Class TestDropout

tests/core_tests.py:744–879

class TestDropout(unittest.TestCase):
    def setUp(self):
        self.skorch_classifier = NeuralNetClassifier(Torch_Model,
                                                     criterion=torch.nn.CrossEntropyLoss,
                                                     optimizer=torch.optim.Adam,
                                                     train_split=None,
                                                     verbose=1)

    def test_mc_dropout_bald(self):
        learner = modAL.models.learners.DeepActiveLearner(
            estimator=self.skorch_classifier,
            query_strategy=modAL.dropout.mc_dropout_bald,
        )
        for random_tie_break in [True, False]:
            for num_cycles, sample_per_forward_pass in product(range(1, 5), range(1, 5)):
                for n_samples, n_classes in product(range(1, 5), range(1, 5)):
                    for n_instances in range(1, n_samples):
                        X_pool = torch.randn(n_samples, n_classes)
                        modAL.dropout.mc_dropout_bald(learner, X_pool, n_instances, random_tie_break, [],
                                                      num_cycles, sample_per_forward_pass)

    def test_mc_dropout_mean_st(self):
        learner = modAL.models.learners.DeepActiveLearner(
            estimator=self.skorch_classifier,
            query_strategy=modAL.dropout.mc_dropout_mean_st,
        )
        for random_tie_break in [True, False]:
            for num_cycles, sample_per_forward_pass in product(range(1, 5), range(1, 5)):
                for n_samples, n_classes in product(range(1, 5), range(1, 5)):
                    for n_instances in range(1, n_samples):
                        X_pool = torch.randn(n_samples, n_classes)
                        modAL.dropout.mc_dropout_mean_st(learner, X_pool, n_instances, random_tie_break, [],
                                                         num_cycles, sample_per_forward_pass)

    def test_mc_dropout_max_entropy(self):
        learner = modAL.models.learners.DeepActiveLearner(
            estimator=self.skorch_classifier,
            query_strategy=modAL.dropout.mc_dropout_max_entropy,
        )
        for random_tie_break in [True, False]:
            for num_cycles, sample_per_forward_pass in product(range(1, 5), range(1, 5)):
                for n_samples, n_classes in product(range(1, 5), range(1, 5)):
                    for n_instances in range(1, n_samples):
                        X_pool = torch.randn(n_samples, n_classes)
                        modAL.dropout.mc_dropout_max_entropy(learner, X_pool, n_instances, random_tie_break, [],
                                                             num_cycles, sample_per_forward_pass)

    def test_mc_dropout_max_variationRatios(self):
        learner = modAL.models.learners.DeepActiveLearner(
            estimator=self.skorch_classifier,
            query_strategy=modAL.dropout.mc_dropout_max_variationRatios,
        )
        for random_tie_break in [True, False]:
            for num_cycles, sample_per_forward_pass in product(range(1, 5), range(1, 5)):
                for n_samples, n_classes in product(range(1, 5), range(1, 5)):
                    for n_instances in range(1, n_samples):
                        X_pool = torch.randn(n_samples, n_classes)
                        modAL.dropout.mc_dropout_max_variationRatios(learner, X_pool, n_instances, random_tie_break, [],