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prepare_finetuning_data.py
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import pandas as pd
import chemprop
def train_val_test_split_multilabel(path, scaffold_split):
main_df = pd.read_csv(path)
main_df.sample(frac=1).reset_index(drop=True) # shuffling
main_df.rename(columns={main_df.columns[0]: "smiles"}, inplace=True)
main_df.fillna(0, inplace=True)
main_df.reset_index(drop=True, inplace=True)
if scaffold_split:
molecule_list = []
for _, row in main_df.iterrows():
molecule_list.append(chemprop.data.data.MoleculeDatapoint(smiles=[row["smiles"]], targets=row[1:].values))
molecule_dataset = chemprop.data.data.MoleculeDataset(molecule_list)
(train, val, test) = chemprop.data.scaffold.scaffold_split(data=molecule_dataset, sizes=(0.8, 0.1, 0.1), seed=42, balanced=True)
return (train, val, test)
else: # random split
from sklearn.model_selection import train_test_split
train, val = train_test_split(main_df, test_size=0.2, random_state=42)
val, test = train_test_split(val, test_size=0.5, random_state=42)
return (train, val, test)
def train_val_test_split(path, target_column_number=1, scaffold_split=False):
main_df = pd.read_csv(path)
main_df.sample(frac=1).reset_index(drop=True) # shuffling
main_df.rename(columns={main_df.columns[0]: "smiles", main_df.columns[target_column_number]: "target"}, inplace=True)
main_df = main_df[["smiles", "target"]]
# main_df.dropna(subset=["target"], inplace=True)
main_df.fillna(0, inplace=True)
main_df.reset_index(drop=True, inplace=True)
if scaffold_split:
molecule_list = []
for _, row in main_df.iterrows():
molecule_list.append(chemprop.data.data.MoleculeDatapoint(smiles=[row["smiles"]], targets=row[1:].values))
molecule_dataset = chemprop.data.data.MoleculeDataset(molecule_list)
(train, val, test) = chemprop.data.scaffold.scaffold_split(data=molecule_dataset, sizes=(0.8, 0.1, 0.1), seed=42, balanced=True)
return (train, val, test)
else: # random split
from sklearn.model_selection import train_test_split
train, val = train_test_split(main_df, test_size=0.2, random_state=42)
val, test = train_test_split(val, test_size=0.5, random_state=42)
return (train, val, test)
def train_val_test_split_with_embs(path, target_column_number=1, scaffold_split=False):
main_df = pd.read_pickle(path)
main_df.sample(frac=1).reset_index(drop=True) # shuffling
main_df.rename(columns={main_df.columns[0]: "smiles", main_df.columns[target_column_number]: "target"}, inplace=True)
# main_df.dropna(subset=["target"], inplace=True)
# fill NaN values in smiles and target columns with 0, but not other columns
main_df[['smiles', 'target']] = main_df[['smiles', 'target']].fillna(0)
main_df.reset_index(drop=True, inplace=True)
if scaffold_split:
molecule_list = []
for _, row in main_df.iterrows():
target_dict = {'target': row['target'],
'selfies': row['selfies'],
'sequence_embeddings': row['sequence_embeddings'],
'text_embeddings': row['text_embeddings'],
'unimol_embeddings': row['unimol_embeddings'],
'kg_embeddings': row['kg_embeddings']}
molecule_list.append(chemprop.data.data.MoleculeDatapoint(smiles=[row["smiles"]], targets=target_dict ))
molecule_dataset = chemprop.data.data.MoleculeDataset(molecule_list)
(train, val, test) = chemprop.data.scaffold.scaffold_split(data=molecule_dataset, sizes=(0.8, 0.1, 0.1), seed=42, balanced=True)
return (train, val, test)
else: # random split
from sklearn.model_selection import train_test_split
train, val = train_test_split(main_df, test_size=0.2, random_state=42)
val, test = train_test_split(val, test_size=0.5, random_state=42)
columns = ["smiles", "target", "selfies", "sequence_embeddings", "text_embeddings", "unimol_embeddings", "kg_embeddings"]
train = train[columns]
val = val[columns]
test = test[columns]
return (train, val, test)