def setup_method(self, method):
seq_len = 480
self.num_samples = 300
self.config = {
"max_y_iterations": 1,
"init_FX_epoch": 1,
"max_FX_epoch": 1,
"max_TCN_epoch": 1,
"alt_iters": 2,
}
self.model = TCMF()
self.Ymat = np.random.rand(self.num_samples, seq_len)
self.horizon = np.random.randint(1, 50)
def test_save_restore(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
result_save = self.model.predict(x=None, horizon=self.horizon)
model_file = "tmp.pkl"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = TCMF()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(x=None, horizon=self.horizon)
assert_array_almost_equal(result_save, result_restore, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)
class TestTCMF(ZooTestCase):
def setup_method(self, method):
seq_len = 480
self.num_samples = 300
self.config = {
"y_iters": 1,
"init_FX_epoch": 1,
"max_FX_epoch": 1,
"max_TCN_epoch": 1,
"alt_iters": 2,
}
self.model = TCMF()
self.Ymat = np.random.rand(self.num_samples, seq_len)
self.horizon = np.random.randint(2, 50)
def teardown_method(self, method):
del self.model
del self.Ymat
def test_tcmf(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
# test predict
result = self.model.predict(horizon=self.horizon)
assert result.shape[1] == self.horizon
# test evaluate
target = np.random.rand(self.num_samples, self.horizon)
evaluate_result = self.model.evaluate(y=target,
metrics=['mae', 'smape'])
assert len(evaluate_result) == 2
assert len(evaluate_result[0]) == self.horizon
assert len(evaluate_result[1]) == self.horizon
# test fit_incremental
Ymat_before = self.model.model.Ymat
self.model.fit_incremental(target)
Ymat_after = self.model.model.Ymat
assert Ymat_after.shape[1] - Ymat_before.shape[1] == self.horizon
incr_result = self.model.predict(horizon=self.horizon)
np.testing.assert_raises(AssertionError, np.testing.assert_array_equal,
result, incr_result)
def test_error(self):
with pytest.raises(ValueError,
match="We don't support input x directly"):
self.model.predict(x=1)
with pytest.raises(ValueError,
match="We don't support input x directly"):
self.model.evaluate(x=1,
y=np.random.rand(self.num_samples,
self.horizon))
with pytest.raises(ValueError, match="Input invalid y of None"):
self.model.evaluate(y=None)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling predict"
):
self.model.predict(x=None)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling predict"
):
self.model.evaluate(
y=np.random.rand(self.num_samples, self.horizon))
with pytest.raises(ValueError, match="Input invalid x of None"):
self.model.fit_incremental(x=None)
with pytest.raises(
Exception,
match="Needs to call fit_eval or restore first before "
"calling fit_incremental"):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon))
with pytest.raises(
Exception,
match=f"Expected incremental input with {self.num_samples} "
f"time series, got {self.num_samples - 1} instead"):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
self.model.fit_incremental(
x=np.random.rand(self.num_samples - 1, self.horizon))
def test_save_restore(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
result_save = self.model.predict(horizon=self.horizon)
model_file = "tmp.pkl"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = TCMF()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(horizon=self.horizon)
assert_array_almost_equal(result_save, result_restore, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)
class TestTCMF(ZooTestCase):
def setup_method(self, method):
seq_len = 480
self.num_samples = 300
self.config = {
"max_y_iterations": 1,
"init_FX_epoch": 1,
"max_FX_epoch": 1,
"max_TCN_epoch": 1,
"alt_iters": 2,
}
self.model = TCMF()
self.Ymat = np.random.rand(self.num_samples, seq_len)
self.horizon = np.random.randint(1, 50)
def teardown_method(self, method):
del self.model
del self.Ymat
def test_fit_predict_evaluate(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
# test predict
result = self.model.predict(x=None, horizon=self.horizon)
assert result.shape[1] == self.horizon
# test evaluate
target = np.random.rand(self.num_samples, self.horizon)
evaluate_result = self.model.evaluate(y=target, metrics=['mae', 'smape'])
assert len(evaluate_result) == 2
assert len(evaluate_result[0]) == self.horizon
assert len(evaluate_result[1]) == self.horizon
def test_predict_evaluate_error(self):
with pytest.raises(ValueError):
self.model.predict(x=1)
with pytest.raises(ValueError):
self.model.evaluate(x=1, y=np.random.rand(self.num_samples, self.horizon))
with pytest.raises(ValueError):
self.model.evaluate(x=None, y=None)
with pytest.raises(Exception):
self.model.predict(x=None)
with pytest.raises(Exception):
self.model.evaluate(x=None, y=np.random.rand(self.num_samples, self.horizon))
def test_save_restore(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
result_save = self.model.predict(x=None, horizon=self.horizon)
model_file = "tmp.pkl"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = TCMF()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(x=None, horizon=self.horizon)
assert_array_almost_equal(result_save, result_restore, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)
class TestTCMF(ZooTestCase):
def setup_method(self, method):
self.seq_len = 480
self.num_samples = 300
self.config = {
"y_iters": 1,
"init_FX_epoch": 1,
"max_FX_epoch": 1,
"max_TCN_epoch": 1,
"alt_iters": 2,
}
self.model = TCMF()
self.Ymat = np.random.rand(self.num_samples, self.seq_len)
self.horizon = np.random.randint(2, 50)
def teardown_method(self, method):
del self.model
del self.Ymat
def test_tcmf(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
