scikit_decorators

Module: missions.nodes.regression.scikit_decorators

Scikit decorators for optimizing hyperparameters

Inheritance diagram for pySPACE.missions.nodes.regression.scikit_decorators:

OptSVRRegressorSklearnNode

class pySPACE.missions.nodes.regression.scikit_decorators.OptSVRRegressorSklearnNode(C=1, epsilon=0.1, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, verbose=False, max_iter=-1, **kwargs)

Bases: pySPACE.missions.nodes.scikit_nodes.SVRRegressorSklearnNode

Decorator wrapper around SVRRegressorSklearnNode

Epsilon-Support Vector Regression.

This node has been automatically generated by wrapping the sklearn.svm.classes.SVR class from the sklearn library. The wrapped instance can be accessed through the scikit_alg attribute.

The free parameters in the model are C and epsilon.

The implementation is based on libsvm.

Read more in the User Guide.

Parameters

C

: float, optional (default=1.0)

Penalty parameter C of the error term.

epsilon

: float, optional (default=0.1)

Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.

kernel

: string, optional (default=’rbf’)

Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

degree

: int, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma

: float, optional (default=’auto’)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.

coef0

: float, optional (default=0.0)

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

shrinking

: boolean, optional (default=True)

Whether to use the shrinking heuristic.

tol

: float, optional (default=1e-3)

Tolerance for stopping criterion.

cache_size

: float, optional

Specify the size of the kernel cache (in MB).

verbose

: bool, default: False

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

max_iter

: int, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

Attributes

support_

: array-like, shape = [n_SV]

Indices of support vectors.

support_vectors_

: array-like, shape = [nSV, n_features]

Support vectors.

dual_coef_

: array, shape = [1, n_SV]

Coefficients of the support vector in the decision function.

coef_

: array, shape = [1, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_.

intercept_

: array, shape = [1]

Constants in decision function.

Examples

>>> from sklearn.svm import SVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y) 
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto',
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

See also

NuSVR

Support Vector Machine for regression implemented using libsvm using a parameter to control the number of support vectors.

LinearSVR

Scalable Linear Support Vector Machine for regression implemented using liblinear.

POSSIBLE NODE NAMES:  

• OptSVRRegressorSklearnNode
• OptSVRRegressorSklearn

POSSIBLE INPUT TYPES:  

• FeatureVector

__init__(C=1, epsilon=0.1, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, verbose=False, max_iter=-1, **kwargs)

__hyperparameters = set([ChoiceParameter<kernel>, LogUniformParameter<C>, NoOptimizationParameter<input_dim>, NoOptimizationParameter<dtype>, NoOptimizationParameter<output_dim>, QLogUniformParameter<max_iter>, NoOptimizationParameter<retrain>, NoOptimizationParameter<store>, NoOptimizationParameter<kwargs_warning>, LogNormalParameter<epsilon>, NoOptimizationParameter<cache_size>, NoOptimizationParameter<shrinking>, LogUniformParameter<gamma>, NoOptimizationParameter<verbose>])