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特征降维三种方式

2019年09月28日  | 移动技术网IT编程  | 我要评论

it解决网,西南科技大学研究生院,黄义达壁纸

降维实际上就是降低特征的个数，最终的结果就是特征和特征之间不相关。

降维：降维是指在某些限定条件下，降低随机变量（特征）个数，得到一组“不相关”主变量的过程

降维的两种方式：

1、特征选择

2、主成分分析（可以理解为一种特征提取的方式）

一、特征选择

定义：数据中包含冗余或相关变量（或称为特征、属性、指标等），旨在从原有特征中找出主要特征

特征选择的2中方法（过滤式 + 嵌入式）

filter（过滤式）：主要探究特征本身特点、特征与特征和目标值之间关联。

    方差选择法：低方差特征过滤.例如鸟类是否可以飞作为特征值是不合适的，此时的方差为0

    相关系数：目的是去除冗余，确定特征与特征之间的相关性

embedded（嵌入式）：算法自动选择特征（特征与目标值之间的关联）

    决策树：信息熵、信息增益

    正则化：l1、l2

    深度学习：卷积等

模块

sklearn.feature_selection

一、降维方式一：特征选择——过滤式——低方差过滤

低方差特征过滤

删除低方差的一些特征，从方差的大小来考虑方式的角度。

特征方差小：某个特征大多样本的值比较相近

特征方差大：某个特征很多样本的值有比较有差别

api

sklearn.feature_selection.variancethreshold( threshold = 0.0 )

    删除所有低方差特征

    variance.fit_transform(x)

        x:numpy array格式的数据 [n_samples, n_features]

        返回：训练集差异低于 threshold的特征将被删除。默认值是保留所有非零方差特征，即删除所有样本中具有相同值的特征。
#删除低方差特征demo

from sklearn.datasets import load_iris
from sklearn.feature_selection import variancethreshold
import pandas as pd

def variance_demo():
    iris = load_iris()
    data = pd.dataframe(iris.data, columns = iris.feature_names)
    data_new = data.iloc[:, :4].values
    
    print("data_new:\n", data_new)
    
    transfer = variancethreshold(threshold = 0.5)
    
    data_variance_value = transfer.fit_transform(data_new)
    print("data_variance_value:\n", data_variance_value)
    
    return none

if __name__ == '__main__':
    variance_demo()





