def test_classifier(self):
index = [i for i in range(len(self.iris.data))]
rf = RandomForestClassifier()
jrf = JoblibedClassifier(rf, "rf", cache_dir='')
jrf.fit(self.iris.data, self.iris.target, index)
prediction = jrf.predict(self.iris.data, index)
score = accuracy_score(self.iris.target, prediction)
self.assertGreater(score, 0.9, "Failed with score = {0}".format(score))
rf = RandomForestClassifier(n_estimators=20)
jrf = JoblibedClassifier(rf, "rf", cache_dir='')
jrf.fit(self.iris.data, self.iris.target)
index = [i for i in range(len(self.iris.data))]
prediction2 = jrf.predict(self.iris.data, index)
self.assertTrue((prediction == prediction2).all())
from sklearn import datasets
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils.validation import check_random_state
from stacked_generalization.lib.joblibed import JoblibedClassifier
iris = datasets.load_iris()
rng = check_random_state(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# Joblibed model
rf = RandomForestClassifier(n_estimators=40, criterion='gini', random_state=1)
clf = JoblibedClassifier(rf, "rf")
train_idx, test_idx = list(StratifiedKFold(3).split(iris.data, iris.target))[0]
xs_train = iris.data[train_idx]
y_train = iris.target[train_idx]
xs_test = iris.data[test_idx]
y_test = iris.target[test_idx]
print("First fit and prediction (not cached).")
clf.fit(xs_train, y_train, train_idx)
score = clf.score(xs_test, y_test, test_idx)
print('Classfier score: {0}'.format(score))
print("Second fit and prediction (load cache).")
clf.fit(xs_train, y_train, train_idx)
score = clf.score(xs_test, y_test, test_idx)