/Auto_ML_C

TastePeptides-Meta convenient machine learning expansion package

Primary LanguageJupyter NotebookMIT LicenseMIT

Auto_ML_C 0.0.12

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 🥂

​ This is a machine learning expansion package for Cui Lianshan and his friends. It has a strong socialist open source sharing spirit, and is extremely creative and combat effective. Let us applaud them here 🥂

Spend

测试集数据位置 Test set data location ：{Example}

The configuration is as follows:

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：

Unwrapped function 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

The running speed comparison results are as follows:

Wrapper function 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

You can refer to learning the environment backup in the current directory: Auto_ML_C.yaml

Package Minimum version-to be tested
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， ​ This package is based on Sklearn, imblance and other machine learning expansion packages. It is planned to be divided into two parts.

  • Classification task

    1. binary_classfication.py

      The internal available functions are as follows 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) Plot the best ROC under scalar hyperparameter search fig
      auto_model(X, Y, k)Model's scalar hyperparameter search results Auc_data, Acc_data,
      Recall_data, Precision_data
      estimator_violion(df1,df2,fig_name) Draw a violin chart for the Dataframe of the auto_model result fig

    2. Multi-class function

      等待

    3. Feature selection function Feature_struction

      Function Function return value
      data_enhance_show(X,Y[["Taste_num"]],location,kind="SMOTE") 多种可视化方法进行降维可视化待增强数据
      A variety of visualization methods to reduce dimensionality to visualize the data to be enhanced      		 Deduplicate DataFrame
      data_enhance(X.iloc[:,:-1],Y[["Taste_num"]]) 采用多指标评价不同增强方法后的结果
      Using multiple indicators to evaluate the results of different enhancement methods      		 Rating DataFrame
      data_enhance_compare(tmp1,location,name) 可视化函数2的返回结果
      Visualize the return result of function 2      		 图片 Fig

    4. 超参数优化的进度条版本！！！！ 下次一定系列！ Super parameter optimization progress bar display function! ! ! ! Update focus of the next version

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==0.0.11
pip install imblearn

# 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

import auto_ml_c.Feature_structure as fs
import pandas as pd
df = pd.read_csv("1_23肽全部的构效数据.csv")
X = df.iloc[:,2:]
Y = df[["Taste"]]
Y["Taste_num"] = 10
for i in range(Y.shape[0]):
    if Y["Taste"].iloc[i] == "Umami":
        Y["Taste_num"].iloc[i] = 1
    elif Y["Taste"].iloc[i] == "Bitter":
        Y["Taste_num"].iloc[i] = 0
        
# Function 1: Use a variety of visualization methods to reduce dimensionality to visualize the data to be enhanced       
# 函数1： 采用多种可视化方法进行降维可视化待增强数据
location = ""
tmp = fs.data_enhance_show(X,Y[["Taste_num"]],location,kind="SMOTE")  
tmp

# Function 2: Using multiple indicators to evaluate the results of different enhancement methods
# 函数2： 采用多指标评价不同增强方法后的结果
tmp1 = fs.data_enhance(X.iloc[:,:-1],Y[["Taste_num"]])
tmp1

# Function 3: Visualize the return result of function 2
# 函数3：可视化函数2的返回结果
location = ""
name="Test"
tmp2 = fs.data_enhance_compare(tmp1,location,name)
tmp2

image-20211026201938046

image-20211026202033919

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")

binary_ROC

estimator_violion

ConTact

VX：Cuizy13390906310_ic

QQ：1776228595

E-mail：1776228595@qq.com

GitHub：https://github.com/SynchronyML/Auto_ML_C