def test_wrong_construction_1(self, dataloader_dummy):
"""Wrong positional arguments."""
with pytest.raises(TypeError):
Run('this_is_fake', MeanReturns(), dataloader_dummy)
with pytest.raises(TypeError):
Run(DummyNet(), 'this_is_fake', dataloader_dummy)
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), 'this_is_fake')
def test_wrong_construction_2(self, dataloader_dummy):
"""Wrong keyword arguments."""
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, metrics='this_is_fake')
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, metrics={'a': 'this_is_fake'})
with pytest.raises(ValueError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, metrics={'loss': MeanReturns()})
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, val_dataloaders='this_is_fake')
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, val_dataloaders={'val': 'this_is_fake'})
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, benchmarks='this_is_fake')
with pytest.raises(TypeError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, benchmarks={'uniform': 'this_is_fake'})
with pytest.raises(ValueError):
Run(DummyNet(), MeanReturns(), dataloader_dummy, benchmarks={'main': OneOverN()})
def test_basic():
n_channels = 2
x = torch.rand(10, n_channels, 4, 5)
network = DummyNet(n_channels=n_channels)
y = network(x)
print(y)
def test_basic(self, Xy_dummy):
X, _, _, _ = Xy_dummy
n_samples, n_channels, lookback, n_assets = X.shape
dtype = X.dtype
device = X.device
network = DummyNet(n_channels=n_channels)
network.to(device=device, dtype=dtype)
weights = network(X)
assert torch.is_tensor(weights)
assert weights.shape == (n_samples, n_assets)
assert X.device == weights.device
assert X.dtype == weights.dtype
assert torch.allclose(weights.sum(dim=1), torch.ones(n_samples).to(dtype=dtype, device=device), atol=1e-4)
def test_launch_interrupt(self, dataloader_dummy, monkeypatch):
network = DummyNet(n_channels=dataloader_dummy.dataset.X.shape[1])
loss = MeanReturns()
class TempCallback(Callback):
def on_train_begin(self, metadata):
raise KeyboardInterrupt()
monkeypatch.setattr('time.sleep', lambda x: None)
run = Run(network, loss, dataloader_dummy, callbacks=[TempCallback()])
run.launch(n_epochs=1)
def test_attributes_after_construction(self, dataloader_dummy, additional_kwargs):
network = DummyNet()
loss = MeanReturns()
kwargs = {}
if additional_kwargs:
kwargs.update({'metrics': {'std': StandardDeviation()},
'val_dataloaders': {'val': dataloader_dummy},
'benchmarks': {'whatever': OneOverN()}})
run = Run(network, loss, dataloader_dummy, **kwargs)
assert network is run.network
assert loss is run.loss
assert dataloader_dummy is run.train_dataloader
assert isinstance(run.metrics, dict)
assert isinstance(run.val_dataloaders, dict)
def test_launch(self, dataloader_dummy):
network = DummyNet(n_channels=dataloader_dummy.dataset.X.shape[1])
loss = MeanReturns()
run = Run(network, loss, dataloader_dummy)
run.launch(n_epochs=1)
def network_dummy(dataset_dummy):
return DummyNet(n_channels=dataset_dummy.n_channels)