mvpa2.measures.base.CrossValidation¶
-
class
mvpa2.measures.base.
CrossValidation
(learner, generator=None, errorfx=<function mean_mismatch_error>, splitter=None, **kwargs)¶ Cross-validate a learner’s transfer on datasets.
A generator is used to resample a dataset into multiple instances (e.g. sets of dataset partitions for leave-one-out folding). For each dataset instance a transfer measure is computed by splitting the dataset into two parts (defined by the dataset generators output space) and train a custom learner on the first part and run it on the next. An arbitrary error function can by used to determine the learner’s error when prediction the dataset part that has been unseen during training.
Notes
Available conditional attributes:
calling_time+
: Time (in seconds) it took to call the nodedatasets
: Store generated datasets for all repetitions. Can be memory expensivenull_prob+
: Nonenull_t
: Noneraw_results
: Computed results before invoking postproc. Stored only if postproc is not None.repetition_results
: Store individual result datasets for each repetitionstats
: Summary statistics about the node performance across all repetitionstrained_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
: Summary statistics about the training status of the learner across all cross-validation fold.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. callback
concat_as
descr
Description of the object if any errorfx
force_train
Whether the Learner enforces training upon every call. generator
learner
node
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 splitter
transfermeasure
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: learner : Learner
Any trainable node that shall be run on the dataset folds.
generator : Node, optional
Generator used to resample the input dataset into multiple instances (i.e. partitioning it). The number of datasets yielded by this generator determines the number of cross-validation folds. IMPORTANT: The
space
of this generator determines the attribute that will be used to split all generated datasets into training and testing sets. If None provided, a single original dataset will be passed to thesplitter
as iserrorfx : Node or callable
Custom implementation of an error function. The callable needs to accept two arguments (1. predicted values, 2. target values). If not a Node, it gets wrapped into a
BinaryFxNode
.splitter : Splitter or None
A Splitter instance to split the dataset into training and testing part. The first split will be used for training and the second for testing – all other splits will be ignored. If None, a default splitter is auto-generated using the
space
setting of thegenerator
. If nogenerator
provided, splitter uses ‘partitions’ sample attribute. The default splitter is configured to return the1
-labeled partition of the input dataset at first, and the2
-labeled partition second. This behavior corresponds to most Partitioners that label the taken-out portion2
and the remainder with1
.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
node : Node
Node or Measure implementing the procedure that is supposed to be run multiple times.
callback : functor, optional
Optional callback to extract information from inside the main loop of the measure. The callback is called with the input ‘data’, the ‘node’ instance that is evaluated repeatedly and the ‘result’ of a single evaluation – passed as named arguments (see labels in quotes) for every iteration, directly after evaluating the node.
concat_as : {‘samples’, ‘features’}, optional
Along which axis to concatenate result dataset from all iterations. By default, results are ‘vstacked’ as multiple samples in the output dataset. Setting this argument to ‘features’ will change this to ‘hstacking’ along the feature axis.
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
Attributes
auto_train
Whether the Learner performs automatic trainingwhen called untrained. callback
concat_as
descr
Description of the object if any errorfx
force_train
Whether the Learner enforces training upon every call. generator
learner
node
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 splitter
transfermeasure
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 -
errorfx
¶
-
learner
¶
-
splitter
¶
-
transfermeasure
¶