prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG] Inaccurracy in how to use autokmeans
  ## Expected Behavior
  
  https://github.com/microsoft/graspologic/blob/10de2bf17b972decbab318568154af226dcd71fa/graspologic/cluster/kclust.py#L16
  
  This line is false; higher silhouette score is better, to my knowledge? https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
  
  ## Actual Behavior
  
  Documentation correctly reports how to use the package.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `graspologic/cluster/kclust.py`
  Content:
  ```
  1 # Copyright (c) Microsoft Corporation and contributors.
  2 # Licensed under the MIT License.
  3 
  4 from typing import Optional, Union
  5 
  6 import numpy as np
  7 from sklearn.cluster import KMeans
  8 from sklearn.metrics import adjusted_rand_score, silhouette_score
  9 
  10 from graspologic.types import List
  11 
  12 from .base import BaseCluster
  13 
  14 
  15 class KMeansCluster(BaseCluster):
  16     ari_: Optional[List[float]]
  17 
  18     """
  19     KMeans Cluster.
  20 
  21     It computes all possible models from one component to
  22     ``max_clusters``. The best model is given by the lowest silhouette score.
  23 
  24     Parameters
  25     ----------
  26     max_clusters : int, defaults to 1.
  27         The maximum number of mixture components to consider.
  28 
  29     random_state : int, RandomState instance or None, optional (default=None)
  30         If int, ``random_state`` is the seed used by the random number generator;
  31         If RandomState instance, ``random_state`` is the random number generator;
  32         If None, the random number generator is the RandomState instance used
  33         by ``np.random``.
  34 
  35     Attributes
  36     ----------
  37     n_clusters_ : int
  38         Optimal number of components. If y is given, it is based on largest
  39         ARI. Otherwise, it is based on smallest loss.
  40 
  41     model_ : KMeans object
  42         Fitted KMeans object fitted with optimal n_components.
  43 
  44     silhouette_ : list
  45         List of silhouette scores computed for all possible number
  46         of clusters given by ``range(2, max_clusters)``.
  47 
  48     ari_ : list
  49         Only computed when y is given. List of ARI values computed for
  50         all possible number of clusters given by ``range(2, max_clusters)``.
  51     """
  52 
  53     def __init__(
  54         self,
  55         max_clusters: int = 2,
  56         random_state: Optional[Union[int, np.random.RandomState]] = None,
  57     ):
  58         if isinstance(max_clusters, int):
  59             if max_clusters <= 1:
  60                 msg = "n_components must be >= 2 or None."
  61                 raise ValueError(msg)
  62             else:
  63                 self.max_clusters = max_clusters
  64         else:
  65             msg = "max_clusters must be an integer, not {}.".format(type(max_clusters))
  66             raise TypeError(msg)
  67         self.random_state = random_state
  68 
  69     def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> "KMeansCluster":
  70         """
  71         Fits kmeans model to the data.
  72 
  73         Parameters
  74         ----------
  75         X : array-like, shape (n_samples, n_features)
  76             List of n_features-dimensional data points. Each row
  77             corresponds to a single data point.
  78 
  79         y : array-like, shape (n_samples,), optional (default=None)
  80             List of labels for `X` if available. Used to compute ARI scores.
  81 
  82         Returns
  83         -------
  84         self
  85         """
  86         # Deal with number of clusters
  87         if self.max_clusters > X.shape[0]:
  88             msg = "n_components must be >= n_samples, but got \
  89                 n_components = {}, n_samples = {}".format(
  90                 self.max_clusters, X.shape[0]
  91             )
  92             raise ValueError(msg)
  93         else:
  94             max_clusters = self.max_clusters
  95 
  96         # Get parameters
  97         random_state = self.random_state
  98 
  99         # Compute all models
  100         models = []
  101         silhouettes = []
  102         aris = []
  103         for n in range(2, max_clusters + 1):
  104             model = KMeans(n_clusters=n, random_state=random_state)
  105 
  106             # Fit and compute values
  107             predictions = model.fit_predict(X)
  108             models.append(model)
  109             silhouettes.append(silhouette_score(X, predictions))
  110             if y is not None:
  111                 aris.append(adjusted_rand_score(y, predictions))
  112 
  113         if y is not None:
  114             self.ari_ = aris
  115             self.silhouette_ = silhouettes
  116             self.n_clusters_ = np.argmax(aris) + 1
  117             self.model_ = models[np.argmax(aris)]
  118         else:
  119             self.ari_ = None
  120             self.silhouette_ = silhouettes
  121             self.n_clusters_ = np.argmax(silhouettes) + 1
  122             self.model_ = models[np.argmax(silhouettes)]
  123 
  124         return self
  125
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/graspologic/cluster/kclust.py b/graspologic/cluster/kclust.py
  --- a/graspologic/cluster/kclust.py
  +++ b/graspologic/cluster/kclust.py
  @@ -18,13 +18,15 @@
       """
       KMeans Cluster.
   
  -    It computes all possible models from one component to
  -    ``max_clusters``. The best model is given by the lowest silhouette score.
  +    It computes all possible models from one component to ``max_clusters``.
  +    When the true labels are known, the best model is given by the model with highest
  +    adjusted Rand index (ARI).
  +    Otherwise, the best model is given by the model with highest silhouette score.
   
       Parameters
       ----------
  -    max_clusters : int, defaults to 1.
  -        The maximum number of mixture components to consider.
  +    max_clusters : int, default=2.
  +        The maximum number of clusters to consider. Must be ``>=2``.
   
       random_state : int, RandomState instance or None, optional (default=None)
           If int, ``random_state`` is the seed used by the random number generator;
  @@ -35,11 +37,11 @@
       Attributes
       ----------
       n_clusters_ : int
  -        Optimal number of components. If y is given, it is based on largest
  -        ARI. Otherwise, it is based on smallest loss.
  +        Optimal number of clusters. If y is given, it is based on largest
  +        ARI. Otherwise, it is based on highest silhouette score.
   
       model_ : KMeans object
  -        Fitted KMeans object fitted with optimal n_components.
  +        Fitted KMeans object fitted with ``n_clusters_``.
   
       silhouette_ : list
           List of silhouette scores computed for all possible number
  
verification_info: {"golden_diff": "diff --git a/graspologic/cluster/kclust.py b/graspologic/cluster/kclust.py\n--- a/graspologic/cluster/kclust.py\n+++ b/graspologic/cluster/kclust.py\n@@ -18,13 +18,15 @@\n     \"\"\"\n     KMeans Cluster.\n \n-    It computes all possible models from one component to\n-    ``max_clusters``. The best model is given by the lowest silhouette score.\n+    It computes all possible models from one component to ``max_clusters``.\n+    When the true labels are known, the best model is given by the model with highest\n+    adjusted Rand index (ARI).\n+    Otherwise, the best model is given by the model with highest silhouette score.\n \n     Parameters\n     ----------\n-    max_clusters : int, defaults to 1.\n-        The maximum number of mixture components to consider.\n+    max_clusters : int, default=2.\n+        The maximum number of clusters to consider. Must be ``>=2``.\n \n     random_state : int, RandomState instance or None, optional (default=None)\n         If int, ``random_state`` is the seed used by the random number generator;\n@@ -35,11 +37,11 @@\n     Attributes\n     ----------\n     n_clusters_ : int\n-        Optimal number of components. If y is given, it is based on largest\n-        ARI. Otherwise, it is based on smallest loss.