feat(matplotlib): implement lift-curve (#2384)

## Implementation: `lift-curve` - matplotlib

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543281060)*

---------

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

Author: github-actions[bot]

plots/lift-curve/implementations/matplotlib.py

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+""" pyplots.ai
+lift-curve: Model Lift Chart
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-27
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Simulate realistic customer response model predictions
+np.random.seed(42)
+n_samples = 1000
+base_rate = 0.15  # 15% baseline response rate
+
+# Generate true labels with base rate
+y_true = np.random.binomial(1, base_rate, n_samples)
+
+# Generate model scores - correlated with true outcomes for realistic model
+# Good responders get higher scores, non-responders get lower scores
+y_score = np.where(
+    y_true == 1,
+    np.clip(np.random.beta(5, 2, n_samples), 0, 1),  # Responders: higher scores
+    np.clip(np.random.beta(2, 5, n_samples), 0, 1),  # Non-responders: lower scores
+)
+
+# Calculate lift curve
+# Sort by predicted score (descending)
+sorted_indices = np.argsort(y_score)[::-1]
+y_true_sorted = y_true[sorted_indices]
+
+# Calculate cumulative response rate and lift
+n_total = len(y_true)
+n_positive = y_true.sum()
+baseline_rate = n_positive / n_total
+
+# Calculate cumulative lift at each percentage
+percentages = np.arange(1, 101)
+lift_values = []
+
+for pct in percentages:
+    n_selected = int(np.ceil(n_total * pct / 100))
+    n_responders = y_true_sorted[:n_selected].sum()
+    response_rate = n_responders / n_selected
+    lift = response_rate / baseline_rate
+    lift_values.append(lift)
+
+lift_values = np.array(lift_values)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot lift curve
+ax.plot(percentages, lift_values, color="#306998", linewidth=3, label="Model Lift", zorder=3)
+
+# Reference line at y=1 (random selection)
+ax.axhline(y=1, color="#FFD43B", linestyle="--", linewidth=2.5, label="Random (Lift = 1)", zorder=2)
+
+# Add markers at key deciles
+decile_pcts = [10, 20, 30, 40, 50]
+for pct in decile_pcts:
+    idx = pct - 1
+    ax.scatter(pct, lift_values[idx], color="#306998", s=150, zorder=4, edgecolors="white", linewidth=2)
+    ax.annotate(
+        f"{lift_values[idx]:.2f}x",
+        (pct, lift_values[idx]),
+        xytext=(0, 15),
+        textcoords="offset points",
+        ha="center",
+        fontsize=14,
+        fontweight="bold",
+        color="#306998",
+    )
+
+# Fill area under curve for visual emphasis
+ax.fill_between(percentages, 1, lift_values, where=(lift_values > 1), alpha=0.15, color="#306998", zorder=1)
+
+# Styling
+ax.set_xlabel("Population Targeted (%)", fontsize=20)
+ax.set_ylabel("Cumulative Lift", fontsize=20)
+ax.set_title("lift-curve · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+
+# Set axis limits
+ax.set_xlim(0, 100)
+ax.set_ylim(0, max(lift_values) * 1.15)
+
+# Grid
+ax.grid(True, alpha=0.3, linestyle="--", zorder=0)
+
+# Legend
+ax.legend(fontsize=16, loc="upper right")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/lift-curve/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: lift-curve
+created: '2025-12-27T19:20:38Z'
+updated: '2025-12-27T19:23:10Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543281060
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/lift-curve/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/lift-curve/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent implementation of lift curve with proper calculation methodology (sort
+    by score, cumulative lift at each percentage)
+  - Clear visual distinction between model lift curve and random baseline with appropriate
+    colors
+  - Well-placed decile annotations that show practical lift values at key targeting
+    thresholds
+  - Shaded area between curves provides intuitive visualization of model value
+  - Realistic customer response data with appropriate base rate and sample size
+  - Clean, well-structured code following KISS principles
+  weaknesses:
+  - Y-axis label could include units or clarification (e.g., Cumulative Lift (ratio))
+  - Does not leverage distinctive matplotlib features beyond basic plotting capabilities