prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Refactor issue #1043
  Fixes #1043 
  
  Removed methods load_UCR_UEA_dataset & _load_dataset from datasets/base.py and moved them to utils/data_io.py
  
  </issue>
  <code>
  [start of sktime/transformations/panel/signature_based/_signature_method.py]
  1 # -*- coding: utf-8 -*-
  2 from sklearn.pipeline import Pipeline
  3 from sktime.transformations.base import _PanelToTabularTransformer
  4 from sktime.transformations.panel.signature_based._compute import (
  5     _WindowSignatureTransform,
  6 )
  7 from sktime.transformations.panel.signature_based._augmentations import (
  8     _make_augmentation_pipeline,
  9 )
  10 from sktime.transformations.panel.signature_based._checks import (
  11     _handle_sktime_signatures,
  12 )
  13 
  14 
  15 class SignatureTransformer(_PanelToTabularTransformer):
  16     """Transformation class from the signature method.
  17 
  18     Follows the methodology laid out in the paper:
  19         "A Generalised Signature Method for Multivariate Time Series"
  20 
  21     Parameters
  22     ----------
  23     augmentation_list: tuple of strings, contains the augmentations to be
  24         applied before application of the signature transform.
  25     window_name: str, The name of the window transform to apply.
  26     window_depth: int, The depth of the dyadic window. (Active only if
  27         `window_name == 'dyadic'`).
  28     window_length: int, The length of the sliding/expanding window. (Active
  29         only if `window_name in ['sliding, 'expanding']`.
  30     window_step: int, The step of the sliding/expanding window. (Active
  31         only if `window_name in ['sliding, 'expanding']`.
  32     rescaling: str or None, The method of signature rescaling.
  33     sig_tfm: str, String to specify the type of signature transform. One of:
  34         ['signature', 'logsignature']).
  35     depth: int, Signature truncation depth.
  36 
  37     Attributes
  38     ----------
  39     signature_method: sklearn.Pipeline, A sklearn pipeline object that contains
  40         all the steps to extract the signature features.
  41     """
  42 
  43     def __init__(
  44         self,
  45         augmentation_list=("basepoint", "addtime"),
  46         window_name="dyadic",
  47         window_depth=3,
  48         window_length=None,
  49         window_step=None,
  50         rescaling=None,
  51         sig_tfm="signature",
  52         depth=4,
  53     ):
  54         super(SignatureTransformer, self).__init__()
  55         self.augmentation_list = augmentation_list
  56         self.window_name = window_name
  57         self.window_depth = window_depth
  58         self.window_length = window_length
  59         self.window_step = window_step
  60         self.rescaling = rescaling
  61         self.sig_tfm = sig_tfm
  62         self.depth = depth
  63 
  64         self.setup_feature_pipeline()
  65 
  66     def _assertions(self):
  67         """Some assertions to run on initialisation."""
  68         assert not all(
  69             [self.sig_tfm == "logsignature", self.rescaling == "post"]
  70         ), "Cannot have post rescaling with the logsignature."
  71 
  72     def setup_feature_pipeline(self):
  73         """Sets up the signature method as an sklearn pipeline."""
  74         augmentation_step = _make_augmentation_pipeline(self.augmentation_list)
  75         transform_step = _WindowSignatureTransform(
  76             window_name=self.window_name,
  77             window_depth=self.window_depth,
  78             window_length=self.window_length,
  79             window_step=self.window_step,
  80             sig_tfm=self.sig_tfm,
  81             sig_depth=self.depth,
  82             rescaling=self.rescaling,
  83         )
  84 
  85         # The so-called 'signature method' as defined in the reference paper
  86         self.signature_method = Pipeline(
  87             [
  88                 ("augmentations", augmentation_step),
  89                 ("window_and_transform", transform_step),
  90             ]
  91         )
  92 
  93     @_handle_sktime_signatures(check_fitted=False)
  94     def fit(self, data, labels=None):
  95         self.signature_method.fit(data, labels)
  96         self._is_fitted = True
  97         return self
  98 
  99     @_handle_sktime_signatures(check_fitted=True)
  100     def transform(self, data, labels=None):
  101         return self.signature_method.transform(data)
  102
  [end of sktime/transformations/panel/signature_based/_signature_method.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/sktime/transformations/panel/signature_based/_signature_method.py b/sktime/transformations/panel/signature_based/_signature_method.py
  --- a/sktime/transformations/panel/signature_based/_signature_method.py
  +++ b/sktime/transformations/panel/signature_based/_signature_method.py
  @@ -1,15 +1,16 @@
   # -*- coding: utf-8 -*-
   from sklearn.pipeline import Pipeline
  +
   from sktime.transformations.base import _PanelToTabularTransformer
  -from sktime.transformations.panel.signature_based._compute import (
  -    _WindowSignatureTransform,
  -)
   from sktime.transformations.panel.signature_based._augmentations import (
       _make_augmentation_pipeline,
   )
   from sktime.transformations.panel.signature_based._checks import (
       _handle_sktime_signatures,
   )
  +from sktime.transformations.panel.signature_based._compute import (
  +    _WindowSignatureTransform,
  +)
   
   
   class SignatureTransformer(_PanelToTabularTransformer):
  @@ -63,14 +64,8 @@
   
           self.setup_feature_pipeline()
   
