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<article id="content">
<header>
<h1 class="title">Module <code>VITAE.utils</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="VITAE.utils.reset_random_seeds"><code class="name flex">
<span>def <span class="ident">reset_random_seeds</span></span>(<span>seed)</span>
</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="VITAE.utils.get_embedding"><code class="name flex">
<span>def <span class="ident">get_embedding</span></span>(<span>z, dimred='umap', **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Get low-dimensional embeddings for visualizations.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>z</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, d]</span><script type="math/tex">[N, d]</script></span> The latent variables.</dd>
<dt><strong><code>dimred</code></strong> :&ensp;<code>str</code>, optional</dt>
<dd>'pca', 'tsne', or umap'.</dd>
<dt><strong><code>**kwargs</code></strong> :&ensp;<code> </code></dt>
<dd>Extra key-value arguments for dimension reduction algorithms.</dd>
</dl>
<h2 id="returns">Returns:</h2>
<p>embed : np.array
<span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</p></div>
</dd>
<dt id="VITAE.utils.get_igraph"><code class="name flex">
<span>def <span class="ident">get_igraph</span></span>(<span>z, random_state=0)</span>
</code></dt>
<dd>
<div class="desc"><p>Get igraph for running Leidenalg clustering.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>z</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, d]</span><script type="math/tex">[N, d]</script></span> The latent variables.</dd>
<dt><strong><code>random_state</code></strong> :&ensp;<code>int</code>, optional</dt>
<dd>The random state.</dd>
</dl>
<h2 id="returns">Returns:</h2>
<p>g : igraph
The igraph object of connectivities.</p></div>
</dd>
<dt id="VITAE.utils.leidenalg_igraph"><code class="name flex">
<span>def <span class="ident">leidenalg_igraph</span></span>(<span>g, res, random_state=0)</span>
</code></dt>
<dd>
<div class="desc"><p>Leidenalg clustering on an igraph object.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>g</code></strong> :&ensp;<code>igraph</code></dt>
<dd>The igraph object of connectivities.</dd>
<dt><strong><code>res</code></strong> :&ensp;<code>float</code></dt>
<dd>The resolution parameter for Leidenalg clustering.</dd>
<dt><strong><code>random_state</code></strong> :&ensp;<code>int</code>, optional</dt>
<dd>The random state.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>labels</code></strong> :&ensp;<code>np.array
</code></dt>
<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The clustered labels.</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.plot_clusters"><code class="name flex">
<span>def <span class="ident">plot_clusters</span></span>(<span>embed_z, labels, plot_labels=False, path=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Plot the clustering results.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>embed_z</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd>
<dt><strong><code>labels</code></strong> :&ensp;<code>np.array
</code></dt>
<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The clustered labels.</dd>
<dt><strong><code>plot_labels</code></strong> :&ensp;<code>boolean</code>, optional</dt>
<dd>Whether to plot text of labels or not.</dd>
<dt><strong><code>path</code></strong> :&ensp;<code>str</code>, optional</dt>
<dd>The path to save the figure.</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.plot_marker_gene"><code class="name flex">
<span>def <span class="ident">plot_marker_gene</span></span>(<span>expression, gene_name: str, embed_z, path=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Plot the marker gene.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>expression</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The expression of the marker gene.</dd>
<dt><strong><code>gene_name</code></strong> :&ensp;<code>str</code></dt>
<dd>The name of the marker gene.</dd>
<dt><strong><code>embed_z</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd>
<dt><strong><code>path</code></strong> :&ensp;<code>str</code>, optional</dt>
<dd>The path to save the figure.</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.plot_uncertainty"><code class="name flex">
<span>def <span class="ident">plot_uncertainty</span></span>(<span>uncertainty, embed_z, path=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Plot the uncertainty for all selected cells.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>uncertainty</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The uncertainty of the all cells.</dd>
<dt><strong><code>embed_z</code></strong> :&ensp;<code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd>
<dt><strong><code>path</code></strong> :&ensp;<code>str</code>, optional</dt>
<dd>The path to save the figure.</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.DE_test"><code class="name flex">
<span>def <span class="ident">DE_test</span></span>(<span>Y, X, gene_names, i_test, alpha: float = 0.05)</span>
</code></dt>
<dd>
