def test_neural_network_training(): model = models.NeuralNetworkModel hyperparams = sample_hp(model.hp_space, rng=RandomState(1)) for dataset in ds.all_datasets: train, test = dataset.get() train, test = model.prepare_dataset(train, test, dataset.categorical_features) estimator = model.build_estimator(hyperparams) X, y, *_ = train estimator.fit(X, y)
def test_linear_regression_training(): model = models.LinearRegressionModel hyperparams = sample_hp(model.hp_space, rng=RandomState(1)) for dataset in ds.all_datasets: train, test = dataset.get() train, test = model.prepare_dataset(train, test, dataset.categorical_features) estimator = model.build_estimator(hyperparams, test=True) X, y, *_ = train estimator.fit(X, y)
def test_gaussian_process_training(): model = models.GaussianProcessModel hyperparams = sample_hp(model.hp_space, rng=RandomState(1)) for dataset in ds.all_datasets: try: print(dataset.__name__) train, test = dataset.get() train, test = model.prepare_dataset(train, test, dataset.categorical_features) estimator = model.build_estimator(hyperparams) X, y, *_ = train estimator.fit(X, y) except MemoryError: # This model has high memory requirements and cannot be used on some big datasets continue
def test_random_forests_hp_space(): sample_hp(models.RandomForestsModel.hp_space)