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自建青龙代理拉取Github仓库或文件

 发布于  收录于 电脑技巧

前言

因为青龙拉取部分作者仓库通过https://ghproxy.com/代理拉取频繁，导致了请求量比较大的仓库被该代理加速作者封禁。所以可以自建代理的链接替换默认的https://github.com/，自己用自己的反代不会封禁，自建代理实现ql repo或ql raw拉取实时的GitHub仓库或文件。

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 Linux

2021年全国大学生数学建模竞赛C题省一等奖论文部分

 发布于  收录于 Python 数学建模

前言

博客展示的是机器转义的markdown格式的，部分内容无法正常展示，完整内容详见博客相关资源。

论文配套的代码均为本人编写，第三四问存在部分代码问题，需要改动细节部分才能运行成功，后面两问的代码时间成本很高，长则数小时，短则十几二十分钟，自己跑酌情修改迭代次数，转换赛题要求的填表格式得通过最后部分的代码才能转换。

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 Python

逻辑回归1-乳腺癌数据案例

 发布于  收录于 Python 数学建模 机器学习

前言

逻辑回归的一个小案例，使用sklearn自带的一个数据

代码

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from sklearn.linear_model import LogisticRegression as LR #逻辑回归
from sklearn.datasets import load_breast_cancer #导入乳腺癌数据集
import numpy as np 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split  # 导入分测试与训练集
from sklearn.metrics import accuracy_score#精确性分数

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data = load_breast_cancer()#乳腺癌数据集
data

{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
         1.189e-01],
        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
         8.902e-02],
        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
         8.758e-02],
        ...,
        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
         7.820e-02],
        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
         1.240e-01],
        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
         7.039e-02]]),
 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),
 'frame': None,
 'target_names': array(['malignant', 'benign'], dtype='<U9'),
 'DESCR': '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 569\n\n    :Number of Attributes: 30 numeric, predictive attributes and the class\n\n    :Attribute Information:\n        - radius (mean of distances from center to points on the perimeter)\n        - texture (standard deviation of gray-scale values)\n        - perimeter\n        - area\n        - smoothness (local variation in radius lengths)\n        - compactness (perimeter^2 / area - 1.0)\n        - concavity (severity of concave portions of the contour)\n        - concave points (number of concave portions of the contour)\n        - symmetry\n        - fractal dimension ("coastline approximation" - 1)\n\n        The mean, standard error, and "worst" or largest (mean of the three\n        worst/largest values) of these features were computed for each image,\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n        10 is Radius SE, field 20 is Worst Radius.\n\n        - class:\n                - WDBC-Malignant\n                - WDBC-Benign\n\n    :Summary Statistics:\n\n    ===================================== ====== ======\n                                           Min    Max\n    ===================================== ====== ======\n    radius (mean):                        6.981  28.11\n    texture (mean):                       9.71   39.28\n    perimeter (mean):                     43.79  188.5\n    area (mean):                          143.5  2501.0\n    smoothness (mean):                    0.053  0.163\n    compactness (mean):                   0.019  0.345\n    concavity (mean):                     0.0    0.427\n    concave points (mean):                0.0    0.201\n    symmetry (mean):                      0.106  0.304\n    fractal dimension (mean):             0.05   0.097\n    radius (standard error):              0.112  2.873\n    texture (standard error):             0.36   4.885\n    perimeter (standard error):           0.757  21.98\n    area (standard error):                6.802  542.2\n    smoothness (standard error):          0.002  0.031\n    compactness (standard error):         0.002  0.135\n    concavity (standard error):           0.0    0.396\n    concave points (standard error):      0.0    0.053\n    symmetry (standard error):            0.008  0.079\n    fractal dimension (standard error):   0.001  0.03\n    radius (worst):                       7.93   36.04\n    texture (worst):                      12.02  49.54\n    perimeter (worst):                    50.41  251.2\n    area (worst):                         185.2  4254.0\n    smoothness (worst):                   0.071  0.223\n    compactness (worst):                  0.027  1.058\n    concavity (worst):                    0.0    1.252\n    concave points (worst):               0.0    0.291\n    symmetry (worst):                     0.156  0.664\n    fractal dimension (worst):            0.055  0.208\n    ===================================== ====== ======\n\n    :Missing Attribute Values: None\n\n    :Class Distribution: 212 - Malignant, 357 - Benign\n\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n    :Donor: Nick Street\n\n    :Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass.  They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree.  Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. topic:: References\n\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n     San Jose, CA, 1993.\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n     July-August 1995.\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n     163-171.',
 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
        'mean smoothness', 'mean compactness', 'mean concavity',
        'mean concave points', 'mean symmetry', 'mean fractal dimension',
        'radius error', 'texture error', 'perimeter error', 'area error',
        'smoothness error', 'compactness error', 'concavity error',
        'concave points error', 'symmetry error',
        'fractal dimension error', 'worst radius', 'worst texture',
        'worst perimeter', 'worst area', 'worst smoothness',
        'worst compactness', 'worst concavity', 'worst concave points',
        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),
 'filename': '/home/ubuntu/.local/lib/python3.8/site-packages/sklearn/datasets/data/breast_cancer.csv'}

