Correlation of a feature values per each target across chunks.
It will assesses feature stability across runs for each unique label by correlating feature values across all labels for pairwise combinations of the chunks.
If there are multiple samples with the same label in a single chunk (as is typically the case) this algorithm will take the featurewise average of the sample activations to get a single value per label, per chunk.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
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 |
Initialize
Parameters : | space : str
enable_ca : None or list of str
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
pass_attr : str, list of str|tuple, optional
postproc : Node instance, optional
descr : str
|
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Methods
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 |