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  <h1>Source code for dscript.language_model</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">subprocess</span> <span class="k">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">h5py</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">.fasta</span> <span class="kn">import</span> <span class="n">parse</span><span class="p">,</span> <span class="n">parse_directory</span><span class="p">,</span> <span class="n">write</span>
<span class="kn">from</span> <span class="nn">.pretrained</span> <span class="kn">import</span> <span class="n">get_pretrained</span>
<span class="kn">from</span> <span class="nn">.alphabets</span> <span class="kn">import</span> <span class="n">Uniprot21</span>
<span class="kn">from</span> <span class="nn">.models.embedding</span> <span class="kn">import</span> <span class="n">SkipLSTM</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>


<div class="viewcode-block" id="lm_embed"><a class="viewcode-back" href="../../api/index.html#dscript.language_model.lm_embed">[docs]</a><span class="k">def</span> <span class="nf">lm_embed</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">use_cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Embed a single sequence using pre-trained language model from `Bepler &amp; Berger &lt;https://github.com/tbepler/protein-sequence-embedding-iclr2019&gt;`_.</span>

<span class="sd">    :param sequence: Input sequence to be embedded</span>
<span class="sd">    :type sequence: str</span>
<span class="sd">    :param use_cuda: Whether to generate embeddings using GPU device [default: False]</span>
<span class="sd">    :type use_cuda: bool</span>
<span class="sd">    :return: Embedded sequence</span>
<span class="sd">    :rtype: torch.Tensor</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">get_pretrained</span><span class="p">(</span><span class="s2">&quot;lm_v1&quot;</span><span class="p">)</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">proj</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">proj</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">100</span><span class="p">))</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">alphabet</span> <span class="o">=</span> <span class="n">Uniprot21</span><span class="p">()</span>
        <span class="n">es</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">alphabet</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">sequence</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">es</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="n">z</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">z</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span></div>


<div class="viewcode-block" id="embed_from_fasta"><a class="viewcode-back" href="../../api/index.html#dscript.language_model.embed_from_fasta">[docs]</a><span class="k">def</span> <span class="nf">embed_from_fasta</span><span class="p">(</span><span class="n">fastaPath</span><span class="p">,</span> <span class="n">outputPath</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Embed sequences using pre-trained language model from `Bepler &amp; Berger &lt;https://github.com/tbepler/protein-sequence-embedding-iclr2019&gt;`_.</span>

<span class="sd">    :param fastaPath: Input sequence file (``.fasta`` format)</span>
<span class="sd">    :type fastaPath: str</span>
<span class="sd">    :param outputPath: Output embedding file (``.h5`` format)</span>
<span class="sd">    :type outputPath: str</span>
<span class="sd">    :param device: Compute device to use for embeddings [default: 0]</span>
<span class="sd">    :type device: int</span>
<span class="sd">    :param verbose: Print embedding progress</span>
<span class="sd">    :type verbose: bool</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">use_cuda</span> <span class="o">=</span> <span class="p">(</span><span class="n">device</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Using CUDA device </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2"> - </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_name</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Using CPU&quot;</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Loading Model...&quot;</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">get_pretrained</span><span class="p">(</span><span class="s2">&quot;lm_v1&quot;</span><span class="p">)</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">proj</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">proj</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">100</span><span class="p">))</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Loading Sequences...&quot;</span><span class="p">)</span>
    <span class="n">names</span><span class="p">,</span> <span class="n">seqs</span> <span class="o">=</span> <span class="n">parse</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">fastaPath</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">))</span>
    <span class="n">alphabet</span> <span class="o">=</span> <span class="n">Uniprot21</span><span class="p">()</span>
    <span class="n">encoded_seqs</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">seqs</span><span class="p">):</span>
        <span class="n">es</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">alphabet</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">s</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">es</span> <span class="o">=</span> <span class="n">es</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="n">encoded_seqs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">es</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
        <span class="n">num_seqs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">encoded_seqs</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# </span><span class="si">{}</span><span class="s2"> Sequences Loaded&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_seqs</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Approximate Storage Required (varies by average sequence length): ~</span><span class="si">{}</span><span class="s2">GB&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_seqs</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">125</span><span class="p">)))</span>

    <span class="n">h5fi</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">outputPath</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Storing to </span><span class="si">{}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">outputPath</span><span class="p">))</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">for</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">encoded_seqs</span><span class="p">),</span><span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">names</span><span class="p">)):</span>
                <span class="n">name</span> <span class="o">=</span> <span class="n">n</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">h5fi</span><span class="p">:</span>
                    <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
                    <span class="n">z</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
                    <span class="n">h5fi</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">z</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">compression</span><span class="o">=</span><span class="s2">&quot;lzf&quot;</span><span class="p">)</span>
        <span class="k">except</span> <span class="ne">KeyboardInterrupt</span><span class="p">:</span>
            <span class="n">h5fi</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">h5fi</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>


<div class="viewcode-block" id="embed_from_directory"><a class="viewcode-back" href="../../api/index.html#dscript.language_model.embed_from_directory">[docs]</a><span class="k">def</span> <span class="nf">embed_from_directory</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">outputPath</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">extension</span><span class="o">=</span><span class="s2">&quot;.seq&quot;</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Embed all files in a directory in ``.fasta`` format using pre-trained language model from `Bepler &amp; Berger &lt;https://github.com/tbepler/protein-sequence-embedding-iclr2019&gt;`_.</span>

<span class="sd">    :param directory: Input directory (``.fasta`` format)</span>
<span class="sd">    :type directory: str</span>
<span class="sd">    :param outputPath: Output embedding file (``.h5`` format)</span>
<span class="sd">    :type outputPath: str</span>
<span class="sd">    :param device: Compute device to use for embeddings [default: 0]</span>
<span class="sd">    :type device: int</span>
<span class="sd">    :param verbose: Print embedding progress</span>
<span class="sd">    :type verbose: bool</span>
<span class="sd">    :param extension: Extension of all files to read in</span>
<span class="sd">    :type extension: str</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">nam</span><span class="p">,</span> <span class="n">seq</span> <span class="o">=</span> <span class="n">parse_directory</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">extension</span><span class="o">=</span><span class="n">extension</span><span class="p">)</span>
    <span class="n">fastaPath</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">directory</span><span class="si">}</span><span class="s2">/allSeqs.fa&quot;</span>
    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">fastaPath</span><span class="p">):</span>
        <span class="n">fastaPath</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fastaPath</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span><span class="o">.</span><span class="n">timestamp</span><span class="p">())</span><span class="si">}</span><span class="s2">&quot;</span>
    <span class="n">write</span><span class="p">(</span><span class="n">nam</span><span class="p">,</span> <span class="n">seq</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">fastaPath</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">))</span>
    <span class="n">embed_from_fasta</span><span class="p">(</span><span class="n">fastaPath</span><span class="p">,</span> <span class="n">outputPath</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
</pre></div>

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