def test_onehot_featurizer_arbitrary_with_max_length(self): """ Test one hot encoding with max_length. """ string = "abcdefghijklmnopqrstuvwxyzvewqmc" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 featurizer = OneHotFeaturizer(charset, max_length=120) feature = featurizer([string]) assert feature.shape == (1, 120, length) # untranform undo_string = featurizer.untransform(feature[0]) assert string == undo_string
def test_onehot_featurizer_SMILES_with_max_length(self): """ Test one hot encoding with max_length. """ from rdkit import Chem length = len(ZINC_CHARSET) + 1 smiles = 'CC(=O)Oc1ccccc1C(=O)O' mol = Chem.MolFromSmiles(smiles) featurizer = OneHotFeaturizer(max_length=120) feature = featurizer([mol]) assert feature.shape == (1, 120, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles
def test_onehot_featurizer_arbitrary(self): """ Test simple one hot encoding for arbitrary string. """ string = "abcdefghijklmnopqrstuvwxyzwebhasw" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 defaultMaxLength = 100 featurizer = OneHotFeaturizer(charset) feature = featurizer([string]) # Implicit call to featurize() assert feature.shape == (1, defaultMaxLength, length) # untransform undo_string = featurizer.untransform(feature[0]) assert string == undo_string
def test_onehot_featurizer_SMILES(self): """ Test simple one hot encoding for SMILES strings. """ from rdkit import Chem length = len(ZINC_CHARSET) + 1 smiles = 'CC(=O)Oc1ccccc1C(=O)O' mol = Chem.MolFromSmiles(smiles) featurizer = OneHotFeaturizer() feature = featurizer([mol]) defaultMaxLength = 100 assert feature.shape == (1, defaultMaxLength, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles
def test_correct_transformation_SMILES(self):
"""
Test correct one hot encoding.
"""
from rdkit import Chem
charset = ['C', 'N', '=', ')', '(', 'O']
smiles = 'CN=C=O'
mol = Chem.MolFromSmiles(smiles)
featurizer = OneHotFeaturizer(charset=charset, max_length=100)
feature = featurizer([mol])
assert np.allclose(feature[0][0], np.array([1, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][1], np.array([0, 1, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][2], np.array([0, 0, 1, 0, 0, 0, 0]))
assert np.allclose(feature[0][3], np.array([1, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][4], np.array([0, 0, 1, 0, 0, 0, 0]))
assert np.allclose(feature[0][5], np.array([0, 0, 0, 0, 0, 1, 0]))
# untranform
undo_smiles = featurizer.untransform(feature[0])
assert smiles == undo_smiles
def test_correct_transformation_arbitrary(self):
"""
Test correct one hot encoding.
"""
charset = "1234567890"
string = "12345"
featurizer = OneHotFeaturizer(charset=charset, max_length=100)
feature = featurizer([string])
assert np.allclose(feature[0][0],
np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][1],
np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][2],
np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][3],
np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]))
assert np.allclose(feature[0][4],
np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]))
assert "This test case has not yet been written."