More in-memory input support for column split (#9685)
This commit is contained in:
Rong Ou
GitHub
@@ -1,3 +1,4 @@
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import sys
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from typing import Type
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import numpy as np
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@@ -6,6 +7,7 @@ from test_dmatrix import set_base_margin_info
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import xgboost as xgb
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from xgboost import testing as tm
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from xgboost.core import DataSplitMode
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from xgboost.testing.data import pd_arrow_dtypes, pd_dtypes
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try:
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@@ -17,114 +19,194 @@ except ImportError:
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pytestmark = pytest.mark.skipif(**tm.no_pandas())
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dpath = 'demo/data/'
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dpath = "demo/data/"
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rng = np.random.RandomState(1994)
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class TestPandas:
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def test_pandas(self):
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df = pd.DataFrame([[1, 2., True], [2, 3., False]],
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columns=['a', 'b', 'c'])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['a', 'b', 'c']
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assert dm.feature_types == ['int', 'float', 'i']
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def test_pandas(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["a", "b", "c"]
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assert dm.feature_types == ["int", "float", "i"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["a", "b", "c"], world_size
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)
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assert dm.feature_types == ["int", "float", "i"] * world_size
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assert dm.num_col() == 3 * world_size
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np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
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# overwrite feature_names and feature_types
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]),
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feature_names=['x', 'y', 'z'],
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feature_types=['q', 'q', 'q'])
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assert dm.feature_names == ['x', 'y', 'z']
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assert dm.feature_types == ['q', 'q', 'q']
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dm = xgb.DMatrix(
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df,
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label=pd.Series([1, 2]),
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feature_names=["x", "y", "z"],
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feature_types=["q", "q", "q"],
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data_split_mode=data_split_mode,
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)
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["x", "y", "z"]
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assert dm.feature_types == ["q", "q", "q"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["x", "y", "z"], world_size
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)
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assert dm.feature_types == ["q", "q", "q"] * world_size
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assert dm.num_col() == 3 * world_size
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# incorrect dtypes
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df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
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columns=['a', 'b', 'c'])
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df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
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with pytest.raises(ValueError):
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xgb.DMatrix(df)
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xgb.DMatrix(df, data_split_mode=data_split_mode)
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# numeric columns
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df = pd.DataFrame([[1, 2., True], [2, 3., False]])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['0', '1', '2']
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assert dm.feature_types == ['int', 'float', 'i']
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df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["0", "1", "2"]
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assert dm.feature_types == ["int", "float", "i"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["0", "1", "2"], world_size
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)
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assert dm.feature_types == ["int", "float", "i"] * world_size
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assert dm.num_col() == 3 * world_size
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np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
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df = pd.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['4', '5', '6']
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assert dm.feature_types == ['int', 'float', 'int']
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df = pd.DataFrame([[1, 2.0, 1], [2, 3.0, 1]], columns=[4, 5, 6])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["4", "5", "6"]
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assert dm.feature_types == ["int", "float", "int"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["4", "5", "6"], world_size
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)
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assert dm.feature_types == ["int", "float", "int"] * world_size
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assert dm.num_col() == 3 * world_size
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df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
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df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
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dummies = pd.get_dummies(df)
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# B A_X A_Y A_Z
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# 0 1 1 0 0
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# 1 2 0 1 0
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# 2 3 0 0 1
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result, _, _ = xgb.data._transform_pandas_df(dummies,
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enable_categorical=False)
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exp = np.array([[1., 1., 0., 0.],
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[2., 0., 1., 0.],
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[3., 0., 0., 1.]])
