tessera-ml 0.2.0

Topological Entity Similarity Structure for Emergent Response Analysis

TESSERA

Topological Entity Similarity Structure for Emergent Response Analysis

A domain-general framework for detecting and exploiting geometric structure in multi-dimensional feature spaces for predictive classification.

Targeting: Nature Computational Science

Core Hypothesis

When entities are characterized by multi-dimensional features and outcomes depend on relational position in feature space, similarity network topology carries predictive information that direct feature-based classifiers miss.

Three Failure Geometries

TESSERA detects geometrically distinct response regions in entity similarity space:

1. Ecotone — responses cluster at boundaries between stable operational domains (analogous to ecological transition zones)
2. Isolated cluster — responses occupy a distinct region, separate from main domains
3. Diffuse — responses scattered within domains, driven by individual feature thresholds

Three Demonstration Domains

	Ecology	Manufacturing	HPC
Entity	Reef survey	Production run	Job
Features	Environmental conditions	Sensor measurements	Resource usage
Outcome	Bleaching	Defect	Failure
Dataset	Global Coral Bleaching DB	UCI SECOM	Synthetic / NØMAD
N	~35,000	1,567	5,000+

Validation Protocol

1. Phase 1 — Ablation: GNN vs. MLP, RF, XGBoost, LogReg (justify network)
2. Phase 2 — Similarity: 7 measures compared (Simpson, Cosine, Bray-Curtis, Jaccard, Pearson, Euclidean, Mahalanobis)
3. Phase 3 — Bin sensitivity: n_bins = 2, 3, 4, 5 (Simpson only)
4. Phase 4 — Temporal: 5-fold temporal cross-validation

Project Structure

tessera/
├── __init__.py                    # Package init (v0.1.0)
├── core/
│   ├── __init__.py
│   ├── similarity.py              # 7 similarity measures
│   └── network.py                 # Graph construction (threshold, kNN, weighted)
├── synthetic/
│   ├── __init__.py
│   ├── landscape.py               # Feature space topology builder
│   ├── outcomes.py                # Failure pattern assignment (3 types + mixed)
│   └── visualize.py               # Diagnostic plots
├── data_acquisition.py            # Real dataset download & preprocessing
├── test_generator.py              # Synthetic data validation
└── test_pipeline.py               # End-to-end pipeline test

Current Status

• Synthetic data generator (3 patterns + mixed, with entity injection)
• Similarity engine (7 measures, vectorized)
• Network construction (threshold, kNN, weighted)
• Pipeline validation (data → similarity → graph)
• GNN model
• Real dataset acquisition (coral bleaching, SECOM)
• Validation pipeline (4 phases)
• Methods section
• Results
• Introduction, Discussion, Conclusions

Paper Writing Order

1. Methods
2. Results
3. Introduction
4. Discussion
5. Conclusions
6. Abstract (last)

References

• van Woesik, R. & Kratochwill, C. (2022). A global coral-bleaching database, 1980–2020. Scientific Data, 9(20).
• McCann, M. & Johnston, A. (2008). SECOM Dataset. UCI ML Repository.
• Tonini, J. (2025). NØMAD-HPC. Journal of Open Research Software.