prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG] Receiving FutureWarning for each label on Calibration Score check
  **Describe the bug**
  Receiving FutureWarning for each label on Calibration Score
  
  **To Reproduce**
  Run a categorical Dataset on Calibration Score check
  
  **Expected behavior**
  No warnings
  
  **Screenshots**
  ![Screen Shot 2022-01-09 at 16 28 07](https://user-images.githubusercontent.com/26321553/148686605-d509df45-37be-4bd7-9e00-2201ca8431b9.png)
  
  
  **Environment (please complete the following information):**
   - OS: mac
   - Python Version: 3.8
   - Deepchecks Version: 0.2.1
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `deepchecks/checks/performance/calibration_score.py`
  Content:
  ```
  1 # ----------------------------------------------------------------------------
  2 # Copyright (C) 2021 Deepchecks (https://www.deepchecks.com)
  3 #
  4 # This file is part of Deepchecks.
  5 # Deepchecks is distributed under the terms of the GNU Affero General
  6 # Public License (version 3 or later).
  7 # You should have received a copy of the GNU Affero General Public License
  8 # along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
  9 # ----------------------------------------------------------------------------
  10 #
  11 """The calibration score check module."""
  12 from sklearn.base import BaseEstimator
  13 from sklearn.calibration import calibration_curve
  14 from sklearn.metrics import brier_score_loss
  15 import plotly.graph_objects as go
  16 
  17 from deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck
  18 from deepchecks.utils.metrics import ModelType, task_type_validation
  19 
  20 
  21 __all__ = ['CalibrationScore']
  22 
  23 
  24 class CalibrationScore(SingleDatasetBaseCheck):
  25     """Calculate the calibration curve with brier score for each class."""
  26 
  27     def run(self, dataset: Dataset, model: BaseEstimator) -> CheckResult:
  28         """Run check.
  29 
  30         Args:
  31             model (BaseEstimator): A scikit-learn-compatible fitted estimator instance
  32             dataset: a Dataset object
  33         Returns:
  34             CheckResult: value is dictionary of class and it's brier score, displays the calibration curve
  35             graph with each class
  36 
  37         Raises:
  38             DeepchecksValueError: If the object is not a Dataset instance with a label
  39         """
  40         return self._calibration_score(dataset, model)
  41 
  42     def _calibration_score(self, dataset: Dataset, model):
  43         Dataset.validate_dataset(dataset)
  44         dataset.validate_label()
  45         task_type_validation(model, dataset, [ModelType.MULTICLASS, ModelType.BINARY])
  46 
  47         ds_x = dataset.features_columns
  48         ds_y = dataset.label_col
  49         # Expect predict_proba to return in order of the sorted classes.
  50         y_pred = model.predict_proba(ds_x)
  51 
  52         briers_scores = {}
  53 
  54         if len(dataset.classes) == 2:
  55             briers_scores[0] = brier_score_loss(ds_y, y_pred[:, 1], pos_label=dataset.classes[1])
  56         else:
  57             for class_index, class_name in enumerate(dataset.classes):
  58                 prob_pos = y_pred[:, class_index]
  59                 clf_score = brier_score_loss(ds_y == class_name, prob_pos, pos_label=class_name)
  60                 briers_scores[class_name] = clf_score
  61 
  62         fig = go.Figure()
  63 
  64         fig.add_trace(go.Scatter(
  65                     x=[0, 1],
  66                     y=[0, 1],
  67                     line_width=2, line_dash='dash',
  68                     name='Perfectly calibrated',
  69                 ))
  70 
  71         if len(dataset.classes) == 2:
  72             fraction_of_positives, mean_predicted_value = calibration_curve(ds_y, y_pred[:, 1], n_bins=10)
  73 
  74             fig.add_trace(go.Scatter(
  75                 x=mean_predicted_value,
  76                 y=fraction_of_positives,
  77                 mode='lines+markers',
  78                 name=f'(brier:{briers_scores[0]:9.4f})',
  79             ))
  80         else:
  81             for class_index, class_name in enumerate(dataset.classes):
  82                 prob_pos = y_pred[:, class_index]
  83 
  84                 fraction_of_positives, mean_predicted_value = \
  85                     calibration_curve(ds_y == class_name, prob_pos, n_bins=10)
  86 
  87                 fig.add_trace(go.Scatter(
  88                     x=mean_predicted_value,
  89                     y=fraction_of_positives,
  90                     mode='lines+markers',
  91                     name=f'{class_name} (brier:{briers_scores[class_name]:9.4f})',
  92                 ))
  93 
  94         fig.update_layout(title_text='Calibration plots (reliability curve)',
  95                           width=700, height=500)
  96         fig.update_yaxes(title='Fraction of positives')
  97         fig.update_xaxes(title='Mean predicted value')
  98 
  99         calibration_text = 'Calibration curves (also known as reliability diagrams) compare how well the ' \
  100                            'probabilistic predictions of a binary classifier are calibrated. It plots the true ' \
  101                            'frequency of the positive label against its predicted probability, for binned predictions.'
  102         brier_text = 'The Brier score metric may be used to assess how well a classifier is calibrated. For more ' \
  103                      'info, please visit https://en.wikipedia.org/wiki/Brier_score'
  104         return CheckResult(briers_scores, header='Calibration Metric',
  105                            display=[calibration_text, fig, brier_text])
  106
  ```
  --- 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/deepchecks/checks/performance/calibration_score.py b/deepchecks/checks/performance/calibration_score.py
  --- a/deepchecks/checks/performance/calibration_score.py
  +++ b/deepchecks/checks/performance/calibration_score.py
  @@ -17,7 +17,6 @@
   from deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck
   from deepchecks.utils.metrics import ModelType, task_type_validation
   
