feat(plotly): implement contour-decision-boundary (#2978)

## Implementation: `contour-decision-boundary` - plotly

Implements the **plotly** version of `contour-decision-boundary`.

**File:** `plots/contour-decision-boundary/implementations/plotly.py`

**Parent Issue:** #2921

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612912968)*

---------

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

Author: github-actions[bot]

plots/contour-decision-boundary/implementations/plotly.py

@@ -0,0 +1,137 @@
+""" pyplots.ai
+contour-decision-boundary: Decision Boundary Classifier Visualization
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.datasets import make_moons
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import StandardScaler
+
+
+# Data - Generate moon-shaped classification data
+np.random.seed(42)
+X, y = make_moons(n_samples=200, noise=0.25, random_state=42)
+
+# Scale features for better visualization
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+
+# Train a KNN classifier
+clf = KNeighborsClassifier(n_neighbors=15)
+clf.fit(X, y)
+
+# Create mesh grid for decision boundary
+x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
+y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
+xx, yy = np.meshgrid(np.linspace(x_min, x_max, 150), np.linspace(y_min, y_max, 150))
+
+# Get predictions for mesh grid
+Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+# Get prediction probabilities for smoother contours
+Z_prob = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
+Z_prob = Z_prob.reshape(xx.shape)
+
+# Create figure
+fig = go.Figure()
+
+# Add decision boundary contour using probability
+fig.add_trace(
+    go.Contour(
+        x=np.linspace(x_min, x_max, 150),
+        y=np.linspace(y_min, y_max, 150),
+        z=Z_prob,
+        colorscale=[[0, "#306998"], [1, "#FFD43B"]],
+        opacity=0.6,
+        showscale=True,
+        colorbar=dict(
+            title=dict(text="Class Probability", font=dict(size=18)), tickfont=dict(size=16), len=0.7, thickness=25
+        ),
+        contours=dict(showlines=False),
+        hoverinfo="skip",
+    )
+)
+
+# Add decision boundary line (where probability = 0.5)
+fig.add_trace(
+    go.Contour(
+        x=np.linspace(x_min, x_max, 150),
+        y=np.linspace(y_min, y_max, 150),
+        z=Z_prob,
+        showscale=False,
+        contours=dict(start=0.5, end=0.5, size=0.1, coloring="lines", showlabels=False),
+        line=dict(color="white", width=3, dash="dash"),
+        hoverinfo="skip",
+    )
+)
+
+# Separate training points by class
+X_class0 = X[y == 0]
+X_class1 = X[y == 1]
+
+# Add training points - Class 0
+fig.add_trace(
+    go.Scatter(
+        x=X_class0[:, 0],
+        y=X_class0[:, 1],
+        mode="markers",
+        marker=dict(size=14, color="#306998", line=dict(color="white", width=2), symbol="circle"),
+        name="Class 0",
+        hovertemplate="Feature 1: %{x:.2f}<br>Feature 2: %{y:.2f}<extra>Class 0</extra>",
+    )
+)
+
+# Add training points - Class 1
+fig.add_trace(
+    go.Scatter(
+        x=X_class1[:, 0],
+        y=X_class1[:, 1],
+        mode="markers",
+        marker=dict(size=14, color="#FFD43B", line=dict(color="black", width=2), symbol="diamond"),
+        name="Class 1",
+        hovertemplate="Feature 1: %{x:.2f}<br>Feature 2: %{y:.2f}<extra>Class 1</extra>",
+    )
+)
+
+# Update layout
+fig.update_layout(
+    title=dict(text="contour-decision-boundary · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Feature 1 (Standardized)", font=dict(size=22)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(128, 128, 128, 0.3)",
+        zeroline=False,
+    ),
+    yaxis=dict(
+        title=dict(text="Feature 2 (Standardized)", font=dict(size=22)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(128, 128, 128, 0.3)",
+        zeroline=False,
+        scaleanchor="x",
+        scaleratio=1,
+    ),
+    template="plotly_white",
+    legend=dict(
+        font=dict(size=18),
+        x=0.02,
+        y=0.98,
+        xanchor="left",
+        yanchor="top",
+        bgcolor="rgba(255, 255, 255, 0.8)",
+        bordercolor="rgba(0, 0, 0, 0.3)",
+        borderwidth=1,
+    ),
+    margin=dict(l=80, r=100, t=100, b=80),
+)
+
+# Save as PNG and HTML
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/contour-decision-boundary/metadata/plotly.yaml

@@ -0,0 +1,25 @@
+library: plotly
+specification_id: contour-decision-boundary
+created: '2025-12-31T05:44:26Z'
+updated: '2025-12-31T05:57:15Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612912968
+issue: 2921
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of probability-based contour coloring rather than just discrete
+    class regions
+  - Clear decision boundary line at probability=0.5 with dashed styling
+  - Good marker differentiation using both shape (circle vs diamond) and color
+  - Professional colorbar showing class probability scale
+  - Responsive layout with proper sizing for 4800x2700 output
+  weaknesses:
+  - Legend position in upper-left overlaps slightly with some Class 0 data points
+  - Could enhance interactivity by adding hover info to the contour regions showing
+    probability values