feat(plotly): implement gain-curve

[github-actions]

Implementation: gain-curve - plotly

Implements the plotly version of gain-curve.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a cumulative gains chart with three lines on a white background. The main model curve (solid dark blue, #306998) shows the cumulative percentage of positives captured as population percentage increases, with a light blue fill area between the model curve and the baseline. A dashed gray diagonal line represents the random baseline (from 0,0 to 100,100). A dotted yellow/gold line shows the perfect model - rising vertically to 100% at around 20% of population, then horizontal to 100%. The x-axis is labeled "Percentage of Population Targeted (%)" (0-100), y-axis is "Percentage of Positives Captured (%)" (0-100). Title "gain-curve · plotly · pyplots.ai" is centered at top. Legend is positioned in lower-right corner with entries for Model, Perfect Model, and Random (Baseline). Grid lines are subtle gray. The model curve demonstrates good discrimination, rising steeply initially and capturing ~80% of positives by targeting ~30% of population.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 28pt, axis labels at 22pt, tick labels at 18pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping elements anywhere
• VQ-03: Element Visibility (7/8) - Lines are well-sized (width 3-4), though model line could be slightly thicker for better visibility against fill area
• VQ-04: Color Accessibility (5/5) - Blue/gray/yellow are colorblind-safe, good contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units (%)
• VQ-07: Grid & Legend (0/2) - Grid is subtle (good), but legend placement in lower-right corner partially overlaps with the baseline diagonal where it crosses

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct cumulative gains chart
• SC-02: Data Mapping (5/5) - X=population %, Y=positives captured % - correctly assigned
• SC-03: Required Features (5/5) - Includes diagonal baseline (random), perfect model curve, and main model curve as specified
• SC-04: Data Range (3/3) - Both axes show 0-100% range appropriately
• SC-05: Legend Accuracy (2/2) - Legend labels are accurate and descriptive
• SC-06: Title Format (2/2) - Uses correct format: "gain-curve · plotly · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows model discrimination well, includes perfect model reference; could show slight variations in model quality to demonstrate different scenarios
• DQ-02: Realistic Context (7/7) - Customer response model evaluation is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (4/5) - 20% positive rate and model performance are realistic; 1000 samples produces slightly granular curve

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean flow: imports → data → calculation → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects used
• CQ-04: No Deprecated API (1/1) - Uses current Plotly API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses fill="tonexty" for area fill which is nice, but doesn't leverage Plotly's interactive features like hover tooltips with additional info (e.g., showing exact values on hover), annotations for key points, or range sliders

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's 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 where it crosses that region
• Could better leverage Plotly's interactive features (hover tooltips with specific values, annotations for key thresholds)

Verdict: APPROVED