mvpa2.clfs.sg.SVM

class mvpa2.clfs.sg.SVM(**kwargs)

Support Vector Machine Classifier(s) based on Shogun

This is a simple base interface

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• estimates+: Internal classifier estimates the most recent predictions are based on
• predicting_time+: Time (in seconds) which took classifier to predict
• predictions+: Most recent set of predictions
• trained_dataset: The dataset it has been trained on
• trained_nsamples+: Number of samples it has been trained on
• trained_targets+: Set of unique targets it has been trained on
• training_stats: Confusion matrix of learning performance
• training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Interface class to Shogun’s classifiers and regressions.

Default implementation is ‘libsvm’.

SVM/SVR definition is dependent on specifying kernel, implementation type, and parameters for each of them which vary depending on the choices made.

Desired implementation is specified in svm_impl argument. Here is the list if implementations known to this class, along with specific to them parameters (described below among the rest of parameters), and what tasks it is capable to deal with (e.g. regression, binary and/or multiclass classification):

Kernel choice is specified as a kernel instance with kwargument kernel. Some kernels (e.g. Linear) might allow computation of per feature sensitivity.

Parameters :

epsilon :

Tolerance of termination criteria. (For nu-SVM default is 0.001). (Default: 5e-05)

kernel :

Kernel object. (Default: None)

num_threads :

Number of threads to utilize. (Default: 1)

retrainable :

Either to enable retraining for ‘retrainable’ classifier. (Default: False)

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

svm

Access to the SVM model.

traindataset

Dataset which was used for training

TODO – might better become conditional attribute I guess