Final Solution Sklearn Task 2

sklearn_questions.py

@@ -65,7 +65,6 @@
 
 class KNearestNeighbors(BaseEstimator, ClassifierMixin):
     """KNearestNeighbors classifier."""
-
     def __init__(self, n_neighbors=1):  # noqa: D107
         self.n_neighbors = n_neighbors
 
@@ -85,7 +84,6 @@ def fit(self, X, y):
             The current instance of the classifier
         """
         # Checks
-
         """
         The check_classification_targets function ensures that target y is of
         a non-regression type. Only the following target types
@@ -94,7 +92,6 @@ def fit(self, X, y):
         'multilabel-sequences'.
         """
         check_classification_targets(y)
-
         """
         The check_X_y function performs an input validation for standard
         estimators (=models). It checks X and y for consistent length,
@@ -118,7 +115,8 @@ def fit(self, X, y):
         return self
 
     def predict(self, X):
-        """Predict function.
+        """
+        Predict function.
 
         Parameters
         ----------
@@ -130,7 +128,6 @@ def predict(self, X):
         y : ndarray, shape (n_test_samples,)
             Predicted class labels for each test data sample (y_test).
         """
-
         """
         The check_is_fitted function is a sklearn.utils.validation function
         used to check whether an estimator (such as a classifier or regressor)
@@ -139,7 +136,6 @@ def predict(self, X):
         an error.
         """
         check_is_fitted(self)
-
         """
         The check_array function is a sklearn.utils.validation function used
         to validate whether an input array is suitable for use in scikit-learn
@@ -150,7 +146,6 @@ def predict(self, X):
         these requirements, check_array will throw an error.
         """
         check_array(X)
-
         # Calculate pairwise distances
         """
         pairwise_distances(X, self.X_):
@@ -162,7 +157,6 @@ def predict(self, X):
         the i-th sample in X and the j-th sample in self.X_.
         """
         dist_matrix = pairwise_distances(X, self.X_)
-
         # Find Indices of Nearest Neighbors
         """
         np.argsort(dist_matrix, axis=1):
@@ -176,7 +170,6 @@ def predict(self, X):
         the nearest neighbors.
         """
         dist_sort_pos = np.argsort(dist_matrix, axis=1)[:, :self.n_neighbors]
-
         # Find Indices of Nearest Neighbors
         """
         np.argsort(dist_matrix, axis=1):
@@ -189,7 +182,6 @@ def predict(self, X):
         This slicing operation takes the first self.n_neighbors
         indices for each row. These are the indices of the nearest neighbors.
         """
-
         # Get labels of nearest neighbors
         """
         self.y_ (y_train) is the array of labels corresponding to the training
@@ -198,7 +190,6 @@ def predict(self, X):
         each sample in X.
         """
         y_closest = self.y_[dist_sort_pos]
-
         # Determine predicted values
         """
         This line predicts the label for each sample in X based on the
@@ -256,7 +247,6 @@ class MonthlySplit(BaseCrossValidator):
         for which this column is not a datetime, it will raise a ValueError.
         To use the index as column just set `time_col` to `'index'`.
     """
-
     def __init__(self, time_col='index'):  # noqa: D107
         self.time_col = time_col