How to use the gala.classify.DefaultRandomForest function in gala

np.random.RandomState(0)
rf = rf.fit(X, y)
classify.save_classifier(rf, 'example-data/rf-1.joblib')
learned_policy = agglo.classifier_probability(fc, rf)
g_test = agglo.Rag(ws_test, pr_test, learned_policy, feature_manager=fc)
g_test.agglomerate(0.5)
seg_test1 = g_test.get_segmentation()
imio.write_h5_stack(seg_test1, 'example-data/test-seg1.lzf.h5', compression='lzf')
g_train4 = agglo.Rag(ws_train, p4_train, feature_manager=fc)
np.random.RandomState(0)
(X4, y4, w4, merges4) = map(np.copy, map(np.ascontiguousarray,
                            g_train4.learn_agglomerate(gt_train, fc)[0]))
print X4.shape
np.savez('example-data/train-set4.npz', X=X4, y=y4)
y4 = y4[:, 0]
rf4 = classify.DefaultRandomForest()
np.random.RandomState(0)
rf4 = rf4.fit(X4, y4)
classify.save_classifier(rf4, 'example-data/rf-4.joblib')
learned_policy4 = agglo.classifier_probability(fc, rf4)
g_test4 = agglo.Rag(ws_test, p4_test, learned_policy4, feature_manager=fc)
g_test4.agglomerate(0.5)
seg_test4 = g_test4.get_segmentation()
imio.write_h5_stack(seg_test4, 'example-data/test-seg4.lzf.h5', compression='lzf')

results = np.vstack((
    ev.split_vi(ws_test, gt_test),
    ev.split_vi(seg_test1, gt_test),
    ev.split_vi(seg_test4, gt_test)
    ))

np.save('example-data/vi-results.npy', results)

def relearn(self):
        """Learn a new merge policy using data gathered so far.

        This resets the state of the RAG to contain only the merges and
        separations received over the course of its history.
        """
        clf = classify.DefaultRandomForest().fit(self.features, self.targets)
        self.policy = agglo.classifier_probability(self.feature_manager, clf)
        self.rag = self.original_rag.copy()
        self.rag.merge_priority_function = self.policy
        self.rag.rebuild_merge_queue()
        for i, (s0, s1) in enumerate(self.separate):
            self.rag.node[s0]['exclusions'].add(i)
            self.rag.node[s1]['exclusions'].add(i)

def classifier():
    X, y = trexamples()
    rf = classify.DefaultRandomForest()
    rf.fit(X, y)
    return rf