diff tabular_learner.xml @ 3:f6a65e05d6ec draft

planemo upload for repository https://github.com/goeckslab/gleam commit b430f8b466655878c3bf63b053655fdbf039ddb0

--- a/tabular_learner.xml	Wed Jul 02 18:59:39 2025 +0000
+++ b/tabular_learner.xml	Wed Jul 09 01:12:48 2025 +0000
@@ -28,10 +28,13 @@
                 --feature_selection
                 #end if
                 #if $enable_cross_validation == "true" 
-                --cross_validation 
+                    --cross_validation
+                    #if $cross_validation_folds
+                        --cross_validation_folds '$cross_validation_folds'
+                    #end if 
                 #end if
-                #if $cross_validation_folds
-                --cross_validation_folds '$cross_validation_folds'
+                #if $enable_cross_validation == "false"
+                    --no_cross_validation
                 #end if
                 #if $remove_outliers
                 --remove_outliers  
@@ -183,6 +186,28 @@
             <param name="target_feature" value="11"/> 
             <param name="model_type" value="classification"/>
             <param name="random_seed" value="42"/>
+            <param name="customize_defaults" value="true"/>
+            <param name="train_size" value="0.8"/>
+            <param name="normalize" value="true"/>
+            <param name="feature_selection" value="true"/>
+            <param name="enable_cross_validation" value="false"/>
+            <param name="remove_outliers" value="true"/>
+            <param name="remove_multicollinearity" value="true"/>
+            <output name="model" file="expected_model_classification_customized_cross_off.h5" compare="sim_size"/>
+            <output name="comparison_result">
+                <assert_contents>
+                    <has_text text="Validation Result Summary" />
+                    <has_text text="Test Results" />
+                    <has_text text="Feature Importance" />
+                </assert_contents>
+            </output>
+            <output name="best_model_csv" value="expected_best_model_classification_customized_cross_off.csv" />
+        </test>
+        <test>
+            <param name="input_file" value="pcr.tsv"/>
+            <param name="target_feature" value="11"/> 
+            <param name="model_type" value="classification"/>
+            <param name="random_seed" value="42"/>
             <output name="model" file="expected_model_classification.h5" compare="sim_size"/>
             <output name="comparison_result"> 
                 <assert_contents>