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

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import shutil
import unittest

import numpy as np
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

from systemds.scuro.representations.concatenation import Concatenation
from systemds.scuro.representations.average import Average
from systemds.scuro.drsearch.fusion_optimizer import FusionOptimizer
from systemds.scuro.drsearch.operator_registry import Registry
from systemds.scuro.models.model import Model
from systemds.scuro.drsearch.task import Task
from systemds.scuro.drsearch.unimodal_representation_optimizer import (
    UnimodalRepresentationOptimizer,
)

from systemds.scuro.representations.spectrogram import Spectrogram
from systemds.scuro.representations.word2vec import W2V
from systemds.scuro.modality.unimodal_modality import UnimodalModality
from systemds.scuro.representations.resnet import ResNet
from tests.scuro.data_generator import (
    setup_data,
    TestDataLoader,
    ModalityRandomDataGenerator,
)

from systemds.scuro.dataloader.audio_loader import AudioLoader
from systemds.scuro.dataloader.video_loader import VideoLoader
from systemds.scuro.dataloader.text_loader import TextLoader
from systemds.scuro.modality.type import ModalityType

from unittest.mock import patch


class TestSVM(Model):
    def __init__(self):
        super().__init__("TestSVM")

    def fit(self, X, y, X_test, y_test):
        if X.ndim > 2:
            X = X.reshape(X.shape[0], -1)
        self.clf = svm.SVC(C=1, gamma="scale", kernel="rbf", verbose=False)
        self.clf = self.clf.fit(X, np.array(y))
        y_pred = self.clf.predict(X)

        return classification_report(
            y, y_pred, output_dict=True, digits=3, zero_division=1
        )["accuracy"]

    def test(self, test_X: np.ndarray, test_y: np.ndarray):
        if test_X.ndim > 2:
            test_X = test_X.reshape(test_X.shape[0], -1)
        y_pred = self.clf.predict(np.array(test_X))  # noqa

        return classification_report(
            np.array(test_y), y_pred, output_dict=True, digits=3, zero_division=1
        )["accuracy"]


class TestCNN(Model):
    def __init__(self):
        super().__init__("TestCNN")

    def fit(self, X, y, X_test, y_test):
        if X.ndim > 2:
            X = X.reshape(X.shape[0], -1)
        self.clf = svm.SVC(C=1, gamma="scale", kernel="rbf", verbose=False)
        self.clf = self.clf.fit(X, np.array(y))
        y_pred = self.clf.predict(X)

        return classification_report(
            y, y_pred, output_dict=True, digits=3, zero_division=1
        )["accuracy"]

    def test(self, test_X: np.ndarray, test_y: np.ndarray):
        if test_X.ndim > 2:
            test_X = test_X.reshape(test_X.shape[0], -1)
        y_pred = self.clf.predict(np.array(test_X))  # noqa

        return classification_report(
            np.array(test_y), y_pred, output_dict=True, digits=3, zero_division=1
        )["accuracy"]


class TestMultimodalRepresentationOptimizer(unittest.TestCase):
    test_file_path = None
    data_generator = None
    num_instances = 0

    @classmethod
    def setUpClass(cls):
        cls.num_instances = 10
        cls.mods = [ModalityType.VIDEO, ModalityType.AUDIO, ModalityType.TEXT]
        cls.labels = np.random.choice([0, 1], size=cls.num_instances)
        cls.indices = np.array(range(cls.num_instances))

        split = train_test_split(
            cls.indices,
            cls.labels,
            test_size=0.2,
            random_state=42,
        )
        cls.train_indizes, cls.val_indizes = [int(i) for i in split[0]], [
            int(i) for i in split[1]
        ]

        cls.tasks = [
            Task(
                "UnimodalRepresentationTask1",
                TestSVM(),
                cls.labels,
                cls.train_indizes,
                cls.val_indizes,
            ),
            Task(
                "UnimodalRepresentationTask2",
                TestCNN(),
                cls.labels,
                cls.train_indizes,
                cls.val_indizes,
            ),
        ]

    def test_multimodal_fusion(self):
        task = Task(
            "UnimodalRepresentationTask1",
            TestSVM(),
            self.labels,
            self.train_indizes,
            self.val_indizes,
        )

        audio_data, audio_md = ModalityRandomDataGenerator().create_audio_data(
            self.num_instances, 100
        )
        text_data, text_md = ModalityRandomDataGenerator().create_text_data(
            self.num_instances
        )
        video_data, video_md = ModalityRandomDataGenerator().create_visual_modality(
            self.num_instances, 60
        )
        audio = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.AUDIO, audio_data, np.float32, audio_md
            )
        )
        video = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.VIDEO, video_data, np.float32, video_md
            )
        )
        text = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.TEXT, text_data, str, text_md
            )
        )

        with patch.object(
            Registry,
            "_representations",
            {
                ModalityType.TEXT: [W2V],
                ModalityType.AUDIO: [Spectrogram],
                ModalityType.TIMESERIES: [ResNet],
                ModalityType.VIDEO: [ResNet],
                ModalityType.EMBEDDING: [],
            },
        ):
            registry = Registry()
            registry._fusion_operators = [Average, Concatenation]
            unimodal_optimizer = UnimodalRepresentationOptimizer(
                [text, audio, video], [task], max_chain_depth=2
            )
            unimodal_optimizer.optimize()

            multimodal_optimizer = FusionOptimizer(
                [audio, text, video],
                task,
                unimodal_optimizer.optimization_results,
                unimodal_optimizer.cache,
                2,
                2,
                debug=False,
            )
            multimodal_optimizer.optimize()


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