mvpa2.mappers.lle.LLEMapper

class mvpa2.mappers.lle.LLEMapper(k, alg='LLE', nodeargs=None, **kwargs)

Locally linear embbeding Mapper.

This mapper performs dimensionality reduction. It wraps two algorithms provided by the Modular Data Processing (MDP) framework.

Locally linear embedding (LLE) approximates the input data with a low-dimensional surface and reduces its dimensionality by learning a mapping to the surface.

This wrapper class provides access to two different LLE algorithms (i.e. the corresponding MDP processing nodes). 1) An algorithm outlined in An Introduction to Locally Linear Embedding by L. Saul and S. Roweis, using improvements suggested in Locally Linear Embedding for Classification by D. deRidder and R.pl.W. Duin (aka LLENode) and 2) Hessian Locally Linear Embedding analysis based on algorithm outlined in Hessian Eigenmaps: new locally linear embedding techniques for high-dimensional data by C. Grimes and D. Donoho, 2003.

For more information see the MDP website at http://mdp-toolkit.sourceforge.net

Notes

This mapper only provides forward-mapping functionality – no reverse mapping is available.

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:

k : int

Number of nearest neighbors to be used by the algorithm.

algorithm : {‘LLE’, ‘HLLE’}

Either use the standard LLE algorithm or Hessian Linear Local Embedding (HLLE).

nodeargs : None or dict

Arguments passed to the MDP node in various stages of its lifetime. See the baseclass for more details.

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 : mdp.Node instance

This node instance is taken as the pristine source of which a copy is made for actual processing upon each training attempt.

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