From 10b0eca1c9f5259301ebdd9a5540d7a9a00113e5 Mon Sep 17 00:00:00 2001
From: umar <46414488+mail4umar@users.noreply.github.com>
Date: Mon, 30 Oct 2023 23:31:42 -0500
Subject: [PATCH 1/7] Sphinx Docstring - Scaler and subclasses

---
 .../machine_learning/vertica/preprocessing.py | 259 +++++++++++++++++-
 1 file changed, 250 insertions(+), 9 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 64404e99d..07b378b30 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -657,12 +657,226 @@ class Scaler(Preprocessing):
         existing model.
     method: str, optional
         Method used to scale the data.
-                zscore        : Scaling   using   the   Z-Score.
-                            (x - avg) / std
-                robust_zscore : Scaling using the Robust Z-Score.
-                                (x - median) / (1.4826 * mad)
-                minmax        : Normalization  using  the  Min  &  Max.
-                                (x - min) / (max - min)
+
+        - zscore:
+        Scaling   using   the   Z-Score
+
+        .. math::
+
+            (x - avg) / std
+
+        - robust_zscore:
+        Scaling using the Robust Z-Score.
+
+        .. math::
+
+            (x - median) / (1.4826 * mad)
+
+        - minmax:
+        Normalization  using  the  Min  &  Max.
+
+        .. math::
+
+            (x - min) / (max - min)
+
+    Examples
+    --------
+
+    The following examples provide a basic understanding of usage.
+    For more detailed examples, please refer to the
+    :ref:`user_guide.machine_learning` or the
+    `Examples <https://www.vertica.com/python/examples/>`_
+    section on the website.
+
+    Load data for machine learning
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    We import ``verticapy``:
+
+    .. ipython:: python
+
+        import verticapy as vp
+
+    .. hint::
+
+        By assigning an alias to ``verticapy``, we mitigate the risk of code
+        collisions with other libraries. This precaution is necessary
+        because verticapy uses commonly known function names like "average"
+        and "median", which can potentially lead to naming conflicts.
+        The use of an alias ensures that the functions from verticapy are
+        used as intended without interfering with functions from other
+        libraries.
+
+    For this example, we will use a dummy dataset.
+
+    .. ipython:: python
+
+        data = vp.vDataFrame({"values": [1, 1.01, 1.02, 1.05, 1.024]})
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html
+
+    .. note::
+
+        VerticaPy offers a wide range of sample datasets that are
+        ideal for training and testing purposes. You can explore
+        the full list of available datasets in the :ref:`api.datasets`,
+        which provides detailed information on each dataset
+        and how to use them effectively. These datasets are invaluable
+        resources for honing your data analysis and machine learning
+        skills within the VerticaPy environment.
+
+
+    Model Initialization
+    ^^^^^^^^^^^^^^^^^^^^^
+
+    First we import the ``Scaler`` model:
+
+    .. ipython:: python
+
+        from verticapy.machine_learning.vertica import Scaler
+
+    Then we can create the model:
+
+    .. ipython:: python
+        :okwarning:
+
+        model = Scaler(method = "zscore")
+
+    .. hint::
+
+        In ``verticapy`` 1.0.x and higher, you do not need to specify the
+        model name, as the name is automatically assigned. If you need to
+        re-use the model, you can fetch the model name from the model's
+        attributes.
+
+    .. important::
+
+        The model name is crucial for the model management system and
+        versioning. It's highly recommended to provide a name if you
+        plan to reuse the model later.
+
+    Model Fitting
+    ^^^^^^^^^^^^^^^
+
+    We can now fit the model:
+
+    .. ipython:: python
+        :okwarning:
+
+        model.fit(data)
+
+    .. important::
+
+        To fit a model, you can directly use the ``vDataFrame``
+        or the name of the relation stored in the database.
+
+    Model Parameters
+    ^^^^^^^^^^^^^^^^^
+
+    To fetch the model parameter (mean) you can use:
+
+    .. ipython:: python
+
+        model.mean_
+
+    Similarly for standard deviation:
+
+    .. ipython:: python
+
+        model.std_
+
+    Conversion/Transformation
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    To get the scaled dataset, we can use the ``transform`` function.
+    Let us transform the data:
+
+
+    .. ipython:: python
+        :okwarning:
+
+        model.transform(data)
+
+    Please refer to
+    :py:mod:`verticapy.machine_learning.Scaler.transform`
+    for more details on transforming a ``vDataFrame``.
+
+    Similarly, you can perform the inverse tranform to get
+    the original features using:
+
+    .. code-block:: python
+
+        model.inverse_transform(data_transformed)
+
+    The variable ``data_transformed`` is the scaled dataset.
+
+    Model Register
+    ^^^^^^^^^^^^^^
+
+    In order to register the model for tracking and versioning:
+
+    .. code-block:: python
+
+        model.register("model_v1")
+
+    Please refer to
+    :ref:`notebooks/ml/model_tracking_versioning/index.html`
+    for more details on model tracking and versioning.
+
+    Model Exporting
+    ^^^^^^^^^^^^^^^^
+
+    **To Memmodel**
+
+    .. code-block:: python
+
+        model.to_memmodel()
+
+    .. note::
+
+        ``MemModel`` objects serve as in-memory representations of
+        machine learning models. They can be used for both in-database
+        and in-memory prediction tasks. These objects can be pickled
+        in the same way that you would pickle a ``scikit-learn`` model.
+
+    The preceding methods for exporting the model use ``MemModel``,
+    and it is recommended to use ``MemModel`` directly.
+
+    **SQL**
+
+    To get the SQL query use below:
+
+    .. ipython:: python
+
+        model.to_sql()
+
+    **To Python**
+
+    To obtain the prediction function in Python syntax, use the
+    following code:
+
+    .. ipython:: python
+
+        X = [[1]]
+        model.to_python()(X)
+
+    .. hint::
+
+        The
+        :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler.to_python`
+        method is used to scale the data. For specific details on how to use this method for
+        different model types, refer to the relevant documentation for
+        each model.
+
+    .. seealso::
+        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.StandardScaler` :
+            Scalar with method set as ``zscore``.
+        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.RobustScaler` :
+            Scalar with method set as ``robust_zscore``.
+        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.MinMaxScaler` :
+            Scalar with method set as ``minmax``.
+
     """
 
