feat(seaborn): implement residual-plot (#2365)

## Implementation: `residual-plot` - seaborn

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528203156)*

---------

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/residual-plot/implementations/seaborn.py

@@ -0,0 +1,81 @@
+""" pyplots.ai
+residual-plot: Residual Plot
+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 pandas as pd
+import seaborn as sns
+
+
+# Data - housing price prediction example
+np.random.seed(42)
+n_samples = 150
+
+# Features: house size (sq ft)
+house_size = np.random.uniform(800, 3500, n_samples)
+
+# True relationship with some non-linearity to show in residuals
+y_true = 50000 + 100 * house_size + 0.02 * house_size**2 + np.random.normal(0, 25000, n_samples)
+
+# Fit linear model using numpy (will show pattern in residuals due to non-linearity)
+slope, intercept = np.polyfit(house_size, y_true, 1)
+y_pred = slope * house_size + intercept
+
+# Calculate residuals
+residuals = y_true - y_pred
+
+# Identify outliers (beyond 2 standard deviations)
+residual_std = np.std(residuals)
+outlier_mask = np.abs(residuals) > 2 * residual_std
+
+# Create DataFrame for seaborn
+df = pd.DataFrame(
+    {"Fitted Values": y_pred, "Residuals": residuals, "Type": np.where(outlier_mask, "Outlier (|z| > 2)", "Normal")}
+)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot using seaborn scatterplot with hue for outliers
+sns.scatterplot(
+    data=df,
+    x="Fitted Values",
+    y="Residuals",
+    hue="Type",
+    palette={"Normal": "#306998", "Outlier (|z| > 2)": "#FFD43B"},
+    s=150,
+    alpha=0.7,
+    ax=ax,
+)
+
+# Add polynomial trend line using seaborn's regplot (order=2 for pattern detection)
+sns.regplot(
+    data=df,
+    x="Fitted Values",
+    y="Residuals",
+    scatter=False,
+    order=2,
+    line_kws={"color": "#C44536", "linewidth": 3, "alpha": 0.8, "label": "Trend"},
+    ax=ax,
+)
+
+# Reference line at y=0
+ax.axhline(y=0, color="#333333", linestyle="-", linewidth=2, zorder=1)
+
+# Add ±2 standard deviation bands
+ax.axhline(y=2 * residual_std, color="#999999", linestyle="--", linewidth=1.5, alpha=0.7, label="±2 SD")
+ax.axhline(y=-2 * residual_std, color="#999999", linestyle="--", linewidth=1.5, alpha=0.7)
+
+# Styling
+ax.set_xlabel("Fitted Values (Predicted Price in $)", fontsize=20)
+ax.set_ylabel("Residuals (Actual - Predicted)", fontsize=20)
+ax.set_title("residual-plot · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=14, loc="upper right", framealpha=0.9)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/residual-plot/metadata/seaborn.yaml

@@ -0,0 +1,28 @@
+library: seaborn
+specification_id: residual-plot
+created: '2025-12-26T19:38:17Z'
+updated: '2025-12-26T19:44:37Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528203156
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/residual-plot/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/residual-plot/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of housing price prediction as a realistic regression context that
+    makes the residual analysis meaningful
+  - Clear U-shaped trend in residuals demonstrates non-linearity detection, which
+    is the primary purpose of residual plots
+  - Good implementation of outlier highlighting with color differentiation (blue/yellow)
+    using hue parameter
+  - Reference line at y=0 and ±2 SD bands provide clear visual guides for interpretation
+  - Proper use of seaborn regplot for polynomial trend line with confidence interval
+  weaknesses:
+  - Does not use seaborn built-in residplot function which is specifically designed
+    for this plot type
+  - The Trend label in regplot line_kws does not appear in the legend (seaborn regplot
+    does not add to legend automatically)