feat(matplotlib): implement gain-curve

[github-actions]

Implementation: gain-curve - matplotlib

Implements the matplotlib version of gain-curve.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a cumulative gains chart on a square canvas with 1:1 aspect ratio. The main model curve is shown in blue (solid line, linewidth=3), starting from origin (0,0) and curving upward to (100,100). A gray dashed diagonal line represents the random baseline. A yellow/gold dotted line shows the perfect model - rising steeply to 100% at approximately 50% of population, then horizontal. A light blue shaded area fills the space between the model curve and the baseline, visually highlighting the model's "lift" over random selection. An annotation box in the lower portion reads "Top 20% captures 35% of positive cases" with an arrow pointing to the corresponding point on the curve. The legend is positioned in the lower right corner. All text is clearly legible with appropriate font sizes.

Quality Score: 93/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, axis labels 20pt, ticks 16pt, legend 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping elements, annotation well-positioned
• VQ-03: Element Visibility (8/8) - Lines are appropriately sized (linewidth=3 for model, 2 for others), clearly distinguishable
• VQ-04: Color Accessibility (5/5) - Blue, gray, and yellow are colorblind-safe and have good contrast
• VQ-05: Layout Balance (4/5) - Good use of canvas with equal aspect, though square format creates some empty space in corners
• VQ-06: Axis Labels (2/2) - "Population Targeted (%)" and "Positive Cases Captured (%)" are descriptive with units
• VQ-07: Grid & Legend (0/2) - Grid at alpha=0.3 is good, but legend could be positioned better (lower right overlaps with data region)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct cumulative gains chart
• SC-02: Data Mapping (5/5) - X=population percentage, Y=cumulative positives captured - correct
• SC-03: Required Features (5/5) - Has diagonal baseline, model curve, perfect model reference, annotation showing key insight
• SC-04: Data Range (3/3) - Axes show 0-100% for both dimensions
• SC-05: Legend Accuracy (2/2) - Legend labels correctly identify Model, Random (Baseline), Perfect Model
• SC-06: Title Format (2/2) - Uses exact format "gain-curve · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows model lift over baseline and comparison to perfect model; the model demonstrates clear discrimination with steeper initial slope
• DQ-02: Realistic Context (7/7) - Customer response model is a real, neutral business scenario (marketing campaign optimization)
• DQ-03: Appropriate Scale (4/5) - Values are sensible; 35% capture at 20% targeting is realistic for a decent model, though the lift could be more dramatic to better showcase the plot type

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API (Axes methods)
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses fill_between for shaded area, annotation with arrowprops, proper Axes methods - good but not exceptional matplotlib features

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's 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

Verdict: APPROVED