     # Properties.
@@ -742,7 +956,16 @@ def to_memmodel(self) -> mm.Scaler:
 
 
 class StandardScaler(Scaler):
-    """i.e. Scaler with param method = 'zscore'"""
+    """
+    i.e. Scaler with param method = 'zscore'
+
+    .. note::
+
+        This is a child class. See
+        :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
+        for more details and examples.
+
+    """
 
     @property
     def _attributes(self) -> list[str]:
@@ -753,7 +976,16 @@ def __init__(self, name: str = None, overwrite_model: bool = False) -> None:
 
 
 class RobustScaler(Scaler):
-    """i.e. Scaler with param method = 'robust_zscore'"""
+    """
+    i.e. Scaler with param method = 'robust_zscore'
+
+    .. note::
+
+        This is a child class. See
+        :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
+        for more details and examples.
+
+    """
 
     @property
     def _attributes(self) -> list[str]:
@@ -764,7 +996,16 @@ def __init__(self, name: str = None, overwrite_model: bool = False) -> None:
 
 
 class MinMaxScaler(Scaler):
-    """i.e. Scaler with param method = 'minmax'"""
+    """
+    i.e. Scaler with param method = 'minmax'
+
+    .. note::
+
+        This is a child class. See
+        :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
+        for more details and examples.
+
+    """
 
     @property
     def _attributes(self) -> list[str]:

From a804b53e69582b18944444fdcbd0ac194732e45b Mon Sep 17 00:00:00 2001
From: umar <46414488+mail4umar@users.noreply.github.com>
Date: Mon, 30 Oct 2023 23:58:49 -0500
Subject: [PATCH 2/7] Added Balance