  -
   __all__ = ['CalibrationScore']
   
   
  @@ -52,11 +51,11 @@
           briers_scores = {}
   
           if len(dataset.classes) == 2:
  -            briers_scores[0] = brier_score_loss(ds_y, y_pred[:, 1], pos_label=dataset.classes[1])
  +            briers_scores[0] = brier_score_loss(ds_y == dataset.classes[1], y_pred[:, 1])
           else:
               for class_index, class_name in enumerate(dataset.classes):
                   prob_pos = y_pred[:, class_index]
  -                clf_score = brier_score_loss(ds_y == class_name, prob_pos, pos_label=class_name)
  +                clf_score = brier_score_loss(ds_y == class_name, prob_pos)
                   briers_scores[class_name] = clf_score
   
           fig = go.Figure()
  
verification_info: {"golden_diff": "diff --git a/deepchecks/checks/performance/calibration_score.py b/deepchecks/checks/performance/calibration_score.py\n--- a/deepchecks/checks/performance/calibration_score.py\n+++ b/deepchecks/checks/performance/calibration_score.py\n@@ -17,7 +17,6 @@\n from deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck\n from deepchecks.utils.metrics import ModelType, task_type_validation\n \n-\n __all__ = ['CalibrationScore']\n \n \n@@ -52,11 +51,11 @@\n         briers_scores = {}\n \n         if len(dataset.classes) == 2:\n-            briers_scores[0] = brier_score_loss(ds_y, y_pred[:, 1], pos_label=dataset.classes[1])\n+            briers_scores[0] = brier_score_loss(ds_y == dataset.classes[1], y_pred[:, 1])\n         else:\n             for class_index, class_name in enumerate(dataset.classes):\n                 prob_pos = y_pred[:, class_index]\n-                clf_score = brier_score_loss(ds_y == class_name, prob_pos, pos_label=class_name)\n+                clf_score = brier_score_loss(ds_y == class_name, prob_pos)\n                 briers_scores[class_name] = clf_score\n \n         fig = go.Figure()\n", "issue": "[BUG] Receiving FutureWarning for each label on Calibration Score check\n**Describe the bug**\r\nReceiving FutureWarning for each label on Calibration Score\r\n\r\n**To Reproduce**\r\nRun a categorical Dataset on Calibration Score check\r\n\r\n**Expected behavior**\r\nNo warnings\r\n\r\n**Screenshots**\r\n![Screen Shot 2022-01-09 at 16 28 07](https://user-images.githubusercontent.com/26321553/148686605-d509df45-37be-4bd7-9e00-2201ca8431b9.png)\r\n\r\n\r\n**Environment (please complete the following information):**\r\n - OS: mac\r\n - Python Version: 3.8\r\n - Deepchecks Version: 0.2.1\r\n\n", "before_files": [{"content": "# ----------------------------------------------------------------------------\n# Copyright (C) 2021 Deepchecks (https://www.deepchecks.com)\n#\n# This file is part of Deepchecks.\n# Deepchecks is distributed under the terms of the GNU Affero General\n# Public License (version 3 or later).\n# You should have received a copy of the GNU Affero General Public License\n# along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.\n# ----------------------------------------------------------------------------\n#\n\"\"\"The calibration score check module.\"\"\"\nfrom sklearn.base import BaseEstimator\nfrom sklearn.calibration import calibration_curve\nfrom sklearn.metrics import brier_score_loss\nimport plotly.graph_objects as go\n\nfrom deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck\nfrom deepchecks.utils.metrics import ModelType, task_type_validation\n\n\n__all__ = ['CalibrationScore']\n\n\nclass CalibrationScore(SingleDatasetBaseCheck):\n    \"\"\"Calculate the calibration curve with brier score for each class.\"\"\"\n\n    def run(self, dataset: Dataset, model: BaseEstimator) -> CheckResult:\n        \"\"\"Run check.