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install.html
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<!DOCTYPE html>
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<title>Install — mvlearn alpha documentation</title>
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0.5.0
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<p class="caption" role="heading"><span class="caption-text">Using mvlearn</span></p>
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<li class="toctree-l1"><a class="reference internal" href="index.html">Overview of mvlearn</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Install</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#pip-installation-instructions">pip installation instructions</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_coregularized_spectral_tutorial.html">Multiview Coregularized Spectral Clustering Comparison</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spherical_kmeans_tutorial.html">Multiview Spherical KMeans Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_tutorial.html">Multiview KMeans Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_vs_singleview_spectral.html">Multiview vs. Singleview Spectral Clustering of UCI Multiview Digits</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_validation_simulated.html">Multiview vs. Singleview KMeans</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spectral_tutorial.html">Multiview Spectral Clustering Tutorial</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spectral_validation_complex.html">Conditional Independence of Views on Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_validation_complex.html">Conditional Independence of Views on Multiview KMeans Clustering</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/compose/plot_multiview_construction.html">Constructing multiple views to classify singleview data</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/compose/plot_pipeline_sklearn_integration.html">Integrating mvlearn with scikit-learn</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_load_ucimultifeature.html">Loading and Viewing the UCI Multiple Features Dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_gaussianmixtures.html">Generating Multiview Data from Gaussian Mixtures</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_nutrimouse.html">An mvlearn case study: the Nutrimouse dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_group_ica_tutorial.html">ICA: a tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_mv_ica_tutorial.html">Multiview Independent Component Analysis (ICA) Comparison</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_ajive_tutorial.html">Angle-based Joint and Individual Variation Explained (AJIVE)</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_gcca_tutorial.html">Generalized Canonical Correlation Analysis (GCCA) Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_mcca_tutorial.html">CCA Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_dcca_tutorial.html">Deep CCA (DCCA) Tutorial</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_cca_comparison.html">Comparing CCA Variants</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_kmcca_tutorial.html">Kernel MCCA (KMCCA) Tutorial</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/plotting/plot_quick_visualize_tutorial.html">Quickly Visualizing Multiview Data</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/plotting/plot_crossviews_plot.html">Plotting Multiview Data with a Cross-view Plot</a></li>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-semi-supervised">Examples on semi_supervised</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/semi_supervised/plot_cotraining_regression.html">2-View Semi-Supervised Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/semi_supervised/plot_cotraining_classification.html">2-View Semi-Supervised Classification</a></li>
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<li class="toctree-l1"><a class="reference internal" href="references/index.html">Reference</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="references/embed.html#canonical-correlation-analysis-cca">Canonical Correlation Analysis (CCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#multiview-canonical-correlation-analysis-mcca">Multiview Canonical Correlation Analysis (MCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#kernel-mcca">Kernel MCCA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#generalized-canonical-correlation-analysis-gcca">Generalized Canonical Correlation Analysis (GCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#deep-canonical-correlation-analysis-dcca">Deep Canonical Correlation Analysis (DCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#multiview-multidimensional-scaling">Multiview Multidimensional Scaling</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#split-autoencoder">Split Autoencoder</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#dcca-utilities">DCCA Utilities</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#dimension-selection">Dimension Selection</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/decomposition.html">Decomposition</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#multiview-ica">Multiview ICA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#group-ica">Group ICA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#group-pca">Group PCA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#angle-based-joint-and-individual-variation-explained-ajive">Angle-Based Joint and Individual Variation Explained (AJIVE)</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="references/cluster.html">Clustering</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-spectral-clustering">Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#co-regularized-multiview-spectral-clustering">Co-Regularized Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-k-means">Multiview K Means</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-spherical-k-means">Multiview Spherical K Means</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/semi_supervised.html">Semi-Supervised</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/semi_supervised.html#cotraining-classifier">Cotraining Classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/semi_supervised.html#cotraining-regressor">Cotraining Regressor</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="references/model_selection.html">Model Selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/model_selection.html#cross-validation">Cross Validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/model_selection.html#train-test-split">Train-Test Split</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/compose.html">Compose</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#averagemerger">AverageMerger</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#concatmerger">ConcatMerger</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#randomgaussianprojection">RandomGaussianProjection</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#randomsubspacemethod">RandomSubspaceMethod</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#simplesplitter">SimpleSplitter</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#viewclassifier">ViewClassifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#viewtransformer">ViewTransformer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/datasets.html">Multiview Datasets</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#uci-multiple-feature-dataset">UCI multiple feature dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#nutrimouse-dataset">Nutrimouse dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#data-simulator">Data Simulator</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#factor-model">Factor Model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="references/plotting.html#quick-visualize">Quick Visualize</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/plotting.html#crossviews-plot">Crossviews Plot</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/utils.html">Utility Functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/utils.html#io">IO</a></li>
