# -*-encoding:utf-8-*-
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
# 读取数据
iris_data_set = pd.read_csv(r"C:Users86156Desktop机器学习iris.csv",encoding='gbk')
# x是4列特征
x = iris_data_set.iloc[:, 0:4].values
# y是1列标签
y = iris_data_set.iloc[:, 4:].values
# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state=5)
# 将特征转为一维数组
y_train = y_train.flatten()
y_test = y_test.flatten()
# 建模、训练、预测
knn_model = KNeighborsClassifier(n_neighbors=10)
knn_model.fit(x_train, y_train)
y_pre = knn_model.predict(x_test)
print("正确标签：", y_test)
print("预测结果：", y_pre)
# 混淆矩阵
conf_mat = confusion_matrix(y_test, y_pre)
print(conf_mat)
# 分类指标文本报告（精确率、召回率、F1值等）
print(classification_report(y_test, y_pre))
