feat(seaborn): implement precision-recall (#2326)

## Implementation: `precision-recall` - seaborn

Implements the **seaborn** version of `precision-recall`.

**File:** `plots/precision-recall/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20527869532)*

---------

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

Author: github-actions[bot]

plots/precision-recall/implementations/seaborn.py

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+""" pyplots.ai
+precision-recall: Precision-Recall Curve
+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
+from sklearn.metrics import average_precision_score, precision_recall_curve
+
+
+# Data - Simulate binary classification with imbalanced classes (fraud detection scenario)
+np.random.seed(42)
+n_samples = 1000
+n_positive = 100  # 10% positive class (imbalanced)
+
+# Ground truth labels
+y_true = np.zeros(n_samples)
+y_true[:n_positive] = 1
+np.random.shuffle(y_true)
+
+# Simulate classifier scores - better separation for positives
+y_scores_good = np.where(
+    y_true == 1,
+    np.random.beta(5, 2, n_samples),  # Positives: higher scores
+    np.random.beta(2, 5, n_samples),  # Negatives: lower scores
+)
+
+y_scores_moderate = np.where(
+    y_true == 1,
+    np.random.beta(3, 2, n_samples),  # Positives: moderately higher
+    np.random.beta(2, 3, n_samples),  # Negatives: moderately lower
+)
+
+# Calculate precision-recall curves
+precision_good, recall_good, _ = precision_recall_curve(y_true, y_scores_good)
+precision_moderate, recall_moderate, _ = precision_recall_curve(y_true, y_scores_moderate)
+
+# Average precision scores
+ap_good = average_precision_score(y_true, y_scores_good)
+ap_moderate = average_precision_score(y_true, y_scores_moderate)
+
+# Baseline (random classifier)
+baseline = n_positive / n_samples
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+sns.set_style("whitegrid")
+
+# Plot PR curves using seaborn's lineplot style with step interpolation
+# Good classifier
+ax.step(recall_good, precision_good, where="post", linewidth=3, color="#306998", label=f"Model A (AP = {ap_good:.2f})")
+ax.fill_between(recall_good, precision_good, step="post", alpha=0.2, color="#306998")
+
+# Moderate classifier
+ax.step(
+    recall_moderate,
+    precision_moderate,
+    where="post",
+    linewidth=3,
+    color="#FFD43B",
+    label=f"Model B (AP = {ap_moderate:.2f})",
+)
+ax.fill_between(recall_moderate, precision_moderate, step="post", alpha=0.2, color="#FFD43B")
+
+# Baseline reference line
+ax.axhline(y=baseline, linestyle="--", linewidth=2, color="#888888", label=f"Random Classifier (P = {baseline:.2f})")
+
+# Styling with seaborn aesthetics
+ax.set_xlabel("Recall (Sensitivity)", fontsize=20)
+ax.set_ylabel("Precision (Positive Predictive Value)", fontsize=20)
+ax.set_title("precision-recall · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+
+# Set axis limits
+ax.set_xlim([0.0, 1.0])
+ax.set_ylim([0.0, 1.05])
+
+# Legend
+ax.legend(loc="upper right", fontsize=16, frameon=True, fancybox=True, framealpha=0.9, edgecolor="#cccccc")
+
+# Grid styling
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/precision-recall/metadata/seaborn.yaml

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+library: seaborn
+specification_id: precision-recall
+created: '2025-12-26T19:11:19Z'
+updated: '2025-12-26T19:13:34Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20527869532
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/precision-recall/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/precision-recall/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with properly sized fonts and stepped line visualization
+  - Well-implemented comparison of two classifiers with different performance levels
+  - Baseline reference line clearly shows random classifier performance at the positive
+    class ratio
+  - Realistic fraud detection scenario with appropriate 10% class imbalance
+  - Good use of fill_between to show area under curves, enhancing visual distinction
+  - Clean, maintainable code following KISS principles
+  weaknesses:
+  - Limited use of seaborn-specific features; primarily uses matplotlib drawing functions
+  - Could benefit from a third classifier curve to show fuller performance spectrum
+    (poor performer)