机器学习数据集读取和预处理

本文以干豆数据集为例，数据集下载位置如下：干豆数据集

import pandas as pd
import sklearn
import numpy as np

数据读取与预处理

dry = pd.read_csv("Dry_Bean.csv")

在info返回的信息中的non-null也能看出数据集不存在缺失值。

dry.info()

RangeIndex: 13611 entries, 0 to 13610
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Area             13611 non-null  int64  
 1   Perimeter        13611 non-null  float64
 2   MajorAxisLength  13611 non-null  float64
 3   MinorAxisLength  13611 non-null  float64
 4   AspectRation     13611 non-null  float64
 5   Eccentricity     13611 non-null  float64
 6   ConvexArea       13611 non-null  int64  
 7   EquivDiameter    13611 non-null  float64
 8   Extent           13611 non-null  float64
 9   Solidity         13611 non-null  float64
 10  roundness        13611 non-null  float64
 11  Compactness      13611 non-null  float64
 12  ShapeFactor1     13611 non-null  float64
 13  ShapeFactor2     13611 non-null  float64
 14  ShapeFactor3     13611 non-null  float64
 15  ShapeFactor4     13611 non-null  float64
 16  Class            13611 non-null  object 
dtypes: float64(14), int64(2), object(1)
memory usage: 1.8+ MB

dry.head()

	Area	Perimeter	MajorAxisLength	MinorAxisLength	AspectRation	Eccentricity	ConvexArea	EquivDiameter	Extent	Solidity	roundness	Compactness	ShapeFactor1	ShapeFactor2	ShapeFactor3	ShapeFactor4	Class
0	28395	610.291	208.178117	173.888747	1.197191	0.549812	28715	190.141097	0.763923	0.988856	0.958027	0.913358	0.007332	0.003147	0.834222	0.998724	SEKER
1	28734	638.018	200.524796	182.734419	1.097356	0.411785	29172	191.272751	0.783968	0.984986	0.887034	0.953861	0.006979	0.003564	0.909851	0.998430	SEKER
2	29380	624.110	212.826130	175.931143	1.209713	0.562727	29690	193.410904	0.778113	0.989559	0.947849	0.908774	0.007244	0.003048	0.825871	0.999066	SEKER
3	30008	645.884	210.557999	182.516516	1.153638	0.498616	30724	195.467062	0.782681	0.976696	0.903936	0.928329	0.007017	0.003215	0.861794	0.994199	SEKER
4	30140	620.134	201.847882	190.279279	1.060798	0.333680	30417	195.896503	0.773098	0.990893	0.984877	0.970516	0.006697	0.003665	0.941900	0.999166	SEKER

dry["Class"].unique()

array(['SEKER', 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ', 'SIRA', 'DERMASON'],
      dtype=object)

dry["Class"].nunique()

7

dry.columns

Index(['Area', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength',
       'AspectRation', 'Eccentricity', 'ConvexArea', 'EquivDiameter', 'Extent',
       'Solidity', 'roundness', 'Compactness', 'ShapeFactor1', 'ShapeFactor2',
       'ShapeFactor3', 'ShapeFactor4', 'Class'],
      dtype='object')

dry.index

RangeIndex(start=0, stop=13611, step=1)

重复值检查

dry.duplicated().sum()

68

数据缺失值检验

dry.isnull().sum()

Area               0
Perimeter          0
MajorAxisLength    0
MinorAxisLength    0
AspectRation       0
Eccentricity       0
ConvexArea         0
EquivDiameter      0
Extent             0
Solidity           0
roundness          0
Compactness        0
ShapeFactor1       0
ShapeFactor2       0
ShapeFactor3       0
ShapeFactor4       0
Class              0
dtype: int64

我们也可以通过定义如下函数来输出更加完整的每一列缺失值的数值和占比

def missing (df):
    """
    计算每一列的缺失值及占比
    """
    missing_number = df.isnull().sum().sort_values(ascending=False)              # 每一列的缺失值求和后降序排序
    missing_percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)          # 每一列缺失值占比
    missing_values = pd.concat([missing_number, missing_percent], axis=1, keys=['Missing_Number', 'Missing_Percent'])      # 合并为一个DataFrame
    return missing_values

missing(dry)

	Missing_Number	Missing_Percent
Area	0	0.0
Solidity	0	0.0
ShapeFactor4	0	0.0
ShapeFactor3	0	0.0
ShapeFactor2	0	0.0
ShapeFactor1	0	0.0
Compactness	0	0.0
roundness	0	0.0
Extent	0	0.0
Perimeter	0	0.0
EquivDiameter	0	0.0
ConvexArea	0	0.0
Eccentricity	0	0.0
AspectRation	0	0.0
MinorAxisLength	0	0.0
MajorAxisLength	0	0.0
Class	0	0.0

dry.describe()

