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:
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. This method solves binary classification problems.
- 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 dislib as ds >>> from dislib.classification import CascadeSVM >>> import numpy as np >>> >>> >>> if __name__ == '__main__': >>> x = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) >>> y = np.array([1, 1, 2, 2]) >>> train_data = ds.array(x, block_size=(4, 2)) >>> train_labels = ds.array(y, block_size=(4, 2)) >>> 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
- load_model(filepath, load_format='json')[source]¶
Loads a model from a file. The model is reinstantiated in the exact same state in which it was saved, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path of the saved the model
load_format (str, optional (default=’json’)) – Format used to load the model.
Examples
>>> from dislib.classification import CascadeSVM >>> import numpy as np >>> import dislib as ds >>> x = ds.array(np.array([[1, 2], [2, 1], [-1, -2], >>> [-2, -1]]), (2, 2)) >>> y = ds.array(np.array([0, 1, 1, 0]).reshape(-1, 1), (2, 1)) >>> model = CascadeSVM(cascade_arity=3, max_iter=10, >>> tol=1e-4, kernel='linear', c=2, gamma=0.1, >>> check_convergence=False, >>> random_state=seed, verbose=False) >>> model.fit(x, y) >>> model.save_model('/tmp/model') >>> loaded_model = CascadeSVM() >>> loaded_model.load_model('/tmp/model') >>> x_test = ds.array(np.array([[1, 2], [2, 1], [-1, -2], [-2, -1], >>> [1, 1], [-1, -1]]), (2, 2)) >>> y_pred = model.predict(x_test) >>> y_loaded_pred = loaded_model.predict(x_test) >>> assert np.allclose(y_pred.collect(), y_loaded_pred.collect())
- 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)
- save_model(filepath, overwrite=True, save_format='json')[source]¶
Saves a model to a file. The model is synchronized before saving and can be reinstantiated in the exact same state, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path where to save the model
overwrite (bool, optional (default=True)) – Whether any existing model at the target location should be overwritten.
save_format (str, optional (default=’json)) – Format used to save the models.
Examples
>>> from dislib.classification import CascadeSVM >>> import numpy as np >>> import dislib as ds >>> x = ds.array(np.array([[1, 2], [2, 1], [-1, -2], >>> [-2, -1]]), (2, 2)) >>> y = ds.array(np.array([0, 1, 1, 0]).reshape(-1, 1), (2, 1)) >>> model = CascadeSVM(cascade_arity=3, max_iter=10, >>> tol=1e-4, kernel='linear', c=2, gamma=0.1, >>> check_convergence=False, >>> random_state=seed, verbose=False) >>> model.fit(x, y) >>> model.save_model('/tmp/model') >>> loaded_model = CascadeSVM() >>> loaded_model.load_model('/tmp/model') >>> x_test = ds.array(np.array([[1, 2], [2, 1], [-1, -2], [-2, -1], >>> [1, 1], [-1, -1]]), (2, 2)) >>> y_pred = model.predict(x_test) >>> y_loaded_pred = loaded_model.predict(x_test) >>> assert np.allclose(y_pred.collect(), >>> y_loaded_pred.collect())
- score(x, y, collect=False)[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.
collect (bool, optional (default=False)) – When True, a synchronized result is returned.
- Returns
score – Mean accuracy of self.predict(x) wrt. y.
- Return type
float (as future object)