def test_create_and_delete(self):
# setup dataset client
dc = DatasetClient(self.project.hid)
self.assertNotEqual(dc, None)
# get initial number of datasets
init_datasets_cnt = len(dc.get_datasets())
# add dataset
my_dataset_1 = dc.add_dataset_if_not_exists(self.X, self.y)
my_dataset_2 = dc.add_dataset_if_not_exists(self.X, y = None)
# get datasets
datasets = dc.get_datasets()
self.assertEqual(len(datasets), init_datasets_cnt+2)
# delete added dataset
dc.delete_dataset(my_dataset_1.hid)
# check number of datasets
datasets = dc.get_datasets()
self.assertEqual(len(datasets), init_datasets_cnt+1)
def test_add_existing_dataset(self):
# setup dataset client
dc = DatasetClient(self.project.hid)
self.assertNotEqual(dc, None)
# get initial number of datasets
init_datasets_cnt = len(dc.get_datasets())
# add dataset
dc.add_dataset_if_not_exists(self.X, self.y)
# get datasets
datasets = dc.get_datasets()
self.assertEqual(len(datasets), init_datasets_cnt+1)
# add the same dataset
# it shouldn't be added
dc.add_dataset_if_not_exists(self.X, self.y)
# number of all datasets in project should be 1
datasets = dc.get_datasets()
self.assertEqual(len(datasets), init_datasets_cnt+1)
def setUp(self):
proj_title = 'Test project-01' + get_postfix()
proj_task = 'bin_class'
self.expt_title = 'Test experiment-01'
self.validation_kfolds = 5
self.validation_shuffle = True
self.validation_stratify = True
self.validation_train_split = None
self.algorithms = ['xgb']
self.metric = 'logloss'
self.tuning_mode = 'Normal'
self.time_constraint = 1
self.create_enseble = False
# setup project
self.project_client = ProjectClient()
self.project = self.project_client.create_project(title=proj_title,
task=proj_task)
# add training data
df = pd.read_csv('tests/data/test_1.csv')
cols = ['sepal length', 'sepal width', 'petal length', 'petal width']
target = 'class'
dc = DatasetClient(self.project.hid)
self.dataset = dc.add_dataset_if_not_exists(df[cols], df[target])
def test_add_dataset_for_prediction(self):
# setup dataset client
dc = DatasetClient(self.project.hid)
self.assertNotEqual(dc, None)
# get datasets, there should be none
datasets = dc.get_datasets()
self.assertEqual(len(datasets), 0)
# add dataset
my_dataset = dc.add_dataset_if_not_exists(self.X, None)
self.assertNotEqual(my_dataset, None)
# get datasets
datasets = dc.get_datasets()
self.assertEqual(len(datasets), 1)
my_dataset_2 = dc.get_dataset(my_dataset.hid)
self.assertEqual(my_dataset.hid, my_dataset_2.hid)
self.assertEqual(my_dataset.title, my_dataset_2.title)
def test_add_dataset_for_training(self):
# setup dataset client
dc = DatasetClient(self.project.hid)
self.assertNotEqual(dc, None)
# get datasets, there should be none
datasets = dc.get_datasets()
self.assertEqual(len(datasets), 0)
# add dataset
my_dataset = dc.add_dataset_if_not_exists(self.X, self.y)
self.assertNotEqual(my_dataset, None)
# get datasets
datasets = dc.get_datasets()
self.assertEqual(len(datasets), 1)
my_dataset_2 = dc.get_dataset(my_dataset.hid)
self.assertEqual(my_dataset.hid, my_dataset_2.hid)
self.assertEqual(my_dataset.title, my_dataset_2.title)
# test __str__ method
self.assertTrue('id' in str(my_dataset_2))
self.assertTrue('title' in str(my_dataset_2))
self.assertTrue('file' in str(my_dataset_2))
def test_get_dataset_for_wrong_hid(self):
""" Get dataset for wrong hid should return None """
dc = DatasetClient(self.project.hid)
dataset = dc.get_dataset('some-wrong-hid')
self.assertTrue(dataset is None)
def test_prepare_data_two_sources_numpy(self):
dc = DatasetClient(self.project.hid)
data_1, data_hash_1 = dc._prepare_data(np.array(self.X),
np.array(self.y))
data_2, data_hash_2 = dc._prepare_data(np.array(self.X), None)
self.assertNotEqual(data_hash_1, data_hash_2)
def test_prepare_data_two_sources(self):
dc = DatasetClient(self.project.hid)
data_1, data_hash_1 = dc._prepare_data(self.X, self.y)
data_2, data_hash_2 = dc._prepare_data(self.X, None)
self.assertNotEqual(data_hash_1, data_hash_2)