Mercurial > repos > bgruening > sklearn_searchcv
changeset 6:7509d7059040 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author | bgruening |
---|---|
date | Thu, 11 Oct 2018 03:30:01 -0400 |
parents | 0987bc3904a0 |
children | 4368259ff821 |
files | search_model_validation.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, 86 insertions(+), 3 deletions(-) [+] |
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--- a/search_model_validation.xml Sat Sep 29 07:26:39 2018 -0400 +++ b/search_model_validation.xml Thu Oct 11 03:30:01 2018 -0400 @@ -85,6 +85,7 @@ options = params["search_schemes"]["options"] options['cv'] = get_cv( options['cv'].strip() ) options['n_jobs'] = N_JOBS +primary_scoring = options['scoring']['primary_scoring'] options['scoring'] = get_scoring(options['scoring']) if options['error_score']: options['error_score'] = 'raise' @@ -114,6 +115,7 @@ print(repr(warning.message)) cv_result = pandas.DataFrame(searcher.cv_results_) +cv_result.rename(inplace=True, columns={"mean_test_primary": "mean_test_"+primary_scoring, "rank_test_primary": "rank_test_"+primary_scoring}) cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False) #if $save: @@ -453,6 +455,75 @@ </assert_contents> </output> </test> + <test> + <param name="selected_search_scheme" value="GridSearchCV"/> + <param name="infile_pipeline" value="pipeline02" ftype="zip"/> + <conditional name="search_param_selector"> + <param name="search_p" value="eps: [0.01, 0.001]"/> + <param name="selected_param_type" value="final_estimator_p"/> + </conditional> + <param name="infile1" value="regression_X.tabular" ftype="tabular"/> + <param name="header1" value="true" /> + <param name="selected_column_selector_option" value="all_columns"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true" /> + <param name="selected_column_selector_option2" value="all_columns"/> + <output name="outfile_result"> + <assert_contents> + <has_n_columns n="12"/> + <has_text text="0.7762968161366681" /> + </assert_contents> + </output> + </test> + <test> + <param name="selected_search_scheme" value="GridSearchCV"/> + <param name="infile_pipeline" value="pipeline05" ftype="zip"/> + <conditional name="search_param_selector"> + <param name="search_p" value="n_estimators: [10, 50, 100, 300]"/> + <param name="selected_param_type" value="final_estimator_p"/> + </conditional> + <param name="infile1" value="regression_X.tabular" ftype="tabular"/> + <param name="header1" value="true" /> + <param name="selected_column_selector_option" value="all_columns"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true" /> + <param name="selected_column_selector_option2" value="all_columns"/> + <output name="outfile_result"> + <assert_contents> + <has_n_columns n="12"/> + <has_text text="0.8176497587057971" /> + </assert_contents> + </output> + </test> + <test expect_failure="true"> + <param name="selected_search_scheme" value="GridSearchCV"/> + <param name="infile_pipeline" value="pipeline01" ftype="zip"/> + <conditional name="search_param_selector"> + <param name="search_p" value="C: open('~/.ssh/authorized_keys', 'r').read()"/> + <param name="selected_param_type" value="final_estimator_p"/> + </conditional> + <param name="infile1" value="regression_X.tabular" ftype="tabular"/> + <param name="header1" value="true" /> + <param name="selected_column_selector_option" value="all_columns"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true" /> + <param name="selected_column_selector_option2" value="all_columns"/> + </test> + <test expect_failure="true"> + <param name="selected_search_scheme" value="GridSearchCV"/> + <param name="infile_pipeline" value="pipeline01" ftype="zip"/> + <conditional name="search_param_selector"> + <param name="search_p" value="C: [1, 10, 100, 1000]"/> + <param name="selected_param_type" value="final_estimator_p"/> + </conditional> + <param name="cv" value="__import__('os').system('ls ~')"/> + <param name="infile1" value="regression_X.tabular" ftype="tabular"/> + <param name="header1" value="true" /> + <param name="selected_column_selector_option" value="all_columns"/> + <param name="infile2" value="regression_y.tabular" ftype="tabular"/> + <param name="header2" value="true" /> + <param name="selected_column_selector_option2" value="all_columns"/> + </test> </tests> <help> <![CDATA[
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abc_result01 Thu Oct 11 03:30:01 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 03:30:01 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 07:26:39 2018 -0400 +++ b/test-data/glm_result08 Thu Oct 11 03:30:01 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