feat(matplotlib): implement gain-curve

plots/gain-curve/implementations/matplotlib.py

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+""" pyplots.ai
+gain-curve: Cumulative Gains Chart
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-29
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Generate synthetic classification data (customer response model)
+np.random.seed(42)
+n_samples = 1000
+
+# Create customer features that influence response
+customer_value = np.random.randn(n_samples)
+customer_engagement = np.random.randn(n_samples)
+
+# True underlying probability (strong signal)
+latent_score = 1.5 * customer_value + 1.0 * customer_engagement
+true_prob = 1 / (1 + np.exp(-latent_score))
+y_true = (np.random.rand(n_samples) < true_prob).astype(int)
+
+# Model predicted probabilities (captures signal well with some noise)
+# A good model that shows clear lift over random
+y_score = 1 / (1 + np.exp(-(latent_score + np.random.randn(n_samples) * 0.5)))
+
+# Calculate cumulative gains curve
+sorted_indices = np.argsort(y_score)[::-1]
+y_true_sorted = y_true[sorted_indices]
+
+# Cumulative gains: percentage of population vs percentage of positives captured
+total_positives = np.sum(y_true)
+cumulative_positives = np.cumsum(y_true_sorted)
+gains = cumulative_positives / total_positives * 100
+
+# Percentage of population targeted
+n_samples = len(y_true)
+population_percentage = np.arange(1, n_samples + 1) / n_samples * 100
+
+# Add origin point (0, 0) for proper plotting
+population_percentage = np.insert(population_percentage, 0, 0)
+gains = np.insert(gains, 0, 0)
+
+# Create perfect model curve (captures all positives immediately)
+positive_rate = total_positives / n_samples * 100
+perfect_x = np.array([0, positive_rate, 100])
+perfect_y = np.array([0, 100, 100])
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot model gains curve
+ax.plot(population_percentage, gains, color="#306998", linewidth=3, label="Model", zorder=3)
+
+# Plot random baseline (diagonal)
+ax.plot([0, 100], [0, 100], color="#888888", linewidth=2, linestyle="--", label="Random (Baseline)", zorder=2)
+
+# Plot perfect model
+ax.plot(perfect_x, perfect_y, color="#FFD43B", linewidth=2, linestyle=":", label="Perfect Model", zorder=2)
+
+# Fill area between model and random baseline
+ax.fill_between(population_percentage, gains, population_percentage, alpha=0.2, color="#306998", zorder=1)
+
+# Styling
+ax.set_xlabel("Population Targeted (%)", fontsize=20)
+ax.set_ylabel("Positive Cases Captured (%)", fontsize=20)
+ax.set_title("gain-curve · matplotlib · pyplots.ai", fontsize=24)
+
+ax.set_xlim(0, 100)
+ax.set_ylim(0, 100)
+ax.set_aspect("equal")
+
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+ax.legend(fontsize=16, loc="lower right")
+
+# Add annotation showing key insight
+# Find where 20% of population is targeted
+idx_20 = np.searchsorted(population_percentage, 20)
+gain_at_20 = gains[idx_20]
+ax.annotate(
+    f"Top 20% captures {gain_at_20:.0f}%\nof positive cases",
+    xy=(20, gain_at_20),
+    xytext=(35, gain_at_20 - 15),
+    fontsize=14,
+    arrowprops={"arrowstyle": "->", "color": "#306998", "lw": 2},
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "white", "edgecolor": "#306998", "alpha": 0.9},
+)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/gain-curve/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: gain-curve
+created: '2025-12-29T22:47:36Z'
+updated: '2025-12-29T22:51:48Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584327624
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/gain-curve/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/gain-curve/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent implementation of the gains curve with all spec-required elements (model,
+    baseline, perfect model)
+  - Clear visual hierarchy with distinct line styles (solid, dashed, dotted) and colors
+  - Informative annotation highlighting the key insight at 20% targeting threshold
+  - Shaded area effectively communicates the model lift over random selection
+  - Clean, well-structured code following KISS principles
+  - Proper use of matplotlib Axes methods throughout
+  weaknesses:
+  - Legend positioned in lower right overlaps with the data region where curves pass
+    through
+  - Square aspect ratio (1:1) while appropriate for the 0-100% axes creates some unused
+    corner space compared to 16:9