葡萄酒数据集的随机森林分类

一：数据集介绍

1：数据集下载

https://archive.ics.uci.edu/ml/datasets/Wine+Quality

我这里选择的是红酒样本

数据的特征与标签

特征：11个 ； 标签：红酒质量0-10之间，11个类别

2：查看数据集

可以看到数据都在一列里，需要改一下

二：数据处理

1：数据分列

观察数据，在一列里用分号隔开，由此对数据分列

选定需要分列的数据–选数据菜单–分列–分隔符–选分号–OK

分列后的数据

2：导入数据

import pandas as pd
#获取数据
data = pd.read_csv("F:\\书籍学习：python数据挖掘与机器学习实战\\葡萄酒数据集的随机森林分类\\winequality-red.csv")
data.head()#查看数据

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

# 导入所有需要的库

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier

3:将数据拆分为特征与标签

features = data.drop('quality', 1)
# df = data.iloc[:, :11] #取前11列数据
labels = data['quality']
print(features.shape)
print(labels.shape)

(1599, 11)
(1599,)


C:\Users\Hp\AppData\Local\Temp\ipykernel_12320\351942566.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  features = data.drop('quality', 1)

三：数据分析

1：数据的描述性分析

# 描述性分析
print(features.describe())

# 直方图
# hist(),输出各个特征对比的直方图
features.hist()

       fixed acidity  volatile acidity  citric acid  residual sugar  \
count    1599.000000       1599.000000  1599.000000     1599.000000   
mean        8.319637          0.527821     0.270976        2.538806   
std         1.741096          0.179060     0.194801        1.409928   
min         4.600000          0.120000     0.000000        0.900000   
25%         7.100000          0.390000     0.090000        1.900000   
50%         7.900000          0.520000     0.260000        2.200000   
75%         9.200000          0.640000     0.420000        2.600000   
max        15.900000          1.580000     1.000000       15.500000   

         chlorides  free sulfur dioxide  total sulfur dioxide      density  \
count  1599.000000          1599.000000           1599.000000  1599.000000   
mean      0.087467            15.874922             46.467792     0.996747   
std       0.047065            10.460157             32.895324     0.001887   
min       0.012000             1.000000              6.000000     0.990070   
25%       0.070000             7.000000             22.000000     0.995600   
50%       0.079000            14.000000             38.000000     0.996750   
75%       0.090000            21.000000             62.000000     0.997835   
max       0.611000            72.000000            289.000000     1.003690   

                pH    sulphates      alcohol  
count  1599.000000  1599.000000  1599.000000  
mean      3.311113     0.658149    10.422983  
std       0.154386     0.169507     1.065668  
min       2.740000     0.330000     8.400000  
25%       3.210000     0.550000     9.500000  
50%       3.310000     0.620000    10.200000  
75%       3.400000     0.730000    11.100000  
max       4.010000     2.000000    14.900000  





array([[,
        ,
        ],
       [,
        ,
        ],
       [,
        ,
        ],
       [,
        , ]],
      dtype=object)

2:各等级酒的描述性分析

分为三个等级：低级（0-3），中级（4-7），高级（8-10）

（最大最小值，平均值，标准差）

2.1: 统计表类别个数df.value_counts()

#查看标签值，有几类标签

print(labels.value_counts())

5    681
6    638
7    199
4     53
8     18
3     10
Name: quality, dtype: int64

2.2:对数据进行分割，低级，中级，高级红酒

暂放-

3 ：变量的相关性分析

1：np.corrcoef（）

#features = data.drop('quality', 1)
df = data.iloc[:, :11] #取前11列数据
#print(df.head())#查看前5列数据


#分析两个变量间的相关性
print(np.corrcoef(data.iloc[1], data.iloc[2]))

#分析所有变量之间的相关性
print(np.corrcoef(df, rowvar = False))

