feat(letsplot): implement lift-curve

[github-actions]

Implementation: lift-curve - python/letsplot

Implements the python/letsplot version of lift-curve.

File: plots/lift-curve/implementations/python/letsplot.py

Parent Issue: #2379

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Warm off-white background (#FAF8F1) with dark text labels and title perfectly readable. The primary lift curve is rendered in #009E73 (Okabe-Ito brand green), starting high (~9) and descending smoothly to baseline. A dashed gray reference line marks y=1 (no lift), with a clear label at position (85, 1.15). The sampled data points are visible and well-spaced. All axis labels descriptive with units: "Population Targeted (%)" and "Cumulative Lift". Subtle gray grid lines support readability without dominating. Overall layout is clean with balanced margins, plot fills 60-70% of canvas.

Dark render (plot-dark.png): Warm near-black background (#1A1A17) with light text labels and title perfectly readable. The lift curve remains identical #009E73—proving data colors are theme-independent. Reference line is the same dashed gray. Grid, axes, and all chrome elements are properly light-colored with no dark-on-dark failures. Baseline label is readable light teal. The plot maintains identical composition and proportions to the light render, with only chrome (background, text, grid) adapted to the dark theme. Both renders are fully legible and properly themed.

Score: 83/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	9	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	4	10
Total	83	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (24/20/16pt), perfectly readable
• VQ-02: No Overlap (6/6) - All text fully readable, no collisions
• VQ-03: Element Visibility (6/6) - Markers and lines perfectly adapted to data density
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette positions 1 & 5, CVD-safe contrast
• VQ-05: Layout & Canvas (4/4) - Perfect proportions, balanced margins, 60-70% fill
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units; correct title format
• VQ-07: Palette Compliance (2/2) - First series #009E73, backgrounds correct, theme chrome proper in both renders

Design Excellence (9/20)

• DE-01: Aesthetic Sophistication (4/8) - Well-configured defaults, not exceptional
• DE-02: Visual Refinement (3/6) - Grid customized but minimal overall refinement
• DE-03: Data Storytelling (2/6) - Data displayed clearly but no visual emphasis or hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct lift curve implementation
• SC-02: Required Features (4/4) - All spec features present and working
• SC-03: Data Mapping (3/3) - X/Y correctly assigned, all data visible
• SC-04: Title & Legend (3/3) - Title format correct, legend appropriately omitted

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows full curve range and key lift-curve characteristics
• DQ-02: Realistic Context (5/5) - Real fraud detection scenario, neutral, 10% base rate realistic
• DQ-03: Appropriate Scale (4/4) - 1200 samples, proportions factually correct

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow, no functions/classes
• CQ-02: Reproducibility (2/2) - Seed set, deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, appropriate complexity
• CQ-05: Output & API (1/1) - Correct plot-{THEME}.png and .html format

Library Mastery (4/10)

• LM-01: Idiomatic Usage (3/5) - Correct ggplot patterns but standard usage
• LM-02: Distinctive Features (1/5) - Generic ggplot-style, easily portable to plotnine

Score Caps Applied

• None

Strengths

• Perfectly legible typography with explicit font sizes in both themes
• Correct implementation of theme-adaptive colors with proper Okabe-Ito usage
• Complete, accurate lift curve visualization with proper reference line
• Clean, reproducible code with proper seeding
• Realistic fraud detection scenario with appropriate proportions
• Proper axis labels with units and descriptive title

Weaknesses

• Design lacks visual storytelling—no emphasis on the critical 0-20% high-lift zone; consider color gradients or size variation to highlight model value
• No distinctive library features—generic ggplot-style easily replicated in plotnine; consider leveraging letsplot's interactive capabilities
• Visual refinement is minimal—spines follow defaults, overall aesthetic needs polish; consider removing top/right spines and refining margins
• Limited visual hierarchy—all elements have equal weight; use alpha/color contrast to guide viewer to key insights

Issues Found

1. DE-01 LOW (4/8): Design lacks sophistication
   • Fix: Remove top/right spines, use subtle alpha on secondary reference line, add visual hierarchy through size or color variation
2. DE-02 LOW (3/6): Minimal visual refinement beyond defaults
   • Fix: Adjust spines, refine whitespace, consider legend or annotation styling enhancements
3. DE-03 LOW (2/6): No visual storytelling or data emphasis
   • Fix: Create focal point by using stronger color on high-lift region, add gradient fill or size-variation to emphasize model performance
4. LM-02 LOW (1/5): Generic, non-distinctive usage
   • Fix: Leverage letsplot-specific interactive features or unique styling not easily replicated in other ggplot-style libraries

AI Feedback for Next Attempt

Priority fixes: (1) Enhance visual storytelling by emphasizing the high-lift zone—use color gradients, size variation, or selective alpha to highlight where the model creates value; (2) Remove top/right spines and refine typography hierarchy to create more polished, publication-ready appearance; (3) Demonstrate library mastery by using a letsplot-specific feature (e.g., interactive tooltips in HTML export, or unique layer composition) that distinguishes this from generic ggplot implementations. These improvements will move Design Excellence and Library Mastery from defaults-only to thoughtful, sophisticated work.

