chore(plotnine): update quality score 86 and review feedback for line-retention-cohort

Author: github-actions[bot]

plots/line-retention-cohort/implementations/plotnine.py

@@ -1,7 +1,7 @@
-"""pyplots.ai
+""" pyplots.ai
 line-retention-cohort: User Retention Curve by Cohort
-Library: plotnine | Python 3.13
-Quality: pending | Created: 2026-03-16
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 86/100 | Created: 2026-03-16
 """
 
 import numpy as np

plots/line-retention-cohort/metadata/plotnine.yaml

@@ -1,10 +1,7 @@
-# Per-library metadata for plotnine implementation of line-retention-cohort
-# Auto-generated by impl-generate.yml
-
 library: plotnine
 specification_id: line-retention-cohort
 created: '2026-03-16T20:44:50Z'
-updated: '2026-03-16T20:44:50Z'
+updated: '2026-03-16T20:50:13Z'
 generated_by: claude-opus-4-5-20251101
 workflow_run: 23164943107
 issue: 4572
@@ -13,7 +10,216 @@ library_version: 0.15.3
 preview_url: https://storage.googleapis.com/pyplots-images/plots/line-retention-cohort/plotnine/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-retention-cohort/plotnine/plot_thumb.png
 preview_html: null
-quality_score: null
+quality_score: 86
 review:
-  strengths: []
-  weaknesses: []
+  strengths:
+  - Full spec compliance with all required features implemented correctly
+  - Thoughtful blue-to-orange color progression with alpha gradient creates effective
+    temporal hierarchy
+  - Clean well-structured code following KISS principles with proper reproducibility
+  - Realistic product analytics data with appropriate cohort sizes and decay patterns
+  weaknesses:
+  - Oldest cohorts at alpha 0.45-0.55 are somewhat faint reducing readability
+  - 20% threshold annotation is small (size=9) compared to other text elements
+  - Design could be more polished with better typography hierarchy and annotation
+    styling
+  - Library mastery stays at basic grammar-of-graphics level without distinctive plotnine
+    features
+  image_description: The plot displays 5 retention curves for monthly signup cohorts
+    (Jan-May 2025) over 12 weeks. Lines transition from light, transparent blue-gray
+    (Jan 2025, oldest) to bold orange-red (May 2025, newest), creating a visual hierarchy
+    emphasizing recent cohorts. All curves start at 100% at week 0 and decay exponentially,
+    with newer cohorts retaining better. A dashed gray horizontal line marks the 20%
+    retention threshold, with a small "20% threshold" annotation near the right end.
+    The Y-axis shows "Retained Users" with percentage labels (0%-100%), X-axis shows
+    "Weeks Since Signup" (0-12). Points mark each data value on the lines. A legend
+    on the right lists each cohort with sample size (e.g., "Jan 2025 (n=1,245)").
+    The title reads "line-retention-cohort · plotnine · pyplots.ai" in bold. Background
+    is clean white with subtle horizontal gridlines.
+  criteria_checklist:
+    visual_quality:
+      score: 28
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 7
+        max: 8
+        passed: true
+        comment: All font sizes explicitly set. Threshold annotation at size=9 is
+          small relative to other text.
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: No overlapping text or elements. Legend well-separated from data.
+      - id: VQ-03
+        name: Element Visibility
+        score: 5
+        max: 6
+        passed: true
+        comment: Lines visible at size=1.5 with points. Oldest cohort at alpha=0.45
+          is somewhat faint.
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Blue-to-orange palette is colorblind-safe with good contrast.
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 4
+        max: 4
+        passed: true
+        comment: 16:9 ratio, plot fills canvas well, legend positioned cleanly.
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: Descriptive labels with percentage units in tick labels.
+    design_excellence:
+      score: 13
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 5
+        max: 8
+        passed: true
+        comment: Custom blue-to-orange palette with alpha gradient. Above defaults
+          but not publication-quality.
+      - id: DE-02
+        name: Visual Refinement
+        score: 4
+        max: 6
+        passed: true
+        comment: Minimal theme, removed minor grid and x-grid. Subtle y-grid with
+          alpha. Axis lines added.
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Alpha and color progression create visual hierarchy. 20% threshold
+          provides benchmark context.
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct line chart with multiple cohort curves.
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: 'All spec features present: 100% start, distinct colors, legend with
+          size, gridlines, opacity gradient, threshold line.'
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X = weeks since signup, Y = retention rate. All data visible.
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title format correct. Legend labels match spec format.
+    data_quality:
+      score: 14
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 5
+        max: 6
+        passed: true
+        comment: 5 cohorts, 13 time periods, different decay rates. All curves follow
+          similar exponential shape.
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: Monthly signup cohorts with realistic sizes. Neutral product analytics
+          context.
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Sensible retention values for SaaS product.
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: 'Flat script: imports, data, plot, save.'
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) set.
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used.
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Clean, appropriate complexity. No fake UI.
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png with current API.
+    library_mastery:
+      score: 6
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good grammar of graphics usage with aes, geom layers, scales, themes.
+          pd.Categorical and scale_alpha_identity are idiomatic.
+      - id: LM-02
+        name: Distinctive Features
+        score: 2
+        max: 5
+        passed: false
+        comment: Uses layer composition and scale_alpha_identity but lacks deeply
+          distinctive plotnine features.
+  verdict: REJECTED
+impl_tags:
+  dependencies: []
+  techniques:
+  - annotations
+  - layer-composition
+  patterns:
+  - data-generation
+  - iteration-over-groups
+  dataprep:
+  - cumulative-sum
+  styling:
+  - alpha-blending
+  - grid-styling