feat(seaborn): implement confusion-matrix (#2283)

## Implementation: `confusion-matrix` - seaborn

Implements the **seaborn** version of `confusion-matrix`.

**File:** `plots/confusion-matrix/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526590019)*

---------

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

Author: github-actions[bot]

plots/confusion-matrix/implementations/seaborn.py

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+""" pyplots.ai
+confusion-matrix: Confusion Matrix Heatmap
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# Data - Multi-class classification results for a sentiment analysis model
+class_names = ["Negative", "Neutral", "Positive", "Mixed"]
+
+# Create realistic confusion matrix with strong diagonal (good model)
+# but with some systematic confusion patterns
+confusion_matrix = np.array(
+    [
+        [156, 12, 5, 8],  # Negative: mostly correct, some confused with Neutral
+        [18, 142, 15, 10],  # Neutral: hardest to classify, confused with all
+        [3, 8, 168, 6],  # Positive: good accuracy
+        [11, 14, 9, 125],  # Mixed: often confused with Neutral
+    ]
+)
+
+# Create figure (square format for symmetric matrix)
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Create heatmap with annotations
+sns.heatmap(
+    confusion_matrix,
+    annot=True,
+    fmt="d",
+    cmap="Blues",
+    xticklabels=class_names,
+    yticklabels=class_names,
+    square=True,
+    linewidths=2,
+    linecolor="white",
+    cbar_kws={"shrink": 0.8, "label": "Count"},
+    annot_kws={"size": 20, "weight": "bold"},
+    ax=ax,
+)
+
+# Style the colorbar
+cbar = ax.collections[0].colorbar
+cbar.ax.tick_params(labelsize=16)
+cbar.ax.set_ylabel("Count", fontsize=18, labelpad=15)
+
+# Labels and title
+ax.set_xlabel("Predicted Label", fontsize=22, labelpad=15)
+ax.set_ylabel("True Label", fontsize=22, labelpad=15)
+ax.set_title("Sentiment Analysis Model · confusion-matrix · seaborn · pyplots.ai", fontsize=24, pad=20)
+
+# Style tick labels
+ax.tick_params(axis="both", labelsize=18)
+
+# Rotate x-axis labels for better readability
+plt.xticks(rotation=45, ha="right")
+plt.yticks(rotation=0)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/confusion-matrix/metadata/seaborn.yaml

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+library: seaborn
+specification_id: confusion-matrix
+created: '2025-12-26T17:36:54Z'
+updated: '2025-12-26T17:40:13Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526590019
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with well-sized annotations and contrasting text colors
+    on cells
+  - Properly follows specification with True/Predicted axis labels and Blues colormap
+  - Realistic sentiment analysis scenario with believable confusion patterns showing
+    systematic errors
+  - Clean, readable code following KISS principles
+  - Square format appropriate for symmetric confusion matrix
+  weaknesses:
+  - Minor colorbar label duplication (label set both in cbar_kws and manually)
+  - Data is deterministic but no explicit np.random.seed() for documentation purposes