Example of building a graph classification pipeline.

Script makes use of grakel.ShortestPath

Out:

Accuracy: 82.45%

from __future__ import print_function
print(__doc__)

import numpy as np

from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_predict
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score

from grakel.datasets import fetch_dataset
from grakel.kernels import ShortestPath

# Loads the Mutag dataset from:
MUTAG = fetch_dataset("MUTAG", verbose=False)
G, y = MUTAG.data, MUTAG.target

# Values of C parameter of SVM
C_grid = (10. ** np.arange(-4,6,1) / len(G)).tolist()

# Creates pipeline
estimator = make_pipeline(
    ShortestPath(normalize=True),
    GridSearchCV(SVC(kernel='precomputed'), dict(C=C_grid),
                 scoring='accuracy', cv=10))

# Performs cross-validation and computes accuracy
n_folds = 10
acc = accuracy_score(y, cross_val_predict(estimator, G, y, cv=n_folds))
print("Accuracy:", str(round(acc*100, 2)) + "%")