sklearn中使用决策树

乘乘凉2023-08-07 19:56

1.示例

criterion可以是信息熵，entropy，可以是基尼系数gini

python 复制代码 
# -*-coding:utf-8-*-
from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
wine=load_wine()

# print ( wine.feature_names )
#(178, 13)
print(wine.data.shape)


Xtrain,Xtest,Ytrain,Ytest=train_test_split(wine.data,wine.target,test_size=0.3)

#random_state=30:输入任意整数，会一直长同一棵树，让模型稳定下来
clf=tree.DecisionTreeClassifier(criterion="entropy",random_state=30,splitter="best")
# clf=tree.DecisionTreeClassifier(criterion="entropy")
clf=clf.fit(Xtrain,Ytrain)
#返回预测准确度accuracy
score=clf.score(Xtest,Ytest)

print( score )

import graphviz
dot_data=tree.export_graphviz(clf,
                              feature_names=wine.feature_names,
                              class_names=["wine1","wine2","wine3"],
                              filled=True,
                              rounded=True)
graph=graphviz.Source(dot_data)
#生成pdf文件
graph.render(view=True, format="pdf", filename="tree_pdf")
print ( graph )
#feature_importances_：每个特征在决策树中的重要成都
print(clf.feature_importances_)
print ( [*zip(wine.feature_names,clf.feature_importances_)] )

决策树生成的pdf

2.示例

max_depth:这参数用来控制决策树的最大深度。以下示例，构建1~10深度的决策时，看哪个深度的决策树的精确率（score）高

python 复制代码 
# -*-coding:utf-8-*-
from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

plt.switch_backend("TkAgg")

wine=load_wine()

# print ( wine.feature_names )
#(178, 13)
print(wine.data.shape)


import pandas as pd
# print (pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1))
#所有的train，test必须是二维矩阵
Xtrain,Xtest,Ytrain,Ytest=train_test_split(wine.data,wine.target,test_size=0.3)

test=[]
bestScore=-1
bestClf=None
for i in range(10):
    clf=tree.DecisionTreeClassifier(max_depth=i+1,
                                    criterion="entropy",
                                    random_state=30,
                                    splitter="random")
    clf=clf.fit(Xtrain,Ytrain)
    score=clf.score(Xtest,Ytest)
    test.append(score)
    if score>bestScore:
        bestScore=score
        bestClf=clf
print(test)
print(test.index(bestScore))
#predict返回每个测试样本的分类/回归结果
predicted=bestClf.predict(Xtest)
print(predicted)

#返回每个测试样本的叶子节点的索引
leaf=bestClf.apply(Xtest)
print(leaf)

plt.plot(range(1,11),test,color="red",label="max_depth")
plt.legend()
plt.show()

结果：

python 复制代码 
(178, 13)
[0.5555555555555556, 0.8148148148148148, 0.9444444444444444, 0.9259259259259259, 0.8518518518518519, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334]
2
[0 1 0 1 2 0 1 1 1 2 2 0 0 2 0 1 1 0 0 0 0 1 1 0 2 1 0 2 2 1 2 1 1 1 1 0 1
 2 2 0 1 1 2 0 2 1 1 0 1 1 2 1 2 2]
[12  7 12 11  3 12  7  7  4  3  3 12 12  3 12  9  7 12 12 12 12  7  9 12
  3  9 12  3  3  4  3  4  7  7  7 12  7  3  3 12  9  9  3 12  3  7  7 12
  7  7  3  7  3  3]

3.交叉熵验证的示例

python 复制代码 
# -*-coding:utf-8-*-
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeRegressor
import sklearn
from sklearn.datasets import fetch_california_housing

housing=fetch_california_housing()
# print(housing)
# print(housing.data)
# print(housing.target)

regressor=DecisionTreeRegressor(random_state=0)

#cv=10,10次交叉验证，default：cv=5
#scoring="neg_mean_squared_error",评价指标是负的均方误差
cross_res=cross_val_score(regressor,
                housing.data,
                housing.target,
                scoring="neg_mean_squared_error",
                cv=10)
print(cross_res)
python 复制代码 
[-1.30551334 -0.78405711 -0.72809865 -0.50413232 -0.79683323 -0.83698199
 -0.56591889 -1.03621067 -1.02786488 -0.51371889]

4.Titanic生存者预测

数据来源：

Titanic - Machine Learning from Disaster | Kaggle

数据预处理

读取数据

python 复制代码 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV
#---------设置pd，在pycharm中显示完全表格-------
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
#----------------------------------------
data=pd.read_csv("./data.csv")
print (data.head(5))
print(data.info())
python 复制代码 
   PassengerId  Survived  Pclass                                                 Name     Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked
0            1         0       3                              Braund, Mr. Owen Harris    male  22.0      1      0         A/5 21171   7.2500   NaN        S
1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1      0          PC 17599  71.2833   C85        C
2            3         1       3                               Heikkinen, Miss. Laina  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S
3            4         1       1         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0            113803  53.1000  C123        S
4            5         0       3                             Allen, Mr. William Henry    male  35.0      0      0            373450   8.0500   NaN        S
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
None

Process finished with exit code 0

筛选特征

python 复制代码 
data.drop(["Cabin","Name","Ticket"],inplace=True,axis=1)
print(data.head())
print(data.info())
python 复制代码 
   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked
0            1         0       3    male  22.0      1      0   7.2500        S
1            2         1       1  female  38.0      1      0  71.2833        C
2            3         1       3  female  26.0      0      0   7.9250        S
3            4         1       1  female  35.0      1      0  53.1000        S
4            5         0       3    male  35.0      0      0   8.0500        S
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Sex          891 non-null    object 
 4   Age          714 non-null    float64
 5   SibSp        891 non-null    int64  
 6   Parch        891 non-null    int64  
 7   Fare         891 non-null    float64
 8   Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(2)
memory usage: 62.8+ KB
None

处理缺失值

python 复制代码 
#年龄用均值填补
data["Age"]=data["Age"].fillna(data["Age"].mean())
print(data.info())
python 复制代码 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Sex          891 non-null    object 
 4   Age          891 non-null    float64
 5   SibSp        891 non-null    int64  
 6   Parch        891 non-null    int64  
 7   Fare         891 non-null    float64
 8   Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(2)
memory usage: 62.8+ KB
None
python 复制代码 
#删除有缺失值的行，Embarked缺了两行
data=data.dropna()
print(data.info())
python 复制代码 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 889 entries, 0 to 890
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  889 non-null    int64  
 1   Survived     889 non-null    int64  
 2   Pclass       889 non-null    int64  
 3   Sex          889 non-null    object 
 4   Age          889 non-null    float64
 5   SibSp        889 non-null    int64  
 6   Parch        889 non-null    int64  
 7   Fare         889 non-null    float64
 8   Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(2)
memory usage: 69.5+ KB
None

处理非数值的列

查看非数值列的所有值

python 复制代码 
print(data["Embarked"].unique())
print(data["Sex"].unique())

#------------结果如下----------
['S' 'C' 'Q']
['male' 'female']
python 复制代码 
labels=data["Embarked"].unique().tolist()
#x代表data[Embarked]的每一行的值,S-->0,C-->1,Q-->2
data["Embarked"]=data["Embarked"].apply(lambda x:labels.index(x))

#把条件为True的转为int行
#也可以这样写：data.loc[:,"Sex"]=(data["Sex"]=="male").astype("int")
#male-->0,female-->1
data["Sex"]=(data["Sex"]=="male").astype("int")

提取数据

python 复制代码 
x=data.iloc[:, data.columns!="Survived"]
y=data.iloc[:,data.columns=="Survived"]

#Xtrain:(622, 8)
#划分数据集和测试集
from sklearn.model_selection import train_test_split
Xtrain,Xtest,Ytrain,Ytest=train_test_split(x,y,test_size=0.3)

