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import numpy as np
import pytest
from chemprop.data import ClassBalanceSampler, MoleculeDatapoint, MoleculeDataset, SeededSampler
from chemprop.featurizers.molgraph import SimpleMoleculeMolGraphFeaturizer
@pytest.fixture(params=[0.0, 0.1, 0.5, 1.0])
def threshold(request):
return request.param
@pytest.fixture
def bin_targets(targets, threshold):
return targets <= threshold
@pytest.fixture
def featurizer():
return SimpleMoleculeMolGraphFeaturizer()
@pytest.fixture
def dataset(mols, targets, featurizer):
data = [MoleculeDatapoint(mol, y) for mol, y in zip(mols, targets)]
return MoleculeDataset(data, featurizer)
@pytest.fixture(params=[0, 24, 100])
def seed(request):
return request.param
@pytest.fixture
def class_sampler(mols, bin_targets, featurizer):
data = [MoleculeDatapoint(mol, y) for mol, y in zip(mols, bin_targets)]
dset = MoleculeDataset(data, featurizer)
return ClassBalanceSampler(dset.Y, shuffle=True)
def test_seeded_no_seed(dataset):
with pytest.raises(ValueError):
SeededSampler(len(dataset), None)
def test_seeded_shuffle(dataset, seed):
sampler = SeededSampler(len(dataset), seed)
assert list(sampler) != list(sampler)
def test_seeded_fixed_shuffle(dataset, seed):
sampler1 = SeededSampler(len(dataset), seed)
sampler2 = SeededSampler(len(dataset), seed)
idxs1 = list(sampler1)
idxs2 = list(sampler2)
assert idxs1 == idxs2
def test_class_balance_length(class_sampler, bin_targets: np.ndarray):
n_actives = bin_targets.any(1).sum(0)
n_inactives = len(bin_targets) - n_actives
expected_length = 2 * min(n_actives, n_inactives)
assert len(class_sampler) == expected_length
def test_class_balance_sample(class_sampler, bin_targets: np.ndarray):
idxs = list(class_sampler)
# sampled indices should be 50/50 actives/inacitves
assert sum(bin_targets[idxs]) == len(idxs) // 2
def test_class_balance_shuffle(class_sampler):
idxs1 = list(class_sampler)
idxs2 = list(class_sampler)
if len(class_sampler) == 0:
pytest.skip("no indices to sample!")
assert idxs1 != idxs2
def test_seed_class_balance_shuffle(smis, bin_targets, featurizer, seed):
data = [MoleculeDatapoint.from_smi(smi, target) for smi, target in zip(smis, bin_targets)]
dset = MoleculeDataset(data, featurizer)
sampler = ClassBalanceSampler(dset.Y, seed, True)
if len(sampler) == 0:
pytest.skip("no indices to sample!")
assert list(sampler) != list(sampler)
def test_seed_class_balance_reproducibility(smis, bin_targets, featurizer, seed):
data = [MoleculeDatapoint.from_smi(smi, target) for smi, target in zip(smis, bin_targets)]
dset = MoleculeDataset(data, featurizer)
sampler1 = ClassBalanceSampler(dset.Y, seed, True)
sampler2 = ClassBalanceSampler(dset.Y, seed, True)
assert list(sampler1) == list(sampler2)
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