How to use the snorkel.classification.MultitaskClassifier function in snorkel

base_task = create_task("task", module_suffixes=["A", "B"])

        # Apply SFs
        slicing_functions = [f, g]  # low-coverage slices
        slice_names = [sf.name for sf in slicing_functions]
        applier = PandasSFApplier(slicing_functions)
        S_train = applier.apply(self.df_train, progress_bar=False)
        S_valid = applier.apply(self.df_valid, progress_bar=False)

        # Add slice labels
        add_slice_labels(dataloaders[0], base_task, S_train)
        add_slice_labels(dataloaders[1], base_task, S_valid)

        # Convert to slice tasks
        tasks = convert_to_slice_tasks(base_task, slice_names)
        model = MultitaskClassifier(tasks=tasks)

        # Train
        # NOTE: Needs more epochs to convergence with more heads
        trainer = Trainer(lr=0.001, n_epochs=60, progress_bar=False)
        trainer.fit(model, dataloaders)
        scores = model.score(dataloaders)

        # Confirm reasonably high slice scores
        # Check train scores
        self.assertGreater(scores["task/TestData/train/f1"], 0.9)
        self.assertGreater(scores["task_slice:f_pred/TestData/train/f1"], 0.9)
        self.assertGreater(scores["task_slice:f_ind/TestData/train/f1"], 0.9)
        self.assertGreater(scores["task_slice:g_pred/TestData/train/f1"], 0.9)
        self.assertGreater(scores["task_slice:g_ind/TestData/train/f1"], 0.9)
        self.assertGreater(scores["task_slice:base_pred/TestData/train/f1"], 0.9)
        self.assertEqual(scores["task_slice:base_ind/TestData/train/f1"], 1.0)

op1 = Operation(module_name=module1_name, inputs=[("_input_", "data")])
    op2 = Operation(module_name=module2_name, inputs=[op1.name])

    op_sequence = [op1, op2]

    task = Task(name=task_name, module_pool=module_pool, op_sequence=op_sequence)

    return task


dataloaders = [create_dataloader(task_name) for task_name in TASK_NAMES]
tasks = [
    create_task(TASK_NAMES[0], module_suffixes=["A", "A"]),
    create_task(TASK_NAMES[1], module_suffixes=["A", "B"]),
]
model = MultitaskClassifier([tasks[0]])


class TrainerTest(unittest.TestCase):
    def test_trainer_onetask(self):
        """Train a single-task model"""
        trainer = Trainer(**base_config)
        trainer.fit(model, [dataloaders[0]])

    def test_trainer_twotask(self):
        """Train a model with overlapping modules and flows"""
        multitask_model = MultitaskClassifier(tasks)
        trainer = Trainer(**base_config)
        trainer.fit(multitask_model, dataloaders)

    def test_trainer_errors(self):
        dataloader = copy.deepcopy(dataloaders[0])

slice_names = [sf.name for sf in slicing_functions]
        applier = PandasSFApplier(slicing_functions)
        S_train = applier.apply(self.df_train, progress_bar=False)
        S_valid = applier.apply(self.df_valid, progress_bar=False)

        self.assertEqual(S_train.shape, (self.N_TRAIN,))
        self.assertEqual(S_valid.shape, (self.N_VALID,))
        self.assertIn("h", S_train.dtype.names)

        # Add slice labels
        add_slice_labels(dataloaders[0], base_task, S_train)
        add_slice_labels(dataloaders[1], base_task, S_valid)

        # Convert to slice tasks
        tasks = convert_to_slice_tasks(base_task, slice_names)
        model = MultitaskClassifier(tasks=tasks)

        # Train
        trainer = Trainer(lr=0.001, n_epochs=50, progress_bar=False)
        trainer.fit(model, dataloaders)
        scores = model.score(dataloaders)

        # Confirm near perfect scores
        self.assertGreater(scores["task/TestData/valid/accuracy"], 0.95)
        self.assertGreater(scores["task_slice:h_pred/TestData/valid/accuracy"], 0.95)
        self.assertGreater(scores["task_slice:h_ind/TestData/valid/f1"], 0.95)

