feat(altair): implement lift-curve (#2409)

## Implementation: `lift-curve` - altair

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543282471)*

---------

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/lift-curve/implementations/altair.py

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+""" pyplots.ai
+lift-curve: Model Lift Chart
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-27
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Simulate customer churn prediction model results
+np.random.seed(42)
+n_samples = 1000
+
+# Create realistic churn prediction scenario
+# True positives have higher scores, some overlap for realism
+y_true = np.concatenate([np.ones(200), np.zeros(800)])  # 20% churn rate
+y_score = np.where(
+    y_true == 1,
+    np.clip(np.random.beta(5, 2, len(y_true)), 0, 1),  # Churners: higher scores
+    np.clip(np.random.beta(2, 5, len(y_true)), 0, 1),  # Non-churners: lower scores
+)
+
+# Calculate lift curve
+sorted_indices = np.argsort(y_score)[::-1]  # Sort by score descending
+y_true_sorted = y_true[sorted_indices]
+
+# Calculate cumulative lift at each percentage
+percentages = np.arange(1, 101)
+n_total = len(y_true)
+n_positives = y_true.sum()
+baseline_rate = n_positives / n_total
+
+lift_values = []
+for pct in percentages:
+    n_selected = int(np.ceil(n_total * pct / 100))
+    n_captured = y_true_sorted[:n_selected].sum()
+    model_rate = n_captured / n_selected
+    lift = model_rate / baseline_rate
+    lift_values.append(lift)
+
+# Create DataFrame for Altair
+df = pd.DataFrame({"Population (%)": percentages, "Cumulative Lift": lift_values})
+
+# Reference line at y=1 (random selection)
+df_reference = pd.DataFrame({"Population (%)": [0, 100], "Reference": [1.0, 1.0]})
+
+# Create lift curve chart
+lift_line = (
+    alt.Chart(df)
+    .mark_line(strokeWidth=4, color="#306998")
+    .encode(
+        x=alt.X("Population (%):Q", scale=alt.Scale(domain=[0, 100]), title="Population Targeted (%)"),
+        y=alt.Y("Cumulative Lift:Q", scale=alt.Scale(domain=[0, 5]), title="Cumulative Lift"),
+        tooltip=[alt.Tooltip("Population (%):Q", format=".0f"), alt.Tooltip("Cumulative Lift:Q", format=".2f")],
+    )
+)
+
+# Reference line at lift = 1
+reference_line = (
+    alt.Chart(df_reference)
+    .mark_line(strokeWidth=2, strokeDash=[8, 4], color="#999999")
+    .encode(x="Population (%):Q", y="Reference:Q")
+)
+
+# Add decile markers
+decile_df = df[df["Population (%)"].isin([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])]
+decile_points = (
+    alt.Chart(decile_df)
+    .mark_point(size=200, color="#306998", filled=True)
+    .encode(
+        x="Population (%):Q",
+        y="Cumulative Lift:Q",
+        tooltip=[
+            alt.Tooltip("Population (%):Q", format=".0f", title="Decile %"),
+            alt.Tooltip("Cumulative Lift:Q", format=".2f", title="Lift"),
+        ],
+    )
+)
+
+# Add annotation for reference line
+annotation = (
+    alt.Chart(pd.DataFrame({"x": [75], "y": [1.25], "text": ["Random Selection (Lift = 1)"]}))
+    .mark_text(fontSize=18, color="#555555", fontWeight="bold", align="center")
+    .encode(x="x:Q", y="y:Q", text="text:N")
+)
+
+# Combine all layers
+chart = (
+    alt.layer(reference_line, lift_line, decile_points, annotation)
+    .properties(
+        width=1600, height=900, title=alt.Title(text="lift-curve · altair · pyplots.ai", fontSize=28, anchor="middle")
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22, gridColor="#dddddd", gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/lift-curve/metadata/altair.yaml

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+library: altair
+specification_id: lift-curve
+created: '2025-12-27T19:22:47Z'
+updated: '2025-12-27T19:25:43Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543282471
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/lift-curve/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/lift-curve/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/lift-curve/altair/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent lift curve visualization that clearly demonstrates model performance
+    vs random selection
+  - Well-implemented decile markers provide clear reference points for business decisions
+  - Realistic customer churn prediction scenario with appropriate lift values
+  - Clean code structure following KISS principles with proper random seed
+  - Good use of Altair layering to combine lift line, reference line, markers, and
+    annotation
+  - Tooltips add interactive value for exploring specific lift values
+  weaknesses:
+  - Could add a small legend to label the lift curve line
+  - Annotation text placement could be slightly adjusted to avoid visual proximity
+    to the reference line