Mercurial > repos > bgruening > sklearn_generalized_linear
changeset 14:10a8543142fc draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
| author | bgruening |
|---|---|
| date | Tue, 10 Jul 2018 03:13:00 -0400 |
| parents | cf635edf37d2 |
| children | f0e215cbade3 |
| files | generalized_linear.xml test-data/gbc_result01 test-data/rfr_result01 |
| diffstat | 3 files changed, 3 insertions(+), 3 deletions(-) [+] |
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--- 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
--- 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
--- 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