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)