PipeGraph extends the concept of Scikit-Learn's Pipeline tool
Scikit-Learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. This eases managing the fit and predict of the complete sequence of steps through a simple call to their fit and predict methods, and in combination with the GridSearchCV function allows to tune the hyperparameters of these steps while in the search for the best model.
PipeGraph extends the concept of Pipeline by using a graph structure that can handle Scikit-Learn’s objects in imaginative layouts. This allows the user to:
- Express simple and complex sequences of steps, from linear workflows to elaborated graphs
- To have access to all the data generated along the intermediate steps
- To use GridSearchCV on those graphs
Let’s see a few examples to understand how we can express these situations through PipeGraph objects.
The MIT License (MIT)
- Copyright (c) 2018 Laura Fernandez Robles,
- Hector Alaiz Moreton, Jaime Cifuentes-Rodriguez, Javier Alfonso-Cendón, Camino Fernández-Llamas, Manuel Castejón-Limas
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