| import datasets | |
| import pyarrow | |
| def test_local_hf_match(dataset_tag): | |
| print(f"For dataset : '{dataset_tag}' testing if local and remote ids match ...") | |
| ids_hf = datasets.load_dataset( | |
| path = "RosettaCommons/MIP", | |
| name = dataset_tag, | |
| data_dir = dataset_tag, | |
| cache_dir = "/scratch/maom_root/maom0/maom", | |
| keep_in_memory = True).data['train'].select(['id']).to_pandas() | |
| ids_local = pyarrow.parquet.read_table( | |
| source = f"intermediate/{dataset_tag}.parquet", | |
| columns = ["id"]).to_pandas() | |
| assert ids_local.equals(ids_hf) | |
| test_local_hf_match("rosetta_high_quality_models") | |
| test_local_hf_match("rosetta_low_quality_models") | |
| test_local_hf_match("dmpfold_high_quality_models") | |
| test_local_hf_match("dmpfold_low_quality_models") | |
| test_local_hf_match("rosetta_high_quality_function_predictions") | |
| test_local_hf_match("rosetta_low_quality_function_predictions") | |
| test_local_hf_match("dmpfold_high_quality_function_predictions") | |
| test_local_hf_match("dmpfold_low_quality_function_predictions") | |
| import pandas | |
| dataset_long = pyarrow.parquet.read_table( | |
| "intermediate/dmpfold_low_quality_function_predictions.parquet").to_pandas() | |
| dataset_wide = pandas.pivot( | |
| dataset_long[["id", "term_id", "Y_hat"]], | |
| columns = "term_id", | |
| index = "id", | |
| values = "Y_hat") | |