def test_unique_util_with_all_missing_values():
# test for all types of missing values for object dtype
values = np.array([np.nan, "a", "c", "c", None, float("nan"), None], dtype=object)
uniques = _unique(values)
assert_array_equal(uniques[:-1], ["a", "c", None])
# last value is nan
assert np.isnan(uniques[-1])
expected_inverse = [3, 0, 1, 1, 2, 3, 2]
_, inverse = _unique(values, return_inverse=True)
assert_array_equal(inverse, expected_inverse)
def test_unique_util_missing_values_numeric():
# Check missing values in numerical values
values = np.array([3, 1, np.nan, 5, 3, np.nan], dtype=float)
expected_uniques = np.array([1, 3, 5, np.nan], dtype=float)
expected_inverse = np.array([1, 0, 3, 2, 1, 3])
uniques = _unique(values)
assert_array_equal(uniques, expected_uniques)
uniques, inverse = _unique(values, return_inverse=True)
assert_array_equal(uniques, expected_uniques)
assert_array_equal(inverse, expected_inverse)
encoded = _encode(values, uniques=uniques)
assert_array_equal(encoded, expected_inverse)
def test_encode_util(values, expected):
uniques = _unique(values)
assert_array_equal(uniques, expected)
result, encoded = _unique(values, return_inverse=True)
assert_array_equal(result, expected)
assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
encoded = _encode(values, uniques=uniques)
assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
result, counts = _unique(values, return_counts=True)
assert_array_equal(result, expected)
assert_array_equal(counts, np.array([2, 1, 2]))
result, encoded, counts = _unique(values,
return_inverse=True,
return_counts=True)
assert_array_equal(result, expected)
assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
assert_array_equal(counts, np.array([2, 1, 2]))
def test_unique_util_missing_values_objects(missing_value, pickle_uniques):
# check for _unique and _encode with missing values with object dtypes
values = np.array(['a', 'c', 'c', missing_value, 'b'], dtype=object)
expected_uniques = np.array(['a', 'b', 'c', missing_value], dtype=object)
uniques = _unique(values)
if missing_value is None:
assert_array_equal(uniques, expected_uniques)
else: # missing_value == np.nan
assert_array_equal(uniques[:-1], expected_uniques[:-1])
assert np.isnan(uniques[-1])
if pickle_uniques:
uniques = pickle.loads(pickle.dumps(uniques))
encoded = _encode(values, uniques=uniques)
assert_array_equal(encoded, np.array([0, 2, 2, 3, 1]))
def test_encode_util(values, expected):
uniques = _unique(values)
assert_array_equal(uniques, expected)
encoded = _encode(values, uniques=uniques)
assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))