def test_lstm_tsp_forecast_oze(user_name, user_password):
"""
Tests the LSTMTimeSeriesPredictor
"""
tsp = TimeSeriesPredictor(
BenchmarkLSTM(hidden_dim=64),
max_epochs=5,
# train_split=None, # default = skorch.dataset.CVSplit(5)
optimizer=torch.optim.Adam)
dataset = _get_dataset(user_name, user_password)
tsp.fit(dataset)
mean_r2_score = tsp.score(tsp.dataset)
assert mean_r2_score > -300
predictions = tsp.forecast(500)
assert len(predictions) == len(dataset) + 500
def test_quantum_lstm_tsp_forecast(device):
"""
Tests the Quantum LSTM forecast
"""
cuda_check(device)
tsp = TimeSeriesPredictor(
QuantumLSTM(hidden_dim = 2),
max_epochs=250,
lr = 1e-4,
early_stopping=EarlyStopping(patience=100, monitor='train_loss'),
train_split=None,
optimizer=Adam,
device=device
)
whole_fd = FlightsDataset()
# leave last N months for error assertion
last_n = 24
start = time.time()
tsp.fit(FlightsDataset(pattern_length = 120, except_last_n = last_n))
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 > -5
netout, _ = tsp.forecast(last_n)
# Select any training example just for comparison
idx = np.random.randint(0, len(tsp.dataset))
_, whole_y = whole_fd[idx]
y_true = whole_y[-last_n:, :] # get only known future outputs
y_pred = netout[idx, -last_n:, :] # get only last N predicted outputs
r2s = r2_score(y_true, y_pred)
assert r2s > -60