final solution

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "."
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

sklearn_questions.py

@@ -59,6 +59,7 @@
 from sklearn.utils.validation import check_array
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.metrics.pairwise import pairwise_distances
+from sklearn.preprocessing import LabelEncoder
 
 
 class KNearestNeighbors(BaseEstimator, ClassifierMixin):
@@ -76,7 +77,20 @@ def fit(self, X, y):
             Data to train the model.
         y : ndarray, shape (n_samples,)
             Labels associated with the training data.
-
+        """
+        # Validate the input
+        X, y = check_X_y(X, y)
+        # Check if the target is suitable for classification
+        check_classification_targets(y)
+        # init LabelEncoder
+        self.label_encoder_ = LabelEncoder()
+        self.X_train_ = X
+        # Convert y using LabelEncoder for different types of labels
+        self.y_train_ = self.label_encoder_.fit_transform(y)
+        # set up classes_ attribut after convert
+        self.classes_ = self.label_encoder_.classes_
+        self.n_features_in_ = X.shape[1]
+        """
         Returns
         ----------
         self : instance of KNearestNeighbors
@@ -91,13 +105,27 @@ def predict(self, X):
         ----------
         X : ndarray, shape (n_test_samples, n_features)
             Data to predict on.
-
+        """
+        # Check if fit had been called
+        check_is_fitted(self, ['X_train_', 'y_train_'])
+        # Input validation
+        X = check_array(X)
+        # Compute distances from each point in X to each point in self.X_train_
+        distances = pairwise_distances(X, self.X_train_, metric='euclidean')
+        y_train_int = self.y_train_.astype(int)
+        # Find the indices of the k closest training samples
+        nearest_indices = np.argsort(distances, axis=1)[:, :self.n_neighbors]
+        # Predict the most common label of the k nearest training samples
+        y_pred_indices = np.array([np.argmax(np.bincount(y_train_int[indices]))
+                                   for indices in nearest_indices])
+        # Predicts and converts integer labels back to the original label
+        y_pred = self.label_encoder_.inverse_transform(y_pred_indices)
+        """
         Returns
         ----------
         y : ndarray, shape (n_test_samples,)
             Predicted class labels for each test data sample.
         """
-        y_pred = np.zeros(X.shape[0])
         return y_pred
 
     def score(self, X, y):
@@ -109,13 +137,21 @@ def score(self, X, y):
             Data to score on.
         y : ndarray, shape (n_samples,)
             target values.
+        """
+        # Check if fit had been called
+        check_is_fitted(self, ['X_train_', 'y_train_'])
 
+        # Input validation
+        X, y = check_X_y(X, y)
+
+        y_pred = self.predict(X)
+        """
         Returns
         ----------
         score : float
             Accuracy of the model computed for the (X, y) pairs.
         """
-        return 0.
+        return np.mean(y_pred == y)
 
 
 class MonthlySplit(BaseCrossValidator):
@@ -155,7 +191,19 @@ def get_n_splits(self, X, y=None, groups=None):
         n_splits : int
             The number of splits.
         """
-        return 0
+        # Check whether time_col points to the index of the DataFrame
+        if self.time_col == 'index':
+            dates = X.index
+        else:
+            # check time_col if exist in column
+            if self.time_col not in X.columns:
+                raise KeyError(f"{self.time_col} is not in the columns of X")
+            dates = X[self.time_col]
+        if not pd.api.types.is_datetime64_any_dtype(dates):
+            raise ValueError("The time column should be datetime type")
+
+        months = np.unique(dates.to_period('M'))
+        return max(0, len(months) - 1)
 
     def split(self, X, y, groups=None):
         """Generate indices to split data into training and test set.
@@ -169,20 +217,35 @@ def split(self, X, y, groups=None):
             Always ignored, exists for compatibility.
         groups : array-like of shape (n_samples,)
             Always ignored, exists for compatibility.
+        """
 
+        """
         Yields
         ------
         idx_train : ndarray
             The training set indices for that split.
         idx_test : ndarray
             The testing set indices for that split.
         """
-
-        n_samples = X.shape[0]
+        # obtain time data
+        if self.time_col == 'index':
+            dates = X.index
+        else:
+            dates = X[self.time_col]
+
+        # Convert to month
+        months = dates.to_period('M')
+        unique_months = np.unique(months)
         n_splits = self.get_n_splits(X, y, groups)
+
         for i in range(n_splits):
-            idx_train = range(n_samples)
-            idx_test = range(n_samples)
+            # Next month as a train set
+            train_mask = months == unique_months[i]
+            idx_train = np.where(train_mask)[0]
+
+            # Next month as a test set
+            test_mask = months == unique_months[i + 1]
+            idx_test = np.where(test_mask)[0]
             yield (
                 idx_train, idx_test
             )