[[1.         0.99368451]
 [0.99368451 1.        ]]
[[ 1.         -0.25613089  0.67170343  0.11477672  0.09370519 -0.15379419
  -0.11318144  0.66804729 -0.68297819  0.18300566 -0.06166827]
 [-0.25613089  1.         -0.55249568  0.00191788  0.06129777 -0.01050383
   0.07647     0.02202623  0.23493729 -0.26098669 -0.20228803]
 [ 0.67170343 -0.55249568  1.          0.14357716  0.20382291 -0.06097813
   0.03553302  0.36494718 -0.54190414  0.31277004  0.10990325]
 [ 0.11477672  0.00191788  0.14357716  1.          0.05560954  0.187049
   0.20302788  0.35528337 -0.08565242  0.00552712  0.04207544]
 [ 0.09370519  0.06129777  0.20382291  0.05560954  1.          0.00556215
   0.04740047  0.20063233 -0.26502613  0.37126048 -0.22114054]
 [-0.15379419 -0.01050383 -0.06097813  0.187049    0.00556215  1.
   0.66766645 -0.02194583  0.0703775   0.05165757 -0.06940835]
 [-0.11318144  0.07647     0.03553302  0.20302788  0.04740047  0.66766645
   1.          0.07126948 -0.06649456  0.04294684 -0.20565394]
 [ 0.66804729  0.02202623  0.36494718  0.35528337  0.20063233 -0.02194583
   0.07126948  1.         -0.34169933  0.14850641 -0.49617977]
 [-0.68297819  0.23493729 -0.54190414 -0.08565242 -0.26502613  0.0703775
  -0.06649456 -0.34169933  1.         -0.1966476   0.20563251]
 [ 0.18300566 -0.26098669  0.31277004  0.00552712  0.37126048  0.05165757
   0.04294684  0.14850641 -0.1966476   1.          0.09359475]
 [-0.06166827 -0.20228803  0.10990325  0.04207544 -0.22114054 -0.06940835
  -0.20565394 -0.49617977  0.20563251  0.09359475  1.        ]]

2：pandas用法，df为datafram数据–df.corr()

print(df.corr())

                      fixed acidity  volatile acidity  citric acid  \
fixed acidity              1.000000         -0.256131     0.671703   
volatile acidity          -0.256131          1.000000    -0.552496   
citric acid                0.671703         -0.552496     1.000000   
residual sugar             0.114777          0.001918     0.143577   
chlorides                  0.093705          0.061298     0.203823   
free sulfur dioxide       -0.153794         -0.010504    -0.060978   
total sulfur dioxide      -0.113181          0.076470     0.035533   
density                    0.668047          0.022026     0.364947   
pH                        -0.682978          0.234937    -0.541904   
sulphates                  0.183006         -0.260987     0.312770   
alcohol                   -0.061668         -0.202288     0.109903   

                      residual sugar  chlorides  free sulfur dioxide  \
fixed acidity               0.114777   0.093705            -0.153794   
volatile acidity            0.001918   0.061298            -0.010504   
citric acid                 0.143577   0.203823            -0.060978   
residual sugar              1.000000   0.055610             0.187049   
chlorides                   0.055610   1.000000             0.005562   
free sulfur dioxide         0.187049   0.005562             1.000000   
total sulfur dioxide        0.203028   0.047400             0.667666   
density                     0.355283   0.200632            -0.021946   
pH                         -0.085652  -0.265026             0.070377   
sulphates                   0.005527   0.371260             0.051658   
alcohol                     0.042075  -0.221141            -0.069408   

                      total sulfur dioxide   density        pH  sulphates  \
fixed acidity                    -0.113181  0.668047 -0.682978   0.183006   
volatile acidity                  0.076470  0.022026  0.234937  -0.260987   
citric acid                       0.035533  0.364947 -0.541904   0.312770   
residual sugar                    0.203028  0.355283 -0.085652   0.005527   
chlorides                         0.047400  0.200632 -0.265026   0.371260   
free sulfur dioxide               0.667666 -0.021946  0.070377   0.051658   
total sulfur dioxide              1.000000  0.071269 -0.066495   0.042947   
density                           0.071269  1.000000 -0.341699   0.148506   
pH                               -0.066495 -0.341699  1.000000  -0.196648   
sulphates                         0.042947  0.148506 -0.196648   1.000000   
alcohol                          -0.205654 -0.496180  0.205633   0.093595   

                       alcohol  
fixed acidity        -0.061668  
volatile acidity     -0.202288  
citric acid           0.109903  
residual sugar        0.042075  
chlorides            -0.221141  
free sulfur dioxide  -0.069408  
total sulfur dioxide -0.205654  
density              -0.496180  
pH                    0.205633  
sulphates             0.093595  
alcohol               1.000000  

