class TestDataset(unittest.TestCase):
def setUp(self):
self._data = {'col1': [1, 2], 'col2': [3, 4], 'col4': [5, 6]}
self._dataframe = pd.DataFrame(data=self._data)
self._dataset = DataModel(self._dataframe)
def test_validate_columns_invalid(self):
with self.assertRaises(RuntimeError):
self._dataset.validate_columns(['col3'])
def test_validate_columns(self):
self._dataset.validate_columns(['col1'])
def test_feature_columns(self):
intended_columns = ['col1', 'col2']
self._dataset.set_feature_columns(intended_columns)
feature_columns = self._dataset.get_feature_columns()
result_columns = list(feature_columns.columns.values)
self.assertEqual(result_columns, intended_columns)
def test_target_column(self):
intended_column = 'col1'
self._dataset.set_target_column(intended_column)
target_column = self._dataset.get_target_column()
self.assertEqual(target_column.tolist(), self._data[intended_column])
def assign_fn(self, data_model: DataModel, fn_name: str, kwargs: dict):
if hasattr(self.fn_holder, fn_name):
return self.load_from_holder(data_model, fn_name, kwargs)
if fn_name == self.PANDAS_FN:
kwargs['x'] = data_model.get_input_fn_x_data()
kwargs['y'] = data_model.get_target_column()
kwargs['target_column'] = data_model.target_column_name
fn = getattr(tf.estimator.inputs, 'pandas_input_fn')
return fn(**kwargs)
def base_fn(data_model: Data.DataModel, batch_size=1, epoch=1):
""" input function one, made for shoes AI.
:param data_model: Data.MLDataset
:param epoch: int
:param batch_size: int
:return:
"""
features = _dataset_to_dict(features=data_model.get_feature_columns())
data_set = tf.data.Dataset.from_tensor_slices(
(features, data_model.get_target_column()))
data_set = data_set.shuffle(100).repeat(epoch).batch(batch_size)
return data_set.make_one_shot_iterator().get_next()