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)

總結(jié)

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