* 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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@ -578,7 +578,7 @@ class TestModels:
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y = rng.randn(rows)
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y = rng.randn(rows)
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feature_names = ["test_feature_" + str(i) for i in range(cols)]
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feature_names = ["test_feature_" + str(i) for i in range(cols)]
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X_pd = pd.DataFrame(X, columns=feature_names)
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X_pd = pd.DataFrame(X, columns=feature_names)
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X_pd.iloc[:, 3] = X_pd.iloc[:, 3].astype(np.int32)
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X_pd[f"test_feature_{3}"] = X_pd.iloc[:, 3].astype(np.int32)
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Xy = xgb.DMatrix(X_pd, y)
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Xy = xgb.DMatrix(X_pd, y)
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assert Xy.feature_types[3] == "int"
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assert Xy.feature_types[3] == "int"
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@ -75,7 +75,10 @@ class TestPandas:
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np.testing.assert_array_equal(result, exp)
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np.testing.assert_array_equal(result, exp)
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dm = xgb.DMatrix(dummies)
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dm = xgb.DMatrix(dummies)
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assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z']
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assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z']
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assert dm.feature_types == ['int', 'int', 'int', 'int']
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if int(pd.__version__[0]) >= 2:
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assert dm.feature_types == ['int', 'i', 'i', 'i']
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else:
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assert dm.feature_types == ['int', 'int', 'int', 'int']
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assert dm.num_row() == 3
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assert dm.num_row() == 3
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assert dm.num_col() == 4
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assert dm.num_col() == 4
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