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Variant-SVMs
============
VarSVM is a Python scikit-learn estimators module for solving variants Support Vector Machines (SVM).
Website: https://variant-svm.readthedocs.io
This project was created by `Ben Dai `_. If there is any problem and suggestion please contact me via .
Installation
------------
Dependencies
~~~~~~~~~~~~
varsvm requires:
- Python
- NumPy
- Pandas
- Sklearn
User installation
~~~~~~~~~~~~~~~~~
Install Variant-SVMs using ``pip`` ::
pip install varsvm
or ::
pip install git+https://github.com/statmlben/varsvm.git
Please install ``python3-dev`` before install ``varsvm`` ::
sudo apt-get install python3-dev
Source code
~~~~~~~~~~~
You can check the latest sources with the command::
git clone https://github.com/statmlben/varsvm.git
Documentation
-------------
weightsvm
~~~~~~~~~
Classical weighted SVMs.
- class VarSVM.weightsvm(alpha=[], beta=[], C=1., max_iter = 1000, eps = 1e-4, print_step = 1)
- Parameters:
- **alpha**: Dual variable.
- **beta**: Primal variable, or coefficients of the support vector in the decision function.
- **C**: Penalty parameter C of the error term.
- **max_iter**: Hard limit on iterations for coordinate descent.
- **eps**: Tolerance for stopping criterion based on the relative l1 norm for difference of beta and beta_old.
- **print_step**: If print the interations for coordinate descent, 1 indicates YES, 0 indicates NO.
- Methods:
- **decision_function(X)**: Evaluates the decision function for the samples in X.
- X : array-like, shape (n_samples, n_features)
- **fit(X, y, sample_weight=1.)**: Fit the SVM model.
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- y : array-like, shape (n_samples,) **NOTE: y must be +1 or -1!**
- sample_weight : array-like, shape (n_samples,), weight for each sample.
Drift SVM
~~~~~~~~~
SVM with dift or fixed intercept for each instance.
- class VarSVM.driftsvm(alpha=[], beta=[], C=1., max_iter = 1000, eps = 1e-4, print_step = 1)
- Parameters:
- **alpha**: Dual variable.
- **beta**: Primal variable, or coefficients of the support vector in the decision function.
- **C**: Penalty parameter C of the error term.
- **max_iter**: Hard limit on iterations for coordinate descent.
- **eps**: Tolerance for stopping criterion based on the relative l1 norm for difference of beta and beta_old.
- **print_step**: If print the interations for coordinate descent, 1 indicates YES, 0 indicates NO.
- Methods:
- **decision_function(X)**: Evaluates the decision function for the samples in X.
- X : array-like, shape (n_samples, n_features)
- **fit(X, y, drift, sample_weight=1.)**: Fit the SVM model.
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- y : array-like, shape (n_samples,). **NOTE: y must be +1 or -1!**
- drift: array-like, shape (n_samples,), drift or fixed intercept for each instance, see `doc <./Variant-SVMs.pdf>`_.
- sample_weight : array-like, shape (n_samples,), weight for each instance.
Non-negative Drift SVM
~~~~~~~~~~~~~~~~~~~~~~
SVM with non-negative constrains for coefficients.
- class VarSVM.noneg_driftsvm(alpha=[], beta=[], C=1., max_iter = 1000, eps = 1e-4, print_step = 1)
- Parameters:
- **alpha**: Dual variable.
- **beta**: Primal variable, or coefficients of the support vector in the decision function.
- **C**: Penalty parameter C of the error term.
- **max_iter**: Hard limit on iterations for coordinate descent.
- **eps**: Tolerance for stopping criterion based on the relative l1 norm for difference of beta and beta_old.
- **print_step**: If print the interations for coordinate descent, 1 indicates YES, 0 indicates NO.
- Methods:
- **decision_function(X)**: Evaluates the decision function for the samples in X.
- X : array-like, shape (n_samples, n_features)
- **fit(X, y, drift, sample_weight=1.)**: Fit the SVM model.
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- y : array-like, shape (n_samples,). **NOTE: y must be +1 or -1!**
- drift: array-like, shape (n_samples,), drift or fixed intercept for each instance, see `doc <./Variant-SVMs.pdf>`_.
- sample_weight : array-like, shape (n_samples,), weight for each instance.
Example
~~~~~~~
.. code-block:: Python
import numpy as np
from sklearn.datasets import make_classification
from VarSVM import noneg_driftsvm
from sklearn.model_selection import GridSearchCV
X, y = make_classification(n_features=4, random_state=0)
y = y * 2 - 1
# fit a single model
n = len(X)
drift = .28*np.ones(n)
clf = noneg_driftsvm()
clf.fit(X=X, y=y, drift=drift)
y_pred = clf.decision_function(X=X, drift=drift)
# Tuning hyperparams based on sklearn.model_selection.GridSearchCV
parameters = {'C':[1, 10]}
psvm = noneg_driftsvm()
clf = GridSearchCV(psvm, parameters)
clf.fit(iris.data, iris.target)