Esm2

create_mock_parquet_train_val_inputs(tmp_path)

创建模拟蛋白质训练和验证集群 parquet。

源代码在 bionemo/testing/data/esm2.py 中

def create_mock_parquet_train_val_inputs(tmp_path):
    """Create a mock protein train and val cluster parquet."""
    train_cluster_path = tmp_path / "train_clusters.parquet"
    train_clusters = pd.DataFrame(
        {
            "ur90_id": [["UniRef90_A"], ["UniRef90_B", "UniRef90_C"]],
        }
    )
    train_clusters.to_parquet(train_cluster_path)

    valid_cluster_path = tmp_path / "valid_clusters.parquet"
    valid_clusters = pd.DataFrame(
        {
            "ur50_id": ["UniRef50_A", "UniRef50_B", "UniRef90_A", "UniRef90_B"],
        }
    )
    valid_clusters.to_parquet(valid_cluster_path)
    return train_cluster_path, valid_cluster_path

create_mock_protein_dataset(tmp_path)

创建模拟蛋白质数据集。

源代码在 bionemo/testing/data/esm2.py 中

def create_mock_protein_dataset(tmp_path):
    """Create a mock protein dataset."""
    db_file = tmp_path / "protein_dataset.db"
    conn = sqlite3.connect(str(db_file))
    cursor = conn.cursor()

    cursor.execute(
        """
        CREATE TABLE protein (
            id TEXT PRIMARY KEY,
            sequence TEXT
        )
    """
    )

    proteins = [
        ("UniRef90_A", "ACDEFGHIKLMNPQRSTVWY"),
        ("UniRef90_B", "DEFGHIKLMNPQRSTVWYAC"),
        ("UniRef90_C", "MGHIKLMNPQRSTVWYACDE"),
        ("UniRef50_A", "MKTVRQERLKSIVRI"),
        ("UniRef50_B", "MRILERSKEPVSGAQLA"),
    ]
    cursor.executemany("INSERT INTO protein VALUES (?, ?)", proteins)

    conn.commit()
    conn.close()

    return db_file