diff tabular_learner.xml @ 9:e7dd78077b72 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam commit 84d5cd0b1fa5c1ff0ad892bc39c95dad1ceb4920

--- a/tabular_learner.xml	Mon Sep 08 22:38:55 2025 +0000
+++ b/tabular_learner.xml	Sat Nov 08 14:20:19 2025 +0000
@@ -59,6 +59,9 @@
             --test_file '$test_file'
         #end if
         --model_type '$model_type'
+        #if $best_model_metric
+            --best_model_metric '$best_model_metric'
+        #end if
         ]]>
     </command>
     <inputs>
@@ -104,6 +107,16 @@
                     <option value="lightgbm">Light Gradient Boosting Machine</option>
                     <option value="catboost">CatBoost Classifier</option>
                 </param>
+                <param name="best_model_metric" type="select" label="Select metric to pick the best model" help="PyCaret will rank models by this metric. Default is Accuracy.">
+                    <option value="Accuracy" selected="true">Accuracy</option>
+                    <option value="AUC">ROC-AUC</option>
+                    <option value="Precision">Precision</option>
+                    <option value="Recall">Recall</option>
+                    <option value="F1">F1</option>
+                    <option value="Kappa">Cohen’s Kappa</option>
+                    <option value="Log Loss">Log Loss (lower is better)</option>
+                    <option value="PR-AUC-Weighted">PR-AUC (weighted)</option>
+                </param>
             </when>
             <when value="regression">
                 <param name="regression_models" type="select" multiple="true" label="Only Select Regression Models if you don't want to compare all models">
@@ -133,6 +146,14 @@
                     <option value="lightgbm">Light Gradient Boosting Machine</option>
                     <option value="catboost">CatBoost Regressor</option>
                 </param>
+                <param name="best_model_metric" type="select" label="Select metric to pick the best model" help="PyCaret will rank models by this metric. Default is R².">
+                    <option value="R2" selected="true">R²</option>
+                    <option value="MAE">MAE</option>
+                    <option value="MSE">MSE</option>
+                    <option value="RMSE">RMSE</option>
+                    <option value="RMSLE">RMSLE</option>
+                    <option value="MAPE">MAPE</option>
+                </param>
             </when>
         </conditional>
         <param name="tune_model" type="boolean" truevalue="True" falsevalue="False" label="Tune hyperparameters" help="Hyperparameter tuning on the best model" />
@@ -179,6 +200,7 @@
             <param name="input_file" value="pcr.tsv"/>
             <param name="target_feature" value="11"/> 
             <param name="model_type" value="classification"/>
+            <param name="best_model_metric" value="F1"/>
             <param name="random_seed" value="42"/>
             <param name="customize_defaults" value="true"/>
             <param name="train_size" value="0.8"/>
@@ -195,6 +217,8 @@
                     <has_text text="Validation Summary" />
                     <has_text text="Test Summary" />
                     <has_text text="Feature Importance" />
+                    <has_text text="Best Model Metric" />
+                    <has_text text="F1" />
                 </assert_contents>
             </output>
             <output name="best_model_csv" value="expected_best_model_classification_customized.csv" />
@@ -257,6 +281,7 @@
             <param name="input_file" value="auto-mpg.tsv"/>
             <param name="target_feature" value="1"/> 
             <param name="model_type" value="regression"/>
+            <param name="best_model_metric" value="RMSE"/>
             <param name="random_seed" value="42"/>
             <output name="model" file="expected_model_regression.h5" compare="sim_size" />
             <output name="comparison_result">
@@ -264,6 +289,8 @@
                     <has_text text="Validation Summary" />
                     <has_text text="Test Summary" />
                     <has_text text="Feature Importance" />
+                    <has_text text="Best Model Metric" />
+                    <has_text text="RMSE" />
                 </assert_contents>
             </output>
             <output name="best_model_csv" value="expected_best_model_regression.csv" />