feat(pygal): implement lift-curve

plots/lift-curve/implementations/pygal.py

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+""" pyplots.ai
+lift-curve: Model Lift Chart
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-27
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Simulated customer response prediction
+np.random.seed(42)
+n_samples = 1000
+
+# Generate realistic model scores and true outcomes
+# Good model: positive correlation between score and actual response
+y_score = np.random.beta(2, 5, n_samples)  # Model predictions (0-1)
+# True labels influenced by score (better score = higher chance of response)
+response_prob = 0.1 + 0.6 * y_score  # Base rate ~10%, top scores ~70%
+y_true = (np.random.random(n_samples) < response_prob).astype(int)
+
+# Calculate lift curve data
+# Sort by score descending
+sorted_indices = np.argsort(y_score)[::-1]
+y_true_sorted = y_true[sorted_indices]
+
+# Calculate cumulative metrics
+n_total = len(y_true)
+n_positive = y_true.sum()
+baseline_rate = n_positive / n_total
+
+# Calculate lift at decile intervals (10%, 20%, ..., 100%)
+deciles = list(range(10, 101, 10))
+lift_values = []
+
+for pct in deciles:
+    n_targeted = int(n_total * pct / 100)
+    positives_captured = y_true_sorted[:n_targeted].sum()
+    model_rate = positives_captured / n_targeted
+    lift = model_rate / baseline_rate if baseline_rate > 0 else 1
+    lift_values.append(round(lift, 2))
+
+# Create custom style for pyplots
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998", "#FFD43B"),
+    title_font_size=56,
+    label_font_size=36,
+    major_label_font_size=32,
+    legend_font_size=32,
+    value_font_size=24,
+    stroke_width=5,
+    opacity=0.9,
+    opacity_hover=1.0,
+)
+
+# Create line chart
+chart = pygal.Line(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="lift-curve · pygal · pyplots.ai",
+    x_title="Population Targeted (%)",
+    y_title="Lift (Model Rate / Baseline Rate)",
+    show_dots=True,
+    dots_size=8,
+    stroke_style={"width": 6},
+    fill=False,
+    show_x_guides=False,
+    show_y_guides=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=2,
+    truncate_legend=-1,
+    range=(0.9, 2.2),
+    margin=50,
+)
+
+# X-axis labels at deciles
+chart.x_labels = [f"{d}%" for d in deciles]
+
+# Add lift curve
+chart.add("Model Lift", lift_values)
+
+# Add baseline reference line at y=1
+baseline = [1.0] * len(deciles)
+chart.add("Random (No Lift)", baseline)
+
+# Save as PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/lift-curve/metadata/pygal.yaml

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+library: pygal
+specification_id: lift-curve
+created: '2025-12-27T19:20:59Z'
+updated: '2025-12-27T19:27:46Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543283456
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/lift-curve/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/lift-curve/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/lift-curve/pygal/plot.html
+quality_score: 91
+review:
+  strengths:
+  - 'Clean, professional visualization with appropriate pyplots color scheme (#306998
+    blue, #FFD43B yellow)'
+  - Correct lift curve implementation showing model performance vs baseline
+  - Excellent realistic data scenario (customer response prediction) that matches
+    spec applications
+  - Good use of pygal style customization with custom Style configuration
+  - Proper 4800x2700 canvas size with appropriately scaled fonts
+  - Includes both PNG and HTML output leveraging pygal interactive capabilities
+  weaknesses:
+  - Legend font appears relatively small compared to axis labels
+  - Missing value annotations at key decile points as suggested in spec
+  - Does not fully leverage pygal interactive tooltip features