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import numpy as np
import pandas as pd
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']
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_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")
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