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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
...

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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