How to use the gala.evaluate.split_vi function in gala

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)

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)

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)

g_test4.agglomerate(0.5)
seg_test4 = g_test4.get_segmentation()

# gala allows implementation of other agglomerative algorithms, including
# the default, mean agglomeration
g_testm = agglo.Rag(ws_test, pr_test,
                    merge_priority_function=agglo.boundary_mean)
g_testm.agglomerate(0.5)
seg_testm = g_testm.get_segmentation()

# examine how well we did with either learning approach, or mean agglomeration
gt_test = imio.read_h5_stack('test-gt.lzf.h5')
import numpy as np
results = np.vstack((
    ev.split_vi(ws_test, gt_test),
    ev.split_vi(seg_testm, gt_test),
    ev.split_vi(seg_test1, gt_test),
    ev.split_vi(seg_test4, gt_test)
    ))

print(results)

def test_split_vi():
    seg_test1 = imio.read_h5_stack(
            os.path.join(rundir, 'example-data/test-seg1.lzf.h5'))
    seg_test4 = imio.read_h5_stack(
            os.path.join(rundir, 'example-data/test-seg4.lzf.h5'))
    result = np.vstack((
        ev.split_vi(ws_test, gt_test),
        ev.split_vi(seg_test1, gt_test),
        ev.split_vi(seg_test4, gt_test)
        ))
    expected = np.load(os.path.join(rundir, 'example-data/vi-results.npy'))
    assert_allclose(result, expected, atol=1e-6)

seg_test4 = g_test4.get_segmentation()

# gala allows implementation of other agglomerative algorithms, including
# the default, mean agglomeration
g_testm = agglo.Rag(ws_test, pr_test,
                    merge_priority_function=agglo.boundary_mean)
g_testm.agglomerate(0.5)
seg_testm = g_testm.get_segmentation()

# examine how well we did with either learning approach, or mean agglomeration
gt_test = imio.read_h5_stack('test-gt.lzf.h5')
import numpy as np
results = np.vstack((
    ev.split_vi(ws_test, gt_test),
    ev.split_vi(seg_testm, gt_test),
    ev.split_vi(seg_test1, gt_test),
    ev.split_vi(seg_test4, gt_test)
    ))

print(results)

def test_split_vi():
    seg_test1 = imio.read_h5_stack(
            os.path.join(rundir, 'example-data/test-seg1.lzf.h5'))
    seg_test4 = imio.read_h5_stack(
            os.path.join(rundir, 'example-data/test-seg4.lzf.h5'))
    result = np.vstack((
        ev.split_vi(ws_test, gt_test),
        ev.split_vi(seg_test1, gt_test),
        ev.split_vi(seg_test4, gt_test)
        ))
    expected = np.load(os.path.join(rundir, 'example-data/vi-results.npy'))
    assert_allclose(result, expected, atol=1e-6)

def split_vi(self, gt=None):
        if self.gt is None and gt is None:
            return array([0,0])
        elif self.gt is not None:
            return split_vi(self.rig)
        else:
            return split_vi(self.get_segmentation(), gt, [0], [0])