def test__init__02(self, array_type):
input_array = array_type(self.test_data)
with pytest.raises(ValueError):
KrigingClass = KrigingModel(input_array)
def test__init__03(self, array_type):
input_array = array_type(self.test_data)
with pytest.raises(Exception):
KrigingClass = KrigingModel(input_array, numerical_gradients=1)
def test_parity_residual_plots(self, mock_show, array_type):
input_array = array_type(self.training_data)
KrigingClass = KrigingModel(input_array, regularization=False)
results = KrigingClass.training()
KrigingClass.parity_residual_plots()
def test__init__01(self, array_type):
input_array = array_type(self.test_data)
KrigingClass = KrigingModel(input_array)
assert KrigingClass.num_grads == True
assert KrigingClass.regularization == True
def test_pickle_load01(self, array_type):
input_array = array_type(self.training_data)
KrigingClass = KrigingModel(input_array, regularization=False)
results = KrigingClass.training()
KrigingClass.pickle_load(KrigingClass.filename)
def test_pickle_load02(self, array_type):
input_array = array_type(self.training_data)
KrigingClass = KrigingModel(input_array, regularization=False)
with pytest.raises(Exception):
KrigingClass.pickle_load('file_not_existing.pickle')
def test__init__07(self, array_type):
input_array = array_type(self.test_data)
with pytest.raises(Exception):
KrigingClass = KrigingModel(input_array, fname=1)
def test_get_feature_vector_02(self, array_type):
input_array = array_type(self.training_data)
KrigingClass = KrigingModel(input_array, regularization=False)
p = KrigingClass.get_feature_vector()
expected_dict = {0: 0, 1: 0}
assert expected_dict == p.extract_values()
def test_get_feature_vector_02(self):
KrigingClass = KrigingModel(self.training_data, regularization=False)
p = KrigingClass.get_feature_vector()
expected_dict = {0: 0, 1: 0}
assert expected_dict == p.extract_values()
def test__init__04(self, array_type):
input_array = array_type(self.test_data)
with pytest.raises(Exception):
KrigingClass = KrigingModel(input_array, regularization=1)
def test_get_feature_vector_01(self):
KrigingClass = KrigingModel(self.full_data, regularization=False)
p = KrigingClass.get_feature_vector()
expected_dict = {'x1': 0, 'x2': 0}
assert expected_dict == p.extract_values()
def test__init__05(self):
with pytest.raises(Exception):
KrigingClass = KrigingModel(self.test_data_numpy, regularization=1)
def test__init__04(self):
with pytest.raises(Exception):
KrigingClass = KrigingModel(self.test_data_numpy,
numerical_gradients=1)
def test__init__03(self):
with pytest.raises(ValueError):
KrigingClass = KrigingModel(list(self.test_data_numpy))
def test__init__02(self):
KrigingClass = KrigingModel(self.test_data_pandas)
assert KrigingClass.num_grads == True
assert KrigingClass.regularization == True