Update typings

README.md

@@ -12,7 +12,8 @@
 
 ## What is this?
 
-This is an algorithm for evenly partitioning data in a `scikit-learn`-like interface. (See [References](#References) for details of the algorithm.)
+This is an algorithm for evenly partitioning data in a `scikit-learn`-like interface.
+(See [References](#References) for details of the algorithm.)
 
 ![simulateion_gif](https://github.com/yu9824/kennard_stone/blob/main/example/simulate.gif?raw=true "Simulateion")
 
@@ -134,7 +135,8 @@ print(cross_validate(estimator, X, y, cv=5))
 There is no notion of `random_state` or `shuffle` because the partitioning is determined uniquely for the dataset.
 If these arguments are included, they do not cause an error. They simply have no effect on the result. Please be careful.
 
-If you want to run the notebook in example directory, you will need to additionally download `pandas`, `matplotlib`, `seaborn`, `tqdm`, and `jupyter` other than the packages in requirements.txt.
+If you want to run the notebook in example directory,
+you will need to additionally install `pandas`, `matplotlib`, `seaborn`, `tqdm`, and `jupyter` other than the packages in requirements.txt.
 
 ## Distance metrics
 
@@ -218,3 +220,8 @@ Copyright (c) 2021 yu9824
 ### v2.1.1
 
 - Fix bug when `metric="nan_euclidean"`.
+
+### v2.1.2
+
+- Fix details.
+  - Update docstrings and typings.

kennard_stone/kennard_stone.py

@@ -11,6 +11,7 @@
 import warnings
 
 import numpy as np
+from numpy.typing import ArrayLike
 from sklearn.model_selection._split import BaseShuffleSplit
 from sklearn.model_selection._split import _BaseKFold
 from sklearn.model_selection._split import _validate_shuffle_split
@@ -294,7 +295,8 @@ def __init__(
         metric: Union[str, Callable] = "euclidean",
         n_jobs: Optional[int] = None,
     ) -> None:
-        """The root program of the Kennard-Stone algorithm.
+        """The root program of the Kennard-Stone algorithm,
+        an algorithm for evenly partitioning data.
 
         Parameters
         ----------
@@ -343,7 +345,19 @@ def __init__(
         self.metric = metric
         self.n_jobs = n_jobs
 
-    def get_indexes(self, X) -> List[List[int]]:
+    def get_indexes(self, X: ArrayLike) -> List[List[int]]:
+        """Sort indexes by the Kennard-Stone algorithm.
+
+        Parameters
+        ----------
+        X : ArrayLike
+            _description_
+
+        Returns
+        -------
+        List[List[int]]
+            _description_
+        """
         # check input array
         X: np.ndarray = check_array(
             X,