Method test_get_predictions

tests/core_tests.py:804–848

(self)

                                                                     num_cycles, sample_per_forward_pass)

    def test_get_predictions(self):
        X = torch.randn(100, 1)

        learner = modAL.models.learners.DeepActiveLearner(
            estimator=self.skorch_classifier,
            query_strategy=mock.MockFunction(return_val=None),
        )

        # num predictions tests
        for num_predictions in range(1, 20):
            for samples_per_forward_pass in range(1, 10):

                predictions = modAL.dropout.get_predictions(
                    learner, X, dropout_layer_indexes=[],
                    num_predictions=num_predictions,
                    sample_per_forward_pass=samples_per_forward_pass)

                self.assertEqual(len(predictions), num_predictions)

        self.assertRaises(AssertionError, modAL.dropout.get_predictions,
                          learner, X, dropout_layer_indexes=[],
                          num_predictions=-1,
                          sample_per_forward_pass=0)

        self.assertRaises(AssertionError, modAL.dropout.get_predictions,
                          learner, X, dropout_layer_indexes=[],
                          num_predictions=10,
                          sample_per_forward_pass=-5)

        # logits adapter function test
        for samples, classes, subclasses in product(range(1, 10),  range(1, 10),  range(1, 10)):
            input_shape = (samples, classes, subclasses)
            desired_shape = (input_shape[0], np.prod(input_shape[1:]))
            X_adaption_needed = torch.randn(input_shape)

            def logits_adaptor(input_tensor, data): return torch.flatten(
                input_tensor, start_dim=1)

            predictions = modAL.dropout.get_predictions(
                learner, X_adaption_needed, dropout_layer_indexes=[],
                num_predictions=num_predictions,
                sample_per_forward_pass=samples_per_forward_pass,
                logits_adaptor=logits_adaptor)

            self.assertEqual(predictions[0].shape, desired_shape)

    def test_set_dropout_mode(self):
        # set dropmout mode for all dropout layers