[wrappers] improve error handling

bob/pipelines/wrappers.py

@@ -138,7 +138,8 @@ class SampleWrapper(BaseWrapper, TransformerMixin):
         if isinstance(samples[0], SampleSet):
             return [
                 SampleSet(
-                    self._samples_transform(sset.samples, method_name), parent=sset,
+                    self._samples_transform(sset.samples, method_name),
+                    parent=sset,
                 )
                 for sset in samples
             ]
@@ -407,7 +408,11 @@ class DaskWrapper(BaseWrapper, TransformerMixin):
     """
 
     def __init__(
-        self, estimator, fit_tag=None, transform_tag=None, **kwargs,
+        self,
+        estimator,
+        fit_tag=None,
+        transform_tag=None,
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.estimator = estimator
@@ -458,13 +463,22 @@ class DaskWrapper(BaseWrapper, TransformerMixin):
         logger.debug(f"{_frmt(self)}.fit")
 
         def _fit(X, y, **fit_params):
-            self.estimator = self.estimator.fit(X, y, **fit_params)
+            try:
+                self.estimator = self.estimator.fit(X, y, **fit_params)
+            except Exception as e:
+                raise RuntimeError(
+                    f"Something went wrong when fitting {self.estimator} "
+                    f"from {self}"
+                ) from e
             copy_learned_attributes(self.estimator, self)
             return self.estimator
 
         # change the name to have a better name in dask graphs
         _fit.__name__ = f"{_frmt(self)}.fit"
-        self._dask_state = delayed(_fit)(X, y,)
+        self._dask_state = delayed(_fit)(
+            X,
+            y,
+        )
 
         if self.fit_tag is not None:
             self.resource_tags[self._dask_state] = self._make_dask_resource_tag(
@@ -582,7 +596,7 @@ def wrap(bases, estimator=None, **kwargs):
         estimator, leftover = _wrap(estimator, **kwargs)
 
     if leftover:
-        raise ValueError(f"Got extra kwargs that were not consumed: {kwargs}")
+        raise ValueError(f"Got extra kwargs that were not consumed: {leftover}")
 
     return estimator