#把索引变为从0~622
for i in [Xtrain,Xtest,Ytrain,Ytest]:
    i.index=range(i.shape[0])

第一种方法构建决策树

python 复制代码 
# clf=DecisionTreeClassifier(random_state=25)
# clf=clf.fit(Xtrain,Ytrain)
# score=clf.score(Xtest,Ytest)
# print(score)
from sklearn.model_selection import cross_val_score
# clf=DecisionTreeClassifier(random_state=25)
# score=cross_val_score(clf,x,y,cv=10).mean()
# print(score)



tr=[]
te=[]
for i in range(10):
    clf=DecisionTreeClassifier(random_state=25,
                               max_depth=i+1,
                               criterion="entropy"
                               )
    clf=clf.fit(Xtrain,Ytrain)
    score_tr=clf.score(Xtrain,Ytrain)
    score_te=cross_val_score(clf,x,y,cv=10).mean()

    tr.append(score_tr)
    te.append(score_te)
print(max(te))
plt.plot(range(1,11),tr,color="red",label="train")
plt.plot(range(1,11),te,color="blue",label="test")
#1~10全部显示
plt.xticks(range(1,11))
plt.legend()
plt.show()

不同深度的决策树的测试集和训练集的表现

第二种方法构建决策树

python 复制代码 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
plt.switch_backend("TkAgg")
from sklearn.model_selection import GridSearchCV
import numpy as np

#---------设置pd，在pycharm中显示完全表格-------
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
#----------------------------------------
data=pd.read_csv("./data.csv")
# print (data.head(5))
# print(data.info())

#去掉姓名、Cabin、票号的特征
data.drop(["Cabin","Name","Ticket"],inplace=True,axis=1)
# print(data.head())
# print(data.info())

#处理缺失值
#年龄用均值填补
data["Age"]=data["Age"].fillna(data["Age"].mean())
# print(data.info())

#删除有缺失值的行，Embarked缺了两行，所有的数据去掉不完整的行
data=data.dropna()
# print(data.info())

# print(data["Embarked"].unique())
# print(data["Sex"].unique())

labels=data["Embarked"].unique().tolist()
#x代表data[Embarked]的每一行的值,S-->0,C-->1,Q-->2
data["Embarked"]=data["Embarked"].apply(lambda x:labels.index(x))

#把条件为True的转为int行
#也可以这样写：data.loc[:,"Sex"]=(data["Sex"]=="male").astype("int")
#male-->0,female-->1
data["Sex"]=(data["Sex"]=="male").astype("int")

x=data.iloc[:, data.columns!="Survived"]
y=data.iloc[:,data.columns=="Survived"]


#Xtrain:(622, 8)
#划分数据集和测试集
from sklearn.model_selection import train_test_split
Xtrain,Xtest,Ytrain,Ytest=train_test_split(x,y,test_size=0.3)

#把索引变为从0~622
for i in [Xtrain,Xtest,Ytrain,Ytest]:
    i.index=range(i.shape[0])


from sklearn.model_selection import cross_val_score


clf=DecisionTreeClassifier(random_state=25)
#GridSearchCV:满足fit，score，交叉验证三个功能
#parameters:一串参数和这些参数对应的，我们希望网格搜索来搜索对应的参数的取值范围
parameters={
    "criterion":("gini","entropy"),
    "splitter":("best","random"),
    "max_depth":[*range(1,10)],
    "min_samples_leaf":[*range(1,50,5)],
    "min_impurity_decrease":[*np.linspace(0,0.5,20)]
}
GS=GridSearchCV(clf,parameters,cv=10)
gs=GS.fit(Xtrain,Ytrain)

#从输入的参数和参数取值中，返回最佳组合
print(gs.best_params_)

#网格搜索后的模型的评判标准
print(gs.best_score_)
python 复制代码 
{'criterion': 'entropy', 'max_depth': 3, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'splitter': 'best'}
0.8297235023041475

这种方法构建的决策树的准确率比第一种的还低

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