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result, _, _ = xgb.data._transform_pandas_df(dummies, enable_categorical=False)
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exp = np.array(
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[[1.0, 1.0, 0.0, 0.0], [2.0, 0.0, 1.0, 0.0], [3.0, 0.0, 0.0, 1.0]]
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)
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np.testing.assert_array_equal(result, exp)
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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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if int(pd.__version__[0]) >= 2:
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assert dm.feature_types == ['int', 'i', 'i', 'i']
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dm = xgb.DMatrix(dummies, data_split_mode=data_split_mode)
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assert dm.num_row() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["B", "A_X", "A_Y", "A_Z"]
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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_col() == 4
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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_col() == 4
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assert dm.feature_names == tm.column_split_feature_names(
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["B", "A_X", "A_Y", "A_Z"], world_size
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)
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if int(pd.__version__[0]) >= 2:
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assert dm.feature_types == ["int", "i", "i", "i"] * world_size
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else:
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assert dm.feature_types == ["int", "int", "int", "int"] * world_size
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assert dm.num_col() == 4 * world_size
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df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
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dm = xgb.DMatrix(df)
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assert dm.feature_names == ['A=1', 'A=2']
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assert dm.feature_types == ['int', 'int']
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df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
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dm = xgb.DMatrix(df, data_split_mode=data_split_mode)
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["A=1", "A=2"]
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assert dm.feature_types == ["int", "int"]
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assert dm.num_col() == 2
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["A=1", "A=2"], world_size
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)
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assert dm.feature_types == ["int", "int"] * world_size
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assert dm.num_col() == 2 * world_size
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df_int = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
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dm_int = xgb.DMatrix(df_int)
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dm_int = xgb.DMatrix(df_int, data_split_mode=data_split_mode)
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df_range = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1))
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dm_range = xgb.DMatrix(df_range)
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assert dm_int.feature_names == ['9', '10'] # assert not "9 "
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dm_range = xgb.DMatrix(df_range, data_split_mode=data_split_mode)
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if data_split_mode == DataSplitMode.ROW:
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assert dm_int.feature_names == ["9", "10"] # assert not "9 "
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else:
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assert dm_int.feature_names == tm.column_split_feature_names(
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["9", "10"], world_size
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)
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assert dm_int.feature_names == dm_range.feature_names
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# test MultiIndex as columns
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df = pd.DataFrame(
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[
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(1, 2, 3, 4, 5, 6),
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(6, 5, 4, 3, 2, 1)
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],
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columns=pd.MultiIndex.from_tuples((
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('a', 1), ('a', 2), ('a', 3),
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('b', 1), ('b', 2), ('b', 3),
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))
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[(1, 2, 3, 4, 5, 6), (6, 5, 4, 3, 2, 1)],
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columns=pd.MultiIndex.from_tuples(
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(
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("a", 1),
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("a", 2),
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("a", 3),
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("b", 1),
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("b", 2),
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("b", 3),
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)
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),
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)
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dm = xgb.DMatrix(df)
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assert dm.feature_names == ['a 1', 'a 2', 'a 3', 'b 1', 'b 2', 'b 3']
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assert dm.feature_types == ['int', 'int', 'int', 'int', 'int', 'int']
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dm = xgb.DMatrix(df, data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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assert dm.num_col() == 6
