feat(matplotlib): implement confusion-matrix (#2282)

## Implementation: `confusion-matrix` - matplotlib

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526590092)*

---------

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/confusion-matrix/implementations/matplotlib.py

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+""" pyplots.ai
+confusion-matrix: Confusion Matrix Heatmap
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-26
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Multi-class classification results (4 classes)
+np.random.seed(42)
+
+class_names = ["Cat", "Dog", "Bird", "Fish"]
+n_classes = len(class_names)
+
+# Create realistic confusion matrix with some misclassifications
+# Diagonal has highest values (correct predictions)
+# Off-diagonal shows realistic confusion patterns
+confusion_matrix = np.array(
+    [
+        [85, 8, 4, 3],  # Cat: mostly correct, some confused with Dog
+        [12, 78, 6, 4],  # Dog: mostly correct, some confused with Cat
+        [3, 5, 88, 4],  # Bird: high accuracy
+        [2, 3, 5, 90],  # Fish: highest accuracy
+    ]
+)
+
+# Create plot (square format for symmetric matrix)
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Create heatmap with Blues colormap
+im = ax.imshow(confusion_matrix, cmap="Blues", aspect="equal")
+
+# Add colorbar
+cbar = ax.figure.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+cbar.ax.tick_params(labelsize=16)
+cbar.set_label("Count", fontsize=18)
+
+# Set ticks and labels
+ax.set_xticks(np.arange(n_classes))
+ax.set_yticks(np.arange(n_classes))
+ax.set_xticklabels(class_names, fontsize=18)
+ax.set_yticklabels(class_names, fontsize=18)
+
+# Rotate x-axis labels for better readability
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+
+# Annotate cells with counts and percentages
+for i in range(n_classes):
+    for j in range(n_classes):
+        count = confusion_matrix[i, j]
+        row_total = confusion_matrix[i, :].sum()
+        percentage = count / row_total * 100
+
+        # Choose text color based on background intensity
+        text_color = "white" if count > confusion_matrix.max() * 0.5 else "black"
+
+        # Display count and percentage
+        text = ax.text(
+            j,
+            i,
+            f"{count}\n({percentage:.1f}%)",
+            ha="center",
+            va="center",
+            color=text_color,
+            fontsize=16,
+            fontweight="bold",
+        )
+
+# Labels and title
+ax.set_xlabel("Predicted Label", fontsize=20)
+ax.set_ylabel("True Label", fontsize=20)
+ax.set_title("confusion-matrix · matplotlib · pyplots.ai", fontsize=24, pad=20)
+
+# Add subtle grid lines between cells
+ax.set_xticks(np.arange(n_classes + 1) - 0.5, minor=True)
+ax.set_yticks(np.arange(n_classes + 1) - 0.5, minor=True)
+ax.grid(which="minor", color="white", linestyle="-", linewidth=2)
+ax.tick_params(which="minor", bottom=False, left=False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/confusion-matrix/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: confusion-matrix
+created: '2025-12-26T17:36:06Z'
+updated: '2025-12-26T17:39:39Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526590092
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent text contrast with adaptive white/black text based on background intensity
+  - Row-normalized percentages provide additional insight beyond raw counts
+  - Realistic multi-class classification scenario with plausible confusion patterns
+  - Clean KISS code structure with proper seed for reproducibility
+  - Square aspect ratio perfect for symmetric confusion matrix
+  - Professional colorbar with clear Count label
+  weaknesses:
+  - Does not leverage matplotlib-specific advanced features (just uses basic imshow)
+  - Grid lines at width=2 are slightly prominent compared to subtle alpha=0.3 guidelines