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comparison svm.xml @ 5:86eb3864c899 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
author bgruening
date Fri, 16 Feb 2018 09:12:19 -0500
parents 4f1b0620ea89
children b70724d5445e
comparison
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4:d5cdb4f35e03 5:86eb3864c899
1 <tool id="svm_classifier" name="Support vector machines (SVMs)" version="@VERSION@"> 1 <tool id="svm_classifier" name="Support vector machines (SVMs)" version="@VERSION@">
2 <description>for classification</description> 2 <description>for classification</description>
3 <expand macro="python_requirements"/>
4 <expand macro="macro_stdio"/>
5 <macros> 3 <macros>
6 <import>main_macros.xml</import> 4 <import>main_macros.xml</import>
7 <!-- macro name="class_weight" argument="class_weight"--> 5 <!-- macro name="class_weight" argument="class_weight"-->
8 </macros> 6 </macros>
7 <expand macro="python_requirements"/>
8 <expand macro="macro_stdio"/>
9 <version_command>echo "@VERSION@"</version_command> 9 <version_command>echo "@VERSION@"</version_command>
10 <command><![CDATA[ 10 <command><![CDATA[
11 python "$svc_script" '$inputs' 11 python "$svc_script" '$inputs'
12 ]]> 12 ]]>
13 </command> 13 </command>
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 #if $selected_tasks.selected_task == "load": 28 #if $selected_tasks.selected_task == "load":
29 29
30 classifier_object = pickle.load(open("$infile_model", 'r')) 30 classifier_object = pickle.load(open("$infile_model", 'rb'))
31 31
32 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 32 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
33 prediction = classifier_object.predict(data) 33 prediction = classifier_object.predict(data)
34 prediction_df = pandas.DataFrame(prediction) 34 prediction_df = pandas.DataFrame(prediction)
35 res = pandas.concat([data, prediction_df], axis=1) 35 res = pandas.concat([data, prediction_df], axis=1)
36 res.to_csv(path_or_buf = "$outfile", sep="\t", index=False) 36 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
37 37
38 #else: 38 #else:
39 39
40 data_train = pandas.read_csv("$selected_tasks.infile_train", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ) 40 data_train = pandas.read_csv("$selected_tasks.infile_train", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
41 41
51 51
52 my_class = getattr(sklearn.svm, selected_algorithm) 52 my_class = getattr(sklearn.svm, selected_algorithm)
53 classifier_object = my_class(**options) 53 classifier_object = my_class(**options)
54 classifier_object.fit(data,labels) 54 classifier_object.fit(data,labels)
55 55
56 pickle.dump(classifier_object,open("$outfile", 'w+')) 56 pickle.dump(classifier_object,open("$outfile_fit", 'w+'))
57 57
58 #end if 58 #end if
59 59
60 ]]> 60 ]]>
61 </configfile> 61 </configfile>
62 </configfiles> 62 </configfiles>
63 <inputs> 63 <inputs>
64 <expand macro="train_loadConditional"> 64 <expand macro="train_loadConditional" model="zip">
65 <param name="selected_algorithm" type="select" label="Classifier type"> 65 <param name="selected_algorithm" type="select" label="Classifier type">
66 <option value="SVC">C-Support Vector Classification</option> 66 <option value="SVC">C-Support Vector Classification</option>
67 <option value="NuSVC">Nu-Support Vector Classification</option> 67 <option value="NuSVC">Nu-Support Vector Classification</option>
68 <option value="LinearSVC">Linear Support Vector Classification</option> 68 <option value="LinearSVC">Linear Support Vector Classification</option>
69 </param> 69 </param>
101 <param argument="intercept_scaling" type="float" optional="true" value="1" label="Add synthetic feature to the instance vector" help=" "/> 101 <param argument="intercept_scaling" type="float" optional="true" value="1" label="Add synthetic feature to the instance vector" help=" "/>
102 </section> 102 </section>