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"]
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assert dm.feature_types == ["int", "int", "int", "int", "int", "int"]
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assert dm.num_col() == 6
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"], world_size
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)
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assert (
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dm.feature_types
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== ["int", "int", "int", "int", "int", "int"] * world_size
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)
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assert dm.num_col() == 6 * world_size
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# test Index as columns
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df = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=pd.Index([1, 2]))
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Xy = xgb.DMatrix(df)
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np.testing.assert_equal(np.array(Xy.feature_names), np.array(["1", "2"]))
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Xy = xgb.DMatrix(df, data_split_mode=data_split_mode)
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if data_split_mode == DataSplitMode.ROW:
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np.testing.assert_equal(np.array(Xy.feature_names), np.array(["1", "2"]))
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else:
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np.testing.assert_equal(
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np.array(Xy.feature_names),
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np.array(tm.column_split_feature_names(["1", "2"], world_size)),
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)
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# test pandas series
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data_series = pd.Series([1, 2, 3, 4, 5])
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dm = xgb.DMatrix(data_series, data_split_mode=data_split_mode)
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assert dm.num_row() == 5
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if data_split_mode == DataSplitMode.ROW:
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assert dm.num_col() == 1
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else:
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assert dm.num_col() == 1 * world_size
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def test_slice(self):
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rng = np.random.RandomState(1994)
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rows = 100
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X = rng.randint(3, 7, size=rows)
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X = pd.DataFrame({'f0': X})
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X = pd.DataFrame({"f0": X})
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y = rng.randn(rows)
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ridxs = [1, 2, 3, 4, 5, 6]
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m = xgb.DMatrix(X, y)
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@@ -132,15 +214,16 @@ class TestPandas:
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assert m.feature_types == sliced.feature_types
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def test_pandas_categorical(self):
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def test_pandas_categorical(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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rng = np.random.RandomState(1994)
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rows = 100
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X = rng.randint(3, 7, size=rows)
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X = pd.Series(X, dtype="category")
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X = pd.DataFrame({'f0': X})
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X = pd.DataFrame({"f0": X})
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y = rng.randn(rows)
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m = xgb.DMatrix(X, y, enable_categorical=True)
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assert m.feature_types[0] == 'c'
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m = xgb.DMatrix(X, y, enable_categorical=True, data_split_mode=data_split_mode)
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assert m.feature_types[0] == "c"
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X_0 = ["f", "o", "o"]
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X_1 = [4, 3, 2]
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@@ -159,22 +242,29 @@ class TestPandas:
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assert not np.any(arr == -1.0)
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X = X["f0"]
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y = y[:X.shape[0]]
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y = y[: X.shape[0]]
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with pytest.raises(ValueError, match=r".*enable_categorical.*"):
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xgb.DMatrix(X, y)
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xgb.DMatrix(X, y, data_split_mode=data_split_mode)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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Xy = xgb.DMatrix(X, y, enable_categorical=True, data_split_mode=data_split_mode)
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assert Xy.num_row() == 3
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assert Xy.num_col() == 1
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if data_split_mode == DataSplitMode.ROW:
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assert Xy.num_col() == 1
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else:
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assert Xy.num_col() == 1 * world_size
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def test_pandas_sparse(self):
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import pandas as pd
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rows = 100
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X = pd.DataFrame(
