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How to use get_mask method in avocado
Best Python code snippet using avocado_python
test_neighborhoods.py
Source:test_neighborhoods.py 
...30 # son 5 entradas en el array porque se cuenta a la celula como un31 # vecino32 mask = np.array([[1, 0, 0, 0, 0]], dtype=bool)3334 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))3536 def test_get_mask_case_2(self):37 """38 Este metodo testea el metodo get_mask de la clase LeftCellNeighborhood39 """40 neighborhood = LeftCellNeighborhood(3, inclusive=True)41 mask = np.array([[1, 0, 0, 1]], dtype=bool)4243 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))4445 def test_get_offset_case_1(self):46 """47 Este metodo testea el metodo get_mask de la clase LeftCellNeighborhood48 """49 neighborhood = LeftCellNeighborhood(4)50 offset = (0, -4)5152 self.assertEqual(neighborhood.get_offset(), offset)5354 def test_get_offset_case_2(self):55 """56 Este metodo testea el metodo get_mask de la clase LeftCellNeighborhood57 """58 neighborhood = LeftCellNeighborhood(3)59 offset = (0, -3)6061 self.assertEqual(neighborhood.get_offset(), offset)626364class TestRightCellNeighborhood(unittest.TestCase):65 """66 Tests para RightCellNeighborhood67 """6869 def test_get_mask_case_1(self):70 """71 Este metodo testea el metodo get_mask de la clase RightCellNeighborhood72 """73 neighborhood = RightCellNeighborhood(4, inclusive=False)74 # son 5 entradas en el array porque se cuenta a la celula como un75 # vecino76 mask = np.array([[0, 0, 0, 0, 1]], dtype=bool)7778 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))7980 def test_get_mask_case_2(self):81 """82 Este metodo testea el metodo get_mask de la clase RightCellNeighborhood83 """84 neighborhood = RightCellNeighborhood(3, inclusive=True)85 mask = np.array([[1, 0, 0, 1]], dtype=bool)8687 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))8889 def test_get_offset_case_1(self):90 """91 Este metodo testea el metodo get_mask de la clase RightCellNeighborhood92 """93 neighborhood = RightCellNeighborhood(4)94 offset = (0, 0)9596 self.assertEqual(neighborhood.get_offset(), offset)979899class TestIntervalCellNeighborhood(unittest.TestCase):100 """101 Tests para IntervalCellNeighborhood102 """103104 def test_get_mask_case_1(self):105 """106 Este metodo testea el metodo get_mask de la clase107 IntervalCellNeighborhood108 """109 neighborhood = IntervalCellNeighborhood(4, 3, inclusive=False)110 # son 5 entradas en el array porque se cuenta a la celula como un111 # vecino112 mask = np.array([[1, 0, 0, 0, 0, 0, 0, 1]], dtype=bool)113114 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))115116 def test_get_mask_case_2(self):117 """118 Este metodo testea el metodo get_mask de la clase119 IntervalCellNeighborhood120 """121 neighborhood = IntervalCellNeighborhood(3, 4, inclusive=True)122 mask = np.array([[1, 0, 0, 1, 0, 0, 0, 1]], dtype=bool)123124 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))125126 def test_get_offset_case_1(self):127 """128 Este metodo testea el metodo get_mask de la clase129 IntervalCellNeighborhood130 """131 neighborhood = IntervalCellNeighborhood(4, 3)132 offset = (0, -4)133134 self.assertEqual(neighborhood.get_offset(), offset)135136 def test_get_offset_case_2(self):137 """138 Este metodo testea el metodo get_mask de la clase139 IntervalCellNeighborhood140 """141 neighborhood = IntervalCellNeighborhood(3, 4)142 offset = (0, -3)143144 self.assertEqual(neighborhood.get_offset(), offset)145146147class TestLeftSideNeighborhood(unittest.TestCase):148 """149 Tests para LeftSideNeighborhood150 """151152 def test_get_mask_case_1(self):153 """154 Este metodo testea el metodo get_mask de la clase155 LeftSideNeighborhood156 """157 neighborhood = LeftSideNeighborhood(4, inclusive=False)158 # son 5 entradas en el array porque se cuenta a la celula como un159 # vecino160 mask = np.array([[1, 1, 1, 1, 0]], dtype=bool)161162 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))163164 def test_get_mask_case_2(self):165 """166 Este metodo testea el metodo get_mask de la clase167 LeftSideNeighborhood168 """169 neighborhood = LeftSideNeighborhood(3, inclusive=True)170 