<div class="desc"><p>Differential gene expression test.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>Y</code></strong> :&ensp;<code>numpy.array</code></dt>
<dd><span><span class="MathJax_Preview">n,</span><script type="math/tex">n,</script></span> the expression matrix.</dd>
<dt><strong><code>X</code></strong> :&ensp;<code>numpy.array</code></dt>
<dd><span><span class="MathJax_Preview">n,1+1+s</span><script type="math/tex">n,1+1+s</script></span> the constant term, the pseudotime and the covariates.</dd>
<dt><strong><code>gene_names</code></strong> :&ensp;<code>numpy.array</code></dt>
<dd><span><span class="MathJax_Preview">n,</span><script type="math/tex">n,</script></span> the names of all genes.</dd>
<dt><strong><code>i_test</code></strong> :&ensp;<code>numpy.array</code></dt>
<dd>The indices of covariates to be tested.</dd>
<dt><strong><code>alpha</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>The cutoff of p-values.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>res_df</code></strong> :&ensp;<code>pandas.DataFrame</code></dt>
<dd>The test results of expressed genes with two columns,
the estimated coefficients and the adjusted p-values.</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.load_data"><code class="name flex">
<span>def <span class="ident">load_data</span></span>(<span>path, file_name, return_dict=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Load h5df data.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>path</code></strong> :&ensp;<code>str</code></dt>
<dd>The path of the h5 files.</dd>
<dt><strong><code>file_name</code></strong> :&ensp;<code>str</code></dt>
<dd>The dataset name.</dd>
<dt><strong><code>return_dict</code></strong> :&ensp;<code>boolean</code>, optional</dt>
<dd>Whether to return the dict of the dataset or not.</dd>
</dl>
<h2 id="returns">Returns:</h2>
<p>data : dict
The dict containing count, grouping, etc. of the dataset.
dd : anndata.AnnData
The AnnData object of the dataset.</p></div>
</dd>
<dt id="VITAE.utils.compute_kernel"><code class="name flex">
<span>def <span class="ident">compute_kernel</span></span>(<span>x, y, kernel='rbf', **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Computes RBF kernel between x and y.</p>
<h2 id="parameters">Parameters</h2>
<pre><code>x: Tensor
    Tensor with shape [batch_size, z_dim]
y: Tensor
    Tensor with shape [batch_size, z_dim]
</code></pre>
<h2 id="returns">Returns</h2>
<pre><code>The computed RBF kernel between x and y
</code></pre></div>
</dd>
<dt id="VITAE.utils.squared_distance"><code class="name flex">
<span>def <span class="ident">squared_distance</span></span>(<span>x, y)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute the pairwise euclidean distance.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>x</code></strong> :&ensp;<code>Tensor</code></dt>
<dd>Tensor with shape [batch_size, z_dim]</dd>
<dt><strong><code>y</code></strong> :&ensp;<code>Tensor</code></dt>
<dd>Tensor with shape [batch_size, z_dim]</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The pairwise euclidean distance between x and y.</p></div>
</dd>
<dt id="VITAE.utils.compute_mmd"><code class="name flex">
<span>def <span class="ident">compute_mmd</span></span>(<span>x, y, kernel, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Computes Maximum Mean Discrepancy(MMD) between x and y.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>x</code></strong> :&ensp;<code>Tensor</code></dt>
<dd>Tensor with shape [batch_size, z_dim]</dd>
<dt><strong><code>y</code></strong> :&ensp;<code>Tensor</code></dt>
<dd>Tensor with shape [batch_size, z_dim]</dd>
<dt><strong><code>kernel</code></strong> :&ensp;<code>str</code></dt>
<dd>The kernel type used in MMD. It can be 'rbf', 'multi-scale-rbf' or 'raphy'.</dd>
<dt><strong><code>**kwargs</code></strong> :&ensp;<code>dict</code></dt>
<dd>The parameters used in kernel function.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>The computed MMD between x and y</code></dt>
<dd>&nbsp;</dd>
</dl></div>
</dd>
<dt id="VITAE.utils.sample_z"><code class="name flex">
<span>def <span class="ident">sample_z</span></span>(<span>args)</span>
</code></dt>
<dd>
<div class="desc"><p>Samples from standard Normal distribution with shape [size, z_dim] and
applies re-parametrization trick. It is actually sampling from latent
space distributions with N(mu, var) computed in <code>_encoder</code> function.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>args</code></strong> :&ensp;<code>list</code></dt>
<dd>List of [mu, log_var] computed in <code>_encoder</code> function.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The computed Tensor of samples with shape [size, z_dim].</p></div>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="VITAE.utils.Early_Stopping"><code class="flex name class">
<span>class <span class="ident">Early_Stopping</span></span>
<span>(</span><span>warmup=0, patience=10, tolerance=0.001, relative=False, is_minimize=True)</span>
</code></dt>
<dd>
<div class="desc"><p>The early-stopping monitor.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Early_Stopping():
    &#39;&#39;&#39;
    The early-stopping monitor.