# test predict
result = self.model.predict(horizon=self.horizon)
assert result.shape[1] == self.horizon
# test evaluate
target = np.random.rand(self.num_samples, self.horizon)
evaluate_result = self.model.evaluate(y=target,
metrics=['mae', 'smape'])
assert len(evaluate_result) == 2
assert len(evaluate_result[0]) == self.horizon
assert len(evaluate_result[1]) == self.horizon
# test fit_incremental
Ymat_before = self.model.model.Ymat
self.model.fit_incremental(target)
Ymat_after = self.model.model.Ymat
assert Ymat_after.shape[1] - Ymat_before.shape[1] == self.horizon
incr_result = self.model.predict(horizon=self.horizon)
np.testing.assert_raises(AssertionError, np.testing.assert_array_equal,
result, incr_result)
def test_tcmf_covariates_dti(self):
# test wrong format in fit
with pytest.raises(ValueError,
match="Input covariates must be a ndarray. Got"):
self.model.fit_eval(x=self.Ymat, covariates='None', **self.config)
with pytest.raises(
ValueError,
match="The second dimension shape of covariates should be"):
self.model.fit_eval(x=self.Ymat,
covariates=np.random.randn(
3, self.seq_len - 1),
**self.config)
with pytest.raises(
ValueError,
match="You should input a 2-D ndarray of covariates."):
self.model.fit_eval(x=self.Ymat,
covariates=np.random.randn(3, 4, 5),
**self.config)
with pytest.raises(
ValueError,
match="Input dti must be a pandas DatetimeIndex. Got"):
self.model.fit_eval(x=self.Ymat,
covariates=np.random.randn(3, self.seq_len),
dti='None',
**self.config)
with pytest.raises(ValueError,
match="Input dti length should be equal to"):
self.model.fit_eval(x=self.Ymat,
covariates=np.random.randn(3, self.seq_len),
dti=pd.date_range('20130101',
periods=self.seq_len - 1),
**self.config)
# valid covariates and dti in fit_eval
self.model.fit_eval(x=self.Ymat,
y=None,
covariates=np.random.rand(3, self.seq_len),
dti=pd.date_range('20130101',
periods=self.seq_len),
**self.config)
# inconsistent covariates and dti
with pytest.raises(
ValueError,
match="Find valid covariates in fit but invalid covariates "
"in predict."):
self.model.predict(horizon=self.horizon)
with pytest.raises(
ValueError,
match="be the same as the input covariates number in fit."):
self.model.predict(
horizon=self.horizon,
future_covariates=np.random.randn(2, self.horizon),
)
with pytest.raises(ValueError,
match="Find valid dti in fit but invalid dti in"):
self.model.predict(
horizon=self.horizon,
future_covariates=np.random.randn(3, self.horizon),
)
with pytest.raises(
ValueError,
match="Find valid covariates in fit but invalid covariates "
"in fit_incremental."):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon))
with pytest.raises(
ValueError,
match="be the same as the input covariates number in fit."):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon),
covariates_new=np.random.randn(2, self.horizon),
)
with pytest.raises(ValueError,
match="Find valid dti in fit but invalid dti in"):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon),
covariates_new=np.random.randn(3, self.horizon),
)
self.model.predict(
horizon=self.horizon,
future_covariates=np.random.randn(3, self.horizon),
future_dti=pd.date_range('20130101', periods=self.horizon),
)
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon),
covariates_new=np.random.randn(3, self.horizon),
dti_new=pd.date_range('20130101', periods=self.horizon),
)
def test_error(self):
with pytest.raises(ValueError,
match="We don't support input x directly"):
self.model.predict(x=1)
with pytest.raises(ValueError,
match="We don't support input x directly"):
self.model.evaluate(x=1,
y=np.random.rand(self.num_samples,
self.horizon))
with pytest.raises(ValueError, match="Input invalid y of None"):
self.model.evaluate(y=None)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling predict"
):
self.model.predict(x=None)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling predict"
):
self.model.evaluate(
y=np.random.rand(self.num_samples, self.horizon))
with pytest.raises(ValueError, match="Input invalid x of None"):
self.model.fit_incremental(x=None)
with pytest.raises(
Exception,
match="Needs to call fit_eval or restore first before "
"calling fit_incremental"):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon))
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
with pytest.raises(
Exception,
match=f"Expected incremental input with {self.num_samples} "
f"time series, got {self.num_samples - 1} instead"):
self.model.fit_incremental(
x=np.random.rand(self.num_samples - 1, self.horizon))
with pytest.raises(ValueError,
match="but invalid covariates in fit. "):
self.model.predict(
horizon=self.horizon,
future_covariates=np.random.randn(3, self.horizon),
)
with pytest.raises(ValueError, match="but invalid dti in fit. "):
self.model.predict(
horizon=self.horizon,
future_dti=pd.date_range('20130101', periods=self.horizon),
)
with pytest.raises(ValueError,
match="but invalid covariates in fit. "):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon),
covariates_new=np.random.randn(3, self.horizon),
)
with pytest.raises(ValueError, match="but invalid dti in fit. "):
self.model.fit_incremental(
x=np.random.rand(self.num_samples, self.horizon),
dti_new=pd.date_range('20130101', periods=self.horizon),
)
def test_save_restore(self):
self.model.fit_eval(x=self.Ymat, y=None, **self.config)
result_save = self.model.predict(horizon=self.horizon)
model_file = "tmp.pkl"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = TCMF()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(horizon=self.horizon)
assert_array_almost_equal(result_save, result_restore, decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)