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X = data.data
X.shape

(569, 30)

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y = data.target
y.shape

(569,)

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X.data.shape#(569, 30)

(569, 30)

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lrl1 = LR(penalty="l1",solver="liblinear",C=0.5,max_iter=1000)#l1 正则化会压缩到0
lrl2 = LR(penalty="l2",solver="liblinear",C=0.5,max_iter=1000)#l2 正则化不会压缩到0，默认l2正则化不填的话

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#逻辑回归的重要属性coef_，查看每个特征所对应的参数
lrl1 = lrl1.fit(X,y)
lrl1.coef_

array([[ 4.00409758,  0.03192262, -0.13763277, -0.01622177,  0.        ,
         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
         0.        ,  0.50529191,  0.        , -0.07128513,  0.        ,
         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
         0.        , -0.2458918 , -0.12855748, -0.01441275,  0.        ,
         0.        , -2.03953157,  0.        ,  0.        ,  0.        ]])

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(lrl1.coef_ != 0).sum(axis=1)#array([10])    30个特征中有10个特征的系数不为0

array([10])

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lrl2 = lrl2.fit(X,y)
lrl2.coef_

array([[ 1.61331113e+00,  1.00124606e-01,  4.60084835e-02,
        -4.19839426e-03, -9.26228937e-02, -3.00484301e-01,
        -4.53250190e-01, -2.19778015e-01, -1.33074668e-01,
        -1.92576286e-02,  1.89635811e-02,  8.74998561e-01,
         1.32421950e-01, -9.53784315e-02, -9.62972408e-03,
        -2.53596204e-02, -5.83890299e-02, -2.67755115e-02,
        -2.73846616e-02, -8.05302922e-05,  1.28529688e+00,
        -3.00088054e-01, -1.74310770e-01, -2.23545072e-02,
        -1.70267493e-01, -8.77272211e-01, -1.15830085e+00,
        -4.22526360e-01, -4.12406225e-01, -8.66393364e-02]])

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lrl2.coef_ != 0

array([[ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True]])

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l1 = []
l2 = []
l1test = []
l2test = []
 
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3,random_state=420)
 
for i in np.linspace(0.05,1.5,19):
    lrl1 = LR(penalty="l1",solver="liblinear",C=i,max_iter=1000)
    lrl2 = LR(penalty="l2",solver="liblinear",C=i,max_iter=1000)
    
    lrl1 = lrl1.fit(Xtrain,Ytrain)
    l1.append(accuracy_score(lrl1.predict(Xtrain),Ytrain))
    l1test.append(accuracy_score(lrl1.predict(Xtest),Ytest))
    lrl2 = lrl2.fit(Xtrain,Ytrain)
    l2.append(accuracy_score(lrl2.predict(Xtrain),Ytrain))
    l2test.append(accuracy_score(lrl2.predict(Xtest),Ytest))
 
graph = [l1,l2,l1test,l2test]
color = ["green","black","lightgreen","gray"]
label = ["L1","L2","L1test","L2test"]    
 
plt.figure(figsize=(6,6))
for i in range(len(graph)):
    plt.plot(np.linspace(0.05,1.5,19),graph[i],color[i],label=label[i])
plt.legend(loc=4) #图例的位置在哪里?4表示，右下角
plt.show()#0.8~0.9比较好

https://spiritlhl-tc.oss-cn-beijing.aliyuncs.com/output_12_0.png

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 Python, 神经网络, 机器学习

删除进程操作

 发布于  收录于 电脑技巧

删除指定端口的所有进程

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sudo kill -9 $(lsof -i:端口号 -t)

