mvlearn is a Python module for machine learning on multiview data (sometimes referred to as multi-modal, multi-table, or multi-block data).
mvlearn aims to serve as a community-driven open-source software package that offers reference implementations for algorithms and methods related to multiview learning, machine learning in settings where there are multiple incommensurate views or feature sets for each sample. It brings together the most widely-used tools in this setting with a standardized scikit-learn like API, well tested code and high-quality documentation. Doing so we aim to facilitate application, extension, comparison of methods, and offer a foundation for research into new multiview algorithms. We welcome new contributors and the addition of methods with proven efficacy and current use.
Multiview data, in which each sample is represented by multiple views of distinct features, are often seen in real-world data, and related methods have grown in popularity. A view is defined as a partition of the complete set of feature variables . Depending on the domain, these views may arise naturally from unique sources, or they may correspond to subsets of the same underlying feature space. For example, a doctor may have an MRI scan, a CT scan, and the answers to a clinical questionnaire for a diseased patient. However, classical methods for inference and analysis are often poorly suited to account for multiple views of the same sample, since they cannot properly account for complementing views that hold differing statistical properties . To deal with this, many multiview learning methods have been developed to take advantage of multiple data views and produce better results in various tasks    .
Decompose two views using multiview PCA to capture joint information
from mvlearn.decomposition import GroupPCA # X1 and X2 are data matrices, each with n samples Xs = [X1, X2] # multiview data Xs_components = GroupPCA().fit_transform(Xs)
Cluster two views using multiview KMeans to find shared labels
from mvlearn.cluster import MultiviewKMeans # X1 and X2 are data matrices, each with n samples Xs = [X1, X2] # multiview data labels = MultiviewKMeans().fit_predict(Xs)
Highlighted full examples¶
- Nutrimouse dataset case study:
- A collection of multiview learning methods across modules provide insights to a 2-view genomics dataset.
- Multiview vs singleview clustering on the UCI multiview digits:
- Multiview clustering strongly outperforms single view clustering on a multiview dataset of handwritten digits.
- A comparison of CCA algorithms:
- Canonical correlation analysis (CCA) variants find linearly correlated projections of each view. Linear and nonlinear variants are compared in various simulated settings.
Python is a powerful programming language that allows concise expressions of network algorithms. Python has a vibrant and growing ecosystem of packages that mvlearn uses to provide more features such as numerical linear algebra. In order to make the most out of mvlearn you will want to know how to write basic programs in Python. Among the many guides to Python, we recommend the Python documentation.
Currently, mvlearn is supported for Python 3.6, 3.7, and 3.8.
mvlearn was developed during the end of 2019 by Richard Guo, Ronan Perry, Gavin Mischler, Theo Lee, Alexander Chang, Arman Koul, and Cameron Franz, a team out of the Johns Hopkins University NeuroData group.
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