        # Calculate/check train/val loss
        train_dataset = dataloaders[0].dataset
        train_loss_output = model.calculate_loss(
            train_dataset.X_dict, train_dataset.Y_dict
        )

def test_twotask_all_overlap_model(self):
        """Add two tasks with identical modules and flows"""
        task1 = create_task("task1", module_suffixes=["A", "A"])
        task2 = create_task("task2", module_suffixes=["A", "A"])
        model = MultitaskClassifier(tasks=[task1, task2])
        self.assertEqual(len(model.task_names), 2)
        self.assertEqual(len(model.op_sequences), 2)
        self.assertEqual(len(model.module_pool), 2)

def test_partially_empty_batch(self):
        dataset = create_dataloader("task1", shuffle=False).dataset
        dataset.Y_dict["task1"][0] = -1
        model = MultitaskClassifier([self.task1])
        loss_dict, count_dict = model.calculate_loss(dataset.X_dict, dataset.Y_dict)
        self.assertEqual(count_dict["task1"], 9)

def test_twotask_none_overlap_model(self):
        """Add two tasks with totally separate modules and flows"""
        model = MultitaskClassifier(tasks=[self.task1, self.task2])
        self.assertEqual(len(model.task_names), 2)
        self.assertEqual(len(model.op_sequences), 2)
        self.assertEqual(len(model.module_pool), 4)

def test_empty_batch(self):
        dataset = create_dataloader("task1", shuffle=False).dataset
        dataset.Y_dict["task1"] = torch.full_like(dataset.Y_dict["task1"], -1)
        model = MultitaskClassifier([self.task1])
        loss_dict, count_dict = model.calculate_loss(dataset.X_dict, dataset.Y_dict)
        self.assertFalse(loss_dict)
        self.assertFalse(count_dict)

def test_no_data_parallel(self):
        model = MultitaskClassifier(tasks=[self.task1, self.task2], dataparallel=False)
        self.assertEqual(len(model.task_names), 2)
        self.assertIsInstance(model.module_pool["linear1A"], nn.Module)

mask = x % 2 == 0
                out = torch.zeros(x.shape[0], 2)
                out[mask, 0] = 1  # class 0
                out[~mask, 1] = 1  # class 1
                return out

        # Create model
        task_name = "VotingTask"
        module_name = "simple_voter"
        module_pool = nn.ModuleDict({module_name: SimpleVoter()})
        op0 = Operation(
            module_name=module_name, inputs=[("_input_", "data")], name="op0"
        )
        op_sequence = [op0]
        task = Task(name=task_name, module_pool=module_pool, op_sequence=op_sequence)
        model = MultitaskClassifier([task])

        # Create dataset
        y_list = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
        x_list = [i for i in range(len(y_list))]
        Y = torch.LongTensor(y_list * 100)
        X = torch.FloatTensor(x_list * 100)
        dataset = DictDataset(
            name="dataset", split="train", X_dict={"data": X}, Y_dict={task_name: Y}
        )

        # Create dataloaders
        dataloader = DictDataLoader(dataset, batch_size=2, shuffle=False)
        scores = model.score([dataloader])

        self.assertEqual(scores["VotingTask/dataset/train/accuracy"], 0.6)

fd, checkpoint_path = tempfile.mkstemp()

        task1 = create_task("task1")
        task2 = create_task("task2")
        # Make task2's second linear layer have different weights
        task2.module_pool["linear2"] = nn.Linear(2, 2)

        model = MultitaskClassifier([task1])
        self.assertTrue(
            torch.eq(
                task1.module_pool["linear2"].weight,
                model.module_pool["linear2"].module.weight,
            ).all()
        )
        model.save(checkpoint_path)
        model = MultitaskClassifier([task2])
        self.assertFalse(
            torch.eq(
                task1.module_pool["linear2"].weight,
                model.module_pool["linear2"].module.weight,
            ).all()
        )
        model.load(checkpoint_path)
        self.assertTrue(
            torch.eq(
                task1.module_pool["linear2"].weight,
                model.module_pool["linear2"].module.weight,
            ).all()
        )

        os.close(fd)