Update black version to fix build (snorkel-team#1693)

Author: fpoms

requirements.txt

@@ -43,7 +43,7 @@ dill>=0.3.0,<0.4.0
 
 #### DEV TOOLS
 
-black>=19.3b0,<20.0
+black>=22.3
 flake8>=3.7.0,<4.0.0
 isort>=4.3.0,<5.0.0
 mypy==0.720

snorkel/utils/lr_schedulers.py

@@ -40,5 +40,7 @@ class LRSchedulerConfig(Config):
     warmup_unit: str = "batches"  # [epochs, batches]
     warmup_percentage: float = 0.0  # warm up percentage
     min_lr: float = 0.0  # minimum learning rate
-    exponential_config: ExponentialLRSchedulerConfig = ExponentialLRSchedulerConfig()  # type:ignore
+    exponential_config: ExponentialLRSchedulerConfig = (
+        ExponentialLRSchedulerConfig()  # type:ignore
+    )
     step_config: StepLRSchedulerConfig = StepLRSchedulerConfig()  # type:ignore

test/augmentation/apply/test_tf_applier.py

@@ -16,15 +16,15 @@
 
 @transformation_function()
 def square(x: DataPoint) -> DataPoint:
-    x.num = x.num ** 2
+    x.num = x.num**2
     return x
 
 
 @transformation_function()
 def square_returns_none(x: DataPoint) -> DataPoint:
     if x.num == 2:
         return None
-    x.num = x.num ** 2
+    x.num = x.num**2
     return x
 
 

test/classification/test_classifier_convergence.py

@@ -32,7 +32,7 @@ def setUpClass(cls):
 
     @pytest.mark.complex
     def test_convergence(self):
-        """ Test multitask classifier convergence with two tasks."""
+        """Test multitask classifier convergence with two tasks."""
 
         dataloaders = []
 

test/classification/test_loss.py

@@ -27,7 +27,7 @@ def test_sce_equals_ce(self):
 
         ce_loss = F.cross_entropy(Y_probs, Y_golds, reduction="mean")
         ces_loss = cross_entropy_with_probs(Y_probs, Y_golds_probs, reduction="mean")
-        np.testing.assert_equal(ce_loss.numpy(), ces_loss.numpy())
+        np.testing.assert_almost_equal(ce_loss.numpy(), ces_loss.numpy())
 
     def test_perfect_predictions(self):
         # Does soft ce loss achieve approx. 0 loss with perfect predictions?

test/classification/training/test_trainer.py

@@ -250,7 +250,12 @@ def dict_check(self, dict1, dict2):
             if isinstance(dict1_, collections.Mapping):
                 self.dict_check(dict1_, dict2_)
             elif isinstance(dict1_, torch.Tensor):
-                self.assertTrue(torch.eq(dict1_, dict2_,).all())
+                self.assertTrue(
+                    torch.eq(
+                        dict1_,
+                        dict2_,
+                    ).all()
+                )
             else:
                 self.assertEqual(dict1_, dict2_)
 

test/labeling/apply/test_lf_applier.py

@@ -17,7 +17,7 @@
 
 @preprocessor()
 def square(x: DataPoint) -> DataPoint:
-    x.num_squared = x.num ** 2
+    x.num_squared = x.num**2
     return x
 
 
@@ -27,7 +27,7 @@ def __init__(self):
 
     def __call__(self, x: float) -> float:
         self.n_hits += 1
-        return x ** 2
+        return x**2
 
 
 @labeling_function()
@@ -247,7 +247,7 @@ def test_lf_applier_dask_preprocessor(self) -> None:
     def test_lf_applier_pandas_preprocessor_memoized(self) -> None:
         @preprocessor(memoize=True)
         def square_memoize(x: DataPoint) -> DataPoint:
-            x.num_squared = x.num ** 2
+            x.num_squared = x.num**2
             return x
 
         @labeling_function(pre=[square_memoize])

test/labeling/apply/test_spark.py

@@ -15,7 +15,7 @@
 
 @preprocessor()
 def square(x: Row) -> Row:
-    return Row(num=x.num, num_squared=x.num ** 2)
+    return Row(num=x.num, num_squared=x.num**2)
 
 
 @labeling_function()
@@ -91,7 +91,7 @@ def test_lf_applier_spark_preprocessor_memoized(self) -> None:
 
         @preprocessor(memoize=True)
         def square_memoize(x: DataPoint) -> DataPoint:
-            return Row(num=x.num, num_squared=x.num ** 2)
+            return Row(num=x.num, num_squared=x.num**2)
 
         @labeling_function(pre=[square_memoize])
         def fp_memoized(x: DataPoint) -> int:

test/labeling/lf/test_core.py

@@ -10,7 +10,7 @@
 
 @preprocessor()
 def square(x: DataPoint) -> DataPoint:
-    x.num = x.num ** 2
+    x.num = x.num**2
     return x
 
 
@@ -74,7 +74,7 @@ def lf(x: DataPoint) -> int:
         self._run_lf(lf)
 
     def test_labeling_function_decorator_args(self) -> None:
-        db = [3, 6, 43 ** 2]
+        db = [3, 6, 43**2]
 
         @labeling_function(name="my_lf", resources=dict(db=db), pre=[square])
         def lf(x: DataPoint, db: List[int]) -> int:

test/map/test_core.py

@@ -59,12 +59,12 @@ def __init__(self):
 
     def __call__(self, x: float) -> float:
         self.n_hits += 1
-        return x ** 2
+        return x**2
 
 
 @lambda_mapper()
 def square(x: DataPoint) -> DataPoint:
-    x.num_squared = x.num ** 2
+    x.num_squared = x.num**2
     return x
 
 
@@ -356,7 +356,7 @@ def test_mapper_decorator_no_parens(self) -> None:
 
             @lambda_mapper
             def square(x: DataPoint) -> DataPoint:
-                x.num_squared = x.num ** 2
+                x.num_squared = x.num**2
                 return x
 
     def test_mapper_with_args_kwargs(self) -> None: