def test_mqcnn_covariate_smoke_test(
use_feat_dynamic_real,
add_time_feature,
add_age_feature,
enable_decoder_dynamic_feature,
hybridize,
):
hps = {
"seed": 42,
"freq": "D",
"prediction_length": 3,
"quantiles": [0.5, 0.1],
"epochs": 3,
"num_batches_per_epoch": 3,
"use_feat_dynamic_real": use_feat_dynamic_real,
"add_time_feature": add_time_feature,
"add_age_feature": add_age_feature,
"enable_decoder_dynamic_feature": enable_decoder_dynamic_feature,
"hybridize": hybridize,
}
dataset_train, dataset_test = make_dummy_datasets_with_features(
cardinality=[3, 10],
num_feat_dynamic_real=2,
freq=hps["freq"],
prediction_length=hps["prediction_length"],
)
estimator = MQCNNEstimator.from_hyperparameters(**hps)
predictor = estimator.train(dataset_train, num_workers=0)
forecasts = list(predictor.predict(dataset_test))
assert len(forecasts) == len(dataset_test)
def test_mqcnn_covariate_smoke_test(
use_past_feat_dynamic_real,
use_feat_dynamic_real,
add_time_feature,
add_age_feature,
enable_encoder_dynamic_feature,
enable_decoder_dynamic_feature,
hybridize,
quantiles,
distr_output,
is_iqf,
):
hps = {
"seed": 42,
"freq": "Y",
"context_length": 5,
"prediction_length": 3,
"quantiles": quantiles,
"distr_output": distr_output,
"epochs": 3,
"num_batches_per_epoch": 3,
"use_past_feat_dynamic_real": use_past_feat_dynamic_real,
"use_feat_dynamic_real": use_feat_dynamic_real,
"add_time_feature": add_time_feature,
"add_age_feature": add_age_feature,
"enable_encoder_dynamic_feature": enable_encoder_dynamic_feature,
"enable_decoder_dynamic_feature": enable_decoder_dynamic_feature,
"hybridize": hybridize,
"is_iqf": is_iqf,
}
dataset_train, dataset_test = make_dummy_datasets_with_features(
cardinality=[3, 10],
num_feat_dynamic_real=2,
num_past_feat_dynamic_real=4,
freq=hps["freq"],
prediction_length=hps["prediction_length"],
)
estimator = MQCNNEstimator.from_hyperparameters(**hps)
predictor = estimator.train(dataset_train, num_workers=None)
forecasts = list(predictor.predict(dataset_test))
assert len(forecasts) == len(dataset_test)