Pipelineopt is a sckit-learn pipeline (http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) optimizer. It provides a scikit-learn like Classifier class which implements fit, predict, and predict_proba. Within fit, several pipelines are tried and the best pipeline according to the validation score is selected. The way pipelines are generated is using a context-free grammar defined in classifier_grammar. Different ways of generating from the grammars are called Walkers. The default Walker used is RandomWalker which selects uniformly from the pipelines of the grammar. Trained/Trainable Walkers can also be used. For instance, an RnnWalker can be initially pre-trained (e.g, it could be trained to generate good pipelines with high probability) and used in Pipelineopt. See https://github.com/machinedesign/grammaropt for more details about RnnWalker.

from pipelineopt.estimator import Classifier

from sklearn.datasets import load_digits

data = load_digits()
X = data['images']
y = data['target']
X = X.reshape((X.shape[0], -1))
clf = Classifier(nb_iter=10)
clf.fit(X, y)
print('Best code :')
print(clf.best_estimator_code_)
print('Best score : {}'.format(clf.best_score_))