def test_move_load(self):
"""Test that datasets can be moved and loaded."""
verbosity = "high"
current_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(self.base_dir, "data")
moved_data_dir = os.path.join(self.base_dir, "moved_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)
dataset = loader.featurize(dataset_file, data_dir)
X, y, w, ids = dataset.to_numpy()
shutil.move(data_dir, moved_data_dir)
moved_dataset = Dataset(
moved_data_dir, reload=reload)
X_moved, y_moved, w_moved, ids_moved = moved_dataset.to_numpy()
np.testing.assert_allclose(X, X_moved)
np.testing.assert_allclose(y, y_moved)
np.testing.assert_allclose(w, w_moved)
np.testing.assert_array_equal(ids, ids_moved)
def test_move_load(self):
"""Test that datasets can be moved and loaded."""
verbosity = "high"
current_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(self.base_dir, "data")
moved_data_dir = os.path.join(self.base_dir, "moved_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)
dataset = loader.featurize(dataset_file, data_dir)
X, y, w, ids = dataset.to_numpy()
shutil.move(data_dir, moved_data_dir)
moved_dataset = Dataset(moved_data_dir, reload=reload)
X_moved, y_moved, w_moved, ids_moved = moved_dataset.to_numpy()
np.testing.assert_allclose(X, X_moved)
np.testing.assert_allclose(y, y_moved)
np.testing.assert_allclose(w, w_moved)
np.testing.assert_array_equal(ids, ids_moved)
def test_multiload(self):
"""Check can re-use featurization for multiple task selections.
TODO(rbharath): This test seems silly after the recent round of
refactoring. Can it be removed?
"""
# Only for debug!
np.random.seed(123)
# Set some global variables up top
reload = True
verbosity = "high"
current_dir = os.path.dirname(os.path.realpath(__file__))
#Make directories to store the raw and featurized datasets.
data_dir = os.path.join(self.base_dir, "dataset")
train_dir = os.path.join(self.base_dir, "train_dataset")
valid_dir = os.path.join(self.base_dir, "valid_dataset")
test_dir = os.path.join(self.base_dir, "test_dataset")
model_dir = os.path.join(self.base_dir, "model")
# Load dataset
print("About to load dataset.")
dataset_file = os.path.join(
current_dir, "../../models/tests/multitask_example.csv")
dataset = load_from_disk(dataset_file)
print("Columns of dataset: %s" % str(dataset.columns.values))
print("Number of examples in dataset: %s" % str(dataset.shape[0]))
# Featurize tox21 dataset
print("About to featurize dataset.")
featurizer = CircularFingerprint(size=1024)
all_tasks = ["task%d"%i for i in range(17)]
####### Do featurization
loader = DataLoader(tasks=all_tasks,
smiles_field="smiles",
featurizer=featurizer,
verbosity=verbosity)
dataset = loader.featurize(
dataset_file, data_dir)
# Do train/valid split.
X_multi, y_multi, w_multi, ids_multi = dataset.to_numpy()
####### Do singletask load
y_tasks, w_tasks, = [], []
for ind, task in enumerate(all_tasks):
print("Processing task %s" % task)
dataset = Dataset(data_dir, verbosity=verbosity, reload=reload)
X_task, y_task, w_task, ids_task = dataset.to_numpy()
y_tasks.append(y_task[:, ind])
w_tasks.append(w_task[:, ind])
################## Do comparison
for ind, task in enumerate(all_tasks):
y_multi_task = y_multi[:, ind]
w_multi_task = w_multi[:, ind]
y_task = y_tasks[ind]
w_task = w_tasks[ind]
np.testing.assert_allclose(y_multi_task.flatten(), y_task.flatten())
np.testing.assert_allclose(w_multi_task.flatten(), w_task.flatten())
def test_multiload(self):
"""Check can re-use featurization for multiple task selections.
TODO(rbharath): This test seems silly after the recent round of
refactoring. Can it be removed?
"""
# Only for debug!
np.random.seed(123)
# Set some global variables up top
reload = True
verbosity = "high"
current_dir = os.path.dirname(os.path.realpath(__file__))
#Make directories to store the raw and featurized datasets.
data_dir = os.path.join(self.base_dir, "dataset")
train_dir = os.path.join(self.base_dir, "train_dataset")
valid_dir = os.path.join(self.base_dir, "valid_dataset")
test_dir = os.path.join(self.base_dir, "test_dataset")
model_dir = os.path.join(self.base_dir, "model")
# Load dataset
print("About to load dataset.")
dataset_file = os.path.join(
current_dir, "../../models/tests/multitask_example.csv")
dataset = load_from_disk(dataset_file)
print("Columns of dataset: %s" % str(dataset.columns.values))
print("Number of examples in dataset: %s" % str(dataset.shape[0]))
# Featurize tox21 dataset
print("About to featurize dataset.")
featurizer = CircularFingerprint(size=1024)
all_tasks = ["task%d" % i for i in range(17)]
####### Do featurization
loader = DataLoader(tasks=all_tasks,
smiles_field="smiles",
featurizer=featurizer,
verbosity=verbosity)
dataset = loader.featurize(dataset_file, data_dir)
# Do train/valid split.
X_multi, y_multi, w_multi, ids_multi = dataset.to_numpy()
####### Do singletask load
y_tasks, w_tasks, = [], []
for ind, task in enumerate(all_tasks):
print("Processing task %s" % task)
dataset = Dataset(data_dir, verbosity=verbosity, reload=reload)
X_task, y_task, w_task, ids_task = dataset.to_numpy()
y_tasks.append(y_task[:, ind])
w_tasks.append(w_task[:, ind])
################## Do comparison
for ind, task in enumerate(all_tasks):
y_multi_task = y_multi[:, ind]
w_multi_task = w_multi[:, ind]
y_task = y_tasks[ind]
w_task = w_tasks[ind]
np.testing.assert_allclose(y_multi_task.flatten(),
y_task.flatten())
np.testing.assert_allclose(w_multi_task.flatten(),
w_task.flatten())