输出结果：
data_new:
 [[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]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.1 1.5 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
data_variance_value:
 [[5.1 1.4 0.2]
 [4.9 1.4 0.2]
 [4.7 1.3 0.2]
 [4.6 1.5 0.2]
 [5.  1.4 0.2]
 [5.4 1.7 0.4]
 [4.6 1.4 0.3]
 [5.  1.5 0.2]
 [4.4 1.4 0.2]
 [4.9 1.5 0.1]
 [5.4 1.5 0.2]
 [4.8 1.6 0.2]
 [4.8 1.4 0.1]
 [4.3 1.1 0.1]
 [5.8 1.2 0.2]
 [5.7 1.5 0.4]
 [5.4 1.3 0.4]
 [5.1 1.4 0.3]
 [5.7 1.7 0.3]
 [5.1 1.5 0.3]
 [5.4 1.7 0.2]
 [5.1 1.5 0.4]
 [4.6 1.  0.2]
 [5.1 1.7 0.5]
 [4.8 1.9 0.2]
 [5.  1.6 0.2]
 [5.  1.6 0.4]
 [5.2 1.5 0.2]
 [5.2 1.4 0.2]
 [4.7 1.6 0.2]
 [4.8 1.6 0.2]
 [5.4 1.5 0.4]
 [5.2 1.5 0.1]
 [5.5 1.4 0.2]
 [4.9 1.5 0.1]
 [5.  1.2 0.2]
 [5.5 1.3 0.2]
 [4.9 1.5 0.1]
 [4.4 1.3 0.2]
 [5.1 1.5 0.2]
 [5.  1.3 0.3]
 [4.5 1.3 0.3]
 [4.4 1.3 0.2]
 [5.  1.6 0.6]
 [5.1 1.9 0.4]
 [4.8 1.4 0.3]
 [5.1 1.6 0.2]
 [4.6 1.4 0.2]
 [5.3 1.5 0.2]
 [5.  1.4 0.2]
 [7.  4.7 1.4]
 [6.4 4.5 1.5]
 [6.9 4.9 1.5]
 [5.5 4.  1.3]
 [6.5 4.6 1.5]
 [5.7 4.5 1.3]
 [6.3 4.7 1.6]
 [4.9 3.3 1. ]
 [6.6 4.6 1.3]
 [5.2 3.9 1.4]
 [5.  3.5 1. ]
 [5.9 4.2 1.5]
 [6.  4.  1. ]
 [6.1 4.7 1.4]
 [5.6 3.6 1.3]
 [6.7 4.4 1.4]
 [5.6 4.5 1.5]
 [5.8 4.1 1. ]
 [6.2 4.5 1.5]
 [5.6 3.9 1.1]
 [5.9 4.8 1.8]
 [6.1 4.  1.3]
 [6.3 4.9 1.5]
 [6.1 4.7 1.2]
 [6.4 4.3 1.3]
 [6.6 4.4 1.4]
 [6.8 4.8 1.4]
 [6.7 5.  1.7]
 [6.  4.5 1.5]
 [5.7 3.5 1. ]
 [5.5 3.8 1.1]
 [5.5 3.7 1. ]
 [5.8 3.9 1.2]
 [6.  5.1 1.6]
 [5.4 4.5 1.5]
 [6.  4.5 1.6]
 [6.7 4.7 1.5]
 [6.3 4.4 1.3]
 [5.6 4.1 1.3]
 [5.5 4.  1.3]
 [5.5 4.4 1.2]
 [6.1 4.6 1.4]
 [5.8 4.  1.2]
 [5.  3.3 1. ]
 [5.6 4.2 1.3]
 [5.7 4.2 1.2]
 [5.7 4.2 1.3]
 [6.2 4.3 1.3]
 [5.1 3.  1.1]
 [5.7 4.1 1.3]
 [6.3 6.  2.5]
 [5.8 5.1 1.9]
 [7.1 5.9 2.1]
 [6.3 5.6 1.8]
 [6.5 5.8 2.2]
 [7.6 6.6 2.1]
 [4.9 4.5 1.7]
 [7.3 6.3 1.8]
 [6.7 5.8 1.8]
 [7.2 6.1 2.5]
 [6.5 5.1 2. ]
 [6.4 5.3 1.9]
 [6.8 5.5 2.1]
 [5.7 5.  2. ]
 [5.8 5.1 2.4]
 [6.4 5.3 2.3]
 [6.5 5.5 1.8]
 [7.7 6.7 2.2]
 [7.7 6.9 2.3]
 [6.  5.  1.5]
 [6.9 5.7 2.3]
 [5.6 4.9 2. ]
 [7.7 6.7 2. ]
 [6.3 4.9 1.8]
 [6.7 5.7 2.1]
 [7.2 6.  1.8]
 [6.2 4.8 1.8]
 [6.1 4.9 1.8]
 [6.4 5.6 2.1]
 [7.2 5.8 1.6]
 [7.4 6.1 1.9]
 [7.9 6.4 2. ]
 [6.4 5.6 2.2]
 [6.3 5.1 1.5]
 [6.1 5.6 1.4]
 [7.7 6.1 2.3]
 [6.3 5.6 2.4]
 [6.4 5.5 1.8]
 [6.  4.8 1.8]
 [6.9 5.4 2.1]
 [6.7 5.6 2.4]
 [6.9 5.1 2.3]
 [5.8 5.1 1.9]
 [6.8 5.9 2.3]
 [6.7 5.7 2.5]
 [6.7 5.2 2.3]
 [6.3 5.  1.9]
 [6.5 5.2 2. ]
 [6.2 5.4 2.3]
 [5.9 5.1 1.8]]

