加载数据

数据集：https://www.kaggle.com/datasets/yuanheqiuye/data-class-raw

# 加载数据
import numpy as np 
import pandas as pd 

data = pd.read_csv('/kaggle/input/data-class-raw/data_class_raw.csv')
X = data.drop(columns=['y'])
y = data.loc[:,'y']
data.head()

输出：

	x1	x2	y
0	0.77	3.97	0
1	1.71	2.81	0
2	2.18	1.31	0
3	3.80	0.69	0
4	5.21	1.14	0

可视化数据

from matplotlib import pyplot as plt
fig1 = plt.figure(figsize=(5,5))
plt.scatter(X.loc[:,'x1'][y==0],X.loc[:,'x2'][y==0])
plt.scatter(X.loc[:,'x1'][y==1],X.loc[:,'x2'][y==1])
plt.show()

异常点检测

这里只找出了一个分类的，其他分类异常点可以类似处理

# 找出异常点
from sklearn.covariance import EllipticEnvelope
# 这里只找出了y=0点中的异常点，相同方法可以找出y=1中的异常点
X_bad = X.loc[y==0]
X_good = X.loc[y==1]
model = EllipticEnvelope(contamination=0.02).fit(X_bad)
y_predict = model.predict(X_bad)
print(y_predict)
# 输出：[ 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1]

# 绘图
fig2 = plt.figure(figsize=(5,5))
bad = plt.scatter(X.loc[:,'x1'][y==0],X.loc[:,'x2'][y==0])
good = plt.scatter(X.loc[:,'x1'][y==1],X.loc[:,'x2'][y==1])
plt.scatter(X.loc[:,'x1'][y==0][y_predict==-1],X.loc[:,'x2'][y==0][y_predict==-1],marker='X',s=100)
plt.legend((bad,good),('bad','good'))
plt.show()

PCA降维

经过上一步将异常数据去掉之后，得到处理后的数据集：
https://www.kaggle.com/datasets/yuanheqiuye/data-class-processed
然后进行PCA主成分分析，尝试降维。

# pca降维
# 读取已经处理好的数据
data2 = pd.read_csv('/kaggle/input/data-class-processed/data_class_processed.csv')
data2.head()
X2 = data2.drop(columns=['y'])
y2 = data2.loc[:,'y']

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
X2_norm = StandardScaler().fit_transform(X2)
pca = PCA(n_components=2)
pca.fit(X2_norm)
var_ratio = pca.explained_variance_ratio_
print(var_ratio)

输出：[0.5369408 0.4630592]
两个维度都很关键，不做降维处理。

使用KNN进行分类并可视化

此处先随机取值n_neighbors=10

# 基于KNN进行分类训练
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X_train,X_test,y_train,y_test = train_test_split(X2,y2,random_state=4,test_size=0.4)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(X_train,y_train)
y_train_predict = knn.predict(X_train)
y_test_predict = knn.predict(X_test)
from sklearn.metrics import accuracy_score
acc_train = accuracy_score(y_train, y_train_predict)
acc_test = accuracy_score(y_test, y_test_predict)
print('acc_train=',acc_train,'acc_test=', acc_test)

# 可视化分类边界
# 数据准备
xx,yy = np.meshgrid(np.arange(0,10,0.05),np.arange(0,10,0.05))
xy_origin = np.c_[xx.ravel(),yy.ravel()]
# 因为训练的时候指定了列名，此处也指定，否则会有警告
xy = pd.DataFrame(xy_origin, columns=['x1', 'x2'])
xy_predict = knn.predict(xy)

# 画图
fig2 = plt.figure(figsize=(5,5))
predict_bad = plt.scatter(xy.loc[:,'x1'][xy_predict==0],xy.loc[:,'x2'][xy_predict==0])
predict_good = plt.scatter(xy.loc[:,'x1'][xy_predict==1],xy.loc[:,'x2'][xy_predict==1])
bad = plt.scatter(X2.loc[:,'x1'][y2==0],X2.loc[:,'x2'][y2==0])
good = plt.scatter(X2.loc[:,'x1'][y2==1],X2.loc[:,'x2'][y2==1])

plt.legend((bad,good,predict_bad,predict_good),('bad','good','predict_bad','predict_good'))
plt.show()

计算混淆矩阵

混淆矩阵图：

from sklearn.metrics import confusion_matrix
m = confusion_matrix(y_test,y_test_predict)
print(m)

TN = m[0][0]
FP = m[0][1]
FN = m[1][0]
TP = m[1][1]
print('TN=',TN,'FP=',FP,'FN=',FN,'TP=',TP)

输出：

[[4 2]
 [3 5]]
 
TN= 4 FP= 2 FN= 3 TP= 5

# 准确率：整体样本中预测正确样本的比例 accuracy = (TP+TN)/(TP+TN+FP+FN)
accuracy = (TP+TN)/(TP+TN+FP+FN)
print(accuracy)

# 召回率：正样本中预测真确的比例 recall = TP/(TP+FN)
recall = TP/(TP+FN)
print('recall=',recall)

# 特异度：负样本中预测为负的比例 specificity = TN/(TN+FP)
specificity = TN/(TN+FP)
print('specificity=',specificity)

# 精确度: 预测结果为正的样本中，预测正确的比例 Precition = TP/(TP+FP)
precition = TP/(TP+FP)
print('precition=',precition)

# F1: 综合precition和recall的一个判断指标 F1 = 2 * precition * recall/(precition+recall)

F1 = 2*precition*recall/(precition+recall)
print('F1=',F1)

输出：

accuracy= 0.6428571428571429
recall= 0.625
specificity= 0.6666666666666666
precition= 0.7142857142857143
F1= 0.6666666666666666

调节n_neighbors参数找到最优值

# 尝试不同的n_neighbors，通过准确度来判断选用什么类型的值
accuary_train_arr = []
accuary_test_arr = []
for i in range(1,20):
    knn_i = KNeighborsClassifier(n_neighbors=i)
    knn_i.fit(X_train,y_train)
    y_i_train_predict = knn_i.predict(X_train)
    y_i_test_predict = knn_i.predict(X_test)
    accuary_i_train = accuracy_score(y_train, y_i_train_predict)
    accuary_i_test = accuracy_score(y_test, y_i_test_predict)
    accuary_train_arr.append(accuary_i_train)
    accuary_test_arr.append(accuary_i_test)

plt.figure(figsize=(10,5))
plt.subplot(121)
plt.plot(range(1,20),accuary_train_arr)
plt.scatter(range(1,20),accuary_train_arr)
plt.xlabel('train n_neighbors')
plt.ylabel('accuracy')
plt.title('train n_neighbors vs accuracy')
plt.subplot(122)
plt.plot(range(1,20),accuary_test_arr)
plt.scatter(range(1,20),accuary_test_arr)
plt.xlabel('test n_neighbors')
plt.ylabel('accuracy')
plt.title('test n_neighbors vs accuracy')
plt.show()

由下图可知：在neighbors=5的情况下取到最好的accuracy