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.multiclass import check_classification_targets
+from sklearn.utils.validation import check_is_fitted, validate_data
+from sklearn.utils.multiclass import unique_labels
 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 +81,9 @@ def fit(self, X, y):
         self : instance of KNearestNeighbors
             The current instance of the classifier
         """
+        self.X_train_, self.y_train_ = validate_data(self, X, y)
+        self.classes_ = unique_labels(y)
+        self.is_fitted_ = True
         return self
 
     def predict(self, X):
@@ -97,7 +99,18 @@ 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])
+        from collections import Counter
+
+        check_is_fitted(self, ['X_train_', 'y_train_'])
+        X = validate_data(self, X, reset=False)
+
+        dist = pairwise_distances(self.X_train_, X, metric="euclidean")
+        y_pred = np.empty(X.shape[0], dtype=self.y_train_.dtype)
+        for i in range(len(X)):
+            idx_nearest = np.argsort(dist[:, i])[:self.n_neighbors]
+            labels = self.y_train_[idx_nearest]
+            most_common_label = Counter(labels).most_common(1)[0][0]
+            y_pred[i] = most_common_label
         return y_pred
 
     def score(self, X, y):
@@ -115,7 +128,10 @@ def score(self, X, y):
         score : float
             Accuracy of the model computed for the (X, y) pairs.
         """
-        return 0.
+        from sklearn.metrics import accuracy_score
+
+        y_pred = self.predict(X)
+        return accuracy_score(y, y_pred)
 
 
 class MonthlySplit(BaseCrossValidator):
@@ -155,7 +171,14 @@ def get_n_splits(self, X, y=None, groups=None):
         n_splits : int
             The number of splits.
         """
-        return 0
+        if self.time_col != 'index':
+            X = X.set_index(self.time_col)
+
+        if not isinstance(X.index, pd.DatetimeIndex):
+            X.index = pd.to_datetime(X.index)
+
+        groups = X.groupby(by=[X.index.year, X.index.month])
+        return len(groups.groups.keys())-1
 
     def split(self, X, y, groups=None):
         """Generate indices to split data into training and test set.
@@ -177,12 +200,24 @@ def split(self, X, y, groups=None):
         idx_test : ndarray
             The testing set indices for that split.
         """
+        if self.time_col != 'index':
+            X = X.set_index(self.time_col)
 
-        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 not isinstance(X.index, pd.DatetimeIndex):
+            X.index = pd.to_datetime(X.index)
+
+        groups = X.groupby(by=[X.index.year, X.index.month])
+        n_splits = len(groups.groups.keys()) - 1
+
+        if n_splits < 1:
+            raise ValueError(
+                "Insufficient data to create splits based on datetime column"
             )
+
+        for i in range(n_splits):
+            idx_tr = X.index.get_indexer_for(
+                groups.groups[list(groups.groups.keys())[i]])
+            idx_te = X.index.get_indexer_for(
+                groups.groups[list(groups.groups.keys())[i + 1]])
+
+            yield (idx_tr, idx_te)