降维方式二：特征选择——过滤式——相关系数

皮尔森相关系数
    反映变量之间相关关系密切程度的统计指标

公式：（在此不列出来了，可以在网上百度一下，了解一下即可）

相关系数的值介于-1与+1之间，即-1≤r≤+1。其性质如下：

    当r>0时，表示两变量正相关，r<0时，两变量为负相关

    当r=|1|时，表示两变量为完全相关，当r=0时，表示狼变量无相关关系

    当0<|r|<1时，表示两变量存在一定程度的相关，|r|越接近1，两变量间线性关系越密切；|r|越接近于0，表示两变量的线性相关越弱

    一般可按三级划分：|r|<0.4为低度相关；0.4≤|r|<0.7为显著相关；0.7≤|r|<1为高度线性相关

api

from scipy.stats import pearsonr
#过滤低方差特征 + 计算相关系数demo
#皮尔森相关系数，计算特征与目标变量之间的相关度
from scipy.stats import pearsonr
from sklearn.datasets import load_iris
from sklearn.feature_selection import variancethreshold
import pandas as pd

def variance_demo():
    iris = load_iris()
    data = pd.dataframe(iris.data, columns = ['sepal length', 'sepal width', 'petal length', 'petal width'])
    data_new = data.iloc[:, :4].values
    print("data_new:\n", data_new)
    
    transfer = variancethreshold(threshold = 0.5)
    
    data_variance_value = transfer.fit_transform(data_new)
    print("data_variance_value:\n", data_variance_value)
    
    #计算两个变量之间的相关系数
    r1 = pearsonr(data['sepal length'], data['petal length'])
    print("sepal length与petal length的相关系数：\n", r1)
    
    r2 = pearsonr(data['petal length'], data['petal width'])
    print("petal length与petal width的相关系数：\n", r2)
    
    import matplotlib.pyplot as plt
    plt.scatter(data['petal length'], data['petal width'])
    plt.show()
    
    return none

if __name__ == '__main__':
    variance_demo()
    




