cool! We have been talking for a while about how to pass other things around grid search and other meta-analysis estimators. This injection approach looks pretty neat as a way to express it. Will need to mull on it.
On 8 Feb 2018 2:51 am, "Manuel Castejón Limas" <manuel.caste...@gmail.com> wrote: > Dear all, > > after some playing with the concept we have developed a module for > implementing the functionality of Pipeline in more general contexts as > first introduced in a former thread ( https://mail.python.org/piperm > ail/scikit-learn/2018-January/002158.html ) > > In order to expand the possibilities of Pipeline for non linearly > sequential workflows a graph like structure has been deployed while keeping > as much as possible the already known syntax we all love and honor: > > X = pd.DataFrame(dict(X=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])) > y = 2 * X > sc = MinMaxScaler() > lm = LinearRegression() > steps = [('scaler', sc), > ('linear_model', lm)] > connections = {'scaler': dict(X='X'), > 'linear_model': dict(X=('scaler', 'predict'), > y='y')} > pgraph = PipeGraph(steps=steps, > connections=connections, > use_for_fit='all', > use_for_predict='all') > > As you can see the biggest difference for the final user is the dictionary > describing the connections. > > Another major contribution for developers wanting to expand scikit learn > is a collection of adapters for scikit learn models in order to provide > them a common API irrespectively of whether they originally implemented > predict, transform or fit_predict as an atomic operation without predict. > These adapters accept as many positional or keyword parameters in their fit > predict methods through *pargs and **kwargs. > > As general as PipeGraph is, it cannot work under the restrictions imposed > by GridSearchCV on the input parameters, namely X and y since PipeGraph can > accept as many input signals as needed. Thus, an adhoc GridSearchCv version > is also needed and we will provide a basic initial version in a later > version. > > We need to write the documentation and we will propose it as a > contrib-project in a few days. > > Best wishes, > Manuel Castejón-Limas > > > > > > > > > > > > > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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