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<p class="caption" role="heading"><span class="caption-text">Developer Information</span></p>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#submitting-a-bug-report-or-a-feature-request">Submitting a bug report or a feature request</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#how-to-make-a-good-bug-report">How to make a good bug report</a></li>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#contributing-code">Contributing Code</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#pull-request-checklist">Pull Request Checklist</a></li>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#guidelines">Guidelines</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#coding-guidelines">Coding Guidelines</a></li>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#docstring-guidelines">Docstring Guidelines</a></li>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#api-of-mvlearn-objects">API of mvlearn Objects</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#estimators">Estimators</a></li>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#additional-functionality">Additional Functionality</a></li>
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<li class="toctree-l1"><a class="reference internal" href="changelog.html">Changelog</a><ul>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-5-0">Version 0.5.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-4-1">Version 0.4.1</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-4-0">Version 0.4.0</a><ul>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id5">mvlearn.compose</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id13">mvlearn.construct</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id15">mvlearn.decomposition</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id17">mvlearn.embed</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id21">mvlearn.model_selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id24">mvlearn.utils</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-3-0">Version 0.3.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#patch-0-2-1">Patch 0.2.1</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-2-0">Version 0.2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-1-0">Version 0.1.0</a></li>
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<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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<p class="caption" role="heading"><span class="caption-text">Useful Links</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn">mvlearn @ GitHub</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pypi.org/project/mvlearn/">mvlearn @ PyPI</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn/issues">Issue Tracker</a></li>
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<section id="install">
<h1>Install<a class="headerlink" href="#install" title="Permalink to this headline">¶</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> can be installed by using <code class="docutils literal notranslate"><span class="pre">pip</span></code>, GitHub, or through the conda-forge
channel into an existing <code class="docutils literal notranslate"><span class="pre">conda</span></code> environment.
See below for <a class="reference internal" href="#pipanchor"><span class="std std-ref">pip installation instructions</span></a> or <a class="reference internal" href="#condaanchor"><span class="std std-ref">conda installation instructions</span></a>.</p>
<p><strong>IMPORTANT NOTE:</strong> <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> has an optional dependencies for certain functions,
and so special instructions must be followed to include these
optional dependencies in the installation (if you do not have those packages already)
in order to access all the features within <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code>.
More details can be found in <a class="reference internal" href="#extradependencyanchor"><span class="std std-ref">Including optional dependencies for full functionality</span></a>.</p>
<section id="pip-installation-instructions">
<span id="pipanchor"></span><h2>pip installation instructions<a class="headerlink" href="#pip-installation-instructions" title="Permalink to this headline">¶</a></h2>
<p>Below we assume you have the default Python3 environment already configured on
your computer and you intend to install <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> inside of it. If you want
to create and work with Python virtual environments, please follow instructions
on <a class="reference external" href="https://docs.python.org/3/library/venv.html">venv</a> and <a class="reference external" href="http://docs.python-guide.org/en/latest/dev/virtualenvs/">virtual
environments</a>.</p>
<p>First, make sure you have the latest version of <code class="docutils literal notranslate"><span class="pre">pip3</span></code> (the Python3 package manager)
installed. If you do not, refer to the <a class="reference external" href="https://pip.pypa.io/en/stable/installing/">Pip documentation</a> and install <code class="docutils literal notranslate"><span class="pre">pip3</span></code> first.</p>
<p>Install the current release of <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> with <code class="docutils literal notranslate"><span class="pre">pip3</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install mvlearn
</pre></div>
</div>
<p>To upgrade to a newer release use the <code class="docutils literal notranslate"><span class="pre">--upgrade</span></code> flag:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install --upgrade mvlearn
</pre></div>
</div>
<p>If you do not have permission to install software systemwide, you can
install into your user directory using the <code class="docutils literal notranslate"><span class="pre">--user</span></code> flag:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install --user mvlearn
</pre></div>
</div>
<p>Alternatively, you can manually download <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> from
<a class="reference external" href="https://github.com/mvlearn/mvlearn">GitHub</a> or
<a class="reference external" href="https://pypi.org/project/mvlearn/">PyPI</a>.
To install one of these versions, unpack it and run the following from the
top-level source directory using the Terminal:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install -e .
