import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
import pydotplus
import matplotlib.pyplot as plt
import graphviz
# from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc, precision_score, recall_score, f1_score, accuracy_score

（1）KNN

clf = KNeighborsClassifier(algorithm='kd_tree', n_neighbors=8)
clf.fit(a, b)

（2）RF

clf = RandomForestClassifier(n_estimators=9, max_depth=7, min_samples_split=3, random_state=0)
clf.fit(a, b)

（3）DT

clf = tree.DecisionTreeClassifier(criterion='gini', max_depth=7, min_samples_leaf=4, min_samples_split=4)
clf.fit(a, b)

（4）SVM

clf = svm.SVC(kernel='rbf', decision_function_shape='ovr', probability=True)
clf.fit(a, b)

检测结果分析TP、FP、ACC、Precision、Recall、F!等

if __name__ == "__main__":
dataSet = pd.read_excel("link_states_train_medium.xlsx")
labelsp = list(dataSet.columns.values)

labels = labelsp[0:4]

clf = fit_model(dataSet, labels)
data = pd.read_excel("link_states_test_medium.xlsx")

y_true = data.iloc[:, -1]
y_pred = clf.predict(data.iloc[:, :-1])
prob = clf.predict_proba(data.iloc[:, :-1])[:, 1]
print(prob.tolist())

tp = sum(y_true & y_pred)
fp = sum((y_true == 0) & (y_pred == 1))
tn = sum((y_true == 0) & (y_pred == 0))
fn = sum((y_true == 1) & (y_pred == 0))
tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
tnr = tn / (fp + tn)
fnr = fn / (tp + fn)
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1_score = f1_score(y_true, y_pred)

print("TP: " + str(tp))
print("FP: " + str(fp))
print("TN: " + str(tn))
print("FN: " + str(fn))
print("TPR: " + str(tpr))
print("FPR: " + str(fpr))
print("TNR: " + str(tnr))
print("FNR: " + str(fnr))
print("Accuracy: " + str(accuracy))
print("Precision: " + str(precision))
print("Recall: " + str(recall))
print("F1 Score: " + str(f1_score))

print(y_true.tolist())
# print(y_pred.tolist())

fpr, tpr, thresholds_keras = roc_curve(y_true, prob)
auc = auc(fpr, tpr)
print("AUC : ", auc)
plt.figure()
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr, tpr, label='SVM (area = {:.3f})'.format(auc))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
# plt.savefig("../images/ROC/ROC_2分类.png")
plt.show()