Mercurial > repos > bgruening > sklearn_generalized_linear

comparison generalized_linear.xml @ 20:9b7d0655f70f draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
author bgruening
date Thu, 23 Aug 2018 16:20:19 -0400
parents a259111a305a
children 212e7adfe65f
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19:a259111a305a 20:9b7d0655f70f
17 import sys 17 import sys
18 import json 18 import json
19 import numpy as np 19 import numpy as np
20 import sklearn.linear_model 20 import sklearn.linear_model
21 import pandas 21 import pandas
22 import pickle
23 from scipy.io import mmread 22 from scipy.io import mmread
24 23
25 execfile("$__tool_directory__/utils.py") 24 execfile("$__tool_directory__/sk_whitelist.py")
25 execfile("$__tool_directory__/utils.py", globals())
26 26
27 input_json_path = sys.argv[1] 27 input_json_path = sys.argv[1]
28 with open(input_json_path, "r") as param_handler: 28 with open(input_json_path, "r") as param_handler:
29 params = json.load(param_handler) 29 params = json.load(param_handler)
30 30
41 with open("$outfile_fit", 'wb') as out_handler: 41 with open("$outfile_fit", 'wb') as out_handler:
42 pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL) 42 pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL)
43 43
44 #else: 44 #else:
45 with open("$selected_tasks.infile_model", 'rb') as model_handler: 45 with open("$selected_tasks.infile_model", 'rb') as model_handler:
46 classifier_object = pickle.load(model_handler) 46 classifier_object = SafePickler.load(model_handler)
47 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 47 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
48 prediction = classifier_object.predict(data) 48 prediction = classifier_object.predict(data)
49 prediction_df = pandas.DataFrame(prediction, columns=["predicted"]) 49 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
50 res = pandas.concat([data, prediction_df], axis=1) 50 res = pandas.concat([data, prediction_df], axis=1)
51 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False, header=None) 51 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False, header=None)