Mercurial > repos > bgruening > sklearn_nn_classifier
diff nn_classifier.xml @ 7:5072ac474cd5 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
author | bgruening |
---|---|
date | Sat, 29 Sep 2018 07:29:02 -0400 |
parents | e972a913e61a |
children | ed7b1654e841 |
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--- a/nn_classifier.xml Thu Aug 23 16:15:30 2018 -0400 +++ b/nn_classifier.xml Sat Sep 29 07:29:02 2018 -0400 @@ -20,8 +20,9 @@ import sklearn.neighbors import pandas -execfile("$__tool_directory__/sk_whitelist.py") -execfile("$__tool_directory__/utils.py", globals()) +with open("$__tool_directory__/sk_whitelist.json", "r") as f: + sk_whitelist = json.load(f) +exec(open("$__tool_directory__/utils.py").read(), globals()) input_json_path = sys.argv[1] with open(input_json_path, "r") as param_handler: @@ -30,7 +31,7 @@ #if $selected_tasks.selected_task == "load": with open("$infile_model", 'rb') as model_handler: - classifier_object = SafePickler.load(model_handler) + classifier_object = load_model(model_handler) header = 'infer' if params["selected_tasks"]["header"] else None data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False) @@ -118,7 +119,7 @@ <param name="selected_algorithm" value="nneighbors"/> <param name="sampling_method" value="KNeighborsClassifier" /> <param name="algorithm" value="brute" /> - <output name="outfile_fit" file="nn_model01.txt"/> + <output name="outfile_fit" file="nn_model01"/> </test> <test> <param name="infile1" value="train_set.tabular" ftype="tabular"/> @@ -128,10 +129,9 @@ <param name="col1" value="1,2,3,4"/> <param name="col2" value="5"/> <param name="selected_task" value="train"/> - <param name="selected_algorithm" value=""/> <param name="selected_algorithm" value="nneighbors"/> <param name="sampling_method" value="RadiusNeighborsClassifier" /> - <output name="outfile_fit" file="nn_model02.txt"/> + <output name="outfile_fit" file="nn_model02"/> </test> <test> <param name="infile1" value="train_set.tabular" ftype="tabular"/> @@ -142,24 +142,24 @@ <param name="col2" value="5"/> <param name="selected_task" value="train"/> <param name="selected_algorithm" value="ncentroid"/> - <output name="outfile_fit" file="nn_model03.txt"/> + <output name="outfile_fit" file="nn_model03"/> </test> <test> - <param name="infile_model" value="nn_model01.txt" ftype="txt"/> + <param name="infile_model" value="nn_model01" ftype="zip"/> <param name="infile_data" value="test_set.tabular" ftype="tabular"/> <param name="header" value="True"/> <param name="selected_task" value="load"/> <output name="outfile_predict" file="nn_prediction_result01.tabular"/> </test> <test> - <param name="infile_model" value="nn_model02.txt" ftype="txt"/> + <param name="infile_model" value="nn_model02" ftype="zip"/> <param name="infile_data" value="test_set.tabular" ftype="tabular"/> <param name="header" value="True"/> <param name="selected_task" value="load"/> <output name="outfile_predict" file="nn_prediction_result02.tabular"/> </test> <test> - <param name="infile_model" value="nn_model03.txt" ftype="txt"/> + <param name="infile_model" value="nn_model03" ftype="zip"/> <param name="infile_data" value="test_set.tabular" ftype="tabular"/> <param name="header" value="True"/> <param name="selected_task" value="load"/>