输出结果：
data_new:
 [[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]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.1 1.5 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
data_variance_value:
 [[5.1 1.4 0.2]
 [4.9 1.4 0.2]
 [4.7 1.3 0.2]
 [4.6 1.5 0.2]
 [5.  1.4 0.2]
 [5.4 1.7 0.4]
 [4.6 1.4 0.3]
 [5.  1.5 0.2]
 [4.4 1.4 0.2]
 [4.9 1.5 0.1]
 [5.4 1.5 0.2]
 [4.8 1.6 0.2]
 [4.8 1.4 0.1]
 [4.3 1.1 0.1]
 [5.8 1.2 0.2]
 [5.7 1.5 0.4]
 [5.4 1.3 0.4]
 [5.1 1.4 0.3]
 [5.7 1.7 0.3]
 [5.1 1.5 0.3]
 [5.4 1.7 0.2]
 [5.1 1.5 0.4]
 [4.6 1.  0.2]
 [5.1 1.7 0.5]
 [4.8 1.9 0.2]
 [5.  1.6 0.2]
 [5.  1.6 0.4]
 [5.2 1.5 0.2]
 [5.2 1.4 0.2]
 [4.7 1.6 0.2]
 [4.8 1.6 0.2]
 [5.4 1.5 0.4]
 [5.2 1.5 0.1]
 [5.5 1.4 0.2]
 [4.9 1.5 0.1]
 [5.  1.2 0.2]
 [5.5 1.3 0.2]
 [4.9 1.5 0.1]
 [4.4 1.3 0.2]
 [5.1 1.5 0.2]
 [5.  1.3 0.3]
 [4.5 1.3 0.3]
 [4.4 1.3 0.2]
 [5.  1.6 0.6]
 [5.1 1.9 0.4]
 [4.8 1.4 0.3]
 [5.1 1.6 0.2]
 [4.6 1.4 0.2]
 [5.3 1.5 0.2]
 [5.  1.4 0.2]
 [7.  4.7 1.4]
 [6.4 4.5 1.5]
 [6.9 4.9 1.5]
 [5.5 4.  1.3]
 [6.5 4.6 1.5]
 [5.7 4.5 1.3]
 [6.3 4.7 1.6]
 [4.9 3.3 1. ]
 [6.6 4.6 1.3]
 [5.2 3.9 1.4]
 [5.  3.5 1. ]
 [5.9 4.2 1.5]
 [6.  4.  1. ]
 [6.1 4.7 1.4]
 [5.6 3.6 1.3]
 [6.7 4.4 1.4]
 [5.6 4.5 1.5]
 [5.8 4.1 1. ]
 [6.2 4.5 1.5]
 [5.6 3.9 1.1]
 [5.9 4.8 1.8]
 [6.1 4.  1.3]
 [6.3 4.9 1.5]
 [6.1 4.7 1.2]
 [6.4 4.3 1.3]
 [6.6 4.4 1.4]
 [6.8 4.8 1.4]
 [6.7 5.  1.7]
 [6.  4.5 1.5]
 [5.7 3.5 1. ]
 [5.5 3.8 1.1]
 [5.5 3.7 1. ]
 [5.8 3.9 1.2]
 [6.  5.1 1.6]
 [5.4 4.5 1.5]
 [6.  4.5 1.6]
 [6.7 4.7 1.5]
 [6.3 4.4 1.3]
 [5.6 4.1 1.3]
 [5.5 4.  1.3]
 [5.5 4.4 1.2]
 [6.1 4.6 1.4]
 [5.8 4.  1.2]
 [5.  3.3 1. ]
 [5.6 4.2 1.3]
 [5.7 4.2 1.2]
 [5.7 4.2 1.3]
 [6.2 4.3 1.3]
 [5.1 3.  1.1]
 [5.7 4.1 1.3]
 [6.3 6.  2.5]
 [5.8 5.1 1.9]
 [7.1 5.9 2.1]
 [6.3 5.6 1.8]
 [6.5 5.8 2.2]
 [7.6 6.6 2.1]
 [4.9 4.5 1.7]
 [7.3 6.3 1.8]
 [6.7 5.8 1.8]
 [7.2 6.1 2.5]
 [6.5 5.1 2. ]
 [6.4 5.3 1.9]
 [6.8 5.5 2.1]
 [5.7 5.  2. ]
 [5.8 5.1 2.4]
 [6.4 5.3 2.3]
 [6.5 5.5 1.8]
 [7.7 6.7 2.2]
 [7.7 6.9 2.3]
 [6.  5.  1.5]
 [6.9 5.7 2.3]
 [5.6 4.9 2. ]
 [7.7 6.7 2. ]
 [6.3 4.9 1.8]
 [6.7 5.7 2.1]
 [7.2 6.  1.8]
 [6.2 4.8 1.8]
 [6.1 4.9 1.8]
 [6.4 5.6 2.1]
 [7.2 5.8 1.6]
 [7.4 6.1 1.9]
 [7.9 6.4 2. ]
 [6.4 5.6 2.2]
 [6.3 5.1 1.5]
 [6.1 5.6 1.4]
 [7.7 6.1 2.3]
 [6.3 5.6 2.4]
 [6.4 5.5 1.8]
 [6.  4.8 1.8]
 [6.9 5.4 2.1]
 [6.7 5.6 2.4]
 [6.9 5.1 2.3]
 [5.8 5.1 1.9]
 [6.8 5.9 2.3]
 [6.7 5.7 2.5]
 [6.7 5.2 2.3]
 [6.3 5.  1.9]
 [6.5 5.2 2. ]
 [6.2 5.4 2.3]
 [5.9 5.1 1.8]]
sepal length与petal length的相关系数：
 (0.8717541573048712, 1.0384540627941809e-47)
petal length与petal width的相关系数：
 (0.9627570970509662, 5.776660988495158e-86)

 

 

特征与特征之间相关性很高，可以采取

1）选取其中一个
2）加权求和
3）主成分分析
 

特征选择注意点！

在所有特征选择方法，方差，selectkbest+各种统计量（卡方过滤、f检验、互信息法），嵌入法和包装法，都有接口get_support，该接口有参数indices，get_support(indices=false)，参数为false的时候可以用来确定原特征矩阵中有哪些特征被选择出来，返回布尔值true或者false，如果设定indices=true，就可以确定被选择出来的特征在原特征矩阵中所在的位置的索引。

x_train_columns = x_train.columns
selector = variancethreshold(0.005071)
x_fsvar = selector.fit_transform(x_train)
x_fsvar.columns = x_train_columns[selector.get_support(indices=true)]


