def test_update(self):
x = np.array([(0,1,2),
(2,5,6),
(3,2,1)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
y = np.array([1,1,0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0,1,1),
(2,5,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
indices = np.array([0,1], dtype=np.int)
box.update(new_box_lim, indices)
self.assertEqual(box.peeling_trajectory['mean'][1], 1)
self.assertEqual(box.peeling_trajectory['coverage'][1], 1)
self.assertEqual(box.peeling_trajectory['density'][1], 1)
self.assertEqual(box.peeling_trajectory['res dim'][1], 1)
self.assertEqual(box.peeling_trajectory['mass'][1], 2/3)
def test_categorical_paste(self):
dtype = [('a', np.float), ('b', np.object)]
x = np.empty((10, ), dtype=dtype)
x['a'] = np.random.rand(10, )
x['b'] = ['a', 'b', 'a', 'b', 'a', 'a', 'b', 'a', 'b', 'a']
y = np.random.randint(0, 2, (10, ))
y = y.astype(np.int)
prim_obj = Prim(x, y, threshold=0.8)
box_lims = np.array([(0, set(['a'])), (1, set(['a']))], dtype=dtype)
yi = np.where(x['b'] == 'a')
box = PrimBox(prim_obj, box_lims, yi)
u = 'b'
pastes = categorical_paste(prim_obj, box, u)
self.assertEquals(len(pastes), 1)
for paste in pastes:
indices, box_lims = paste
self.assertEquals(indices.shape[0], 10)
self.assertEqual(box_lims[u][0], set(['a', 'b']))
def test_init(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = np.array([0, 1, 2])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
self.assertTrue(box.peeling_trajectory.shape == (1, 5))
def test_update(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = np.array([1, 1, 0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0, 1, 1), (2, 5, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
indices = np.array([0, 1], dtype=np.int)
box.update(new_box_lim, indices)
self.assertEqual(box.peeling_trajectory['mean'][1], 1)
self.assertEqual(box.peeling_trajectory['coverage'][1], 1)
self.assertEqual(box.peeling_trajectory['density'][1], 1)
self.assertEqual(box.peeling_trajectory['res dim'][1], 1)
self.assertEqual(box.peeling_trajectory['mass'][1], 2 / 3)
def test_drop_restriction(self):
x = np.array([(0,1,2),
(2,5,6),
(3,2,1)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
y = np.array([1,1,0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0,1,1),
(2,2,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
indices = np.array([0,1], dtype=np.int)
box.update(new_box_lim, indices)
box.drop_restriction('b')
correct_box_lims = np.array([(0,1,1),
(2,5,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
box_lims = box._box_lims[-1]
names = recfunctions.get_names(correct_box_lims.dtype)
for entry in names:
lim_correct = correct_box_lims[entry]
lim_box = box_lims[entry]
for i in range(len(lim_correct)):
self.assertEqual(lim_correct[i], lim_box[i])
self.assertEqual(box.peeling_trajectory['mean'][2], 1)
self.assertEqual(box.peeling_trajectory['coverage'][2], 1)
self.assertEqual(box.peeling_trajectory['density'][2], 1)
self.assertEqual(box.peeling_trajectory['res dim'][2], 1)
self.assertEqual(box.peeling_trajectory['mass'][2], 2/3)
def test_drop_restriction(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = np.array([1, 1, 0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0, 1, 1), (2, 2, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
indices = np.array([0, 1], dtype=np.int)
box.update(new_box_lim, indices)
box.drop_restriction('b')
correct_box_lims = np.array([(0, 1, 1), (2, 5, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
box_lims = box._box_lims[-1]
names = recfunctions.get_names(correct_box_lims.dtype)
for entry in names:
lim_correct = correct_box_lims[entry]
lim_box = box_lims[entry]
for i in range(len(lim_correct)):
self.assertEqual(lim_correct[i], lim_box[i])
self.assertEqual(box.peeling_trajectory['mean'][2], 1)
self.assertEqual(box.peeling_trajectory['coverage'][2], 1)
self.assertEqual(box.peeling_trajectory['density'][2], 1)
self.assertEqual(box.peeling_trajectory['res dim'][2], 1)
self.assertEqual(box.peeling_trajectory['mass'][2], 2 / 3)
def test_select(self):
x = np.array([(0,1,2),
(2,5,6),
(3,2,1)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
y = np.array([1,1,0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0,1,1),
(2,5,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
indices = np.array([0,1], dtype=np.int)
box.update(new_box_lim, indices)
box.select(0)
self.assertTrue(np.all(box.yi==prim_obj.yi))
def test_select(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = np.array([1, 1, 0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0, 1, 1), (2, 5, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
indices = np.array([0, 1], dtype=np.int)
box.update(new_box_lim, indices)
box.select(0)
self.assertTrue(np.all(box.yi == prim_obj.yi))
def test_categorical_peel(self):
dtype = [('a', np.float), ('b', np.object)]
x = np.empty((10, ), dtype=dtype)
x['a'] = np.random.rand(10, )
x['b'] = ['a', 'b', 'a', 'b', 'a', 'a', 'b', 'a', 'b', 'a']
y = np.random.randint(0, 2, (10, ))
y = y.astype(np.int)
prim_obj = Prim(x, y, threshold=0.8)
box_lims = np.array([(0, set(['a', 'b'])), (1, set(['a', 'b']))],
dtype=dtype)
box = PrimBox(prim_obj, box_lims, prim_obj.yi)
u = 'b'
peels = categorical_peel(prim_obj, box, u)
self.assertEquals(len(peels), 2)
for peel in peels:
pl = peel[1][u]
self.assertEquals(len(pl[0]), 1)
self.assertEquals(len(pl[1]), 1)
