# create nodes using scikit-learn's support vector classification
# over logarithmic range of C from 0.01 to 1000
for exp in range(-1, 3):
# use one vs all node for multi-classification
# note use of node naming
P.add_node("SVM %s" % 10 ** exp, SklearnOneVsRestNode(svm.SVC, C=10 ** exp))
# create node for scoring the classifers
P.add_node("Metrics", MetricNode([accuracy_score, f1_score],
verbose=True))
# construct the DAG of the pipeline by defining layers using lists
# that are connected in full feedforward fashion, note the use of regexes
# in order to find nodes by their name
P << [["Training Data", "Validation Data"], "SVM\d*", "Metrics"]
P << ["Validation Data", "Metrics"]
# Dag Representation:
# ----> SVM 0.1 ---->
# Training Data ----> SVM 1 ----> Metrics <-------|
# Validation Data ----> SVM 10 ----> |
# | ----> SVM 1000 ----> |
# | |
# --------------------------------------------------------
# show Pipeline
visualize_pipeline(P)
# execute pipline
P.run(timer=True, verbose=False)