	Area	Perimeter	MajorAxisLength	MinorAxisLength	AspectRation	Eccentricity	ConvexArea	EquivDiameter	Extent	Solidity	roundness	Compactness	ShapeFactor1	ShapeFactor2	ShapeFactor3	ShapeFactor4
count	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000	13611.000000
mean	53048.284549	855.283459	320.141867	202.270714	1.583242	0.750895	53768.200206	253.064220	0.749733	0.987143	0.873282	0.799864	0.006564	0.001716	0.643590	0.995063
std	29324.095717	214.289696	85.694186	44.970091	0.246678	0.092002	29774.915817	59.177120	0.049086	0.004660	0.059520	0.061713	0.001128	0.000596	0.098996	0.004366
min	20420.000000	524.736000	183.601165	122.512653	1.024868	0.218951	20684.000000	161.243764	0.555315	0.919246	0.489618	0.640577	0.002778	0.000564	0.410339	0.947687
25%	36328.000000	703.523500	253.303633	175.848170	1.432307	0.715928	36714.500000	215.068003	0.718634	0.985670	0.832096	0.762469	0.005900	0.001154	0.581359	0.993703
50%	44652.000000	794.941000	296.883367	192.431733	1.551124	0.764441	45178.000000	238.438026	0.759859	0.988283	0.883157	0.801277	0.006645	0.001694	0.642044	0.996386
75%	61332.000000	977.213000	376.495012	217.031741	1.707109	0.810466	62294.000000	279.446467	0.786851	0.990013	0.916869	0.834270	0.007271	0.002170	0.696006	0.997883
max	254616.000000	1985.370000	738.860154	460.198497	2.430306	0.911423	263261.000000	569.374358	0.866195	0.994677	0.990685	0.987303	0.010451	0.003665	0.974767	0.999733

dry.groupby("Class").mean()

	Area	Perimeter	MajorAxisLength	MinorAxisLength	AspectRation	Eccentricity	ConvexArea	EquivDiameter	Extent	Solidity	roundness	Compactness	ShapeFactor1	ShapeFactor2	ShapeFactor3	ShapeFactor4
Class
BARBUNYA	69804.133132	1046.105764	370.044279	240.309352	1.544395	0.754665	71025.729198	297.311018	0.749273	0.982804	0.800200	0.805001	0.005357	0.001394	0.649144	0.995739
BOMBAY	173485.059387	1585.619079	593.152075	374.352547	1.585550	0.770518	175813.116858	468.941426	0.776559	0.986902	0.864421	0.792622	0.003442	0.000844	0.629195	0.991841
CALI	75538.211043	1057.634282	409.499538	236.370616	1.733663	0.814804	76688.503067	309.535280	0.758953	0.985021	0.845934	0.756703	0.005459	0.001107	0.573022	0.990584
DERMASON	32118.710942	665.209536	246.557279	165.657143	1.490471	0.736632	32498.435138	201.683813	0.752953	0.988226	0.908114	0.819110	0.007755	0.002161	0.671636	0.996914
HOROZ	53648.508817	919.859676	372.570290	184.170663	2.026119	0.867443	54440.091805	260.730715	0.706393	0.985480	0.794420	0.700880	0.007007	0.001048	0.491791	0.991926
SEKER	39881.299951	727.672440	251.291957	201.909653	1.245182	0.584781	40269.567341	224.948441	0.771674	0.990351	0.944508	0.896841	0.006334	0.002541	0.805149	0.998383
SIRA	44729.128604	796.418737	299.380258	190.800250	1.570083	0.767277	45273.099772	238.335316	0.749445	0.987971	0.884652	0.797345	0.006720	0.001683	0.636358	0.995385

查看标签字段的取值分布情况

import seaborn as sns
import matplotlib.pyplot as plt

dry["Class"].value_counts()

DERMASON    3546
SIRA        2636
SEKER       2027
HOROZ       1928
CALI        1630
BARBUNYA    1322
BOMBAY       522
Name: Class, dtype: int64

sns.displot(dry['Class'])

数据标准化与归一化

  当然，除了离散变量的重编码外，有的时候我们也需要对连续变量进行转化，以提升模型表现或模型训练效率。在之前的内容中我们曾介绍了关于连续变量标准化和归一化的相关内容，对连续变量而言，标准化可以消除量纲影响并且加快梯度下降的迭代效率，而归一化则能够对每条数据进行进行范数单位化处理，我们可以通过下面的内容进行标准化和归一化相关内容回顾。

