Auto-ML-C 0.0.11

A small example package

Auto_ML_C 0.0.10

Illustrate：

​ 这是崔连山和小伙伴们的机器学习拓展包，代有浓厚的社会主义开源分享精神，极富创造力和战斗力。在这里让我们为他们鼓掌 :clinking_glasses:

Spend

测试集数据位置：{Example}

配置如下：

	Windows	Windows	MacOS	Linux
型号	i7-9750H	i7-9750H	M1	E5-2640 V4
核心	6核心12线程	6核心12线程	8核心	20核心40线程
频率	2.67GHz	3.2GHz		2.40GHz

运行速度对比结果如下：

未集成	Windows1	Windows2	MacOS	Linux
ALL_FUNCTION	47.364	43.681	34.013	==27.282==
binary_ROC()	45.809	42.964	32.751	==32.143==
auto_model()	53.498	48.649	==38.267==	40.794
estimator_violion()	1.191	==1.021==	1.678	2.395

集成	Windows1	Windows2	MacOS	Linux
binary_ROC()	46.2 s	43.2 s	CPU times: user 4.82 s, sys: 365 ms, total: 5.18 s Wall time: ==32.9s==	CPU times: user 9.59 s, sys: 3.89 s, total: 13.5 s Wall time: 33.3 s
auto_model()	50.4 s	47.1 s	CPU times: user 9.75 s, sys: 247 ms, total: 10 s Wall time: ==38.1 s==	CPU times: user 15.1 s, sys: 1.68 s, total: 16.8 s Wall time: 41.1 s
estimator_violion()	1.16 s	Wall time: ==1.01 s==	CPU times: user 2.02 s, sys: 70.1 ms, total: 2.09 s Wall time: 1.69 s	CPU times: user 3.85 s, sys: 2.32 s, total: 6.17 s Wall time: 2.23 s

Request_install

可以参考学习当前目录下的环境备份：Auto_ML_C.yaml

主要是涉及到的软件如下：

Package	最低版本——待检测
python=3.8.10
seaborn=0.11.2
pandas=1.3.3
matplotlib=3.4.2
numpy=1.20.3

Content:

​ 该包是基于Sklearn，imblance等机器学习拓展包之上的Package，共计划分为两个部分，

• 分类任务

  1. binary_classfication.py

     内部可用函数如下

     函数名	功能	返回值
     cal_add_1(num1,num2):wave:	简单的欢迎函数	num1,num2
     LogisticRegressionCV_mdoel(X, Y,cv)
     SGDClassifier_model(X,Y,cv)
     LinearDiscriminantAnalysis_model(X, Y,cv)
     LinearSVC_model(X, Y,cv)
     SVC_model(X, Y,cv)
     DecisionTreeClassifier_model(X,Y,cv)
     AdaBoostClassifier_model(X,Y,cv)
     BaggingClassifier_model(X, Y,cv)
     GradientBoostingClassifier_model(X, Y,cv)
     RandomForestClassifier_model(X, Y,cv)
     KNeighborsClassifier_model(X, Y,cv)
     BernoulliNB_model(X, Y,cv)
     GaussianNB_model(X,Y,cv)
     下面是总函数
     binary_ROC(X,Y,k,fig_name)	绘制标量超参数搜索下最佳的ROC	fig
     auto_model(X, Y, k)	模型的标量超参数搜索结果	Auc_data, Acc_data, Recall_data, Precision_data
     estimator_violion(df1,df2,fig_name)	为auto_model结果的Dataframe绘制小提琴图	fig

  2. 多分类函数

     等待

  3. 特征筛选函数Feature_struction

  4. waited

How to Use

Installation

# Method 1
# Create a new environment, here is conda as an example
conda create --name Auto_ML_C python=3.8.10

# Activate the newly created environment
conda activate Auto_ML_C

# Installation package
pip install Auto_ML_C==0.0.8

# Suggest the pipeline of Jupyter notebook [optional, recommended]
conda install jupyter notebook
conda install ipykernel 
python -m ipykernel install --user --name Auto_ML_C --display-name   "Auto_ML_C"
# Install Sklearn 0.6.  this will fixed next version
conda install -c conda-forge sklearn-contrib-lightning

# Method2
# Use the yaml environment file on the GitHub homepage to directly copy the current environment
conda env create -n Auto_ML_C -f Auto_ML_C.yaml

# Activate the newly created environment
conda activate 

# Suggest the pipeline of Jupyter notebook [optional, recommended]
conda install jupyter notebook
conda install ipykernel 
python -m ipykernel install --user --name Auto_ML_C --display-name   "Auto_ML_C"

Feature_struction

# 

Binary Classication

# Here is an example of the function binary_classfication_ws  
# 这里以函数binary_classfication_ws举例

# 开始加载环境
import pandas as pd
import numpy as np
import auto_ml_c.binary_classfication as abc

# 读取测试数据
df = pd.read_csv("2_data_deal_smote.csv")
X = df.iloc[:,:-1]
Y = df["label"]
score = 'accuracy'

# The first function, draw ROC image
tmp_a = abc.binary_ROC(X,Y,cv,"111","accuracy")

# The second function, get Auc_data, Acc_data, Recall_data, Precision_data
tmp_b1,tmp_b2,tmp_b3,tmp_b4 = abc.auto_model(X,Y,cv,"accuracy")

# The third function, draw the evaluation graph obtained by function 2 auto_model
tmp_c = abc.estimator_violion(tmp_b1,tmp_b2,"Violionplot")

ConTact

VX：Cuizy13390906310_ic

QQ：1776228595

E-mail：[e-mail]

GitHub：地址待填写