Python-Wrapper for Francesco Parrella's OnlineSVR C++ implementation.
Project description
PyOnlineSVR
Python-Wrapper for Francesco Parrella's OnlineSVR [PAR2007] C++ implementation with scikit-learn-compatible interfaces. You can find more information about the OnlineSVR here and the original source code here.
Installation
Dependencies
PyOnlineSVR requires the following dependencies:
- python (>=3.6.10)
- numpy (>=1.13.3)
- scipy (>=0.19.1)
- joblib (>=0.11)
- scikit-learn (>=0.23.0)
Binaries
PyOnlineSVR is published to PyPi and can be installed using pip
.
Prerequisites
- python (>=3.6.10)
- pip
Steps
You can use pip
to install PyOnlineSVR using:
pip install PyOnlineSVR
From Source (Linux)
If you are installing PyOnlineSVR from source, you will need Python 3.6.10 or later and a modern C++ compiler. We highly recommend using an Anaconda environment for building this project.
In the following, we explain the steps to build PyOnlineSVR using Anaconda and git.
Prepare environment
Create a new Anaconda environment and install the required dependencies. This includes python, SWIG to generate the C++ wrapper, and the C and C++ compiler toolchains.
conda create -n pyonlinesvr python swig gcc_linux-64 gxx_linux-64
conda activate pyonlinesvr
Install dependencies
conda install -n pyonlinesvr numpy scipy scikit-learn
Get the source code
git clone https://gitlab.hpi.de/akita/pyonlinesvr
cd pyonlinesvr
Install PyOnlineSVR
python setup.py install
Note that if your are using Anaconda, you may experience an error caused by the linker:
build/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized
collect2: error: ld returned 1 exit status
error: command 'g++' failed with exit status 1
This is caused by the linker ld
from the Conda environment shadowing the system ld
.
You should use a newer version of Python in your environment that fixes this issue.
The recommended Python versions are 3.6.10+, 3.7.6+ and 3.8.1+.
For further details see the issue.
Usage
>>> import numpy as np
>>> from pyonlinesvr import OnlineSVR
>>> X = np.array([[0, 0], [2, 2]])
>>> y = np.array([0.5, 2.5])
>>> regr = OnlineSVR()
>>> regr.fit(X[:1], y[:1])
OnlineSVR()
>>> regr.predict([[1, 1]])
array([ 0.4])
>>> regr.partial_fit(X[1:], y[1:])
OnlineSVR()
>>> regr.predict([[1, 1]])
array([ 1.5])
License
PyOnlineSVR is free software under the terms of the GNU General Public License, as found in the LICENSE file.
References
[PAR2007]: Parrelly, Francesco (2007). "Online Support Vector Machines for Regression." Master thesis. University of Genoa, Italy. PDF
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for PyOnlineSVR-0.0.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 576923641100c8324c12938562b489fd0e92ab9de476c9518e69e248c0597422 |
|
MD5 | fedaeb336c86ac94c853de1eff6a65af |
|
BLAKE2b-256 | 385d147cd2bc7ea9758487f520fa60897e55b9d00d0176edb8cf060b1cf45da6 |
Hashes for PyOnlineSVR-0.0.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 829d32f709f4c01cd93dd64c9fc6bc70f5a841699ff2a03ddf148b96c0f66055 |
|
MD5 | 489416d6270f9d71e41890eec5e3a82f |
|
BLAKE2b-256 | 18c1a55cdcc9ca123f9c9e40e5f1b790cdacbb9c202937e19686f0a3404cfc2c |
Hashes for PyOnlineSVR-0.0.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0ada42b7707124b3ef0981e2ea422acae1e40cb1aceca6aa59ce4e435ea3b110 |
|
MD5 | abb7e9afe12329840e77e6f354cd7207 |
|
BLAKE2b-256 | aadc55488df0a7442cd3c43ac7a92fec14437b21ca15f644ac131c6897060daa |