adaptivepca 1.0.0

Adaptive PCA with parallel scaling and dimensionality reduction

AdaptivePCA AdaptivePCA is a flexible, scalable Python package that enables dimensionality reduction with PCA, automatically selecting the best scaler and the optimal number of components to meet a specified variance threshold. Built for efficiency, AdaptivePCA includes parallel processing capabilities to speed up large-scale data transformations, making it ideal for data scientists and machine learning practitioners working with high-dimensional datasets.

Features Automatic Component Selection: Automatically selects the optimal number of principal components based on a specified variance threshold. Scaler Selection: Compares multiple scalers (StandardScaler and MinMaxScaler) to find the best fit for the data. Parallel Processing: Option to use concurrent scaling for faster computations. Easy Integration: Built on top of widely-used libraries like scikit-learn and numpy. Installation You can install AdaptivePCA via pip:

bash Copy code pip install adaptivepca Usage Import and Initialize python Copy code from adaptivepca import AdaptivePCA import pandas as pd

Load your dataset

X = pd.read_csv("your_data.csv") # Ensure your dataset is loaded as a Pandas DataFrame Basic Usage Initialize AdaptivePCA and fit it to your data:

python Copy code

Initialize AdaptivePCA with desired variance threshold and maximum components

adaptive_pca = AdaptivePCA(variance_threshold=0.95, max_components=10)

Fit and transform data

X_transformed = adaptive_pca.fit_transform(X) Parallel Processing For larger datasets, enable parallel processing to speed up computations:

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Fit AdaptivePCA with parallel processing

adaptive_pca.fit(X, parallel=True) Accessing Best Parameters After fitting, you can retrieve the best scaler, number of components, and explained variance score:

python Copy code print(f"Best Scaler: {adaptive_pca.best_scaler}") print(f"Optimal Components: {adaptive_pca.best_n_components}") print(f"Explained Variance Score: {adaptive_pca.best_explained_variance}") Parameters variance_threshold (float): Desired variance threshold for component selection. Default is 0.95. max_components (int): Maximum number of PCA components to consider. Default is 10. Methods fit(X, parallel=False): Fits AdaptivePCA to the dataset X. Use parallel=True to enable parallel processing. transform(X): Transforms the dataset X using the previously fitted configuration. fit_transform(X): Combines fit and transform steps in one call. Example python Copy code from adaptivepca import AdaptivePCA import pandas as pd

Example dataset

X = pd.DataFrame({ 'feature1': [1, 2, 3, 4, 5], 'feature2': [10, 9, 8, 7, 6], 'feature3': [2, 4, 6, 8, 10] })

adaptive_pca = AdaptivePCA(variance_threshold=0.95, max_components=2) X_transformed = adaptive_pca.fit_transform(X)

Retrieve best configuration details

print(f"Best Scaler: {adaptive_pca.best_scaler}") print(f"Optimal Components: {adaptive_pca.best_n_components}") print(f"Explained Variance Score: {adaptive_pca.best_explained_variance}") Dependencies scikit-learn>=0.24 numpy>=1.19 pandas>=1.1 License This project is licensed under the MIT License. See the LICENSE file for details.