feat(plotly): implement confusion-matrix (#2284)

## Implementation: `confusion-matrix` - plotly

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526590160)*

---------

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

Author: github-actions[bot]

plots/confusion-matrix/implementations/plotly.py

@@ -0,0 +1,78 @@
+""" pyplots.ai
+confusion-matrix: Confusion Matrix Heatmap
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data: Multi-class classification results (4 classes - sentiment analysis)
+np.random.seed(42)
+class_names = ["Negative", "Neutral", "Positive", "Very Positive"]
+n_classes = len(class_names)
+
+# Create a realistic confusion matrix with good diagonal but some misclassifications
+# Simulating a sentiment classifier that sometimes confuses adjacent classes
+confusion_matrix = np.array(
+    [
+        [85, 12, 2, 1],  # Negative: mostly correct, some confused with Neutral
+        [8, 72, 15, 5],  # Neutral: confused with both adjacent classes
+        [3, 18, 78, 11],  # Positive: some confusion with Neutral and Very Positive
+        [1, 3, 14, 82],  # Very Positive: mostly correct, some confused with Positive
+    ]
+)
+
+# Create heatmap
+fig = go.Figure(
+    data=go.Heatmap(
+        z=confusion_matrix,
+        x=class_names,
+        y=class_names,
+        colorscale="Blues",
+        showscale=True,
+        colorbar=dict(title=dict(text="Count", font=dict(size=20)), tickfont=dict(size=16), thickness=25, len=0.8),
+        hovertemplate="True: %{y}<br>Predicted: %{x}<br>Count: %{z}<extra></extra>",
+    )
+)
+
+# Add text annotations for each cell
+annotations = []
+for i in range(n_classes):
+    for j in range(n_classes):
+        value = confusion_matrix[i, j]
+        # Use white text on dark backgrounds (high values), black on light
+        text_color = "white" if value > 50 else "black"
+        annotations.append(
+            dict(
+                x=class_names[j],
+                y=class_names[i],
+                text=str(value),
+                font=dict(size=24, color=text_color),
+                showarrow=False,
+            )
+        )
+
+# Update layout
+fig.update_layout(
+    title=dict(text="confusion-matrix · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Predicted Label", font=dict(size=22)), tickfont=dict(size=18), side="bottom", tickangle=0
+    ),
+    yaxis=dict(
+        title=dict(text="True Label", font=dict(size=22)),
+        tickfont=dict(size=18),
+        autorange="reversed",  # Put first class at top
+    ),
+    annotations=annotations,
+    template="plotly_white",
+    margin=dict(l=120, r=100, t=100, b=100),
+)
+
+# Make cells square
+fig.update_xaxes(scaleanchor="y", scaleratio=1)
+
+# Save outputs
+fig.write_image("plot.png", width=1200, height=1200, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/confusion-matrix/metadata/plotly.yaml

@@ -0,0 +1,25 @@
+library: plotly
+specification_id: confusion-matrix
+created: '2025-12-26T17:36:58Z'
+updated: '2025-12-26T17:40:52Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526590160
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/confusion-matrix/plotly/plot.html
+quality_score: 93
+review:
+  strengths:
+  - Excellent use of Plotly hovertemplate for interactive tooltips showing True/Predicted/Count
+  - Smart text color contrast logic - white text on dark cells, black text on light
+    cells
+  - Square cells using scaleanchor ensures proper matrix visualization
+  - Clean professional appearance with plotly_white template
+  - Realistic sentiment analysis data with believable misclassification patterns
+  weaknesses:
+  - Image dimensions use square format (3600x3600) instead of standard 4800x2700 landscape,
+    though appropriate for confusion matrices
+  - Axis labels could be more descriptive (e.g., True Sentiment Class vs True Label)