Delete meta_modulus.py

meta_modulus.py

@@ -1,71 +0,0 @@
-import datasets
-import torch
-from torch_geometric.data import Data
-
-
-_CITATION = """\
-@article{metamatbench,
-  title={MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery},
-  author={Chen, Jianpeng and Zhan, Wangzhi and Wang, Haohui and Jia, Zian and Gan, Jingru and Zhang, Junkai and Qi, Jingyuan and Chen, Tingwei and Huang, Lifu and Chen, Muhao and others},
-  journal={arXiv preprint arXiv:2505.20299},
-  year={2025}
-}
-"""
-
-_DESCRIPTION = """
-This dataset contains lattice structure data for predicting modulus properties, preprocessed into PyTorch Geometric (PyG) compatible format.
-"""
-
-
-class LatticeModulusDataset(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0")),
-    ]
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            features=datasets.Features({
-                'frac_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
-                'cart_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
-                'node_feat': datasets.Array2D(shape=(None, 4), dtype='float32'),
-                'node_type': datasets.Sequence(datasets.Value('int64')),
-                'edge_feat': datasets.Array2D(shape=(None, 1), dtype='float32'),
-                'edge_index': datasets.Array2D(shape=(2, None), dtype='int64'),
-                'lengths': datasets.Array2D(shape=(1, 3), dtype='float32'),
-                'num_nodes': datasets.Value('int64'),
-                'num_atoms': datasets.Value('int64'),
-                'angles': datasets.Array2D(shape=(1, 3), dtype='float32'),
-                'vector': datasets.Array2D(shape=(1, 9), dtype='float32'),
-                'y': datasets.Array2D(shape=(1, 12), dtype='float32'),
-                'young': datasets.Array2D(shape=(1, 3), dtype='float32'),
-                'shear': datasets.Array2D(shape=(1, 3), dtype='float32'),
-                'poisson': datasets.Array2D(shape=(1, 6), dtype='float32'),
-            }),
-        )
-
-    def _split_generators(self, dl_manager):
-        file_path = dl_manager.download_and_extract('data.pkl')
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': file_path})]
-
-    def _generate_examples(self, filepath):
-        dataset = torch.load(filepath)
-
-        for idx, data in enumerate(dataset):
-            yield idx, {
-                'frac_coords': data.frac_coords.numpy(),
-                'cart_coords': data.cart_coords.numpy(),
-                'node_feat': data.node_feat.numpy(),
-                'node_type': data.node_type.numpy().tolist(),
-                'edge_feat': data.edge_feat.numpy(),
-                'edge_index': data.edge_index.numpy(),
-                'lengths': data.lengths.numpy(),
-                'num_nodes': data.num_nodes,
-                'num_atoms': data.num_atoms,
-                'angles': data.angles.numpy(),
-                'vector': data.vector.numpy(),
-                'y': data.y.numpy(),
-                'young': data.young.numpy(),
-                'shear': data.shear.numpy(),
-                'poisson': data.poisson.numpy(),
-            }