\n+        Optimal number of clusters. If y is given, it is based on largest\n+        ARI. Otherwise, it is based on highest silhouette score.\n \n     model_ : KMeans object\n-        Fitted KMeans object fitted with optimal n_components.\n+        Fitted KMeans object fitted with ``n_clusters_``.\n \n     silhouette_ : list\n         List of silhouette scores computed for all possible number\n", "issue": "[BUG] Inaccurracy in how to use autokmeans\n## Expected Behavior\r\n\r\nhttps://github.com/microsoft/graspologic/blob/10de2bf17b972decbab318568154af226dcd71fa/graspologic/cluster/kclust.py#L16\r\n\r\nThis line is false; higher silhouette score is better, to my knowledge? https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html\r\n\r\n## Actual Behavior\r\n\r\nDocumentation correctly reports how to use the package.\r\n\n", "before_files": [{"content": "# Copyright (c) Microsoft Corporation and contributors.\n# Licensed under the MIT License.\n\nfrom typing import Optional, Union\n\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import adjusted_rand_score, silhouette_score\n\nfrom graspologic.types import List\n\nfrom .base import BaseCluster\n\n\nclass KMeansCluster(BaseCluster):\n    ari_: Optional[List[float]]\n\n    \"\"\"\n    KMeans Cluster.\n\n    It computes all possible models from one component to\n    ``max_clusters``. The best model is given by the lowest silhouette score.\n\n    Parameters\n    ----------\n    max_clusters : int, defaults to 1.\n        The maximum number of mixture components to consider.\n\n    random_state : int, RandomState instance or None, optional (default=None)\n        If int, ``random_state`` is the seed used by the random number generator;\n        If RandomState instance, ``random_state`` is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by ``np.random``.\n\n    Attributes\n    ----------\n    n_clusters_ : int\n        Optimal number of components. If y is given, it is based on largest\n        ARI. Otherwise, it is based on smallest loss.\n\n    model_ : KMeans object\n        Fitted KMeans object fitted with optimal n_components.\n\n    silhouette_ : list\n        List of silhouette scores computed for all possible number\n        of clusters given by ``range(2, max_clusters)``.\n\n    ari_ : list\n        Only computed when y is given. List of ARI values computed for\n        all possible number of clusters given by ``range(2, max_clusters)``.\n    \"\"\"\n\n    def __init__(\n        self,\n        max_clusters: int = 2,\n        random_state: Optional[Union[int, np.random.RandomState]] = None,\n    ):\n        if isinstance(max_clusters, int):\n            if max_clusters <= 1:\n                msg = \"n_components must be >= 2 or None.\"\n                raise ValueError(msg)\n            else:\n                self.max_clusters = max_clusters\n        else:\n            msg = \"max_clusters must be an integer, not {}.\".format(type(max_clusters))\n            raise TypeError(msg)\n        self.random_state = random_state\n\n    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> \"KMeansCluster\":\n        \"\"\"\n        Fits kmeans model to the data.\n\n        Parameters\n        ----------\n        X : array-like, shape (n_samples, n_features)\n            List of n_features-dimensional data points. Each row\n            corresponds to a single data point.\n\n        y : array-like, shape (n_samples,), optional (default=None)\n            List of labels for `X` if available. Used to compute ARI scores.\n\n        Returns\n        -------\n        self\n        \"\"\"\n        # Deal with number of clusters\n        if self.max_clusters > X.shape[0]:\n            msg = \"n_components must be >= n_samples, but got \\\n                n_components = {}, n_samples = {}\".format(\n                self.max_clusters, X.shape[0]\n            )\n            raise ValueError(msg)\n        else:\n            max_clusters = self.max_clusters\n\n        # Get parameters\n        random_state = self.random_state\n\n        # Compute all models\n        models = []\n        silhouettes = []\n        aris = []\n        for n in range(2, max_clusters + 1):\n            model = KMeans(n_clusters=n, random_state=random_state)\n\n            # Fit and compute values\n            predictions = model.fit_predict(X)\n            models.append(model)\n            silhouettes.append(silhouette_score(X, predictions))\n            if y is not None:\n                aris.append(adjusted_rand_score(y, predictions))\n\n        if y is not None:\n            self.ari_ = aris\n            self.silhouette_ = silhouettes\n            self.n_clusters_ = np.argmax(aris) + 1\n            self.model_ = models[np.argmax(aris)]\n        else:\n            self.ari_ = None\n            self.silhouette_ = silhouettes\n            self.n_clusters_ = np.argmax(silhouettes) + 1\n            self.model_ = models[np.argmax(silhouettes)]\n\n        return self\n", "path": "graspologic/cluster/kclust.py"}], "after_files": [{"content": "# Copyright (c) Microsoft Corporation and contributors.