Difference: KernelQuickTour (1 vs. 11)

Revision 112013-06-07 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 18 to 18
 by the OpenFst library. The OpenFst quick tour contains the relevant information for accomplishing this.
Changed:
<
<
A dataset is then represented by a Fst archive (far file) or by a text file containing a list of fst file (specified using an
>
>
A dataset is then represented by a Fst archive (far file) or by a text file containing a list of fst file (specified using an
 absolute path). The i -th entry in the far archive or in the fst list being the fst representing the i -th point in the dataset.
Line: 41 to 41
 
Changed:
<
<
In addition to n-gram kernels, the library provides tools for the creation of gappy n-gram kernels, mismatch kernels and arbitrary rational kernels.
>
>
In addition to n-gram kernels, the library provides tools for the creation of gappy n-gram kernels (klngram), mismatch kernels (klmismatch) and arbitrary rational kernels (klrational).
  Kernels can also be combined by taking their sum (klsum) or product (klproduct) or can be composed with a polynomial (klpolynomial), a gaussian (klgaussian) or

Revision 102010-04-09 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 18 to 18
 by the OpenFst library. The OpenFst quick tour contains the relevant information for accomplishing this.
Changed:
<
<
A dataset is then represented by a text file containing a list of fst file (specified using an absolute path). The i -th file in the list being the fst representing the i -th point
>
>
A dataset is then represented by a Fst archive (far file) or by a text file containing a list of fst file (specified using an absolute path). The i -th entry in the far archive or in the fst list being the fst representing the i -th point
 in the dataset.

This dataset should contain both your training and testing data.

Line: 37 to 37
 that contains both the kernel function and the dataset it is defined on.
Changed:
<
<
$ klngram -order=3 -sigma=2 fst.list > 3-gram.kar
>
>
$ klngram -order=3 -sigma=2 data.far > 3-gram.kar
 

Revision 92010-04-09 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 24 to 24
  This dataset should contain both your training and testing data.
Changed:
<
<
An example of dataset, a subset of Reuters-21578, is provided with the library
>
>
IDEA! An example of dataset, a subset of Reuters-21578, is provided with the library
 and can be used to become familiar with its usage.

Revision 82008-05-14 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 111 to 111
 specifies that the 3rd and 5th points of the dataset are in the test set (the labels are optional here and will only be used for scoring).
Changed:
<
<
The svm-train utility needs to be call with two additional options. The -k option
>
>
The svm-train utility needs to be called with two additional options. The -k option
 specifies the type of kernel and should be openkernel when using the OpenKernel library. The -K option specifies the kar file defining the kernel and dataset to be used. All the other svm-train options are still available.

Revision 72007-12-06 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 34 to 34
 specifies the n-gram order and the -sigma option the size of the alphabet (i.e. the maximum label id). The fst.list specifies the dataset the kernel is operating on. The output of klngram is a kar file (for kernel archive)
Changed:
<
<
that contains both the kernel and the data it is defined on.
>
>
that contains both the kernel function and the dataset it is defined on.
 
$ klngram -order=3 -sigma=2  fst.list > 3-gram.kar
Line: 62 to 62
 n floats on each line. The j -th value on the i -th line correspond to the value of the kernel for the i -th and j -th points in the dataset.
Added:
>
>
The kernel matrix can be partially computed by restricting the set of values to be evaluated using the -xmin, -xmax, -ymin and -ymax flags. Assuming the lines and columns are indexed from 0 to n - 1, the following command can be used to only compute the (i, j) value if and only if 10 ≤ i, j < 20:
kleval -xmin=10 -ymin=10 -xmax=20 -ymax=20 3-gram.kar
 Using the -libsvm option will generate a file in the format used by LIBSVM to specify precomputed kernels. LIBSVM users are however encouraged to use the LIBSVM plugin as described below.
Added:
>
>
Finally, the -kar option allows the kernel matrix to be stored in a kar file in addition to the kernel function and dataset.

$ kleval 3-gram.kar > 3-gram.matrix.kar
 

Using the LIBSVM plugin

Line: 112 to 128
 $ svm-predict test 3-gram.model 3-gram.pred
Added:
>
>
When using the LIBSVM plugin, the kernel values are computed "on the fly" as requested by the LIBSVM utilities. When performing several experiments using the same kernel (on the same dataset), it is recommended, in order to avoid unnecessary computations, to first compute the (partial) kernel matrix using kleval -kar and use the resulting kar file as a parameter to the LIBSVM utilities.
 

