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Initial support for multioutput regression. (#7514)

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* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.
This commit is contained in:
Jiaming Yuan
2021-12-18 09:28:38 +08:00
committed by GitHub
parent 9ab73f737e
commit 58a6723eb1
22 changed files with 306 additions and 67 deletions
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20
tests/python/test_with_sklearn.py
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@@ -1118,10 +1118,10 @@ def run_boost_from_prediction_binary(tree_method, X, y, as_frame: Optional[Calla
def run_boost_from_prediction_multi_clasas(
tree_method, X, y, as_frame: Optional[Callable]
estimator, tree_method, X, y, as_frame: Optional[Callable]
):
# Multi-class
model_0 = xgb.XGBClassifier(
model_0 = estimator(
learning_rate=0.3, random_state=0, n_estimators=4, tree_method=tree_method
)
model_0.fit(X=X, y=y)
@@ -1129,7 +1129,7 @@ def run_boost_from_prediction_multi_clasas(
if as_frame is not None:
margin = as_frame(margin)
model_1 = xgb.XGBClassifier(
model_1 = estimator(
learning_rate=0.3, random_state=0, n_estimators=4, tree_method=tree_method
)
model_1.fit(X=X, y=y, base_margin=margin)
@@ -1137,7 +1137,7 @@ def run_boost_from_prediction_multi_clasas(
xgb.DMatrix(X, base_margin=margin), output_margin=True
)
model_2 = xgb.XGBClassifier(
model_2 = estimator(
learning_rate=0.3, random_state=0, n_estimators=8, tree_method=tree_method
)
model_2.fit(X=X, y=y)
@@ -1152,8 +1152,9 @@ def run_boost_from_prediction_multi_clasas(
@pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
def test_boost_from_prediction(tree_method):
from sklearn.datasets import load_breast_cancer, load_digits
from sklearn.datasets import load_breast_cancer, load_digits, make_regression
import pandas as pd
X, y = load_breast_cancer(return_X_y=True)
run_boost_from_prediction_binary(tree_method, X, y, None)
@@ -1161,8 +1162,13 @@ def test_boost_from_prediction(tree_method):
X, y = load_digits(return_X_y=True)
run_boost_from_prediction_multi_clasas(tree_method, X, y, None)
run_boost_from_prediction_multi_clasas(tree_method, X, y, pd.DataFrame)
run_boost_from_prediction_multi_clasas(xgb.XGBClassifier, tree_method, X, y, None)
run_boost_from_prediction_multi_clasas(
xgb.XGBClassifier, tree_method, X, y, pd.DataFrame
)
X, y = make_regression(n_samples=100, n_targets=4)
run_boost_from_prediction_multi_clasas(xgb.XGBRegressor, tree_method, X, y, None)
def test_estimator_type():