Third test

sklearn_questions.py

@@ -69,8 +69,10 @@ def __init__(self, n_neighbors=1):  # noqa: D107
         self.n_neighbors = n_neighbors
 
     def fit(self, X, y):
-        """Fitting function.
-         Parameters
+        """
+        Fitting function.
+        
+        Parameters
         ----------
         X : ndarray, shape (n_samples, n_features)
             Data to train the model.
@@ -98,7 +100,9 @@ def fit(self, X, y):
         return self
 
     def predict(self, X):
-        """Predict function.
+        """
+        Predict function.
+        
         Parameters
         ----------
         X : ndarray, shape (n_test_samples, n_features)
@@ -127,7 +131,9 @@ def predict(self, X):
         return y_pred
 
     def score(self, X, y):
-        """Calculate the score of the prediction.
+        """
+        Calculate the score of the prediction.
+        
         Parameters
         ----------
         X : ndarray, shape (n_samples, n_features)
@@ -152,7 +158,9 @@ def score(self, X, y):
 
 
 class MonthlySplit(BaseCrossValidator):
-    """CrossValidator based on monthly split.
+    """
+    CrossValidator based on monthly split.
+    
     Split data based on the given `time_col` (or default to index). Each split
     corresponds to one month of data for the training and the next month of
     data for the test.
@@ -170,7 +178,9 @@ def __init__(self, time_col='index'):  # noqa: D107
         self.time_col = time_col
 
     def get_n_splits(self, X, y=None, groups=None):
-        """Return the number of splitting iterations in the cross-validator.
+        """
+        Return the number of splitting iterations in the cross-validator.
+        
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
@@ -201,7 +211,9 @@ def get_n_splits(self, X, y=None, groups=None):
         return max(0, len(months) - 1)
 
     def split(self, X, y, groups=None):
-        """Generate indices to split data into training and test set.
+        """
+        Generate indices to split data into training and test set.
+        
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)