eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles

• URL: http://arxiv.org/abs/2401.09582v1
• Date: Wed, 17 Jan 2024 20:07:47 GMT
• Title: eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles
• Authors: Jamie J. R. Bennett, Yan Chak Li, Gaurav Pandey
• Abstract summary: eipy is an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing data integration and predictive modeling methods.
• Score: 3.465746303617158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we introduce eipy--an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy simultaneously provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing multi-modal data integration and predictive modeling methods by systematically evaluating their performance using nested cross-validation. The package is designed to leverage scikit-learn-like estimators as components to build multi-modal predictive models. An up-to-date user guide, including API reference and tutorials, for eipy is maintained at https://eipy.readthedocs.io . The main repository for this project can be found on GitHub at https://github.com/GauravPandeyLab/eipy .

Related papers

• MALPOLON: A Framework for Deep Species Distribution Modeling [3.1457219084519004]
  MALPOLON aims to facilitate training and inferences of deep species distribution models (deep-SDM) It is written in Python and built upon the PyTorch library. The framework is open-sourced on GitHub and PyPi.
  arXiv  Detail & Related papers  (2024-09-26T17:45:10Z)
• VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models [89.63342806812413]
  We present an open-source toolkit for evaluating large multi-modality models based on PyTorch. VLMEvalKit implements over 70 different large multi-modality models, including both proprietary APIs and open-source models. We host OpenVLM Leaderboard to track the progress of multi-modality learning research.
  arXiv  Detail & Related papers  (2024-07-16T13:06:15Z)
• Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models [4.60168321737677]
  textttMixture-Models is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants. It streamlines the implementation and analysis of these models using various first/second order optimization routines. The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation.
  arXiv  Detail & Related papers  (2024-02-08T19:34:24Z)
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series [0.0]
  PyPOTS is an open-source Python library dedicated to data mining and analysis on partially-observed time series. It provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• DADApy: Distance-based Analysis of DAta-manifolds in Python [51.37841707191944]
  DADApy is a python software package for analysing and characterising high-dimensional data. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics.
  arXiv  Detail & Related papers  (2022-05-04T08:41:59Z)
• PyHHMM: A Python Library for Heterogeneous Hidden Markov Models [63.01207205641885]
  PyHHMM is an object-oriented Python implementation of Heterogeneous-Hidden Markov Models (HHMMs) PyHHMM emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License.
  arXiv  Detail & Related papers  (2022-01-12T07:32:36Z)
• IMBENS: Ensemble Class-imbalanced Learning in Python [26.007498723608155]
  imbens is an open-source Python toolbox for implementing and deploying ensemble learning algorithms on class-imbalanced data. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2021-11-24T20:14:20Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• mvlearn: Multiview Machine Learning in Python [103.55817158943866]
  mvlearn is a Python library which implements the leading multiview machine learning methods. The package can be installed from Python Package Index (PyPI) and the conda package manager.
  arXiv  Detail & Related papers  (2020-05-25T02:35:35Z)
• Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
  We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
  arXiv  Detail & Related papers  (2020-01-10T16:14:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.