\n\n        Args:\n            model (BaseEstimator): A scikit-learn-compatible fitted estimator instance\n            dataset: a Dataset object\n        Returns:\n            CheckResult: value is dictionary of class and it's brier score, displays the calibration curve\n            graph with each class\n\n        Raises:\n            DeepchecksValueError: If the object is not a Dataset instance with a label\n        \"\"\"\n        return self._calibration_score(dataset, model)\n\n    def _calibration_score(self, dataset: Dataset, model):\n        Dataset.validate_dataset(dataset)\n        dataset.validate_label()\n        task_type_validation(model, dataset, [ModelType.MULTICLASS, ModelType.BINARY])\n\n        ds_x = dataset.features_columns\n        ds_y = dataset.label_col\n        # Expect predict_proba to return in order of the sorted classes.\n        y_pred = model.predict_proba(ds_x)\n\n        briers_scores = {}\n\n        if len(dataset.classes) == 2:\n            briers_scores[0] = brier_score_loss(ds_y, y_pred[:, 1], pos_label=dataset.classes[1])\n        else:\n            for class_index, class_name in enumerate(dataset.classes):\n                prob_pos = y_pred[:, class_index]\n                clf_score = brier_score_loss(ds_y == class_name, prob_pos, pos_label=class_name)\n                briers_scores[class_name] = clf_score\n\n        fig = go.Figure()\n\n        fig.add_trace(go.Scatter(\n                    x=[0, 1],\n                    y=[0, 1],\n                    line_width=2, line_dash='dash',\n                    name='Perfectly calibrated',\n                ))\n\n        if len(dataset.classes) == 2:\n            fraction_of_positives, mean_predicted_value = calibration_curve(ds_y, y_pred[:, 1], n_bins=10)\n\n            fig.add_trace(go.Scatter(\n                x=mean_predicted_value,\n                y=fraction_of_positives,\n                mode='lines+markers',\n                name=f'(brier:{briers_scores[0]:9.4f})',\n            ))\n        else:\n            for class_index, class_name in enumerate(dataset.classes):\n                prob_pos = y_pred[:, class_index]\n\n                fraction_of_positives, mean_predicted_value = \\\n                    calibration_curve(ds_y == class_name, prob_pos, n_bins=10)\n\n                fig.add_trace(go.Scatter(\n                    x=mean_predicted_value,\n                    y=fraction_of_positives,\n                    mode='lines+markers',\n                    name=f'{class_name} (brier:{briers_scores[class_name]:9.4f})',\n                ))\n\n        fig.update_layout(title_text='Calibration plots (reliability curve)',\n                          width=700, height=500)\n        fig.update_yaxes(title='Fraction of positives')\n        fig.update_xaxes(title='Mean predicted value')\n\n        calibration_text = 'Calibration curves (also known as reliability diagrams) compare how well the ' \\\n                           'probabilistic predictions of a binary classifier are calibrated. It plots the true ' \\\n                           'frequency of the positive label against its predicted probability, for binned predictions.'\n        brier_text = 'The Brier score metric may be used to assess how well a classifier is calibrated. For more ' \\\n                     'info, please visit https://en.wikipedia.org/wiki/Brier_score'\n        return CheckResult(briers_scores, header='Calibration Metric',\n                           display=[calibration_text, fig, brier_text])\n", "path": "deepchecks/checks/performance/calibration_score.py"}], "after_files": [{"content": "# ----------------------------------------------------------------------------\n# Copyright (C) 2021 Deepchecks (https://www.deepchecks.com)\n#\n# This file is part of Deepchecks.\n# Deepchecks is distributed under the terms of the GNU Affero General\n# Public License (version 3 or later).\n# You should have received a copy of the GNU Affero General Public License\n# along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.\n# ----------------------------------------------------------------------------\n#\n\"\"\"The calibration score check module.