sparse-pls 0.1.1

Sparse Partial Least Squares (Sparse PLS) Regression

Sparse Partial Least Squares (Sparse PLS) Regression

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Introduction

The Sparse Partial Least Squares (Sparse PLS) project implements a regression model that combines dimensionality reduction with variable selection. This model is particularly useful for high-dimensional datasets where interpretability and feature selection are important. By incorporating sparsity into the Partial Least Squares (PLS) regression framework, the model selects the most relevant features while capturing the underlying relationship between predictors and responses.

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Features

• Dimensionality Reduction: Reduces the dataset to a lower-dimensional latent space.
• Variable Selection: Introduces sparsity to select the most relevant features.
• Customizable Scaling: Supports data preprocessing with different scaling methods.
• Hyperparameter Optimization: Includes methods for cross-validation and hyperparameter tuning.
• Scikit-learn Compatibility: Designed to integrate seamlessly with scikit-learn's ecosystem.

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Table of Contents

• Installation
• Dependencies
• Usage
  • Example Workflow
• Classes and Methods
  • SparsePLS
  • DataPreprocessor
• Mathematical Background
• Contributing
• License

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Installation

To install the required packages, you can use the following command:

pip install -r requirements.txt

Alternatively, you can install the packages individually:

pip install numpy pandas scikit-learn scipy joblib

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Dependencies

• Python 3.12.4
• numpy==1.26.4
• pandas==2.2.2
• scikit-learn==1.5.1
• scipy==1.13.1
• joblib==1.4.2

Ensure that all dependencies are installed before running the project. The requirements.txt file contains all the necessary packages and their versions.

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Usage

Example Workflow

import pandas as pd
from model import SparsePLS
from preprocessing import DataPreprocessor
from sklearn.model_selection import train_test_split

# Load your dataset
data = pd.read_csv('your_dataset.csv')
X = data.drop('target_column', axis=1)
y = data['target_column']

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Initialize the model
model = SparsePLS(n_components=2, alpha=0.5)

# Fit the model
model.fit(X_train, y_train)

# Transform the data
X_scores = model.transform(X_test)

# Predict target values
y_pred = model.predict(X_test)

# Optimize hyperparameters
param_grid = {
    'n_components': [1, 2, 3],
    'alpha': [0.1, 0.5, 1.0]
}
model.optimize_parameters(
    X_train, y_train,
    param_grid=param_grid,
    cv=5,
    scoring='neg_mean_squared_error',
    n_jobs=-1,
    verbose=1
)

# Access selected variables
selected_features = model.feature_names_in_[model.selected_variables_]
print("Selected Features:")
print(selected_features)

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Classes and Methods

SparsePLS Class

Overview

The SparsePLS class implements the Sparse Partial Least Squares regression model. It performs both dimensionality reduction and variable selection by introducing sparsity into the PLS framework.

Initialization Parameters

• n_components (int, default=2): Number of components to extract.
• alpha (float, default=1.0): Regularization parameter controlling sparsity.
• max_iter (int, default=500): Maximum number of iterations.
• tol (float, default=1e-6): Tolerance for convergence.
• scale (bool, default=True): Whether to scale the data.
• scale_method (str, default='standard'): Scaling method to use.
• **kwargs: Additional keyword arguments for the scaler.

Key Methods

• fit(X, Y): Fits the model to the data.
• transform(X): Transforms new data using the learned components.
• fit_transform(X, Y): Fits the model and transforms the data.
• predict(X): Predicts target values for new data.
• optimize_parameters(X, Y, param_grid, cv, scoring, n_jobs, verbose, return_models): Optimizes hyperparameters using cross-validation.

Attributes

• x_weights_: Weights for predictors.
• y_weights_: Weights for responses.
• x_loadings_: Loadings for predictors.
• y_loadings_: Loadings for responses.
• x_scores_: Scores for predictors.
• y_scores_: Scores for responses.
• coef_: Regression coefficients.
• selected_variables_: Indices of selected variables.
• feature_names_in_: Feature names seen during fitting.
• cv_results_: Cross-validation results.

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DataPreprocessor Class

Overview

The DataPreprocessor class handles data scaling and preprocessing. It supports different scaling methods and ensures that the data is appropriately transformed before modeling.

Initialization Parameters

• method (str, default='standard'): Scaling method to use.
• **kwargs: Additional arguments specific to the scaling method.

Key Methods

• fit(X): Fits the scaler to the data.
• transform(X): Transforms the data using the fitted scaler.
• fit_transform(X): Fits the scaler and transforms the data.
• inverse_transform(X_scaled): Reverts the data to its original scale.

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Mathematical Background

Partial Least Squares (PLS)

PLS regression seeks to find latent components that capture the maximum covariance between predictors (X) and responses (Y). It projects both X and Y into a lower-dimensional space.

Introducing Sparsity

Sparse PLS incorporates sparsity by applying an $\ell_1$-norm penalty on the weight vectors, encouraging many coefficients to be zero. This results in variable selection and enhances interpretability.

Optimization Problem

The optimization problem in Sparse PLS can be formulated as:

$$ \max_{\mathbf{w}, \mathbf{c}} \ \mathbf{w}^\top \mathbf{X}^\top \mathbf{Y} \mathbf{c} - \alpha (|\mathbf{w}|_1 + |\mathbf{c}|_1) $$

Subject to:

$$ |\mathbf{w}|_2 = 1, \quad |\mathbf{c}|_2 = 1 $$

Where:

• $\mathbf{w}$ and $\mathbf{c}$ are weight vectors.
• $\alpha$ controls the level of sparsity.
• $|\cdot|_1$ is the $\ell_1$-norm.
• $|\cdot|_2$ is the $\ell_2$-norm.

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Contributing

We welcome contributions to improve the Sparse PLS project. If you'd like to contribute, please follow these steps:

1. Fork the Repository: Create a fork of this repository to your GitHub account.
2. Clone the Fork: Clone your forked repository to your local machine.
3. Create a Feature Branch: Create a new branch for your feature or bug fix.
4. Make Changes: Implement your changes and ensure that the code is well-documented.
5. Test Your Changes: Run existing tests and add new ones if necessary.
6. Commit and Push: Commit your changes and push them to your fork.
7. Submit a Pull Request: Open a pull request to the main repository with a clear description of your changes.

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License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software in accordance with the license terms.

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Contact Information

If you have any questions, suggestions, or issues, please feel free to open an issue on the GitHub repository or contact the project maintainers.

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Author and Acknowledgments

Author and Owner of this repository : Younes AJEDDIG, Ph.D in Process & Chemical Engineering

Thanks to ChatGPT o1-preview, it's nice to have someone that do things you do not like to do even if you need it.

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Additional Resources

• Scikit-learn Documentation: https://scikit-learn.org/stable/
• NumPy Documentation: https://numpy.org/doc/
• Pandas Documentation: https://pandas.pydata.org/docs/
• SciPy Documentation: https://docs.scipy.org/doc/

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Note: Ensure that the preprocessing.py file containing the DataPreprocessor class is included in your project directory. This file should define the class and methods as used in the SparsePLS class.

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Disclaimer: This README provides a high-level overview of the project. For detailed information, please refer to the code documentation and docstrings within the codebase.