feat(matplotlib): implement lift-curve

[github-actions]

Implementation: lift-curve - matplotlib

Implements the matplotlib version of lift-curve.

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

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a lift curve with a solid blue line showing the model lift decreasing from approximately 6.5x at 0% population down to 1x at 100% population. A horizontal yellow dashed reference line at y=1 represents random selection. Key decile markers are shown at 10%, 20%, 30%, 40%, and 50% with their lift values annotated (6.14x, 4.28x, 3.14x, 2.45x, 2.00x respectively). The area between the lift curve and the reference line is filled with a light blue shade for visual emphasis. The title reads "lift-curve · matplotlib · pyplots.ai", x-axis is labeled "Population Targeted (%)", and y-axis is labeled "Cumulative Lift". A legend in the upper right identifies the blue line as "Model Lift" and yellow dashed line as "Random (Lift = 1)". The plot uses a subtle gray grid with dashed lines.

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements; annotations are well-spaced above markers
• VQ-03: Element Visibility (8/8) - Line width of 3, marker size of 150, appropriate for line chart with clear visibility
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) are colorblind-safe and high contrast
• VQ-05: Layout Balance (5/5) - Excellent canvas utilization, plot fills appropriate area with balanced margins
• VQ-06: Axis Labels (1/2) - Labels are descriptive but y-axis could include units (e.g., "Cumulative Lift (ratio)")
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), legend well-placed; minor: legend could be slightly smaller or positioned to avoid crowding top-right

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct lift curve chart type
• SC-02: Data Mapping (5/5) - X-axis shows percentage of population, Y-axis shows cumulative lift ratio
• SC-03: Required Features (5/5) - Includes reference line at y=1 as specified, decile markers at key percentiles
• SC-04: Data Range (3/3) - X-axis 0-100%, Y-axis shows full lift range appropriately
• SC-05: Legend Accuracy (2/2) - Legend correctly labels both curves
• SC-06: Title Format (2/2) - Exact format "lift-curve · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows characteristic lift curve behavior: high lift at low percentages, gradual decay to 1
• DQ-02: Realistic Context (7/7) - Customer response model with 15% baseline rate is realistic marketing scenario
• DQ-03: Appropriate Scale (5/5) - 1000 samples, 15% base rate, lift values (6.5x to 1x) are realistic for a good model

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear script: imports → data → calculation → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set at the start
• 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' with dpi=300

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Uses basic matplotlib features only; could leverage fill_between more creatively or add interactive annotations

Strengths

• Excellent implementation of lift curve with proper calculation methodology (sort by score, cumulative lift at each percentage)
• Clear visual distinction between model lift curve and random baseline with appropriate colors
• Well-placed decile annotations that show practical lift values at key targeting thresholds
• Shaded area between curves provides intuitive visualization of model value
• Realistic customer response data with appropriate base rate and sample size
• Clean, well-structured code following KISS principles

Weaknesses

• Y-axis label "Cumulative Lift" could include units or clarification (e.g., "Cumulative Lift (ratio)" or "Cumulative Lift (×)")
• Does not leverage distinctive matplotlib features beyond basic plotting capabilities

Verdict: APPROVED