predictors : array (N*(N-1)/2, n_predictors)
array containing the upper triangular matrix in vector form of the
predictor Dissimilarity Matrices. Each column is a predictor dsm.
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’]
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]
method : {ridge, lasso}, optional
Compute Ridge (l2) or Lasso (l1) regression. Constraints: value must
be one of (‘ridge’, ‘lasso’). [Default: ‘ridge’]
alpha : float, optional
alpha parameter for lassoregression. Constraints: value must be
convertible to type ‘float’. [Default: 1.0]
fit_intercept : bool, optional
whether to fit theintercept. Constraints: value must be convertible
to type bool. [Default: True]
rank_data : bool, optional
whether to rank the neural dsm and the predictor dsms before running
the regression model. Constraints: value must be convertible to type
bool. [Default: True]
normalize : bool, optional
if True the predictors and neural dsm will benormalized (z-scored)
prior to the regression (and after the data ranking, if
rank_data=True). 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
keep_pairs : None or list or array
indices in range(N*(N-1)/2) to keep before running the regression.
All other elements will be removed. If None, the regression is run
on the entire DSM.
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