faf0f2df10
- Implement a columnar adapter. - Refactor Python pandas handling code to avoid converting into a single numpy array. - Add support in R for transforming columns. - Support R data.frame and factor type.
149 lines
5.3 KiB
Python
149 lines
5.3 KiB
Python
import numpy as np
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import pytest
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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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try:
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import modin.pandas as md
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except ImportError:
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pass
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pytestmark = pytest.mark.skipif(**tm.no_modin())
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class TestModin:
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@pytest.mark.xfail
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def test_modin(self) -> None:
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df = md.DataFrame([[1, 2., True], [2, 3., False]],
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columns=['a', 'b', 'c'])
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dm = xgb.DMatrix(df, label=md.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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assert dm.num_row() == 2
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assert dm.num_col() == 3
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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=md.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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assert dm.num_row() == 2
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assert dm.num_col() == 3
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# incorrect dtypes
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df = md.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
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columns=['a', 'b', 'c'])
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with pytest.raises(ValueError):
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xgb.DMatrix(df)
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# numeric columns
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df = md.DataFrame([[1, 2., True], [2, 3., False]])
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dm = xgb.DMatrix(df, label=md.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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assert dm.num_row() == 2
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assert dm.num_col() == 3
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np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
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df = md.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
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dm = xgb.DMatrix(df, label=md.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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assert dm.num_row() == 2
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assert dm.num_col() == 3
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df = md.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
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dummies = md.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.]]).T
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np.testing.assert_array_equal(result.columns, 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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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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df = md.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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assert dm.num_row() == 3
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assert dm.num_col() == 2
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df_int = md.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
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dm_int = xgb.DMatrix(df_int)
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df_range = md.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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assert dm_int.feature_names == dm_range.feature_names
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# test MultiIndex as columns
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df = md.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=md.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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)
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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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assert dm.num_row() == 2
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assert dm.num_col() == 6
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def test_modin_label(self):
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# label must be a single column
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df = md.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")
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# label must be supported dtype
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df = md.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")
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df = md.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"
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)
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np.testing.assert_array_equal(
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np.stack(result.columns, axis=1),
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np.array([[1.0], [2.0], [3.0]], dtype=float),
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)
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dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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def test_modin_weight(self):
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kRows = 32
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kCols = 8
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X = np.random.randn(kRows, kCols)
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y = np.random.randn(kRows)
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w = np.random.uniform(size=kRows).astype(np.float32)
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w_pd = md.DataFrame(w)
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data = xgb.DMatrix(X, y, w_pd)
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assert data.num_row() == kRows
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assert data.num_col() == kCols
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np.testing.assert_array_equal(data.get_weight(), w)
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def test_base_margin(self):
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set_base_margin_info(md.DataFrame, xgb.DMatrix, "hist")
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