Comments (2)
Hi @kkovary,
Thank you for your question!
Similar to how Scikit-learn does this, you can achieve this by simply splitting the train set again. For example:
import datamol as dm
from splito import ScaffoldSplit
# Load some data
data = dm.data.chembl_drugs()
all_smiles = data["smiles"].tolist()
# Generate the trainval-test split
splitter = ScaffoldSplit(smiles=all_smiles, test_size=0.2)
trainval_idx, test_idx = next(splitter.split(X=all_smiles))
# Generate the train-val split
trainval_smiles = all_smiles[trainval_idx]
splitter = ScaffoldSplit(smiles=trainval_smiles, test_size=0.2)
train_idx, val_idx = next(splitter.split(X=trainval_smiles))
I do agree that with our current setup, this is a bit verbose. Having something like #8 would probably help to make this easier!
Let me know if that helps!
from splito.
Thanks so much!
from splito.
Related Issues (11)
- Documentation HOT 1
- SIMPD new implementation
- Library name HOT 9
- Add proxy functions for common splitting method such as random
- Make a general function for all splitting strategies. HOT 1
- Unpinned dependencies HOT 3
- visualize_chemspace is missing HOT 1
- Functional forms of splitters
- Add support for SPECTRA
- rdkit.Chem.rdFingerprintGenerator.GetMorganGenerator() did not match C++ signature HOT 2
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from splito.