删除指定名称的所有进程

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sudo kill -9 $(pidof 名字)

查看指定端口占用情况

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sudo lsof -i:端口号

删除指定pid的进程

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sudo kill -9 pid号
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 Linux

Okteto常用项目

 发布于  收录于 电脑技巧

docker-compose搭建

wordpress

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version: '3.3'
 
services:
   db:
     image: mysql:latest
     volumes:
       - /data/wordpress_db:/var/lib/mysql
     restart: always
     environment:
       MYSQL_ROOT_PASSWORD: rootwordpress
       MYSQL_DATABASE: wordpress
       MYSQL_USER: wordpress
       MYSQL_PASSWORD: wordpress
 
   wordpress:
     depends_on:
       - db
     image: wordpress:latest
     ports:
       - "8000:80"
     restart: always
     environment:
       WORDPRESS_DB_HOST: db:3306
       WORDPRESS_DB_USER: wordpress
       WORDPRESS_DB_PASSWORD: wordpress
       WORDPRESS_DB_NAME: wordpress

Cloudreve

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services:
    cloudreve:
        public: true
        container_name: cloudreve
        image: jialezi/cloudreve
        ports:
            - 5212:5212
        volumes: 
            - /root/cloudreve

Bitwarden

管理地址为“xxx/admin”。

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 Github, Linux, Docker

预处理3-连续值与特征选取

 发布于  收录于 数学建模 Python 机器学习

前言

使用的相关数据链接如下：

点我跳转

代码

连续值预处理

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import pandas as pd
data = pd.read_csv("Narrativedata.csv"
                   ,index_col=0
                  )#index_col=0将第0列作为索引，不写则认为第0列为特征
data.loc[:,"Age"] = data.loc[:,"Age"].fillna(data.loc[:,"Age"].median())

#将年龄二值化 

data_2 = data.copy()
data_2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Age       891 non-null    float64
 1   Sex       891 non-null    object 
 2   Embarked  889 non-null    object 
 3   Survived  891 non-null    object 
dtypes: float64(1), object(3)
memory usage: 34.8+ KB

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from sklearn.preprocessing import Binarizer
X = data_2.iloc[:,0].values.reshape(-1,1)               #类为特征专用，所以不能使用一维数组
transformer = Binarizer(threshold=30).fit_transform(X)  #阈值threshold=n, 小于等于n的数值转为0, 大于n的数值转为1

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data_2.iloc[:,0] = transformer
data_2.head()

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 Python, 数学建模, 神经网络, 机器学习

预处理2-随机森林填补缺失值

 发布于  收录于 数学建模 Python 机器学习

随机森林填补缺失值(实测回归比较好)

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%matplotlib inline
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier#随机森林分类树
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

