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توضیحات

Calculate a Machine Learning (ML) performance metric called MLcps: ML Cumulative Performance Score.

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	MLcps-0.0.6
نام	MLcps
نسخه کتابخانه	0.0.6
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Akshay
ایمیل نویسنده	akshaysuhag1996@gmail.com
آدرس صفحه اصلی	https://github.com/FunctionalUrology/MLcps
آدرس اینترنتی	https://pypi.org/project/MLcps/
مجوز	-

# MLcps **MLcps: Machine Learning cumulative performance score** is a performance metric that combines multiple performance metrics and reports a cumulative score enabling researchers to compare the ML models using a single metric. MLcps provides a comprehensive platform to identify the best-performing ML model on any given dataset. ### ***Note*** If you want to use MLcps without installing it on your local machine, please click here [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main). It will launch a Jupyterlab server (all the requirements for MLcps are pre-installed ) where you can run the already available example Jupyter notebook for MLcps analysis. It may take a while to launch! You can also upload your data or notebook to perform the analysis. # Prerequisites 1. Python >=3.8 2. R >=4.0. R should be accessible through terminal/command prompt. 3. ```radarchart, tibble,``` and ```dplyr``` R packages. MLcps can install all these packages at first import if unavailable, but we highly recommend installing them before using MLcps. The user could run the following R code in the R environment to install them: ``` ## Install the unavailable packages install.packages(c('radarchart','tibble','dplyr'),dependencies = TRUE,repos="https://cloud.r-project.org") ``` # Installation ``` pip install MLcps ``` # Binder environment for MLcps As an alternative, we have built a binder computational environment where all the requirements are pre-installed for MLcps. It allows the user to ***use MLcps without any installation***. Please click here [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main) to launch the Jupyterlab server where you can run the already available example Jupyter notebook for MLcps analysis. It may take a while to launch! You can also upload your data or notebook to perform the analysis. # Usage #### **Quick Start** ```python #import MLcps from MLcps import getCPS #calculate Machine Learning cumulative performance score cps=getCPS.calculate(object) ``` > * ***object***: A pandas dataframe where rows are different metrics scores and columns are different ML models. **Or** a GridSearchCV object. > * ***cps***: A pandas dataframe with models name and corresponding MLcps. **Or** a GridSearchCV object. #### **Example 0.1** Create Input dataframe for MLcps ```python import pandas as pd metrics_list=[] #Metrics from SVC model (kernel=rbf) acc = 0.88811 #accuracy bacc = 0.86136 #balanced_accuracy prec = 0.86 #precision rec = 0.97727 #recall f1 = 0.91489 #F1 mcc = 0.76677 #Matthews_correlation_coefficient metrics_list.append([acc,bacc,prec,rec,f1,mcc]) #Metrics from SVC model (kernel=linear) acc = 0.88811 bacc = 0.87841 prec = 0.90 rec = 0.92045 f1 = 0.91011 mcc = 0.76235 metrics_list.append([acc,bacc,prec,rec,f1,mcc]) #Metrics from KNN acc = 0.78811 bacc = 0.82841 prec = 0.80 rec = 0.82 f1 = 0.8911 mcc = 0.71565 metrics_list.append([acc,bacc,prec,rec,f1,mcc]) metrics=pd.DataFrame(metrics_list,index=["SVM rbf","SVM linear","KNN"], columns=["accuracy","balanced_accuracy","precision","recall", "f1","Matthews_correlation_coefficient"]) print(metrics) ``` #### **Example 1** Calculate MLcps for a pandas dataframe where rows are different metrics scores and columns are different ML models. ```python #import MLcps from MLcps import getCPS #read input data (a dataframe) or load an example data metrics=getCPS.sample_metrics() #calculate Machine Learning cumulative performance score cpsScore=getCPS.calculate(metrics) print(cpsScore) ######################################################### #plot MLcps import plotly.express as px from plotly.offline import plot import plotly.io as pio pio.renderers.default = 'iframe' #or pio.renderers.default = 'browser' fig = px.bar(cpsScore, x='Score', y='Algorithms',color='Score',labels={'MLcps Score'}, width=700,height=1000,text_auto=True) fig.update_xaxes(title_text="MLcps") plot(fig) fig ``` #### **Example 2** Calculate MLcps using the mean test score of all the metrics available in the given GridSearch object and return an updated GridSearch object. Returned GridSearch object contains ```mean_test_MLcps``` and ```rank_test_MLcps``` arrays, which can be used to rank the models similar to any other metric. ```python #import MLcps from MLcps import getCPS #load GridSearch object or load it from package gsObj=getCPS.sample_GridSearch_Object() #calculate Machine Learning cumulative performance score gsObj_updated=getCPS.calculate(gsObj) ######################################################### #access MLcps print("MLcps: ",gsObj_updated.cv_results_["mean_test_MLcps"]) #access rank array based on MLcps print("Ranking based on MLcps:",gsObj_updated.cv_results_["rank_test_MLcps"]) ``` #### **Example 3** Certain metrics are more significant than others in some cases. As an example, if the dataset is imbalanced, a high F1 score might be preferred to higher accuracy. A user can provide weights for metrics of interest while calculating MLcps in such a scenario. Weights should be a dictionary object where keys are metric names and values are corresponding weights. It can be passed as a parameter in ```getCPS.calculate()``` function. * **3.a)** ```python #import MLcps from MLcps import getCPS #read input data (a dataframe) or load an example data metrics=getCPS.sample_metrics() #define weights weights={"Accuracy":0.75,"F1": 1.25} #calculate Machine Learning cumulative performance score cpsScore=getCPS.calculate(metrics,weights) print(cpsScore) ######################################################### #plot weighted MLcps import plotly.express as px from plotly.offline import plot import plotly.io as pio pio.renderers.default = 'iframe' #or pio.renderers.default = 'browser' fig = px.bar(cpsScore, x='Score', y='Algorithms',color='Score',labels={'MLcps Score'}, width=700,height=1000,text_auto=True) fig.update_xaxes(title_text="MLcps") plot(fig) fig ``` * **3.b)** ```python #import MLcps from MLcps import getCPS ######################################################### #load GridSearch object or load it from package gsObj=getCPS.sample_GridSearch_Object() #define weights weights={"accuracy":0.75,"f1": 1.25} #calculate Machine Learning cumulative performance score gsObj_updated=getCPS.calculate(gsObj,weights) ######################################################### #access MLcps print("MLcps: ",gsObj_updated.cv_results_["mean_test_MLcps"]) #access rank array based on MLcps print("Ranking based on MLcps:",gsObj_updated.cv_results_["rank_test_MLcps"]) ``` # Links  * MLcps source code and a Jupyter notebook with sample analyses is available on the **[MLcps GitHub repository](https://github.com/FunctionalUrology/MLcps/blob/main/Example-Notebook.ipynb)** and binder [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main). * Please use the **[MLcps GitHub](https://github.com/FunctionalUrology/MLcps/issues)** repository to report all the issues. # Citations Information If **MLcps** in any way help you in your research work, please cite the MLcps publication. ***

نیازمندی

مقدار	نام
-	rpy2
-	pandas
-	scikit-learn
-	imbalanced-learn
-	plotly

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl MLcps-0.0.6:

pip install MLcps-0.0.6.whl

نصب پکیج tar.gz MLcps-0.0.6:

pip install MLcps-0.0.6.tar.gz