Auto-ML-C 0.0.9

A small example package

Auto_ML_C 0.0.8

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}

如果全流程,源码的速度是 Fast_Example.ipynb是 38s

函数【有感知机版本】	耗时	函数【无感知机版本】	耗时
以下数据在i9-9750H		12核上测试
binary_ROC(X,Y,5,)	566.51	也就是去掉NuSVC	52.63
auto_model(X,Y,5)	556.12		52.60

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
py-xgboost=1.4.0

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)
     NuSVC_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)
     xgboost_model(X, Y,cv)
     KNeighborsClassifier_model(X, Y,cv)
     NearestNeighbors_model(X, Y,cv)
     BernoulliNB_model(X, Y,cv)
     GaussianNB_model(X,Y,cv)
     下面是新增函数
     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. binary_classfication_ws.py

     这是为了速度考虑，舍弃占用90%时间的NuSVC函数的函数

  3. 多分类函数

     等待

  4. 数据增强函数

  5. waited

How to Use

Install

:warning: MacOS not pass

# 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
conda install xgboost
[之后完善可以看到，这里依赖项比较多
  _py-xgboost-mutex  anaconda/pkgs/main/osx-64::_py-xgboost-mutex-2.0-cpu_0
  libxgboost         anaconda/pkgs/main/osx-64::libxgboost-1.3.3-h23ab428_0
  py-xgboost         anaconda/pkgs/main/osx-64::py-xgboost-1.3.3-py38hecd8cb5_0
  xgboost            anaconda/pkgs/main/osx-64::xgboost-1.3.3-py38hecd8cb5_0
]

brew install scipy

# 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"

Binary Classication

# Here is an example of the function binary_classfication_ws  
# 这里以函数binary_classfication_ws举例
# if you use macOS and can't load xgboost ,  please use function binary_classfication_ws_for_mac，This function abandons xgboost which is more troublesome for mac

ConTact

VX：Cuizy13390906310_ic

QQ：1776228595

E-mail：[e-mail]

GitHub：地址待填写