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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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6
tests/python/test_basic_models.py
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@@ -434,7 +434,13 @@ class TestModels:
booster[...:end] = booster
sliced_0 = booster[1:3]
np.testing.assert_allclose(
booster.predict(dtrain, iteration_range=(1, 3)), sliced_0.predict(dtrain)
)
sliced_1 = booster[3:7]
np.testing.assert_allclose(
booster.predict(dtrain, iteration_range=(3, 7)), sliced_1.predict(dtrain)
)
predt_0 = sliced_0.predict(dtrain, output_margin=True)
predt_1 = sliced_1.predict(dtrain, output_margin=True)