9f73127a23
* Cleanup Python GPU tests. - Remove the use of `gpu_hist` and `gpu_id` in cudf/cupy tests. - Move base margin test into the testing directory.
149 lines
5.3 KiB
Python
149 lines
5.3 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import xgboost as xgb
|
|
from xgboost import testing as tm
|
|
from xgboost.testing.data import run_base_margin_info
|
|
|
|
try:
|
|
import modin.pandas as md
|
|
except ImportError:
|
|
pass
|
|
|
|
|
|
pytestmark = pytest.mark.skipif(**tm.no_modin())
|
|
|
|
|
|
class TestModin:
|
|
@pytest.mark.xfail
|
|
def test_modin(self) -> None:
|
|
df = md.DataFrame([[1, 2., True], [2, 3., False]],
|
|
columns=['a', 'b', 'c'])
|
|
dm = xgb.DMatrix(df, label=md.Series([1, 2]))
|
|
assert dm.feature_names == ['a', 'b', 'c']
|
|
assert dm.feature_types == ['int', 'float', 'i']
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 3
|
|
np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
|
|
|
|
# overwrite feature_names and feature_types
|
|
dm = xgb.DMatrix(df, label=md.Series([1, 2]),
|
|
feature_names=['x', 'y', 'z'],
|
|
feature_types=['q', 'q', 'q'])
|
|
assert dm.feature_names == ['x', 'y', 'z']
|
|
assert dm.feature_types == ['q', 'q', 'q']
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 3
|
|
|
|
# incorrect dtypes
|
|
df = md.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
|
|
columns=['a', 'b', 'c'])
|
|
with pytest.raises(ValueError):
|
|
xgb.DMatrix(df)
|
|
|
|
# numeric columns
|
|
df = md.DataFrame([[1, 2., True], [2, 3., False]])
|
|
dm = xgb.DMatrix(df, label=md.Series([1, 2]))
|
|
assert dm.feature_names == ['0', '1', '2']
|
|
assert dm.feature_types == ['int', 'float', 'i']
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 3
|
|
np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
|
|
|
|
df = md.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
|
|
dm = xgb.DMatrix(df, label=md.Series([1, 2]))
|
|
assert dm.feature_names == ['4', '5', '6']
|
|
assert dm.feature_types == ['int', 'float', 'int']
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 3
|
|
|
|
df = md.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
|
|
dummies = md.get_dummies(df)
|
|
# B A_X A_Y A_Z
|
|
# 0 1 1 0 0
|
|
# 1 2 0 1 0
|
|
# 2 3 0 0 1
|
|
result, _, _ = xgb.data._transform_pandas_df(dummies,
|
|
enable_categorical=False)
|
|
exp = np.array([[1., 1., 0., 0.],
|
|
[2., 0., 1., 0.],
|
|
[3., 0., 0., 1.]]).T
|
|
np.testing.assert_array_equal(result.columns, exp)
|
|
dm = xgb.DMatrix(dummies)
|
|
assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z']
|
|
assert dm.feature_types == ['int', 'int', 'int', 'int']
|
|
assert dm.num_row() == 3
|
|
assert dm.num_col() == 4
|
|
|
|
df = md.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
|
|
dm = xgb.DMatrix(df)
|
|
assert dm.feature_names == ['A=1', 'A=2']
|
|
assert dm.feature_types == ['int', 'int']
|
|
assert dm.num_row() == 3
|
|
assert dm.num_col() == 2
|
|
|
|
df_int = md.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
|
|
dm_int = xgb.DMatrix(df_int)
|
|
df_range = md.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1))
|
|
dm_range = xgb.DMatrix(df_range)
|
|
assert dm_int.feature_names == ['9', '10'] # assert not "9 "
|
|
assert dm_int.feature_names == dm_range.feature_names
|
|
|
|
# test MultiIndex as columns
|
|
df = md.DataFrame(
|
|
[
|
|
(1, 2, 3, 4, 5, 6),
|
|
(6, 5, 4, 3, 2, 1)
|
|
],
|
|
columns=md.MultiIndex.from_tuples((
|
|
('a', 1), ('a', 2), ('a', 3),
|
|
('b', 1), ('b', 2), ('b', 3),
|
|
))
|
|
)
|
|
dm = xgb.DMatrix(df)
|
|
assert dm.feature_names == ['a 1', 'a 2', 'a 3', 'b 1', 'b 2', 'b 3']
|
|
assert dm.feature_types == ['int', 'int', 'int', 'int', 'int', 'int']
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 6
|
|
|
|
def test_modin_label(self):
|
|
# label must be a single column
|
|
df = md.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
|
|
with pytest.raises(ValueError):
|
|
xgb.data._transform_pandas_df(df, False, None, None, "label")
|
|
|
|
# label must be supported dtype
|
|
df = md.DataFrame({"A": np.array(["a", "b", "c"], dtype=object)})
|
|
with pytest.raises(ValueError):
|
|
xgb.data._transform_pandas_df(df, False, None, None, "label")
|
|
|
|
df = md.DataFrame({"A": np.array([1, 2, 3], dtype=int)})
|
|
result, _, _ = xgb.data._transform_pandas_df(
|
|
df, False, None, None, "label"
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.stack(result.columns, axis=1),
|
|
np.array([[1.0], [2.0], [3.0]], dtype=float),
|
|
)
|
|
dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
|
|
assert dm.num_row() == 3
|
|
assert dm.num_col() == 2
|
|
|
|
def test_modin_weight(self):
|
|
kRows = 32
|
|
kCols = 8
|
|
|
|
X = np.random.randn(kRows, kCols)
|
|
y = np.random.randn(kRows)
|
|
w = np.random.uniform(size=kRows).astype(np.float32)
|
|
w_pd = md.DataFrame(w)
|
|
data = xgb.DMatrix(X, y, w_pd)
|
|
|
|
assert data.num_row() == kRows
|
|
assert data.num_col() == kCols
|
|
|
|
np.testing.assert_array_equal(data.get_weight(), w)
|
|
|
|
def test_base_margin(self):
|
|
run_base_margin_info(md.DataFrame, xgb.DMatrix, "cpu")
|