直接上代码，因为都是函数都是ppt里面的也没有太多解释，具体看注释即可
from sklearn import datasets
from sklearn import model_selection
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
import matplotlib.pyplot as plt
# 旧版
# from sklearn import cross_validation
NB_accuracy = []
NB_f1_score = []
NB_auc_roc = []
SVM_accuracy = []
SVM_f1_score = []
SVM_auc_roc = []
RAMF_accuracy = []
RAMF_f1_score = []
RAMF_auc_roc = []
def evaluate(y_test, pred):
acc = metrics.accuracy_score(y_test, pred)
print('\tAccuracy:', acc)
f1 = metrics.f1_score(y_test, pred)
print('\tF1-score:', f1)
auc = metrics.roc_auc_score(y_test, pred)
print('\tAUC ROC:', auc)
return acc, f1, auc
# Train the algorithm
# Naive Bayes
def NaiveBayesTrain(X_train, y_train, X_test):
clf = GaussianNB()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
return pred
# SVM
def SVMTrain(X_train, y_train, X_test):
clf = SVC(C=1e-01, kernel='rbf', gamma=0.1)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
return pred
# Random Forest
def RandomForestTrain(X_train, y_train, X_test):
clf = RandomForestClassifier(n_estimators=10)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
return pred
# Create a classification dataset (n samples >= 1000, n features >= 10)
dataset = datasets.make_classification(
n_samples=1000, n_features=10, n_informative=2, n_redundant=2,
n_repeated=0, n_classes=2)
X = dataset[0]
y = dataset[1]
# Split the dataset using 10-fold cross validation
# 新版写法
index = 1
kf = model_selection.KFold(n_splits=10)
for tran_index, test_index in kf.split(X):
X_train, X_test = X[tran_index], X[test_index]
y_train, y_test = y[tran_index], y[test_index]
# Evaluate the cross-validated performance
print('train', index)
index += 1
print('Naive Bayes:')
acc, f1, auc = evaluate(y_test, NaiveBayesTrain(X_train, y_train, X_test))
NB_accuracy.append(acc)
NB_f1_score.append(f1)
NB_auc_roc.append(auc)
print('SVM:')
acc, f1, auc = evaluate(y_test, SVMTrain(X_train, y_train, X_test))
SVM_accuracy.append(acc)
SVM_f1_score.append(f1)
SVM_auc_roc.append(auc)
print('Random Forest:')
acc, f1, auc = evaluate(
y_test, RandomForestTrain(X_train, y_train, X_test))
RAMF_accuracy.append(acc)
RAMF_f1_score.append(f1)
RAMF_auc_roc.append(auc)
print()
# 旧版写法
# kf = cross_validation.KFold(len(X), n_folds=10)
# for tran_index, test_index in kf:
# X_train, X_test = X[tran_index], X[test_index]
# y_train, y_test = y[tran_index], y[test_index]
# NB_accuracy = []
# NB_f1_score = []
# NB_auc_roc = []
# SVM_accuracy = []
# SVM_f1_score = []
# SVM_auc_roc = []
# RAMF_accuracy = []
# RAMF_f1_score = []
# RAMF_auc_roc = []
x = [i + 1 for i in range(len(NB_accuracy))]
plt.figure()
plt.plot(x, NB_accuracy, label='NB_accuracy')
plt.plot(x, SVM_accuracy, label='SVM_accuracy')
plt.plot(x, RAMF_accuracy, label='RAMF_accuracy')
plt.xlabel('train')
plt.ylabel('value')
plt.title('Accuracy')
plt.legend(loc='upper left')
plt.savefig('Accuracy.png')
plt.figure()
plt.plot(x, NB_f1_score, label='NB_f1_score')
plt.plot(x, SVM_f1_score, label='SVM_f1_score')
plt.plot(x, RAMF_f1_score, label='RAMF_f1_score')
