KRidge-GIN 1.0.0

Kernel Ridge Regression Optimized by GBO, INFO, and RUN Algorithms (GIN)

Optimized Kernel Ridge Regression (KRidge)

This code implements a Kernel Ridge Regression (KRidge) model optimized using the INFO (Weighted Mean of Vectors Optimization) algorithm. The provided implementation focuses on predicting a target variable from a dataset and evaluating its performance using various metrics.

Features

• KRidge Regression: A powerful non-linear regression technique using kernel functions to capture complex relationships in data.
• Optimization: Employs the INFO, GBO, and RUN algorithms to determine optimal hyperparameters for the KRidge model, enhancing its predictive accuracy.
• Performance Evaluation: Calculates and saves a comprehensive set of regression metrics (R2, RMSE, MAPE, KGE, NSE, WHD, VSD, WAI) to assess model performance on training and testing datasets.
• Excel Output: Saves the calculated metrics and actual vs. predicted values into a well-structured Excel file for easy analysis and comparison.

Requirements

• Python 3.x
• Libraries: pandas, scikit-learn, openpyxl (install using pip install pandas scikit-learn openpyxl)
• Metrics.py: A separate Python file containing the Save_Metrics function (provided in previous responses).
• Run_INFO.py: A separate Python file containing the RUN_INFO and PredictedValue_TrainTest functions.

Usage

1. Data Preparation:

   • Ensure your dataset is in an Excel file (e.g., 'Data.xlsx').
   • The last column of the dataset should contain the target variable you want to predict.

2. Configuration:

   • nTs: Specifies the percentage of data to be used for testing (e.g., 0.3 for 30%).
   • kernel_type: Defines the kernel function to be used in KRidge (e.g., 'wavelet', 'rbf', etc.).
   • nP: The number of particles for the INFO optimization algorithm.
   • MaxIt: The maximum number of iterations for the INFO algorithm.
   • UC: Upper bound for the KRidge regularization parameter (C).
   • UKF: Upper bound for the KRidge kernel function coefficient.

3. Running the Code:

   • Execute the Python script.
   • The optimized KRidge model will be trained, and results will be saved to 'Results of KRidge.xlsx'.

Output

The code will generate an Excel file named 'Results of KRidge.xlsx' containing the following sheets:

• Metrics: Summarizes the calculated regression metrics for both training and testing sets.
• Train_Predictions: Shows the actual target values (y_train) and the corresponding model predictions (y_train_pred) for the training dataset.
• **Test_Predict

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Install

• pip install KRidge_INFO
• pip install IM_Metrics
• pip install info_optimizer
• pip install IM_GBO_optimizer
• pip install IM_RUN_optimizer

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Eample:

-- coding: utf-8 --

""" @author: Iman Ahmadianfar """ import pandas as pd
from sklearn.model_selection import train_test_split
from IM_Metrics import Save_Metrics from KRidge_GIN.KRidge import RUN_GIN,PredictedValue_TrainTest

Read data

data = pd.read_excel('Data.xlsx')

nTs = 0.3 # Percentage of test dataset
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=nTs, random_state=0)

kernel_type = 'wavelet'
nP = 50 MaxIt = 20 UC = 2e10 #Upper Bound for C coefficient in KRidge model. UKF = 2e10 #Upper Bound for kernel function coefficient in KRidge model.

optimization_method = 'RUN' # GBO or INFO best_parameters = RUN_GIN(nP, MaxIt,X_train, X_test, y_train, y_test,kernel_type,optimization_method,UC,UKF)

After obtaining final predictions

y_train_pred, y_train,y_test_pred,y_test = PredictedValue_TrainTest(best_parameters, kernel_type, X_train, y_train,X_test, y_test)

metrics_filename = 'Results of KRidge.xlsx' Save_Metrics(y_train, y_train_pred, y_test, y_test_pred,metrics_filename)