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{"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
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"B": pd.arrays.SparseArray(np.random.randn(rows)),
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"C": pd.arrays.SparseArray(np.random.permutation(
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[True, False] * (rows // 2)))}
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{
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"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
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"B": pd.arrays.SparseArray(np.random.randn(rows)),
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"C": pd.arrays.SparseArray(
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np.random.permutation([True, False] * (rows // 2))
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),
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}
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)
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y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
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dtrain = xgb.DMatrix(X, y)
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@@ -183,27 +273,36 @@ class TestPandas:
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predt_dense = booster.predict(xgb.DMatrix(X.sparse.to_dense()))
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np.testing.assert_allclose(predt_sparse, predt_dense)
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def test_pandas_label(self):
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def test_pandas_label(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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# label must be a single column
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df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
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df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
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with pytest.raises(ValueError):
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xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
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xgb.data._transform_pandas_df(df, False, None, None, "label", "float")
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# label must be supported dtype
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df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
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df = pd.DataFrame({"A": np.array(["a", "b", "c"], dtype=object)})
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with pytest.raises(ValueError):
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xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
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xgb.data._transform_pandas_df(df, False, None, None, "label", "float")
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df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
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result, _, _ = xgb.data._transform_pandas_df(df, False, None, None,
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'label', 'float')
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np.testing.assert_array_equal(result, np.array([[1.], [2.], [3.]],
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dtype=float))
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dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
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df = pd.DataFrame({"A": np.array([1, 2, 3], dtype=int)})
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result, _, _ = xgb.data._transform_pandas_df(
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df, False, None, None, "label", "float"
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)
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np.testing.assert_array_equal(
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result, np.array([[1.0], [2.0], [3.0]], dtype=float)
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)
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dm = xgb.DMatrix(
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np.random.randn(3, 2), label=df, data_split_mode=data_split_mode
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)
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.num_col() == 2
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else:
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assert dm.num_col() == 2 * world_size
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def test_pandas_weight(self):
|
||||
def test_pandas_weight(self, data_split_mode=DataSplitMode.ROW):
|
||||
world_size = xgb.collective.get_world_size()
|
||||
kRows = 32
|
||||
kCols = 8
|
||||
|
||||
@@ -211,11 +310,13 @@ class TestPandas:
|
||||
y = np.random.randn(kRows)
|
||||
w = np.random.uniform(size=kRows).astype(np.float32)
|
||||
w_pd = pd.DataFrame(w)
|
||||
data = xgb.DMatrix(X, y, weight=w_pd)
|
||||
data = xgb.DMatrix(X, y, weight=w_pd, data_split_mode=data_split_mode)
|
||||
|
||||
assert data.num_row() == kRows
|
||||
assert data.num_col() == kCols
|
||||
|
||||
if data_split_mode == DataSplitMode.ROW:
|
||||
assert data.num_col() == kCols
|
||||
else:
|
||||
assert data.num_col() == kCols * world_size
|
||||
np.testing.assert_array_equal(data.get_weight(), w)
|
||||
|
||||
def test_base_margin(self):
|
||||
@@ -223,81 +324,128 @@ class TestPandas:
|
||||
|
||||
def test_cv_as_pandas(self):
|
||||
dm, _ = tm.load_agaricus(__file__)
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic', 'eval_metric': 'error'}
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
"eval_metric": "error",
|
||||
}
|
||||
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
|
||||
assert isinstance(cv, pd.DataFrame)
|
||||
exp = pd.Index([u'test-error-mean', u'test-error-std',
|
||||
u'train-error-mean', u'train-error-std'])
|
||||
exp = pd.Index(
|
||||
["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
|
||||
)
|
||||
assert len(cv.columns.intersection(exp)) == 4
|
||||
|
||||
# show progress log (result is the same as above)
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
verbose_eval=True)
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, verbose_eval=True)
|
||||
assert isinstance(cv, pd.DataFrame)
|
||||
exp = pd.Index([u'test-error-mean', u'test-error-std',
|
||||
