Fix abstractmethod errors

recordlinkage/adapters.py

@@ -38,10 +38,10 @@ def _predict(self, features):
  try:
  prediction = self.kernel.predict(features)
  except NotFittedError as err:
- raise NotFittedError from err(
+ raise NotFittedError(
  "{} is not fitted yet. Call 'fit' with appropriate "
  "arguments before using this method.".format(type(self).__name__)
- )
+ ) from err
 
  return prediction
 
@@ -109,10 +109,10 @@ def _predict(self, features):
  try:
  prediction = self.kernel.predict_classes(features)[:, 0]
  except NotFittedError as err:
- raise NotFittedError from err(
+ raise NotFittedError(
  "{} is not fitted yet. Call 'fit' with appropriate "
  "arguments before using this method.".format(type(self).__name__)
- )
+ ) from err
 
  return prediction
 

recordlinkage/base.py

@@ -868,7 +868,6 @@ def learn(self, *args, **kwargs):
  )
  return self.fit_predict(*args, **kwargs)
 
- @abstractmethod
  def _initialise_classifier(self, comparison_vectors):
  """Initialise the classifier.
 
@@ -877,7 +876,7 @@ def _initialise_classifier(self, comparison_vectors):
  comparison_vectors : pandas.DataFrame
  The comparison vectors (or features) to fit the classifier with.
  """
- pass
+ return
 
  @abstractmethod
  def _fit(self, *args, **kwargs):
@@ -999,7 +998,6 @@ def predict(self, comparison_vectors):
  # format and return the result
  return self._return_result(prediction, comparison_vectors)
 
- @abstractmethod
  def _post_predict(self, result):
  """Method called after prediction.
 
@@ -1008,7 +1006,7 @@ def _post_predict(self, result):
  result : pandas.Series
  The resulting classification.
  """
- pass
+ return
 
  @abstractmethod
  def _prob_match(self, *args, **kwargs):

recordlinkage/classifiers.py

@@ -415,14 +415,14 @@ def _predict(self, features):
  self.kernel.classes_ = numpy.array([0, 1])
 
  if not fit_bool:
- raise NotFittedError
+ raise NotFittedError()
 
  prediction = self.kernel.predict(features)
  except NotFittedError as err:
- raise NotFittedError from err(
+ raise NotFittedError(
  "{} is not fitted yet. Call 'fit' with appropriate "
  "arguments before using this method.".format(type(self).__name__)
- )
+ ) from err
 
  return prediction