| import pandas | |
| import pyarrow.parquet | |
| import pickle | |
| # Gabe requested that the splits defined in ThermoMPNN of the MegaScale dataset be the default splits | |
| # ThermoMPNN/datsets.py | |
| # class MegaScaleDataset | |
| # def __init__(csg, split): | |
| # fname = self.cfg.data_loc.megascale_csv | |
| # df = pd.read_csv(fname, usecols=["ddG_ML", "mut_type", "WT_name", "aa_seq", "dG_ML"]) | |
| # | |
| # # remove unreliable data and more complicated mutations | |
| # df = df.loc[df.ddG_ML != '-', :].reset_index(drop=True) | |
| # df = df.loc[ | |
| # ~df.mut_type.str.contains("ins") & | |
| # ~df.mut_type.str.contains("del") & | |
| # ~df.mut_type.str.contains(":"), :].reset_index(drop=True) | |
| # | |
| # splits = <load from self.cfg.data_loc.megascale_splits> | |
| # | |
| # if self.split != 'all' and (cfg.reduce != 'prot' or self.split != 'train'): | |
| # self.wt_names = splits[split] | |
| # | |
| # | |
| # | |
| # local.yaml | |
| # data_loc: | |
| # megascale_csv: "<truncated>/Processed_K50_dG_datasets/Tsuboyama2023_Dataset2_Dataset3_20230416.csv" | |
| with open("data/ThermoMPNN/dataset_splits/mega_splits.pkl", "rb") as f: | |
| mega_splits = pickle.load(f) | |
| splits = [] | |
| for split_name, split_ids in mega_splits.items(): | |
| splits.append( | |
| pandas.DataFrame({ | |
| 'split_name': split_name, | |
| 'id': split_ids})) | |
| splits = pandas.concat(splits) | |
| splits.reset_index(drop=True, inplace=True) | |
| pyarrow.parquet.write_table( | |
| pyarrow.Table.from_pandas(splits), | |
| where = "intermediate/ThermoMPNN_splits.parquet") | |
| parquet_file = pyarrow.parquet.ParquetFile('intermediate/ThermoMPNN_splits.parquet') | |
| parquet_file.metadata | |
| # <pyarrow._parquet.FileMetaData object at 0x149f5d2667a0> | |
| # created_by: parquet-cpp-arrow version 17.0.0 | |
| # num_columns: 2 | |
| # num_rows: 2020 | |
| # num_row_groups: 1 | |
| # format_version: 2.6 | |
| # serialized_size: 1881 | |