Mercurial > repos > bgruening > sklearn_numeric_clustering

comparison numeric_clustering.xml @ 31:83938131dd46 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 05:44:48 -0500
parents 37e193b3fdd7
children 816b65d52c33
comparison
equal deleted inserted replaced
30:772db6f8bc24 31:83938131dd46
43 cluster_object.set_params( n_jobs=N_JOBS ) 43 cluster_object.set_params( n_jobs=N_JOBS )
44 44
45 #if $input_types.selected_input_type == "sparse": 45 #if $input_types.selected_input_type == "sparse":
46 data_matrix = mmread("$infile") 46 data_matrix = mmread("$infile")
47 #else: 47 #else:
48 data = pandas.read_csv("$infile", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 48 data = pandas.read_csv("$infile", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None)
49 header = 'infer' if params["input_types"]["header"] else None 49 header = 'infer' if params["input_types"]["header"] else None
50 column_option = params["input_types"]["column_selector_options"]["selected_column_selector_option"] 50 column_option = params["input_types"]["column_selector_options"]["selected_column_selector_option"]
51 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]: 51 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
52 c = params["input_types"]["column_selector_options"]["col"] 52 c = params["input_types"]["column_selector_options"]["col"]
53 else: 53 else:
57 c = c, 57 c = c,
58 c_option = column_option, 58 c_option = column_option,
59 sep='\t', 59 sep='\t',
60 header=header, 60 header=header,
61 parse_dates=True, 61 parse_dates=True,
62 encoding=None, 62 encoding=None)
63 tupleize_cols=False)
64 #end if 63 #end if
65 64
66 prediction = cluster_object.fit_predict( data_matrix ) 65 prediction = cluster_object.fit_predict( data_matrix )
67 66
68 if len(np.unique(prediction)) > 1: 67 if len(np.unique(prediction)) > 1: