prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [DOCS] Simple Model Comparison example improvement 
  Currently, the Simple Model Comparison notebook lacks some explanations.
  Please follow the guidelines from this issue: #543 to improve it
  
  </issue>
  <code>
  [start of deepchecks/utils/validation.py]
  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 """objects validation utilities."""
  12 import typing as t
  13 
  14 import pandas as pd
  15 
  16 from deepchecks import base  # pylint: disable=unused-import, is used in type annotations
  17 from deepchecks import errors
  18 from deepchecks.utils.typing import Hashable, BasicModel
  19 
  20 __all__ = ['model_type_validation', 'ensure_hashable_or_mutable_sequence', 'validate_model', 'ensure_dataframe_type']
  21 
  22 
  23 def model_type_validation(model: t.Any):
  24     """Receive any object and check if it's an instance of a model we support.
  25 
  26     Raises:
  27         DeepchecksValueError: If the object is not of a supported type
  28     """
  29     if not isinstance(model, BasicModel):
  30         raise errors.DeepchecksValueError(
  31             'Model must inherit from one of supported '
  32             'models: sklearn.base.BaseEstimator or CatBoost, '
  33             f'Received: {model.__class__.__name__}'
  34         )
  35 
  36 
  37 def validate_model(
  38     data: t.Union['base.Dataset', pd.DataFrame],
  39     model: t.Any
  40 ):
  41     """Check model is able to predict on the dataset.
  42 
  43     Args:
  44         data (Dataset, pandas.DataFrame):
  45         model (BaseEstimator):
  46 
  47     Raise:
  48         DeepchecksValueError: if dataset does not match model
  49     """
  50     model_type_validation(model)
  51 
  52     error_message = (
  53         'In order to evaluate model correctness we need not empty dataset '
  54         'with the same set of features that was used to fit the model. {0}'
  55     )
  56 
  57     if isinstance(data, base.Dataset):
  58         features = data.features_columns
  59         features_names = set(data.features)
  60     else:
  61         features = data
  62         features_names = set(data.columns)
  63 
  64     model_features = getattr(model, 'feature_names_in_', None)
  65 
  66     if features is None:
  67         raise errors.DeepchecksValueError(error_message.format(
  68             'But function received dataset without feature columns.'
  69         ))
  70 
  71     if len(features) == 0:
  72         raise errors.DeepchecksValueError(error_message.format(
  73             'But function received empty dataset.'
  74         ))
  75 
  76     try:
  77         model_features = set(model_features)  # type: ignore
  78         if model_features != features_names:
  79             raise errors.DeepchecksValueError(error_message.format(
  80                 'But function received dataset with a different set of features.'
  81             ))
  82     except (TypeError, ValueError):
  83         # in case if 'model.feature_names_in_' was None or not iterable
  84         pass
  85 
  86     try:
  87         model.predict(features.head(1))
  88     except Exception as exc:
  89         raise errors.DeepchecksValueError(
  90             f'Got error when trying to predict with model on dataset: {str(exc)}'
  91         )
  92 
  93 
  94 T = t.TypeVar('T', bound=Hashable)
  95 
  96 
  97 def ensure_hashable_or_mutable_sequence(
  98     value: t.Union[T, t.MutableSequence[T]],
  99     message: str = (
  100         'Provided value is neither hashable nor mutable '
  101         'sequence of hashable items. Got {type}')
  102 ) -> t.List[T]:
  103     """Validate that provided value is either hashable or mutable sequence of hashable values."""
  104     if isinstance(value, Hashable):
  105         return [value]
  106 
  107     if isinstance(value, t.MutableSequence):
  108         if len(value) > 0 and not isinstance(value[0], Hashable):
  109             raise errors.DeepchecksValueError(message.format(
  110                 type=f'MutableSequence[{type(value).__name__}]'
  111             ))
  112         return list(value)
  113 
  114     raise errors.DeepchecksValueError(message.format(
  115         type=type(value).__name__
  116     ))
  117 
  118 
  119 def ensure_dataframe_type(obj: t.Any) -> pd.DataFrame:
  120     """Ensure that given object is of type DataFrame or Dataset and return it as DataFrame. else raise error.
  121 
  122     Args:
  123         obj: Object to ensure it is DataFrame or Dataset
  124 
  125     Returns:
  126         (pd.DataFrame)
  127     """
  128     if isinstance(obj, pd.DataFrame):
  129         return obj
  130     elif isinstance(obj, base.Dataset):
  131         return obj.data
  132     else:
  133         raise errors.DeepchecksValueError(
  134             f'dataset must be of type DataFrame or Dataset, but got: {type(obj).__name__}'
  135         )
  136
  [end of deepchecks/utils/validation.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/deepchecks/utils/validation.py b/deepchecks/utils/validation.py
  --- a/deepchecks/utils/validation.py
  +++ b/deepchecks/utils/validation.py
  @@ -56,12 +56,8 @@
   
       if isinstance(data, base.Dataset):
           features = data.features_columns
  -        features_names = set(data.features)
       else:
           features = data
  -        features_names = set(data.columns)
  -
  -    model_features = getattr(model, 'feature_names_in_', None)
   
       if features is None:
           raise errors.DeepchecksValueError(error_message.format(
  @@ -73,16 +69,6 @@
               'But function received empty dataset.'
           ))
   
  -    try:
  -        model_features = set(model_features)  # type: ignore
  -        if model_features != features_names:
  -            raise errors.DeepchecksValueError(error_message.format(
  -                'But function received dataset with a different set of features.'
  -            ))
  -    except (TypeError, ValueError):
  -        # in case if 'model.feature_names_in_' was None or not iterable
  -        pass
  -
       try:
           model.predict(features.head(1))
       except Exception as exc:
  
verification_info: {"golden_diff": "diff --git a/deepchecks/utils/validation.py b/deepchecks/utils/validation.py\n--- a/deepchecks/utils/validation.py\n+++ b/deepchecks/utils/validation.py\n@@ -56,12 +56,8 @@\n \n     if isinstance(data, base.Dataset):\n         features = data.features_columns\n-        features_names = set(data.features)\n     else:\n         features = data\n-        features_names = set(data.columns)\n-\n-    model_features = getattr(model, 'feature_names_in_', None)\n \n     if features is None:\n         raise errors.DeepchecksValueError(error_message.format(\n@@ -73,16 +69,6 @@\n             'But function received empty dataset.'\n         ))\n \n-    try:\n-        model_features = set(model_features)  # type: ignore\n-        if model_features != features_names:\n-            raise errors.DeepchecksValueError(error_message.format(\n-                'But function received dataset with a different set of features.'\n-            ))\n-    except (TypeError, ValueError):\n-        # in case if 'model.feature_names_in_' was None or not iterable\n-        pass\n-\n     try:\n         model.predict(features.head(1))\n     except Exception as exc:\n", "issue": "[DOCS] Simple Model Comparison example improvement \nCurrently, the Simple Model Comparison notebook lacks some explanations.\r\nPlease follow the guidelines from this issue: #543 to improve it\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\"\"\"objects validation utilities.\"\"\"\nimport typing as t\n\nimport pandas as pd\n\nfrom deepchecks import base  # pylint: disable=unused-import, is used in type annotations\nfrom deepchecks import errors\nfrom deepchecks.utils.typing import Hashable, BasicModel\n\n__all__ = ['model_type_validation', 'ensure_hashable_or_mutable_sequence', 'validate_model', 'ensure_dataframe_type']\n\n\ndef model_type_validation(model: t.Any):\n    \"\"\"Receive any object and check if it's an instance of a model we support.\n\n    Raises:\n        DeepchecksValueError: If the object is not of a supported type\n    \"\"\"\n    if not isinstance(model, BasicModel):\n        raise errors.DeepchecksValueError(\n            'Model must inherit from one of supported '\n            'models: sklearn.base.BaseEstimator or CatBoost, '\n            f'Received: {model.__class__.__name__}'\n        )\n\n\ndef validate_model(\n    data: t.Union['base.Dataset', pd.DataFrame],\n    model: t.Any\n):\n    \"\"\"Check model is able to predict on the dataset.\n\n    Args:\n        data (Dataset, pandas.DataFrame):\n        model (BaseEstimator):\n\n    Raise:\n        DeepchecksValueError: if dataset does not match model\n    \"\"\"\n    model_type_validation(model)\n\n    error_message = (\n        'In order to evaluate model correctness we need not empty dataset '\n        'with the same set of features that was used to fit the model. {0}'\n    )\n\n    if isinstance(data, base.Dataset):\n        features = data.features_columns\n        features_names = set(data.features)\n    else:\n        features = data\n        features_names = set(data.columns)\n\n    model_features = getattr(model, 'feature_names_in_', None)\n\n    if features is None:\n        raise errors.DeepchecksValueError(error_message.format(\n            'But function received dataset without feature columns.'\n        ))\n\n    if len(features) == 0:\n        raise errors.DeepchecksValueError(error_message.format(\n            'But function received empty dataset.'\n        ))\n\n    try:\n        model_features = set(model_features)  # type: ignore\n        if model_features != features_names:\n            raise errors.DeepchecksValueError(error_message.format(\n                'But function received dataset with a different set of features.'\n            ))\n    except (TypeError, ValueError):\n        # in case if 'model.feature_names_in_' was None or not iterable\n        pass\n\n    try:\n        model.predict(features.head(1))\n    except Exception as exc:\n        raise errors.DeepchecksValueError(\n            f'Got error when trying to predict with model on dataset: {str(exc)}'\n        )\n\n\nT = t.TypeVar('T', bound=Hashable)\n\n\ndef ensure_hashable_or_mutable_sequence(\n    value: t.Union[T, t.MutableSequence[T]],\n    message: str = (\n        'Provided value is neither hashable nor mutable '\n        'sequence of hashable items. Got {type}')\n) -> t.List[T]:\n    \"\"\"Validate that provided value is either hashable or mutable sequence of hashable values.\"\"\"\n    if isinstance(value, Hashable):\n        return [value]\n\n    if isinstance(value, t.MutableSequence):\n        if len(value) > 0 and not isinstance(value[0], Hashable):\n            raise errors.DeepchecksValueError(message.format(\n                type=f'MutableSequence[{type(value).__name__}]'\n            ))\n        return list(value)\n\n    raise errors.DeepchecksValueError(message.format(\n        type=type(value).__name__\n    ))\n\n\ndef ensure_dataframe_type(obj: t.Any) -> pd.DataFrame:\n    \"\"\"Ensure that given object is of type DataFrame or Dataset and return it as DataFrame. else raise error.\n\n    Args:\n        obj: Object to ensure it is DataFrame or Dataset\n\n    Returns:\n        (pd.DataFrame)\n    \"\"\"\n    if isinstance(obj, pd.DataFrame):\n        return obj\n    elif isinstance(obj, base.Dataset):\n        return obj.data\n    else:\n        raise errors.DeepchecksValueError(\n            f'dataset must be of type DataFrame or Dataset, but got: {type(obj).__name__}'\n        )\n", "path": "deepchecks/utils/validation.py"}]}