60526100e3
- Use pandas extension type for pyarrow support. - Additional support for QDM. - Additional support for inplace_predict.
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
import os
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import xgboost as xgb
|
|
from xgboost import testing as tm
|
|
|
|
try:
|
|
import pandas as pd
|
|
import pyarrow as pa
|
|
import pyarrow.csv as pc
|
|
except ImportError:
|
|
pass
|
|
|
|
pytestmark = pytest.mark.skipif(
|
|
tm.no_arrow()["condition"] or tm.no_pandas()["condition"],
|
|
reason=tm.no_arrow()["reason"] + " or " + tm.no_pandas()["reason"],
|
|
)
|
|
|
|
dpath = "demo/data/"
|
|
|
|
|
|
class TestArrowTable:
|
|
def test_arrow_table(self):
|
|
df = pd.DataFrame(
|
|
[[0, 1, 2.0, 3.0], [1, 2, 3.0, 4.0]], columns=["a", "b", "c", "d"]
|
|
)
|
|
table = pa.Table.from_pandas(df)
|
|
dm = xgb.DMatrix(table)
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 4
|
|
|
|
def test_arrow_table_with_label(self):
|
|
df = pd.DataFrame([[1, 2.0, 3.0], [2, 3.0, 4.0]], columns=["a", "b", "c"])
|
|
table = pa.Table.from_pandas(df)
|
|
label = np.array([0, 1])
|
|
dm = xgb.DMatrix(table)
|
|
dm.set_label(label)
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 3
|
|
np.testing.assert_array_equal(dm.get_label(), np.array([0, 1]))
|
|
|
|
def test_arrow_table_from_np(self):
|
|
coldata = np.array(
|
|
[[1.0, 1.0, 0.0, 0.0], [2.0, 0.0, 1.0, 0.0], [3.0, 0.0, 0.0, 1.0]]
|
|
)
|
|
cols = list(map(pa.array, coldata))
|
|
table = pa.Table.from_arrays(cols, ["a", "b", "c"])
|
|
dm = xgb.DMatrix(table)
|
|
assert dm.num_row() == 4
|
|
assert dm.num_col() == 3
|
|
|
|
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
|
|
def test_arrow_train(self, DMatrixT):
|
|
import pandas as pd
|
|
|
|
rows = 100
|
|
X = pd.DataFrame(
|
|
{
|
|
"A": np.random.randint(0, 10, size=rows),
|
|
"B": np.random.randn(rows),
|
|
"C": np.random.permutation([1, 0] * (rows // 2)),
|
|
}
|
|
)
|
|
y = pd.Series(np.random.randn(rows))
|
|
|
|
table = pa.Table.from_pandas(X)
|
|
dtrain1 = DMatrixT(table)
|
|
dtrain1.set_label(pa.Table.from_pandas(pd.DataFrame(y)))
|
|
bst1 = xgb.train({}, dtrain1, num_boost_round=10)
|
|
preds1 = bst1.predict(DMatrixT(X))
|
|
|
|
dtrain2 = DMatrixT(X, y)
|
|
bst2 = xgb.train({}, dtrain2, num_boost_round=10)
|
|
preds2 = bst2.predict(DMatrixT(X))
|
|
|
|
np.testing.assert_allclose(preds1, preds2)
|
|
|
|
preds3 = bst2.inplace_predict(table)
|
|
np.testing.assert_allclose(preds1, preds3)
|
|
assert bst2.feature_names == ["A", "B", "C"]
|
|
assert bst2.feature_types == ["int", "float", "int"]
|
|
|
|
def test_arrow_survival(self):
|
|
data = os.path.join(tm.data_dir(__file__), "veterans_lung_cancer.csv")
|
|
table = pc.read_csv(data)
|
|
y_lower_bound = table["Survival_label_lower_bound"]
|
|
y_upper_bound = table["Survival_label_upper_bound"]
|
|
X = table.drop(["Survival_label_lower_bound", "Survival_label_upper_bound"])
|
|
|
|
dtrain = xgb.DMatrix(
|
|
X, label_lower_bound=y_lower_bound, label_upper_bound=y_upper_bound
|
|
)
|
|
y_np_up = dtrain.get_float_info("label_upper_bound")
|
|
y_np_low = dtrain.get_float_info("label_lower_bound")
|
|
np.testing.assert_equal(y_np_up, y_upper_bound.to_pandas().values)
|
|
np.testing.assert_equal(y_np_low, y_lower_bound.to_pandas().values)
|