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wine = load_wine()
wine

{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,
         1.065e+03],
        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,
         1.050e+03],
        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,
         1.185e+03],
        ...,
        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,
         8.350e+02],
        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,
         8.400e+02],
        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,
         5.600e+02]]),
 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2]),
 'frame': None,
 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),
 'DESCR': '.. _wine_dataset:\n\nWine recognition dataset\n------------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 178 (50 in each of three classes)\n    :Number of Attributes: 13 numeric, predictive attributes and the class\n    :Attribute Information:\n \t\t- Alcohol\n \t\t- Malic acid\n \t\t- Ash\n\t\t- Alcalinity of ash  \n \t\t- Magnesium\n\t\t- Total phenols\n \t\t- Flavanoids\n \t\t- Nonflavanoid phenols\n \t\t- Proanthocyanins\n\t\t- Color intensity\n \t\t- Hue\n \t\t- OD280/OD315 of diluted wines\n \t\t- Proline\n\n    - class:\n            - class_0\n            - class_1\n            - class_2\n\t\t\n    :Summary Statistics:\n    \n    ============================= ==== ===== ======= =====\n                                   Min   Max   Mean     SD\n    ============================= ==== ===== ======= =====\n    Alcohol:                      11.0  14.8    13.0   0.8\n    Malic Acid:                   0.74  5.80    2.34  1.12\n    Ash:                          1.36  3.23    2.36  0.27\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n    Magnesium:                    70.0 162.0    99.7  14.3\n    Total Phenols:                0.98  3.88    2.29  0.63\n    Flavanoids:                   0.34  5.08    2.03  1.00\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n    Proanthocyanins:              0.41  3.58    1.59  0.57\n    Colour Intensity:              1.3  13.0     5.1   2.3\n    Hue:                          0.48  1.71    0.96  0.23\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n    Proline:                       278  1680     746   315\n    ============================= ==== ===== ======= =====\n\n    :Missing Attribute Values: None\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%[email protected])\n    :Date: July, 1988\n\nThis is a copy of UCI ML Wine recognition datasets.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n\nThe data is the results of a chemical analysis of wines grown in the same\nregion in Italy by three different cultivators. There are thirteen different\nmeasurements taken for different constituents found in the three types of\nwine.\n\nOriginal Owners: \n\nForina, M. et al, PARVUS - \nAn Extendible Package for Data Exploration, Classification and Correlation. \nInstitute of Pharmaceutical and Food Analysis and Technologies,\nVia Brigata Salerno, 16147 Genoa, Italy.\n\nCitation:\n\nLichman, M. (2013). UCI Machine Learning Repository\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\nSchool of Information and Computer Science. \n\n.. topic:: References\n\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \n  Comparison of Classifiers in High Dimensional Settings, \n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n  Mathematics and Statistics, James Cook University of North Queensland. \n  (Also submitted to Technometrics). \n\n  The data was used with many others for comparing various \n  classifiers. The classes are separable, though only RDA \n  has achieved 100% correct classification. \n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n  (All results using the leave-one-out technique) \n\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \n  "THE CLASSIFICATION PERFORMANCE OF RDA" \n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n  Mathematics and Statistics, James Cook University of North Queensland. \n  (Also submitted to Journal of Chemometrics).\n',
 'feature_names': ['alcohol',
  'malic_acid',
  'ash',
  'alcalinity_of_ash',
  'magnesium',
  'total_phenols',
  'flavanoids',
  'nonflavanoid_phenols',
  'proanthocyanins',
  'color_intensity',
  'hue',
  'od280/od315_of_diluted_wines',
  'proline']}

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wine.data.shape#特征矩阵

(178, 13)

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wine.target.shape#标签，以数的形式呈现

(178,)

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#实例化
clf = DecisionTreeClassifier(random_state=25)
xtrain,xtest,ytrain,ytest = train_test_split(wine.data,wine.target,test_size=0.3)#数据和特征分开导入

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clf = DecisionTreeClassifier(random_state=0)
rfc = RandomForestClassifier(random_state=0)

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#导入训练
clf = clf.fit(xtrain,ytrain)
rfc = rfc.fit(xtrain,ytrain)

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#打分
score_c = clf.score(xtest,ytest)
score_r = rfc.score(xtest,ytest)

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print("Single Tree:{}".format(score_c)
      ,"Random Forest:{}".format(score_r))

Single Tree:0.8703703703703703 Random Forest:0.9629629629629629

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# 交叉验证
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt

rfc = RandomForestClassifier(random_state=25)
rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10)

clf = DecisionTreeClassifier(random_state=25)
clf_s = cross_val_score(clf,wine.data,wine.target,cv=10)

plt.plot(range(1,11),rfc_s,label="RandomForest")
plt.plot(range(1,11),clf_s,label="DecisionTree")
plt.legend()
plt.show()

https://gitee.com/spiritlhl/picture/raw/master/output_10_0.png

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 Python, 数学建模, 神经网络, 机器学习