mask = np.array([[1, 1, 1, 1]], dtype=bool)171172 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))173174 def test_get_offset_case_1(self):175 """176 Este metodo testea el metodo get_mask de la clase177 LeftSideNeighborhood178 """179 neighborhood = LeftSideNeighborhood(4)180 offset = (0, -4)181182 self.assertEqual(neighborhood.get_offset(), offset)183184 def test_get_offset_case_2(self):185 """186 Este metodo testea el metodo get_mask de la clase187 LeftSideNeighborhood188 """189 neighborhood = LeftSideNeighborhood(3)190 offset = (0, -3)191192 self.assertEqual(neighborhood.get_offset(), offset)193194195class TestRightSideNeighborhood(unittest.TestCase):196 """197 Tests para RightSideNeighborhood198 """199200 def test_get_mask_case_1(self):201 """202 Este metodo testea el metodo get_mask de la clase RightSideNeighborhood203 """204 neighborhood = RightSideNeighborhood(4, inclusive=False)205 # son 5 entradas en el array porque se cuenta a la celula como un206 # vecino207 mask = np.array([[0, 1, 1, 1, 1]], dtype=bool)208209 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))210211 def test_get_mask_case_2(self):212 """213 Este metodo testea el metodo get_mask de la clase RightSideNeighborhood214 """215 neighborhood = RightSideNeighborhood(3, inclusive=True)216 mask = np.array([[1, 1, 1, 1]], dtype=bool)217218 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))219220 def test_get_offset_case_1(self):221 """222 Este metodo testea el metodo get_mask de la clase RightSideNeighborhood223 """224 neighborhood = RightSideNeighborhood(4)225 offset = (0, 0)226227 self.assertEqual(neighborhood.get_offset(), offset)228229230class TestBothSideNeighborhood(unittest.TestCase):231 """232 Tests para BothSideNeighborhood233 """234235 def test_get_mask_case_1(self):236 """237 Este metodo testea el metodo get_mask de la clase238 BothSideNeighborhood239 """240 neighborhood = BothSideNeighborhood(4, 3, inclusive=False)241 # son 5 entradas en el array porque se cuenta a la celula como un242 # vecino243 mask = np.array([[1, 1, 1, 1, 0, 1, 1, 1]], dtype=bool)244245 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))246247 def test_get_mask_case_2(self):248 """249 Este metodo testea el metodo get_mask de la clase250 BothSideNeighborhood251 """252 neighborhood = BothSideNeighborhood(3, 4, inclusive=True)253 mask = np.array([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=bool)254255 self.assertTrue(np.array_equal(neighborhood.get_mask(), mask))256257 def test_get_offset_case_1(self):258 """259 Este metodo testea el metodo get_mask de la clase260 BothSideNeighborhood261 """262 neighborhood = BothSideNeighborhood(4, 3)263 offset = (0, -4)264265 self.assertEqual(neighborhood.get_offset(), offset)266267 def test_get_offset_case_2(self):268 """269 Este metodo testea el metodo get_mask de la clase
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splits.py
Source:splits.py
...19 for c in range(data.y.max() + 1):20 class_idx = development_idx[np.where(data.y[development_idx].cpu() == c)[0]]21 train_idx.extend(rnd_state.choice(class_idx, min(num_per_class, ceil(len(class_idx) * 0.5)), replace=False))22 val_idx = [i for i in development_idx if i not in train_idx]23 def get_mask(idx):24 mask = torch.zeros(num_nodes, dtype=torch.bool)25 mask[idx] = 126 return mask27 data.train_mask = get_mask(train_idx)28 data.val_mask = get_mask(val_idx)29 data.test_mask = get_mask(test_idx)30 return data31 32def set_train_val_test_split_frac(seed: int, data: Data, val_frac: float, test_frac: float):33 num_nodes = data.y.shape[0]34 val_size = ceil(val_frac * num_nodes)35 test_size = ceil(test_frac * num_nodes)36 train_size = num_nodes - val_size - test_size37 nodes = list(range(num_nodes))38 # Take same test set every time using development seed for robustness39 random.seed(development_seed)40 random.shuffle(nodes)41 test_idx = sorted(nodes[:test_size])42 nodes = [x for x in nodes if x not in test_idx]43 # Take train / val split according to seed44 random.seed(seed)45 random.shuffle(nodes)46 train_idx = sorted(nodes[:train_size])47 val_idx = sorted(nodes[train_size:])48 49 assert