Auto-Taste-ML 0.0.1

A small example package

Auto_Taste_ML 0.0.1

Illustrate：

​ 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 :clinking_glasses: :clinking_glasses:

Spend

Test set data location:：{Example}

The configuration is as follows：

	Windows	Windows	MacOS	Linux
Config	i7-9750H	i7-9750H	M1	E5-2640 V4
Cores	6 cores 12 threads	6 cores 12 threads	8 cores	20 cores 40 threads
Freq	2.67GHz	3.2GHz		2.40GHz

运行速度对比结果如下：

Not integrated	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

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

The main software involved is as follows:

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:

​ This package is based on Sklearn, imblance and other machine learning expansion packages. It is currently planned to be divided into two parts:

• Classification task

  1. binary_classfication.py

     	Function	Rturn
     cal_add_1(num1,num2):wave:	welcome	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)
     Below is the total function
     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

     Develeping :man_health_worker:

  3. 特征筛选函数Feature_struction

     Name	Function	Rturn
     data_enhance_show(X,Y[["Taste_num"]],location,kind="SMOTE")	A variety of visualization methods to reduce dimensionality to visualize the data to be enhanced	DataFRmae
     data_enhance(X.iloc[:,:-1],Y[["Taste_num"]])	Use multiple indicators to evaluate the results of different enhancement methods	DataFrame
     data_enhance_compare(tmp1,location,name)	Visualize the return result of function 2	Fig

  4. The progress bar version of hyperparameter optimization! ! ! ! Surely series next time!

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

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