---
 .../machine_learning/vertica/preprocessing.py | 128 ++++++++++++++++--
 1 file changed, 113 insertions(+), 15 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 07b378b30..1db7231da 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -70,18 +70,25 @@ def Balance(
         Response column.
     method: str, optional
         Method used to do the balancing.
-            hybrid : Performs  over-sampling   and
-                     under-sampling  on  different
-                     classes so that each class is
-                     equally represented.
-            over   : Over-samples on  all classes,
-                     except the most represented
-                     class, towards the  most
-                     represented class's cardinality.
-            under  : Under-samples on  all classes,
-                     except the least represented
-                     class,  towards  the least
-                     represented class's cardinality.
+
+        - hybrid :
+            Performs  over-sampling   and
+            under-sampling  on  different
+            classes so that each class is
+            equally represented.
+
+        - over   :
+            Over-samples on  all classes,
+            except the most represented
+            class, towards the  most
+            represented class's cardinality.
+
+        - under:
+            Under-samples on  all classes,
+            except the least represented
+            class,  towards  the least
+            represented class's cardinality.
+
     ratio: float, optional
         The desired ratio between the majority class
         and the minority class. This value has no
@@ -92,6 +99,100 @@ def Balance(
     -------
     vDataFrame
         vDataFrame of the created view.
+
+
+    Examples
+    --------
+
+    The following examples provide a basic understanding of usage.
+    For more detailed examples, please refer to the
+    :ref:`user_guide.machine_learning` or the
+    `Examples <https://www.vertica.com/python/examples/>`_
+    section on the website.
+
+    Load data for machine learning
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    We import ``verticapy``:
+
+    .. ipython:: python
+
+        import verticapy as vp
+
+    .. hint::
+
+        By assigning an alias to ``verticapy``, we mitigate the risk of code
+        collisions with other libraries. This precaution is necessary
+        because verticapy uses commonly known function names like "average"
+        and "median", which can potentially lead to naming conflicts.
+        The use of an alias ensures that the functions from verticapy are
+        used as intended without interfering with functions from other
+        libraries.
+
+    For this example, we will use the Titanic dataset.
+
+    .. code-block:: python
+
+        import verticapy.datasets as vpd
+
+        data = vpd.load_titanic()
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html
+
+    .. ipython:: python
+        :suppress:
+
+        import verticapy.datasets as vpd
+        data = vpd.load_titanic()
+
+    .. note::
+
+        VerticaPy offers a wide range of sample datasets that are
+        ideal for training and testing purposes. You can explore
+        the full list of available datasets in the :ref:`api.datasets`,
+        which provides detailed information on each dataset
+        and how to use them effectively. These datasets are invaluable
+        resources for honing your data analysis and machine learning
+        skills within the VerticaPy environment.
+
+
+    Model Application
+    ^^^^^^^^^^^^^^^^^^^
+
+    First we import the ``Balance`` model:
+
+    .. ipython:: python
+
+        from verticapy.machine_learning.vertica import Balance
+
+    Then we can directly apply it to the dataset:
+
+    .. ipython:: python
+        :okwarning:
+
+        @suppress
+        vp.drop("balance_model")
+
+        Balance(name = "balance_model",
+                input_relation = data,
+                y = "survived",
+                method = "under"
+        )
+
+    .. important::
+
+        The model name is crucial for the model management system and
+        versioning. It's highly recommended to provide a name if you
+        plan to reuse the model later.
+
+    The output vDataFrame can then be used or stored for later analysis.
+
+
+
+    .. seealso::
+        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
+          Normalizing the dataset.
     """
     _executeSQL(
         query=f"""
@@ -713,9 +814,6 @@ class Scaler(Preprocessing):
 
         data = vp.vDataFrame({"values": [1, 1.01, 1.02, 1.05, 1.024]})
 
-    .. raw:: html
-        :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html
-
     .. note::
 
         VerticaPy offers a wide range of sample datasets that are

From fa0449a41a8965b643fe38973859b194ab7c17e8 Mon Sep 17 00:00:00 2001
From: Badr <badr.ouali@outlook.fr>
Date: Tue, 31 Oct 2023 09:09:49 -0400
Subject: [PATCH 3/7] Update preprocessing.py

---
 .../machine_learning/vertica/preprocessing.py | 42 ++++++++-----------
 1 file changed, 17 insertions(+), 25 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 1db7231da..6c5aea26a 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -100,7 +100,6 @@ def Balance(
     vDataFrame
         vDataFrame of the created view.
 
-
     Examples
     --------
 
@@ -156,11 +155,10 @@ def Balance(
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
+    Function Application
+    ^^^^^^^^^^^^^^^^^^^^^
 
-    Model Application
-    ^^^^^^^^^^^^^^^^^^^
-
-    First we import the ``Balance`` model:
+    First we import the ``Balance`` function:
 
     .. ipython:: python
 
@@ -174,10 +172,11 @@ def Balance(
         @suppress
         vp.drop("balance_model")
 
-        Balance(name = "balance_model",
-                input_relation = data,
-                y = "survived",
-                method = "under"
+        Balance(
+            name = "balance_model",
+            input_relation = data,
+            y = "survived",
+            method = "under"
         )
 
     .. important::
@@ -188,8 +187,6 @@ def Balance(
 
     The output vDataFrame can then be used or stored for later analysis.
 
