updating documentation #1

korem-lab · Sep 29, 2024 · ceb73c8 · ceb73c8
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diff --git a/AdaptationDebiasMClassifier.html b/AdaptationDebiasMClassifier.html
@@ -263,11 +263,14 @@
     <li>
       <a href="DebiasMClassifier-demo.html">Demo</a>
     </li>
+    <li>
+      <a href="MultitaskDebiasMClassifier.html">MultitaskDebiasMClassifier</a>
+    </li>
   </ul>
 </li>
 <li class="dropdown">
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    <span class="fa fa-arrow-trend-up"></span>
+    <span class="fa fa-sliders"></span>
 
     Regression
 
@@ -280,30 +283,16 @@
     <li>
       <a href="DebiasMRegressor-demo.html">Demo</a>
     </li>
-  </ul>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    <span class="fa fa-sliders"></span>
-
-    Multitask
-
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="MultitaskDebiasMClassifier.html">MultitaskDebiasMClassifier</a>
-    </li>
     <li>
-      <a href="MultitaskDebiasMClassifier-demo.html">Demo</a>
+      <a href="MultitaskDebiasMRegressor.html">MultitaskDebiasMRegressor</a>
     </li>
   </ul>
 </li>
 <li class="dropdown">
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
     <span class="fa fa-globe"></span>
 
-    Adaptation
+    Online learning
 
     <span class="caret"></span>
   </a>

diff --git a/DebiasMClassifier.Rmd b/DebiasMClassifier.Rmd
@@ -79,10 +79,12 @@ klippy::klippy(c('r', 'python', 'bash'),
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                l2_strength=0,<br>
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                w_l2=0,<br>
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                random_state=None,<br>
-  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                x_val=None<br>
-  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                )
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                x_val=None,<br>
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;                prediction_loss=torch.nn.functional.binary_cross_entropy<br>
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&ensp; &emsp;)
 </div>
 
+
 <br>
 The DEBIAS-M Classifier. <br> <br>
 This class implements multiplicative DEBIAS-M bias-correction using aggregated microbiome n_samples \times n_taxa read count matrices from multiple `X` samples, along with a provided binary `y` label. It can handle both read count and relative abundance inputs. <br> <br>
@@ -113,6 +115,9 @@ Parameters
 * <hr style="height: 2.5px; border: 1px solid #987CAC; background-color: #987CAC; margin: 2px">
 * <span style="color:#DD4814">x_val: {array-like, sparse matrix} of shape (n_samples, 1 + n_taxa), default=None</span>
    *  An `n_samples` x `1 + n_taxa` matrix describing the read counts of held-out validation and/or test sets, for which any validation or testing labels will not be available during training. The first column of <span style="color:#DD4814">x_val</span> denotes the batch of each sample, as non-negative integers which are interpreted alongside batches specified in the <span style="color:#DD4814">train</span> inputs. Providing <span style="color:#DD4814">x_val</span> allows DEBIAS-M to account for distribution shifts from these samples during training.
+* <hr style="height: 2.5px; border: 1px solid #987CAC; background-color: #987CAC; margin: 2px">
+* <span style="color:#DD4814">prediction_loss: loss function, default=torch.nn.functional.binary_cross_entropy</span>
+   *  Used to specify the prediction loss function to be used during training.
 </div>
 
 <br><br>

diff --git a/DebiasMClassifier.html b/DebiasMClassifier.html
@@ -261,13 +261,13 @@
       <a href="DebiasMClassifier.html">DebiasMClassifier</a>
     </li>
     <li>
-      <a href="DebiasMClassifier-demo.html">Demo</a>
+      <a href="MultitaskDebiasMClassifier.html">MultitaskDebiasMClassifier</a>
     </li>
   </ul>
 </li>
 <li class="dropdown">
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    <span class="fa fa-arrow-trend-up"></span>
+    <span class="fa fa-sliders"></span>
 
     Regression
 
@@ -278,41 +278,21 @@
       <a href="DebiasMRegressor.html">DebiasMRegressor</a>
     </li>
     <li>
-      <a href="DebiasMRegressor-demo.html">Demo</a>
-    </li>
-  </ul>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    <span class="fa fa-sliders"></span>
-
-    Multitask
-
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="MultitaskDebiasMClassifier.html">MultitaskDebiasMClassifier</a>
-    </li>
-    <li>
-      <a href="MultitaskDebiasMClassifier-demo.html">Demo</a>
+      <a href="MultitaskDebiasMRegressor.html">MultitaskDebiasMRegressor</a>
     </li>
   </ul>
 </li>
 <li class="dropdown">
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
     <span class="fa fa-globe"></span>
 
