Mercurial > repos > bgruening > sklearn_numeric_clustering

comparison numeric_clustering.xml @ 30:60d80322e1e9 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
author bgruening
date Tue, 14 May 2019 17:45:57 -0400
parents c156b85a6389
children a36e1455971d
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29:c156b85a6389 30:60d80322e1e9
14 <inputs name="inputs"/> 14 <inputs name="inputs"/>
15 <configfile name="cluster_script"> 15 <configfile name="cluster_script">
16 <![CDATA[ 16 <![CDATA[
17 import sys 17 import sys
18 import json 18 import json
19 import numpy as np
19 import sklearn.cluster 20 import sklearn.cluster
20 import pandas 21 import pandas
21 from sklearn import metrics 22 from sklearn import metrics
22 from scipy.io import mmread 23 from scipy.io import mmread
23 24
24 exec(open("$__tool_directory__/utils.py").read(), globals()) 25 sys.path.insert(0, '$__tool_directory__')
26 from utils import read_columns
27
28 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
25 29
26 input_json_path = sys.argv[1] 30 input_json_path = sys.argv[1]
27 with open(input_json_path, "r") as param_handler: 31 with open(input_json_path, "r") as param_handler:
28 params = json.load(param_handler) 32 params = json.load(param_handler)
29 33
54 c_option = column_option, 58 c_option = column_option,
55 sep='\t', 59 sep='\t',
56 header=header, 60 header=header,
57 parse_dates=True, 61 parse_dates=True,
58 encoding=None, 62 encoding=None,
59 tupleize_cols=False 63 tupleize_cols=False)
60 )
61 #end if 64 #end if
62 65
63 prediction = cluster_object.fit_predict( data_matrix ) 66 prediction = cluster_object.fit_predict( data_matrix )
64 67
65 if len(np.unique(prediction)) > 1: 68 if len(np.unique(prediction)) > 1: