---
license: mit
datasets:
- PLAID-datasets/Tensile2d
language:
- en
pipeline_tag: graph-ml
---
# PCA-GP model for VKILS59 dataset

The code used to train this model is given in `train.py`.

## Install

```bash
conda env create -n mmgp_tensile2d -f https://huggingface.co/fabiencasenave/mmgp_tensile2d/resolve/main/environment.yml
conda activate mmgp_tensile2d
pip install git+https://huggingface.co/fabiencasenave/mmgp_tensile2d
```

## Use

```python
from datasets import load_dataset
from plaid.bridges.huggingface_bridge import huggingface_dataset_to_plaid
import mmgp_tensile2d

model = mmgp_tensile2d.load()

hf_dataset = load_dataset("PLAID-datasets/Tensile2d", split="all_samples")
ids_test = hf_dataset.description["split"]['test'][:5]

dataset_test, _ = huggingface_dataset_to_plaid(hf_dataset, ids = ids_test, processes_number = 5, verbose = True)

print("Check that 'U1' field is not present in test dataset: dataset_test[0].get_field('U1') =", dataset_test[0].get_field('U1'))

print("Run prediction...")
dataset_pred = model.predict(dataset_test)

print("Check that 'U1' field is now present in pred dataset: dataset_pred[0].get_field('U1') =", dataset_pred[0].get_field('U1'))
```