dislib.classification.CascadeSVM

class dislib.classification.csvm.base.CascadeSVM(cascade_arity=2, max_iter=5, tol=0.001, kernel='rbf', c=1, gamma='auto', check_convergence=True, random_state=None, verbose=False)[source]

Bases: sklearn.base.BaseEstimator

Cascade Support Vector classification.

Implements distributed support vector classification based on Graf et al. [1]. The optimization process is carried out using scikit-learn’s SVC.

Parameters:
  • cascade_arity (int, optional (default=2)) – Arity of the reduction process.

  • max_iter (int, optional (default=5)) – Maximum number of iterations to perform.

  • tol (float, optional (default=1e-3)) – Tolerance for the stopping criterion.

  • kernel (string, optional (default=’rbf’)) – Specifies the kernel type to be used in the algorithm. Supported kernels are ‘linear’ and ‘rbf’.

  • c (float, optional (default=1.0)) – Penalty parameter C of the error term.

  • gamma (float, optional (default=’auto’)) – Kernel coefficient for ‘rbf’.

    Default is ‘auto’, which uses 1 / (n_features).

  • check_convergence (boolean, optional (default=True)) – Whether to test for convergence. If False, the algorithm will run for max_iter iterations. Checking for convergence adds a synchronization point after each iteration.

    If ``check_convergence=False’’ synchronization does not happen until a call to ``predict’’ or ``decision_function’‘. This can be useful to fit multiple models in parallel.

  • random_state (int, RandomState instance or None, optional (default=None)) – The seed of the pseudo random number generator used when shuffling the data for probability estimates. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

  • verbose (boolean, optional (default=False)) – Whether to print progress information.

Variables:
  • iterations (int) – Number of iterations performed.
  • converged (boolean) – Whether the model has converged.

References

[1]Graf, H. P., Cosatto, E., Bottou, L., Dourdanovic, I., & Vapnik, V. (2005). Parallel support vector machines: The cascade svm. In Advances in neural information processing systems (pp. 521-528).

Examples

>>> import numpy as np
>>> x = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> import dislib as ds
>>> train_data = ds.array(x, block_size=(4, 2))
>>> train_labels = ds.array(y, block_size=(4, 2))
>>> from dislib.classification import CascadeSVM
>>> svm = CascadeSVM()
>>> svm.fit(train_data, train_labels)
>>> test_data = ds.array(np.array([[-0.8, -1]]), block_size=(1, 2))
>>> y_pred = svm.predict(test_data)
>>> print(y_pred)
decision_function(x)[source]

Evaluates the decision function for the samples in x.

Parameters:x (ds-array, shape=(n_samples, n_features)) – Input samples.
Returns:df – The decision function of the samples for each class in the model.
Return type:ds-array, shape=(n_samples, 2)
fit(x, y)[source]

Fits a model using training data.

Parameters:
  • x (ds-array, shape=(n_samples, n_features)) – Training samples.
  • y (ds-array, shape=(n_samples, 1)) – Class labels of x.
Returns:

self

Return type:

CascadeSVM

predict(x)[source]

Perform classification on samples.

Parameters:x (ds-array, shape=(n_samples, n_features)) – Input samples.
Returns:y – Class labels of x.
Return type:ds-array, shape(n_samples, 1)
score(x, y)[source]

Returns the mean accuracy on the given test data and labels.

Parameters:
  • x (ds-array, shape=(n_samples, n_features)) – Test samples.
  • y (ds-array, shape=(n_samples, 1)) – True labels for x.
Returns:

score – Mean accuracy of self.predict(x) wrt. y.

Return type:

float (as future object)