    &#39;&#39;&#39;
    def __init__(self, warmup=0, patience=10, tolerance=1e-3, 
            relative=False, is_minimize=True):
        self.warmup = warmup
        self.patience = patience
        self.tolerance = tolerance
        self.is_minimize = is_minimize
        self.relative = relative

        self.step = -1
        self.best_step = -1
        self.best_metric = np.inf

        if not self.is_minimize:
            self.factor = -1.0
        else:
            self.factor = 1.0

    def __call__(self, metric):
        self.step += 1
        
        if self.step &lt; self.warmup:
            return False
        elif (self.best_metric==np.inf) or \
                (self.relative and (self.best_metric-metric)/self.best_metric &gt; self.tolerance) or \
                ((not self.relative) and self.factor*metric&lt;self.factor*self.best_metric-self.tolerance):
            self.best_metric = metric
            self.best_step = self.step
            return False
        elif self.step - self.best_step&gt;self.patience:
            print(&#39;Best Epoch: %d. Best Metric: %f.&#39;%(self.best_step, self.best_metric))
            return True
        else:
            return False</code></pre>
</details>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="VITAE" href="index.html">VITAE</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="two-column">
<li><code><a title="VITAE.utils.reset_random_seeds" href="#VITAE.utils.reset_random_seeds">reset_random_seeds</a></code></li>
<li><code><a title="VITAE.utils.get_embedding" href="#VITAE.utils.get_embedding">get_embedding</a></code></li>
<li><code><a title="VITAE.utils.get_igraph" href="#VITAE.utils.get_igraph">get_igraph</a></code></li>
<li><code><a title="VITAE.utils.leidenalg_igraph" href="#VITAE.utils.leidenalg_igraph">leidenalg_igraph</a></code></li>
<li><code><a title="VITAE.utils.plot_clusters" href="#VITAE.utils.plot_clusters">plot_clusters</a></code></li>
<li><code><a title="VITAE.utils.plot_marker_gene" href="#VITAE.utils.plot_marker_gene">plot_marker_gene</a></code></li>
<li><code><a title="VITAE.utils.plot_uncertainty" href="#VITAE.utils.plot_uncertainty">plot_uncertainty</a></code></li>
<li><code><a title="VITAE.utils.DE_test" href="#VITAE.utils.DE_test">DE_test</a></code></li>
<li><code><a title="VITAE.utils.load_data" href="#VITAE.utils.load_data">load_data</a></code></li>
<li><code><a title="VITAE.utils.compute_kernel" href="#VITAE.utils.compute_kernel">compute_kernel</a></code></li>
<li><code><a title="VITAE.utils.squared_distance" href="#VITAE.utils.squared_distance">squared_distance</a></code></li>
<li><code><a title="VITAE.utils.compute_mmd" href="#VITAE.utils.compute_mmd">compute_mmd</a></code></li>
<li><code><a title="VITAE.utils.sample_z" href="#VITAE.utils.sample_z">sample_z</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="VITAE.utils.Early_Stopping" href="#VITAE.utils.Early_Stopping">Early_Stopping</a></code></h4>
</li>
</ul>
</li>
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