  -    def _assertions(self):
  -        """Some assertions to run on initialisation."""
  -        assert not all(
  -            [self.sig_tfm == "logsignature", self.rescaling == "post"]
  -        ), "Cannot have post rescaling with the logsignature."
  -
       def setup_feature_pipeline(self):
  -        """Sets up the signature method as an sklearn pipeline."""
  +        """Set up the signature method as an sklearn pipeline."""
           augmentation_step = _make_augmentation_pipeline(self.augmentation_list)
           transform_step = _WindowSignatureTransform(
               window_name=self.window_name,
  @@ -92,10 +87,38 @@
   
       @_handle_sktime_signatures(check_fitted=False)
       def fit(self, data, labels=None):
  +        """Fit to data, then transform it.
  +
  +        Parameters
  +        ----------
  +        data: pd.Dataframe or np.ndarray (3d array)
  +            Data to transform.
  +        labels: np.ndarray (1d array) or pd.series or list
  +            Labels for the data.
  +
  +        Returns
  +        -------
  +        pd.Dataframe or np.ndarray or pd.series
  +        Transformed data.
  +        """
           self.signature_method.fit(data, labels)
           self._is_fitted = True
           return self
   
       @_handle_sktime_signatures(check_fitted=True)
       def transform(self, data, labels=None):
  +        """Transform the class from the signature method.
  +
  +        Parameters
  +        ----------
  +        data: pd.Dataframe or np.ndarray (3d array)
  +            Data to transform.
  +        labels: np.ndarray (1d array) or pd.series or list
  +            Labels for the data.
  +
  +        Returns
  +        -------
  +        pd.Dataframe or np.ndarray or pd.series
  +            Transformed data.
  +        """
           return self.signature_method.transform(data)
  
