black linting

gwatkinson · Oct 18, 2022 · c2ccf13 · c2ccf13
1 parent c055965
commit c2ccf13
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Showing 3 changed files with 36 additions and 13 deletions.
diff --git a/goli/ipu/ipu_metrics.py b/goli/ipu/ipu_metrics.py
@@ -772,7 +772,9 @@ def fbeta_score_ipu(
         weights = tp + fn
         fbeta = (weights * fbeta).sum() / weights.sum()
     else:
-        raise ValueError(f"`average={average}` not yet supported. Chose between None, Micro, Macro, or Weighted")
+        raise ValueError(
+            f"`average={average}` not yet supported. Chose between None, Micro, Macro, or Weighted"
+        )
 
     return fbeta
 
@@ -810,7 +812,18 @@ def f1_score_ipu(
             ``average`` parameter)
     """
 
-    return fbeta_score_ipu(preds, target, beta=beta, average=average, mdmc_average=mdmc_average, ignore_index=ignore_index, num_classes=num_classes, threshold=threshold, top_k=top_k, multiclass=multiclass,)
+    return fbeta_score_ipu(
+        preds,
+        target,
+        beta=beta,
+        average=average,
+        mdmc_average=mdmc_average,
+        ignore_index=ignore_index,
+        num_classes=num_classes,
+        threshold=threshold,
+        top_k=top_k,
+        multiclass=multiclass,
+    )
 
 
 def mean_squared_error_ipu(preds: Tensor, target: Tensor, squared: bool) -> Tensor:

diff --git a/goli/trainer/metrics.py b/goli/trainer/metrics.py
@@ -197,7 +197,9 @@ def _parse_target_nan_mask(target_nan_mask):
             # Only a few str options are accepted
             target_nan_mask = target_nan_mask.lower()
             accepted_str = ["ignore", "none"]
-            assert target_nan_mask in accepted_str, f"Provided {target_nan_mask} not in accepted_str={accepted_str}"
+            assert (
+                target_nan_mask in accepted_str
+            ), f"Provided {target_nan_mask} not in accepted_str={accepted_str}"
 
             if target_nan_mask == "none":
                 target_nan_mask = None
@@ -211,22 +213,26 @@ def _parse_multitask_handling(multitask_handling, target_nan_mask):
         Parse the `multitask_handling` parameter
         """
 
-        if (multitask_handling is None):
+        if multitask_handling is None:
             # None is accepted
             pass
         elif isinstance(multitask_handling, str):
             # Only a few str options are accepted
             multitask_handling = multitask_handling.lower()
             accepted_str = ["flatten", "mean-per-label", "none"]
-            assert multitask_handling in accepted_str, f"Provided {multitask_handling} not in accepted_str={accepted_str}"
+            assert (
+                multitask_handling in accepted_str
+            ), f"Provided {multitask_handling} not in accepted_str={accepted_str}"
 
             if multitask_handling == "none":
                 multitask_handling = None
         else:
             raise ValueError(f"Unrecognized option `multitask_handling={multitask_handling}`")
 
         if (target_nan_mask == "ignore") and (multitask_handling is None):
-            raise ValueError("Cannot use the option `multitask_handling=None` when `target_nan_mask=ignore`. Use either 'flatten' or 'mean-per-label'")
+            raise ValueError(
+                "Cannot use the option `multitask_handling=None` when `target_nan_mask=ignore`. Use either 'flatten' or 'mean-per-label'"
+            )
 
         return multitask_handling
 
@@ -261,7 +267,9 @@ def compute(self, preds: Tensor, target: Tensor) -> Tensor:
 
         if self.multitask_handling is None:
             # In case of no multi-task handling, apply the nan filtering, then compute the metrics
-            assert self.target_nan_mask != "ignore", f"Cannot use the option `multitask_handling=None` when `target_nan_mask=ignore`. Use either 'flatten' or 'mean-per-label'"
+            assert (
+                self.target_nan_mask != "ignore"
+            ), f"Cannot use the option `multitask_handling=None` when `target_nan_mask=ignore`. Use either 'flatten' or 'mean-per-label'"
             preds, target = self._filter_nans(preds, target)
             metric_val = self.metric(preds, target, **self.kwargs)
         elif self.multitask_handling == "flatten":

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
@@ -108,7 +108,9 @@ def test_target_nan_mask(self):
             self.assertAlmostEqual(score4, mean_squared_error(this_preds, this_target), msg=err_msg)
 
             # Ignore NaNs in each column and average the score
-            metric = MetricWrapper(metric="mse", target_nan_mask="ignore", multitask_handling="mean-per-label")
+            metric = MetricWrapper(
+                metric="mse", target_nan_mask="ignore", multitask_handling="mean-per-label"
+            )
             score5 = metric(preds, target)
 
             this_target = target.clone()
@@ -153,11 +155,11 @@ def test_pickling(self):
                                 # Raise with incompatible options
                                 with self.assertRaises(ValueError):
                                     MetricWrapper(
-                                    metric=metric,
-                                    threshold_kwargs=threshold_kwargs,
-                                    target_nan_mask=target_nan_mask,
-                                    multitask_handling=multitask_handling,
-                                    **kwargs,
+                                        metric=metric,
+                                        threshold_kwargs=threshold_kwargs,
+                                        target_nan_mask=target_nan_mask,
+                                        multitask_handling=multitask_handling,
+                                        **kwargs,
                                     )
 
                             else: