feat(plotly): implement gain-curve (#2492)

## Implementation: `gain-curve` - plotly

Implements the **plotly** version of `gain-curve`.

**File:** `plots/gain-curve/implementations/plotly.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20584841141)*

---------

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

Author: github-actions[bot]

plots/gain-curve/implementations/plotly.py

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+""" pyplots.ai
+gain-curve: Cumulative Gains Chart
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-29
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Customer response model evaluation
+np.random.seed(42)
+n_samples = 1000
+
+# Simulate a classification model with moderate discrimination
+# Positive class ~20% of population
+positive_rate = 0.20
+y_true = np.random.binomial(1, positive_rate, n_samples)
+
+# Generate predicted scores that correlate with true labels
+# Good predictions: positives tend to have higher scores
+y_score = np.where(
+    y_true == 1,
+    np.random.beta(5, 2, n_samples),  # Positives: skewed toward higher scores
+    np.random.beta(2, 5, n_samples),  # Negatives: skewed toward lower scores
+)
+# Add some noise to make it realistic
+y_score = np.clip(y_score + np.random.normal(0, 0.1, n_samples), 0, 1)
+
+# Calculate cumulative gains
+sorted_indices = np.argsort(y_score)[::-1]  # Sort by score descending
+y_true_sorted = y_true[sorted_indices]
+
+# Cumulative sum of positives
+cumulative_positives = np.cumsum(y_true_sorted)
+total_positives = y_true.sum()
+
+# Percentage of population (x-axis)
+pct_population = np.arange(1, n_samples + 1) / n_samples * 100
+
+# Percentage of positives captured (y-axis)
+pct_positives_captured = cumulative_positives / total_positives * 100
+
+# Add origin point for complete curve
+pct_population = np.insert(pct_population, 0, 0)
+pct_positives_captured = np.insert(pct_positives_captured, 0, 0)
+
+# Perfect model curve
+pct_for_perfect = positive_rate * 100
+perfect_x = [0, pct_for_perfect, 100]
+perfect_y = [0, 100, 100]
+
+# Plot
+fig = go.Figure()
+
+# Random baseline (diagonal)
+fig.add_trace(
+    go.Scatter(
+        x=[0, 100], y=[0, 100], mode="lines", name="Random (Baseline)", line=dict(color="#888888", width=3, dash="dash")
+    )
+)
+
+# Perfect model
+fig.add_trace(
+    go.Scatter(
+        x=perfect_x, y=perfect_y, mode="lines", name="Perfect Model", line=dict(color="#FFD43B", width=3, dash="dot")
+    )
+)
+
+# Model gains curve
+fig.add_trace(
+    go.Scatter(
+        x=pct_population,
+        y=pct_positives_captured,
+        mode="lines",
+        name="Model",
+        line=dict(color="#306998", width=4),
+        fill="tonexty",
+        fillcolor="rgba(48, 105, 152, 0.2)",
+    )
+)
+
+# Layout
+fig.update_layout(
+    title=dict(text="gain-curve · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Percentage of Population Targeted (%)", font=dict(size=22)),
+        tickfont=dict(size=18),
+        range=[0, 100],
+        dtick=20,
+        showgrid=True,
+        gridcolor="rgba(0, 0, 0, 0.1)",
+    ),
+    yaxis=dict(
+        title=dict(text="Percentage of Positives Captured (%)", font=dict(size=22)),
+        tickfont=dict(size=18),
+        range=[0, 100],
+        dtick=20,
+        showgrid=True,
+        gridcolor="rgba(0, 0, 0, 0.1)",
+    ),
+    template="plotly_white",
+    legend=dict(
+        x=0.98,
+        y=0.02,
+        xanchor="right",
+        yanchor="bottom",
+        font=dict(size=18),
+        bgcolor="rgba(255, 255, 255, 0.8)",
+        bordercolor="rgba(0, 0, 0, 0.2)",
+        borderwidth=1,
+    ),
+    margin=dict(l=80, r=40, t=80, b=80),
+)
+
+# Save
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/gain-curve/metadata/plotly.yaml

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+library: plotly
+specification_id: gain-curve
+created: '2025-12-29T23:20:47Z'
+updated: '2025-12-29T23:23:00Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584841141
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/gain-curve/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/gain-curve/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/gain-curve/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of cumulative gains chart with all three required curves
+    (model, random baseline, perfect model)
+  - Clear, readable text sizing following the library guidelines (28pt title, 22pt
+    labels, 18pt ticks)
+  - Good use of Plotly fill feature to highlight the gain over baseline
+  - Well-structured code with clear data simulation using beta distributions for realistic
+    model scores
+  - Correct title format and professional appearance
+  weaknesses:
+  - Legend placement in lower-right corner has minor overlap with the baseline diagonal
+    line
+  - Could better leverage Plotly interactive features (hover tooltips with specific
+    values, annotations for key thresholds)