Increase minimum Python version to 3.8 (#2306)

doc/source/ref-changelog.md

@@ -12,7 +12,7 @@
 
 ### Incompatible changes
 
-- **Remove support for Python 3.7** ([#2280](https:/adap/flower/pull/2280), [#2299](https:/adap/flower/pull/2299), [2304](https:/adap/flower/pull/2304))
+- **Remove support for Python 3.7** ([#2280](https:/adap/flower/pull/2280), [#2299](https:/adap/flower/pull/2299), [2304](https:/adap/flower/pull/2304), [#2306](https:/adap/flower/pull/2306))
 
  Python 3.7 support was deprecated in Flower 1.5, and this release removes support. Flower now requires Python 3.8.
 

pyproject.toml

@@ -30,7 +30,6 @@ classifiers = [
  "Programming Language :: Python",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3 :: Only",
- "Programming Language :: Python :: 3.7",
  "Programming Language :: Python :: 3.8",
  "Programming Language :: Python :: 3.9",
  "Programming Language :: Python :: 3.10",
@@ -58,7 +57,7 @@ flower-server = "flwr.server:run_server"
 flower-client = "flwr.client:run_client"
 
 [tool.poetry.dependencies]
-python = "^3.7"
+python = "^3.8"
 # Mandatory dependencies
 numpy = "^1.21.0"
 grpcio = "^1.48.2,!=1.52.0"
@@ -132,7 +131,7 @@ known_first_party = ["flwr", "flwr_experimental", "flwr_tool"]
 
 [tool.black]
 line-length = 88
-target-version = ["py37", "py38", "py39", "py310", "py311"]
+target-version = ["py38", "py39", "py310", "py311"]
 
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
@@ -178,7 +177,7 @@ wrap-summaries = 88
 wrap-descriptions = 88
 
 [tool.ruff]
-target-version = "py37"
+target-version = "py38"
 line-length = 88
 select = ["D", "E", "F", "W", "B", "ISC", "C4", "UP"]
 fixable = ["D", "E", "F", "W", "B", "ISC", "C4", "UP"]

src/py/flwr/common/dp.py

@@ -26,7 +26,7 @@
 def _get_update_norm(update: NDArrays) -> float:
  flattened_update = update[0]
  for i in range(1, len(update)):
- flattened_update = np.append(flattened_update, update[i]) # type: ignore
+ flattened_update = np.append(flattened_update, update[i])
  return float(np.sqrt(np.sum(np.square(flattened_update))))
 
 

src/py/flwr/common/parameter.py

@@ -40,7 +40,7 @@ def ndarray_to_bytes(ndarray: NDArray) -> bytes:
  # WARNING: NEVER set allow_pickle to true.
  # Reason: loading pickled data can execute arbitrary code
  # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html
- np.save(bytes_io, ndarray, allow_pickle=False) # type: ignore
+ np.save(bytes_io, ndarray, allow_pickle=False)
  return bytes_io.getvalue()
 
 
@@ -50,5 +50,5 @@ def bytes_to_ndarray(tensor: bytes) -> NDArray:
  # WARNING: NEVER set allow_pickle to true.
  # Reason: loading pickled data can execute arbitrary code
  # Source: https://numpy.org/doc/stable/reference/generated/numpy.load.html
- ndarray_deserialized = np.load(bytes_io, allow_pickle=False) # type: ignore
+ ndarray_deserialized = np.load(bytes_io, allow_pickle=False)
  return cast(NDArray, ndarray_deserialized)

src/py/flwr/common/parameter_test.py

@@ -32,8 +32,8 @@ def test_serialisation_deserialisation() -> None:
  arr_deserialized = bytes_to_ndarray(arr_serialized)
 
  # Assert deserialized array is equal to original
- np.testing.assert_equal(arr_deserialized, arr) # type: ignore
+ np.testing.assert_equal(arr_deserialized, arr)
 
  # Test false positive
  with pytest.raises(AssertionError, match="Arrays are not equal"):
- np.testing.assert_equal(arr_deserialized, np.ones((3, 2))) # type: ignore
+ np.testing.assert_equal(arr_deserialized, np.ones((3, 2)))

src/py/flwr/server/strategy/aggregate.py

@@ -48,7 +48,7 @@ def aggregate_median(results: List[Tuple[NDArrays, int]]) -> NDArrays:
 
  # Compute median weight of each layer
  median_w: NDArrays = [
- np.median(np.asarray(layer), axis=0) for layer in zip(*weights) # type: ignore
+ np.median(np.asarray(layer), axis=0) for layer in zip(*weights)
  ]
  return median_w
 
@@ -122,14 +122,12 @@ def _compute_distances(weights: List[NDArrays]) -> NDArray:
  Input: weights - list of weights vectors
  Output: distances - matrix distance_matrix of squared distances between the vectors
  """
- flat_w = np.array(
- [np.concatenate(p, axis=None).ravel() for p in weights] # type: ignore
- )
+ flat_w = np.array([np.concatenate(p, axis=None).ravel() for p in weights])
  distance_matrix = np.zeros((len(weights), len(weights)))
  for i, _ in enumerate(flat_w):
  for j, _ in enumerate(flat_w):
  delta = flat_w[i] - flat_w[j]
- norm = np.linalg.norm(delta) # type: ignore
+ norm = np.linalg.norm(delta)
  distance_matrix[i, j] = norm**2
  return distance_matrix
 

src/py/flwr/server/strategy/aggregate_test.py

@@ -37,7 +37,7 @@ def test_aggregate() -> None:
  actual = aggregate(results)
 
  # Assert
- np.testing.assert_equal(expected, actual) # type: ignore
+ np.testing.assert_equal(expected, actual)
 
 
 def test_weighted_loss_avg_single_value() -> None:

src/py/flwr/server/strategy/fedavg_android.py

@@ -242,5 +242,5 @@ def ndarray_to_bytes(self, ndarray: NDArray) -> bytes:
  # pylint: disable=R0201
  def bytes_to_ndarray(self, tensor: bytes) -> NDArray:
  """Deserialize NumPy array from bytes."""
- ndarray_deserialized = np.frombuffer(tensor, dtype=np.float32) # type: ignore
+ ndarray_deserialized = np.frombuffer(tensor, dtype=np.float32)
  return cast(NDArray, ndarray_deserialized)

src/py/flwr/server/strategy/fedavgm_test.py

@@ -78,7 +78,7 @@ def test_aggregate_fit_using_near_one_server_lr_and_no_momentum() -> None:
  # Assert
  assert actual
  for w_act, w_exp in zip(parameters_to_ndarrays(actual), expected):
- assert_almost_equal(w_act, w_exp) # type: ignore
+ assert_almost_equal(w_act, w_exp)
 
 
 def test_aggregate_fit_server_learning_rate_and_momentum() -> None:
@@ -136,4 +136,4 @@ def test_aggregate_fit_server_learning_rate_and_momentum() -> None:
  # Assert
  assert actual
  for w_act, w_exp in zip(parameters_to_ndarrays(actual), expected):
- assert_almost_equal(w_act, w_exp) # type: ignore
+ assert_almost_equal(w_act, w_exp)

src/py/flwr/server/strategy/qfedavg.py

@@ -174,7 +174,7 @@ def norm_grad(grad_list: NDArrays) -> float:
  # output: square of the L-2 norm
  client_grads = grad_list[0]
  for i in range(1, len(grad_list)):
- client_grads = np.append( # type: ignore
+ client_grads = np.append(
  client_grads, grad_list[i]
  ) # output a flattened array
  squared = np.square(client_grads)