Mercurial > repos > bgruening > sklearn_ensemble
changeset 22:2e69c6ca6e91 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author | bgruening |
---|---|
date | Thu, 11 Oct 2018 03:34:03 -0400 |
parents | 9ce3e347506c |
children | 39ae276e75d9 |
files | ensemble.xml test-data/abc_model01 test-data/abc_result01 test-data/abr_model01 test-data/abr_result01 test-data/glm_model08 test-data/glm_result08 |
diffstat | 7 files changed, 47 insertions(+), 3 deletions(-) [+] |
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--- a/ensemble.xml Sat Sep 29 07:30:08 2018 -0400 +++ b/ensemble.xml Thu Oct 11 03:34:03 2018 -0400 @@ -292,6 +292,38 @@ <param name="selected_task" value="load"/> <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/> </test> + <test> + <param name="infile1" value="train.tabular" ftype="tabular"/> + <param name="infile2" value="train.tabular" ftype="tabular"/> + <param name="col1" value="1,2,3,4"/> + <param name="col2" value="5"/> + <param name="selected_task" value="train"/> + <param name="selected_algorithm" value="AdaBoostClassifier"/> + <param name="random_state" value="10"/> + <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="500"/> + </test> + <test> + <param name="infile_model" value="abc_model01" ftype="zip"/> + <param name="infile_data" value="test.tabular" ftype="tabular"/> + <param name="selected_task" value="load"/> + <output name="outfile_predict" file="abc_result01" compare="sim_size" delta="500"/> + </test> + <test> + <param name="infile1" value="regression_train.tabular" ftype="tabular"/> + <param name="infile2" value="regression_train.tabular" ftype="tabular"/> + <param name="col1" value="1,2,3,4,5"/> + <param name="col2" value="6"/> + <param name="selected_task" value="train"/> + <param name="selected_algorithm" value="AdaBoostRegressor"/> + <param name="random_state" value="10"/> + <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="500"/> + </test> + <test> + <param name="infile_model" value="abr_model01" ftype="zip"/> + <param name="infile_data" value="regression_test.tabular" ftype="tabular"/> + <param name="selected_task" value="load"/> + <output name="outfile_predict" file="abr_result01" compare="sim_size" delta="500"/> + </test> </tests> <help><![CDATA[ ***What it does***
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abc_result01 Thu Oct 11 03:34:03 2018 -0400 @@ -0,0 +1,6 @@ +0 1 2 3 predicted +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abr_result01 Thu Oct 11 03:34:03 2018 -0400 @@ -0,0 +1,6 @@ +0 1 2 3 4 predicted +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.323842059244 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 1.1503117056799999 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.7191695359690001 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1503117056799999 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.7191695359690001
--- a/test-data/glm_result08 Sat Sep 29 07:30:08 2018 -0400 +++ b/test-data/glm_result08 Thu Oct 11 03:34:03 2018 -0400 @@ -1,5 +1,5 @@ -3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 0 0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 -2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 -1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1