导入库

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.datasets import load_iris

from sklearn.cluster import KMeans

from sklearn.preprocessing import MinMaxScaler

%matplotlib inline

sns.set(style="white")

pd.set_option("display.max_rows", 1000)

sklearn自带iris数据集(nrow=150)

4个预测变量

3分类结局

iris = load_iris()

X = iris["data"]

Y = iris["target"]

display(X[:5])

display(pd.Series(Y).value_counts())

Y = Y.reshape(-1, 1) # Y的形状转换为[150, 1]

array([[5.1, 3.5, 1.4, 0.2],

[4.9, 3. , 1.4, 0.2],

[4.7, 3.2, 1.3, 0.2],

[4.6, 3.1, 1.5, 0.2],

[5. , 3.6, 1.4, 0.2]])

2 50

1 50

0 50

dtype: int64

data = pd.DataFrame(np.concatenate((X, Y), axis=1),

columns=["x1", "x2", "x3", "x4", "y"])

data["y"] = data["y"].astype("int64")

data.head()

x1

x2

x3

x4

y

0

5.1

3.5

1.4

0.2

0

1

4.9

3.0

1.4

0.2

0

2

4.7

3.2

1.3

0.2

0

3

4.6

3.1

1.5

0.2

0

4

5.0

3.6

1.4

0.2

0

观察数据分布

4个预测变量两两散点图

sns.pairplot(data, hue="y")

数据标准化

Kmeans聚类前应对数据进行标准化

scaler = MinMaxScaler()

data.iloc[:, :4] = scaler.fit_transform(data.iloc[:, :4])

data.head()

x1

x2

x3

x4

y

0

0.222222

0.625000

0.067797

0.041667

0

1

0.166667

0.416667

0.067797

0.041667

0

2

0.111111

0.500000

0.050847

0.041667

0

3

0.083333

0.458333

0.084746

0.041667

0

4

0.194444

0.666667

0.067797

0.041667

0

设置类别数为3，进行Kmeans聚类

clus = KMeans(n_clusters=3)

clus = clus.fit(data.iloc[:, 1:4])

聚类完成后150个样本的聚类标签

clus.labels_

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,

1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,

1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,

2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,

2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)

聚类完成后三个聚类中心

clus.cluster_centers_

array([[0.595 , 0.07830508, 0.06083333],

[0.2975 , 0.55661017, 0.50583333],

[0.42916667, 0.76745763, 0.8075 ]])

聚类的评估指标

clus.inertia_

4.481991774793322

确定最佳聚类数目

尝试不同的类别数，查看criterion值(越小越好)，画出“肘线图”

L = []

for i in range(1, 9):

clus = KMeans(n_clusters=i)

clus.fit(data.iloc[:, 1:3])

L.append([i, clus.inertia_])

L = pd.DataFrame(L, columns=["k", "criterion"])

L

k

criterion

0

1

18.253249

1

2

5.106290

2

3

3.312646

3

4

2.585065

4

5

1.946648

5

6

1.637264

6

7

1.387541

7

8

1.175937

sns.pointplot(x="k", y="criterion", data=L)

sns.despine()

根据选定的聚类模型，对样本进行预测

从“肘线图”可看出最佳类别数等于3或4较好，此处使用3

clus = KMeans(n_clusters=3)

clus = clus.fit(data.iloc[:, 1:4])

data["pred"] = clus.predict(data.iloc[:, 1:4])

data.loc[data["pred"] == 0, "Pred"] = 11

data.loc[data["pred"] == 1, "Pred"] = 0

data.loc[data["pred"] == 2, "Pred"] = 2

data.loc[data["Pred"] == 11, "Pred"] = 1

data["Pred"] = data["Pred"].astype("int64")

data.head()

x1

x2

x3

x4

y

pred

Pred

0

0.222222

0.625000

0.067797

0.041667

0

1

0

1

0.166667

0.416667

0.067797

0.041667

0

1

0

2

0.111111

0.500000

0.050847

0.041667

0

1

0

3

0.083333

0.458333

0.084746

0.041667

0

1

0

4

0.194444

0.666667

0.067797

0.041667

0

1

0

画出预测混淆矩阵，计算准确率

df = pd.crosstab(data["y"], data["Pred"])

df

Pred

0

1

2

y

0

50

0

0

1

0

46

4

2

0

4

46

L = []

for i in range(df.shape[0]):

for j in range(df.shape[1]):

if i != j:

L.append(df.iloc[i, j])

print("预测准确率为：", round((150 - sum(L)) / 150 * 100, 1), "%")

预测准确率为： 94.7 %

本文地址：https://blog.csdn.net/weixin_40575651/article/details/107334269

希望与广大网友互动？？

点此进行留言吧！