Code machine learning practical --PCA (Principal Component Analysis)

1. The basic concept of principal component analysis

2. Code

Import the necessary libraries

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris  #sklearn中导入load_iris数据
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
%matplotlib inline

Loading data, build DataFrame

iris = load_iris()    #加载数据集
df = pd.DataFrame(iris.data, columns = iris.feature_names)   #创建DataFrame对象
df['label'] = iris.target	 #iris.target是标签
df.columns = ['speal length','speal width', 'petal length', 'petal width', 'label']
df.label.value_counts()	#统计不同标签的个数

X = df.iloc[:,0:4]        #对df进行切分第0、1、2、3列为X
print(X)
y = df.iloc[:,4]          #第4列为Y
print(y)

In scikit-learn, the category associated with the PCA in sklearn.decomposition package. PCA is the most common type sklearn.decomposition.PCA
PCA class does not need to adjust the basic parameters, generally speaking, we only need to specify that we need to reduce dimension to dimension, or we hope variance of the principal component after accounting for the original dimensions and dimension reduction All of the features and the ratio of the variance of the threshold value can be a

sklearn_pca = sklearnPCA(n_components = 2)       #n_components这个参数可以帮我们指定希望PCA降维后的特征维度数目(PCA算法中所要保留的主成分个数n，也即保留下来的特征个数n)
Y = sklearn_pca.fit_transform(X)         #用X来训练PCA模型，同时返回降维后的数据

Construction of principal component analysis data

principalDf = pd.DataFrame(data = np.array(Y),columns = ['principal component 1','principal component 2'])   
print(principalDf)
Df = pd.concat([principalDf,y],axis = 1) #需要连接的对象，eg [df1, df2]  #axis = 0, 表示在水平方向(row)进行连接 axis = 1, 表示在垂直方向(column)进行连接 
print(Df)    

After the scatter plot shown Principal Component Analysis

fig = plt.figure(figsize = (5,5))     #控制图片大小
ax = fig.add_subplot(1,1,1)      #“111”表示“1×1网格，第一子图”，“234”表示“2×3网格，第四子图”。

ax.set_xlabel('Principal Component 1',fontsize = 15)
ax.set_ylabel('Principal Component 2',fontsize = 15)
ax.set_title('2 component PCA',fontsize = 20)

targets = [0,1,2]
colors = ['r', 'g', 'b']
for target, color in zip(targets, colors):   #zip() 函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。
    indicesToKeep = Df['label'] == target
    ax.scatter(Df.loc[indicesToKeep,'principal component 1'],Df.loc[indicesToKeep,'principal component 2'], c = color ,s = 50)

ax.legend(targets)
ax.grid()