-    Adaptation
+    Online learning
 
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
     <li>
-      <a href="AdaptationDebiasMClassifier.html">AdaptationDebiasMClassifier</a>
-    </li>
-    <li>
-      <a href="AdaptationDebiasMClassifier-demo.html">Demo</a>
+      <a href="OnlineDebiasMClassifier.html">OnlineDebiasMClassifier</a>
     </li>
   </ul>
 </li>
@@ -328,9 +308,6 @@
     <li>
       <a href="DebiasMClassifierLogAdd.html">DebiasMClassifierLogAdd</a>
     </li>
-    <li>
-      <a href="DebiasMClassifierLogAdd-demo.html">Demo</a>
-    </li>
   </ul>
 </li>
 <li class="dropdown">
@@ -442,7 +419,9 @@ <h1>
 = ‘infer’,<br>                    learning_rate=0.005, <br>            
        min_epochs=25,<br>                    l2_strength=0,<br>        
            w_l2=0,<br>                    random_state=None,<br>        
-           x_val=None<br>                    )</p>
+           x_val=None,<br>                   
+prediction_loss=torch.nn.functional.binary_cross_entropy<br>            
+       )</p>
 </div>
 <p><br> The DEBIAS-M Classifier. <br> <br> This class implements
 multiplicative DEBIAS-M bias-correction using aggregated microbiome
@@ -511,6 +490,13 @@ <h2>Parameters</h2>
 style="color:#DD4814">x_val</span> allows DEBIAS-M to account for
 distribution shifts from these samples during training.</li>
 </ul></li>
+<li><hr style="height: 2.5px; border: 1px solid #987CAC; background-color: #987CAC; margin: 2px"></li>
+<li><span style="color:#DD4814">prediction_loss: loss function,
+default=torch.nn.functional.binary_cross_entropy</span>
+<ul>
+<li>Used to specify the prediction loss function to be used during
+training.</li>
+</ul></li>
 </ul>
 </div>
 <p><br><br></p>

diff --git a/DebiasMClassifierLogAdd.Rmd b/DebiasMClassifierLogAdd.Rmd
@@ -79,10 +79,12 @@ klippy::klippy(c('r', 'python', 'bash'),
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                l2_strength=0,<br>
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                w_l2=0,<br>
   &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                random_state=None,<br>
-  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                x_val=None<br>
-  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                )
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                x_val=None,<br>
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp;&ensp;&emsp;&emsp;&emsp;&ensp;                prediction_loss=torch.nn.functional.binary_cross_entropy<br>
+  &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;&emsp; &emsp;&emsp;&emsp;&emsp;)
 </div>
 
+
 <br>
 The DEBIAS-M Classifier implementation for logspace inputs. <br> <br>
 This class implements additive DEBIAS-M bias-correction, which models the processing-bias mechanism in logarithmic space representations of read count samples, such as the center log ratio transform.<br>
@@ -116,6 +118,9 @@ Parameters
 * <hr style="height: 2.5px; border: 1px solid #987CAC; background-color: #987CAC; margin: 2px">
 * <span style="color:#DD4814">x_val: {array-like, sparse matrix} of shape (n_samples, 1 + n_taxa), default=None</span>
    *  An `n_samples` x `1 + n_taxa` matrix describing the log-processed read counts of held-out validation and/or test sets, for which any validation or testing labels will not be available during training. The first column of <span style="color:#DD4814">x_val</span> denotes the batch of each sample, as non-negative integers which are interpreted alongside batches specified in the <span style="color:#DD4814">train</span> inputs. Providing <span style="color:#DD4814">x_val</span> allows DEBIAS-M to account for distribution shifts from these samples during training.
+* <hr style="height: 2.5px; border: 1px solid #987CAC; background-color: #987CAC; margin: 2px">
+* <span style="color:#DD4814">prediction_loss: loss function, default=torch.nn.functional.binary_cross_entropy</span>
+   *  Used to specify the prediction loss function to be used during training.
 </div>
 
 <br><br>