def test_compute_features_on_infinity_distance():
"""Test that WeaveModel correctly transforms WeaveMol objects into tensors with infinite max_pair_distance."""
featurizer = dc.feat.WeaveFeaturizer(max_pair_distance=None)
X = featurizer(["C", "CCC"])
batch_size = 20
model = WeaveModel(1,
batch_size=batch_size,
mode='classification',
fully_connected_layer_sizes=[2000, 1000],
batch_normalize=True,
batch_normalize_kwargs={
"fused": False,
"trainable": True,
"renorm": True
},
learning_rage=0.0005)
atom_feat, pair_feat, pair_split, atom_split, atom_to_pair = model.compute_features_on_batch(
X)
# There are 4 atoms each of which have 75 atom features
assert atom_feat.shape == (4, 75)
# There are 10 pairs with infinity distance and 14 pair features
assert pair_feat.shape == (10, 14)
# 4 atoms in total
assert atom_split.shape == (4, )
assert np.all(atom_split == np.array([0, 1, 1, 1]))
# 10 pairs in total
assert pair_split.shape == (10, )
assert np.all(pair_split == np.array([0, 1, 1, 1, 2, 2, 2, 3, 3, 3]))
# 10 pairs in total each with start/finish
assert atom_to_pair.shape == (10, 2)
assert np.all(
atom_to_pair == np.array([[0, 0], [1, 1], [1, 2], [1, 3], [2, 1],
[2, 2], [2, 3], [3, 1], [3, 2], [3, 3]]))
def test_compute_features_on_distance_1():
"""Test that WeaveModel correctly transforms WeaveMol objects into tensors with finite max_pair_distance."""
featurizer = dc.feat.WeaveFeaturizer(max_pair_distance=1)
X = featurizer(["C", "CCC"])
batch_size = 20
model = WeaveModel(
1,
batch_size=batch_size,
mode='classification',
fully_connected_layer_sizes=[2000, 1000],
batch_normalize=True,
batch_normalize_kwargs={
"fused": False,
"trainable": True,
"renorm": True
},
learning_rate=0.0005)
atom_feat, pair_feat, pair_split, atom_split, atom_to_pair = model.compute_features_on_batch(
X)
# There are 4 atoms each of which have 75 atom features
assert atom_feat.shape == (4, 75)
# There are 8 pairs with distance 1 and 14 pair features. (To see why 8,
# there's the self pair for "C". For "CCC" there are 7 pairs including self
# connections and accounting for symmetry.)
assert pair_feat.shape == (8, 14)
# 4 atoms in total
assert atom_split.shape == (4,)
assert np.all(atom_split == np.array([0, 1, 1, 1]))
# 10 pairs in total
assert pair_split.shape == (8,)
# The center atom is self connected and to both neighbors so it appears
# thrice. The canonical ranking used in MolecularFeaturizer means this
# central atom is ranked last in ordering.
assert np.all(pair_split == np.array([0, 1, 1, 2, 2, 3, 3, 3]))
# 10 pairs in total each with start/finish
assert atom_to_pair.shape == (8, 2)
assert np.all(atom_to_pair == np.array([[0, 0], [1, 1], [1, 3], [2, 2],
[2, 3], [3, 1], [3, 2], [3, 3]]))
