diff keras_train_and_eval.xml @ 14:818f9b69d8a0 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a

--- a/keras_train_and_eval.xml	Fri Sep 22 17:04:27 2023 +0000
+++ b/keras_train_and_eval.xml	Mon Oct 02 10:00:27 2023 +0000
@@ -8,7 +8,7 @@
     <expand macro="macro_stdio" />
     <version_command>echo "@VERSION@"</version_command>
     <command>
-        <![CDATA[
+        <![CDATA[        
         export HDF5_USE_FILE_LOCKING='FALSE';
         #if $input_options.selected_input == 'refseq_and_interval'
         bgzip -c '$input_options.target_file' > '${target_file.element_identifier}.gz' &&
@@ -29,6 +29,9 @@
             #end if
             --infile2 '$input_options.infile2'
             --outfile_result '$outfile_result'
+            #if $save and 'save_csvlogger' in str($save)
+            --outfile_history '$outfile_history'
+            #end if
             #if $save and 'save_estimator' in str($save)
             --outfile_object '$outfile_object'
             #end if
@@ -39,7 +42,6 @@
             #if $experiment_schemes.test_split.split_algos.shuffle == 'group'
             --groups '$experiment_schemes.test_split.split_algos.groups_selector.infile_g'
             #end if
-
         ]]>
     </command>
     <configfiles>
@@ -81,10 +83,14 @@
         <param name="save" type="select" multiple='true' display="checkboxes" label="Save the fitted model" optional="true" help="Evaluation scores will be output by default.">
             <option value="save_estimator" selected="true">Fitted estimator</option>
             <option value="save_prediction">True labels and prediction results from evaluation for downstream analysis</option>
+            <option value="save_csvlogger">Display CSVLogger if selected as a callback in the Keras model builder tool</option>
         </param>
     </inputs>
     <outputs>
         <data format="tabular" name="outfile_result" />
+         <data format="tabular" name="outfile_history" label="Deep learning training history log on ${on_string}">
+            <filter>str(save) and 'save_csvlogger' in str(save)</filter>
+        </data>
         <data format="h5mlm" name="outfile_object" label="Fitted estimator or estimator skeleton on ${on_string}">
             <filter>str(save) and 'save_estimator' in str(save)</filter>
         </data>