format

avalanche/core.py

@@ -5,6 +5,7 @@
 Most of these protocols are checked dynamically at runtime, so it is often not
 necessary to inherit explicit from them or implement all the methods.
 """
+
 from abc import ABC
 from typing import Any, TypeVar, Generic, Protocol, runtime_checkable
 from typing import TYPE_CHECKING

avalanche/evaluation/collector.py

@@ -79,7 +79,9 @@ def update(self, res, *, stream=None):
             else:
                 self.metrics_res[k] = [v]
 
-    def get(self, name, *, time_reduce=None, exp_reduce=None, stream=None, weights=None):
+    def get(
+        self, name, *, time_reduce=None, exp_reduce=None, stream=None, weights=None
+    ):
         """Returns a metric value given its name and aggregation method.
 
         :param name: name of the metric.
@@ -102,9 +104,13 @@ def get(self, name, *, time_reduce=None, exp_reduce=None, stream=None, weights=N
         assert time_reduce in {None, "last", "mean"}
         assert exp_reduce in {None, "sample_mean", "experience_mean", "weighted_sum"}
         if exp_reduce == "weighted_sum":
-            assert weights is not None, "You should set the `weights` argument when `exp_reduce == 'weighted_sum'`."
+            assert (
+                weights is not None
+            ), "You should set the `weights` argument when `exp_reduce == 'weighted_sum'`."
         else:
-            assert weights is None, "Can't use the `weights` argument when `exp_reduce != 'weighted_sum'`"
+            assert (
+                weights is None
+            ), "Can't use the `weights` argument when `exp_reduce != 'weighted_sum'`"
 
         if stream is not None:
             name = f"{stream.name}/{name}"

avalanche/models/dynamic_optimizers.py

@@ -33,7 +33,9 @@
 colors[None] = colors["END"]
 
 
-@experimental("New dynamic optimizers. The API may slightly change in the next versions.")
+@experimental(
+    "New dynamic optimizers. The API may slightly change in the next versions."
+)
 class DynamicOptimizer(Adaptable):
     """Avalanche dynamic optimizer.
 
@@ -64,6 +66,7 @@ class DynamicOptimizer(Adaptable):
         # first model.pre_adapt, then optimizer.pre_adapt
         agent.pre_adapt(experience)
     """
+
     def __init__(self, optim):
         self.optim = optim
 
@@ -78,7 +81,7 @@ def pre_adapt(self, agent: Agent, exp: CLExperience):
         update_optimizer(
             self.optim,
             new_params=dict(agent.model.named_parameters()),
-            optimized_params=dict(agent.model.named_parameters())
+            optimized_params=dict(agent.model.named_parameters()),
         )
 
 

tests/evaluation/test_collector.py

@@ -57,10 +57,10 @@ def __len__(self):
             time_reduce=None,
             exp_reduce="weighted_sum",
             weights=[1, 2],
-            stream=fake_stream
+            stream=fake_stream,
         )
         np.testing.assert_array_almost_equal(
-            v, [(1 + 3*2), (5 + 7*2), (11 + 13*2)]
+            v, [(1 + 3 * 2), (5 + 7 * 2), (11 + 13 * 2)]
         )
 
         # time = "last"
@@ -87,9 +87,9 @@ def __len__(self):
             time_reduce="last",
             exp_reduce="weighted_sum",
             stream=fake_stream,
-            weights=[1, 2]
+            weights=[1, 2],
         )
-        self.assertAlmostEqual(v, 11 + 13*2)
+        self.assertAlmostEqual(v, 11 + 13 * 2)
 
         # time_reduce = "mean"
         v = mc.get(
@@ -115,9 +115,9 @@ def __len__(self):
             time_reduce="mean",
             exp_reduce="weighted_sum",
             stream=fake_stream,
-            weights=[1, 2]
+            weights=[1, 2],
         )
-        self.assertAlmostEqual(v, ((1 + 3*2) + (5 + 7*2) + (11 + 13*2)) / 3)
+        self.assertAlmostEqual(v, ((1 + 3 * 2) + (5 + 7 * 2) + (11 + 13 * 2)) / 3)
 
 
 if __name__ == "__main__":