def test_tcn_forecaster_onnx_methods(self):
train_data, val_data, test_data = create_data()
forecaster = TCNForecaster(past_seq_len=24,
future_seq_len=5,
input_feature_num=1,
output_feature_num=1,
kernel_size=4,
num_channels=[16, 16],
lr=0.01)
forecaster.fit(train_data[0], train_data[1], epochs=2)
try:
import onnx
import onnxruntime
pred = forecaster.predict(test_data[0])
pred_onnx = forecaster.predict_with_onnx(test_data[0])
np.testing.assert_almost_equal(pred, pred_onnx, decimal=5)
mse = forecaster.evaluate(test_data[0],
test_data[1],
multioutput="raw_values")
mse_onnx = forecaster.evaluate_with_onnx(test_data[0],
test_data[1],
multioutput="raw_values")
np.testing.assert_almost_equal(mse, mse_onnx, decimal=5)
mse = forecaster.evaluate(test_data[0], test_data[1])
mse_onnx = forecaster.evaluate_with_onnx(test_data[0],
test_data[1])
np.testing.assert_almost_equal(mse, mse_onnx, decimal=5)
except ImportError:
pass
def test_tcn_forecaster_save_restore(self):
train_data, val_data, test_data = create_data()
forecaster = TCNForecaster(past_seq_len=24,
future_seq_len=5,
input_feature_num=1,
output_feature_num=1,
kernel_size=3,
lr=0.01)
train_mse = forecaster.fit(train_data[0], train_data[1], epochs=2)
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
test_pred_save = forecaster.predict(test_data[0])
forecaster.save(ckpt_name)
forecaster.restore(ckpt_name)
test_pred_restore = forecaster.predict(test_data[0])
np.testing.assert_almost_equal(test_pred_save, test_pred_restore)
def test_tcn_forecaster_runtime_error(self):
train_data, val_data, test_data = create_data()
forecaster = TCNForecaster(past_seq_len=24,
future_seq_len=5,
input_feature_num=1,
output_feature_num=1,
kernel_size=3,
lr=0.01)
with pytest.raises(RuntimeError):
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
forecaster.save(ckpt_name)
with pytest.raises(RuntimeError):
forecaster.predict(test_data[0])
with pytest.raises(RuntimeError):
forecaster.evaluate(test_data[0], test_data[1])
def test_tcn_forecaster_fit_eva_pred(self):
train_data, val_data, test_data = create_data()
forecaster = TCNForecaster(past_seq_len=24,
future_seq_len=5,
input_feature_num=1,
output_feature_num=1,
kernel_size=3,
lr=0.01)
train_mse = forecaster.fit(train_data[0], train_data[1], epochs=2)
test_pred = forecaster.predict(test_data[0])
assert test_pred.shape == test_data[1].shape
test_mse = forecaster.evaluate(test_data[0], test_data[1])