Multivariate Pattern Analysis in Python

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# mvpa2.clfs.gnb.GNB

class `mvpa2.clfs.gnb.``GNB`(**kwargs)

  Gaussian Naive Bayes `Classifier`.

  `GNB` is a probabilistic classifier relying on Bayes rule to estimate posterior probabilities of labels given the data. Naive assumption in it is an independence of the features, which allows to combine per-feature likelihoods by a simple product across likelihoods of “independent” features. See http://en.wikipedia.org/wiki/Naive_bayes for more information.

  Provided here implementation is “naive” on its own – various aspects could be improved, but it has its own advantages:

  - implementation is simple and straightforward
  - no data copying while considering samples of specific class
  - provides alternative ways to assess prior distribution of the classes in the case of unbalanced sets of samples (see parameter `prior`)
  - makes use of NumPy broadcasting mechanism, so should be relatively efficient
  - should work for any dimensionality of samples

  `GNB` is listed both as linear and non-linear classifier, since specifics of separating boundary depends on the data and/or parameters: linear separation is achieved whenever samples are balanced (or `prior='uniform'`) and features have the same variance across different classes (i.e. if `common_variance=True` to enforce this).

  Whenever decisions are made based on log-probabilities (parameter `logprob=True`, which is the default), then conditional attribute `values`, if enabled, would also contain log-probabilities. Also mention that normalization by the evidence (P(data)) is disabled by default since it has no impact per se on classification decision. You might like to set parameter normalize to True if you want to access properly scaled probabilities in `values` conditional attribute.

  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
  - `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) | Factory method to return an appropriate sensitivity analyzer for the respective classifier.
  `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`() | Providing summary over the 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

  Initialize an GNB classifier.

  Parameters:

  common_variance :

  > Use the same variance across all classes. (Default: False)

  prior :

  > How to compute prior distribution. (Default: ‘laplacian_smoothing’)

  logprob :

  > Operate on log probabilities. Preferable to avoid unneeded exponentiation and loose precision. If set, logprobs are stored in `values`. (Default: True)

  normalize :

  > Normalize (log)prob by P(data). Requires probabilities thus for `logprob` case would require exponentiation of ‘logprob’s, thus disabled by default since does not impact classification output. (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

  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) | Factory method to return an appropriate sensitivity analyzer for the respective classifier.
  `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`() | Providing summary over the 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

  `means` = None

    Means of features per class

  `priors` = None

    Class probabilities

  `ulabels` = None

    Labels classifier was trained on

  `variances` = None

    Variances per class, but “vars” is taken ;)

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