Mercurial > repos > bgruening > sklearn_data_preprocess

comparison pre_process.xml @ 21:f156acc7239b draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
author bgruening
date Sat, 29 Sep 2018 07:31:15 -0400
parents f196d4715cfb
children d6b8103c909c
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20:2bda387c73e4 21:f156acc7239b
21 import pickle 21 import pickle
22 from scipy.io import mmread 22 from scipy.io import mmread
23 from scipy.io import mmwrite 23 from scipy.io import mmwrite
24 from sklearn import preprocessing 24 from sklearn import preprocessing
25 25
26 execfile("$__tool_directory__/utils.py") 26 exec(open("$__tool_directory__/utils.py").read(), globals())
27 27
28 input_json_path = sys.argv[1] 28 input_json_path = sys.argv[1]
29 with open(input_json_path, "r") as param_handler: 29 with open(input_json_path, "r") as param_handler:
30 params = json.load(param_handler) 30 params = json.load(param_handler)
31 31
58 estimator = my_class(**options) 58 estimator = my_class(**options)
59 estimator.fit(X) 59 estimator.fit(X)
60 result = estimator.transform(X) 60 result = estimator.transform(X)
61 61
62 #if $input_type.selected_input_type == "sparse": 62 #if $input_type.selected_input_type == "sparse":
63 with open("$outfile_transform", "w+") as transform_handler: 63 with open("$outfile_transform", "wb") as transform_handler:
64 mmwrite(transform_handler, result) 64 mmwrite(transform_handler, result)
65 #else: 65 #else:
66 res = pandas.DataFrame(result) 66 res = pandas.DataFrame(result)
67 res.to_csv(path_or_buf = "$outfile_transform", sep="\t", index=False, header=None) 67 res.to_csv(path_or_buf = "$outfile_transform", sep="\t", index=False, header=None)
68 #end if 68 #end if