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Fix coef_ and intercept_ signature to be compatible with sklearn.RFECV (#3873)

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* Fix coef_ and intercept_ signature to be compatible with sklearn.RFECV

* Fix lint

* Fix lint
This commit is contained in:
Philip Hyunsu Cho
2018-11-08 19:41:35 -08:00
committed by GitHub
parent 19ee0a3579
commit ad6e0d55f1
2 changed files with 45 additions and 4 deletions
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33
tests/python/test_with_sklearn.py
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@@ -544,3 +544,36 @@ def test_save_load_model():
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
def test_RFECV():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_boston
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_iris
from sklearn.feature_selection import RFECV
# Regression
X, y = load_boston(return_X_y=True)
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='reg:linear',
random_state=0, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_mean_squared_error')
rfecv.fit(X, y)
# Binary classification
X, y = load_breast_cancer(return_X_y=True)
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='binary:logistic',
random_state=0, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='roc_auc')
rfecv.fit(X, y)
# Multi-class classification
X, y = load_iris(return_X_y=True)
bst = xgb.XGBClassifier(base_score=0.4, booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='multi:softprob',
random_state=0, reg_alpha=0.001, reg_lambda=0.01,
scale_pos_weight=0.5, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_log_loss')
rfecv.fit(X, y)