对数据进行二项式逻辑回归分类（准确度百分之百），并将数据结果可视化

import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error,r2_score


data = pd.read_csv("./examdata.csv",encoding="gbk")
data.head()
x1 = data.loc[:,"Exam1"]
x2 = data.loc[:,"Exam2"]
x3 = data.drop(['Pass'],axis=1)
y = data.loc[:,"Pass"]
mask = data.loc[:,"Pass"]==1

x11 = np.array(x1).reshape(-1,1)
x22 = np.array(x2).reshape(-1,1)

LR = LogisticRegression()
LR.fit(x3,y)
y_predict = LR.predict(x3)
y_test = LR.predict([[40,100]])
print('ok'if y_test == 1 else 'ng')
accuray = accuracy_score(y,y_predict)
print(accuray)


# t0 = LR.intercept_
# t1,t2=LR.coef_[0][0],LR.coef_[0][1]
# x2_new = -(t0+t1*x1)/t2

x1_2 = x1*x1
x2_2 = x2*x2
x1_x2 = x1*x2
##创建二阶边界函数
x_new = {'x11':x1,'x22':x2,'x1_2 ':x1_2 ,'x2_2':x2_2,'x1_x2':x1_x2}
x_new = pd.DataFrame(x_new)
print(x_new)
LR2 = LogisticRegression()
LR2.fit(x_new,y)
# print(t0)
t0 = LR2.intercept_
t1 = LR2.coef_[0][0]
t2 = LR2.coef_[0][1]
t3 = LR2.coef_[0][2]
t4 = LR2.coef_[0][3]
t5 = LR2.coef_[0][4]
print(t1,t2)
print(t3,t4,t5)
y2_predict = LR2.predict(x_new)
print(y2_predict)
accuray2 = accuracy_score(y,y2_predict)
print(accuray2)

x1_new = x1.sort_values()#排序
print(x1_new)

a = t4
b = t5*x1_new + t2
c = t0 + t1 *x1_new + t3 *x1_new*x1_new
x2_new_2 = (-b+np.sqrt(b*b-4*a*c))/(2*a)
print(x2_new_2)


fig = plt.figure()
# # plt.subplot(1,2,1)
ok = plt.scatter(x1[mask],x2[mask])
ng = plt.scatter(x1[~mask],x2[~mask])
plt.legend((ok,ng),('ok','ng'))
plt.plot(x1_new,x2_new_2)
# # plt.subplot(1,2,2)
# # plt.scatter(x1,x2,c='b',marker="s")
# # plt.xlabel("test1")
# # plt.ylabel("test2")
plt.show()

 结果展示：

数据： 

Exam1	Exam2	Pass
34.62365962	78.02469282	0
30.28671077	43.89499752	0
35.84740877	72.90219803	0
60.18259939	86.3085521	1
79.03273605	75.34437644	1
45.08327748	56.31637178	0
61.10666454	96.51142588	1
75.02474557	46.55401354	1
76.0987867	87.42056972	1
84.43281996	43.53339331	1
95.86155507	38.22527806	0
75.01365839	30.60326323	0
82.30705337	76.4819633	1
69.36458876	97.71869196	1
39.53833914	76.03681085	0
53.97105215	89.20735014	1
69.07014406	52.74046973	1
67.94685548	46.67857411	0
70.66150955	92.92713789	1
76.97878373	47.57596365	1
67.37202755	42.83843832	0
89.67677575	65.79936593	1
50.53478829	48.85581153	0
34.21206098	44.2095286	0
77.92409145	68.97235999	1
62.27101367	69.95445795	1
80.19018075	44.82162893	1
93.1143888	38.80067034	0
61.83020602	50.25610789	0
38.7858038	64.99568096	0
61.37928945	72.80788731	1
85.40451939	57.05198398	1
52.10797973	63.12762377	0
52.04540477	69.43286012	1
40.23689374	71.16774802	0
54.63510555	52.21388588	0
33.91550011	98.86943574	0
64.17698887	80.90806059	1
74.78925296	41.57341523	0
34.18364003	75.23772034	0
83.90239366	56.30804622	1
51.54772027	46.85629026	0
94.44336777	65.56892161	1
82.36875376	40.61825516	0
51.04775177	45.82270146	0
62.22267576	52.06099195	0
77.19303493	70.4582	1
97.77159928	86.72782233	1
62.0730638	96.76882412	1
91.5649745	88.69629255	1
79.94481794	74.16311935	1
99.27252693	60.999031	1
90.54671411	43.39060181	1
34.52451385	60.39634246	0
50.28649612	49.80453881	0
49.58667722	59.80895099	0
97.64563396	68.86157272	1
32.57720017	95.59854761	0
74.24869137	69.82457123	1
71.79646206	78.45356225	1
75.39561147	85.75993667	1
35.28611282	47.02051395	0
56.2538175	39.26147251	0
30.05882245	49.59297387	0
44.66826172	66.45008615	0
66.56089447	41.09209808	0
40.45755098	97.53518549	1
49.07256322	51.88321182	0
80.27957401	92.11606081	1
66.74671857	60.99139403	1
32.72283304	43.30717306	0
64.03932042	78.03168802	1
72.34649423	96.22759297	1
60.45788574	73.0949981	1
58.84095622	75.85844831	1
99.8278578	72.36925193	1
47.26426911	88.475865	1
50.4581598	75.80985953	1
60.45555629	42.50840944	0
82.22666158	42.71987854	0
88.91389642	69.8037889	1
94.83450672	45.6943068	1
67.31925747	66.58935318	1
57.23870632	59.51428198	1
80.366756	90.9601479	1
68.46852179	85.5943071	1
42.07545454	78.844786	0
75.47770201	90.424539	1
78.63542435	96.64742717	1
52.34800399	60.76950526	0
94.09433113	77.15910509	1
90.44855097	87.50879176	1
55.48216114	35.57070347	0
74.49269242	84.84513685	1
89.84580671	45.35828361	1
83.48916274	48.3802858	1
42.26170081	87.10385094	1
99.31500881	68.77540947	1
55.34001756	64.93193801	1
74.775893	89.5298129	1