降维方式三：主成分分析（pca）


定义：高维数据转化为地位数据的过程，在此过程中可能会舍弃原有数据、创造新的变量

作用：是数据维数的压缩，尽可能降低原数据的维数（复杂度），损失少量信息

应用：回归分析或者据类分析当中


api


sklearn.decomposition.pca(n_components = none)
    将数据分解为较低维数空间

    n_components:
        小数：表示包留百分之多少的信息

        整数：减少到多少特征

    pca.fit_transform(x)

        x:numpy array格式的数据[n_samples, n_features]

        返回：转换后指定维度的array






from sklearn.decomposition import pca
from sklearn.datasets import load_iris
import pandas as pd

def pca_demo():
    iris = load_iris()
    data = pd.dataframe(iris.data, columns = iris.feature_names)
    data_array = data.iloc[:, :4].values
    print("data_array:\n", data_array)
    
    transfer = pca(n_components = 2)
    #ransfer = pca(n_components = 0.95)
    
    data_pca_value = transfer.fit_transform(data_array)
    print("data_pca_value:\n", data_pca_value)
    
    return none

if __name__ == '__main__':
    pca_demo()




输出结果：
data_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]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.1 1.5 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
data_pca_value:
 [[-2.68420713  0.32660731]
 [-2.71539062 -0.16955685]
 [-2.88981954 -0.13734561]
 [-2.7464372  -0.31112432]
 [-2.72859298  0.33392456]
 [-2.27989736  0.74778271]
 [-2.82089068 -0.08210451]
 [-2.62648199  0.17040535]
 [-2.88795857 -0.57079803]
 [-2.67384469 -0.1066917 ]
 [-2.50652679  0.65193501]
 [-2.61314272  0.02152063]
 [-2.78743398 -0.22774019]
 [-3.22520045 -0.50327991]
 [-2.64354322  1.1861949 ]
 [-2.38386932  1.34475434]
 [-2.6225262   0.81808967]
 [-2.64832273  0.31913667]
 [-2.19907796  0.87924409]
 [-2.58734619  0.52047364]
 [-2.3105317   0.39786782]
 [-2.54323491  0.44003175]
 [-3.21585769  0.14161557]
 [-2.30312854  0.10552268]
 [-2.35617109 -0.03120959]
 [-2.50791723 -0.13905634]
 [-2.469056    0.13788731]
 [-2.56239095  0.37468456]
 [-2.63982127  0.31929007]
 [-2.63284791 -0.19007583]
 [-2.58846205 -0.19739308]
 [-2.41007734  0.41808001]
 [-2.64763667  0.81998263]
 [-2.59715948  1.10002193]
 [-2.67384469 -0.1066917 ]
 [-2.86699985  0.0771931 ]
 [-2.62522846  0.60680001]
 [-2.67384469 -0.1066917 ]
 [-2.98184266 -0.48025005]
 [-2.59032303  0.23605934]
 [-2.77013891  0.27105942]
 [-2.85221108 -0.93286537]
 [-2.99829644 -0.33430757]
 [-2.4055141   0.19591726]
 [-2.20883295  0.44269603]
 [-2.71566519 -0.24268148]
 [-2.53757337  0.51036755]
 [-2.8403213  -0.22057634]
 [-2.54268576  0.58628103]
 [-2.70391231  0.11501085]
 [ 1.28479459  0.68543919]
 [ 0.93241075  0.31919809]
 [ 1.46406132  0.50418983]
 [ 0.18096721 -0.82560394]
 [ 1.08713449  0.07539039]
 [ 0.64043675 -0.41732348]
 [ 1.09522371  0.28389121]
 [-0.75146714 -1.00110751]
 [ 1.04329778  0.22895691]
 [-0.01019007 -0.72057487]
 [-0.5110862  -1.26249195]
 [ 0.51109806 -0.10228411]
