我在对数据集进行10折交叉验证的SVM分析，使用不同的C和gamma值
from sklearn.datasets import load_digits, load_iris, load_breast_cancer, load_winefrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.utils import shufflefrom sklearn import preprocessingfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.model_selection import StratifiedKFoldfrom sklearn.metrics import accuracy_score, zero_one_loss, confusion_matriximport pandas as pdimport numpy as npz = pd.read_csv('/home/user/iris.csv', header=0)X = z.iloc[:, :-1]y = z.iloc[:, -1:]X = np.array(X)y = np.array(y)# 执行标准化处理scaler = preprocessing.MinMaxScaler()X_scaled = scaler.fit_transform(X)c = [0.1, 0.5]gamma_values = [1e-1, 1] for z in c: for v in gamma_values: # 定义使用'rbf'核的SVM svc = SVC(kernel='rbf',C=z, gamma=v, random_state=50) skf = StratifiedKFold(n_splits=10, shuffle=True) acc_score = [] #skf.get_n_splits(X, y) for train_index, test_index in skf.split(X, y): X_train, X_test = X_scaled[train_index], X_scaled[test_index] y_train, y_test = y[train_index], y[test_index] # 训练模型 svc.fit(X_train, np.ravel(y_train)) # 在测试数据上进行预测 y_pred = svc.predict(X_test) # 获得模型的准确性得分 score = accuracy_score(y_test, y_pred) acc_score.append(score) print(np.array(acc_score)) #打印每个C值的准确性得分 print('Mean accuracy score: %0.3f' % np.array(acc_score).mean())这会产生如下输出
[0.52 0.6 0.49 0.6 0.55 0.6 0.5 0.51 0.63 0.54]Mean accuracy score: 0.554[0.51 0.45 0.54 0.42 0.53 0.45 0.52 0.48 0.5 0.39]Mean accuracy score: 0.479[0.73 0.76 0.7 0.64 0.61 0.68 0.71 0.61 0.71 0.71]Mean accuracy score: 0.686[0.76 0.6 0.66 0.61 0.67 0.66 0.69 0.74 0.63 0.65]Mean accuracy score: 0.667然而，我希望能更有意义地打印结果，如下所示：
[0.52 0.6 0.49 0.6 0.55 0.6 0.5 0.51 0.63 0.54]Mean accuracy score for (C=0.1,gamma=0.1): 0.554[0.51 0.45 0.54 0.42 0.53 0.45 0.52 0.48 0.5 0.39]Mean accuracy score (C=0.1, gamma = 1): 0.479[0.73 0.76 0.7 0.64 0.61 0.68 0.71 0.61 0.71 0.71]Mean accuracy score (C=0.5, gamma = 0.1): 0.686[0.76 0.6 0.66 0.61 0.67 0.66 0.69 0.74 0.63 0.65]Mean accuracy score (C=0.5, gamma = 1): 0.667如何在现有代码中更有意义地打印结果？
回答：
尝试这样做：
# (1)print('Mean accuracy score (C=%0.1f, gamma=%0.1f): %0.3f' % (z, v, np.array(acc_score).mean()))# (2)print("Mean accuracy score (C={}, gamma={}): {}".format(z, v, np.array(acc_score).mean()))# (3)print("Mean accuracy score (C="+str(z)+", gamma="+str(v)+"): "+str(np.array(acc_score).mean()))输出：
Mean accuracy score (C=0.1, gamma=0.1): 0.554