verification_info: {"golden_diff": "diff --git a/sktime/transformations/panel/signature_based/_signature_method.py b/sktime/transformations/panel/signature_based/_signature_method.py\n--- a/sktime/transformations/panel/signature_based/_signature_method.py\n+++ b/sktime/transformations/panel/signature_based/_signature_method.py\n@@ -1,15 +1,16 @@\n # -*- coding: utf-8 -*-\n from sklearn.pipeline import Pipeline\n+\n from sktime.transformations.base import _PanelToTabularTransformer\n-from sktime.transformations.panel.signature_based._compute import (\n-    _WindowSignatureTransform,\n-)\n from sktime.transformations.panel.signature_based._augmentations import (\n     _make_augmentation_pipeline,\n )\n from sktime.transformations.panel.signature_based._checks import (\n     _handle_sktime_signatures,\n )\n+from sktime.transformations.panel.signature_based._compute import (\n+    _WindowSignatureTransform,\n+)\n \n \n class SignatureTransformer(_PanelToTabularTransformer):\n@@ -63,14 +64,8 @@\n \n         self.setup_feature_pipeline()\n \n-    def _assertions(self):\n-        \"\"\"Some assertions to run on initialisation.\"\"\"\n-        assert not all(\n-            [self.sig_tfm == \"logsignature\", self.rescaling == \"post\"]\n-        ), \"Cannot have post rescaling with the logsignature.\"\n-\n     def setup_feature_pipeline(self):\n-        \"\"\"Sets up the signature method as an sklearn pipeline.\"\"\"\n+        \"\"\"Set up the signature method as an sklearn pipeline.\"\"\"\n         augmentation_step = _make_augmentation_pipeline(self.augmentation_list)\n         transform_step = _WindowSignatureTransform(\n             window_name=self.window_name,\n@@ -92,10 +87,38 @@\n \n     @_handle_sktime_signatures(check_fitted=False)\n     def fit(self, data, labels=None):\n+        \"\"\"Fit to data, then transform it.\n+\n+        Parameters\n+        ----------\n+        data: pd.Dataframe or np.ndarray (3d array)\n+            Data to transform.\n+        labels: np.ndarray (1d array) or pd.series or list\n+            Labels for the data.\n+\n+        Returns\n+        -------\n+        pd.Dataframe or np.ndarray or pd.series\n+        Transformed data.\n+        \"\"\"\n         self.signature_method.fit(data, labels)\n         self._is_fitted = True\n         return self\n \n     @_handle_sktime_signatures(check_fitted=True)\n     def transform(self, data, labels=None):\n+        \"\"\"Transform the class from the signature method.\n+\n+        Parameters\n+        ----------\n+        data: pd.Dataframe or np.ndarray (3d array)\n+            Data to transform.\n+        labels: np.ndarray (1d array) or pd.series or list\n+            Labels for the data.\n+\n+        Returns\n+        -------\n+        pd.Dataframe or np.ndarray or pd.series\n+            Transformed data.\n+        \"\"\"\n         return self.signature_method.transform(data)\n", "issue": "Refactor issue #1043\nFixes #1043 \r\n\r\nRemoved methods load_UCR_UEA_dataset & _load_dataset from datasets/base.py and moved them to utils/data_io.py\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\nfrom sklearn.pipeline import Pipeline\nfrom sktime.transformations.base import _PanelToTabularTransformer\nfrom sktime.transformations.panel.signature_based._compute import (\n    _WindowSignatureTransform,\n)\nfrom sktime.transformations.panel.signature_based._augmentations import (\n    _make_augmentation_pipeline,\n)\nfrom sktime.transformations.panel.signature_based._checks import (\n    _handle_sktime_signatures,\n)\n\n\nclass SignatureTransformer(_PanelToTabularTransformer):\n    \"\"\"Transformation class from the signature method.\n\n    Follows the methodology laid out in the paper:\n        \"A Generalised Signature Method for Multivariate Time Series\"\n\n    Parameters\n    ----------\n    augmentation_list: tuple of strings, contains the augmentations to be\n        applied before application of the signature transform.\n    window_name: str, The name of the window transform to apply.\n    window_depth: int, The depth of the dyadic window. (Active only if\n        `window_name == 'dyadic'`).\n    window_length: int, The length of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    window_step: int, The step of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    rescaling: str or None, The method of signature rescaling.\n    sig_tfm: str, String to specify the type of signature transform. One of:\n        ['signature', 'logsignature']).\n    depth: int, Signature truncation depth.\n\n    Attributes\n    ----------\n    signature_method: sklearn.Pipeline, A sklearn pipeline object that contains\n        all the steps to extract the signature features.\n    \"\"\"\n\n    def __init__(\n        self,\n        augmentation_list=(\"basepoint\", \"addtime\"),\n        window_name=\"dyadic\",\n        window_depth=3,\n        window_length=None,\n        window_step=None,\n        rescaling=None,\n        sig_tfm=\"signature\",\n        depth=4,\n    ):\n        super(SignatureTransformer, self).__init__()\n        self.augmentation_list = augmentation_list\n        self.window_name = window_name\n        self.window_depth = window_depth\n        self.window_length = window_length\n        self.window_step = window_step\n        self.rescaling = rescaling\n        self.sig_tfm = sig_tfm\n        self.depth = depth\n\n        self.setup_feature_pipeline()\n\n    def _assertions(self):\n        \"\"\"Some assertions to run on initialisation.\"\"\"\n        assert not all(\n            [self.sig_tfm == \"logsignature\", self.rescaling == \"post\"]\n        ), \"Cannot have post rescaling with the logsignature.\"\n\n    def setup_feature_pipeline(self):\n        \"\"\"Sets up the signature method as an sklearn pipeline.\"\"\"\n        augmentation_step = _make_augmentation_pipeline(self.augmentation_list)\n        transform_step = _WindowSignatureTransform(\n            window_name=self.window_name,\n            window_depth=self.window_depth,\n            window_length=self.window_length,\n            window_step=self.window_step,\n            sig_tfm=self.sig_tfm,\n            sig_depth=self.depth,\n            rescaling=self.rescaling,\n        )\n\n        # The so-called 'signature method' as defined in the reference paper\n        self.signature_method = Pipeline(\n            [\n                (\"augmentations\", augmentation_step),\n                (\"window_and_transform\", transform_step),\n            ]\n        )\n\n    @_handle_sktime_signatures(check_fitted=False)\n    def fit(self, data, labels=None):\n        self.signature_method.fit(data, labels)\n        self._is_fitted = True\n        return self\n\n    @_handle_sktime_signatures(check_fitted=True)\n    def transform(self, data, labels=None):\n        return self.signature_method.transform(data)\n", "path": "sktime/transformations/panel/signature_based/_signature_method.py"}]}