-
-
     .. seealso::
         | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
           Normalizing the dataset.
@@ -824,7 +821,6 @@ class Scaler(Preprocessing):
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
-
     Model Initialization
     ^^^^^^^^^^^^^^^^^^^^^
 
@@ -887,9 +883,8 @@ class Scaler(Preprocessing):
     Conversion/Transformation
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    To get the scaled dataset, we can use the ``transform`` function.
-    Let us transform the data:
-
+    To get the scaled dataset, we can use the ``transform`` 
+    function. Let us transform the data:
 
     .. ipython:: python
         :okwarning:
@@ -910,7 +905,7 @@ class Scaler(Preprocessing):
     The variable ``data_transformed`` is the scaled dataset.
 
     Model Register
-    ^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 
@@ -963,9 +958,9 @@ class Scaler(Preprocessing):
 
         The
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler.to_python`
-        method is used to scale the data. For specific details on how to use this method for
-        different model types, refer to the relevant documentation for
-        each model.
+        method is used to scale the data. For specific details on how 
+        to use this method for different model types, refer to the 
+        relevant documentation for each model.
 
     .. seealso::
         | :py:mod:`verticapy.machine_learning.vertica.preprocessing.StandardScaler` :
@@ -1062,7 +1057,6 @@ class StandardScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1082,7 +1076,6 @@ class RobustScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1102,7 +1095,6 @@ class MinMaxScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1286,8 +1278,8 @@ class OneHotEncoder(Preprocessing):
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     To get the transformed dataset in the form that is encoded,
-    we can use the ``transform`` function. Let us transform the data
-    and display the first 20 datapoints.
+    we can use the ``transform`` function. Let us transform the
+    data and display the first 20 datapoints.
 
     .. ipython:: python
         :okwarning:
@@ -1309,7 +1301,7 @@ class OneHotEncoder(Preprocessing):
     components.
 
     Model Register
-    ^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 

From feceec48d857f3f4a97929410cff4456182b5fb8 Mon Sep 17 00:00:00 2001
From: Badr <badr.ouali@outlook.fr>
Date: Tue, 31 Oct 2023 09:15:01 -0400
Subject: [PATCH 4/7] Update preprocessing.py

---
 .../machine_learning/vertica/preprocessing.py | 21 +++++++------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 6c5aea26a..167cc6053 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -57,8 +57,8 @@ def Balance(
     ratio: float = 0.5,
 ) -> vDataFrame:
     """
-    Creates a view with an equal distribution of the
-    input data based on the response_column.
+    Creates a view with an equal distribution of
+    the input data based on the response_column.
 
     Parameters
     ----------
@@ -179,13 +179,8 @@ def Balance(
             method = "under"
         )
 
-    .. important::
-
-        The model name is crucial for the model management system and
-        versioning. It's highly recommended to provide a name if you
-        plan to reuse the model later.
-
-    The output vDataFrame can then be used or stored for later analysis.
+    The output vDataFrame can then be used or stored for later
+    analysis.
 
     .. seealso::
         | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
@@ -851,7 +846,7 @@ class Scaler(Preprocessing):
         plan to reuse the model later.
 
     Model Fitting
-    ^^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^
 
     We can now fit the model:
 
@@ -883,7 +878,7 @@ class Scaler(Preprocessing):
     Conversion/Transformation
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    To get the scaled dataset, we can use the ``transform`` 
+    To get the scaled dataset, we can use the ``transform``
     function. Let us transform the data:
 
     .. ipython:: python
@@ -958,8 +953,8 @@ class Scaler(Preprocessing):
 
         The
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler.to_python`
-        method is used to scale the data. For specific details on how 
-        to use this method for different model types, refer to the 
+        method is used to scale the data. For specific details on how
+        to use this method for different model types, refer to the
         relevant documentation for each model.
 
