Mercurial > repos > bgruening > sklearn_generalized_linear
comparison generalized_linear.xml @ 20:9b7d0655f70f draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
author | bgruening |
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date | Thu, 23 Aug 2018 16:20:19 -0400 |
parents | a259111a305a |
children | 212e7adfe65f |
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19:a259111a305a | 20:9b7d0655f70f |
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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) |