3:绘图

3.1：散点图–seaborn或者pandas

此处只取前3列数据

第一行代码结果如图所示，是一张大图，其中包含9个子图，每个子图都是每个维度和其他某个维度的相关关系图，这其中主对角线上的图，则是每个维度的数据分布直方图。

而第二行代码是画出同样的图形，但却以fixed acidity（第一列数据）这个维度的数据为标准，从图中可以看出，sepal_width这列数据共5个不同的数值，每个数值一种颜色，所以生成的图是彩色的。

import scipy.stats as ss
import seaborn as sns ##导入库

dff=data.iloc[:, :3]

sns.pairplot(dff)
sns.pairplot(dff , hue ='fixed acidity')

3.2：热力图–heatmap（）

import scipy.stats as ss
import seaborn as sns ##导入库
import matplotlib.pyplot as plt

figure, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(dff.corr(), square=True, annot=True, ax=ax)

这个颜色太丑了–换一个

博文详解

https://blog.csdn.net/weixin_45492560/article/details/106227864

颜色参数：

cmap：指定一个colormap对象，用于热力图的填充色

center：指定颜色中心值，通过该参数可以调整热力图的颜色深浅

figure, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(dff.corr(),cmap='GnBu' ,square=True, annot=True, ax=ax)

figure, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(dff.corr(),cmap='YlGnBu' ,square=True, annot=True, ax=ax)

figure, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(dff.corr(),cmap='summer' ,square=True, annot=True, ax=ax)

四：使用随机森林构建模型

1:使用模型前的数据处理

# 特征与标签
features = data.drop('quality', 1)
# df = data.iloc[:, :11] #取前11列数据
labels = data['quality']
print(features.shape)
print(labels.shape)

# 拆分训练集与测试集
# 构造训练集和测试集
# 

from sklearn.model_selection import train_test_split

# 交叉验证
X_train,X_test,y_train,y_test=train_test_split(features,labels,random_state=1,test_size=0.3)
# print(X_train.shape)
# print(X_test.shape)
# print(y_train.shape)
# print(y_test.shape)
# 默认为75%为训练，25%为测试

 
  
(1599, 11)
(1599,)


C:\Users\Hp\AppData\Local\Temp\ipykernel_12320\1883349980.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  features = data.drop('quality', 1)
 
  
2:复习sklearn建模的基本流程
 
  
 
  
3： 建模与分析
 
  
画出随机森林和决策树在一组交叉验证下的效果对比
 
  

 
 
  
# 使用默认参数
model = RandomForestClassifier(oob_score=True, random_state=10)
model.fit(X_train,y_train)
test_predict = model.predict(X_test)

from sklearn.metrics import accuracy_score
accuracy_score(y_test, test_predict)



 
  
0.6979166666666666
 
  
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt

rfc = RandomForestClassifier(n_estimators=25)
rfc_s = cross_val_score(rfc,X_train,y_train,cv=10)
# 交叉验证划分为10折，
clf = DecisionTreeClassifier()
clf_s = cross_val_score(clf,X_train,y_train,cv=10)
plt.plot(range(1,11),rfc_s,label = "RandomForest")
plt.plot(range(1,11),clf_s,label = "Decision Tree")
plt.legend()
plt.show()

 
  
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
 
  
 
  
画出随机森林和决策树在10组交叉验证下的效果比较
 
  
rfc_l = []
clf_l = []
for i in range(10):
    rfc = RandomForestClassifier(n_estimators=25)
    rfc_s = cross_val_score(rfc,X_train,y_train,cv=10).mean()
    rfc_l.append(rfc_s)
    clf = DecisionTreeClassifier()
    clf_s = cross_val_score(clf,X_train,y_train,cv=10).mean()
    clf_l.append(clf_s)
    
plt.plot(range(1,11),rfc_l,label = "Random Forest")
plt.plot(range(1,11),clf_l,label = "Decision Tree")
plt.legend()
plt.show()
#是否有注意到，单个决策树的波动轨迹和随机森林一致？
#再次验证了我们之前提到的，单个决策树的准确率越高，随机森林的准确率也会越高

 
  
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(
D:\anaconda\lib\site-packages\sklearn\model_selection\_split.py:676: UserWarning: The least populated class in y has only 8 members, which is less than n_splits=10.
  warnings.warn(