Python - Scikit-Learn 的数据加载+数据标准化+特征的选取

叁歲伎倆 2022-06-18 02:22 170阅读 0赞

加载数据到内存。Scikit-Learn库在它的实现用使用了NumPy数组，用NumPy来加载\*.csv文件。

[UCI Machine Learning Repository][]数据集库。

其中一个：http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data

部分数据样本，9维数组，如下

6,148,72,35,0,33.6,0.627,50,1
    1,85,66,29,0,26.6,0.351,31,0
    8,183,64,0,0,23.3,0.672,32,1
    1,89,66,23,94,28.1,0.167,21,0
    0,137,40,35,168,43.1,2.288,33,1
    5,116,74,0,0,25.6,0.201,30,0
    3,78,50,32,88,31.0,0.248,26,1
    10,115,0,0,0,35.3,0.134,29,0
    2,197,70,45,543,30.5,0.158,53,1
    8,125,96,0,0,0.0,0.232,54,1
    4,110,92,0,0,37.6,0.191,30,0
    10,168,74,0,0,38.0,0.537,34,1
    10,139,80,0,0,27.1,1.441,57,0
    1,189,60,23,846,30.1,0.398,59,1
    5,166,72,19,175,25.8,0.587,51,1
    7,100,0,0,0,30.0,0.484,32,1
    0,118,84,47,230,45.8,0.551,31,1
    7,107,74,0,0,29.6,0.254,31,1
    1,103,30,38,83,43.3,0.183,33,0
    1,115,70,30,96,34.6,0.529,32,1
    3,126,88,41,235,39.3,0.704,27,0
    8,99,84,0,0,35.4,0.388,50,0
    7,196,90,0,0,39.8,0.451,41,1

## 在运行算法之前，要进行标准化，或所谓的规格化。 ##

## 标准化包括替换所有特征的名义值，让它们每一个的值在0和1之间。 ##

## 而规格化，它包括数据的预处理，使得每个特征的值有0和1的离差。 ##

## 特征选取和特征工程。如树算法就可以计算特征的信息量。 ##

## 对特征子集的高效搜索，从而找到最好的子集，意味着演化了的模型在这个子集上有最好的质量。 ##

## 搜索算法：递归特征消除算法（RFE） ##

# -*- coding: utf-8 -*- 
    import numpy as np
    import urllib
    from sklearn import preprocessing
    from sklearn import metrics
    from sklearn.ensemble import ExtraTreesClassifier
    from sklearn.feature_selection import RFE
    from sklearn.linear_model import LogisticRegression
    
    # 数据集url
    url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
    # 下载文件
    raw_data = urllib.urlopen(url)
    # 载入文件为数组矩阵
    dataset = np.loadtxt(raw_data, delimiter=",")
    # 分离数据与目标类别
    X = dataset[:,0:7]
    y = dataset[:,8]
    print X,y
    # 规格化数据属性
    normalized_X = preprocessing.normalize(X)
    print normalized_X
    # 标准化数据属性
    standardized_X = preprocessing.scale(X)
    print '___________________________'
    print standardized_X
    
    model = ExtraTreesClassifier()#特征的选取
    model.fit(X, y)
    # 显示每个属性的相对重要性
    print(model.feature_importances_)
    
    model = LogisticRegression()
    # 创造 RFE 模型 and 选择 3 属性
    rfe = RFE(model, 3) # 递归特征消除算法（RFE）
    rfe = rfe.fit(X, y)
    # 总结属性的选择
    print(rfe.support_)
    print(rfe.ranking_)

输出如下：  
================ RESTART: F:/课程资料/神经网络/get\_ML\_data.py ================  
\[\[   6.     148.      72.    ...,    0.      33.6      0.627\]  
 \[   1.      85.      66.    ...,    0.      26.6      0.351\]  
 \[   8.     183.      64.    ...,    0.      23.3      0.672\]  
 ...,   
 \[   5.     121.      72.    ...,  112.      26.2      0.245\]  
 \[   1.     126.      60.    ...,    0.      30.1      0.349\]  
 \[   1.      93.      70.    ...,    0.      30.4      0.315\]\]\#原始数据  
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_  
\[ 1.  0.  1.  0.  1.  0.  1.  0.  1.  1.  0.  1.  0.  1.  1.  1.  1.  1.  
 ...,   
  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  0.  0.  0.  0.  0.  
  0.  1.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  
  0.  1.  0.  1.  0.  1.  0.  0.  0.  0.  1.  0.\]\#类别属性  
\[\[ 0.03494617  0.86200564  0.41935409 ...,  0.          0.19569858  
   0.00365188\]  
 \[ 0.00872683  0.74178025  0.57597054 ...,  0.          0.23213358  
   0.00306312\]  
 \[ 0.04093566  0.93640332  0.32748532 ...,  0.          0.11922512  
   0.0034386 \]  
 ...,   
 \[ 0.02727338  0.66001582  0.39273669 ...,  0.61092373  0.14291252  
   0.0013364 \]  
 \[ 0.0070043   0.8825414   0.42025781 ...,  0.          0.21082934  
   0.0024445 \]  
 \[ 0.00804902  0.74855891  0.56343144 ...,  0.          0.24469022  
   0.00253544\]\]\# 规格化数据属性  
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_  
\[\[ 0.63994726  0.84832379  0.14964075 ..., -0.69289057  0.20401277  
   0.46849198\]  
 \[-0.84488505 -1.12339636 -0.16054575 ..., -0.69289057 -0.68442195  
  -0.36506078\]  
 \[ 1.23388019  1.94372388 -0.26394125 ..., -0.69289057 -1.10325546  
   0.60439732\]  
 ...,   
 \[ 0.3429808   0.00330087  0.14964075 ...,  0.27959377 -0.73518964  
  -0.68519336\]  
 \[-0.84488505  0.1597866  -0.47073225 ..., -0.69289057 -0.24020459  
  -0.37110101\]  
 \[-0.84488505 -0.8730192   0.04624525 ..., -0.69289057 -0.20212881  
  -0.47378505\]\]\# 标准化数据属性  
\[ 0.13977213  0.25230235  0.11835827  0.09398053  0.07977048  0.16745061  
  0.14836562\]\# 显示每个属性的相对重要性  
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_  
\[ True False False False False  True  True\]\#总结属性的选择  
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_  
\[1 2 3 5 4 1 1\]

[UCI Machine Learning Repository]: http://archive.ics.uci.edu/ml/