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More general predict proba. (#6817)

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* Use `output_margin` for `softmax`.
* Add test for dask binary cls.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2021-04-01 19:52:12 +08:00
committed by GitHub
parent a5c852660b
commit 47b62480af
4 changed files with 72 additions and 40 deletions
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45
tests/python/test_with_dask.py
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@@ -318,14 +318,17 @@ def run_dask_classifier(
w: xgb.dask._DaskCollection,
model: str,
client: "Client",
n_classes,
) -> None:
metric = "merror" if n_classes > 2 else "logloss"
if model == "boosting":
classifier = xgb.dask.DaskXGBClassifier(
verbosity=1, n_estimators=2, eval_metric="merror"
verbosity=1, n_estimators=2, eval_metric=metric
)
else:
classifier = xgb.dask.DaskXGBRFClassifier(
verbosity=1, n_estimators=2, eval_metric="merror"
verbosity=1, n_estimators=2, eval_metric=metric
)
assert classifier._estimator_type == "classifier"
@@ -343,7 +346,7 @@ def run_dask_classifier(
assert isinstance(history, dict)
assert list(history.keys())[0] == "validation_0"
assert list(history["validation_0"].keys())[0] == "merror"
assert list(history["validation_0"].keys())[0] == metric
assert len(list(history["validation_0"])) == 1
forest = int(
json.loads(classifier.get_booster().save_config())["learner"][
@@ -351,34 +354,35 @@ def run_dask_classifier(
]["gbtree_train_param"]["num_parallel_tree"]
)
if model == "boosting":
assert len(history["validation_0"]["merror"]) == 2
assert len(history["validation_0"][metric]) == 2
assert forest == 1
else:
assert len(history["validation_0"]["merror"]) == 1
assert len(history["validation_0"][metric]) == 1
assert forest == 2
# Test .predict_proba()
probas = classifier.predict_proba(X).compute()
assert classifier.n_classes_ == 10
assert classifier.n_classes_ == n_classes
assert probas.ndim == 2
assert probas.shape[0] == kRows
assert probas.shape[1] == 10
assert probas.shape[1] == n_classes
cls_booster = classifier.get_booster()
single_node_proba = cls_booster.inplace_predict(X.compute())
if n_classes > 2:
cls_booster = classifier.get_booster()
single_node_proba = cls_booster.inplace_predict(X.compute())
# test shared by CPU and GPU
if isinstance(single_node_proba, np.ndarray):
np.testing.assert_allclose(single_node_proba, probas)
else:
import cupy
cupy.testing.assert_allclose(single_node_proba, probas)
# test shared by CPU and GPU
if isinstance(single_node_proba, np.ndarray):
np.testing.assert_allclose(single_node_proba, probas)
else:
import cupy
cupy.testing.assert_allclose(single_node_proba, probas)
# Test with dataframe, not shared with GPU as cupy doesn't work well with da.unique.
if isinstance(X, da.Array):
if isinstance(X, da.Array) and n_classes > 2:
X_d: dd.DataFrame = X.to_dask_dataframe()
assert classifier.n_classes_ == 10
assert classifier.n_classes_ == n_classes
prediction_df = classifier.predict(X_d).compute()
assert prediction_df.ndim == 1
@@ -393,7 +397,12 @@ def run_dask_classifier(
def test_dask_classifier(model: str, client: "Client") -> None:
X, y, w = generate_array(with_weights=True)
y = (y * 10).astype(np.int32)
run_dask_classifier(X, y, w, model, client)
run_dask_classifier(X, y, w, model, client, 10)
y_bin = y.copy()
y_bin[y > 5] = 1.0
y_bin[y <= 5] = 0.0
run_dask_classifier(X, y_bin, w, model, client, 2)
@pytest.mark.skipif(**tm.no_sklearn())