-- CyrilAllauzen - 08 Oct 2007 \ No newline at end of file

Revision 62007-10-30 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 98 to 98
 The svm-train utility needs to be call with two additional options. The -k option specifies the type of kernel and should be openkernel when using the OpenKernel library. The -K option specifies the kar file defining the kernel and dataset to be used.
Added:
>
>
All the other svm-train options are still available.
 For example:

Revision 52007-10-30 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 24 to 24
  This dataset should contain both your training and testing data.
Added:
>
>
An example of dataset, a subset of Reuters-21578, is provided with the library and can be used to become familiar with its usage.
 

Creating an n -gram kernel

Line: 93 to 96
 will only be used for scoring).

The svm-train utility needs to be call with two additional options. The -k option

Changed:
<
<
specifies the type of kernel and should be kernellib when using the OpenKernel library.
>
>
specifies the type of kernel and should be openkernel when using the OpenKernel library.
 The -K option specifies the kar file defining the kernel and dataset to be used. For example:
Changed:
<
<
$ svm-train -k kernellib -K 3-gram.kar train 3-gram.model
>
>
$ svm-train -k openkernel -K 3-gram.kar train 3-gram.model
 

The svm-predict utility does not required any additional options. The kernel information

Revision 42007-10-19 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

ALERT! Under construction.

Added:
>
>
 

Using the library

Changed:
<
<
In this quick tour, we will focus on the command-line utilities and libsvm plugin. The command-line utilities are available in the bin sub-directory.
>
>
In this quick tour, we will focus on the command-line utilities and LIBSVM plugin. The command-line utilities are available in the kernel/bin sub-directory.
 
Changed:
<
<

Creating an n-gram kernel

>
>

Preparing your data

In order to use the library, you need to represent each point in your dataset as an fst, i.e., a weighted transducer (or automaton) represented in the binary format used by the OpenFst library. The OpenFst quick tour contains the relevant information for accomplishing this.

A dataset is then represented by a text file containing a list of fst file (specified using an absolute path). The i -th file in the list being the fst representing the i -th point in the dataset.

This dataset should contain both your training and testing data.

Creating an n -gram kernel

  The klngram utility can be used to generate an n-gram kernel. The -order option specifies the n-gram order and the -sigma option the size of the alphabet (i.e.
Changed:
<
<
the maximum label id). The output of klngram is a kar file (for kernel archive)
>
>
the maximum label id). The fst.list specifies the dataset the kernel is operating on. The output of klngram is a kar file (for kernel archive)
 that contains both the kernel and the data it is defined on.
Line: 24 to 41
 In addition to n-gram kernels, the library provides tools for the creation of gappy n-gram kernels, mismatch kernels and arbitrary rational kernels.
Changed:
<
<

Generating a kernel matrix

>
>
Kernels can also be combined by taking their sum (klsum) or product (klproduct) or can be composed with a polynomial (klpolynomial), a gaussian (klgaussian) or a sigmoid (klsigmoid).
 
Added:
>
>

Generating a kernel matrix

 
Added:
>
>
The kernel matrix corresponding to the evaluation of the kernel on the specified dataset can be computed using the kleval utility as shown here:
 
$ kleval 3-gram.kar > 3-gram.matrix
Added:
>
>
Assuming the size of the dataset is n, the result will be a text file with n lines and n floats on each line. The j -th value on the i -th line correspond to the value of the kernel for the i -th and j -th points in the dataset.

Using the -libsvm option will generate a file in the format used by LIBSVM to specify precomputed kernels. LIBSVM users are however encouraged to use the LIBSVM plugin as described below.

 
Added:
>
>
 

Using the LIBSVM plugin

The OpenKernel library package includes a modified version of LIBSVMExternal site that allows the definition of arbitrary plugins to handle the kernel computations. This version of LIBSVM is available in the libsvm sub-directory. A specific plugin to allow the use of the OpenKernel library

Changed:
<
<
with libsvm is provided in the plugin sub-directory. In order to use this plugin, you need to add the path to the plugin sub-directory to your dynamic loader path (LD_LIBRARY_PATH
>
>
with libsvm is provided in the kernel/plugin sub-directory. In order to use this plugin, you need to add the path to the kernel/plugin sub-directory to your dynamic loader path (LD_LIBRARY_PATH
 on Linux, DYLD_LIBRARY_PATH on MacOS X).
Added:
>
>
The training and test dataset need to be specified in the usual LIBSVM format (if you are not familiar with LIBSVM check out the official website or the README file in the libsvm directory). For instance a text file train such as:
1 1:1.0
-1 2:1.0
1 4:1.0
specifies that the 1st, 2nd and 4th points of the dataset are in the training set with labels 1, -1 and 1. And a text file test such as:
-1 3:1.0
1 5:1.0
specifies that the 3rd and 5th points of the dataset are in the test set (the labels are optional here and will only be used for scoring).
 
