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Support categorical data for dask functional interface and DQM. (#7043)

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* Support categorical data for dask functional interface and DQM.

* Implement categorical data support for GPU GK-merge.
* Add support for dask functional interface.
* Add support for DQM.

* Get newer cupy.
This commit is contained in:
Jiaming Yuan
2021-06-18 13:06:52 +08:00
committed by GitHub
parent 7dd29ffd47
commit 86715e4cd4
16 changed files with 364 additions and 167 deletions
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47
tests/python-gpu/test_from_cudf.py
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@@ -225,19 +225,28 @@ class IterForDMatrixTest(xgb.core.DataIter):
ROWS_PER_BATCH = 100 # data is splited by rows
BATCHES = 16
def __init__(self):
def __init__(self, categorical):
'''Generate some random data for demostration.
Actual data can be anything that is currently supported by XGBoost.
'''
import cudf
self.rows = self.ROWS_PER_BATCH
rng = np.random.RandomState(1994)
self._data = [
cudf.DataFrame(
{'a': rng.randn(self.ROWS_PER_BATCH),
'b': rng.randn(self.ROWS_PER_BATCH)})] * self.BATCHES
self._labels = [rng.randn(self.rows)] * self.BATCHES
if categorical:
self._data = []
self._labels = []
for i in range(self.BATCHES):
X, y = tm.make_categorical(self.ROWS_PER_BATCH, 4, 13, False)
self._data.append(cudf.from_pandas(X))
self._labels.append(y)
else:
rng = np.random.RandomState(1994)
self._data = [
cudf.DataFrame(
{'a': rng.randn(self.ROWS_PER_BATCH),
'b': rng.randn(self.ROWS_PER_BATCH)})] * self.BATCHES
self._labels = [rng.randn(self.rows)] * self.BATCHES
self.it = 0 # set iterator to 0
super().__init__()
@@ -272,24 +281,26 @@ class IterForDMatrixTest(xgb.core.DataIter):
@pytest.mark.skipif(**tm.no_cudf())
def test_from_cudf_iter():
@pytest.mark.parametrize("enable_categorical", [True, False])
def test_from_cudf_iter(enable_categorical):
rounds = 100
it = IterForDMatrixTest()
it = IterForDMatrixTest(enable_categorical)
params = {"tree_method": "gpu_hist"}
# Use iterator
m_it = xgb.DeviceQuantileDMatrix(it)
reg_with_it = xgb.train({'tree_method': 'gpu_hist'}, m_it,
num_boost_round=rounds)
predict_with_it = reg_with_it.predict(m_it)
m_it = xgb.DeviceQuantileDMatrix(it, enable_categorical=enable_categorical)
reg_with_it = xgb.train(params, m_it, num_boost_round=rounds)
# Without using iterator
m = xgb.DMatrix(it.as_array(), it.as_array_labels())
X = it.as_array()
y = it.as_array_labels()
m = xgb.DMatrix(X, y, enable_categorical=enable_categorical)
assert m_it.num_col() == m.num_col()
assert m_it.num_row() == m.num_row()
reg = xgb.train({'tree_method': 'gpu_hist'}, m,
num_boost_round=rounds)
predict = reg.predict(m)
reg = xgb.train(params, m, num_boost_round=rounds)
predict = reg.predict(m)
predict_with_it = reg_with_it.predict(m_it)
np.testing.assert_allclose(predict_with_it, predict)