https://github.com/aalto-ics-kepaco/projse
Supervised variables selection by projection operators in kernel space
Science Score: 26.0%
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Low similarity (11.6%) to scientific vocabulary
Repository
Supervised variables selection by projection operators in kernel space
Basic Info
- Host: GitHub
- Owner: aalto-ics-kepaco
- License: mit
- Language: Python
- Default Branch: main
- Size: 41 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ProjSe-VaSP: Variable selection by projection operators
Requirements
The application of the ProjSe assumes the Python interpreter, version at least 3.7, and the numpy package, version at least 1.20.
To run the examples also requires the matplotlib. All these packages can be freely downloaded and installed from pypi.org.
Installation
The ProjSe package might be installed by the following procedures.
Directly from the github
pip3 install git+https://github.com/aalto-ics-kepaco/ProjSe.git#egg=ProjSe
Downloading from github
mkdir projective_selection
cd projective_selection
git clone https://github.com/aalto-ics-kepaco/ProjSe
After downloading the ProjSe package it can be installed by the following command:
pip3 install projective_selection/ProjSe
Before installing the ProjSe package the latest version of the Python packages pip and build need to be installed.
pip3 install --upgrade pip
pip3 install --upgrade build
The ProjSe can be imported as
import ProjSe
Running the projective selection algorithm
There is a demonstration in the "examples" directory. It requires the installation of the matplotlib.
Interface:
The basic class definition:
cprojector = clsprojectorkern(funckernel = None, **kernelparams)
class clsprojectorkern:
def init(self, funckernel = None, **kernelparams):
"""
Input:
func_kernreference to a kernel function, it assumes two 2d array X1,X2 inputs, a 2d array of kernel matrix. The inner product are computed between the rows of X1 and X2. The number of rows in X1 and X2 can be different. See the default example: self.linkern in this class.
If funckern == None then the linear kernel is used.kernel_params
dictionary of parameters transferred to the function given in the func_kern.
"""
Running the variable selection:
cprojector.full_cycle(Y, X, nitem, ilocal = 1, iscale = 1)
def full_cycle(self,Y, X, nitem, ilocal = 1, iscale = 1):
"""
Task: to enumerate the x variables best correlating with Y but conditionally uncorrelating with the previous selection
Input:
Y 2d array reference set, variables in the columns X 2d array of variables to be selected variables in the columns nitem maximum number of x variables selected ilocal =1 X,Y centralized =0 not iscale =1 X,Y normalized row wise to have length 1 = 0 notOutput lorder list of x variables arranged by selection order
"""
The selection score can be read out of cprojector.xstat which contains the scores in the order of selection.
Owner
- Name: KEPACO
- Login: aalto-ics-kepaco
- Kind: organization
- Location: Espoo, Finland
- Website: http://research.ics.aalto.fi/kepaco/
- Repositories: 29
- Profile: https://github.com/aalto-ics-kepaco
Kernel Machines, Pattern Analysis and Computational Metabolomics - Research group at Aalto University
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Dependencies
- numpy >=1.20.0
- numpy >=1.20.0