---
language: en
license: apache-2.0
tags:
  - marine-biology
  - metagenomics
  - environmental-modeling
  - protein-domains
  - tara-oceans
  - vicreg
  - joint-embedding
  - self-supervised-learning
  - pytorch
library_name: pytorch
pipeline_tag: tabular-regression
---

# TARA-WorldModel-VICReg

Joint environment--genome embedding model for marine ecosystem productivity prediction, trained with Variance-Invariance-Covariance Regularization (VICReg) on TARA Oceans data.

## Model Description

The World Model learns a shared latent space that aligns environmental context (satellite-derived variables) with microalgal protein domain composition (Pfam module abundances), then predicts marine productivity (chlorophyll-a, POC, NFLH) from the joint embedding.

### Architecture

```
Training:
  env (24 dims) → EncoderE(128 → 32) → z_env ─┐
                                                 ├→ VICReg loss
  pfam (20 dims) → EncoderP(256 → 128 → 32) → z_pfam
                                                 └→ Predictor(64 → 3) → productivity

Inference (environment-only):
  env → EncoderE → z_env → Predictor → [chl-a, POC, NFLH]
```

- **EncoderE**: Linear(24→128) + BN + ReLU + Dropout(0.3) → Linear(128→32) + BN + ReLU + Dropout(0.3)
- **EncoderP**: Linear(20→256) + BN + ReLU + Dropout(0.3) → Linear(256→128) + BN + ReLU + Dropout(0.3) → Linear(128→32) + BN + ReLU + Dropout(0.3)
- **Predictor**: Linear(32→64) + ReLU → Linear(64→3)
- **Total parameters**: 53,187

### VICReg Loss

Non-contrastive self-supervised alignment (Bardes et al., ICLR 2022):
- **Invariance**: MSE between co-located env/pfam embeddings (λ=25)
- **Variance**: Hinge loss preventing embedding collapse (λ=25)
- **Covariance**: Off-diagonal penalty decorrelating dimensions (λ=1)
- **Prediction**: MSE on productivity targets (α=1)

## Performance

Joint embedding improves POC prediction (R² 0.422 → 0.532, 26% relative improvement) over environment-only baseline. Chlorophyll-a and NFLH are better predicted by environment alone (directly satellite-measured).

## Files

### Fold Checkpoints (leave-one-basin-out spatial CV)

Two training runs are provided:
- `world_model_fold_*_20260127_110243.pt` — Initial configuration (latent_dim=16)
- `world_model_fold_*_20260127_111754.pt` — Best configuration from hyperparameter sweep (latent_dim=32)

Six folds per run: Arctic, Atlantic, Indian, Mediterranean, Pacific, Southern.

### Configuration

- `phase2_best_config.json` — Hyperparameter sweep results (54 configurations, 3 seeds each)

## Hyperparameters (Best Config)

| Parameter | Value |
|-----------|-------|
| latent_dim | 32 |
| dropout | 0.3 |
| λ_invariance | 25.0 |
| λ_variance | 25.0 |
| λ_covariance | 1.0 |
| pred_alpha | 1.0 |
| learning_rate | 0.001 |
| weight_decay | 1e-4 |
| batch_size | 128 |
| max_epochs | 300 |
| patience | 30 |
| grad_clip | 1.0 |

## Usage

```python
import torch

# Load fold checkpoint
ckpt = torch.load("world_model_fold_Atlantic_20260127_111754.pt", map_location="cpu")

# ckpt contains model_state_dict for the full WorldModel
# Requires WorldModel class from the training codebase
```

## Dataset

- **1,810 ocean samples** with co-located environment and Pfam profiles
- **24 environmental variables** (GEE oceanographic/atmospheric)
- **20 Pfam module features** (aggregated from 9,466 domains via co-occurrence clustering)
- **3 productivity targets** (chlorophyll-a, POC, NFLH)
- **Spatial cross-validation**: Leave-one-basin-out (6 ocean basins)

## Related Models

- [GreenGenomicsLab/algaGPT](https://huggingface.co/GreenGenomicsLab/algaGPT) — AlgaGPT protein classification
- [GreenGenomicsLab/LA4SR-Pythia70m-b-ckpt-55000](https://huggingface.co/GreenGenomicsLab/LA4SR-Pythia70m-b-ckpt-55000) — LA4SR-Pythia classification
- [GreenGenomicsLab/TARA-ELF-NET](https://huggingface.co/GreenGenomicsLab/TARA-ELF-NET) — Deep bidirectional env↔pfam models
- [GreenGenomicsLab/TARA-XGBoost-Bidirectional](https://huggingface.co/GreenGenomicsLab/TARA-XGBoost-Bidirectional) — XGBoost bidirectional models

## Citation

LA4SR classification models:
> Nelson DR, Jaiswal AK, Ismail NS, Mystikou A, Salehi-Ashtiani K. *Patterns*. 2024;6(11).

## License

Apache 2.0