| OpenMS 2.5.0

SvmTheoreticalSpectrumGeneratorTrainer

Trainer for SVM model as input for SvmTheoreticalSpectrumGenerator.

This application requires mzML file with ms2 spectra and annotations in an idXml file and trains a SVM model usable by SvmTheoreticalSpectrumGenerator. Please refer to the documentation of the corresponding class OpenMS::SvmTheoreticalSpectrumGeneratorTrainer

Note
  This tool is experimental!
  Currently mzIdentML (mzid) is not directly supported as an input/output format of this tool. Convert mzid files to/from idXML using IDFileConverter if necessary.

The command line parameters of this tool are:

    SvmTheoreticalSpectrumGeneratorTrainer -- Trainer for SVM models as input for SvmTheoreticalSpectrumGenerator

    Full documentation: http://www.openms.de/documentation/UTILS_SvmTheoreticalSpectrumGeneratorTrainer.html

    Version: 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Sep 10 2020, 12:20:39, Revision: GIT-NOTFOUND

    To cite OpenMS:

      Rost HL, Sachsenberg T, Aiche S, Bielow C et al.. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Meth. 2016; 13, 9: 741-748. doi:10.1038/nmeth.3959.

    Usage:

      SvmTheoreticalSpectrumGeneratorTrainer <options>

    This tool has algorithm parameters that are not shown here! Please check the ini file for a detailed descript

    ion or use the --helphelp option.

    Options (mandatory options marked with '*'):

      -in_spectra <file>*          Input Training Spectra in mzML (valid formats: 'mzML')

      -in_identifications <file>*  Input file with corresponding sequences in idXML (valid formats: 'idXML')

      -model_output_file <file>*   Name for output files. For each ion_type one file <filename>_residue_loss_char

                                   ge.svm and one <filename>.info which has to be passed to the SvmTheoretical

                                   SpectrumGenerator

      -precursor_charge <Int>      Precursor charge state used for model training (default: '2' min: '1' max:

                                   '3')

      -write_training_files        No models are trained but input training files for libSVM command line tools

                                   are produced

    Common UTIL options:

      -ini <file>                  Use the given TOPP INI file

      -threads <n>                 Sets the number of threads allowed to be used by the TOPP tool (default: '1')

      -write_ini <file>            Writes the default configuration file

      --help                       Shows options

      --helphelp                   Shows all options (including advanced)

    The following configuration subsections are valid:

     - algorithm

    You can write an example INI file using the '-write_ini' option.

    Documentation of subsection parameters can be found in the doxygen documentation or the INIFileEditor.

    For more information, please consult the online documentation for this tool:

      - http://www.openms.de/documentation/UTILS_SvmTheoreticalSpectrumGeneratorTrainer.html

INI file documentation of this tool:

Legend:
required parameter
advanced parameter
+SvmTheoreticalSpectrumGeneratorTrainer Trainer for SVM models as input for SvmTheoreticalSpectrumGenerator
version 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Version of the tool that generated this parameters file.
++1 Instance '1' section for 'SvmTheoreticalSpectrumGeneratorTrainer'
in_spectra Input Training Spectra in mzML input file*.mzML
in_identifications Input file with corresponding sequences in idXML input file*.idXML
model_output_file Name for output files. For each ion_type one file _residue_loss_charge.svm and one .info which has to be passed to the SvmTheoretical SpectrumGenerator output file
precursor_charge 2 Precursor charge state used for model training 1:3
write_training_files false No models are trained but input training files for libSVM command line tools are produced true,false
log Name of log file (created only when specified)
debug 0 Sets the debug level
threads 1 Sets the number of threads allowed to be used by the TOPP tool
no_progress false Disables progress logging to command line true,false
force false Overwrite tool specific checks.true,false
test false Enables the test mode (needed for internal use only)true,false
+++algorithm
number_intensity_levels 7 The number of intensity bins (for secondary type models)
number_regions 3 The number of regions each spectrum is split to (for secondary type models)
parent_tolerance 2.5 The maximum difference between theoretical and experimental parent mass to accept training spectrum
peak_tolerance 0.5 The maximum mass error for a peak to the expected mass of some ion type
add_b_ions true Train simulator for b-ions true,false
add_y_ions true Train simulator for y-ions true,false
add_a_ions false Train simulator for a-ions true,false
add_c_ions false Train simulator for c-ions true,false
add_x_ions false Train simulator for x-ions true,false
add_z_ions false Train simulator for z-ions true,false
add_losses false Train simulator for neutral losses of H2O and NH3 for b-ions and y-ions true,false
add_b2_ions false Train simulator for doubly charged b-ions true,false
add_y2_ions false Train simulator for double charged y-ions true,false
++++svm Parameters controlling SVM trainig behaviour. All parameter names are chosen as in the libSVM library. Please refer to libSVM documentation for explanation
svc_type 0 Type of the SVC: 0=C_SVC 1=NU_SVC 0:1
svr_type 1 Type of the SVR: 0=EPSILON_SVR 1=NU_SVR 0:1
scaling true Apply scaling of feature values true,false
scaling_lower 0.0 Lower bound for scaling
scaling_upper 1.0 Upper bound for scaling
n_fold 5 n_fold cross validation is performed 1:∞
grid false Perform grid search true,false
additive_cv false Additive step size (if false multiplicative)true,false
+++++svc Parameters for svm - classification of missing/abundant
kernel_type 2 Type of the kernel: 0=LINEAR 1=POLY 2=RBF 3=SIGMOID 0:3
degree 3 For POLY 1:∞
gamma 0.0 For POLY/RBF/SIGMOID 0.0:∞
C 1.0 Cost of constraint violation
nu 0.5 For NU_SVC, ONE_CLASS and NU_SVR
balancing true Use class balanced SVC training true,false
degree_start 1 starting point of degree 1:∞
degree_step_size 2 step size point of degree
degree_stop 4 stopping point of degree
gamma_start 1.0e-05 starting point of gamma 0.0:1.0
gamma_step_size 100 step size point of gamma
gamma_stop 0.1 stopping point of gamma
c_start 0.1 starting point of c
c_step_size 100 step size of c
c_stop 1000 stopping point of c
nu_start 0.3 starting point of nu 0.0:1.0
nu_step_size 2 step size of nu
nu_stop 0.6 stopping point of nu 0.0:1.0
+++++svr Parameters for svm - regression of peak intensities
kernel_type 2 Type of the kernel: 0=LINEAR 1=POLY 2=RBF 3=SIGMOID 0:3
degree 3 For POLY 1:∞
gamma 0.0 For POLY/RBF/SIGMOID 0.0:∞
C 1.0 Cost of constraint violation
p 0.1 The epsilon for the loss function in epsilon-SVR
nu 0.5 For NU_SVC, ONE_CLASS and NU_SVR
degree_start 1 starting point of degree 1:∞
degree_step_size 2 step size point of degree
degree_stop 4 stopping point of degree
gamma_start 1.0e-05 starting point of gamma 0.0:1.0
gamma_step_size 100 step size point of gamma
gamma_stop 0.1 stopping point of gamma
p_start 1.0e-05 starting point of p
p_step_size 100 step size point of p
p_stop 0.1 stopping point of p
c_start 0.1 starting point of c
c_step_size 100 step size of c
c_stop 1000 stopping point of c
nu_start 0.3 starting point of nu 0.0:1.0
nu_step_size 2 step size of nu
nu_stop 0.6 stopping point of nu 0.0:1.0