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pca_gp_vkils59

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metadata
license: mit
datasets:
  - PLAID-datasets/VKI-LS59
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 

pip install git+https://huggingface.co/fabiencasenave/pca_gp_vkils59

Use 

from datasets import load_dataset
from plaid.bridges.huggingface_bridge import huggingface_dataset_to_plaid
import pca_gp_vkils59

model = pca_gp_vkils59.load()

hf_dataset = load_dataset("PLAID-datasets/VKI-LS59", split="all_samples")
ids_test = hf_dataset.description["split"]['test']

dataset_test, _ = huggingface_dataset_to_plaid(hf_dataset, ids = ids_test, processes_number = 6, verbose = True)

print("Check that 'mach' field is not present in test dataset: dataset_test[0].get_field('mach', base_name='Base_2_2') =", dataset_test[0].get_field('mach', base_name='Base_2_2'))

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

print("Check that 'mach' field is now present in pred dataset: dataset_pred[0].get_field('mach', base_name='Base_2_2') =", dataset_pred[0].get_field('mach', base_name='Base_2_2'))