Mercurial > repos > bgruening > sklearn_generalized_linear
diff generalized_linear.xml @ 15:f0e215cbade3 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
author | bgruening |
---|---|
date | Fri, 13 Jul 2018 03:56:33 -0400 |
parents | 10a8543142fc |
children | a259111a305a |
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--- a/generalized_linear.xml Tue Jul 10 03:13:00 2018 -0400 +++ b/generalized_linear.xml Fri Jul 13 03:56:33 2018 -0400 @@ -26,7 +26,8 @@ @GET_X_y_FUNCTION@ input_json_path = sys.argv[1] -params = json.load(open(input_json_path, "r")) +with open(input_json_path, "r") as param_handler: + params = json.load(param_handler) #if $selected_tasks.selected_task == "train": @@ -38,10 +39,12 @@ my_class = getattr(sklearn.linear_model, algorithm) estimator = my_class(**options) estimator.fit(X,y) -pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL) +with open("$outfile_fit", 'wb') as out_handler: + pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL) #else: -classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r')) +with open("$selected_tasks.infile_model", 'rb') as model_handler: + classifier_object = pickle.load(model_handler) data = pandas.read_csv("$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, columns=["predicted"])