UP MY SOLUTION

sklearn_questions.py

@@ -52,12 +52,14 @@
 
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
+from collections import Counter
 
 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.multiclass import (
+    check_classification_targets, unique_labels)
 from sklearn.metrics.pairwise import pairwise_distances
 
 
@@ -82,6 +84,13 @@ def fit(self, X, y):
         self : instance of KNearestNeighbors
             The current instance of the classifier
         """
+
+        X, y = check_X_y(X, y)
+        check_classification_targets(y)
+        self.X_ = X
+        self.y_ = y
+        self.classes_ = unique_labels(y)
+        self.n_features_in_ = X.shape[1]
         return self
 
     def predict(self, X):
@@ -97,8 +106,21 @@ 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])
-        return y_pred
+        check_is_fitted(self)
+        X = check_array(X)
+        y_pred = []
+        for i, x in enumerate(X):
+            distances = pairwise_distances(self.X_, x[np.newaxis, :]).ravel()
+            # get top k nearest neighbors
+            args_k_nearest = np.argpartition(distances, self.n_neighbors)[
+                : self.n_neighbors
+            ]
+            # most frequent strategy
+            y_pred.append(
+                Counter(self.y_[args_k_nearest]).most_common(1)[0][0]
+            )
+
+        return np.array(y_pred)
 
     def score(self, X, y):
         """Calculate the score of the prediction.
@@ -115,7 +137,10 @@ def score(self, X, y):
         score : float
             Accuracy of the model computed for the (X, y) pairs.
         """
-        return 0.
+        X, y = check_X_y(X, y)
+        check_classification_targets(y)
+        y_pred = self.predict(X)
+        return (y_pred == y).mean()
 
 
 class MonthlySplit(BaseCrossValidator):
@@ -155,7 +180,14 @@ def get_n_splits(self, X, y=None, groups=None):
         n_splits : int
             The number of splits.
         """
-        return 0
+        if not isinstance(X.index, pd.RangeIndex):
+            X = X.reset_index()
+
+        # check datatype
+        if X[self.time_col].dtype != "datetime64[ns]":
+            raise ValueError("datetime")
+
+        return len(X.resample("M", on=self.time_col)) - 1
 
     def split(self, X, y, groups=None):
         """Generate indices to split data into training and test set.
@@ -177,12 +209,11 @@ def split(self, X, y, groups=None):
         idx_test : ndarray
             The testing set indices for that split.
         """
-
-        n_samples = X.shape[0]
+        if isinstance(X, pd.Series):
+            X = X.to_frame()
+        X = X.reset_index()
         n_splits = self.get_n_splits(X, y, groups)
+        ids_by_mth = [groups.index.values
+                      for _, groups in X.resample("M", on=self.time_col)]
         for i in range(n_splits):
-            idx_train = range(n_samples)
-            idx_test = range(n_samples)
-            yield (
-                idx_train, idx_test
-            )
+            yield ids_by_mth[i], ids_by_mth[i + 1]