feat(seaborn): implement lift-curve (#2388)

## Implementation: `lift-curve` - seaborn

Implements the **seaborn** version of `lift-curve`.

**File:** `plots/lift-curve/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543281168)*

---------

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

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plots/lift-curve/implementations/seaborn.py

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+""" pyplots.ai
+lift-curve: Model Lift Chart
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-27
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Set seaborn style
+sns.set_theme(style="whitegrid")
+
+# Data - simulate customer response prediction for marketing campaign
+np.random.seed(42)
+n_samples = 1000
+base_response_rate = 0.10  # 10% overall response rate
+
+# Create a model with good predictive power
+# True propensity is a latent variable
+latent_propensity = np.random.normal(0, 1, n_samples)
+
+# Model score approximates the latent propensity with some noise
+y_score = latent_propensity + np.random.normal(0, 0.3, n_samples)
+y_score = (y_score - y_score.min()) / (y_score.max() - y_score.min())  # Normalize to 0-1
+
+# Actual responses based on latent propensity (strong correlation)
+response_threshold = np.percentile(latent_propensity, 100 * (1 - base_response_rate))
+y_true = (latent_propensity >= response_threshold).astype(int)
+
+# Calculate lift curve data
+# Sort by predicted scores descending
+sorted_indices = np.argsort(y_score)[::-1]
+y_true_sorted = y_true[sorted_indices]
+
+# Calculate cumulative gains
+n_positives = y_true.sum()
+cumulative_positives = np.cumsum(y_true_sorted)
+population_percentages = np.arange(1, n_samples + 1) / n_samples * 100
+
+# Calculate lift: (cumulative positive rate) / (overall positive rate)
+cumulative_positive_rate = cumulative_positives / np.arange(1, n_samples + 1)
+baseline_rate = n_positives / n_samples
+lift = cumulative_positive_rate / baseline_rate
+
+# Create dataframe for seaborn
+df = pd.DataFrame({"Population Targeted (%)": population_percentages, "Cumulative Lift": lift})
+
+# Create plot (16:9 landscape format - 4800x2700 at 300 dpi)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot lift curve using seaborn lineplot
+sns.lineplot(
+    data=df,
+    x="Population Targeted (%)",
+    y="Cumulative Lift",
+    ax=ax,
+    color="#306998",  # Python Blue
+    linewidth=3,
+    label="Model Lift",
+)
+
+# Add baseline reference line (random selection = lift of 1)
+ax.axhline(y=1, color="#FFD43B", linestyle="--", linewidth=2.5, label="Random (No Lift)", zorder=3)
+
+# Add decile markers
+decile_indices = [int(n_samples * p / 100) - 1 for p in [10, 20, 30, 40, 50]]
+for idx in decile_indices:
+    pct = population_percentages[idx]
+    lift_val = lift[idx]
+    ax.plot(pct, lift_val, "o", color="#306998", markersize=12, zorder=5)
+    ax.annotate(
+        f"{lift_val:.2f}x",
+        (pct, lift_val),
+        textcoords="offset points",
+        xytext=(0, 15),
+        ha="center",
+        fontsize=14,
+        fontweight="bold",
+        color="#306998",
+    )
+
+# Styling
+ax.set_xlabel("Population Targeted (%)", fontsize=20)
+ax.set_ylabel("Cumulative Lift", fontsize=20)
+ax.set_title("lift-curve · seaborn · pyplots.ai", fontsize=24, fontweight="bold")
+ax.tick_params(axis="both", labelsize=16)
+
+# Configure grid
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Set axis limits
+ax.set_xlim(0, 100)
+ax.set_ylim(0, max(lift) * 1.15)
+
+# Legend
+ax.legend(fontsize=16, loc="upper right")
+
+# Tight layout
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/lift-curve/metadata/seaborn.yaml

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+library: seaborn
+specification_id: lift-curve
+created: '2025-12-27T19:20:51Z'
+updated: '2025-12-27T19:26:29Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543281168
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/lift-curve/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/lift-curve/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with appropriate font sizes and line widths for high-resolution
+    output
+  - Proper lift curve calculation methodology with clear documentation in comments
+  - Good use of decile markers with lift value annotations to highlight key insights
+  - Reference baseline at y=1 clearly distinguishes model performance from random
+    selection
+  - Realistic marketing campaign scenario with appropriate 10% base response rate
+  - Clean, well-organized code following KISS principles
+  weaknesses:
+  - Seaborn is used minimally - only for sns.lineplot and theming; the core visualization
+    could be done entirely in matplotlib
+  - First decile annotation appears slightly cramped near the top of the curve
+  - Y-axis label could clarify that lift is a ratio