UP my solution

sklearn_questions.py

@@ -55,13 +55,12 @@
 
 from sklearn.model_selection import BaseCrossValidator
 
-from sklearn.utils.validation import check_X_y, check_is_fitted
-from sklearn.utils.validation import check_array
+from sklearn.utils.validation import check_X_y, check_is_fitted, validate_data
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.metrics.pairwise import pairwise_distances
 
 
-class KNearestNeighbors(BaseEstimator, ClassifierMixin):
+class KNearestNeighbors(ClassifierMixin, BaseEstimator):
     """KNearestNeighbors classifier."""
 
     def __init__(self, n_neighbors=1):  # noqa: D107
@@ -82,6 +81,12 @@ def fit(self, X, y):
         self : instance of KNearestNeighbors
             The current instance of the classifier
         """
+        X, y = check_X_y(X, y)
+        self._X = X
+        self._y = y
+        self.n_features_in_ = X.shape[1]
+        check_classification_targets(y)
+        self.classes_ = np.unique(y)
         return self
 
     def predict(self, X):
@@ -97,7 +102,15 @@ def predict(self, X):
         y : ndarray, shape (n_test_samples,)
             Predicted class labels for each test data sample.
         """
-        y_pred = np.zeros(X.shape[0])
+        check_is_fitted(self)
+        X = validate_data(self, X, reset=False)
+        distances = pairwise_distances(X, self._X)
+        nearest_neighbors = np.argsort(distances, axis=1)[:, :self.n_neighbors]
+        unique_classes, y_indices = np.unique(self._y,
+                                              return_inverse=True)
+        neighbor_labels = y_indices[nearest_neighbors]
+        y_pred = np.array([unique_classes[np.bincount(labels).argmax()]
+                           for labels in neighbor_labels])
         return y_pred
 
     def score(self, X, y):
@@ -115,7 +128,8 @@ def score(self, X, y):
         score : float
             Accuracy of the model computed for the (X, y) pairs.
         """
-        return 0.
+        y_pred = self.predict(X)
+        return np.mean(y_pred == y)
 
 
 class MonthlySplit(BaseCrossValidator):
@@ -155,7 +169,17 @@ def get_n_splits(self, X, y=None, groups=None):
         n_splits : int
             The number of splits.
         """
-        return 0
+        if isinstance(X, pd.Series):
+            times = X.index
+        elif isinstance(X, pd.DataFrame):
+            times = X.index if self.time_col == 'index' else X[self.time_col]
+        else:
+            raise ValueError("X should be a pandas DataFrame or Series.")
+
+        if not pd.api.types.is_datetime64_any_dtype(times):
+            raise ValueError("time_col must be a datetime column.")
+        periods = pd.Series(times).dt.to_period("M")
+        return len(periods.unique()) - 1
 
     def split(self, X, y, groups=None):
         """Generate indices to split data into training and test set.
@@ -177,12 +201,35 @@ def split(self, X, y, groups=None):
         idx_test : ndarray
             The testing set indices for that split.
         """
-
-        n_samples = X.shape[0]
-        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)
-            yield (
-                idx_train, idx_test
-            )
+        if isinstance(X, pd.Series):
+            times = X.index
+        elif isinstance(X, pd.DataFrame):
+            times = X.index if self.time_col == "index" else X[self.time_col]
+        else:
+            raise ValueError("X should be a pandas DataFrame or Series.")
+
+        if not pd.api.types.is_datetime64_any_dtype(times):
+            raise ValueError("time_col must be a datetime column.")
+        sorted_indices = np.argsort(times)
+        times = times[sorted_indices]
+        X = X.iloc[sorted_indices] if isinstance(
+            X, pd.DataFrame
+            ) else X[
+            sorted_indices
+            ]
+        y = y.iloc[sorted_indices] if isinstance(
+            y, pd.DataFrame
+            ) else y[
+                sorted_indices
+                ]
+
+        periods = pd.Series(times).dt.to_period("M")
+        unique_months = periods.unique()
+        for i in range(len(unique_months) - 1):
+            train_month = unique_months[i]
+            test_month = unique_months[i + 1]
+
+            idx_train = np.array(periods[periods == train_month].index)
+            idx_test = np.array(periods[periods == test_month].index)
+
+            yield idx_train, idx_test