# HG changeset patch
# User bgruening
# Date 1531206780 14400
# Node ID 10a8543142fc7b49a7d5e1af611904073d8afff6
# Parent  cf635edf37d206e68771b252a914d274000a636a
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf

diff -r cf635edf37d2 -r 10a8543142fc generalized_linear.xml
--- a/generalized_linear.xml	Mon Jul 09 14:33:39 2018 -0400
+++ b/generalized_linear.xml	Tue Jul 10 03:13:00 2018 -0400
@@ -44,7 +44,7 @@
 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
 prediction = classifier_object.predict(data)
-prediction_df = pandas.DataFrame(prediction)
+prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
 res = pandas.concat([data, prediction_df], axis=1)
 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False, header=None)
 #end if
diff -r cf635edf37d2 -r 10a8543142fc test-data/gbc_result01
--- a/test-data/gbc_result01	Mon Jul 09 14:33:39 2018 -0400
+++ b/test-data/gbc_result01	Tue Jul 10 03:13:00 2018 -0400
@@ -1,4 +1,4 @@
-0	1	2	3	0
+0	1	2	3	predicted
 3.68258022948	2.82110345641	-3.990140724	-1.9523364774	1
 0.015942057224	-0.711958594347	0.125502976978	-0.972218263337	0
 2.08690768825	0.929399321468	-2.12924084484	-1.99714022188	1
diff -r cf635edf37d2 -r 10a8543142fc test-data/rfr_result01
--- a/test-data/rfr_result01	Mon Jul 09 14:33:39 2018 -0400
+++ b/test-data/rfr_result01	Tue Jul 10 03:13:00 2018 -0400
@@ -1,4 +1,4 @@
-86.9702122735	1.00532111569	-1.01739601979	-0.613139481654	0.641846874331	0
+86.9702122735	1.00532111569	-1.01739601979	-0.613139481654	0.641846874331	predicted
 91.2021798817	-0.6215229712070001	1.11914889596	0.390012184498	1.28956938152	0.8511213285107001
 -47.4101632272	-0.638416457964	-0.7327774684530001	-0.8640261049779999	-1.06109770116	0.05344095304070007
 61.712804630200004	-1.0999480057700002	-0.739679672932	0.585657963012	1.4890682753600002	1.1892759745694002