def test_on_one_row():
test_argument = np.array([[1382.0, 390167.0]])
# Store information about raised ValueError in exc_info
with pytest.raises(ValueError) as exc_info:
split_into_training_and_testing_sets(test_argument)
expected_error_msg = "Argument data_array must have at least 2 rows, it actually has just 1"
# Check if the raised ValueError contains the correct message
assert exc_info.match(expected_error_msg)
def test_on_six_rows():
example_argument = np.array([[2081.0, 314942.0], [1059.0, 186606.0],
[1148.0, 206186.0], [1506.0, 248419.0],
[1210.0, 214114.0], [1697.0, 277794.0]]
)
# Fill in with training array's expected number of rows
expected_training_array_num_rows = 4
# Fill in with testing array's expected number of rows
expected_testing_array_num_rows = 2
actual = split_into_training_and_testing_sets(example_argument)
# Write the assert statement checking training array's number of rows
assert actual[0].shape[0] == expected_training_array_num_rows, "The actual number of rows in the training array is not {}".format(expected_training_array_num_rows)
def test_on_one_row():
test_argument = np.array([[1382.0, 390167.0]])
# Fill in with a context manager for checking ValueError
with pytest.raises(ValueError):
split_into_training_and_testing_sets(test_argument)
def test_on_one_row():
test_argument = np.array([[1382.0, 390167.0]])
# Store information about raised ValueError in exc_info
with pytest.raises(ValueError) as exc_info:
split_into_training_and_testing_sets(test_argument)