     .. seealso::

From ec6af1d7aee0207730c20778b978b757e7f40733 Mon Sep 17 00:00:00 2001
From: umar <46414488+mail4umar@users.noreply.github.com>
Date: Tue, 31 Oct 2023 09:48:32 -0500
Subject: [PATCH 5/7] resolving comments

---
 .../machine_learning/vertica/preprocessing.py | 84 ++++++++++++-------
 1 file changed, 56 insertions(+), 28 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 167cc6053..62870a516 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -57,8 +57,8 @@ def Balance(
     ratio: float = 0.5,
 ) -> vDataFrame:
     """
-    Creates a view with an equal distribution of
-    the input data based on the response_column.
+    Creates a view with an equal distribution of the
+    input data based on the response_column.
 
     Parameters
     ----------
@@ -100,6 +100,7 @@ def Balance(
     vDataFrame
         vDataFrame of the created view.
 
+
     Examples
     --------
 
@@ -155,10 +156,11 @@ def Balance(
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
-    Function Application
-    ^^^^^^^^^^^^^^^^^^^^^
 
-    First we import the ``Balance`` function:
+    Model Application
+    ^^^^^^^^^^^^^^^^^^^
+
+    First we import the ``Balance`` model:
 
     .. ipython:: python
 
@@ -168,23 +170,44 @@ def Balance(
 
     .. ipython:: python
         :okwarning:
+        :suppress:
+
 
-        @suppress
         vp.drop("balance_model")
+        result = Balance(name = "balance_model",
+                input_relation = data,
+                y = "survived",
+                method = "under"
+        )
+        html_file = open("SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html", "w")
+        html_file.write(result._repr_html_())
+        html_file.close()
+
+    .. code-block:: python
 
-        Balance(
-            name = "balance_model",
-            input_relation = data,
-            y = "survived",
-            method = "under"
+        Balance(name = "balance_model",
+                input_relation = data,
+                y = "survived",
+                method = "under"
         )
 
-    The output vDataFrame can then be used or stored for later
-    analysis.
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html
+
+
+    .. important::
+
+        The model name is crucial for the model management system and
+        versioning. It's highly recommended to provide a name if you
+        plan to reuse the model later.
+
+    The output vDataFrame can then be used or stored for later analysis.
+
+
 
     .. seealso::
-        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
-          Normalizing the dataset.
+        | :py:mod:`verticapy.vDataFrame.sample` :
+          Sampling the dataset.
     """
     _executeSQL(
         query=f"""
@@ -756,21 +779,21 @@ class Scaler(Preprocessing):
 
         .. math::
 
-            (x - avg) / std
+            Z_score = (x - avg) / std
 
         - robust_zscore:
         Scaling using the Robust Z-Score.
 
         .. math::
 
-            (x - median) / (1.4826 * mad)
+            Z_rscore = (x - median) / (1.4826 * mad)
 
         - minmax:
         Normalization  using  the  Min  &  Max.
 
         .. math::
 
-            (x - min) / (max - min)
+            Z_minmax = (x - min) / (max - min)
 
     Examples
     --------
@@ -816,6 +839,7 @@ class Scaler(Preprocessing):
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
+
     Model Initialization
     ^^^^^^^^^^^^^^^^^^^^^
 
@@ -846,7 +870,7 @@ class Scaler(Preprocessing):
         plan to reuse the model later.
 
     Model Fitting
-    ^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^^
 
     We can now fit the model:
 
@@ -878,8 +902,9 @@ class Scaler(Preprocessing):
     Conversion/Transformation
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    To get the scaled dataset, we can use the ``transform``
-    function. Let us transform the data:
+    To get the scaled dataset, we can use the ``transform`` function.
+    Let us transform the data:
+
 
     .. ipython:: python
         :okwarning:
@@ -900,7 +925,7 @@ class Scaler(Preprocessing):
     The variable ``data_transformed`` is the scaled dataset.
 
     Model Register
-    ^^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 
@@ -953,9 +978,9 @@ class Scaler(Preprocessing):
 
         The
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler.to_python`
-        method is used to scale the data. For specific details on how
-        to use this method for different model types, refer to the
-        relevant documentation for each model.
+        method is used to scale the data. For specific details on how to use this method for
+        different model types, refer to the relevant documentation for
+        each model.
 
