mvpa2.clfs.skl.SKLLearnerAdapter¶
- class mvpa2.clfs.skl.SKLLearnerAdapter(skl_learner, tags=None, enforce_dim=None, **kwargs)¶
Generic adapter for instances of learners provided by scikits.learn
Provides basic adaptation of interface (e.g. train -> fit) and wraps the constructed instance of a learner from skl, so it looks like any other learner present within PyMVPA (so obtains all the conditional attributes defined at the base level of a Classifier)
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- estimates+: Internal classifier estimates the most recent predictions are based on
- predicting_time+: Time (in seconds) which took classifier to predict
- predictions+: Most recent set of predictions
- raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
- trained_dataset: The dataset it has been trained on
- trained_nsamples+: Number of samples it has been trained on
- trained_targets+: Set of unique targets it has been trained on
- training_stats: Confusion matrix of learning performance
- training_time+: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with +)
Examples
TODO
Parameters : skl_learner :
Existing instance of a learner from skl. It should implement fit and predict. If predict_proba is available in the interface, then conditional attribute probabilities becomes available as well
tags : list of string
What additional tags to attach to this learner. Tags are used in the queries to classifier or regression warehouses.
enforce_dim : None or int, optional
If not None, it would enforce given dimensionality for predict call, if all other trailing dimensions are degenerate.
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.
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