Verdict: REJECTED

Score of 83 falls below the 90-point threshold for Attempt 1. Implementation is technically sound and above-median quality (calibration: median = 72-78), but needs design sophistication and distinctive library features to reach publication quality. Repair focused on visual storytelling, aesthetic refinement, and library-specific features.

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot displays a classic lift curve with a warm off-white background (#FAF8F1). The main green data line (#009E73) starts high (~8x lift) at the left and curves gracefully down toward the baseline at y=1. A shaded green region under the curve highlights the "high-lift zone" where the model provides significant value. The gray dashed reference line at y=1 clearly marks the random baseline. Axis labels "Population Targeted (%)" and "Cumulative Lift" are dark and fully readable against the light background. Title "lift-curve · letsplot · anyplot.ai" is prominent at the top. Grid lines are subtle and don't compete with the data. The annotation "High-lift zone" in the upper left and "Baseline (Lift = 1)" on the right guide interpretation. All text, tick labels, and data elements are perfectly readable.

Dark render (plot-dark.png): The identical plot renders on a warm near-black background (#1A1A17) with excellent theme adaptation. The data colors remain identical—the same green line and shaded area—proving correct chrome-only adaptation. All text is now light-colored (titles, labels, ticks) and remains clearly readable against the dark surface. No "dark-on-dark" failures; all text has adequate contrast. Grid lines are subtle light gray. The plot maintains the same visual hierarchy and storytelling as the light version. Both renders demonstrate perfect theme-adaptive implementation.

Score: 94/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	94	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title=24pt, axis_title=20pt, axis_text=16pt); perfectly readable in both themes
• VQ-02: No Overlap (6/6) - No overlapping elements; all text fully readable
• VQ-03: Element Visibility (6/6) - Curve (size=2.5) is thick and prominent; point markers (size=4, every 5th) are well-sized; reference line is clear
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette; excellent contrast; colorblind-safe
• VQ-05: Layout & Canvas (4/4) - Perfect layout; plot fills 50-70% of canvas with balanced margins; nothing cut off
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units: "Population Targeted (%)" and "Cumulative Lift"
• VQ-07: Palette Compliance (2/2) - First series is #009E73 ✓; light bg is #FAF8F1 ✓; dark bg is #1A1A17 ✓; theme tokens applied to all chrome elements; both renders identical in data colors

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Thoughtful design with shaded region under curve to highlight high-lift zone; explicit annotations guide interpretation; brand color used effectively; above default styling but not quite FiveThirtyEight-level
• DE-02: Visual Refinement (4/6) - Subtle grid, well-distributed whitespace, thoughtful annotation placement; some customization visible; could remove more spines for even cleaner look
• DE-03: Data Storytelling (6/6) - Clear visual hierarchy: thick green curve draws eye, shaded area emphasizes value zone, labels guide interpretation; viewer immediately understands the business insight; tells a compelling story about model lift

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct lift curve with cumulative lift vs. population percentage
• SC-02: Required Features (4/4) - All features present: reference line at y=1, proper axes, realistic fraud detection scenario
• SC-03: Data Mapping (3/3) - X correctly mapped to population_pct; Y to lift; axes show full range
• SC-04: Title & Legend (3/3) - Title format correct; annotations serve as contextual labels; no redundant legend needed

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows full curve from high lift to baseline; demonstrates steep decline clearly; exponential distributions realistic
• DQ-02: Realistic Context (5/5) - Fraud detection is real, comprehensible, and neutral; 10% fraud rate is realistic
• DQ-03: Appropriate Scale (4/4) - 1200 samples (within 100-10000 spec range); lift values (8x → 1x) are factually correct for the scenario

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple linear flow; imports → setup → data → plot → save; no unnecessary functions
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set; fully deterministic
• CQ-03: Clean Imports (2/2) - Only used imports (os, numpy, pandas, lets_plot)
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, appropriate complexity; effective use of theme tokens
• CQ-05: Output & API (1/1) - Correct output: plot-{THEME}.png + plot-{THEME}.html; scale=3 for resolution

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Expert use of ggplot() + geom_*() + theme() pattern; proper aes() mappings; comprehensive theme styling; correct API usage throughout
• LM-02: Distinctive Features (3/5) - Uses multiple geom layers effectively (line + point + text + area); theme-adaptive design; well-executed but not particularly library-distinctive

Score Caps Applied

• None / Plot is technically excellent with no deficiencies

Strengths

• Perfect theme adaptation: identical data colors in both renders with only chrome switching
• Strong visual storytelling: shaded region and annotations clearly communicate the value of targeting
• Comprehensive text styling with explicit font sizes ensuring readability at high resolution
• Realistic fraud detection scenario that demonstrates the practical utility of lift curves
• Clean, maintainable code with proper random seed for reproducibility
• Excellent palette compliance: Okabe-Ito brand green used correctly; all text colors theme-appropriate

Weaknesses

• Library Mastery (LM-02) is solid but not distinctive—implementation uses standard LetsPlot techniques without leveraging library-unique interactive features (though this is appropriate for a static PNG output)
• Design Excellence (DE-01) could reach 7-8 with even more refined typography choices or color harmony details (but current level is polished and professional)

Issues Found

None—this is a high-quality implementation that merits approval.

AI Feedback for Next Attempt

This implementation is approved on first review. The visual polish, clear storytelling, and correct technical execution exceed the quality threshold. No repairs needed.

Verdict: APPROVED