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[breaking] Add prediction fucntion for DMatrix and use inplace predict for dask. (#6668)

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* Add a new API function for predicting on `DMatrix`.  This function aligns
with rest of the `XGBoosterPredictFrom*` functions on semantic of function
arguments.
* Purge `ntree_limit` from libxgboost, use iteration instead.
* [dask] Use `inplace_predict` by default for dask sklearn models.
* [dask] Run prediction shape inference on worker instead of client.

The breaking change is in the Python sklearn `apply` function, I made it to be
consistent with other prediction functions where `best_iteration` is used by
default.
This commit is contained in:
Jiaming Yuan
2021-02-08 18:26:32 +08:00
committed by GitHub
parent dbb5208a0a
commit 4656b09d5d
29 changed files with 1134 additions and 604 deletions
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15
tests/python-gpu/test_from_cupy.py
View File

@@ -112,17 +112,24 @@ def _test_cupy_metainfo(DMatrixT):
@pytest.mark.skipif(**tm.no_sklearn())
def test_cupy_training_with_sklearn():
import cupy as cp
np.random.seed(1)
cp.random.seed(1)
X = cp.random.randn(50, 10, dtype='float32')
y = (cp.random.randn(50, dtype='float32') > 0).astype('int8')
X = cp.random.randn(50, 10, dtype="float32")
y = (cp.random.randn(50, dtype="float32") > 0).astype("int8")
weights = np.random.random(50) + 1
cupy_weights = cp.array(weights)
base_margin = np.random.random(50)
cupy_base_margin = cp.array(base_margin)
clf = xgb.XGBClassifier(gpu_id=0, tree_method='gpu_hist', use_label_encoder=False)
clf.fit(X, y, sample_weight=cupy_weights, base_margin=cupy_base_margin, eval_set=[(X, y)])
clf = xgb.XGBClassifier(gpu_id=0, tree_method="gpu_hist", use_label_encoder=False)
clf.fit(
X,
y,
sample_weight=cupy_weights,
base_margin=cupy_base_margin,
eval_set=[(X, y)],
)
pred = clf.predict(X)
assert np.array_equal(np.unique(pred), np.array([0, 1]))

68
tests/python-gpu/test_gpu_with_dask.py
View File

@@ -16,13 +16,15 @@ if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
sys.path.append("tests/python")
from test_with_dask import run_empty_dmatrix_reg # noqa
from test_with_dask import run_empty_dmatrix_cls # noqa
from test_with_dask import _get_client_workers # noqa
from test_with_dask import generate_array # noqa
from test_with_dask import kCols as random_cols # noqa
from test_with_dask import suppress # noqa
import testing as tm # noqa
from test_with_dask import run_empty_dmatrix_reg # noqa
from test_with_dask import run_boost_from_prediction # noqa
from test_with_dask import run_dask_classifier # noqa
from test_with_dask import run_empty_dmatrix_cls # noqa
from test_with_dask import _get_client_workers # noqa
from test_with_dask import generate_array # noqa
from test_with_dask import kCols as random_cols # noqa
from test_with_dask import suppress # noqa
import testing as tm # noqa
try:
@@ -132,9 +134,9 @@ def run_gpu_hist(
num_rounds: int,
dataset: tm.TestDataset,
DMatrixT: Type,
client: Client
client: Client,
) -> None:
params['tree_method'] = 'gpu_hist'
params["tree_method"] = "gpu_hist"
params = dataset.set_params(params)
# It doesn't make sense to distribute a completely
# empty dataset.
@@ -143,26 +145,40 @@ def run_gpu_hist(
chunk = 128
X = to_cp(dataset.X, DMatrixT)
X = da.from_array(X,
chunks=(chunk, dataset.X.shape[1]))
X = da.from_array(X, chunks=(chunk, dataset.X.shape[1]))
y = to_cp(dataset.y, DMatrixT)
y = da.from_array(y, chunks=(chunk, ))
y = da.from_array(y, chunks=(chunk,))
if dataset.w is not None:
w = to_cp(dataset.w, DMatrixT)
w = da.from_array(w, chunks=(chunk, ))
w = da.from_array(w, chunks=(chunk,))
else:
w = None
if DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
m = DMatrixT(client, data=X, label=y, weight=w,
max_bin=params.get('max_bin', 256))
m = DMatrixT(
client, data=X, label=y, weight=w, max_bin=params.get("max_bin", 256)
)
else:
m = DMatrixT(client, data=X, label=y, weight=w)
history = dxgb.train(client, params=params, dtrain=m,
num_boost_round=num_rounds,
evals=[(m, 'train')])['history']
history = dxgb.train(
client,
params=params,
dtrain=m,
num_boost_round=num_rounds,
evals=[(m, "train")],
)["history"]
note(history)
assert tm.non_increasing(history['train'][dataset.metric])
assert tm.non_increasing(history["train"][dataset.metric])
def test_boost_from_prediction(local_cuda_cluster: LocalCUDACluster) -> None:
import cudf
from sklearn.datasets import load_breast_cancer
with Client(local_cuda_cluster) as client:
X_, y_ = load_breast_cancer(return_X_y=True)
X = dd.from_array(X_, chunksize=100).map_partitions(cudf.from_pandas)
y = dd.from_array(y_, chunksize=100).map_partitions(cudf.from_pandas)
run_boost_from_prediction(X, y, "gpu_hist", client)
class TestDistributedGPU:
@@ -246,6 +262,20 @@ class TestDistributedGPU:
dump = booster.get_dump(dump_format='json')
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.parametrize("model", ["boosting"])
def test_dask_classifier(self, model, local_cuda_cluster: LocalCUDACluster) -> None:
import dask_cudf
with Client(local_cuda_cluster) as client:
X_, y_, w_ = generate_array(with_weights=True)
y_ = (y_ * 10).astype(np.int32)
X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X_))
y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y_))
w = dask_cudf.from_dask_dataframe(dd.from_dask_array(w_))
run_dask_classifier(X, y, w, model, client)
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.mgpu