Module neuralops.tests.config.test_training_config

Tests for neuralops.config.training_config.

Classes

class TestTrainingConfig

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class TestTrainingConfig:
    """Test TrainingConfig dataclass hyperparameters and scheduler settings."""

    def test_creation(self):
        cfg = TrainingConfig()
        assert cfg is not None

    def test_batch_size(self):
        cfg = TrainingConfig(batch_size=16)
        assert cfg.batch_size == 16

    def test_learning_rate(self):
        cfg = TrainingConfig(learning_rate=1e-4)
        assert cfg.learning_rate == 1e-4

    def test_epochs(self):
        cfg = TrainingConfig(epochs=5)
        assert cfg.epochs == 5

    def test_optimizer_type(self):
        cfg = TrainingConfig(optimizer="adamw")
        assert cfg.optimizer == "adamw"

    def test_lr_scheduler(self):
        cfg = TrainingConfig(lr_scheduler_type="cosine")
        assert cfg.lr_scheduler_type == "cosine"

    def test_all_fields_present(self):
        cfg = TrainingConfig()
        field_names = {f.name for f in fields(cfg)}
        expected = {"batch_size", "epochs", "learning_rate"}
        assert expected.issubset(field_names)

Test TrainingConfig dataclass hyperparameters and scheduler settings.

Methods

def test_all_fields_present(self)

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def test_all_fields_present(self):
    cfg = TrainingConfig()
    field_names = {f.name for f in fields(cfg)}
    expected = {"batch_size", "epochs", "learning_rate"}
    assert expected.issubset(field_names)

def test_batch_size(self)

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def test_batch_size(self):
    cfg = TrainingConfig(batch_size=16)
    assert cfg.batch_size == 16

def test_creation(self)

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def test_creation(self):
    cfg = TrainingConfig()
    assert cfg is not None

def test_epochs(self)

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def test_epochs(self):
    cfg = TrainingConfig(epochs=5)
    assert cfg.epochs == 5

def test_learning_rate(self)

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def test_learning_rate(self):
    cfg = TrainingConfig(learning_rate=1e-4)
    assert cfg.learning_rate == 1e-4

def test_lr_scheduler(self)

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def test_lr_scheduler(self):
    cfg = TrainingConfig(lr_scheduler_type="cosine")
    assert cfg.lr_scheduler_type == "cosine"

def test_optimizer_type(self)

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def test_optimizer_type(self):
    cfg = TrainingConfig(optimizer="adamw")
    assert cfg.optimizer == "adamw"