feat(matplotlib): implement pdp-basic

[github-actions]

Implementation: pdp-basic - matplotlib

Implements the matplotlib version of pdp-basic.

File: plots/pdp-basic/implementations/matplotlib.py

Parent Issue: #2922

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a Partial Dependence Plot (PDP) with a blue color scheme. The main PDP line is a thick dark blue line (#306998) showing a gentle upward slope from approximately -50 at feature value -2 to +40 at feature value +1.7. A light blue 95% confidence band surrounds the main line, spanning from about -300 to +250 in the middle range. Faint ICE (Individual Conditional Expectation) lines are visible as semi-transparent blue curves within the confidence band. A yellow/gold rug plot at the bottom of the chart (at y ≈ -290) shows the distribution of training data values as vertical tick marks, concentrated between -1 and +1 feature values. The title reads "pdp-basic · matplotlib · pyplots.ai" at the top. X-axis is labeled "Feature Value" and Y-axis is labeled "Partial Dependence (Predicted Value)". A legend in the upper left shows three items: 95% Confidence Interval, Partial Dependence, and Data Distribution. The grid is subtle with dashed lines at alpha=0.3.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 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, legend is well-positioned in upper left
• VQ-03: Element Visibility (7/8) - Main PDP line is thick (linewidth=4), ICE lines sampled every 10th for clarity, rug plot visible but markers could be slightly larger for the 4800x2700 canvas
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) color scheme is colorblind-safe with good contrast
• VQ-05: Layout Balance (4/5) - Good use of canvas space, but the rug plot at the very bottom edge creates slight imbalance
• VQ-06: Axis Labels (2/2) - Descriptive labels: "Feature Value" and "Partial Dependence (Predicted Value)"
• VQ-07: Grid & Legend (0/2) - Grid is subtle at alpha=0.3, but legend overlaps with the ICE lines making it slightly distracting

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct PDP visualization showing marginal effect of feature on prediction
• SC-02: Data Mapping (5/5) - Feature values on x-axis, partial dependence on y-axis as specified
• SC-03: Required Features (5/5) - All spec features present: PDP line, confidence band, ICE lines, rug plot
• SC-04: Data Range (3/3) - Axes properly show full range of data
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies all three plot elements
• SC-06: Title Format (2/2) - Correct format: "pdp-basic · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows PDP with ICE lines showing individual variation, confidence band, and data distribution. Shows the upward trend well but all ICE lines have similar shape (less variation than ideal)
• DQ-02: Realistic Context (7/7) - Uses sklearn's regression data with GradientBoostingRegressor - a standard ML interpretability scenario
• DQ-03: Appropriate Scale (4/5) - Feature values in -2 to 3 range is reasonable for standardized data, but partial dependence range (-300 to 250) is very wide compared to the actual PDP line movement

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Follows imports → data → plot → save structure without functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) and random_state=42 for model
• CQ-03: Clean Imports (2/2) - All imports are used: matplotlib, numpy, sklearn components
• CQ-04: No Deprecated API (1/1) - Uses current sklearn.inspection.partial_dependence API
• CQ-05: Output Correct (1/1) - Saves as plot.png with dpi=300

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses matplotlib's fill_between for confidence band, scatter for rug plot, but could leverage more matplotlib-specific features like ax.axhline for a reference line at y=0

Strengths

• Excellent implementation of PDP with ICE lines using sklearn's partial_dependence function with kind="both"
• Proper confidence band visualization using fill_between with mean ± 1.96*std
• Rug plot effectively shows training data distribution along x-axis
• Clean, readable code that follows KISS principles
• Good color scheme with Python blue (#306998) and yellow (#FFD43B) that is colorblind-safe
• Appropriate text sizing for 4800x2700 canvas (title 24pt, labels 20pt, ticks 16pt)

Weaknesses

• The legend in the upper left slightly overlaps with ICE lines - consider using frameon=True with white background or relocating
• The rug plot markers (s=100) could be larger (s=150-200) for better visibility on the high-resolution canvas
• The y-axis range is dominated by the wide confidence band, making the actual PDP line's variation appear minimal - consider whether centering at zero as suggested in spec would help interpretation

Verdict: APPROVED