grid_search.py 文件源码

def LogisticRegression(X_train, y_train):
    from sklearn.linear_model import LogisticRegression
    parameters = {
        'C':[0.6, 0.8, 1.0, 1.2],
        'class_weight':[None, 'balanced'],
    }

    LR = LogisticRegression()
    grid_search = GridSearchCV(estimator=LR, param_grid=parameters, cv=5, scoring='neg_log_loss',n_jobs=4)

    now = datetime.datetime.now()
    print ("logestic regression grid_search start in " + now.strftime('%Y-%m-%d %H:%M:%S'))

    grid_search.fit(X_train, y_train)
    print ("logestic regression grid_search done in " + now.strftime('%Y-%m-%d %H:%M:%S'))

    results = grid_search.grid_scores_
    for result in results:
        print(result)
    print("\nBest score: %0.3f\n" % grid_search.best_score_)
    print ("---------best parameters---------")
    best_parameters = grid_search.best_estimator_.get_params()
    for param_name in sorted(parameters.keys()):
        print ("%s: %r" % (param_name, best_parameters[param_name]))