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Coordinated Minima Search: An Efficient Approach for Optimizing Linear and Non-Linear Regression Models.
CMS is a Python package that provides tools for compiling and managing custom models for linear and non-linear regression analysis. It includes functions to compile linear and non-linear regression models, and to predict.
Clone the repository:
git clone https://github.com/RHassan1609/cms.git
Install the package:
pip install .
To use the CMS package in your project, import the module and use the available functions.
from cms import compile_custom_model, compile_best_model, compile_all_models, predict
# Example: Using the compile_custom_model function
import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_custom_model(data=data,powers=[1,2,1],feature_columns=[0,1,2],label_column=4,print_loss=True,model_name='cms_model',print_model=True)
# Example: Using the compile_best_model function
import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_best_model(data=data,max_power=2,feature_columns=[0,1,2],label_column=4,print_loss=True,print_best_model=True,best_model_name='best_cms_model',save_first_model=True,first_model_name='first_cms_model')
# Example: Using the compile_all_models function
import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_all_models(data=data,powers=[1,2,1],feature_columns=[0,1,2],label_column=4,print_loss=True,model_name='cms_model',print_model=True)
# Example: Using the predict function
test_data=pd.read_csv(r'D:\CMS\test_data.csv')
predict(data=test_data,load_model='best_cms_model',print_output=True,save_output_file=True,saved_file_name='predicted cms file')
FAQs
Coordinated Minima Search: An Efficient Approach for Optimizing Linear and Non-Linear Regression Models.
We found that cms-model demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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