def test_data_splitter():
a = synthetic_iid()
# test leaving validataion_size empty works
ds = DataSplitter(a, train_size=0.4)
ds.setup()
# check the number of indices
_, _, _ = ds.train_dataloader(), ds.val_dataloader(), ds.test_dataloader()
n_train_idx = len(ds.train_idx)
n_validation_idx = len(ds.val_idx) if ds.val_idx is not None else 0
n_test_idx = len(ds.test_idx) if ds.test_idx is not None else 0
assert n_train_idx + n_validation_idx + n_test_idx == a.n_obs
assert np.isclose(n_train_idx / a.n_obs, 0.4)
assert np.isclose(n_validation_idx / a.n_obs, 0.6)
assert np.isclose(n_test_idx / a.n_obs, 0)
# test test size
ds = DataSplitter(a, train_size=0.4, validation_size=0.3)
ds.setup()
# check the number of indices
_, _, _ = ds.train_dataloader(), ds.val_dataloader(), ds.test_dataloader()
n_train_idx = len(ds.train_idx)
n_validation_idx = len(ds.val_idx) if ds.val_idx is not None else 0
n_test_idx = len(ds.test_idx) if ds.test_idx is not None else 0
assert n_train_idx + n_validation_idx + n_test_idx == a.n_obs
assert np.isclose(n_train_idx / a.n_obs, 0.4)
assert np.isclose(n_validation_idx / a.n_obs, 0.3)
assert np.isclose(n_test_idx / a.n_obs, 0.3)
# test that 0 < train_size <= 1
with pytest.raises(ValueError):
ds = DataSplitter(a, train_size=2)
ds.setup()
ds.train_dataloader()
with pytest.raises(ValueError):
ds = DataSplitter(a, train_size=-2)
ds.setup()
ds.train_dataloader()
# test that 0 <= validation_size < 1
with pytest.raises(ValueError):
ds = DataSplitter(a, train_size=0.1, validation_size=1)
ds.setup()
ds.val_dataloader()
with pytest.raises(ValueError):
ds = DataSplitter(a, train_size=0.1, validation_size=-1)
ds.setup()
ds.val_dataloader()
# test that train_size + validation_size <= 1
with pytest.raises(ValueError):
ds = DataSplitter(a, train_size=1, validation_size=0.1)
ds.setup()
ds.train_dataloader()
ds.val_dataloader()
def train(
self,
max_epochs: int = 200,
use_gpu: Optional[Union[str, int, bool]] = None,
kappa: int = 5,
train_size: float = 0.9,
validation_size: Optional[float] = None,
batch_size: int = 128,
plan_kwargs: Optional[dict] = None,
**kwargs,
):
"""
Train the model.
Parameters
----------
max_epochs
Number of passes through the dataset. If `None`, defaults to
`np.min([round((20000 / n_cells) * 400), 400])`
use_gpu
Use default GPU if available (if None or True), or index of GPU to use (if int),
or name of GPU (if str, e.g., `'cuda:0'`), or use CPU (if False).
kappa
Scaling parameter for the discriminator loss.
train_size
Size of training set in the range [0.0, 1.0].
validation_size
Size of the test set. If `None`, defaults to 1 - `train_size`. If
`train_size + validation_size < 1`, the remaining cells belong to a test set.
batch_size
Minibatch size to use during training.
plan_kwargs
Keyword args for model-specific Pytorch Lightning task. Keyword arguments passed to
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
**kwargs
Other keyword args for :class:`~scvi.train.Trainer`.
"""
gpus, device = parse_use_gpu_arg(use_gpu)
self.trainer = Trainer(
max_epochs=max_epochs,
gpus=gpus,
**kwargs,
)
self.train_indices_, self.test_indices_, self.validation_indices_ = [], [], []
train_dls, test_dls, val_dls = [], [], []
for i, adm in enumerate(self.adata_managers.values()):
ds = DataSplitter(
adm,
train_size=train_size,
validation_size=validation_size,
batch_size=batch_size,
use_gpu=use_gpu,
)
ds.setup()
train_dls.append(ds.train_dataloader())
test_dls.append(ds.test_dataloader())
val = ds.val_dataloader()
val_dls.append(val)
val.mode = i
self.train_indices_.append(ds.train_idx)
self.test_indices_.append(ds.test_idx)
self.validation_indices_.append(ds.val_idx)
train_dl = TrainDL(train_dls)
plan_kwargs = plan_kwargs if isinstance(plan_kwargs, dict) else dict()
self._training_plan = GIMVITrainingPlan(
self.module,
adversarial_classifier=True,
scale_adversarial_loss=kappa,
**plan_kwargs,
)
if train_size == 1.0:
# circumvent the empty data loader problem if all dataset used for training
self.trainer.fit(self._training_plan, train_dl)
else:
# accepts list of val dataloaders
self.trainer.fit(self._training_plan, train_dl, val_dls)
try:
self.history_ = self.trainer.logger.history
except AttributeError:
self.history_ = None
self.module.eval()
self.to_device(device)
self.is_trained_ = True