Pipeline et al. design issues
This page is to collate issues related to Pipelines and other meta-estimator API design. In general, a meta-estimator M with (primary) sub-estimator S should be more-or-less usable in place of S. Deficiencies in the current models mean this is not always the case; which of these deficiencies should be fixed and how? Other issues related to meta-estimator support (e.g. nested parameter setting) may also be relevant.
FIXED
hasattr may be used to check an estimator supports a particular functionality (e.g. fit_transform, predict_proba). In meta-estimators this is conditioned on the presence of that method on a sub-estimator. This behaviour can be ensured using magic methods (__getattr__ or __getattribute__) or using descriptors (e.g. property): when these raise AttributeError, hasattr returns false.
PR #2019 supports common methods using property, sacrificing some readability; it also tests for their conditional availability. The question of which common methods need to be supported is a further issue.
A further concern is that in traditional estimators, hasattr will work before or after fitting. If something like GridSearchCV delegates hasattr to its best_estimator_, this will only have effect after fitting.
It can be cumbersome to access a fitted attribute of an estimator (e.g. in a Pipeline within GridSearchCV, this may involve gs.best_estimator_.steps[-1][1].coef_). To be interpreted with respect to the input space, this may require further transformation (e.g. Pipeline(gs.best_estimator_.steps[:-1]).inverse_transform(gs.best_estimator_.steps[-1][1].coef_)).
Moreover, some fitted attributes are used by meta-estimators; AdaBoostClassifier assumes its sub-estimator has a classes_ attribute after fitting, which means that presently Pipeline cannot be used as the sub-estimator of AdaBoostClassifier. Either meta-estimators such as AdaBoostClassifier need to be configurable in how they access this attribute, or meta-estimators such as Pipeline need to make some fitted attributes of sub-estimators accessible.
Passing parameters such as sample_weight to methods (cf. #2630)
It should be possible to pass more than X and y to a pipeline's fit, score, etc. methods, and the most common use-case is sample_weight. Pipeline.fit presently forwards kwargs to individual sub-estimators based on a prefixing system (e.g. fit(X, y, clf__sample_weight=...)). This is not usable within AdaBoost* or grid search (pending support) which expect to provide the sample_weight kwarg.
sample_weight cannot be passed to all estimators of a pipeline; some will not have support. Implicitly detecting support is not future-proof (i.e. a transformer may introduce support for sample_weight, affecting earlier code). @jcrudy has suggested specifying the forwarding paths for kwargs in the Pipeline constructor:
Pipeline([('t1', t1), ('t2', t2), ('c', c)], forward_kwargs={'sample_weight': ['t1', 'c']})Note that this would be presumed to apply for any Pipeline method to which sample_weight is passed (e.g. fit, score).
Pipeline.get_feature_names() (#2007)
Feature extractors provide get_feature_names() to identify the extraced features and their ordering. FeatureUnion.get_feature_names concatenates the output of this function on each of its transformers. It would be similarly to get the feature names produced by a transformation Pipeline. This faces the following problems:
- not all transformers support
get_feature_names(), and the main extractor may not be the first Pipeline step. - many transformers alter the set of features, and
get_feature_names()must apply corresponding feature selection masks (this is justselector.transform(names)). - some transformers produce features that cannot be named (e.g. PCA, random projection)
- where the features are already extracted to array form (i.e. the pipeline does not contain a feature extractor), it would also be useful to get the transformed feature names given a set of input feature names
@kmike's suggestion is to extend the get_feature_names() API to take an optional list of input feature names, allowing them to be transformed and output.
[Aside: It's possible that scikit-learn's handling of feature names needs reconsideration. For example, building a feature selector by name is best implemented as a feature-extracting meta-estimator, which seems awkward:
class SelectByName(FeatureSelectorMixin, BaseEstimator):
def __init__(self, extractor, selected_names): ...
def fit(self, X, y):
names = {name: i for i, name in enuemrate(self.extractor.fit(X, y).get_feature_names())}
self.indices_ = [names[name] for name in self.selected_names]
def transform(self, X):
return self.extractor.transform(X, y)[:, self.indices_]]
FIXED
In order to support the __ meta-estimator parameter notation, get_params needs to return a mapping to sub-estimators from their parameter prefixes (e.g. 'clf' in 'clf__C'). In Pipeline and FeatureUnion, unlike other meta-estimators, these prefixes do not correspond to attributes. Hence using set_params with one of these prefixes is broken (and may overwrite an existing attribute!) but raises no error.
Solutions include:
- Make it possible to set steps using
set_params(or, indeed, as attributes). - Do not use
get_paramsto map parameter name prefixes, and introduceget_sub_estimatorsto return this mapping.
Efficiently reusing partial models/transformations during grid search (#2086)
FIXED
Pipelines and FeatureUnions in grid search may perform a lot of redundant work in fitting and transforming the same data for sub-pipelines. Caching these partial results may provide great efficiency gains; the simplest solution is to allow individual estimators to be cached, without special handling at the Pipeline level.