| standard MIS-based support | ||||
| coco.so | (433 kb) | GNU/Linux Python 3.10 shared object | ||
| coco.pyd | (121 kb) | Windows Python 3.10 dynamic module | ||
| influence map overlap based support | ||||
| ccnovl.so | (413 kb) | GNU/Linux Python 3.10 shared object | ||
| ccnovl.pyd | (110 kb) | Windows Python 3.10 dynamic module | ||
| pycoco.zip | (414 kb) | C sources, version 2.44 (2016.11.21) | ||
| pycoco.tar.gz | (394 kb) | |||
| psf+psr.zip | (78 kb) | Python scripts for Pattern Spectrum Filtering | ||
| psf+psr.tar.gz | (70 kb) | and Pattern Set Reduction | ||
PyCoCo is an extension module that makes the CoCoNAD implementation available as a function in Python 3.10 or later and also includes a function for generating a pattern spectrum with the help of surrogate data and CoCoNAD analysis.
How to use the function coconad can be seen in the
script testcoco.py in the source package (directory
pycoco/ex). For the function patspec see the
script ccn+psf+psr.py in the psf+psr archives.
Alternatively, from a Python command prompt interface, call
help(coco), help(coconad)
(or help(coco.coconad)) and help(patspec)
(or help(coco.patspec)) or print
coconad.__doc__ and patspec.__doc__ for
a description of these functions and their arguments.
The Python scripts in the archives psf+psr implement the
full analysis process of parallel (spike) trains/point processes as
it is described (although for standard frequent item set mining) in
[Picado-Muiño et al.
2013] and [Torre et al.
2013]. A full description of these scripts can be found
here.
Call the main script ccn+psf+psr.py without any arguments
to obtain a help message that shows the invocation and the available
options.
If instead of a command prompt interface as the psf+psr
scripts provide it, a graphical user interface is desired, the
Java-based CoCoGUI
program is worth looking at.
If you have a GNU/Linux system, you can use this extension module
by simply downloading the shared object made available above and
storing it in a directory that is on your PYTHONPATH
(environment variable). A typical (local) installation directory
is $HOME/lib/ while a typical (global) installation
directory is /usr/local/lib/python3.10/site-packages/ .
(Note that you may need root rights to copy into the latter
directories.) A typical (local) installation directory
for the Anaconda Python distribution is
$HOME/anaconda/lib/python310/site-packages/.
If you have a Windows system, downloading the Python dynamic
module made available above and placing it into the extension
module directory of your Python installation should work.
Consult the manual of your Python installation to find the
correct directory. A typical directory for (global) installation is
C:\Program Files\Python310\Lib\site-packages\
(Note that you may need administrator rights to copy into
these directory.) A typical installation directory
for the Anaconda Python distribution is
C:\Anaconda3\Lib\site-packages\.
If you have trouble on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2022 (see under "Other Tools and Frameworks") installed, as the library was compiled with Microsoft Visual Studio 2022.
Another way to install the extension module for your system is to
use the Python script setup_fim.py (in the source package),
which uses Python's
setuptools
package to build and install the module.
On a GNU/Linux system call the script with
./setup_coco.py install
in a terminal window to build and install the extension module.
If you get a "Permission denied" error message, check whether the
file setup_coco.py is marked as executable. If it is not,
add the executable flag with the command
chmod +x setup_coco.py
Alternatively, call the script explicitly through Python:
python3 setup_coco.py install
On a Microsoft Windows system call the script with
python setup_coco.py install
in a command prompt window to build and install the extension module.
Note, however, that this direct call to Python is possible on Microsoft
Windows only if the directory, in which the program python.exe
resides, is contained in your PATH variable (environment
variable, check its contents at a command prompt with
echo %PATH%). Otherwise you may have to specify the full path
to the Python program. A typical form of the command for this case is
"C:\Program Files\Python310\python.exe" setup_coco.py install
In addition, building the module requires a C compiler.
On a GNU/Linux system Python uses the system C compiler,
which for GNU/Linux is usually the GNU
C compiler gcc. This compiler is essentially part of
the system and thus basically always available. One only may have to
install the Python development files (package python-dev or
python3-dev for Debian based GNU/Linux distributions).
On a Windows system Python commonly uses Microsoft Visual Studio C/C++, which therefore needs to be installed. Note that the Community Edition of this C compiler can be obtained (perfectly legally) free of charge.
Note generally (for GNU/Linux as well as for Microsoft Windows) that installing this extension module for all users may require root/administrator rights in order to copy the shared object/Python dynamic module to the standard extension module directory. Local installations (for individual users) are also possible.
On GNU/Linux (provided the Python development files are installed
– package python3-dev on Debian based distributions),
you may also install the extension module by simply calling
make all
in the source directory pycoco/src and copying the
resulting shared object coco.so to a directory that is on
your PYTHONPATH (environment variable).
On Windows you may also install the extension module by simply calling
nmake /f pycoco.mak all
in a command prompt of Microsoft Visual Studio C/C++ in the source
directory pycoco/src and copying the resulting dynamic
module coco.pyd to the extension module directory of your
Python installation.
Should the compilation fail, check the definition of the
variable PYDIR in the makefile
(Gnu/Linux) or pycoco.mak (Windows)
If you are using the Anaconda Python distribution, you may use the
special makefile pycoco_conda.mak, which is configured for
Anaconda 1.8.0 installed in the default path. If you have a different
version or installed to a non-standard path, you may have to adapt
the definitions of CONDAINC and CONDALIB in
pycoco_conda.mak.
More information about the CoCoNAD algorithm can be found in the following papers:
The use of pattern spectra to evaluate the statistical significance of found frequent item sets is explained in these papers (although for standard frequent item set mining, but the idea can be transferred, with a few adaptations, to CoCoNAD):