12.4 grid_search.py 文件源码

def test_RandomizedSearchCV():

    '''
    Use RandomizedSearchCV and LogisticRegression, to improve C, multi_class.
    :return:  None
    '''
    digits = load_digits()
    X_train,X_test,y_train,y_test=train_test_split(digits.data, digits.target,
                test_size=0.25,random_state=0,stratify=digits.target)

    tuned_parameters ={  'C': scipy.stats.expon(scale=100),
                        'multi_class': ['ovr','multinomial']}
    clf=RandomizedSearchCV(LogisticRegression(penalty='l2',solver='lbfgs',tol=1e-6),
                        tuned_parameters,cv=10,scoring="accuracy",n_iter=100)
    clf.fit(X_train,y_train)
    print("Best parameters set found:",clf.best_params_)
    print("Randomized Grid scores:")
    for params, mean_score, scores in clf.grid_scores_:
             print("\t%0.3f (+/-%0.03f) for %s" % (mean_score, scores.std() * 2, params))

    print("Optimized Score:",clf.score(X_test,y_test))
    print("Detailed classification report:")
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))