def __init__(self,
client_manager: ClientManager,
strategy: Optional[Strategy] = None) -> None:
self._client_manager: ClientManager = client_manager
self.parameters: Parameters = Parameters(tensors=[],
tensor_type="numpy.ndarray")
self.strategy: Strategy = strategy if strategy is not None else FedAvg(
)
def test_aggregate_fit_no_failures() -> None:
"""Test evaluate function."""
# Prepare
strategy = FaultTolerantFedAvg(min_completion_rate_fit=0.99)
results: List[Tuple[ClientProxy, FitRes]] = [
(MagicMock(), FitRes(Parameters(tensors=[], tensor_type=""), 1, 1, 0.1))
]
failures: List[BaseException] = []
expected: Optional[Weights] = []
# Execute
actual = strategy.aggregate_fit(1, results, failures)
# Assert
assert actual == expected
def test_fit_clients() -> None:
"""Test fit_clients."""
# Prepare
clients: List[ClientProxy] = [
FailingCLient("0"),
SuccessClient("1"),
]
arr = np.array([[1, 2], [3, 4], [5, 6]])
arr_serialized = ndarray_to_bytes(arr)
ins: FitIns = FitIns(Parameters(tensors=[arr_serialized], tensor_type=""), {})
client_instructions = [(c, ins) for c in clients]
# Execute
results, failures = fit_clients(client_instructions)
# Assert
assert len(results) == 1
assert len(failures) == 1
assert results[0][1].num_examples == 1
def fit(self, ins: FitIns) -> FitRes:
arr = np.array([[1, 2], [3, 4], [5, 6]])
arr_serialized = ndarray_to_bytes(arr)
return FitRes(Parameters(tensors=[arr_serialized], tensor_type=""), 1, 1, 12.3)
def weights_to_parameters(self, weights: Weights) -> Parameters:
"""Convert NumPy weights to parameters object."""
tensors = [self.ndarray_to_bytes(ndarray) for ndarray in weights]
return Parameters(tensors=tensors, tensor_type="numpy.nda")