len(train_idx) + len(val_idx) + len(test_idx) == num_nodes50 def get_mask(idx):51 mask = torch.zeros(num_nodes, dtype=torch.bool)52 mask[idx] = 153 return mask54 data.train_mask = get_mask(train_idx)55 data.val_mask = get_mask(val_idx)56 data.test_mask = get_mask(test_idx)57 return data58# def set_train_val_test_split_classic(seed: int, data: Data, val_frac: float, test_frac: float):59# random.seed(seed)60# num_nodes = data.y.shape[0]61# val_size = ceil(val_frac * num_nodes)62# test_size = ceil(test_frac * num_nodes)63# train_size = num_nodes - val_size - test_size64# nodes = list(range(num_nodes))65# random.shuffle(nodes)66# train_idx = sorted(nodes[:train_size])67# val_idx = sorted(nodes[train_size:train_size+val_size])68# test_idx = sorted(nodes[train_size+val_size:])69# def get_mask(idx):70# mask = torch.zeros(num_nodes, dtype=torch.bool)71# mask[idx] = 172# return mask73# data.train_mask = get_mask(train_idx)74# data.val_mask = get_mask(val_idx)75# data.test_mask = get_mask(test_idx)76# return data77 78# def set_train_val_test_split_robust(seed: int, data: Data, val_frac: float, test_frac: float):79# num_nodes = data.y.shape[0]80# val_size = ceil(val_frac * num_nodes)81# test_size = ceil(test_frac * num_nodes)82# train_size = num_nodes - val_size - test_size83# nodes = list(range(num_nodes))84# # Take same test set every time using development seed for robustness85# random.seed(development_seed)86# random.shuffle(nodes)87# test_idx = sorted(nodes[:test_size])88# nodes = [x for x in nodes if x not in test_idx]89# # Take train / val split according to seed90# random.seed(seed)91# random.shuffle(nodes)92# train_idx = sorted(nodes[:train_size])93# val_idx = sorted(nodes[train_size:])94 95# assert len(train_idx) + len(val_idx) + len(test_idx) == num_nodes96# def get_mask(idx):97# mask = torch.zeros(num_nodes, dtype=torch.bool)98# mask[idx] = 199# return mask100# data.train_mask = get_mask(train_idx)101# data.val_mask = get_mask(val_idx)102# data.test_mask = get_mask(test_idx)103# return data104# def set_train_val_test_split(105# seed: int, data: Data, num_development: int = 1500, num_per_class: int = 20106# ) -> Data:107# rnd_state = np.random.RandomState(development_seed)108# num_nodes = data.y.shape[0]109# development_idx = rnd_state.choice(num_nodes, num_development, replace=False)110# test_idx = [i for i in np.arange(num_nodes) if i not in development_idx]111# train_idx = []112# rnd_state = np.random.RandomState(seed)113# for c in range(data.y.max() + 1):114# class_idx = development_idx[np.where(data.y[development_idx].cpu() == c)[0]]115# train_idx.extend(116# rnd_state.choice(117# class_idx, min(num_per_class, int(len(class_idx) * 0.7)), replace=False118# )119# )120# val_idx = [i for i in development_idx if i not in train_idx]121# def get_mask(idx):122# mask = torch.zeros(num_nodes, dtype=torch.bool)123# mask[idx] = 1124# return mask125# data.train_mask = get_mask(train_idx)126# data.val_mask = get_mask(val_idx)127# data.test_mask = get_mask(test_idx)128# return data129# def set_train_val_test_split_webkb(130# seed: int,131# data: Data,132# num_development: int = 1500,133# num_per_class: int = 20,134# train_proportion: float = None,135# ) -> Data:136# rnd_state = np.random.RandomState(development_seed)137# num_nodes = data.y.shape[0]138# development_idx = rnd_state.choice(num_nodes, num_development, replace=False)139# test_idx = [i for i in np.arange(num_nodes) if i not in development_idx]140# rnd_state = np.random.RandomState(seed)141# if train_proportion:142# train_idx = rnd_state.choice(143# development_idx, int(train_proportion * len(development_idx)), replace=False144# )145# else:146# train_idx = []147# for c in range(data.y.max() + 1):148# class_idx = development_idx[np.where(data.y[development_idx].cpu() == c)[0]]149# train_idx.extend(rnd_state.choice(class_idx, num_per_class, replace=False))150# val_idx = [i for i in development_idx if i not in train_idx]151# def get_mask(idx):152# mask = torch.zeros(num_nodes, dtype=torch.bool)153# mask[idx] = 1154# return mask155# data.train_mask = get_mask(train_idx)156# data.val_mask = get_mask(val_idx)157# data.test_mask = get_mask(test_idx)...
