#!/user/bin/env python3
# -*- coding: utf-8 -*-
# 常用的科学运算库
import numpy as np
# 常用于画图包含 数据集分布情况、训练后的模型训练情况
import matplotlib.pyplot as plt
# 官方数据库
from sklearn.datasets import load_iris
# 数据集拆分
from sklearn.model_selection import train_test_split
# 导入模型（机器学习一般都是定义好的模型较多，包括不限于随机森林、决策树、线性回归、逻辑回归）
from sklearn.linear_model import LinearRegression
# 导出模型训练结果的损失函数\得分函数
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report


iris = load_iris()
X = iris.data[:,:2]
y = (iris.target != 0) * 1

X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.2,random_state=42)
model = LinearRegression()
model.fit(X_train,Y_train)
y_pred = model.predict(X_test)
acc = accuracy_score(Y_test,y_pred)
print(f"精确率:{acc}")
matrix = confusion_matrix(Y_test,y_pred)
print(f"混淆矩阵:{matrix}")