plt.xlabel('train')
plt.ylabel('value')
plt.title('F1_score')
plt.legend(loc='upper left')
plt.savefig('F1_score.png')
plt.figure()
plt.plot(x, NB_auc_roc, label='NB_auc_roc')
plt.plot(x, SVM_auc_roc, label='SVM_auc_roc')
plt.plot(x, RAMF_auc_roc, label='RAMF_auc_roc')
plt.xlabel('train')
plt.ylabel('value')
plt.title('Auc_roc')
plt.legend(loc='upper left')
plt.savefig('Auc_roc.png')注意我的程序会打印出每次训练后每个算法的accuracy，f1 score，auc roc，并且最后会画图作比较并且图片自动保存在当前目录，结果如下：
train 1
Naive Bayes:
Accuracy: 0.9
F1-score: 0.9056603773584905
AUC ROC: 0.8991596638655462
SVM:
Accuracy: 0.94
F1-score: 0.9433962264150944
AUC ROC: 0.9391756702681072
Random Forest:
Accuracy: 0.97
F1-score: 0.9702970297029702
AUC ROC: 0.9701880752300921
train 2
Naive Bayes:
Accuracy: 0.78
F1-score: 0.8135593220338982
AUC ROC: 0.7698898408812729
SVM:
Accuracy: 0.85
F1-score: 0.8717948717948718
AUC ROC: 0.8427172582619339
Random Forest:
Accuracy: 0.92
F1-score: 0.9310344827586206
AUC ROC: 0.9155446756425948
train 3
Naive Bayes:
Accuracy: 0.8
F1-score: 0.8
AUC ROC: 0.8012820512820513
SVM:
Accuracy: 0.77
F1-score: 0.7628865979381444
AUC ROC: 0.7700320512820513
Random Forest:
Accuracy: 0.8
F1-score: 0.7916666666666666
AUC ROC: 0.7996794871794872
train 4
Naive Bayes:
Accuracy: 0.85
F1-score: 0.854368932038835
AUC ROC: 0.8525641025641024
SVM:
Accuracy: 0.89
F1-score: 0.8952380952380952
AUC ROC: 0.8934294871794872
Random Forest:
Accuracy: 0.91
F1-score: 0.910891089108911
AUC ROC: 0.9118589743589745
train 5
Naive Bayes:
Accuracy: 0.87
F1-score: 0.8686868686868686
AUC ROC: 0.8725411481332798
SVM:
Accuracy: 0.92
F1-score: 0.9183673469387754
AUC ROC: 0.9221196306704136
Random Forest:
Accuracy: 0.98
F1-score: 0.9791666666666666
AUC ROC: 0.9811320754716981
train 6
Naive Bayes:
Accuracy: 0.82
F1-score: 0.85
AUC ROC: 0.8102521703183133
SVM:
Accuracy: 0.84
F1-score: 0.8666666666666666
AUC ROC: 0.8309218685407193
Random Forest:
Accuracy: 0.91
F1-score: 0.9217391304347826
AUC ROC: 0.9125671765192229
train 7
Naive Bayes:
Accuracy: 0.94
F1-score: 0.9387755102040817
AUC ROC: 0.9421918908069049
SVM:
Accuracy: 0.96
F1-score: 0.9591836734693878
AUC ROC: 0.9622641509433962
Random Forest:
Accuracy: 0.98
F1-score: 0.9787234042553191
AUC ROC: 0.9799277398635086
train 8
Naive Bayes:
Accuracy: 0.85
F1-score: 0.8571428571428572
AUC ROC: 0.85
SVM:
Accuracy: 0.88
F1-score: 0.888888888888889
AUC ROC: 0.88
Random Forest:
Accuracy: 0.9
F1-score: 0.9056603773584904
AUC ROC: 0.8999999999999999
train 9
Naive Bayes:
Accuracy: 0.89
F1-score: 0.8952380952380952
AUC ROC: 0.89
SVM:
Accuracy: 0.9
F1-score: 0.9038461538461539
AUC ROC: 0.8999999999999999
Random Forest:
Accuracy: 0.95
F1-score: 0.9484536082474226
AUC ROC: 0.95
train 10
Naive Bayes:
Accuracy: 0.88
F1-score: 0.875
AUC ROC: 0.8918808649530804
SVM:
Accuracy: 0.87
F1-score: 0.8571428571428572
AUC ROC: 0.8745410036719705
Random Forest:
Accuracy: 0.9
F1-score: 0.8809523809523809
AUC ROC: 0.8951448388412893
[Finished in 2.6s]对结果画图：
由上面三幅图来看，可以明显看出，从accuracy，f1 score，auc roc这三个值的比较得到，性能最高的是Random Forest，然后到SVM，最后到Naive Bayes。