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            <a href="../index.html" class="icon icon-home"> DeepPurpose





  
  

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  <div class="section" id="configuration">





  
  

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<h1>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h1>





  
  

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<p><strong>generate_config</strong> generate all the configuration that can be used in learning and inference.</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">utils</span><span class="o">.</span><span class="n">generate_config</span><span class="p">(</span>





  
  

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       <span class="n">drug_encoding</span><span class="p">,</span>





  
  

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       <span class="n">target_encoding</span><span class="p">,</span>





  
  

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       <span class="n">result_folder</span> <span class="o">=</span> <span class="s2">&quot;./result/&quot;</span><span class="p">,</span>





  
  

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       <span class="n">input_dim_drug</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">,</span>





  
  

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       <span class="n">input_dim_protein</span> <span class="o">=</span> <span class="mi">8420</span><span class="p">,</span>





  
  

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       <span class="n">hidden_dim_drug</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>





  
  

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       <span class="n">hidden_dim_protein</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>





  
  

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       <span class="n">cls_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>





  
  

  274
  
       <span class="n">mlp_hidden_dims_drug</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>





  
  

  275
  
       <span class="n">mlp_hidden_dims_target</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>





  
  

  276
  
       <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>





  
  

  277
  
       <span class="n">train_epoch</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>





  
  

  278
  
       <span class="n">test_every_X_epoch</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span>





  
  

  279
  
       <span class="n">LR</span> <span class="o">=</span> <span class="mf">1e-4</span><span class="p">,</span>





  
  

  280
  
       <span class="n">transformer_emb_size_drug</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>





  
  

  281
  
       <span class="n">transformer_intermediate_size_drug</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>





  
  

  282
  
       <span class="n">transformer_num_attention_heads_drug</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span>





  
  

  283
  
       <span class="n">transformer_n_layer_drug</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span>





  
  

  284
  
       <span class="n">transformer_emb_size_target</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>





  
  

  285
  
       <span class="n">transformer_intermediate_size_target</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>





  
  

  286
  
       <span class="n">transformer_num_attention_heads_target</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span>





  
  

  287
  
       <span class="n">transformer_n_layer_target</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>





  
  

  288
  
       <span class="n">transformer_dropout_rate</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>





  
  

  289
  
       <span class="n">transformer_attention_probs_dropout</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>





  
  

  290
  
       <span class="n">transformer_hidden_dropout_rate</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>





  
  

  291
  
       <span class="n">mpnn_hidden_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span>





  
  

  292
  
       <span class="n">mpnn_depth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>





  
  

  293
  
       <span class="n">cnn_drug_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">96</span><span class="p">],</span>





  
  

  294
  
       <span class="n">cnn_drug_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span>





  
  

  295
  
       <span class="n">cnn_target_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">96</span><span class="p">],</span>





  
  

  296
  
       <span class="n">cnn_target_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">],</span>





  
  

  297
  
       <span class="n">rnn_Use_GRU_LSTM_drug</span> <span class="o">=</span> <span class="s1">&#39;GRU&#39;</span><span class="p">,</span>





  
  

  298
  
       <span class="n">rnn_drug_hid_dim</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>





  
  

  299
  
       <span class="n">rnn_drug_n_layers</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>





  
  

  300
  
       <span class="n">rnn_drug_bidirectional</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>





  
  

  301
  
       <span class="n">rnn_Use_GRU_LSTM_target</span> <span class="o">=</span> <span class="s1">&#39;GRU&#39;</span><span class="p">,</span>





  
  

  302
  
       <span class="n">rnn_target_hid_dim</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>





  
  

  303
  
       <span class="n">rnn_target_n_layers</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>





  
  

  304
  
       <span class="n">rnn_target_bidirectional</span> <span class="o">=</span> <span class="kc">True</span>





  
  

  305
  
       <span class="p">)</span>





  
  

  306
  
</pre></div>





  
  

  307
  
</div>





  
  

  308
  
<ul class="simple">





  
  

  309
  
<li><p><strong>drug_encoding</strong> (str) - Encoder mode for drug. It can be “transformer”, “MPNN”, “CNN”, “CNN_RNN” …,</p></li>





  
  

  310
  
<li><p><strong>target_encoding</strong> (str) - Encoder mode for protein. It can be “transformer”, “CNN”, “CNN_RNN” …,</p></li>





  
  

  311
  
<li><p><strong>input_dim_drug</strong> (int) - Dimension of input drug feature.</p></li>





  
  

  312
  
<li><p><strong>input_dim_protein</strong> (int) - Dimension of input protein feature.</p></li>





  
  

  313
  
<li><p><strong>hidden_dim_drug</strong> (int) - Dimension of hidden layer of drug feature.</p></li>





  
  

  314
  
<li><p><strong>hidden_dim_protein</strong> (int) - Dimension of hidden layer of protein feature.</p></li>





  
  

  315
  
<li><p><strong>batch_size</strong> (int) - batch size</p></li>





  
  

  316
  
<li><p><strong>train_epoch</strong> (int) - training epoch</p></li>





  
  

  317
  
<li><p><strong>test_every_X_epoch</strong> (int) - test every X epochs</p></li>





  
  

  318
  
<li><p><strong>LR</strong> (float) - Learning rate.</p></li>





  
  

  319
  
<li><p><strong>cls_hidden_dims</strong> (list of int) - hidden dimensions of classifier.</p></li>





  
  

  320
  
<li><p><strong>mlp_hidden_dims_drug</strong> (list of int) - hidden dimension of MLP when encoding drug.</p></li>





  
  

  321
  
<li><p><strong>mlp_hidden_dims_target</strong> (list of int) - hidden dimension of MLP when encoding protein.</p></li>





  
  

  322
  
<li><p><strong>transformer_emb_size_drug</strong> (int) - embedding size of transformer when encoding drug.</p></li>





  
  

  323
  
<li><p><strong>transformer_intermediate_size_drug</strong> (int) -</p></li>





  
  

  324
  
<li><p><strong>transformer_num_attention_heads_drug</strong> (int) -</p></li>





  
  

  325
  
<li><p><strong>transformer_n_layer_drug</strong> (int) -</p></li>





  
  

  326
  
<li><p><strong>transformer_emb_size_target</strong> (int) -</p></li>





  
  

  327
  
<li><p><strong>transformer_intermediate_size_target</strong> (int) -</p></li>





  
  

  328
  
<li><p><strong>transformer_num_attention_heads_target</strong> (int) -</p></li>





  
  

  329
  
<li><p><strong>transformer_n_layer_target</strong> (int) -</p></li>





  
  

  330
  
<li><p><strong>transformer_dropout_rate</strong> (float) -</p></li>





  
  

  331
  
</ul>





  
  

  332
  
</div>





  
  

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