test_boolean_mask.py
Source:test_boolean_mask.py
...9LOGGING = False10MASK_SIZE = 1611class TestBooleanMasks(unittest.TestCase):12 def test_boolean_mask_is_all_false(self):13 self.assertFalse(get_mask(MASK_SIZE, type='empty').any())14 def test_boolean_mask_is_all_true(self):15 self.assertTrue(get_mask(MASK_SIZE, type='full').all())16 def test_boolean_mask_random(self):17 mask = get_mask(MASK_SIZE, type='random')18 print(f'A random mask is {mask}\n')19 self.assertTrue(True)20 def test_operator_and__on_boolean_mask(self):21 empty_mask = get_mask(MASK_SIZE, type='empty')22 full_mask = get_mask(MASK_SIZE, type='full')23 np.testing.assert_array_equal(empty_mask, np.logical_and(empty_mask, full_mask))24 def test_operator_or__on_boolean_mask(self):25 empty_mask = get_mask(MASK_SIZE, type='empty')26 full_mask = get_mask(MASK_SIZE, type='full')27 np.testing.assert_array_equal(full_mask, np.logical_or(empty_mask, full_mask))28 def test_boolean_mask_for_half(self):29 exp_mask = np.array([True, True, False, False])30 act_mask_0 = get_mask(4, type='half_0')31 act_mask_1 = get_mask(4, type='half_1')32 self.assertFalse(np.logical_xor(act_mask_0, exp_mask).all())33 self.assertFalse(np.logical_xor(act_mask_1, np.logical_not(exp_mask)).all())34 def test_boolean_operator_and_mask_repetitively (self):35 mask_set = self.get_set()36 b = bool_and(get_mask(MASK_SIZE, type='random'), get_mask(MASK_SIZE, type='random'))37 for i in range(100):38 b = bool_and(b, get_mask(MASK_SIZE, type='random'))39 if LOGGING:40 print(f'After multiple operation we have {b}')41 self.assertTrue(True)42 def test_random_boolean_operator_and_mask_repetitively (self):43 function_set = self.get_functions()44 function = random.choice(function_set)45 b = function(random.choice(get_mask(MASK_SIZE, type='random')), get_mask(MASK_SIZE, type='random'))46 for i in range(100):47 print(b)48 function = random.choice(function_set)49 if function == bool_not:50 b = function(b)51 else:52 b = function(b, get_mask(MASK_SIZE, type='random'))53 if LOGGING:54 print(f'After multiple operation we have {b}')55 self.assertTrue(True)56 @staticmethod57 def get_set():58 empty_mask = get_mask(MASK_SIZE, type='empty')59 full_mask = get_mask(MASK_SIZE, type='full')60 act_mask_0 = get_mask(MASK_SIZE, type='half_0')61 act_mask_1 = get_mask(MASK_SIZE, type='half_1')62 return [empty_mask, full_mask, act_mask_0, act_mask_1]63 @staticmethod64 def get_functions():65 return [bool_and, bool_or, bool_xor, bool_not]66 def test_get_mask_from_string_using_1111(self):67 full_mask = get_mask(4, type='full')68 np.testing.assert_array_equal(full_mask, get_mask_from_string("1111"))69 def test_get_mask_from_string_using_0000(self):70 empty_mask = get_mask(4, type='empty')71 np.testing.assert_array_equal(empty_mask, get_mask_from_string("0000"))72 def test_get_mask_from_string_using_1100(self):73 half_0_mask = get_mask(4, type='half_0')74 np.testing.assert_array_equal(half_0_mask, get_mask_from_string("1100"))75 def test_get_mask_from_string_using_0011(self):76 half_1_mask = get_mask(4, type='half_1')77 np.testing.assert_array_equal(half_1_mask, get_mask_from_string("0011"))78 def test_mask_to_string_from_1111(self):79 full_mask = get_mask(4, type='full')80 self.assertEqual("1111", mask_to_string(full_mask))81 def test_mask_to_string_from_0000(self):82 empty_mask = get_mask(4, type='empty')83 self.assertEqual("0000", mask_to_string(empty_mask))84 def test_mask_to_string_from_1100(self):85 half_0_mask = get_mask(4, type='half_0')86 self.assertEqual("1100", mask_to_string(half_0_mask))87 def test_mask_to_string_from_0011(self):88 half_1_mask = get_mask(4, type='half_1')...
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