fix new black requirements (#118)

Author: TimotheeMathieu

sklearn_extra/cluster/_k_medoids.py

@@ -152,7 +152,7 @@ def _check_nonnegative_int(self, value, desc, strict=True):
             )
 
     def _check_init_args(self):
-        """Validates the input arguments. """
+        """Validates the input arguments."""
 
         # Check n_clusters and max_iter
         self._check_nonnegative_int(self.n_clusters, "n_clusters")
@@ -298,7 +298,7 @@ def _update_medoid_idxs_in_place(self, D, labels, medoid_idxs):
                 medoid_idxs[k] = cluster_k_idxs[min_cost_idx]
 
     def _compute_cost(self, D, medoid_idxs):
-        """ Compute the cose for a given configuration of the medoids"""
+        """Compute the cose for a given configuration of the medoids"""
         return self._compute_inertia(D[:, medoid_idxs])
 
     def transform(self, X):

sklearn_extra/kernel_approximation/_fastfood.py

@@ -116,7 +116,7 @@ def _uniform_vector(self, rng):
             return None
 
     def _apply_approximate_gaussian_matrix(self, B, G, P, X):
-        """ Create mapping of all x_i by applying B, G and P step-wise """
+        """Create mapping of all x_i by applying B, G and P step-wise"""
         num_examples = X.shape[0]
 
         result = np.multiply(B, X.reshape((1, num_examples, 1, self._d)))
@@ -134,7 +134,7 @@ def _apply_approximate_gaussian_matrix(self, B, G, P, X):
         return result
 
     def _scale_transformed_data(self, S, VX):
-        """ Scale mapped data VX to match kernel(e.g. RBF-Kernel) """
+        """Scale mapped data VX to match kernel(e.g. RBF-Kernel)"""
         VX = VX.reshape(-1, self._times_to_stack_v * self._d)
 
         return (

sklearn_extra/robust/robust_weighted_estimator.py

@@ -418,7 +418,7 @@ def fit(self, X, y=None):
         return self
 
     def _get_loss_function(self, loss):
-        """Get concrete ''LossFunction'' object for str ''loss''. """
+        """Get concrete ''LossFunction'' object for str ''loss''."""
         if type(loss) == str:
             eff_loss = LOSS_FUNCTIONS.get(loss)
             if eff_loss is None: