python 混淆矩陣繪圖

y_predicted=bdt.predict(X_test)
from sklearn.metrics import confusion_matrix
from sklearn import cross_validation, metrics
import matplotlib.pyplot as plt
plt.rc('figure',figsize=(5,5))

cm = confusion_matrix(y_test,y_predicted)
plt.matshow(cm,cmap = plt.cm.Blues) # 背景顏色
plt.colorbar() # 顏色標籤

# 內部添加圖例標籤
for x in range(len(cm)):
    for y in range(len(cm)):
        plt.annotate(cm[x,y], xy = (x,y), horizontalalignment = 'center', verticalalignment = 'center')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.title('Cart_train')
plt.show()
y_predprob = bdt.predict_proba(X_test)[:,1]
print("AUC Score (Train): %f" % metrics.roc_auc_score(y_test, y_predprob))


# 3.1> 畫混淆矩陣
# 方法1：
# from cm_plot import *# 導入自行編寫的混淆矩陣可視化函數 等價於下面的語句
# cm_plot(train[:,3],predict_CartResult).show()#顯示混淆矩陣可視化結果

# 方法2：
# 導入相關庫，生成混淆矩陣
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
plt.rc('figure',figsize=(5,5))

cm = confusion_matrix(train[:,3], predict_CartResult)
plt.matshow(cm,cmap = plt.cm.Blues) # 背景顏色
plt.colorbar() # 顏色標籤

# 內部添加圖例標籤
for x in range(len(cm)):
    for y in range(len(cm)):
        plt.annotate(cm[x,y], xy = (x,y), horizontalalignment = 'center', verticalalignment = 'center')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.title('Cart_train')
plt.show()