标准化与归一化

  从功能上划分，sklearn中的归一化其实是分为标准化（Standardization）和归一化（Normalization）两类。其中，此前所介绍的Z-Score标准化和0-1标准化，都属于Standardization的范畴，而在sklearn中，Normalization则特指针对单个样本（一行数据）利用其范数进行放缩的过程。不过二者都属于数据预处理范畴，都在sklearn中的Preprocessing data模块下。

需要注意的是，此前我们介绍数据归一化时有讨论过标准化和归一化名称上的区别，在大多数场景下其实我们并不会对其进行特意的区分，但sklearn中标准化和归一化则各指代一类数据处理方法，此处需要注意。

标准化 Standardization

  sklearn的标准化过程，即包括Z-Score标准化，也包括0-1标准化，并且即可以通过实用函数来进行标准化处理，同时也可以利用评估器来执行标准化过程。接下来我们分不同功能以的不同实现形式来进行讨论：

• Z-Score标准化的评估器实现方法

  实用函数进行标准化处理，尽管从代码实现角度来看清晰易懂，但却不适用于许多实际的机器学习建模场景。其一是因为在进行数据集的训练集和测试集切分后，我们首先要在训练集进行标准化、然后统计训练集上统计均值和方差再对测试集进行标准化处理，因此其实还需要一个统计训练集相关统计量的过程；其二则是因为相比实用函数，sklearn中的评估器其实会有一个非常便捷的串联的功能，sklearn中提供了Pipeline工具能够对多个评估器进行串联进而组成一个机器学习流，从而简化模型在重复调用时候所需代码量，因此通过评估器的方法进行数据标准化，其实是一种更加通用的选择。

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

scaler.fit()

将特征和标签分开：

#.提取特征数据、标签数据
cols = [i for i in dry.columns if i not in ['Class']] #获取种特征名称，不包含标签
print(cols)

['Area', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'ConvexArea', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'Compactness', 'ShapeFactor1', 'ShapeFactor2', 'ShapeFactor3', 'ShapeFactor4']

data = dry[cols]
data.head()

	Area	Perimeter	MajorAxisLength	MinorAxisLength	AspectRation	Eccentricity	ConvexArea	EquivDiameter	Extent	Solidity	roundness	Compactness	ShapeFactor1	ShapeFactor2	ShapeFactor3	ShapeFactor4
0	28395	610.291	208.178117	173.888747	1.197191	0.549812	28715	190.141097	0.763923	0.988856	0.958027	0.913358	0.007332	0.003147	0.834222	0.998724
1	28734	638.018	200.524796	182.734419	1.097356	0.411785	29172	191.272751	0.783968	0.984986	0.887034	0.953861	0.006979	0.003564	0.909851	0.998430
2	29380	624.110	212.826130	175.931143	1.209713	0.562727	29690	193.410904	0.778113	0.989559	0.947849	0.908774	0.007244	0.003048	0.825871	0.999066
3	30008	645.884	210.557999	182.516516	1.153638	0.498616	30724	195.467062	0.782681	0.976696	0.903936	0.928329	0.007017	0.003215	0.861794	0.994199
4	30140	620.134	201.847882	190.279279	1.060798	0.333680	30417	195.896503	0.773098	0.990893	0.984877	0.970516	0.006697	0.003665	0.941900	0.999166

target = dry["Class"]
target.head()

0    SEKER
1    SEKER
2    SEKER
3    SEKER
4    SEKER
Name: Class, dtype: object

data_Standard = scaler.fit_transform(data)
data_Standard

array([[-0.84074853, -1.1433189 , -1.30659814, ...,  2.4021726 ,
         1.92572347,  0.83837102],
       [-0.82918764, -1.01392388, -1.39591111, ...,  3.10089364,
         2.68970162,  0.77113831],
       [-0.80715717, -1.07882906, -1.25235661, ...,  2.23509111,
         1.84135576,  0.91675506],
       ...,
       [-0.37203825, -0.44783294, -0.45047814, ...,  0.28920501,
         0.33632829,  0.39025106],
       [-0.37176543, -0.42702856, -0.42897404, ...,  0.22837456,
         0.2489734 ,  0.03644007],
       [-0.37135619, -0.38755718, -0.2917356 , ..., -0.12777538,
        -0.27648141,  0.71371941]])

from sklearn.preprocessing import Normalizer

normlize = Normalizer()
data_normlize.fit_transform(data)