mvpa2.measures.rsa.PDistConsistency

class mvpa2.measures.rsa.PDistConsistency(**kwargs)

Calculate the correlations of PDist measures across chunks

This measures the consistency in similarity structure across runs within individuals, or across individuals if the target dataset is made from several subjects in some common space and where the sample attribute specified as the chunks_attr codes for subject identity.

@author: ACC Aug 2013

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• null_prob+: None
• null_t: None
• 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.
null_dist	Return Null Distribution estimator
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)
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_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:

chunks_attr : str, optional

Chunks attribute to use for chunking dataset. Can be any samples attribute. Constraints: value must be a string. [Default: ‘chunks’]

pairwise_metric : str, optional

Distance metric to use for calculating dissimilarity matrices from the set of samples in each chunk specified. See spatial.distance.pdist for all possible metrics. Constraints: value must be a string. [Default: ‘correlation’]

consistency_metric : {pearson, spearman}, optional

Correlation measure to use for the correlation between dissimilarity matrices. Constraints: value must be one of (‘pearson’, ‘spearman’). [Default: ‘pearson’]

center_data : bool, optional

If True then center each column of the data matrix by subtracting the column mean from each element. This is recommended especially when using pairwise_metric=’correlation’. Constraints: value must be convertible to type bool. [Default: False]

square : bool, optional

If True return the square distance matrix, if False, returns the flattened upper triangle. 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

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.

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

Returns:

Dataset

Contains the pairwise correlations between the DSMs computed from each chunk of the input dataset. If square is False, this is a column vector of length N(N-1)/2 for N chunks. If square is True, this is a square matrix of size NxN for N chunks.

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.
null_dist	Return Null Distribution estimator
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)
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_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

is_trained = True

Indicate that this measure is always trained.