     .. seealso::
         | :py:mod:`verticapy.machine_learning.vertica.preprocessing.StandardScaler` :
@@ -1052,6 +1077,7 @@ class StandardScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
+
     """
 
     @property
@@ -1071,6 +1097,7 @@ class RobustScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
+
     """
 
     @property
@@ -1090,6 +1117,7 @@ class MinMaxScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
+
     """
 
     @property
@@ -1273,8 +1301,8 @@ class OneHotEncoder(Preprocessing):
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     To get the transformed dataset in the form that is encoded,
-    we can use the ``transform`` function. Let us transform the
-    data and display the first 20 datapoints.
+    we can use the ``transform`` function. Let us transform the data
+    and display the first 20 datapoints.
 
     .. ipython:: python
         :okwarning:
@@ -1296,7 +1324,7 @@ class OneHotEncoder(Preprocessing):
     components.
 
     Model Register
-    ^^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 

From 66d1002a04c5c65197e452caa4af69085f410acc Mon Sep 17 00:00:00 2001
From: umar <46414488+mail4umar@users.noreply.github.com>
Date: Tue, 31 Oct 2023 09:56:13 -0500
Subject: [PATCH 6/7] Revert "resolving comments"

This reverts commit ec6af1d7aee0207730c20778b978b757e7f40733.
---
 .../machine_learning/vertica/preprocessing.py | 84 +++++++------------
 1 file changed, 28 insertions(+), 56 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 62870a516..167cc6053 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -57,8 +57,8 @@ def Balance(
     ratio: float = 0.5,
 ) -> vDataFrame:
     """
-    Creates a view with an equal distribution of the
-    input data based on the response_column.
+    Creates a view with an equal distribution of
+    the input data based on the response_column.
 
     Parameters
     ----------
@@ -100,7 +100,6 @@ def Balance(
     vDataFrame
         vDataFrame of the created view.
 
-
     Examples
     --------
 
@@ -156,11 +155,10 @@ def Balance(
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
+    Function Application
+    ^^^^^^^^^^^^^^^^^^^^^
 
-    Model Application
-    ^^^^^^^^^^^^^^^^^^^
-
-    First we import the ``Balance`` model:
+    First we import the ``Balance`` function:
 
     .. ipython:: python
 
@@ -170,44 +168,23 @@ def Balance(
 
     .. ipython:: python
         :okwarning:
-        :suppress:
-
 
+        @suppress
         vp.drop("balance_model")
-        result = Balance(name = "balance_model",
-                input_relation = data,
-                y = "survived",
-                method = "under"
-        )
-        html_file = open("SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html", "w")
-        html_file.write(result._repr_html_())
-        html_file.close()
-
-    .. code-block:: python
 
-        Balance(name = "balance_model",
-                input_relation = data,
-                y = "survived",
-                method = "under"
+        Balance(
+            name = "balance_model",
+            input_relation = data,
+            y = "survived",
+            method = "under"
         )
 
-    .. raw:: html
-        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html
-
-
-    .. important::
-
-        The model name is crucial for the model management system and
-        versioning. It's highly recommended to provide a name if you
-        plan to reuse the model later.
-
-    The output vDataFrame can then be used or stored for later analysis.
-
-
+    The output vDataFrame can then be used or stored for later
+    analysis.
 
     .. seealso::
-        | :py:mod:`verticapy.vDataFrame.sample` :
-          Sampling the dataset.
+        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
+          Normalizing the dataset.
     """
     _executeSQL(
         query=f"""
@@ -779,21 +756,21 @@ class Scaler(Preprocessing):
 
         .. math::
 
-            Z_score = (x - avg) / std
+            (x - avg) / std
 
         - robust_zscore:
         Scaling using the Robust Z-Score.
 
         .. math::
 
-            Z_rscore = (x - median) / (1.4826 * mad)
+            (x - median) / (1.4826 * mad)
 
         - minmax:
         Normalization  using  the  Min  &  Max.
 
         .. math::
 
-            Z_minmax = (x - min) / (max - min)
+            (x - min) / (max - min)
 
     Examples
     --------
@@ -839,7 +816,6 @@ class Scaler(Preprocessing):
         resources for honing your data analysis and machine learning
         skills within the VerticaPy environment.
 
-
     Model Initialization
     ^^^^^^^^^^^^^^^^^^^^^
 
@@ -870,7 +846,7 @@ class Scaler(Preprocessing):
         plan to reuse the model later.
 
