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Fix weighted samples in multi-class AUC. (#7300)

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This commit is contained in:
Jiaming Yuan
2021-10-11 15:12:29 +08:00
committed by GitHub
parent 69d3b1b8b4
commit 298af6f409
6 changed files with 41 additions and 17 deletions
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34
tests/python/test_eval_metrics.py
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@@ -191,11 +191,11 @@ class TestEvalMetrics:
np.testing.assert_allclose(skl_auc, auc, rtol=1e-6)
@pytest.mark.skipif(**tm.no_sklearn())
@pytest.mark.parametrize("n_samples", [4, 100, 1000])
@pytest.mark.parametrize("n_samples", [100, 1000])
def test_roc_auc(self, n_samples):
self.run_roc_auc_binary("hist", n_samples)
def run_roc_auc_multi(self, tree_method, n_samples):
def run_roc_auc_multi(self, tree_method, n_samples, weighted):
import numpy as np
from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score
@@ -213,8 +213,14 @@ class TestEvalMetrics:
n_classes=n_classes,
random_state=rng
)
if weighted:
weights = rng.randn(n_samples)
weights -= weights.min()
weights /= weights.max()
else:
weights = None
Xy = xgb.DMatrix(X, y)
Xy = xgb.DMatrix(X, y, weight=weights)
booster = xgb.train(
{
"tree_method": tree_method,
@@ -226,16 +232,22 @@ class TestEvalMetrics:
num_boost_round=8,
)
score = booster.predict(Xy)
skl_auc = roc_auc_score(y, score, average="weighted", multi_class="ovr")
skl_auc = roc_auc_score(
y, score, average="weighted", sample_weight=weights, multi_class="ovr"
)
auc = float(booster.eval(Xy).split(":")[1])
np.testing.assert_allclose(skl_auc, auc, rtol=1e-6)
X = rng.randn(*X.shape)
score = booster.predict(xgb.DMatrix(X))
skl_auc = roc_auc_score(y, score, average="weighted", multi_class="ovr")
auc = float(booster.eval(xgb.DMatrix(X, y)).split(":")[1])
np.testing.assert_allclose(skl_auc, auc, rtol=1e-6)
score = booster.predict(xgb.DMatrix(X, weight=weights))
skl_auc = roc_auc_score(
y, score, average="weighted", sample_weight=weights, multi_class="ovr"
)
auc = float(booster.eval(xgb.DMatrix(X, y, weight=weights)).split(":")[1])
np.testing.assert_allclose(skl_auc, auc, rtol=1e-5)
@pytest.mark.parametrize("n_samples", [4, 100, 1000])
def test_roc_auc_multi(self, n_samples):
self.run_roc_auc_multi("hist", n_samples)
@pytest.mark.parametrize(
"n_samples,weighted", [(4, False), (100, False), (1000, False), (1000, True)]
)
def test_roc_auc_multi(self, n_samples, weighted):
self.run_roc_auc_multi("hist", n_samples, weighted)