你可以自己得分：from sklearn.metrics import make_scorer
from sklearn.metrics import roc_curve, auc
# define scoring function
def custom_auc(ground_truth, predictions):
# I need only one column of predictions[“0” and “1”]. You can get an error here
# while trying to return both columns at once
fpr, tpr, _ = roc_curve(ground_truth, predictions[:, 1], pos_label=1)
return auc(fpr, tpr)
# to be standart sklearn’s scorer
my_auc = make_scorer(custom_auc, greater_is_better=True, needs_proba=True)
pipeline = Pipeline(
[(“transformer”, TruncatedSVD(n_components=70)),
(“classifier”, xgb.XGBClassifier(scale_pos_weight=1.0, learning_rate=0.1,
max_depth=5, n_estimators=50, min_child_weight=5))])
parameters_grid = {‘transformer__n_components’: [60, 40, 20] }
grid_cv = GridSearchCV(pipeline, parameters_grid, scoring = my_auc, n_jobs=-1,
cv = StratifiedShuffleSplit(n_splits=5,test_size=0.3,random_state = 0))
grid_cv.fit(X, y)
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