kSVMlib

Kernel SVM library based on sklearn and GPlib. Provides similar functionality to GPlib for SVMs.

Setup kSVMlib

• Create and activate virtualenv (for python2) or venv (for python3)

  # for python3
  python3 -m venv .env
  # or for python2
  python2 -m virtualenv .env

  source .env/bin/activate

• Upgrade pip

  python -m pip install --upgrade pip

• Install kSVMlib package

  python -m pip install ksvmlib

Use kSVMlib

• Import kSVMlib to use it in your python script.

  import ksvmlib

• Generate some random data.

  import numpy as np
  data = {}
  data['X'] = np.vstack((
      np.random.multivariate_normal([1, 1], [[1, 0], [0, 1]], 100),
      np.random.multivariate_normal([3, 3], [[1, 0], [0, 1]], 100)
  ))
  data['Y'] = np.vstack((
      np.ones((100, 1)),
      np.zeros((100, 1)),
  ))

  validation = ksvmlib.dm.RandFold(fold_len=0.2, n_folds=1)
  train_set, test_set = validation.get_folds(data)[0]

• Initialize the KSVM model and a metric to measure the results.

  model = ksvmlib.KSVM(ksvmlib.ker.SquaredExponential())
  accuracy = ksvmlib.me.Accuracy()

• Fit the model to the data.

  fitting_method = ksvmlib.fit.GridSearch(
      obj_fun=accuracy.fold_measure,
      max_fun_call=300
  )
  train_validation = ksvmlib.dm.RandFold(fold_len=0.2, n_folds=3)

  log = fitting_method.fit(model, train_validation.get_folds(
      train_set
  ))
  print("Fitting log: {}".format(log))

• Finally plot the results.

  print("Accuracy: {}".format(accuracy.measure(model, train_set, test_set)))
  ksvmlib.plot.kernel_sort_data(model, test_set)

• There are more examples in examples/ directory. Check them out!

Develop kSVMlib

• Update API documentation

  source ./.env/bin/activate
  pip install Sphinx
  cd docs/
  sphinx-apidoc -f -o ./ ../ksvmlib