Changed:
<
<
>
>
The svm-train utility needs to be call with two additional options. The -k option specifies the type of kernel and should be kernellib when using the OpenKernel library. The -K option specifies the kar file defining the kernel and dataset to be used. For example:
 
$ svm-train -k kernellib -K 3-gram.kar train 3-gram.model
Deleted:
<
<
$ svm-predict test 3-gram.model 3-gram.pred
 
Added:
>
>
The svm-predict utility does not required any additional options. The kernel information is included in the model file:

$ svm-predict test 3-gram.model 3-gram.pred
 

Revision 32007-10-09 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Changed:
<
<
Warning, important Under construction.
>
>
ALERT! Under construction.
 

Using the library

Line: 36 to 36
 

Using the LIBSVM plugin

The OpenKernel library package includes a modified version of

Changed:
<
<
LIBSVMExternal site that allows the definition
>
>
LIBSVMExternal site that allows the definition
 of arbitrary plugins to handle the kernel computations. This version of LIBSVM is available in the libsvm sub-directory. A specific plugin to allow the use of the OpenKernel library with libsvm is provided in the plugin sub-directory. In order to use this plugin, you need to add the path to the plugin sub-directory to your dynamic loader path (LD_LIBRARY_PATH
Changed:
<
<
on Linux, DYLD_LIBRARY_PATH on MacOS X).
>
>
on Linux, DYLD_LIBRARY_PATH on MacOS X).
 

Revision 22007-10-09 - CyrilAllauzen

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Line: 11 to 11
 

Creating an n-gram kernel

Added:
>
>
The klngram utility can be used to generate an n-gram kernel. The -order option specifies the n-gram order and the -sigma option the size of the alphabet (i.e. the maximum label id). The output of klngram is a kar file (for kernel archive) that contains both the kernel and the data it is defined on.
 
$ klngram -order=3 -sigma=2  fst.list > 3-gram.kar
Line: 26 to 33
 
Changed:
<
<

Using the libsvm plugin

>
>

Using the LIBSVM plugin

 
Changed:
<
<
The OpenKernel library package includes a modified version of libsvm that allows the definition of arbitrary plugins to handle the kernel computations. This version of libsvm is available
>
>
The OpenKernel library package includes a modified version of LIBSVMExternal site that allows the definition of arbitrary plugins to handle the kernel computations. This version of LIBSVM is available
 in the libsvm sub-directory. A specific plugin to allow the use of the OpenKernel library
Changed:
<
<
with libsvm is provided in the plugin sub-directory.
>
>
with libsvm is provided in the plugin sub-directory. In order to use this plugin, you need to add the path to the plugin sub-directory to your dynamic loader path (LD_LIBRARY_PATH on Linux, DYLD_LIBRARY_PATH on MacOS X).
 

Revision 12007-10-08 - CyrilAllauzen

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="WebHome"

OpenKernel Quick Tour

Warning, important Under construction.

Using the library

In this quick tour, we will focus on the command-line utilities and libsvm plugin. The command-line utilities are available in the bin sub-directory.

Creating an n-gram kernel

$ klngram -order=3 -sigma=2  fst.list > 3-gram.kar

In addition to n-gram kernels, the library provides tools for the creation of gappy n-gram kernels, mismatch kernels and arbitrary rational kernels.

Generating a kernel matrix

$ kleval 3-gram.kar > 3-gram.matrix

Using the libsvm plugin

The OpenKernel library package includes a modified version of libsvm that allows the definition of arbitrary plugins to handle the kernel computations. This version of libsvm is available in the libsvm sub-directory. A specific plugin to allow the use of the OpenKernel library with libsvm is provided in the plugin sub-directory.

$ svm-train -k kernellib -K 3-gram.kar train 3-gram.model
$ svm-predict test 3-gram.model 3-gram.pred

-- CyrilAllauzen - 08 Oct 2007

 
This site is powered by the TWiki collaboration platform Powered by PerlCopyright © 2008-2019 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback