mvpa2.clfs.gpr.Classifier¶
-
class
mvpa2.clfs.gpr.
Classifier
(space=None, **kwargs)¶ Abstract classifier class to be inherited by all classifiers
Notes
Available conditional attributes:
calling_time+
: Time (in seconds) it took to call the nodeestimates+
: Internal classifier estimates the most recent predictions are based onpredicting_time+
: Time (in seconds) which took classifier to predictpredictions+
: Most recent set of predictionsraw_results
: Computed results before invoking postproc. Stored only if postproc is not None.trained_dataset
: The dataset it has been trained ontrained_nsamples+
: Number of samples it has been trained ontrained_targets+
: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)training_stats
: Confusion matrix of learning performancetraining_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. 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 trained
Either classifier was already trained Methods
__call__
(ds)clone
()Create full copy of the classifier. generate
(ds)Yield processing results. get_postproc
()Returns the post-processing node or None. get_sensitivity_analyzer
(\*\*kwargs)Factory method to return an appropriate sensitivity analyzer for the respective classifier. get_space
()Query the processing space name of this node. is_trained
([dataset])Either classifier was already trained. predict
(obj, data, \*args, \*\*kwargs)repredict
(obj, data, \*args, \*\*kwargs)reset
()retrain
(dataset, \*\*kwargs)Helper to avoid check if data was changed actually changed set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. summary
()Providing summary over the classifier 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 instance of Classifier
Parameters: 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
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
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. 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 trained
Either classifier was already trained Methods
__call__
(ds)clone
()Create full copy of the classifier. generate
(ds)Yield processing results. get_postproc
()Returns the post-processing node or None. get_sensitivity_analyzer
(\*\*kwargs)Factory method to return an appropriate sensitivity analyzer for the respective classifier. get_space
()Query the processing space name of this node. is_trained
([dataset])Either classifier was already trained. predict
(obj, data, \*args, \*\*kwargs)repredict
(obj, data, \*args, \*\*kwargs)reset
()retrain
(dataset, \*\*kwargs)Helper to avoid check if data was changed actually changed set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. summary
()Providing summary over the classifier train
(ds)The default implementation calls _pretrain()
,_train()
, and finally_posttrain()
.untrain
()Reverts changes in the state of this node caused by previous training -
clone
()¶ Create full copy of the classifier.
It might require classifier to be untrained first due to present SWIG bindings.
TODO: think about proper re-implementation, without enrollment of deepcopy
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get_sensitivity_analyzer
(**kwargs)¶ Factory method to return an appropriate sensitivity analyzer for the respective classifier.
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is_trained
(dataset=None)¶ Either classifier was already trained.
MUST BE USED WITH CARE IF EVER
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predict
(obj, data, *args, **kwargs)¶
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repredict
(obj, data, *args, **kwargs)¶
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retrain
(dataset, **kwargs)¶ Helper to avoid check if data was changed actually changed
Useful if just some aspects of classifier were changed since its previous training. For instance if dataset wasn’t changed but only classifier parameters, then kernel matrix does not have to be computed.
Words of caution: classifier must be previously trained, results always should first be compared to the results on not ‘retrainable’ classifier (without calling retrain). Some additional checks are enabled if debug id ‘CHECK_RETRAIN’ is enabled, to guard against obvious mistakes.
Parameters: kwargs
that is what _changedData gets updated with. So, smth like
(params=['C'], targets=True)
if parameter C and targets got changed
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summary
()¶ Providing summary over the classifier
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trained
¶ Either classifier was already trained