Package mdp :: Package nodes :: Class LibSVMClassifier

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Class LibSVMClassifier

The LibSVMClassifier class acts as a wrapper around the LibSVM library for support vector machines.

Information to the parameters can be found on http://www.csie.ntu.edu.tw/~cjlin/libsvm/

The class provides access to change kernel and svm type with a text string.

Additionally self.parameter is exposed which allows to change all other svm parameters directly.

This node depends on libsvm.

Instance Methods

[hide private]

__init__(self, kernel=None, classifier=None, probability=True, params=None, input_dim=None, output_dim=None, dtype=None)
kernel -- The kernel to use classifier -- The type of the SVM params -- a dict of parameters to be passed to the svm_parameter probability -- Must be set to True, if algorithms based on probability shall be used.

_get_supported_dtypes(self)
Return the list of dtypes selfupported by this node.

_label(self, x)

_prob(self, x)

_stop_training(self)
Transform the data and labels lists to array objects and reshape them.

_train(self, x, labels)
Cumulate all input data in a one dimensional list.

label(self, x)
Returns an array with best class labels.

predict_probability(self, x)

prob(self, x)
Returns the probability for each datapoint and label (e.g., [{1:0.1, 2:0.0, 3:0.9}, {1:1.0, 2:0.0, 3:0.0}, ...])

set_classifier(self, classifier)
Sets the classifier.

set_kernel(self, kernel)
Sets the kernel.

stop_training(self)
Transform the data and labels lists to array objects and reshape them.

train(self, x, labels)
Cumulate all input data in a one dimensional list.

Inherited from PreserveDimNode (private): _set_input_dim, _set_output_dim

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from ClassifierCumulator

_check_train_args(self, x, labels)

Inherited from ClassifierNode

_execute(self, x)

execute(self, x)
Process the data contained in x.

rank(self, x, threshold=None)
Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert y.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

Inherited from unreachable._SVMClassifier: is_invertible

Inherited from Node

is_trainable()
Return True if the node can be trained, False otherwise.

Class Variables

[hide private]

classifiers = ['C_SVC', 'NU_SVC', 'ONE_CLASS', 'EPSILON_SVR', ...

kernels = ['RBF', 'LINEAR', 'POLY', 'SIGMOID']

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, kernel=None, classifier=None, probability=True, params=None, input_dim=None, output_dim=None, dtype=None)
(Constructor)


kernel -- The kernel to use
classifier -- The type of the SVM
params -- a dict of parameters to be passed to the svm_parameter
probability -- Must be set to True, if algorithms based on probability
               shall be used.

Overrides: object.__init__

_get_supported_dtypes(self)

Return the list of dtypes selfupported by this node.

Overrides: Node._get_supported_dtypes

_label(self, x)

Overrides: ClassifierNode._label

_prob(self, x)

Overrides: ClassifierNode._prob

_stop_training(self)

Transform the data and labels lists to array objects and reshape them.

Overrides: Node._stop_training

_train(self, x, labels)

Cumulate all input data in a one dimensional list.

Overrides: Node._train

label(self, x)

Returns an array with best class labels.

By default, subclasses should overwrite _label to implement their label. The docstring of the '_label' method overwrites this docstring.

Overrides: ClassifierNode.label

predict_probability(self, x)

prob(self, x)

Returns the probability for each datapoint and label (e.g., [{1:0.1, 2:0.0, 3:0.9}, {1:1.0, 2:0.0, 3:0.0}, ...])

By default, subclasses should overwrite _prob to implement their prob. The docstring of the '_prob' method overwrites this docstring.

Overrides: ClassifierNode.prob

set_classifier(self, classifier)

Sets the classifier.

classifier -- A string with the name of the classifier which: should be used. Possible values are in self.classifiers

set_kernel(self, kernel)

Sets the kernel.

kernel -- A string with the name of the classifier which: should be used. Possible values are in self.kernels

stop_training(self)

Transform the data and labels lists to array objects and reshape them.

Overrides: Node.stop_training

train(self, x, labels)

Cumulate all input data in a one dimensional list.

Overrides: Node.train

Class Variable Details

[hide private]

classifiers

Value:

['C_SVC', 'NU_SVC', 'ONE_CLASS', 'EPSILON_SVR', 'NU_SVR']

kernels

Value:

['RBF', 'LINEAR', 'POLY', 'SIGMOID']

Class LibSVMClassifier

__init__(self, kernel=None, classifier=None, probability=True, params=None, input_dim=None, output_dim=None, dtype=None) (Constructor)

_get_supported_dtypes(self)

_label(self, x)

_prob(self, x)

_stop_training(self)

_train(self, x, labels)

label(self, x)

predict_probability(self, x)

prob(self, x)

set_classifier(self, classifier)

set_kernel(self, kernel)

stop_training(self)

train(self, x, labels)

classifiers

kernels

init(self, kernel=None, classifier=None, probability=True, params=None, input_dim=None, output_dim=None, dtype=None)
(Constructor)