History Module Tests⚓︎
Overview⚓︎
The history module tests (test_history.py) verify the metrics tracking and history management functionality, including the StopWatch context manager and History class.
Test Cases⚓︎
test_stopwatch_context_manager⚓︎
Tests the StopWatch context manager for timing code blocks.
def test_stopwatch_context_manager(self):
"""Test StopWatch context manager."""
with history.StopWatch("test timer") as sw:
# Do some work
result = 1 + 1
assert result == 2
# The stopwatch should have recorded some data
assert hasattr(sw, "data")
assert isinstance(sw.data, dict)
test_history_initialization⚓︎
Verifies the History class initialization.
def test_history_initialization(self):
"""Test History class initialization."""
hist = history.History()
assert hist is not None
assert hasattr(hist, "data")
assert isinstance(hist.data, dict)
test_history_update⚓︎
Tests the History update method for adding metrics.
def test_history_update(self):
"""Test History update method."""
hist = history.History()
# Add some test data
metrics = {"loss": 0.5, "accuracy": 0.8}
summary = hist.update(metrics)
assert isinstance(summary, str)
assert "loss" in summary
assert "accuracy" in summary
test_grab_tensor⚓︎
Verifies the tensor conversion utility function.
def test_grab_tensor(self):
"""Test grab_tensor function."""
# Test with numpy array
np_array = np.array([1, 2, 3])
result = history.grab_tensor(np_array)
assert np.array_equal(result, np_array)
# Test with torch tensor
torch_tensor = torch.tensor([1, 2, 3])
result = history.grab_tensor(torch_tensor)
assert isinstance(result, np.ndarray)
assert np.array_equal(result, torch_tensor.numpy())
# Test with scalar
scalar = 5
result = history.grab_tensor(scalar)
assert result == scalar
# Test with list
test_list = [1, 2, 3]
result = history.grab_tensor(test_list)
assert isinstance(result, np.ndarray)
assert np.array_equal(result, np.array(test_list))