prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Improve Canberra metric
  ## 🚀 Feature
  
  Actual implementation of Canberra metric does not use absolute value on terms in denominator. Moreover, `sklearn` can be used in test.
  
  See https://arxiv.org/pdf/1411.7474.pdf 
  
  See https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `ignite/contrib/metrics/regression/canberra_metric.py`
  Content:
  ```
  1 import torch
  2 
  3 from ignite.contrib.metrics.regression._base import _BaseRegression
  4 
  5 
  6 class CanberraMetric(_BaseRegression):
  7     r"""
  8     Calculates the Canberra Metric.
  9 
  10     :math:`\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{A_j + P_j}`
  11 
  12     where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
  13 
  14     More details can be found in `Botchkarev 2018`__.
  15 
  16     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
  17     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
  18 
  19     __ https://arxiv.org/abs/1809.03006
  20     """
  21 
  22     def reset(self):
  23         self._sum_of_errors = 0.0
  24 
  25     def _update(self, output):
  26         y_pred, y = output
  27         errors = torch.abs(y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred))
  28         self._sum_of_errors += torch.sum(errors).item()
  29 
  30     def compute(self):
  31         return self._sum_of_errors
  32
  ```
  --- 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/ignite/contrib/metrics/regression/canberra_metric.py b/ignite/contrib/metrics/regression/canberra_metric.py
  --- a/ignite/contrib/metrics/regression/canberra_metric.py
  +++ b/ignite/contrib/metrics/regression/canberra_metric.py
  @@ -7,16 +7,19 @@
       r"""
       Calculates the Canberra Metric.
   
  -    :math:`\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{A_j + P_j}`
  +    :math:`\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|A_j| + |P_j|}`
   
       where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
   
  -    More details can be found in `Botchkarev 2018`__.
  +    More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_
   
       - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
       - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
   
  -    __ https://arxiv.org/abs/1809.03006
  +    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006
  +    .. _scikit-learn distance metrics:
  +        https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
  +
       """
   
       def reset(self):
  @@ -24,7 +27,7 @@
   
       def _update(self, output):
           y_pred, y = output
  -        errors = torch.abs(y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred))
  +        errors = torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(y.view_as(y_pred)))
           self._sum_of_errors += torch.sum(errors).item()
   
       def compute(self):
  
verification_info: {"golden_diff": "diff --git a/ignite/contrib/metrics/regression/canberra_metric.py b/ignite/contrib/metrics/regression/canberra_metric.py\n--- a/ignite/contrib/metrics/regression/canberra_metric.py\n+++ b/ignite/contrib/metrics/regression/canberra_metric.py\n@@ -7,16 +7,19 @@\n     r\"\"\"\n     Calculates the Canberra Metric.\n \n-    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{A_j + P_j}`\n+    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{|A_j| + |P_j|}`\n \n     where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.\n \n-    More details can be found in `Botchkarev 2018`__.\n+    More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_\n \n     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n \n-    __ https://arxiv.org/abs/1809.03006\n+    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006\n+    .. _scikit-learn distance metrics:\n+        https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n+\n     \"\"\"\n \n     def reset(self):\n@@ -24,7 +27,7 @@\n \n     def _update(self, output):\n         y_pred, y = output\n-        errors = torch.abs(y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred))\n+        errors = torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(y.view_as(y_pred)))\n         self._sum_of_errors += torch.sum(errors).item()\n \n     def compute(self):\n", "issue": "Improve Canberra metric\n## \ud83d\ude80 Feature\r\n\r\nActual implementation of Canberra metric does not use absolute value on terms in denominator. Moreover, `sklearn` can be used in test.\r\n\r\nSee https://arxiv.org/pdf/1411.7474.pdf \r\n\r\nSee https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\r\n\n", "before_files": [{"content": "import torch\n\nfrom ignite.contrib.metrics.regression._base import _BaseRegression\n\n\nclass CanberraMetric(_BaseRegression):\n    r\"\"\"\n    Calculates the Canberra Metric.\n\n    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{A_j + P_j}`\n\n    where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.\n\n    More details can be found in `Botchkarev 2018`__.\n\n    - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n    - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n\n    __ https://arxiv.org/abs/1809.03006\n    \"\"\"\n\n    def reset(self):\n        self._sum_of_errors = 0.0\n\n    def _update(self, output):\n        y_pred, y = output\n        errors = torch.abs(y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred))\n        self._sum_of_errors += torch.sum(errors).item()\n\n    def compute(self):\n        return self._sum_of_errors\n", "path": "ignite/contrib/metrics/regression/canberra_metric.py"}], "after_files": [{"content": "import torch\n\nfrom ignite.contrib.metrics.regression._base import _BaseRegression\n\n\nclass CanberraMetric(_BaseRegression):\n    r\"\"\"\n    Calculates the Canberra Metric.\n\n    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{|A_j| + |P_j|}`\n\n    where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.\n\n    More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_\n\n    - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n    - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n\n    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006\n    .. _scikit-learn distance metrics:\n        https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n\n    \"\"\"\n\n    def reset(self):\n        self._sum_of_errors = 0.0\n\n    def _update(self, output):\n        y_pred, y = output\n        errors = torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(y.view_as(y_pred)))\n        self._sum_of_errors += torch.sum(errors).item()\n\n    def compute(self):\n        return self._sum_of_errors\n", "path": "ignite/contrib/metrics/regression/canberra_metric.py"}]}