feat(seaborn): implement lift-curve

[github-actions]

Implementation: lift-curve - seaborn

Implements the seaborn version of lift-curve.

File: plots/lift-curve/implementations/seaborn.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a lift curve chart with a 16:9 landscape format on a white background with a subtle gray grid. The main lift curve is rendered as a thick blue line (#306998 Python Blue) starting at approximately 10x lift on the left and smoothly decreasing to approach 1x at 100% population. A dashed yellow/gold horizontal reference line at y=1 represents random selection (no lift). Six decile markers are shown as blue circles at the 10%, 20%, 30%, 40%, and 50% population points, with bold blue annotations showing lift values (8.30x, 4.95x, 3.33x, 2.50x, 2.00x). The title "lift-curve · seaborn · pyplots.ai" is prominently displayed at the top in bold. X-axis shows "Population Targeted (%)" from 0-100, Y-axis shows "Cumulative Lift" from 0-11. A legend in the upper right identifies "Model Lift" (solid blue) and "Random (No Lift)" (dashed yellow).

Quality Score: 92/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - All text is clearly readable: title at 24pt bold, axis labels at 20pt, tick labels at 16pt
• VQ-02: No Overlap (8/8) - No overlapping text elements; decile annotations are well-spaced
• VQ-03: Element Visibility (8/8) - Line width of 3 is appropriate, markers at size 12 are clearly visible
• VQ-04: Color Accessibility (5/5) - Blue and yellow are colorblind-safe combination with good contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, legend positioned appropriately
• VQ-06: Axis Labels (1/2) - Descriptive labels but Y-axis lacks units (ratio is unitless, but could use "x" suffix)
• VQ-07: Grid & Legend (1/2) - Grid at alpha=0.3 is subtle; legend well-placed but could be slightly smaller

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct lift curve visualization
• SC-02: Data Mapping (5/5) - X=population percentage, Y=cumulative lift ratio correctly mapped
• SC-03: Required Features (5/5) - Includes reference line at y=1, decile markers with values, proper curve shape
• SC-04: Data Range (3/3) - X-axis 0-100%, Y-axis shows full lift range with appropriate headroom
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies both lines
• SC-06: Title Format (2/2) - Uses exact format: "lift-curve · seaborn · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows high initial lift (~10x), gradual decay, convergence to baseline
• DQ-02: Realistic Context (6/7) - Marketing campaign response prediction is plausible; 10% base rate is realistic
• DQ-03: Appropriate Scale (5/5) - 1000 samples, 10% response rate, lift values 1-10x are all realistic

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear structure: imports → data → calculations → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Uses current seaborn API correctly
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Only uses sns.lineplot and sns.set_theme; the actual lift curve calculation and most visualization is done with matplotlib (ax.axhline, ax.plot, ax.annotate). Seaborn is primarily used for theming rather than its distinctive statistical visualization features.

Strengths

• Excellent visual clarity with appropriate font sizes and line widths for high-resolution output
• Proper lift curve calculation methodology with clear documentation in comments
• Good use of decile markers with lift value annotations to highlight key insights
• Reference baseline at y=1 clearly distinguishes model performance from random selection
• Realistic marketing campaign scenario with appropriate 10% base response rate
• Clean, well-organized code following KISS principles

Weaknesses

• Seaborn is used minimally - only for sns.lineplot and theming; the core visualization could be done entirely in matplotlib
• First decile annotation "8.30x" appears slightly cramped near the top of the curve
• Y-axis label could clarify that lift is a ratio (e.g., "Cumulative Lift (ratio)")

Verdict: APPROVED