| import pandas as pd |
| import numpy as np |
|
|
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
| from molfeat.calc import FPCalculator |
| from molfeat.trans import MoleculeTransformer |
|
|
| calc = FPCalculator("ecfp") |
| mol_transf = MoleculeTransformer(calc, n_jobs=5) |
|
|
| data_train_moldata = pq.read_table("intermediate_data/data_train.parquet").to_pandas() |
| data_train_features = mol_transf(data_train_moldata["SMILES"].values) |
| data_train_features = np.stack(data_train_features) |
| data_train_features = pd.DataFrame(data_train_features) |
| pq.write_table( |
| pa.Table.from_pandas(data_train_features), |
| "intermediate_data/data_train_features.parquet") |
|
|
| data_test_moldata = pq.read_table("intermediate_data/data_test.parquet").to_pandas() |
| data_test_features = mol_transf(data_test_moldata['SMILES'].values) |
| data_test_features = np.stack(data_test_features) |
| data_test_features = pd.DataFrame(data_test_features) |
| pq.write_table( |
| pa.Table.from_pandas(data_test_features), |
| "intermediate_data/data_test_features.parquet") |
|
|
