src/main/python/tests/scuro/test_window_operations.py

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import unittest
import math

import numpy as np

from tests.scuro.data_generator import ModalityRandomDataGenerator
from systemds.scuro.modality.type import ModalityType


class TestWindowOperations(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.num_instances = 40
        cls.data_generator = ModalityRandomDataGenerator()
        cls.aggregations = ["mean", "sum", "max", "min"]

    def test_window_operations_on_audio_representations(self):
        window_size = 10
        self.run_window_operations_for_modality(ModalityType.AUDIO, window_size)

    def test_window_operations_on_video_representations(self):
        window_size = 10
        self.run_window_operations_for_modality(ModalityType.VIDEO, window_size)

    def test_window_operations_on_text_representations(self):
        window_size = 10

        self.run_window_operations_for_modality(ModalityType.TEXT, window_size)

    def run_window_operations_for_modality(self, modality_type, window_size):
        r = self.data_generator.create1DModality(40, 100, modality_type)
        for aggregation in self.aggregations:
            windowed_modality = r.window_aggregation(window_size, aggregation)

            self.verify_window_operation(aggregation, r, windowed_modality, window_size)

    def verify_window_operation(
        self, aggregation, modality, windowed_modality, window_size
    ):
        assert windowed_modality.data is not None
        assert len(windowed_modality.data) == self.num_instances

        for i, instance in enumerate(windowed_modality.data):
            # assert (
            #     list(windowed_modality.metadata.values())[i]["data_layout"]["shape"][0]
            #     == list(modality.metadata.values())[i]["data_layout"]["shape"][0]
            # )
            assert len(instance) == math.ceil(len(modality.data[i]) / window_size)
            for j in range(0, len(instance)):
                if aggregation == "mean":
                    np.testing.assert_almost_equal(
                        instance[j],
                        np.mean(
                            modality.data[i][j * window_size : (j + 1) * window_size],
                            axis=0,
                        ),
                    )
                elif aggregation == "sum":
                    np.testing.assert_almost_equal(
                        instance[j],
                        np.sum(
                            modality.data[i][j * window_size : (j + 1) * window_size],
                            axis=0,
                        ),
                    )
                elif aggregation == "max":
                    np.testing.assert_almost_equal(
                        instance[j],
                        np.max(
                            modality.data[i][j * window_size : (j + 1) * window_size],
                            axis=0,
                        ),
                    )
                elif aggregation == "min":
                    np.testing.assert_almost_equal(
                        instance[j],
                        np.min(
                            modality.data[i][j * window_size : (j + 1) * window_size],
                            axis=0,
                        ),
                    )


if __name__ == "__main__":
    unittest.main()