Result-Analyzer Skill

Purpose

The final piece of the reproduction pipeline. After running experiments, this skill compares your results against the paper's reported metrics, figures, and training curves — then generates a structured report.

When to Use

• After code-reproducer has finished training on the remote GPU
• User says "compare my results" or "how close are we to the paper?"
• When writing a reproduction report or paper
• To decide if reproduction was successful

Architecture

Inputs                           Comparators                     Outputs
───────────────────             ──────────────                  ────────────────
paper-parser JSON ──┐
                    ├──→ table_extractor ───┐
paper metrics     ──┘                      │
                                           ├──→ metric_comparator ──→ Markdown Report
repro metrics   ────────────────────────────┘                    ──→ JSON Report
                                                                 ──→ Beamer Data
repro train.csv ────→ curve_comparator ──→ comparison plots      ──→ latex-paper-skills
                                                                 ──→ beamer-skill PPT
repro images    ────→ image_comparator ──→ SSIM/PSNR/FID
paper figures   ────┘                  ──→ side-by-side figures

Quick Start

Simple Metric Comparison

python result-analyzer/analyze.py \
    --paper-metrics '{"accuracy": 95.3, "FID": 3.17}' \
    --repro-metrics '{"accuracy": 94.8, "FID": 3.45}' \
    --title "DDPM Reproduction" \
    -o report/

Full Pipeline (paper JSON + training log + images)

python result-analyzer/analyze.py \
    --paper-json workspace/<paper>/paper_content.json \
    --repro-log workspace/<paper>/train_log.csv \
    --repro-images workspace/<paper>/generated/ \
    --paper-figures workspace/<paper>/figures/ \
    --fid \
    --beamer \
    -o workspace/<paper>/report/

From Paper-Parser JSON Only

python result-analyzer/analyze.py \
    --paper-json paper_content.json \
    --repro-metrics '{"accuracy": 94.8}' \
    --method-name "Ours" \
    -o report/

Comparators

1. Metric Comparator (Core)

Compares reproduced values against paper values with tolerance-based judgment.

Built-in Knowledge Base: 50+ metrics with direction info:

• higher_better: accuracy, BLEU, PSNR, SSIM, IS, mAP, AUC, F1...
• lower_better: FID, loss, perplexity, WER, MAE, RMSE, LPIPS...

Judgment Logic:

2. Curve Comparator

• Loads CSV/JSON training logs
• Compares final values against paper
• Pearson correlation between curves
• Convergence speed analysis
• Generates matplotlib comparison plots

3. Image Comparator

• SSIM (Structural Similarity) — via scikit-image
• PSNR (Peak Signal-to-Noise Ratio)
• FID (Fréchet Inception Distance) — optional, requires pytorch-fid
• Side-by-side comparison figures

4. Table Extractor

• Reads paper-parser JSON output → finds result tables
• Auto-detects "Ours" / "Proposed" / last row
• Handles ±, bold markers, % signs
• Free-text metric extraction (e.g., "We achieve 95.3% accuracy")

Output Formats

Markdown Report (reproduction_report.md)

# Reproduction Report
## 🟢 Overall: PASS
| Status | Metric | Paper | Reproduced | Diff | Note |
|--------|--------|-------|-----------|------|------|
| ✅ PASS | Accuracy | 95.3 | 94.8 | -0.5 ↓ | Within tolerance |
| ⚠️ WARN | FID | 3.17 | 3.45 | +0.28 ↑ | Close but slightly off |

JSON Report (reproduction_report.json)

Structured data for downstream consumption:

• latex-paper-skills → results-backfill skill
• beamer-skill → reproduction PPT

Beamer Data (beamer_report_data.json)

Slide-by-slide data for generating a Beamer PPT:

1. Title slide (paper name + overall status)
2. Metric comparison table
3. Training curves figure
4. Sample comparison figure
5. Conclusion slide

Integration with Other Skills

→ beamer-skill (Generate PPT)

# 1. Run result-analyzer with --beamer flag
python result-analyzer/analyze.py ... --beamer -o report/

# 2. Auto-generate Beamer .tex from JSON
python beamer-skill/generate_beamer_report.py report/beamer_report_data.json -o report/reproduction_slides.tex

# 3. Compile to PDF
python beamer-skill/generate_beamer_report.py report/beamer_report_data.json -o slides.tex --compile

→ latex-paper-skills (Write LaTeX Paper via latex_bridge.py)

# 1. Run result-analyzer
python result-analyzer/analyze.py ... -o report/

# 2. Convert JSON to latex-paper-skills format
python result-analyzer/latex_bridge.py report/reproduction_report.json -o paper/results/

# This generates:
#   paper/results/main_results.csv        ← CSV for results-backfill
#   paper/results/main_results.tex        ← LaTeX table: \input{results/main_results.tex}
#   paper/results/reproduction_summary.tex ← Summary text for abstract/conclusion

# 3. In your paper:
#   \input{results/main_results.tex}       % metric comparison table
#   \input{results/reproduction_summary.tex} % auto-written reproduction summary

← paper-parser (Input)

paper-parser outputs paper_content.json with tables
    → result-analyzer extracts paper metrics from tables
    → compares against reproduced values

← code-reproducer (Input)

code-reproducer outputs train_log.csv + generated images
    → result-analyzer loads CSV for curve comparison
    → compares images via SSIM/PSNR

Dependencies

pip install -r result-analyzer/requirements.txt
# Core: numpy, matplotlib, scikit-image, pandas
# Optional: pytorch-fid (for FID score)

📚 Reference URLs (for agent self-help)

Limitations

• SSIM is sensitive to image alignment — ensure consistent cropping
• FID requires ≥2048 images per directory for reliable scores
• Table extraction depends on paper-parser JSON quality
• Free-text metric extraction uses regex patterns — may miss complex phrasing