def test_singletask_random_k_fold_split(self):
"""
Test singletask RandomSplitter class.
"""
solubility_dataset = self.load_solubility_data()
random_splitter = RandomSplitter()
ids_set = set(solubility_dataset.ids)
K = 5
fold_dirs = [tempfile.mkdtemp() for i in range(K)]
fold_datasets = random_splitter.k_fold_split(solubility_dataset,
fold_dirs)
for fold in range(K):
fold_dataset = fold_datasets[fold]
# Verify lengths is 10/k == 2
assert len(fold_dataset) == 2
# Verify that compounds in this fold are subset of original compounds
fold_ids_set = set(fold_dataset.ids)
assert fold_ids_set.issubset(ids_set)
# Verify that no two folds have overlapping compounds.
for other_fold in range(K):
if fold == other_fold:
continue
other_fold_dataset = fold_datasets[other_fold]
other_fold_ids_set = set(other_fold_dataset.ids)
assert fold_ids_set.isdisjoint(other_fold_ids_set)
merge_dir = tempfile.mkdtemp()
merged_dataset = DiskDataset.merge(merge_dir, fold_datasets)
assert len(merged_dataset) == len(solubility_dataset)
assert sorted(merged_dataset.ids) == (sorted(solubility_dataset.ids))
def test_singletask_stratified_k_fold_split(self):
"""
Test RandomStratifiedSplitter k-fold class.
"""
n_samples = 100
n_positives = 20
n_features = 10
n_tasks = 1
X = np.random.rand(n_samples, n_features)
y = np.zeros(n_samples)
y[:n_positives] = 1
w = np.ones(n_samples)
ids = np.arange(n_samples)
data_dir = tempfile.mkdtemp()
dataset = DiskDataset.from_numpy(data_dir, X, y, w, ids)
stratified_splitter = RandomStratifiedSplitter()
ids_set = set(dataset.ids)
K = 5
fold_dirs = [tempfile.mkdtemp() for i in range(K)]
fold_datasets = stratified_splitter.k_fold_split(dataset, fold_dirs)
for fold in range(K):
fold_dataset = fold_datasets[fold]
# Verify lengths is 100/k == 20
# Note: This wouldn't work for multitask str
# assert len(fold_dataset) == n_samples/K
fold_labels = fold_dataset.y
# Verify that each fold has n_positives/K = 4 positive examples.
assert np.count_nonzero(fold_labels == 1) == n_positives / K
# Verify that compounds in this fold are subset of original compounds
fold_ids_set = set(fold_dataset.ids)
assert fold_ids_set.issubset(ids_set)
# Verify that no two folds have overlapping compounds.
for other_fold in range(K):
if fold == other_fold:
continue
other_fold_dataset = fold_datasets[other_fold]
other_fold_ids_set = set(other_fold_dataset.ids)
assert fold_ids_set.isdisjoint(other_fold_ids_set)
merge_dir = tempfile.mkdtemp()
merged_dataset = DiskDataset.merge(merge_dir, fold_datasets)
assert len(merged_dataset) == len(dataset)
assert sorted(merged_dataset.ids) == (sorted(dataset.ids))
def test_singletask_index_split(self):
"""
Test singletask RandomSplitter class.
"""
solubility_dataset = self.load_solubility_data()
random_splitter = IndexSplitter()
train_data, valid_data, test_data = \
random_splitter.train_valid_test_split(
solubility_dataset,
self.train_dir, self.valid_dir, self.test_dir,
frac_train=0.8, frac_valid=0.1, frac_test=0.1)
assert len(train_data) == 8
assert len(valid_data) == 1
assert len(test_data) == 1
merge_dir = tempfile.mkdtemp()
merged_dataset = DiskDataset.merge(merge_dir,
[train_data, valid_data, test_data])
assert sorted(merged_dataset.ids) == (sorted(solubility_dataset.ids))
def test_merge(self):
"""Test that datasets can be merged."""
verbosity = "high"
current_dir = os.path.dirname(os.path.realpath(__file__))
first_data_dir = os.path.join(self.base_dir, "first_dataset")
second_data_dir = os.path.join(self.base_dir, "second_dataset")
merged_data_dir = os.path.join(self.base_dir, "merged_data")
dataset_file = os.path.join(current_dir,
"../../models/tests/example.csv")
featurizer = CircularFingerprint(size=1024)
tasks = ["log-solubility"]
loader = DataLoader(tasks=tasks,
smiles_field="smiles",
featurizer=featurizer,
verbosity=verbosity)
first_dataset = loader.featurize(dataset_file, first_data_dir)
second_dataset = loader.featurize(dataset_file, second_data_dir)
merged_dataset = DiskDataset.merge(merged_data_dir,
[first_dataset, second_dataset])
assert len(merged_dataset) == len(first_dataset) + len(second_dataset)