Multivariate Pattern Analysis in Python

# mvpa2.mappers.glm.GLMMapper

class `mvpa2.mappers.glm.``GLMMapper`(regs, add_regs=None, **kwargs)

  Transform a dataset into parameter estimates for a general linear model.

  This is a univariate measure were the model is fitted independently to each input feature.

  The GLM design matrix is created from two different source: a) sample attributes of the input dataset, and b) common regressors stored in the mapper itself upon instantiation. The order of the design matrix columns is as follows: First regressors from the dataset in the order in which their names were specified, then additional regressors stored in the mapper – appended in their given order, and, lastly, a potential constant column.

  The nature of the values returned with the mapped dataset depends on the implementation details and parameter settings of the actual GLMMapper subclass. Most commonly, however, is a mapped dataset that has the same number of features as the input, and each sample contains the parameter estimates corresponding to a design matrix column.

  This is a base class, thus is not supposed to be used directly by users which should use specific implementations suchas NiPyGLMMapper and StatsmodelsGLMMapper.

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `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 (or any other space) it has been trained on (if present in the dataset trained on)
  - `training_time+`: Time (in seconds) it took to train the learner

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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon every call.
  `is_trained` | Whether the Learner is currently trained.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node

  Methods

  `__call__`(ds) |
  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `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`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node
  `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:

  regs : list or tuple

  > Names of sample attributes to be extracted from an input dataset and used as design matrix columns.

  add_regs : tuple, optional

  > Additional regressors to be used in the design matrix. Each tuple element is a 2-tuple: the first element is a literal label for the regressor, and the second element is a 1D array with the regressor values. The length of the array needs to match the length of any input dataset.

  add_constant : bool, optional

  > If True, a constant will be added as last column in the design matrix. Constraints: value must be convertible to type bool. [Default: False]

  return_design : bool, optional

  > If True, the mapped dataset will contain a sample attribute `regressors` with the design matrix columns. Constraints: value must be convertible to type bool. [Default: False]

  return_model : bool, optional

  > If True, the mapped dataset will contain am attribute `model` for an instance of the fitted GLM. The type of this instance dependent on the actual implementation used. Constraints: value must be convertible to type bool. [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.

  pass_attr : str, list of str|tuple, optional

  > Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see `Dataset.get_attr()`), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

  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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon every call.
  `is_trained` | Whether the Learner is currently trained.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node

  Methods

  `__call__`(ds) |
  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `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`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node
  `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