模型評估
import pickle
from matplotlib import pyplot as plt
from sklearn.externals import joblib
from sklearn.metrics import accuracy_score, recall_score, f1_score, roc_auc_score, roc_curve
path = "E:/mypython/Machine_learning_GoGoGo/"
"""=====================================================================================================================
1 讀取特徵
"""
print("0 讀取特徵")
f = open(path + 'feature/feature_V1.pkl', 'rb')
train, test, y_train,y_test= pickle.load(f)
f.close()
"""=====================================================================================================================
2 讀取模型
"""
print("1 讀取模型")
SVM_linear = joblib.load( path + "model/SVM_linear.pkl")
SVM_rbf = joblib.load( path + "model/SVM_rbf.pkl")
SVM_sigmoid = joblib.load( path + "model/SVM_sigmoid.pkl")
lg_120 = joblib.load( path + "model/lg_120.pkl")
DT = joblib.load( path + "model/DT.pkl")
xgb_sklearn = joblib.load( path + "model/xgb_sklearn.pkl")
lgb_sklearn = joblib.load( path + "model/lgb_sklearn.pkl")
xgb = joblib.load( path + "model/xgb.pkl")
lgb = joblib.load( path + "model/lgb.pkl")
"""=====================================================================================================================
3 模型評估
"""
def model_evalua(clf, X_train, X_test, y_train, y_test,name):
y_train_pred = clf.predict(X_train)
y_test_pred = clf.predict(X_test)
y_train_pred_proba = clf.predict_proba(X_train)[:, 1]
y_test_pred_proba = clf.predict_proba(X_test)[:, 1]
"""【AUC Score】"""
print(name+'_AUC Score')
print(name+"_Train_AUC Score ：{:.4f}".format(roc_auc_score(y_train, y_train_pred)))
print(name+"_Test_AUC Score ：{:.4f}".format(roc_auc_score(y_test, y_test_pred)))
"""【準確性】"""
print(name+'_準確性：')
print(name+'_Train_準確性：{:.4f}'.format(accuracy_score(y_train, y_train_pred)))
print(name+'_Test_準確性：{:.4f}'.format(accuracy_score(y_test, y_test_pred)))
"""【召回率】"""
print(name+'_召回率：')
print(name+'_Train_召回率：{:.4f}'.format(recall_score(y_train, y_train_pred)))
print(name+'_Test_召回率：{:.4f}'.format(recall_score(y_test, y_test_pred)))
"""【f1_score】"""
print(name+'_f1_score：')
print(name+'_Train_f1_score：{:.4f}'.format(f1_score(y_train, y_train_pred)))
print(name+'_Test_f1_score：{:.4f}'.format(f1_score(y_test, y_test_pred)))
#描繪 ROC 曲線
fpr_tr, tpr_tr, _ = roc_curve(y_train, y_train_pred_proba)
fpr_te, tpr_te, _ = roc_curve(y_test, y_test_pred_proba)
# KS
print(name+'_KS：')
print(name+'_Train：{:.4f}'.format(max(abs((fpr_tr - tpr_tr)))))
print(name+'_Test：{:.4f}'.format(max(abs((fpr_te - tpr_te)))))
plt.plot(fpr_tr, tpr_tr, 'r-',
label = name+"_Train:AUC: {:.3f} KS:{:.3f}".format(roc_auc_score(y_train, y_train_pred_proba),
max(abs((fpr_tr - tpr_tr)))))
plt.plot(fpr_te, tpr_te, 'g-',
label=name+"_Test:AUC: {:.3f} KS:{:.3f}".format(roc_auc_score(y_test, y_test_pred_proba),
max(abs((fpr_tr - tpr_tr)))))
plt.plot([0, 1], [0, 1], 'd--')
plt.legend(loc='best')
plt.title(name+"_ROC curse")
plt.savefig(path +'picture/'+name+'.jpg')
plt.show()
print('-------------------SVM_linear-------------------')
model_evalua(SVM_linear, train, test, y_train, y_test,'SVM_linear')
print('-------------------SVM_rbf-------------------：')
model_evalua(SVM_rbf, train, test, y_train, y_test,'SVM_rbf')
print('-------------------SVM_sigmoid-------------------：')
model_evalua(SVM_sigmoid, train, test, y_train, y_test,'SVM_sigmoid')
print('-------------------lg_120-------------------')
model_evalua(lg_120, train, test, y_train, y_test,'lg_120')
print('-------------------DT-------------------')
model_evalua(DT, train, test, y_train, y_test,'DT')
print('-------------------xgb_sklearn-------------------')
model_evalua(xgb_sklearn, train, test, y_train, y_test,'xgb_sklearn')
# print('-------------------xgb-------------------')
# model_evalua(xgb, train, test, y_train, y_test)
print('-------------------lgb_sklearn-------------------')
model_evalua(lgb_sklearn, train, test, y_train, y_test,'lgb_sklearn')
# print('-------------------lgb-------------------')
# model_evalua(lgb, train, test, y_train, y_test)
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作者：lgy54321
來源：CSDN
原文：blog.csdn.net/lgy54321/ar…
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