Multivariate Pattern Analysis in Python

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# mvpa2.measures.base.CrossValidation

class `mvpa2.measures.base.``CrossValidation`(learner, generator, errorfx=<function mean_mismatch_error at 0xce999cc>, splitter=None, **kwargs)

  Cross-validate a learner’s transfer on datasets.

  A generator is used to resample a dataset into multiple instances (e.g. sets of dataset partitions for leave-one-out folding). For each dataset instance a transfer measure is computed by splitting the dataset into two parts (defined by the dataset generators output space) and train a custom learner on the first part and run it on the next. An arbitray error function can by used to determine the learner’s error when prediction the dataset part that has been unseen during training.

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `datasets`: Store generated datasets for all repetitions. Can be memory expensive
  - `null_prob+`: None
  - `null_t`: None
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `repetition_results`: Store individual result datasets for each repetition
  - `stats`: Summary statistics about the node performance across all repetitions
  - `training_stats`: Summary statistics about the training status of the learner across all cross-validation fold.
  - `training_time+`: Time (in seconds) it took to train the learner

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Parameters:

  learner : Learner

  > Any trainable node that shall be run on the dataset folds.

  generator : Node

  > Generator used to resample the input dataset into multiple instances (i.e. partitioning it). The number of datasets yielded by this generator determines the number of cross-validation folds. IMPORTANT: The `space` of this generator determines the attribute that will be used to split all generated datasets into training and testing sets.

  errorfx : Node or callable

  > Custom implementation of an error function. The callable needs to accept two arguments (1. predicted values, 2. target values). If not a Node, it gets wrapped into a `BinaryFxNode`.

  splitter : Splitter or None

  > A Splitter instance to split the dataset into training and testing part. The first split will be used for training and the second for testing – all other splits will be ignored. If None, a default splitter is auto-generated using the `space` setting of the `generator`. The default splitter is configured to return the `1`-labeled partition of the input dataset at first, and the `2`-labeled partition second. This behavior corresponds to most Partitioners that label the taken-out portion `2` and the remainder with `1`.

  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

  node : Node

  > Node or Measure implementing the procedure that is supposed to be run multiple times.

  callback : functor

  > Optional callback to extract information from inside the main loop of the measure. The callback is called with the input ‘data’, the ‘node’ instance that is evaluated repeatedly and the ‘result’ of a single evaluation – passed as named arguments (see labels in quotes) for every iteration, directly after evaluating the node.

  concat_as : {‘samples’, ‘features’}

  > Along which axis to concatenate result dataset from all iterations. By default, results are ‘vstacked’ as multiple samples in the output dataset. Setting this argument to ‘features’ will change this to ‘hstacking’ along the feature axis.

  null_dist : instance of distribution estimator

  > The estimated distribution is used to assign a probability for a certain value of the computed measure.

  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

  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `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.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  `errorfx`
  `learner`
  `splitter`
  `transfermeasure`

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