--- a/macros.xml	Mon Aug 12 17:58:14 2024 +0000
+++ b/macros.xml	Mon Apr 14 09:56:46 2025 +0000
@@ -1,6 +1,6 @@
 <macros>
-    <token name="@TOOL_VERSION@">0.2.10</token>
-    <token name="@VERSION_SUFFIX@">0</token>
+    <token name="@TOOL_VERSION@">0.2.17</token>
+    <token name="@VERSION_SUFFIX@">1</token>
     <token name="@PROFILE@">24.1</token>
     <xml name="requirements">
         <requirements>
@@ -65,10 +65,16 @@
             <param argument="--log_transform" type="boolean" truevalue="--log_transform True" falsevalue="" checked="false" label="Whether to apply log-transformation to input data matrices" />
             <param argument="--early_stop_patience" type="integer" min="-1" value="10" label="How many epochs to wait when no improvements in validation loss are observed." help="Set to -1 to disable early stopping." />
             <param argument="--hpo_iter" type="integer" min="1" value="100" label="Number of iterations for hyperparameter optimisation." />
+            <param argument="--val_size" type="float" min="0.0" max="1" value="0.2" label="Proportion of training data to be used as validation split"/>
             <param argument="--hpo_patience" type="integer" min="0" value="10" label="How many hyperparameter optimisation iterations to wait for when no improvements are observed." help="Set to 0 to disable early stopping." />
             <param argument="--use_cv" type="boolean" truevalue="--use_cv" falsevalue="" checked="false" label="Cross validation" help="If set, a 5-fold cross-validation training will be done. Otherwise, a single training on 80 percent of the dataset is done. " />
             <param argument="--use_loss_weighting" type="boolean" truevalue="--use_loss_weighting True" falsevalue="" checked="true" label="Whether to apply loss-balancing using uncertainty weights method." />
             <param argument="--evaluate_baseline_performance" type="boolean" truevalue="--evaluate_baseline_performance" falsevalue="" checked="false" label="Enable modeling also with Random Forest + SVMs to see the performance of off-the-shelf tools on the same dataset." />
+            <param argument="--feature_importance_method" type="select" label="which method(s) to use to compute feature importance scores.">
+                <option value="Both" selected="true">Both</option>
+                <option value="IntegratedGradients">IntegratedGradients</option>
+                <option value="GradientShap">GradientShap</option>
+            </param>
             <param argument="--disable_marker_finding" type="boolean" truevalue="--disable_marker_finding" falsevalue="" checked="false" label="Disable marker discovery after model training." />
         </section>
     </xml>