103 </when> 103 </when>
104 </expand> 104 </expand>
105 </inputs> 105 </inputs>
106 <outputs> 106
107 <data format="txt" name="outfile"/> 107 <expand macro="output"/>
108 </outputs> 108
109 <tests> 109 <tests>
110 <test> 110 <test>
111 <param name="infile_train" value="train_set.tabular" ftype="tabular"/> 111 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
112 <param name="selected_task" value="train"/> 112 <param name="selected_task" value="train"/>
113 <param name="selected_algorithm" value="SVC"/> 113 <param name="selected_algorithm" value="SVC"/>
114 <param name="random_state" value="5"/> 114 <param name="random_state" value="5"/>
115 <output name="outfile" file="svc_model01.txt"/> 115 <output name="outfile_fit" file="svc_model01.txt"/>
116 </test> 116 </test>
117 <test> 117 <test>
118 <param name="infile_train" value="train_set.tabular" ftype="tabular"/> 118 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
119 <param name="selected_task" value="train"/> 119 <param name="selected_task" value="train"/>
120 <param name="selected_algorithm" value="NuSVC"/> 120 <param name="selected_algorithm" value="NuSVC"/>
121 <param name="random_state" value="5"/> 121 <param name="random_state" value="5"/>
122 <output name="outfile" file="svc_model02.txt"/> 122 <output name="outfile_fit" file="svc_model02.txt"/>
123 </test> 123 </test>
124 <test> 124 <test>
125 <param name="infile_train" value="train_set.tabular" ftype="tabular"/> 125 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
126 <param name="selected_task" value="train"/> 126 <param name="selected_task" value="train"/>
127 <param name="selected_algorithm" value="LinearSVC"/> 127 <param name="selected_algorithm" value="LinearSVC"/>
128 <param name="random_state" value="5"/> 128 <param name="random_state" value="5"/>
129 <output name="outfile" file="svc_model03.txt"/> 129 <output name="outfile_fit" file="svc_model03.txt"/>
130 </test> 130 </test>
131 <test> 131 <test>
132 <param name="infile_model" value="svc_model01.txt" ftype="txt"/> 132 <param name="infile_model" value="svc_model01.txt" ftype="txt"/>
133 <param name="infile_data" value="test_set.tabular" ftype="tabular"/> 133 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
134 <param name="selected_task" value="load"/> 134 <param name="selected_task" value="load"/>
135 <output name="outfile" file="svc_prediction_result01.tabular"/> 135 <output name="outfile_predict" file="svc_prediction_result01.tabular"/>
136 </test> 136 </test>
137 <test> 137 <test>
138 <param name="infile_model" value="svc_model02.txt" ftype="txt"/> 138 <param name="infile_model" value="svc_model02.txt" ftype="txt"/>
139 <param name="infile_data" value="test_set.tabular" ftype="tabular"/> 139 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
140 <param name="selected_task" value="load"/> 140 <param name="selected_task" value="load"/>
141 <output name="outfile" file="svc_prediction_result02.tabular"/> 141 <output name="outfile_predict" file="svc_prediction_result02.tabular"/>
142 </test> 142 </test>
143 <test> 143 <test>
144 <param name="infile_model" value="svc_model03.txt" ftype="txt"/> 144 <param name="infile_model" value="svc_model03.txt" ftype="txt"/>
145 <param name="infile_data" value="test_set.tabular" ftype="tabular"/> 145 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
146 <param name="selected_task" value="load"/> 146 <param name="selected_task" value="load"/>
147 <output name="outfile" file="svc_prediction_result03.tabular"/> 147 <output name="outfile_predict" file="svc_prediction_result03.tabular"/>
148 </test> 148 </test>
149 </tests> 149 </tests>
150 <help><![CDATA[ 150 <help><![CDATA[
151 **What it does** 151 **What it does**
152 This module implements the Support Vector Machine (SVM) classification algorithms. 152 This module implements the Support Vector Machine (SVM) classification algorithms.