & (y_true == 0))
+    tpr3.append(tp / (tp + fn) if (tp + fn) > 0 else 0)
+    fpr3.append(fp / (fp + tn) if (fp + tn) > 0 else 0)
+fpr3, tpr3 = np.array(fpr3), np.array(tpr3)
+
+# Calculate AUC scores using trapezoidal rule
+idx1 = np.argsort(fpr1)
+auc1 = np.trapezoid(tpr1[idx1], fpr1[idx1])
+idx2 = np.argsort(fpr2)
+auc2 = np.trapezoid(tpr2[idx2], fpr2[idx2])
+idx3 = np.argsort(fpr3)
+auc3 = np.trapezoid(tpr3[idx3], fpr3[idx3])
+
+# Set seaborn style
+sns.set_style("whitegrid")
+sns.set_context("talk", font_scale=1.2)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot ROC curves using seaborn lineplot
+sns.lineplot(x=fpr1, y=tpr1, ax=ax, linewidth=3.5, color="#306998", label=f"Random Forest (AUC = {auc1:.3f})")
+sns.lineplot(x=fpr2, y=tpr2, ax=ax, linewidth=3.5, color="#FFD43B", label=f"Logistic Regression (AUC = {auc2:.3f})")
+sns.lineplot(x=fpr3, y=tpr3, ax=ax, linewidth=3.5, color="#E74C3C", label=f"Decision Tree (AUC = {auc3:.3f})")
+
+# Diagonal reference line (random classifier)
+ax.plot([0, 1], [0, 1], linestyle="--", linewidth=2.5, color="#7F8C8D", label="Random Classifier (AUC = 0.500)")
+
+# Styling
+ax.set_xlabel("False Positive Rate (FPR)", fontsize=22)
+ax.set_ylabel("True Positive Rate (TPR)", fontsize=22)
+ax.set_title("roc-curve · seaborn · pyplots.ai", fontsize=26, fontweight="bold")
+ax.tick_params(axis="both", labelsize=18)
+
+# Set axis limits and aspect
+ax.set_xlim([-0.02, 1.02])
+ax.set_ylim([-0.02, 1.02])
+ax.set_aspect("equal", adjustable="box")
+
+# Legend
+ax.legend(loc="lower right", fontsize=18, framealpha=0.95)
+
+# Grid styling
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/roc-curve/metadata/seaborn.yaml

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+library: seaborn
+specification_id: roc-curve
+created: '2025-12-26T17:38:39Z'
+updated: '2025-12-26T17:47:35Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526593384
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/roc-curve/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/roc-curve/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with properly sized text and well-contrasted line colors
+  - Complete spec compliance including diagonal reference line, AUC annotations, and
+    correct title format
+  - Realistic machine learning context comparing three classifiers with believable
+    performance differences
+  - Clean, readable code structure following KISS principles
+  - Equal aspect ratio makes the curve shapes easy to interpret correctly
+  weaknesses:
+  - Uses seaborn minimally - only lineplot and styling, which does not showcase seaborn
+    distinctive features
+  - Yellow/gold color for Logistic Regression could be slightly harder to distinguish
+    for colorblind viewers