feat(altair): implement calibration-curve

[github-actions]

Implementation: calibration-curve - altair

Implements the altair version of calibration-curve.

File: plots/calibration-curve/implementations/altair.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a calibration curve visualization with two vertically stacked charts. The main chart (top) displays the calibration curve with a solid blue line (#306998) connecting filled circular markers representing binned mean predicted probabilities on the X-axis vs. fraction of positives on the Y-axis. A yellow dashed diagonal line represents perfect calibration. The title reads "calibration-curve · altair · pyplots.ai" with a subtitle showing "Brier Score: 0.1388". The bottom chart is a histogram showing the distribution of predicted probabilities using blue bars. Both axes are clearly labeled with appropriate font sizes. The calibration curve demonstrates a slightly overconfident classifier that falls below the diagonal for lower probabilities and approaches/crosses it at higher values.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 28pt, axis labels at 18pt, tick labels at 16pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Points are well-sized (size=300), line thickness appropriate; minor deduction for points being slightly large
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) are colorblind-safe and high contrast
• VQ-05: Layout Balance (5/5) - Good use of canvas with vconcat layout, charts well proportioned
• VQ-06: Axis Labels (2/2) - Descriptive labels: "Mean Predicted Probability", "Fraction of Positives", "Count"
• VQ-07: Grid & Legend (0/2) - No grid lines present; while not always necessary, subtle grid would aid reading values

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct calibration curve with diagonal reference line
• SC-02: Data Mapping (5/5) - X=mean predicted probability, Y=fraction of positives (correct)
• SC-03: Required Features (5/5) - Has diagonal reference line, 10 bins, Brier score metric, histogram of predictions
• SC-04: Data Range (3/3) - Both axes show 0-1 range appropriately
• SC-05: Legend Accuracy (2/2) - No legend needed as single model; colors are self-explanatory
• SC-06: Title Format (2/2) - Uses correct format "calibration-curve · altair · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows overconfident classifier behavior well, demonstrates deviation from perfect calibration; could show underconfident region more clearly
• DQ-02: Realistic Context (6/7) - Simulated classifier predictions are plausible, though generic
• DQ-03: Appropriate Scale (5/5) - 2000 samples, probabilities in 0-1 range, Brier score of 0.1388 is realistic

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean sequential structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (altair, numpy, pandas)
• CQ-04: No Deprecated API (1/1) - Uses current Altair API
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html; spec doesn't require HTML but this is acceptable for interactive libraries

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses Altair's declarative grammar with layering, vconcat, and configure methods; could leverage tooltips more prominently or interactive features

Strengths

• Excellent implementation of calibration curve with all required spec features (diagonal reference, binning, Brier score, histogram)
• Clean, readable code following KISS principles with proper seed for reproducibility
• Good use of Altair's layering and vconcat for combining charts
• Appropriate color scheme with good contrast between calibration line (blue) and reference line (yellow dashed)
• Subtitle elegantly displays the Brier score metric
• Tooltips included on data points for interactivity

Weaknesses

• Missing subtle grid lines which would aid in reading exact values from the calibration curve
• Histogram bars could use slight spacing/gap for better visual separation

Verdict: APPROVED