\"\"\"\nfrom sklearn.base import BaseEstimator\nfrom sklearn.calibration import calibration_curve\nfrom sklearn.metrics import brier_score_loss\nimport plotly.graph_objects as go\n\nfrom deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck\nfrom deepchecks.utils.metrics import ModelType, task_type_validation\n\n__all__ = ['CalibrationScore']\n\n\nclass CalibrationScore(SingleDatasetBaseCheck):\n    \"\"\"Calculate the calibration curve with brier score for each class.\"\"\"\n\n    def run(self, dataset: Dataset, model: BaseEstimator) -> CheckResult:\n        \"\"\"Run check.\n\n        Args:\n            model (BaseEstimator): A scikit-learn-compatible fitted estimator instance\n            dataset: a Dataset object\n        Returns:\n            CheckResult: value is dictionary of class and it's brier score, displays the calibration curve\n            graph with each class\n\n        Raises:\n            DeepchecksValueError: If the object is not a Dataset instance with a label\n        \"\"\"\n        return self._calibration_score(dataset, model)\n\n    def _calibration_score(self, dataset: Dataset, model):\n        Dataset.validate_dataset(dataset)\n        dataset.validate_label()\n        task_type_validation(model, dataset, [ModelType.MULTICLASS, ModelType.BINARY])\n\n        ds_x = dataset.features_columns\n        ds_y = dataset.label_col\n        # Expect predict_proba to return in order of the sorted classes.\n        y_pred = model.predict_proba(ds_x)\n\n        briers_scores = {}\n\n        if len(dataset.classes) == 2:\n            briers_scores[0] = brier_score_loss(ds_y == dataset.classes[1], y_pred[:, 1])\n        else:\n            for class_index, class_name in enumerate(dataset.classes):\n                prob_pos = y_pred[:, class_index]\n                clf_score = brier_score_loss(ds_y == class_name, prob_pos)\n                briers_scores[class_name] = clf_score\n\n        fig = go.Figure()\n\n        fig.add_trace(go.Scatter(\n                    x=[0, 1],\n                    y=[0, 1],\n                    line_width=2, line_dash='dash',\n                    name='Perfectly calibrated',\n                ))\n\n        if len(dataset.classes) == 2:\n            fraction_of_positives, mean_predicted_value = calibration_curve(ds_y, y_pred[:, 1], n_bins=10)\n\n            fig.add_trace(go.Scatter(\n                x=mean_predicted_value,\n                y=fraction_of_positives,\n                mode='lines+markers',\n                name=f'(brier:{briers_scores[0]:9.4f})',\n            ))\n        else:\n            for class_index, class_name in enumerate(dataset.classes):\n                prob_pos = y_pred[:, class_index]\n\n                fraction_of_positives, mean_predicted_value = \\\n                    calibration_curve(ds_y == class_name, prob_pos, n_bins=10)\n\n                fig.add_trace(go.Scatter(\n                    x=mean_predicted_value,\n                    y=fraction_of_positives,\n                    mode='lines+markers',\n                    name=f'{class_name} (brier:{briers_scores[class_name]:9.4f})',\n                ))\n\n        fig.update_layout(title_text='Calibration plots (reliability curve)',\n                          width=700, height=500)\n        fig.update_yaxes(title='Fraction of positives')\n        fig.update_xaxes(title='Mean predicted value')\n\n        calibration_text = 'Calibration curves (also known as reliability diagrams) compare how well the ' \\\n                           'probabilistic predictions of a binary classifier are calibrated. It plots the true ' \\\n                           'frequency of the positive label against its predicted probability, for binned predictions.'\n        brier_text = 'The Brier score metric may be used to assess how well a classifier is calibrated. For more ' \\\n                     'info, please visit https://en.wikipedia.org/wiki/Brier_score'\n        return CheckResult(briers_scores, header='Calibration Metric',\n                           display=[calibration_text, fig, brier_text])\n", "path": "deepchecks/checks/performance/calibration_score.py"}]}