 [ 0.26233576 -0.5478933 ]
 [ 0.98404455 -0.12436042]
 [-0.174864   -0.25181557]
 [ 0.92757294  0.46823621]
 [ 0.65959279 -0.35197629]
 [ 0.23454059 -0.33192183]
 [ 0.94236171 -0.54182226]
 [ 0.0432464  -0.58148945]
 [ 1.11624072 -0.08421401]
 [ 0.35678657 -0.06682383]
 [ 1.29646885 -0.32756152]
 [ 0.92050265 -0.18239036]
 [ 0.71400821  0.15037915]
 [ 0.89964086  0.32961098]
 [ 1.33104142  0.24466952]
 [ 1.55739627  0.26739258]
 [ 0.81245555 -0.16233157]
 [-0.30733476 -0.36508661]
 [-0.07034289 -0.70253793]
 [-0.19188449 -0.67749054]
 [ 0.13499495 -0.31170964]
 [ 1.37873698 -0.42120514]
 [ 0.58727485 -0.48328427]
 [ 0.8072055   0.19505396]
 [ 1.22042897  0.40803534]
 [ 0.81286779 -0.370679  ]
 [ 0.24519516 -0.26672804]
 [ 0.16451343 -0.67966147]
 [ 0.46303099 -0.66952655]
 [ 0.89016045 -0.03381244]
 [ 0.22887905 -0.40225762]
 [-0.70708128 -1.00842476]
 [ 0.35553304 -0.50321849]
 [ 0.33112695 -0.21118014]
 [ 0.37523823 -0.29162202]
 [ 0.64169028  0.01907118]
 [-0.90846333 -0.75156873]
 [ 0.29780791 -0.34701652]
 [ 2.53172698 -0.01184224]
 [ 1.41407223 -0.57492506]
 [ 2.61648461  0.34193529]
 [ 1.97081495 -0.18112569]
 [ 2.34975798 -0.04188255]
 [ 3.39687992  0.54716805]
 [ 0.51938325 -1.19135169]
 [ 2.9320051   0.35237701]
 [ 2.31967279 -0.24554817]
 [ 2.91813423  0.78038063]
 [ 1.66193495  0.2420384 ]
 [ 1.80234045 -0.21615461]
 [ 2.16537886  0.21528028]
 [ 1.34459422 -0.77641543]
 [ 1.5852673  -0.53930705]
 [ 1.90474358  0.11881899]
 [ 1.94924878  0.04073026]
 [ 3.48876538  1.17154454]
 [ 3.79468686  0.25326557]
 [ 1.29832982 -0.76101394]
 [ 2.42816726  0.37678197]
 [ 1.19809737 -0.60557896]
 [ 3.49926548  0.45677347]
 [ 1.38766825 -0.20403099]
 [ 2.27585365  0.33338653]
 [ 2.61419383  0.55836695]
 [ 1.25762518 -0.179137  ]
 [ 1.29066965 -0.11642525]
 [ 2.12285398 -0.21085488]
 [ 2.3875644   0.46251925]
 [ 2.84096093  0.37274259]
 [ 3.2323429   1.37052404]
 [ 2.15873837 -0.21832553]
 [ 1.4431026  -0.14380129]
 [ 1.77964011 -0.50146479]
 [ 3.07652162  0.68576444]
 [ 2.14498686  0.13890661]
 [ 1.90486293  0.04804751]
 [ 1.16885347 -0.1645025 ]
 [ 2.10765373  0.37148225]
 [ 2.31430339  0.18260885]
 [ 1.92245088  0.40927118]
 [ 1.41407223 -0.57492506]
 [ 2.56332271  0.2759745 ]
 [ 2.41939122  0.30350394]
 [ 1.94401705  0.18741522]
 [ 1.52566363 -0.37502085]
 [ 1.76404594  0.07851919]
 [ 1.90162908  0.11587675]
 [ 1.38966613 -0.28288671]]




 





参考链接：https://www.cnblogs.com/ftl1012/p/10498480.html



                    

                        

                            
                        

                    

                    


                    
                    

                        
    
                            
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