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mmgp_tensile2d
fabiencasenave/mmgp_tensile2d
mmgp_tensile2d is a graph machine learning model from fabiencasenave. Use it for the graph machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
The code used to train this model is given in train.py.
Access
Public
Updated Aug 10, 2025
Repo size
73.5 MB
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1
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Repo makeup
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.joblib71.5 MB · 100%
At a glance
- Task
- Graph Machine Learning
- License
- mit
- Access
- Public
- Created
- Aug 10, 2025
- Updated
- Aug 10, 2025
- SHA
- 7b2fa554
Datasets
Model card
From the Hugging Face model README
PCA-GP model for VKILS59 dataset
The code used to train this model is given in train.py.
Install
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
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'))
- Task
- Graph Machine Learning
- License
- mit
- Languages
- en
- Created
- Aug 10, 2025
- Updated
- Aug 10, 2025