feat(letsplot): implement lift-curve (#2398)

## Implementation: `lift-curve` - letsplot

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543283655)*

---------

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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/letsplot.py

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+# ruff: noqa: F405
+"""pyplots.ai
+lift-curve: Model Lift Chart
+Library: lets-plot | Python 3.13
+Quality: pending | Created: 2025-12-27
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *  # noqa: F403, F405
+
+
+LetsPlot.setup_html()
+
+# Data: Simulated customer response model
+np.random.seed(42)
+n_samples = 1000
+
+# Create realistic model scores with good discrimination
+# Higher scores for positive class, lower for negative
+y_true = np.concatenate([np.ones(200), np.zeros(800)])  # 20% response rate
+positive_scores = np.random.beta(5, 2, 200)  # Skewed high
+negative_scores = np.random.beta(2, 5, 800)  # Skewed low
+y_score = np.concatenate([positive_scores, negative_scores])
+
+# Shuffle data
+shuffle_idx = np.random.permutation(n_samples)
+y_true = y_true[shuffle_idx]
+y_score = y_score[shuffle_idx]
+
+# Calculate lift curve
+sorted_idx = np.argsort(y_score)[::-1]  # Sort by score descending
+y_true_sorted = y_true[sorted_idx]
+
+# Calculate cumulative metrics
+n_positive = np.sum(y_true)
+baseline_rate = n_positive / n_samples
+cumsum_positive = np.cumsum(y_true_sorted)
+population_pct = np.arange(1, n_samples + 1) / n_samples * 100
+response_rate = cumsum_positive / np.arange(1, n_samples + 1)
+lift = response_rate / baseline_rate
+
+# Sample points for smoother curve (every 1%)
+sample_points = np.arange(10, n_samples + 1, 10)
+df = pd.DataFrame({"population_pct": population_pct[sample_points - 1], "lift": lift[sample_points - 1]})
+
+# Add starting point
+df = pd.concat([pd.DataFrame({"population_pct": [0], "lift": [lift[0]]}), df], ignore_index=True)
+
+# Reference line data (horizontal at y=1)
+ref_df = pd.DataFrame({"population_pct": [0, 100], "lift": [1, 1]})
+
+# Create plot
+plot = (
+    ggplot()
+    + geom_line(aes(x="population_pct", y="lift"), data=ref_df, color="#888888", size=1.5, linetype="dashed")
+    + geom_line(aes(x="population_pct", y="lift"), data=df, color="#306998", size=2.5)
+    + geom_point(
+        aes(x="population_pct", y="lift"), data=df[df["population_pct"] % 10 == 0], color="#306998", size=5, alpha=0.8
+    )
+    + labs(x="Population Targeted (%)", y="Cumulative Lift", title="lift-curve · letsplot · pyplots.ai")
+    + scale_x_continuous(breaks=list(range(0, 101, 10)))
+    + scale_y_continuous(breaks=[1, 2, 3, 4, 5, 6])
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Add annotation for reference line
+plot = plot + geom_text(
+    aes(x="x", y="y", label="label"),
+    data=pd.DataFrame({"x": [85], "y": [1.15], "label": ["Random (Lift = 1)"]}),
+    size=14,
+    color="#666666",
+)
+
+# Save as PNG and HTML (path='.' to save in current directory)
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")

plots/lift-curve/metadata/letsplot.yaml

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+library: letsplot
+specification_id: lift-curve
+created: '2025-12-27T19:21:39Z'
+updated: '2025-12-27T19:29:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543283655
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/lift-curve/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/lift-curve/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/lift-curve/letsplot/plot.html
+quality_score: 98
+review:
+  strengths:
+  - Excellent lift curve implementation with proper mathematical calculation of cumulative
+    lift ratio
+  - Clear reference line at y=1 with helpful annotation explaining random selection
+    baseline
+  - 'Well-chosen data: beta distributions create realistic score separation between
+    positive/negative classes'
+  - Decile markers (points at 10% intervals) aid interpretation per spec recommendation
+  - 'Proper lets-plot idioms: ggsize for dimensions, scale=3 for 4800x2700 output,
+    theme customization'
+  weaknesses: []