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Python library for Variants of Support Vector Machines

Project description

PyPi License Python3 downloads downloads_month DOI

Variant-SVMs

VarSVM is a Python module for solving variants Support Vector Machines (SVM).

This project was created by Ben Dai. If there is any problem and suggestion please contact me via <bdai@umn.edu>.

Installation

Dependencies

Variant-SVMs requires:

  • Python

  • NumPy

  • SciPy

  • Cython

User installation

Install Variant-SVMs using pip

pip install VarSVM
pip install git+https://github.com/statmlben/Variant-SVM.git

Source code

You can check the latest sources with the command:

git clone https://github.com/statmlben/Variant-SVM.git

Documentation

The mathematical formulation for each model can be found in VariantSVMs.

Weighted SVM

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.

        • 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.

        • sample_weight : array-like, shape (n_samples,), weight for each instance.

Example

import numpy as np
from sklearn.datasets import make_classification
from VarSVM import noneg_driftsvm

X, y = make_classification(n_features=4, random_state=0)
y = y * 2 - 1

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)

Project details


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