Mercurial > repos > bgruening > scipy_sparse

diff main_macros.xml @ 27:eb3091b4ee4b draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
author bgruening
date Fri, 13 Sep 2019 12:26:41 -0400
parents d0ed8e976b79
children 2a32d1d9c8da
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line diff
--- a/main_macros.xml	Fri Aug 09 07:27:03 2019 -0400
+++ b/main_macros.xml	Fri Sep 13 12:26:41 2019 -0400
@@ -1,12 +1,12 @@
 <macros>
-  <token name="@VERSION@">1.0.7.10</token>
+  <token name="@VERSION@">1.0.7.12</token>
 
   <token name="@ENSEMBLE_VERSION@">0.2.0</token>
 
   <xml name="python_requirements">
       <requirements>
           <requirement type="package" version="3.6">python</requirement>
-          <requirement type="package" version="0.7.10">Galaxy-ML</requirement>
+          <requirement type="package" version="0.7.12">Galaxy-ML</requirement>
           <yield/>
       </requirements>
   </xml>
@@ -1379,7 +1379,7 @@
       <expand macro="model_validation_common_options"/>
       <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/-->
       <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/>
-      <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset."/>
+      <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!"/>
       <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/>
       <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
   </xml>