def test_vector_conversion_to_datetime():
flights_dataset = FlightsDataset()
dt_vector = convert_year_month_array_to_datetime(
flights_dataset.x[0, 0:5, :])
expected_output_vector = [
datetime(1949, 1, 15),
datetime(1949, 2, 15),
datetime(1949, 3, 15),
datetime(1949, 4, 15),
datetime(1949, 5, 15),
]
assert np.all(expected_output_vector == dt_vector)
def test_train_loss_monitor_no_train_split():
"""
Tests the LSTMTimeSeriesPredictor fitting
"""
tsp = TimeSeriesPredictor(BenchmarkLSTM(hidden_dim=10),
early_stopping=EarlyStopping(
monitor='train_loss', patience=15),
max_epochs=150,
train_split=None,
optimizer=torch.optim.Adam)
tsp.fit(FlightsDataset())
mean_r2_score = tsp.score(tsp.dataset)
assert mean_r2_score > -300
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
def test_transformer_tsp(device):
'''univariate test'''
cuda_check(device)
start = time.time()
tsp = TimeSeriesPredictor(
Transformer(),
max_epochs=50,
train_split=None,
device=device,
)
tsp.fit(FlightsDataset())
score = tsp.score(tsp.dataset)
assert score > -1
end = time.time()
elapsed = timedelta(seconds=end - start)
print(f"Fitting in {device} time delta: {elapsed}")
def test_quantum_lstm_tsp_fitting(device):
"""
Tests the Quantum LSTM TimeSeriesPredictor fitting
"""
cuda_check(device)
tsp = TimeSeriesPredictor(
QuantumLSTM(),
lr=1E-1,
max_epochs=50,
train_split=None,
optimizer=Adam,
device=device
)
start = time.time()
tsp.fit(FlightsDataset())
end = time.time()
elapsed = timedelta(seconds = end - start)
print("Fitting in {} time delta: {}".format(device, elapsed))
mean_r2_score = tsp.score(tsp.dataset)
assert mean_r2_score > -10
def test_dataset_shape():
flights_dataset = FlightsDataset()
assert flights_dataset.get_y_shape() == (1, 144, 1)
assert flights_dataset.get_x_shape() == (1, 144, 2)
fd = flights_dataset.make_future_dataframe(12)
assert fd.shape == (1, 156, 2)
def test_conversion_to_datetime():
flights_dataset = FlightsDataset()
first_date = flights_dataset.x[0, 0, :]
dt = convert_year_month_array_to_datetime(first_date)
assert dt == datetime(1949, 1, 15)