mvpa2.measures.rsa.PDistTargetSimilarity

class mvpa2.measures.rsa.PDistTargetSimilarity(target_dsm, **kwargs)

Calculate the correlations of PDist measures with a target

Target dissimilarity correlation Measure. Computes the correlation between the dissimilarity matrix defined over the pairwise distances between the samples of dataset and the target dissimilarity matrix.

Notes

Available conditional attributes:

• calling_time+: None
• null_prob+: None
• null_t: None
• raw_results: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

target_dsm : array (length N*(N-1)/2)

Target dissimilarity matrix

pairwise_metric : str, optional

Distance metric to use for calculating pairwise vector distances for dissimilarity matrix (DSM). See scipy.spatial.distance.pdist for all possible metrics. Constraints: value must be a string. [Default: ‘correlation’]

comparison_metric : {pearson, spearman}, optional

Similarity measure to be used for comparing dataset DSM with the target DSM. 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 subtracing 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]

corrcoef_only : bool, optional

If True, return only the correlation coefficient (rho), otherwise return rho and probability, p. 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 :

If corrcoef_only is True, contains one feature: the correlation coefficient (rho); or otherwise two-fetaures: rho plus p.

Methods

is_trained = True

Indicate that this measure is always trained.