python数据分析与应用课后实训_python数据分析与应用

python数据分析与应用笔记

使用sklearn构建模型

1.使用sklearn转换器处理数据

import numpy as np

from sklearn.datasets import load_breast_cancer

from sklearn.preprocessing import MinMaxScaler #该函数时对数据做标准化处理

from sklearn.decomposition import PCA #该函数时对数据进行降维处理

from sklearn.model_selection import train_test_split #该函数是对数据做训练集和测试集的划分

cancer = load_breast_cancer() #将数据集赋值给cancer变量

cancer_data = cancer['data'] #提取数据集中的数据

cancer_target = cancer['target'] #提取数据集中的标签

cancer_names = cancer['feature_names'] #查看特征数目

cancer_desc = cancer['DESCR']

#划分训练集和测试集，其中20%的作为测试集

cancer_train_data,cancer_test_data,cancer_train_target,cancer_test_target = train_test_split(cancer_data,cancer_target,test_size = 0.2,random_state = 42)

scaler = MinMaxScaler().fit(cancer_train_data) #生成规则

# 将规则应用于训练集和测试集

cancer_trainScaler = scaler.transform(cancer_train_data)

cancer_testScaler = scaler.transform(cancer_test_data)

#构建pca降维模型

pca_model = PCA(n_components = 10).fit(cancer_trainScaler)

#将降维模型应用于标准化之后的训练数据和测试数据

cancer_trainPca = pca_model.transform(cancer_trainScaler)

cancer_testPca = pca_model.transform(cancer_testScaler)

print('降维前训练数据的形状：',cancer_trainScaler.shape)

print('降维后训练数据的形状：',cancer_trainPca.shape)

print('降维前测试数据的形状：',cancer_testScaler.shape)

print('降维后测试数据的形状：',cancer_testPca.shape)

降维前训练数据的形状： (455, 30)

降维后训练数据的形状： (455, 10)

降维前测试数据的形状： (114, 30)

降维后测试数据的形状： (114, 10)

任务：使用sklearn实现数据处理和降维操作

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

boston = load_boston()

boston_data = boston['data']

boston_target = boston['target']

boston_names = boston['feature_names']

boston_train_data,boston_test_data,boston_train_target,boston_test_target = train_test_split(boston_data,boston_target,test_size = 0.2,random_state = 42)

stdScale = StandardScaler().fit(boston_train_data)

boston_trainScaler = stdScale.transform(boston_train_data)

boston_testScaler = stdScale.transform(boston_test_data)

pca_model = PCA(n_components = 5).fit(boston_trainScaler)

boston_trainPca = pca_model.transform(boston_trainScaler)

boston_testPca = pca_model.transform(boston_testScaler)

2.构建并评价聚类模型

常用的聚类算法如表所示：

sklearn常用的聚类算法模块cluster提供的聚类算法及其适用范围如图：

import pandas as pd

from sklearn.manifold import TSNE #TSNE函数可实现多维数据的可视化展现

import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

from sklearn.preprocessing import MinMaxScaler

from sklearn.cluster import KMeans

iris = load_iris()

iris_data = iris['data']

iris_target = iris['target']

iris_names = iris['feature_names']

scale = MinMaxScaler().fit(iris_data) #构建规则

iris_dataScale = scale.transform(iris_data) #将规则应用于数据

kmeans = KMeans(n_clusters = 3,random_state = 123).fit(iris_dataScale) #构建并训练聚类模型

result = kmeans.predict([[1.5,1.5,1.5,1.5]]) #用模型进行预测

tsne = TSNE(n_components = 2,init = 'random',random_state=177).fit(iris_data) #使用TSNE对数据进行降维，降成两维

df = pd.DataFrame(tsne.embedding_) #将原始数据转化为DataFrame

df['labels']=kmeans.labels_ #将聚类结果存储进df数据集

df1 = df[df['labels']==0]

df2 = df[df['labels']==1]

df3 = df[df['labels']==2]

fig = plt.figure(figsize=(9,6))

plt.plot(df1[0],df1[1],'bo',df2[0],df2[1],'r*',df3[0],df3[1],'gD')

#plt.axis([-60,60,-80,80])

plt.savefig('聚类结果.png')

plt.show()

# print(df)

# print(df1)

# print(kmeans.labels_)

print(iris_names)

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

评价聚类模型

标准是：组内相似性越大，组间差别越大，其聚类效果越好

sklearn 的metrics模块提供的聚类模型评价指标有：

使用FMI评级法去评价K-Means聚类模型

from sklearn.metrics import fowlkes_mallows_score

for i in range(2,7):

kmeans = KMeans(n_clusters = i,random_state = 123).fit(iris_data)

score = fowlkes_mallows_score(iris_target,kmeans.labels_)

print('iris数据聚%d类FMI评价分值为：%f'%(i,score))

iris数据聚2类FMI评价分值为：0.750473

iris数据聚3类FMI评价分值为：0.820808

iris数据聚4类FMI评价分值为：0.753970

iris数据聚5类FMI评价分值为：0.725483

iris数据聚6类FMI评价分值为：0.600691

使用轮廓系数评价法

from sklearn.metrics import silhouette_score

import matplotlib.pyplot as plt

silhouettteScore = []

for i in range(2,15):

kmeans = KMeans(n_clusters = i,random_state = 123).fit(iris_data)

score = silhouette_score(iris_data,kmeans.labels_)

silhouettteScore.append(score)

plt.figure(figsize=(10,6))

plt.plot(range(2,15),silhouettteScore,linewidth = 1.5,linestyle = '-')

plt.show()

使用Calinski-Harabasz指数评价K-Means聚类模型

from sklearn.metrics import calinski_harabaz_score

for i in range(2,7):

kmeans = KMeans(n_clusters = i,random_state = 123).fit(iris_data)

score = calinski_harabaz_score(iris_data,kmeans.labels_)

print('iris数据聚%d类calinski_harabaz指数为：%f'%(i,score))

iris数据聚2类calinski_harabaz指数为：513.303843

iris数据聚3类calinski_harabaz指数为：560.399924

iris数据聚4类calinski_harabaz指数为：529.120719

iris数据聚5类calinski_harabaz指数为：494.094382

iris数据聚6类calinski_harabaz指数为：474.753604