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alegonz / baikal, Hacker News

alegonz / baikal, Hacker News
                    

        

baikalbaikal

baikal is written in pure Python. It supports Python 3.5 and above.

Note: baikal is still a young project and there might be backward incompatible changes. The next development steps and backwards-incompatible changes are announced and discussed in this issue . Please subscribe to it if you use baikal . What is baikal?

baikal is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API . It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) (graphkit package.

baikal aims to provide an API that allows to build complex, non-linear machine learning pipelines that look like this:

with code that looks like this:

x1=(Input () x2=(Input () y_t=(Input () y1=ExtraTreesClassifier () (x1, y_t ) y2=

 RandomForestClassifier () (x2, y_t ) z=PowerTransformer () (x2) z=PCA () (z) y3=LogisticRegression () (z, y_t )  ensemble_features=Stack () ([y1, y2, y3] ) y=SVC () (ensemble_features, y_t )  model=Model ([x1, x2], y, y_t) 

What can I do with it?

With baikal you can

(build non-linear pipelines effortlessly) (handle multiple inputs and outputs)

and come up with some clever combination of the above composite estimators ( (Pipeline) s, (ColumnTransformer) (s, and (StackingClassifier) ​​s, etc), but you might end up with code that feels hard-to-follow and verbose.

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