     Model Fitting
-    ^^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^
 
     We can now fit the model:
 
@@ -902,9 +878,8 @@ class Scaler(Preprocessing):
     Conversion/Transformation
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    To get the scaled dataset, we can use the ``transform`` function.
-    Let us transform the data:
-
+    To get the scaled dataset, we can use the ``transform``
+    function. Let us transform the data:
 
     .. ipython:: python
         :okwarning:
@@ -925,7 +900,7 @@ class Scaler(Preprocessing):
     The variable ``data_transformed`` is the scaled dataset.
 
     Model Register
-    ^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 
@@ -978,9 +953,9 @@ class Scaler(Preprocessing):
 
         The
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler.to_python`
-        method is used to scale the data. For specific details on how to use this method for
-        different model types, refer to the relevant documentation for
-        each model.
+        method is used to scale the data. For specific details on how
+        to use this method for different model types, refer to the
+        relevant documentation for each model.
 
     .. seealso::
         | :py:mod:`verticapy.machine_learning.vertica.preprocessing.StandardScaler` :
@@ -1077,7 +1052,6 @@ class StandardScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1097,7 +1071,6 @@ class RobustScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1117,7 +1090,6 @@ class MinMaxScaler(Scaler):
         This is a child class. See
         :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler`
         for more details and examples.
-
     """
 
     @property
@@ -1301,8 +1273,8 @@ class OneHotEncoder(Preprocessing):
     ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     To get the transformed dataset in the form that is encoded,
-    we can use the ``transform`` function. Let us transform the data
-    and display the first 20 datapoints.
+    we can use the ``transform`` function. Let us transform the
+    data and display the first 20 datapoints.
 
     .. ipython:: python
         :okwarning:
@@ -1324,7 +1296,7 @@ class OneHotEncoder(Preprocessing):
     components.
 
     Model Register
-    ^^^^^^^^^^^^^^
+    ^^^^^^^^^^^^^^^
 
     In order to register the model for tracking and versioning:
 

From 78d6117e1c3dad85be53ed4da266d6e626aefb8a Mon Sep 17 00:00:00 2001
From: umar <46414488+mail4umar@users.noreply.github.com>
Date: Tue, 31 Oct 2023 10:00:59 -0500
Subject: [PATCH 7/7] resolving comments again

---
 .../machine_learning/vertica/preprocessing.py | 36 ++++++++++++-------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/verticapy/machine_learning/vertica/preprocessing.py b/verticapy/machine_learning/vertica/preprocessing.py
index 167cc6053..535c9d746 100755
--- a/verticapy/machine_learning/vertica/preprocessing.py
+++ b/verticapy/machine_learning/vertica/preprocessing.py
@@ -168,23 +168,33 @@ def Balance(
 
     .. ipython:: python
         :okwarning:
+        :suppress:
+
 
-        @suppress
         vp.drop("balance_model")
+        result = Balance(name = "balance_model",
+                input_relation = data,
+                y = "survived",
+                method = "under"
+        )
+        html_file = open("SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html", "w")
+        html_file.write(result._repr_html_())
+        html_file.close()
 
-        Balance(
-            name = "balance_model",
-            input_relation = data,
-            y = "survived",
-            method = "under"
+    .. code-block:: python
+
+        Balance(name = "balance_model",
+                input_relation = data,
+                y = "survived",
+                method = "under"
         )
 
-    The output vDataFrame can then be used or stored for later
-    analysis.
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_preprocessing_balance.html
 
     .. seealso::
-        | :py:mod:`verticapy.machine_learning.vertica.preprocessing.Scaler` :
-          Normalizing the dataset.
+        | :py:mod:`verticapy.vDataFrame.sample` :
+          Sampling the dataset.
     """
     _executeSQL(
         query=f"""
@@ -756,21 +766,21 @@ class Scaler(Preprocessing):
 
         .. math::
 
-            (x - avg) / std
+            Z_score = (x - avg) / std
 
         - robust_zscore:
         Scaling using the Robust Z-Score.
 
         .. math::
 
-            (x - median) / (1.4826 * mad)
+            Z_rscore = (x - median) / (1.4826 * mad)
 
         - minmax:
         Normalization  using  the  Min  &  Max.
 
         .. math::
 
-            (x - min) / (max - min)
+            Z_minmax = (x - min) / (max - min)
 
     Examples
     --------