u'train-error-mean', u'train-error-std'])
|
||||
exp = pd.Index(
|
||||
["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
|
||||
)
|
||||
assert len(cv.columns.intersection(exp)) == 4
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
verbose_eval=True, show_stdv=False)
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, verbose_eval=True, show_stdv=False
|
||||
)
|
||||
assert isinstance(cv, pd.DataFrame)
|
||||
exp = pd.Index([u'test-error-mean', u'test-error-std',
|
||||
u'train-error-mean', u'train-error-std'])
|
||||
exp = pd.Index(
|
||||
["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
|
||||
)
|
||||
assert len(cv.columns.intersection(exp)) == 4
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic', 'eval_metric': 'auc'}
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
"eval_metric": "auc",
|
||||
}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
|
||||
assert 'eval_metric' in params
|
||||
assert 'auc' in cv.columns[0]
|
||||
assert "eval_metric" in params
|
||||
assert "auc" in cv.columns[0]
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic', 'eval_metric': ['auc']}
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
"eval_metric": ["auc"],
|
||||
}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
|
||||
assert 'eval_metric' in params
|
||||
assert 'auc' in cv.columns[0]
|
||||
assert "eval_metric" in params
|
||||
assert "auc" in cv.columns[0]
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic', 'eval_metric': ['auc']}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, early_stopping_rounds=1)
|
||||
assert 'eval_metric' in params
|
||||
assert 'auc' in cv.columns[0]
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
"eval_metric": ["auc"],
|
||||
}
|
||||
cv = xgb.cv(
|
||||
params,
|
||||
dm,
|
||||
num_boost_round=10,
|
||||
nfold=10,
|
||||
as_pandas=True,
|
||||
early_stopping_rounds=1,
|
||||
)
|
||||
assert "eval_metric" in params
|
||||
assert "auc" in cv.columns[0]
|
||||
assert cv.shape[0] < 10
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic'}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, metrics='auc')
|
||||
assert 'auc' in cv.columns[0]
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
}
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics="auc"
|
||||
)
|
||||
assert "auc" in cv.columns[0]
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic'}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, metrics=['auc'])
|
||||
assert 'auc' in cv.columns[0]
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
}
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["auc"]
|
||||
)
|
||||
assert "auc" in cv.columns[0]
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
|
||||
'objective': 'binary:logistic', 'eval_metric': ['auc']}
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, metrics='error')
|
||||
assert 'eval_metric' in params
|
||||
assert 'auc' not in cv.columns[0]
|
||||
assert 'error' in cv.columns[0]
|
||||
params = {
|
||||
"max_depth": 2,
|
||||
"eta": 1,
|
||||
"verbosity": 0,
|
||||
"objective": "binary:logistic",
|
||||
"eval_metric": ["auc"],
|
||||
}
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics="error"
|
||||
)
|
||||
assert "eval_metric" in params
|
||||
assert "auc" not in cv.columns[0]
|
||||
assert "error" in cv.columns[0]
|
||||
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, metrics=['error'])
|
||||
assert 'eval_metric' in params
|
||||
assert 'auc' not in cv.columns[0]
|
||||
assert 'error' in cv.columns[0]
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["error"]
|
||||
)
|
||||
assert "eval_metric" in params
|
||||
assert "auc" not in cv.columns[0]
|
||||
assert "error" in cv.columns[0]
|
||||
|
||||
params = list(params.items())
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
|
||||
as_pandas=True, metrics=['error'])
|
||||
cv = xgb.cv(
|
||||
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["error"]
|
||||
)
|
||||
assert isinstance(params, list)
|
||||
assert 'auc' not in cv.columns[0]
|
||||
assert 'error' in cv.columns[0]
|
||||
assert "auc" not in cv.columns[0]
|
||||
assert "error" in cv.columns[0]
|
||||
|
||||
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
|
||||
def test_nullable_type(self, DMatrixT) -> None:
|
||||
@@ -358,3 +506,60 @@ class TestPandas:
|
||||
if y is not None:
|
||||
np.testing.assert_allclose(m_orig.get_label(), m_etype.get_label())
|
||||
np.testing.assert_allclose(m_etype.get_label(), y.values)
|
||||
|
||||
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
||||
def test_pandas_column_split(self):
|
||||
tm.run_with_rabit(
|
||||
world_size=3, test_fn=self.test_pandas, data_split_mode=DataSplitMode.COL
|
||||
)
|
||||
|
||||
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
||||
def test_pandas_categorical_column_split(self):
|
||||
tm.run_with_rabit(
|
||||
world_size=3,
|
||||
test_fn=self.test_pandas_categorical,
|
||||
data_split_mode=DataSplitMode.COL,
|
||||
)
|
||||
|
||||
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
||||
def test_pandas_sparse_column_split(self):
|
||||
rows = 100
|
||||
X = pd.DataFrame(
|
||||
{
|
||||
"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
|
||||
"B": pd.arrays.SparseArray(np.random.randn(rows)),
|
||||
"C": pd.arrays.SparseArray(
|
||||
np.random.permutation([True, False] * (rows // 2))
|
||||
),
|
||||
}
|
||||
)
|
||||
y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
|
||||
|
||||
def verify_pandas_sparse():
|
||||
dtrain = xgb.DMatrix(X, y, data_split_mode=DataSplitMode.COL)
|
||||
booster = xgb.train({}, dtrain, num_boost_round=4)
|
||||
predt_sparse = booster.predict(
|
||||
xgb.DMatrix(X, data_split_mode=DataSplitMode.COL)
|
||||
)
|
||||
predt_dense = booster.predict(
|
||||
xgb.DMatrix(X.sparse.to_dense(), data_split_mode=DataSplitMode.COL)
|
||||
)
|
||||
np.testing.assert_allclose(predt_sparse, predt_dense)
|
||||
|
||||
tm.run_with_rabit(world_size=3, test_fn=verify_pandas_sparse)
|
||||
|
||||
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
||||
def test_pandas_label_column_split(self):
|
||||
tm.run_with_rabit(
|
||||
world_size=3,
|
||||
test_fn=self.test_pandas_label,
|
||||
data_split_mode=DataSplitMode.COL,
|
||||
)
|
||||
|
||||
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
||||
def test_pandas_weight_column_split(self):
|
||||
tm.run_with_rabit(
|
||||
world_size=3,
|
||||
test_fn=self.test_pandas_weight,
|
||||
data_split_mode=DataSplitMode.COL,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user