275 lines
9.4 KiB
Python
275 lines
9.4 KiB
Python
import pickle
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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from sklearn.utils._encode import _check_unknown, _encode, _get_counts, _unique
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@pytest.mark.parametrize(
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"values, expected",
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[
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(np.array([2, 1, 3, 1, 3], dtype="int64"), np.array([1, 2, 3], dtype="int64")),
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(
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np.array([2, 1, np.nan, 1, np.nan], dtype="float32"),
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np.array([1, 2, np.nan], dtype="float32"),
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),
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(
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np.array(["b", "a", "c", "a", "c"], dtype=object),
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np.array(["a", "b", "c"], dtype=object),
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),
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(
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np.array(["b", "a", None, "a", None], dtype=object),
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np.array(["a", "b", None], dtype=object),
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),
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(np.array(["b", "a", "c", "a", "c"]), np.array(["a", "b", "c"])),
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],
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ids=["int64", "float32-nan", "object", "object-None", "str"],
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)
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def test_encode_util(values, expected):
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uniques = _unique(values)
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assert_array_equal(uniques, expected)
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result, encoded = _unique(values, return_inverse=True)
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assert_array_equal(result, expected)
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assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
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encoded = _encode(values, uniques=uniques)
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assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
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result, counts = _unique(values, return_counts=True)
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assert_array_equal(result, expected)
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assert_array_equal(counts, np.array([2, 1, 2]))
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result, encoded, counts = _unique(values, return_inverse=True, return_counts=True)
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assert_array_equal(result, expected)
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assert_array_equal(encoded, np.array([1, 0, 2, 0, 2]))
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assert_array_equal(counts, np.array([2, 1, 2]))
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def test_encode_with_check_unknown():
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# test for the check_unknown parameter of _encode()
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uniques = np.array([1, 2, 3])
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values = np.array([1, 2, 3, 4])
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# Default is True, raise error
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with pytest.raises(ValueError, match="y contains previously unseen labels"):
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_encode(values, uniques=uniques, check_unknown=True)
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# dont raise error if False
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_encode(values, uniques=uniques, check_unknown=False)
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# parameter is ignored for object dtype
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uniques = np.array(["a", "b", "c"], dtype=object)
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values = np.array(["a", "b", "c", "d"], dtype=object)
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with pytest.raises(ValueError, match="y contains previously unseen labels"):
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_encode(values, uniques=uniques, check_unknown=False)
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def _assert_check_unknown(values, uniques, expected_diff, expected_mask):
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diff = _check_unknown(values, uniques)
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assert_array_equal(diff, expected_diff)
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diff, valid_mask = _check_unknown(values, uniques, return_mask=True)
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assert_array_equal(diff, expected_diff)
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assert_array_equal(valid_mask, expected_mask)
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@pytest.mark.parametrize(
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"values, uniques, expected_diff, expected_mask",
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[
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(np.array([1, 2, 3, 4]), np.array([1, 2, 3]), [4], [True, True, True, False]),
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(np.array([2, 1, 4, 5]), np.array([2, 5, 1]), [4], [True, True, False, True]),
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(np.array([2, 1, np.nan]), np.array([2, 5, 1]), [np.nan], [True, True, False]),
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(
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np.array([2, 1, 4, np.nan]),
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np.array([2, 5, 1, np.nan]),
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[4],
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[True, True, False, True],
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),
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(
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np.array([2, 1, 4, np.nan]),
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np.array([2, 5, 1]),
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[4, np.nan],
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[True, True, False, False],
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),
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(
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np.array([2, 1, 4, 5]),
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np.array([2, 5, 1, np.nan]),
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[4],
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[True, True, False, True],
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),
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(
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np.array(["a", "b", "c", "d"], dtype=object),
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np.array(["a", "b", "c"], dtype=object),
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np.array(["d"], dtype=object),
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[True, True, True, False],
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),
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(
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np.array(["d", "c", "a", "b"], dtype=object),
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np.array(["a", "c", "b"], dtype=object),
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np.array(["d"], dtype=object),
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[False, True, True, True],
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),
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(
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np.array(["a", "b", "c", "d"]),
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np.array(["a", "b", "c"]),
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np.array(["d"]),
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[True, True, True, False],
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),
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(
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np.array(["d", "c", "a", "b"]),
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np.array(["a", "c", "b"]),
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np.array(["d"]),
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[False, True, True, True],
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),
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],
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)
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def test_check_unknown(values, uniques, expected_diff, expected_mask):
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_assert_check_unknown(values, uniques, expected_diff, expected_mask)
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@pytest.mark.parametrize("missing_value", [None, np.nan, float("nan")])
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@pytest.mark.parametrize("pickle_uniques", [True, False])
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def test_check_unknown_missing_values(missing_value, pickle_uniques):
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# check for check_unknown with missing values with object dtypes
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values = np.array(["d", "c", "a", "b", missing_value], dtype=object)
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uniques = np.array(["c", "a", "b", missing_value], dtype=object)
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if pickle_uniques:
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uniques = pickle.loads(pickle.dumps(uniques))
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expected_diff = ["d"]
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expected_mask = [False, True, True, True, True]
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_assert_check_unknown(values, uniques, expected_diff, expected_mask)
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values = np.array(["d", "c", "a", "b", missing_value], dtype=object)
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uniques = np.array(["c", "a", "b"], dtype=object)
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if pickle_uniques:
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uniques = pickle.loads(pickle.dumps(uniques))
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expected_diff = ["d", missing_value]
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expected_mask = [False, True, True, True, False]
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_assert_check_unknown(values, uniques, expected_diff, expected_mask)
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values = np.array(["a", missing_value], dtype=object)
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uniques = np.array(["a", "b", "z"], dtype=object)
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if pickle_uniques:
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uniques = pickle.loads(pickle.dumps(uniques))
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expected_diff = [missing_value]
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expected_mask = [True, False]
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_assert_check_unknown(values, uniques, expected_diff, expected_mask)
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@pytest.mark.parametrize("missing_value", [np.nan, None, float("nan")])
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@pytest.mark.parametrize("pickle_uniques", [True, False])
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def test_unique_util_missing_values_objects(missing_value, pickle_uniques):
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# check for _unique and _encode with missing values with object dtypes
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values = np.array(["a", "c", "c", missing_value, "b"], dtype=object)
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expected_uniques = np.array(["a", "b", "c", missing_value], dtype=object)
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uniques = _unique(values)
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if missing_value is None:
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assert_array_equal(uniques, expected_uniques)
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else: # missing_value == np.nan
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assert_array_equal(uniques[:-1], expected_uniques[:-1])
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assert np.isnan(uniques[-1])
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if pickle_uniques:
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uniques = pickle.loads(pickle.dumps(uniques))
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encoded = _encode(values, uniques=uniques)
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assert_array_equal(encoded, np.array([0, 2, 2, 3, 1]))
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def test_unique_util_missing_values_numeric():
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# Check missing values in numerical values
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values = np.array([3, 1, np.nan, 5, 3, np.nan], dtype=float)
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expected_uniques = np.array([1, 3, 5, np.nan], dtype=float)
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expected_inverse = np.array([1, 0, 3, 2, 1, 3])
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uniques = _unique(values)
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assert_array_equal(uniques, expected_uniques)
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uniques, inverse = _unique(values, return_inverse=True)
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assert_array_equal(uniques, expected_uniques)
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assert_array_equal(inverse, expected_inverse)
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encoded = _encode(values, uniques=uniques)
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assert_array_equal(encoded, expected_inverse)
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def test_unique_util_with_all_missing_values():
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# test for all types of missing values for object dtype
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values = np.array([np.nan, "a", "c", "c", None, float("nan"), None], dtype=object)
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uniques = _unique(values)
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assert_array_equal(uniques[:-1], ["a", "c", None])
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# last value is nan
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assert np.isnan(uniques[-1])
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expected_inverse = [3, 0, 1, 1, 2, 3, 2]
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_, inverse = _unique(values, return_inverse=True)
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assert_array_equal(inverse, expected_inverse)
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def test_check_unknown_with_both_missing_values():
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# test for both types of missing values for object dtype
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values = np.array([np.nan, "a", "c", "c", None, np.nan, None], dtype=object)
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diff = _check_unknown(values, known_values=np.array(["a", "c"], dtype=object))
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assert diff[0] is None
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assert np.isnan(diff[1])
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diff, valid_mask = _check_unknown(
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values, known_values=np.array(["a", "c"], dtype=object), return_mask=True
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)
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assert diff[0] is None
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assert np.isnan(diff[1])
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assert_array_equal(valid_mask, [False, True, True, True, False, False, False])
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@pytest.mark.parametrize(
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"values, uniques, expected_counts",
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[
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(np.array([1] * 10 + [2] * 4 + [3] * 15), np.array([1, 2, 3]), [10, 4, 15]),
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(
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np.array([1] * 10 + [2] * 4 + [3] * 15),
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np.array([1, 2, 3, 5]),
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[10, 4, 15, 0],
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),
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(
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np.array([np.nan] * 10 + [2] * 4 + [3] * 15),
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np.array([2, 3, np.nan]),
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[4, 15, 10],
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),
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(
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np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object),
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["a", "b", "c"],
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[16, 4, 20],
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),
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(
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np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object),
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["c", "b", "a"],
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[20, 4, 16],
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),
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(
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np.array([np.nan] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object),
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["c", np.nan, "a"],
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[20, 4, 16],
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),
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(
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np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object),
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["a", "b", "c", "e"],
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[16, 4, 20, 0],
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),
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],
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)
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def test_get_counts(values, uniques, expected_counts):
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counts = _get_counts(values, uniques)
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assert_array_equal(counts, expected_counts)
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