\n# Licensed under the MIT License.\n\nfrom typing import Optional, Union\n\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import adjusted_rand_score, silhouette_score\n\nfrom graspologic.types import List\n\nfrom .base import BaseCluster\n\n\nclass KMeansCluster(BaseCluster):\n    ari_: Optional[List[float]]\n\n    \"\"\"\n    KMeans Cluster.\n\n    It computes all possible models from one component to ``max_clusters``.\n    When the true labels are known, the best model is given by the model with highest\n    adjusted Rand index (ARI).\n    Otherwise, the best model is given by the model with highest silhouette score.\n\n    Parameters\n    ----------\n    max_clusters : int, default=2.\n        The maximum number of clusters to consider. Must be ``>=2``.\n\n    random_state : int, RandomState instance or None, optional (default=None)\n        If int, ``random_state`` is the seed used by the random number generator;\n        If RandomState instance, ``random_state`` is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by ``np.random``.\n\n    Attributes\n    ----------\n    n_clusters_ : int\n        Optimal number of clusters. If y is given, it is based on largest\n        ARI. Otherwise, it is based on highest silhouette score.\n\n    model_ : KMeans object\n        Fitted KMeans object fitted with ``n_clusters_``.\n\n    silhouette_ : list\n        List of silhouette scores computed for all possible number\n        of clusters given by ``range(2, max_clusters)``.\n\n    ari_ : list\n        Only computed when y is given. List of ARI values computed for\n        all possible number of clusters given by ``range(2, max_clusters)``.\n    \"\"\"\n\n    def __init__(\n        self,\n        max_clusters: int = 2,\n        random_state: Optional[Union[int, np.random.RandomState]] = None,\n    ):\n        if isinstance(max_clusters, int):\n            if max_clusters <= 1:\n                msg = \"n_components must be >= 2 or None.\"\n                raise ValueError(msg)\n            else:\n                self.max_clusters = max_clusters\n        else:\n            msg = \"max_clusters must be an integer, not {}.\".format(type(max_clusters))\n            raise TypeError(msg)\n        self.random_state = random_state\n\n    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> \"KMeansCluster\":\n        \"\"\"\n        Fits kmeans model to the data.\n\n        Parameters\n        ----------\n        X : array-like, shape (n_samples, n_features)\n            List of n_features-dimensional data points. Each row\n            corresponds to a single data point.\n\n        y : array-like, shape (n_samples,), optional (default=None)\n            List of labels for `X` if available. Used to compute ARI scores.\n\n        Returns\n        -------\n        self\n        \"\"\"\n        # Deal with number of clusters\n        if self.max_clusters > X.shape[0]:\n            msg = \"n_components must be >= n_samples, but got \\\n                n_components = {}, n_samples = {}\".format(\n                self.max_clusters, X.shape[0]\n            )\n            raise ValueError(msg)\n        else:\n            max_clusters = self.max_clusters\n\n        # Get parameters\n        random_state = self.random_state\n\n        # Compute all models\n        models = []\n        silhouettes = []\n        aris = []\n        for n in range(2, max_clusters + 1):\n            model = KMeans(n_clusters=n, random_state=random_state)\n\n            # Fit and compute values\n            predictions = model.fit_predict(X)\n            models.append(model)\n            silhouettes.append(silhouette_score(X, predictions))\n            if y is not None:\n                aris.append(adjusted_rand_score(y, predictions))\n\n        if y is not None:\n            self.ari_ = aris\n            self.silhouette_ = silhouettes\n            self.n_clusters_ = np.argmax(aris) + 1\n            self.model_ = models[np.argmax(aris)]\n        else:\n            self.ari_ = None\n            self.silhouette_ = silhouettes\n            self.n_clusters_ = np.argmax(silhouettes) + 1\n            self.model_ = models[np.argmax(silhouettes)]\n\n        return self\n", "path": "graspologic/cluster/kclust.py"}]}