Multivariate Pattern Analysis in Python

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# mvpa2.testing.clfs.SVM

class `mvpa2.testing.clfs.``SVM`(**kwargs)

  Support Vector Machine Classifier.

  This is a simple interface to the libSVM package.

  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
  - `probabilities`: Estimates of samples probabilities as provided by LibSVM
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `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 `+`)

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(**kwargs) | Returns an appropriate SensitivityAnalyzer.
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() | Provide quick summary over the SVM classifier
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Interface class to LIBSVM classifiers and regressions.

  Default implementation (C/nu/epsilon SVM) is chosen depending on the given parameters (C/nu/tube_epsilon).

  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):

  ONE_CLASS : one-class-SVM Capabilities: oneclass C_SVC : C-SVM classification

  > Parameters: C Capabilities: binary, multiclass

  NU_SVR : nu-SVM regression
    Parameters: nu, tube_epsilon Capabilities: regression
  NU_SVC : nu-SVM classification
    Parameters: nu Capabilities: binary, multiclass
  EPSILON_SVR : epsilon-SVM regression
    Parameters: C, tube_epsilon Capabilities: regression

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

  Parameters:

  tube_epsilon :

  > Epsilon in epsilon-insensitive loss function of epsilon-SVM regression (SVR). (Default: 0.01)

  C :

  > Trade-off parameter between width of the margin and number of support vectors. Higher C – more rigid margin SVM. In linear kernel, negative values provide automatic scaling of their value according to the norm of the data. (Default: -1.0)

  weight :

  > Custom weights per label. (Default: [])

  probability :

  > Flag to signal either probability estimate is obtained within LIBSVM. (Default: 0)

  epsilon :

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

  weight_label :

  > To be used in conjunction with weight for custom per-label weight. (Default: [])

  shrinking :

  > Either shrinking is to be conducted. (Default: 1)

  nu :

  > Fraction of datapoints within the margin. (Default: 0.5)

  kernel :

  > Kernel object. (Default: None)

  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

  Methods

  `clone`() | Create full copy of the classifier.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_sensitivity_analyzer`(**kwargs) | Returns an appropriate SensitivityAnalyzer.
  `get_space`() | Query the processing space name of this node.
  `is_trained`([dataset]) | Either classifier was already trained.
  `predict`(obj, data, *args, **kwargs) |
  `repredict`(obj, data, *args, **kwargs) |
  `reset`() |
  `retrain`(dataset, **kwargs) | Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `summary`() | Provide quick summary over the SVM classifier
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  `model`

    Access to the SVM model.

  `summary`()

    Provide quick summary over the SVM classifier

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