Fixing linting

Author: cajchristian

src/skmatter/_selection.py

@@ -570,7 +570,6 @@ def score(self, X, y=None):
         score : numpy.ndarray of (n_to_select_from_)
             :math:`\pi` importance for the given samples or features
         """
-
         X, y = validate_data(self, X, y, reset=False)
 
         return self.pi_
@@ -747,7 +746,6 @@ def score(self, X, y=None):
         score : numpy.ndarray of (n_to_select_from_)
             :math:`\pi` importance for the given samples or features
         """
-
         X, y = validate_data(self, X, y, reset=False)
 
         return self.pi_
@@ -943,7 +941,6 @@ def score(self, X, y=None):
         -------
         hausdorff : Hausdorff distances
         """
-
         X, y = validate_data(self, X, y, reset=False)
 
         return self.hausdorff_
@@ -1107,7 +1104,6 @@ def score(self, X, y=None):
         -------
         hausdorff : Hausdorff distances
         """
-
         X, y = validate_data(self, X, y, reset=False)
 
         return self.hausdorff_

src/skmatter/decomposition/_pcovr.py

@@ -221,7 +221,6 @@ def fit(self, X, Y, W=None):
             Regression weights, optional when regressor=`precomputed`. If not
             passed, it is assumed that `W = np.linalg.lstsq(X, Y, self.tol)[0]`
         """
-
         X, Y = validate_data(self, X, Y, y_numeric=True, multi_output=True)
 
         # saved for inverse transformations from the latent space,
@@ -636,7 +635,6 @@ def score(self, X, y, T=None):
             Negative sum of the loss in reconstructing X from the latent-space
             projection T and the loss in predicting Y from the latent-space projection T
         """
-
         X, y = validate_data(self, X, y, reset=False)
 
         if T is None:

src/skmatter/linear_model/_ridge.py

@@ -1,6 +1,6 @@
 import numpy as np
 from joblib import Parallel, delayed
-from sklearn.base import RegressorMixin, MultiOutputMixin, BaseEstimator
+from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
 from sklearn.metrics import check_scoring
 from sklearn.model_selection import KFold, check_cv
 from sklearn.utils.validation import check_is_fitted, validate_data