def test_transformer_tsp_multisamples(device):
'''multivariate test'''
cuda_check(device)
start = time.time()
tsp = TimeSeriesPredictor(
Transformer(d_model=12),
lr=1e-5,
lambda1=1e-8,
optimizer__weight_decay=1e-8,
iterator_train__shuffle=True,
early_stopping=EarlyStopping(patience=100),
max_epochs=500,
train_split=CVSplit(10),
optimizer=Adam,
device=device,
)
past_pattern_length = 24
future_pattern_length = 12
pattern_length = past_pattern_length + future_pattern_length
# pylint: disable-next=line-too-long
fsd = FlightSeriesDataset(pattern_length,
future_pattern_length,
pattern_length,
stride=1,
generate_test_dataset=True)
tsp.fit(fsd)
end = time.time()
elapsed = timedelta(seconds=end - start)
print(f"Fitting in {device} time delta: {elapsed}")
mean_r2_score = tsp.score(tsp.dataset)
assert mean_r2_score > -0.5
netout = tsp.predict(fsd.test.x)
idx = np.random.randint(0, len(fsd.test.x))
y_true = fsd.test.y[idx, :, :]
y_hat = netout[idx, :, :]
r2s = r2_score(y_true, y_hat)
assert r2s > -1
print(f"Final R2 score: {r2s}")
def test_main(stride, test_main_context):
context = test_main_context(stride)
past_pattern_length = context['past_pattern_length']
future_pattern_length = context['future_pattern_length']
pattern_length = past_pattern_length + future_pattern_length
tsp = TimeSeriesPredictor(
BenchmarkLSTM(
initial_forget_gate_bias=1,
hidden_dim=7,
num_layers=1,
),
lr=context['lr'],
lambda1=1e-8,
optimizer__weight_decay=1e-8,
iterator_train__shuffle=True,
early_stopping=EarlyStopping(patience=100),
max_epochs=500,
train_split=CVSplit(context['n_cv_splits']),
optimizer=Adam,
)
fsd = FlightSeriesDataset(pattern_length,
future_pattern_length,
context['except_last_n'],
stride=stride,
generate_test_dataset=True)
tsp.fit(fsd)
mean_r2_score = tsp.score(tsp.dataset)
assert mean_r2_score > context['mean_r2_score']
netout = tsp.predict(fsd.test.x)
idx = np.random.randint(0, len(fsd.test.x))
y_true = fsd.test.y[idx, :, :]
y_hat = netout[idx, :, :]
r2s = r2_score(y_true, y_hat)
print("Final R2 score: {}".format(r2s))
assert r2s > context['final_r2_score']