xgboost/tests/python/test_dt.py
Jiaming Yuan e07245f110
Take datatable as row major input. (#8472)
* Take datatable as row major input.

Try to avoid a transform with dense table.
2022-11-24 09:20:13 +08:00

42 lines
1.3 KiB
Python

import numpy as np
import pytest
import xgboost as xgb
dt = pytest.importorskip("datatable")
pd = pytest.importorskip("pandas")
class TestDataTable:
def test_dt(self) -> None:
df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
dtable = dt.Frame(df)
labels = dt.Frame([1, 2])
dm = xgb.DMatrix(dtable, label=labels)
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(np.array([1, 2]), dm.get_label())
# overwrite feature_names
dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]), feature_names=["x", "y", "z"])
assert dm.feature_names == ["x", "y", "z"]
assert dm.num_row() == 2
assert dm.num_col() == 3
# incorrect dtypes
df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
dtable = dt.Frame(df)
with pytest.raises(ValueError):
xgb.DMatrix(dtable)
df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
dtable = dt.Frame(df)
dm = xgb.DMatrix(dtable)
assert dm.feature_names == ["A=1", "A=2"]
assert dm.feature_types == ["int", "int"]
assert dm.num_row() == 3
assert dm.num_col() == 2