|
|
|
|
| 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") |
|
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|
|
|
|
| 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") |
|
|
