def test_add_time_features():
tran = transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=10,
)
tran2 = clone(
tran,
{
"time_features": [
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
]
},
)
assert equals(tran, clone(tran))
assert not equals(tran, tran2)
def test_gluon_predictor():
train_length = 100
pred_length = 10
estimator = CanonicalRNNEstimator("5min", train_length, pred_length)
assert equals(estimator, clone(estimator))
assert not equals(estimator, clone(estimator, {"freq": "1h"}))
def test_map_transformation():
tran = transform.VstackFeatures(
output_field="dynamic_feat",
input_fields=["age", "time_feat"],
drop_inputs=True,
)
assert equals(tran, clone(tran))
assert not equals(tran, clone(tran, {"drop_inputs": False}))
def test_chain():
chain = transform.Chain(trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=10,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=10,
log_scale=True,
),
transform.AddObservedValuesIndicator(target_field=FieldName.TARGET,
output_field="observed_values"),
])
assert equals(chain, clone(chain))
assert not equals(chain, clone(chain, {"trans": []}))
another_chain = transform.Chain(trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=10,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=10,
log_scale=False,
),
transform.AddObservedValuesIndicator(target_field=FieldName.TARGET,
output_field="observed_values"),
])
assert not equals(chain, another_chain)
def test_instance_splitter():
splitter = transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=transform.ExpectedNumInstanceSampler(num_instances=4),
past_length=100,
future_length=10,
time_series_fields=["dynamic_feat", "observed_values"],
)
splitter2 = clone(
splitter,
{
"instance_sampler":
transform.ExpectedNumInstanceSampler(num_instances=5)
},
)
assert equals(splitter, clone(splitter))
assert not equals(splitter, splitter2)
def test_continuous_time_splitter():
splitter = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1,
future_interval_length=1,
instance_sampler=transform.ContinuousTimePointSampler(),
)
splitter2 = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1,
future_interval_length=1,
instance_sampler=transform.ContinuousTimePointSampler(min_past=1.0),
)
assert equals(splitter, clone(splitter))
assert not equals(splitter, splitter2)
def test_continuous_time_sampler():
sampler = transform.ContinuousTimeUniformSampler(num_instances=4)
assert equals(sampler, clone(sampler))
assert not equals(sampler, clone(sampler, {"num_instances": 5}))
def test_exp_num_sampler():
sampler = transform.ExpectedNumInstanceSampler(num_instances=4)
assert equals(sampler, clone(sampler))
assert not equals(sampler, clone(sampler, {"num_instances": 5}))