Mercurial > repos > bgruening > sklearn_ensemble

comparison ensemble.xml @ 2:6e6726be0728 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 641ac64ded23fbb6fe85d5f13926da12dcce4e76
author bgruening
date Tue, 13 Mar 2018 04:56:08 -0400
parents 569eefee7ed8
children 0431274c367d
comparison
equal deleted inserted replaced
1:883f2973d37d 2:6e6726be0728
23 from scipy.io import mmread 23 from scipy.io import mmread
24 24
25 input_json_path = sys.argv[1] 25 input_json_path = sys.argv[1]
26 params = json.load(open(input_json_path, "r")) 26 params = json.load(open(input_json_path, "r"))
27 27
28 @COLUMNS_FUNCTION@
29
28 #if $selected_tasks.selected_task == "train": 30 #if $selected_tasks.selected_task == "train":
29 31
30 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"] 32 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
31 options = params["selected_tasks"]["selected_algorithms"]["options"] 33 options = params["selected_tasks"]["selected_algorithms"]["options"]
32 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"] 34 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
33 if input_type=="tabular": 35 if input_type=="tabular":
34 col1 = params["selected_tasks"]["selected_algorithms"]["input_options"]["col1"] 36 X = read_columns(
35 col1 = list(map(lambda x: x - 1, col1)) 37 "$selected_tasks.selected_algorithms.input_options.infile1",
36 f1 = pandas.read_csv("$selected_tasks.selected_algorithms.input_options.infile1", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 38 "$selected_tasks.selected_algorithms.input_options.col1",
37 X = f1.iloc[:,col1].values 39 sep='\t',
40 header=None,
41 parse_dates=True
42 )
38 else: 43 else:
39 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r')) 44 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r'))
40 45
41 col2 = params["selected_tasks"]["selected_algorithms"]["input_options"]["col2"] 46 y = read_columns(
42 col2 = list(map(lambda x: x - 1, col2)) 47 "$selected_tasks.selected_algorithms.input_options.infile2",
43 f2 = pandas.read_csv("$selected_tasks.selected_algorithms.input_options.infile2", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 48 "$selected_tasks.selected_algorithms.input_options.col2",
44 y = f2.iloc[:,col2].values 49 sep='\t',
50 header=None,
51 parse_dates=True
52 )
45 53
46 my_class = getattr(sklearn.ensemble, algorithm) 54 my_class = getattr(sklearn.ensemble, algorithm)
47 estimator = my_class(**options) 55 estimator = my_class(**options)
48 estimator.fit(X,y) 56 estimator.fit(X,y)
49 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL) 57 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
50 58
51 #else: 59 #else:
52 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r')) 60 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
53 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 61 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
54 prediction = classifier_object.predict(data) 62 prediction = classifier_object.predict(data)
55 prediction_df = pandas.DataFrame(prediction) 63 prediction_df = pandas.DataFrame(prediction)
56 res = pandas.concat([data, prediction_df], axis=1) 64 res = pandas.concat([data, prediction_df], axis=1)
57 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False) 65 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
58 #end if 66 #end if