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Fix tests with pandas 2.0. (#9014)

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* Fix tests with pandas 2.0.

- `is_categorical` is replaced by `is_categorical_dtype`.
- one hot encoding returns boolean type instead of integer type.
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Jiaming Yuan 2023-04-11 00:17:34 +08:00 committed by GitHub
parent ebd64f6e22
commit 2c8d735cb3
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2 changed files with 8 additions and 5 deletions
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2
tests/python/test_basic_models.py
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@ -664,7 +664,7 @@ class TestModels:
y = rng.randn(rows) y = rng.randn(rows)
feature_names = ["test_feature_" + str(i) for i in range(cols)] feature_names = ["test_feature_" + str(i) for i in range(cols)]
X_pd = pd.DataFrame(X, columns=feature_names) X_pd = pd.DataFrame(X, columns=feature_names)
X_pd.iloc[:, 3] = X_pd.iloc[:, 3].astype(np.int32) X_pd[f"test_feature_{3}"] = X_pd.iloc[:, 3].astype(np.int32)
Xy = xgb.DMatrix(X_pd, y) Xy = xgb.DMatrix(X_pd, y)
assert Xy.feature_types[3] == "int" assert Xy.feature_types[3] == "int"

11
tests/python/test_with_pandas.py
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@ -77,7 +77,10 @@ class TestPandas:
np.testing.assert_array_equal(result, exp) np.testing.assert_array_equal(result, exp)
dm = xgb.DMatrix(dummies) dm = xgb.DMatrix(dummies)
assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z'] assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z']
assert dm.feature_types == ['int', 'int', 'int', 'int'] if int(pd.__version__[0]) >= 2:
assert dm.feature_types == ['int', 'i', 'i', 'i']
else:
assert dm.feature_types == ['int', 'int', 'int', 'int']
assert dm.num_row() == 3 assert dm.num_row() == 3
assert dm.num_col() == 4 assert dm.num_col() == 4
@ -298,14 +301,14 @@ class TestPandas:
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix]) @pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
def test_nullable_type(self, DMatrixT) -> None: def test_nullable_type(self, DMatrixT) -> None:
from pandas.api.types import is_categorical from pandas.api.types import is_categorical_dtype
for orig, df in pd_dtypes(): for orig, df in pd_dtypes():
if hasattr(df.dtypes, "__iter__"): if hasattr(df.dtypes, "__iter__"):
enable_categorical = any(is_categorical for dtype in df.dtypes) enable_categorical = any(is_categorical_dtype for dtype in df.dtypes)
else: else:
# series # series
enable_categorical = is_categorical(df.dtype) enable_categorical = is_categorical_dtype(df.dtype)
f0_orig = orig[orig.columns[0]] if isinstance(orig, pd.DataFrame) else orig f0_orig = orig[orig.columns[0]] if isinstance(orig, pd.DataFrame) else orig
f0 = df[df.columns[0]] if isinstance(df, pd.DataFrame) else df f0 = df[df.columns[0]] if isinstance(df, pd.DataFrame) else df