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comparison PTModel.xml @ 0:3d84209d3178 draft

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author bgruening
date Fri, 10 Oct 2014 18:20:03 -0400
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1 <?xml version='1.0' encoding='UTF-8'?>
2 <tool id="PTModel" name="PTModel" version="1.12.0">
3 <description>Trains a model for the prediction of proteotypic peptides from a training set.</description>
4 <macros>
5 <token name="@EXECUTABLE@">PTModel</token>
6 <import>macros.xml</import>
7 </macros>
8 <expand macro="stdio"/>
9 <expand macro="requirements"/>
10 <command>PTModel
11
12 -in_positive ${param_in_positive}
13 -in_negative ${param_in_negative}
14 -out ${param_out}
15 -c ${param_c}
16 -svm_type ${param_svm_type}
17 -nu ${param_nu}
18 -kernel_type ${param_kernel_type}
19 -degree ${param_degree}
20 -border_length ${param_border_length}
21 -k_mer_length ${param_k_mer_length}
22 -sigma ${param_sigma}
23 -max_positive_count ${param_max_positive_count}
24 -max_negative_count ${param_max_negative_count}
25 ${param_redundant}
26 ${param_additive_cv}
27 -threads \${GALAXY_SLOTS:-24}
28 ${param_skip_cv}
29 -cv:number_of_runs ${param_number_of_runs}
30 -cv:number_of_partitions ${param_number_of_partitions}
31 -cv:degree_start ${param_degree_start}
32 -cv:degree_step_size ${param_degree_step_size}
33 -cv:degree_stop ${param_degree_stop}
34 -cv:c_start ${param_c_start}
35 -cv:c_step_size ${param_c_step_size}
36 -cv:c_stop ${param_c_stop}
37 -cv:nu_start ${param_nu_start}
38 -cv:nu_step_size ${param_nu_step_size}
39 -cv:nu_stop ${param_nu_stop}
40 -cv:sigma_start ${param_sigma_start}
41 -cv:sigma_step_size ${param_sigma_step_size}
42 -cv:sigma_stop ${param_sigma_stop}
43 </command>
44 <inputs>
45 <param name="param_in_positive" type="data" format="idXML" optional="False" label="input file with positive examples" help="(-in_positive)"/>
46 <param name="param_in_negative" type="data" format="idXML" optional="False" label="input file with negative examples" help="(-in_negative)"/>
47 <param name="param_c" type="float" value="1.0" label="the penalty parameter of the svm" help="(-c)"/>
48 <param name="param_svm_type" type="select" optional="True" value="C_SVC" label="the type of the svm (NU_SVC or C_SVC)" help="(-svm_type)">
49 <option value="NU_SVC">NU_SVC</option>
50 <option value="C_SVC">C_SVC</option>
51 </param>
52 <param name="param_nu" type="float" min="0.0" max="1.0" optional="True" value="0.5" label="the nu parameter [0..1] of the svm (for nu-SVR)" help="(-nu)"/>
53 <param name="param_kernel_type" type="select" optional="True" value="OLIGO" label="the kernel type of the svm" help="(-kernel_type)">
54 <option value="LINEAR">LINEAR</option>
55 <option value="RBF">RBF</option>
56 <option value="POLY">POLY</option>
57 <option value="OLIGO">OLIGO</option>
58 </param>
59 <param name="param_degree" type="integer" min="1" optional="True" value="1" label="the degree parameter of the kernel function of the svm (POLY kernel)" help="(-degree)"/>
60 <param name="param_border_length" type="integer" min="1" optional="True" value="22" label="length of the POBK" help="(-border_length)"/>
61 <param name="param_k_mer_length" type="integer" min="1" optional="True" value="1" label="k_mer length of the POBK" help="(-k_mer_length)"/>
62 <param name="param_sigma" type="float" value="5.0" label="sigma of the POBK" help="(-sigma)"/>
63 <param name="param_max_positive_count" type="integer" min="1" optional="True" value="1000" label="quantity of positive samples for training (randomly chosen if smaller than available quantity)" help="(-max_positive_count)"/>
64 <param name="param_max_negative_count" type="integer" min="1" optional="True" value="1000" label="quantity of positive samples for training (randomly chosen if smaller than available quantity)" help="(-max_negative_count)"/>
65 <param name="param_redundant" type="boolean" truevalue="-redundant true" falsevalue="-redundant false" checked="false" optional="True" label="if the input sets are redundant and the redundant peptides should occur more than once in the training set, this flag has to be set" help="(-redundant)"/>
66 <param name="param_additive_cv" type="boolean" truevalue="-additive_cv true" falsevalue="-additive_cv false" checked="false" optional="True" label="if the step sizes should be interpreted additively (otherwise the actual value is multiplied with the step size to get the new value" help="(-additive_cv)"/>
67 <param name="param_skip_cv" type="boolean" truevalue="-cv:skip_cv true" falsevalue="-cv:skip_cv false" checked="false" optional="True" label="Has to be set if the cv should be skipped and the model should just be trained with the specified parameters." help="(-skip_cv)"/>
68 <param name="param_number_of_runs" type="integer" min="1" optional="True" value="10" label="number of runs for the CV" help="(-number_of_runs)"/>
69 <param name="param_number_of_partitions" type="integer" min="2" optional="True" value="10" label="number of CV partitions" help="(-number_of_partitions)"/>
70 <param name="param_degree_start" type="integer" min="1" optional="True" value="1" label="starting point of degree" help="(-degree_start)"/>
71 <param name="param_degree_step_size" type="integer" value="2" label="step size point of degree" help="(-degree_step_size)"/>
72 <param name="param_degree_stop" type="integer" value="4" label="stopping point of degree" help="(-degree_stop)"/>
73 <param name="param_c_start" type="float" value="1.0" label="starting point of c" help="(-c_start)"/>
74 <param name="param_c_step_size" type="float" value="100.0" label="step size of c" help="(-c_step_size)"/>
75 <param name="param_c_stop" type="float" value="1000.0" label="stopping point of c" help="(-c_stop)"/>
76 <param name="param_nu_start" type="float" min="0.0" max="1.0" optional="True" value="0.1" label="starting point of nu" help="(-nu_start)"/>
77 <param name="param_nu_step_size" type="float" value="1.3" label="step size of nu" help="(-nu_step_size)"/>
78 <param name="param_nu_stop" type="float" min="0.0" max="1.0" optional="True" value="0.9" label="stopping point of nu" help="(-nu_stop)"/>
79 <param name="param_sigma_start" type="float" value="1.0" label="starting point of sigma" help="(-sigma_start)"/>
80 <param name="param_sigma_step_size" type="float" value="1.3" label="step size of sigma" help="(-sigma_step_size)"/>
81 <param name="param_sigma_stop" type="float" value="15.0" label="stopping point of sigma" help="(-sigma_stop)"/>
82 </inputs>
83 <outputs>
84 <data name="param_out" label="output file: the model in libsvm format" format="txt"/>
85 </outputs>
86 <help>**What it does**
87
88 Trains a model for the prediction of proteotypic peptides from a training set.
89
90
91 For more information, visit http://ftp.mi.fu-berlin.de/OpenMS/release-documentation/html/TOPP_PTModel.html
92
93 @REFERENCES@
94 </help>
95 </tool>