feat(plotly): implement residual-plot (#2356)

## Implementation: `residual-plot` - plotly

Implements the **plotly** version of `residual-plot`.

**File:** `plots/residual-plot/implementations/plotly.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528203388)*

---------

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

Author: github-actions[bot]

plots/residual-plot/implementations/plotly.py

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+""" pyplots.ai
+residual-plot: Residual Plot
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Generate realistic regression scenario with varying residual patterns
+np.random.seed(42)
+n_samples = 150
+
+# Create features with some non-linearity to show interesting residual patterns
+X = np.linspace(0, 10, n_samples)
+# True relationship with slight curvature (linear model will miss this)
+y_true = 2 * X + 0.3 * X**1.5 + np.random.randn(n_samples) * 2
+
+# Simple linear regression (manual fit)
+X_mean = np.mean(X)
+y_mean = np.mean(y_true)
+slope = np.sum((X - X_mean) * (y_true - y_mean)) / np.sum((X - X_mean) ** 2)
+intercept = y_mean - slope * X_mean
+y_pred = slope * X + intercept
+
+# Calculate residuals
+residuals = y_true - y_pred
+std_residuals = np.std(residuals)
+
+# Identify outliers (beyond ±2 standard deviations)
+outlier_mask = np.abs(residuals) > 2 * std_residuals
+normal_mask = ~outlier_mask
+
+# Create figure
+fig = go.Figure()
+
+# Add ±2 standard deviation bands (dashed lines)
+fig.add_trace(
+    go.Scatter(
+        x=[y_pred.min(), y_pred.max()],
+        y=[2 * std_residuals, 2 * std_residuals],
+        mode="lines",
+        line=dict(color="rgba(255, 212, 59, 0.7)", width=3, dash="dash"),
+        name="+2 SD",
+        showlegend=True,
+    )
+)
+
+fig.add_trace(
+    go.Scatter(
+        x=[y_pred.min(), y_pred.max()],
+        y=[-2 * std_residuals, -2 * std_residuals],
+        mode="lines",
+        line=dict(color="rgba(255, 212, 59, 0.7)", width=3, dash="dash"),
+        name="-2 SD",
+        showlegend=True,
+    )
+)
+
+# Add horizontal reference line at y=0
+fig.add_trace(
+    go.Scatter(
+        x=[y_pred.min(), y_pred.max()],
+        y=[0, 0],
+        mode="lines",
+        line=dict(color="#333333", width=3),
+        name="Zero Line",
+        showlegend=False,
+    )
+)
+
+# Add normal residuals
+fig.add_trace(
+    go.Scatter(
+        x=y_pred[normal_mask],
+        y=residuals[normal_mask],
+        mode="markers",
+        marker=dict(size=14, color="#306998", opacity=0.7, line=dict(width=1, color="#1e4263")),
+        name="Residuals",
+        hovertemplate="Fitted: %{x:.2f}<br>Residual: %{y:.2f}<extra></extra>",
+    )
+)
+
+# Add outlier residuals
+if np.any(outlier_mask):
+    fig.add_trace(
+        go.Scatter(
+            x=y_pred[outlier_mask],
+            y=residuals[outlier_mask],
+            mode="markers",
+            marker=dict(size=16, color="#FFD43B", opacity=0.9, line=dict(width=2, color="#cc9900"), symbol="diamond"),
+            name="Outliers (>2 SD)",
+            hovertemplate="Fitted: %{x:.2f}<br>Residual: %{y:.2f}<extra></extra>",
+        )
+    )
+
+# Add smoothing line to detect patterns (moving average with numpy)
+sorted_indices = np.argsort(y_pred)
+window_size = 15
+kernel = np.ones(window_size) / window_size
+smoothed_residuals = np.convolve(residuals[sorted_indices], kernel, mode="same")
+
+fig.add_trace(
+    go.Scatter(
+        x=y_pred[sorted_indices],
+        y=smoothed_residuals,
+        mode="lines",
+        line=dict(color="#cc4444", width=4),
+        name="Trend Line",
+        hovertemplate="Fitted: %{x:.2f}<br>Smoothed Residual: %{y:.2f}<extra></extra>",
+    )
+)
+
+# Update layout
+fig.update_layout(
+    title=dict(
+        text="residual-plot · plotly · pyplots.ai", font=dict(size=32, color="#333333"), x=0.5, xanchor="center"
+    ),
+    xaxis=dict(
+        title=dict(text="Fitted Values", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(0,0,0,0.1)",
+        zeroline=False,
+    ),
+    yaxis=dict(
+        title=dict(text="Residuals (y_true - y_pred)", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(0,0,0,0.1)",
+        zeroline=False,
+    ),
+    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(0,0,0,0.2)",
+        borderwidth=1,
+    ),
+    margin=dict(l=100, r=50, t=100, b=80),
+    plot_bgcolor="white",
+)
+
+# Save as PNG (4800 x 2700 px)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save as HTML for interactivity
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/residual-plot/metadata/plotly.yaml

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+library: plotly
+specification_id: residual-plot
+created: '2025-12-26T19:37:08Z'
+updated: '2025-12-26T19:43:44Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528203388
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/residual-plot/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/residual-plot/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/residual-plot/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - 'Excellent implementation of all key residual plot features: zero reference line,
+    ±2 SD bands, outlier highlighting, and smoothing trend line'
+  - Good color scheme with distinct visual encoding for normal residuals (blue circles),
+    outliers (yellow diamonds), and trend (red line)
+  - Interactive hover templates provide useful diagnostic information (fitted value
+    and residual)
+  - Data generation creates interesting non-linear pattern that demonstrates the diagnostic
+    purpose of residual plots
+  - Clean code structure following KISS principles
+  weaknesses:
+  - Legend shows +2 SD and -2 SD as separate entries; could be combined into single
+    ±2 SD Bounds entry
+  - Axis labels lack units (though residuals are unitless, y-axis label is verbose)
+  - Moving average smoothing could benefit from using a more sophisticated LOWESS
+    approach for smoother trend detection