</pre></div>
</div>
<p>This will install <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> and the required dependencies (see below).</p>
</section>
<section id="conda-installation-instructions">
<span id="condaanchor"></span><h2>conda installation instructions<a class="headerlink" href="#conda-installation-instructions" title="Permalink to this headline">¶</a></h2>
<p>Here, we assume you have created a conda environment with one of the
accepted python versions, and you intend to install the <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code>
into it.</p>
<p>To install <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> with conda, run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ conda install -c conda-forge mvlearn
</pre></div>
</div>
<p>To list all versions of <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> available on your platform, use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ conda search mvlearn --channel conda-forge
</pre></div>
</div>
<section id="including-optional-dependencies-for-full-functionality">
<span id="extradependencyanchor"></span><h3>Including optional dependencies for full functionality<a class="headerlink" href="#including-optional-dependencies-for-full-functionality" title="Permalink to this headline">¶</a></h3>
<p>A small subset of functions require specific extra dependencies not installed
by default with the core installation. Each bullet point denotes a collection
of functions, with corresponding keyword enclosed in the brackets [].</p>
<ul class="simple">
<li><p>[torch]: <code class="docutils literal notranslate"><span class="pre">DCCA</span></code>, <code class="docutils literal notranslate"><span class="pre">SplitAE</span></code></p></li>
<li><p>[multiviewica]: <code class="docutils literal notranslate"><span class="pre">MultiviewICA</span></code>, <code class="docutils literal notranslate"><span class="pre">GroupICA</span></code></p></li>
</ul>
<p>If you want to use any of the above functionality within mvlearn, please
follow the directions below to install the additional dependencies.
These dependencies are listed in the package requirements folder
with corresponding keyword names for manual installation.</p>
<p>They can be installed from PyPI by simply calling:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install mvlearn[keyword]
</pre></div>
</div>
<p>where 'keyword' is from the list above, bracketed.
To upgrade the package and torch requirements:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install --upgrade mvlearn[keyword]
</pre></div>
</div>
<p>If you have the package locally, from the top level folder call:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip3 install -e .[keyword]
</pre></div>
</div>
<p>To install the optional dependencies in with conda, consult the following for the dependencies you need:</p>
<ul class="simple">
<li><p>[torch]: Please consult the <a class="reference external" href="https://pytorch.org/get-started/locally/">PyTorch Installation Guide</a></p></li>
</ul>
<p>to install it properly for your specific system specifications. Then, install tqdm:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ conda install -c conda-forge tqdm
</pre></div>
</div>
<ul>
<li><p>[multiviewica]: There are two package dependencies for this functionality, which can be installed through conda-forge:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ conda install -c conda-forge python-picard
$ conda install -c conda-forge multiviewica
</pre></div>
</div>
</li>
</ul>
</section>
</section>
<section id="python-package-dependencies">
<h2>Python package dependencies<a class="headerlink" href="#python-package-dependencies" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> requires the following packages:</p>
<ul class="simple">
<li><p>matplotlib >=3.0.0</p></li>
<li><p>numpy >=1.17.0</p></li>
<li><p>scikit-learn >=0.19.1</p></li>
<li><p>scipy >=1.5.0</p></li>
<li><p>seaborn >=0.9.0</p></li>
<li><p>joblib >=0.11</p></li>
</ul>
<p>with optional [torch] dependencies,</p>
<ul class="simple">
<li><p>torch >=1.1.0</p></li>
<li><p>tqdm</p></li>
</ul>
<p>and optional [multiviewica] dependencies,</p>
<ul class="simple">
<li><p>python-picard >=0.4</p></li>
<li><p>multiviewica >=0.0.1</p></li>
</ul>
<p>Currently, <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> is supported for Python 3.6, 3.7, and 3.8.</p>
</section>
<section id="hardware-requirements">
<h2>Hardware requirements<a class="headerlink" href="#hardware-requirements" title="Permalink to this headline">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> package requires only a standard computer with enough RAM to support the in-memory operations and free memory to install required packages.</p>
</section>
<section id="os-requirements">
<h2>OS Requirements<a class="headerlink" href="#os-requirements" title="Permalink to this headline">¶</a></h2>
<p>This package is supported for <em>Linux</em> and <em>macOS</em> and can also be run on Windows machines.</p>
</section>
<section id="testing">
<h2>Testing<a class="headerlink" href="#testing" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">mvlearn</span></code> uses the Python <code class="docutils literal notranslate"><span class="pre">pytest</span></code> testing package. If you don't already have
that package installed, follow the directions on the <a class="reference external" href="https://docs.pytest.org/en/latest/">pytest homepage</a>.</p>
</section>
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