问使用对数损失函数的sklearn.linear_model.SGDClassifier运行时间非常长

EN

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDClassifier

pipeline_sgdlogreg = Pipeline([
    ('imputer', Imputer(copy=False)), # Mean imputation by default
    ('scaler', StandardScaler(copy=False)),
    ('model', SGDClassifier(loss='log', max_iter=1000, tol=1e-3, random_state=1, warm_start=True))
])


param_grid_sgdlogreg = {
    'model__alpha': [10**-5, 10**-2, 10**1],
    'model__penalty': ['l1', 'l2']
}

grid_sgdlogreg = GridSearchCV(estimator=pipeline_sgdlogreg, param_grid=param_grid_sgdlogreg, 
                              scoring='roc_auc', n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, 
                              verbose=1, return_train_score=False)
grid_sgdlogreg.fit(X_train, y_train)

GridSearchCV(cv=5, error_score='raise',
       estimator=Pipeline(memory=None,
       steps=[('imputer', Imputer(axis=0, copy=False, missing_values='NaN', strategy='mean', verbose=0)), 
              ('scaler', StandardScaler(copy=False, with_mean=True, with_std=True)), 
              ('model', SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
       penalty='l2', power_t=0.5, random_state=1, shuffle=True,
       tol=0.001, verbose=0, warm_start=True))]),
       fit_params=None, iid=True, n_jobs=-1,
       param_grid={'model__alpha': [1e-05, 0.01, 10], 'model__penalty': ['l1', 'l2']},
       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
       scoring='roc_auc', verbose=1)
grid_sgdlogreg.best_score_
grid_sgdlogreg.best_params_