DimSense is a Python library designed to simplify feature selection and extraction tasks for machine learning. With DimSense, you can easily transform and preprocess your data using a variety of feature selection and extraction methods.
You can install DimSense using pip:
pip install dimsense
Let's dive into a quick example of how to use DimSense for feature extraction:
import numpy as np
from dimsense.feature_extraction import PCAExtractor
# Generate synthetic data
np.random.seed(42)
X = np.random.rand(100, 5)
# Initialize the PCAExtractor
extractor = PCAExtractor(num_components=2)
# Transform the data
X_extracted = extractor.fit_transform(X)
print("Original data shape:", X.shape)
print("Extracted data shape:", X_extracted.shape)DimSense offers a range of feature selection and extraction methods, including:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Term Frequency-Inverse Document Frequency (TF-IDF) extraction
- Word count-based feature extraction
- Topic modeling using Latent Dirichlet Allocation (LDA)
- Independent Component Analysis (ICA)
- Autoencoders for feature extraction
For more details on each method, refer to the Feature Extraction Methods documentation.
Explore the detailed documentation for more information on how to use DimSense effectively. Check out the Usage Guide for step-by-step instructions and examples.
DimSense is an open-source project, and contributions are welcome! If you have ideas for improvements or new feature extraction methods, check out our Contributing Guidelines for more information.
DimSense v0.1.2